presentation on lymphoma

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About half of the blood cancers that occur each year are lymphomas, or cancers of the lymphatic system. This system - composed of lymph nodes in your neck, armpits, groin, chest, and abdomen - removes excess fluids from your body and produces immune cells. Abnormal lymphocytes, a type of white blood cell that fights infection, become lymphoma cells, which multiply and collect in your lymph nodes. Over time, these cancerous cells impair your immune system.

Lymphomas are divided into two categories: Hodgkin lymphoma and non-Hodgkin lymphoma. About 12 percent of people with lymphoma have Hodgkin lymphoma. Because of  breakthrough research , this once fatal diagnosis has been transformed into a curable condition. Most non-Hodgkin lymphomas are B-cell lymphomas, and either grow quickly (high-grade) or slowly (low-grade). There are over a dozen types of B-cell non-Hodgkin lymphomas. The rest are T-cell lymphomas, named after a different cancerous white blood cell, or lymphocyte. 

How Lymphoma Develops

Many lymphoma patients are able to lead active lives as they receive treatment for their symptoms and are monitored by their doctors.

Am I at Risk?

The exact causes of lymphoma remain unknown; however, the following factors increase your risk of developing the disease:

• Having an autoimmune disease
• Diet high in meats and fat
• Being exposed to certain pesticides

Symptoms of lymphoma include the following:

• Swollen lymph nodes in the neck, armpits, or groin
• Weakness and fatigue
• Weight loss
• Difficulty breathing or chest pain

How is Lymphoma Treated?

Your doctor will perform a lymph node biopsy to diagnose lymphoma. Additional tests are then conducted to determine the stage (extent) of the lymphoma including blood tests, bone marrow biopsies, and imaging tests, such as a CT scan or PET scan. Imaging tests show whether the lymphoma has spread to other parts of your body, like the spleen and lungs. Decisions about treatment are then determined by your doctor, who will consider your age, general health, and stage and type of lymphoma. Hodgkin lymphoma is one of the most curable types of cancer.

Treatment options include the following:

• Chemotherapy
• Chemotherapy and radiation that  directly targets the lymphoma
• Biological therapies, such as antibodies, directed at lymphoma cells
• Stem cell transplant

For some patients, participating in a  clinical trial  provides access to experimental therapies. If you are diagnosed with lymphoma, talk with your doctor about whether joining a clinical trial is right for you.

Is Lymphoma Preventable?

Because the cause of lymphoma remains unknown, there is no real way to prevent it. However, if you think you may be exhibiting signs of lymphoma, being aware of the risk factors and symptoms and talking with your doctor are critical to early diagnosis and treatment. It is especially important if you have a family history of lymphoma to look out for symptoms and share your family medical history with your doctor.

If you suspect that you have or are at risk for lymphoma, talk with your doctor about detection and treatment. Depending on your physical condition, genetics, and medical history, you may be referred to a hematologist, a doctor who specializes in blood conditions.

Lymphoma: A Patient's Journey

Where Can I Find More Information?

If you find that you are interested in learning more about blood diseases and disorders, here are a few other resources that may be of some help:

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Lymphoma is a cancer that starts in cells that are part of the body's immune system. Knowing which type of lymphoma you have is important because it affects your treatment options and your outlook (prognosis). If you aren’t sure which type you have, ask your doctor so you can get the right information.

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Non-Hodgkin Lymphoma (NHL) Clinical Presentation

• Author: Sanjay Vinjamaram, MD, MPH; Chief Editor: Emmanuel C Besa, MD  more...
• Sections Non-Hodgkin Lymphoma (NHL)
• Practice Essentials
• Pathophysiology
• Epidemiology
• Patient Education
• Physical Examination
• Complications
• Approach Considerations
• Complete Blood Cell Count
• Serum Chemistry Studies
• Other Laboratory Studies
• Radiography
• CT, Bone Scan, and Gallium Scan
• Positron Emission Tomography and Ultrasonography
• Multiple Gated Acquisition Scanning
• Magnetic Resonance Imaging
• Lumbar Puncture
• Histologic Findings
• Immunophenotypic Analysis
• Cytogenetic Studies
• Management of Indolent NHL
• Management of Aggressive NHL
• Management of Indolent Recurrent NHL
• Management of Aggressive Recurrent Adult NHL
• Management of T-cell Lymphomas
• Surgical Care
• Complications of Therapy
• Dietary Modification
• Activity Restriction
• Management of NHL in Special Populations
• CAR T-cell Therapy
• Consultations
• Long-Term Monitoring
• Guidelines Summary
• Risk Stratification
• Follicular Lymphoma
• Marginal Zone Lymphomas of MALT Type
• Mantle Cell Lymphoma
• Diffuse Large B-Cell Lymphoma
• Burkitt Lymphoma
• Primary Cutaneous B-Cell Lymphomas
• Cutaneous T-Cell Lymphoma
• Primary Cutaneous CD30+ T-Cell Lymphoproliferative Disorders
• Medication Summary
• Cytotoxic agents
• Antineoplastic Agents, Histone Deacetylase Inhibitors
• Antineoplastics, PI3K Inhibitors
• Monoclonal Antibodies
• Antineoplastic Agents, Proteasome Inhibitors
• Antineoplastic Agents, mTOR Kinase Inhibitors
• Antineoplastics, Angiogenesis Inhibitor
• PD-1/PD-L1 Inhibitors
• Antineoplastics, Anti-CD19 Monoclonal Antibodies
• Colony-Stimulating Factor Growth Factors
• Immunomodulators
• Corticosteroids
• Questions & Answers
• Media Gallery

The clinical manifestations of non-Hodgkin lymphoma (NHL) vary with such factors as the location of the lymphomatous process, the rate of tumor growth, and the function of the organ being compromised or displaced by the malignant process.

The Working Formulation classification groups the subtypes of NHL by clinical behavior—that is, low-grade, intermediate-grade, and high-grade. Because the Working Formulation is limited to classification based upon morphology, it cannot encompass the complex spectrum of NHL disease, excluding important subtypes such as mantle cell lymphoma or T cell/natural killer cell lymphomas. However, it continues to serve as a basis for understanding the clinical behavior of groups of NHLs.

Low-grade lymphomas

Peripheral adenopathy that is painless and slowly progressive is the most common clinical presentation in these patients. Spontaneous regression of enlarged lymph nodes can occur in low-grade lymphoma, potentially causing confusion with an infectious condition.

Primary extranodal involvement and B symptoms (ie, temperature >38°C, night sweats, weight loss >10% from baseline within 6 mo) are not common at presentation, but they are common in patients with advanced, malignant transformation (ie, evolution from a low-grade to an intermediate- or high-grade lymphoma) or end-stage disease.

Bone marrow is frequently involved and may be associated with cytopenia or cytopenias. [ 13 ] Fatigue and weakness are more common in patients with advanced-stage disease.

Intermediate- and high-grade lymphomas

These types of lymphomas cause a more varied clinical presentation. Most patients present with adenopathy. More than one third of patients present with extranodal involvement; the most common sites are the gastrointestinal (GI) tract (including the Waldeyer ring), skin, bone marrow, sinuses, genitourinary (GU) tract, thyroid, and central nervous system (CNS). B-symptoms are more common, occurring in approximately 30-40% of patients.

Lymphoblastic lymphoma, a high-grade lymphoma, often manifests with an anterior superior mediastinal mass, superior vena cava (SVC) syndrome, and leptomeningeal disease with cranial nerve palsies.

Patients with Burkitt lymphoma (occurring in the United States) often present with a large abdominal mass and symptoms of bowel obstruction. Obstructive hydronephrosis secondary to bulky retroperitoneal lymphadenopathy obstructing the ureters can also be observed in these patients.

Primary CNS lymphomas are high-grade neoplasms of B-cell origin. Most lymphomas originating in the CNS are large cell lymphomas or immunoblastomas, and they account for 1% of all intracranial neoplasms. These lymphomas are more commonly observed in patients who are immunodeficient because of conditions such as Wiskott-Aldrich syndrome, transplantation, or AIDS (see HIV-Associated Opportunistic Neoplasms-CNS Lymphoma for more information on this topic). [ 4 ]

Fernberg et al examined time trends in risk and risk determinants in posttransplant patients with lymphoma and found that posttransplant NHL risk decreased during the 2000s compared with the 1990s among patients who underwent nonkidney transplants. [ 14 ]

Low-grade lymphomas may produce peripheral adenopathy, splenomegaly, and hepatomegaly. Splenomegaly is observed in approximately 40% of patients; the spleen is rarely the only involved site at presentation.

Intermediate- and high-grade lymphomas may produce the following physical examination findings:

• Rapidly growing and bulky lymphadenopathy
• Splenomegaly
• Hepatomegaly
• Large abdominal mass : this usually occurs in Burkitt lymphoma
• Testicular mass
• Skin lesions: lesions are associated with cutaneous T-cell lymphoma (mycosis fungoides), anaplastic large-cell lymphoma , and angioimmunoblastic lymphoma [ 15 ]

Potential disease-related complications include the following:

Cytopenias (ie, neutropenia, anemia, thrombocytopenia) secondary to bone marrow infiltration; alternatively, autoimmune hemolytic anemia is observed in some types of NHL (eg, small lymphocytic lymphoma /chronic lymphocytic leukemia [SLL/CLL])

Bleeding secondary to thrombocytopenia, disseminated intravascular coagulation (DIC), or vascular invasion by the tumor

Infection secondary to leukopenia, especially neutropenia

Cardiac problems secondary to large pericardial effusion or arrhythmias secondary to cardiac metastases

Respiratory problems secondary to pleural effusion and/or parenchymal lesions

Superior vena cava (SVC) syndrome secondary to a large mediastinal tumor

Spinal cord compression secondary to vertebral metastases

Neurologic problems secondary to primary CNS lymphoma or lymphomatous meningitis

GI obstruction, perforation, and bleeding in a patient with GI lymphoma (may also be caused by chemotherapy)

Pain secondary to tumor invasion

Leukocytosis (lymphocytosis) in leukemic phase of disease

Surveillance, Epidemiology, and End Results Program. Cancer Stat Facts: Non-Hodgkin Lymphoma. National Cancer Institute. Available at https://seer.cancer.gov/statfacts/html/nhl.html . Accessed: May 28, 2024.

PDQ Adult Treatment Editorial Board. Adult Non-Hodgkin Lymphoma Treatment (PDQ®): Health Professional Version. May 18, 2023. [QxMD MEDLINE Link] . [Full Text] .

Shustik J, Quinn M, Connors JM, et al. Follicular non-Hodgkin lymphoma grades 3A and 3B have a similar outcome and appear incurable with anthracycline-based therapy. Ann Oncol . 2011 May. 22(5):1164-9. [QxMD MEDLINE Link] .

Martí-Carvajal AJ, Cardona AF, Lawrence A. Interventions for previously untreated patients with AIDS-associated non-Hodgkin's lymphoma. Cochrane Database Syst Rev . 2009 Jul 8. CD005419. [QxMD MEDLINE Link] .

Antonopoulos CN, Sergentanis TN, et al. Maternal smoking during pregnancy and childhood lymphoma: a meta-analysis. Int J Cancer . 2011 Dec 1. 129(11):2694-703. [QxMD MEDLINE Link] .

Elfstrom P, Granath F, Ekstrom Smedby K, et al. Risk of lymphoproliferative malignancy in relation to small intestinal histopathology among patients with celiac disease. J Natl Cancer Inst . 2011 Mar 2. 103(5):436-44. [QxMD MEDLINE Link] .

American Cancer Society. Cancer Facts & Figures 2024. Available at https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2024/2024-cancer-facts-and-figures-acs.pdf . Accessed: May 28, 2024.

Abla O, Weitzman S, Blay JY, O'Neill BP, Abrey LE, Neuwelt E, et al. Primary CNS lymphoma in children and adolescents: a descriptive analysis from the international primary CNS lymphoma collaborative group (IPCG). Clin Cancer Res . 2011 Jan 15. 17(2):346-52. [QxMD MEDLINE Link] .

Solal-Céligny P, Roy P, Colombat P, et al. Follicular lymphoma international prognostic index. Blood . 2004 Sep 1. 104 (5):1258-65. [QxMD MEDLINE Link] . [Full Text] .

Drake MT, Maurer MJ, Link BK, et al. Vitamin D insufficiency and prognosis in non-Hodgkin's lymphoma. J Clin Oncol . 2010 Sep 20. 28(27):4191-8. [QxMD MEDLINE Link] .

Chang ET, Canchola AJ, Cockburn M, et al. Adulthood residential ultraviolet radiation, sun sensitivity, dietary vitamin D, and risk of lymphoid malignancies in the California Teachers Study. Blood . 2011 Aug 11. 118(6):1591-9. [QxMD MEDLINE Link] . [Full Text] .

Donin N, Filson C, Drakaki A, Tan HJ, Castillo A, Kwan L, et al. Risk of second primary malignancies among cancer survivors in the United States, 1992 through 2008. Cancer . 2016 Jul 5. [QxMD MEDLINE Link] .

Zhang QY, Foucar K. Bone marrow involvement by Hodgkin and non-Hodgkin lymphomas. Hematol Oncol Clin North Am . 2009 Aug. 23(4):873-902. [QxMD MEDLINE Link] .

Fernberg P, Edgren G, Adami J, et al. Time trends in risk and risk determinants of non-hodgkin lymphoma in solid organ transplant recipients. Am J Transplant . 2011 Nov. 11(11):2472-82. [QxMD MEDLINE Link] .

Hsia CC, Howson-Jan K, Rizkalla KS. Hodgkin lymphoma with cutaneous involvement. Dermatol Online J . 2009 May 15. 15(5):5. [QxMD MEDLINE Link] .

Hussain SK, Zhu W, Chang SC, Breen EC, Vendrame E, Magpantay L, et al. Serum Levels of the Chemokine CXCL13, Genetic Variation in CXCL13 and Its Receptor CXCR5, and HIV-Associated Non-Hodgkin B-Cell Lymphoma Risk. Cancer Epidemiol Biomarkers Prev . 2013 Feb. 22(2):295-307. [QxMD MEDLINE Link] .

Rossi M, Pertovaara H, Dastidar P, et al. Computed tomography-based tumor volume in non-Hodgkin lymphoma: clinical correlation and comparison with magnetic resonance imaging. J Comput Assist Tomogr . 2009 Jul-Aug. 33(4):641-9. [QxMD MEDLINE Link] .

von Falck C, Rodt T, Joerdens S, et al. F-18 2-fluoro-2-deoxy-glucose positron emission tomography/computed tomography for the detection of radicular and peripheral neurolymphomatosis: correlation with magnetic resonance imaging and ultrasound. Clin Nucl Med . 2009 Aug. 34(8):493-5. [QxMD MEDLINE Link] .

Zinzani PL, Gandolfi L, Broccoli A, et al. Midtreatment 18F-fluorodeoxyglucose positron-emission tomography in aggressive non-Hodgkin lymphoma. Cancer . 2011 Mar 1. 117(5):1010-8. [QxMD MEDLINE Link] .

Terezakis SA, Hunt MA, Kowalski A, et al. [¹8F]FDG-positron emission tomography coregistration with computed tomography scans for radiation treatment planning of lymphoma and hematologic malignancies. Int J Radiat Oncol Biol Phys . 2011 Nov 1. 81(3):615-22. [QxMD MEDLINE Link] .

Khan AB, Barrington SF, Mikhaeel NG, et al. PET-CT staging of DLBCL accurately identifies and provides new insight into the clinical significance of bone marrow involvement. Blood . 2013 May 9. [QxMD MEDLINE Link] .

Mac Manus MP, Rainer Bowie CA, Hoppe RT. What is the prognosis for patients who relapse after primary radiation therapy for early-stage low-grade follicular lymphoma?. Int J Radiat Oncol Biol Phys . 1998 Sep 1. 42(2):365-71. [QxMD MEDLINE Link] .

Rossier C, Schick U, Miralbell R, et al. Low-dose radiotherapy in indolent lymphoma. Int J Radiat Oncol Biol Phys . 2011 Nov 1. 81(3):e1-6. [QxMD MEDLINE Link] .

Peyrade F, Jardin F, Thieblemont C, et al. Attenuated immunochemotherapy regimen (R-miniCHOP) in elderly patients older than 80 years with diffuse large B-cell lymphoma: a multicentre, single-arm, phase 2 trial. Lancet Oncol . 2011 May. 12(5):460-8. [QxMD MEDLINE Link] .

Rummel MJ, Niederle N, Maschmeyer G, Banat GA, etc. Bendamustine plus rituximab versus CHOP plus rituximab as first-line treatment for patients with indolent and mantle-cell lymphomas: an open-label, multicentre, randomised, phase 3 non-inferiority trial. Lancet . 2013 Feb 19. [QxMD MEDLINE Link] .

[Guideline] NCCN Clinical Practice Guidelines in Oncology. B-cell Lymphomas. National Comprehensive Cancer Network. Available at https://www.nccn.org/professionals/physician_gls/pdf/b-cell.pdf . Version 2.2024 — April 30, 2024; Accessed: May 28, 2024.

Horvath B, Demeter J, Eros N, et al. Intravascular large B-cell lymphoma: Remission after rituximab-cyclophosphamide, doxorubicin, vincristine, and prednisolone chemotherapy. J Am Acad Dermatol . 2009 Jul 24. [QxMD MEDLINE Link] .

Barr PM, Fu P, Lazarus HM, et al. Phase I trial of fludarabine, bortezomib and rituximab for relapsed and refractory indolent and mantle cell non-Hodgkin lymphoma. Br J Haematol . 2009 Jun 29. [QxMD MEDLINE Link] . [Full Text] .

Tobinai K, Ishizawa KI, Ogura M, et al. Phase II study of oral fludarabine in combination with rituximab for relapsed indolent B-cell non-Hodgkin lymphoma. Cancer Sci . 2009 Jun 17. [QxMD MEDLINE Link] .

Watanabe T, Tobinai K, Shibata T, et al. Phase II/III Study of R-CHOP-21 Versus R-CHOP-14 for Untreated Indolent B-Cell Non-Hodgkin's Lymphoma: JCOG 0203 Trial. J Clin Oncol . 2011 Oct 20. 29(30):3990-8. [QxMD MEDLINE Link] .

Czuczman MS, Koryzna A, Mohr A, et al. Rituximab in combination with fludarabine chemotherapy in low-grade or follicular lymphoma. J Clin Oncol . 2005 Feb 1. 23(4):694-704. [QxMD MEDLINE Link] .

Barta SK, Li H, Hochster HS, Hong F, Weller E, Gascoyne RD, et al. Randomized phase 3 study in low-grade lymphoma comparing maintenance anti-CD20 antibody with observation after induction therapy: A trial of the ECOG-ACRIN Cancer Research Group (E1496). Cancer . 2016 Jun 28. [QxMD MEDLINE Link] .

Sehn LH, Chua N, Mayer J, Dueck G, Trněný M, Bouabdallah K, et al. Obinutuzumab plus bendamustine versus bendamustine monotherapy in patients with rituximab-refractory indolent non-Hodgkin lymphoma (GADOLIN): a randomised, controlled, open-label, multicentre, phase 3 trial. Lancet Oncol . 2016 Jun 23. [QxMD MEDLINE Link] .

Fisher RI, Gaynor ER, Dahlberg S. Comparison of a standard regimen (CHOP) with three intensive chemotherapy regimens for advanced non-Hodgkin's lymphoma. N Engl J Med . 1993 Apr 8. 328(14):1002-6. [QxMD MEDLINE Link] .

Romaguera JE, Fayad L, Rodriguez MA, et al. High rate of durable remissions after treatment of newly diagnosed aggressive mantle-cell lymphoma with rituximab plus hyper-CVAD alternating with rituximab plus high-dose methotrexate and cytarabine. J Clin Oncol . 2005 Oct 1. 23(28):7013-23. [QxMD MEDLINE Link] .

Rummel MJ, et al. Bendamustine plus rituximab is superior in respect to progression free survival and CR rate when compared to CHOP plus rituximab as first-line treatment in patients with advanced follicular, indolent, and mantle cell lymphomas. [abstract: American Society of Hematology]. Blood . 2009. 114:405.

Weidmann E, Neumann A, Fauth F, et al. Phase II study of bendamustine in combination with rituximab as first-line treatment in patients 80 years or older with aggressive B-cell lymphomas. Ann Oncol . 2011 Aug. 22(8):1839-44. [QxMD MEDLINE Link] .

Goy A, Bernstein SH, Kahl BS, et al. Bortezomib in patients with relapsed or refractory mantle cell lymphoma: updated time-to-event analyses of the multicenter phase 2 PINNACLE study. Ann Oncol . 2009 Mar. 20(3):520-5. [QxMD MEDLINE Link] .

Feugier P, Van Hoof A, Sebban C, Solal-Celigny P, Bouabdallah R, Fermé C, et al. Long-term results of the R-CHOP study in the treatment of elderly patients with diffuse large B-cell lymphoma: a study by the Groupe d'Etude des Lymphomes de l'Adulte. J Clin Oncol . 2005 Jun 20. 23(18):4117-26. [QxMD MEDLINE Link] .

Pfreundschuh M, Trümper L, Osterborg A, et al for the MabThera International Trial Group. CHOP-like chemotherapy plus rituximab versus CHOP-like chemotherapy alone in young patients with good-prognosis diffuse large-B-cell lymphoma: a randomised controlled trial by the MabThera International Trial (MInT) Group. Lancet Oncol . 2006 May. 7(5):379-91. [QxMD MEDLINE Link] .

Smith SM, van Besien K, Karrison T, Dancey J, McLaughlin P, Younes A, et al. Temsirolimus has activity in non-mantle cell non-Hodgkin's lymphoma subtypes: The University of Chicago phase II consortium. J Clin Oncol . 2010 Nov 1. 28(31):4740-6. [QxMD MEDLINE Link] .

Leahy MF, Seymour JF, Hicks RJ, Turner JH. Multicenter phase II clinical study of iodine-131-rituximab radioimmunotherapy in relapsed or refractory indolent non-Hodgkin's lymphoma. J Clin Oncol . 2006 Sep 20. 24(27):4418-25. [QxMD MEDLINE Link] .

Gopal AK, Rajendran JG, Gooley TA, Pagel JM, Fisher DR, Petersdorf SH. High-dose [131I]tositumomab (anti-CD20) radioimmunotherapy and autologous hematopoietic stem-cell transplantation for adults > or = 60 years old with relapsed or refractory B-cell lymphoma. J Clin Oncol . 2007 Apr 10. 25(11):1396-402. [QxMD MEDLINE Link] .

Kaminski MS, Zelenetz AD, Press OW, Saleh M, Leonard J, Fehrenbacher L. Pivotal study of iodine I 131 tositumomab for chemotherapy-refractory low-grade or transformed low-grade B-cell non-Hodgkin's lymphomas. J Clin Oncol . 2001 Oct 1. 19(19):3918-28. [QxMD MEDLINE Link] .

Milpied N, Deconinck E, Gaillard F, Delwail V, Foussard C, Berthou C. Initial treatment of aggressive lymphoma with high-dose chemotherapy and autologous stem-cell support. N Engl J Med . 2004 Mar 25. 350(13):1287-95. [QxMD MEDLINE Link] .

Leonard, John P., et al. AUGMENT: A Phase III Study of Lenalidomide Plus Rituximab Versus Placebo Plus Rituximab in Relapsed or Refractory Indolent Lymphoma. J Clin Oncol . 2019 May 10. 37 (14):1188-1199. [QxMD MEDLINE Link] . [Full Text] .

Gopal AK, Kahl BS, de Vos S, Wagner-Johnston ND, Schuster SJ, Jurczak WJ, et al. PI3Kd inhibition by idelalisib in patients with relapsed indolent lymphoma. N Engl J Med . 2014 Mar 13. 370(11):1008-18. [QxMD MEDLINE Link] . [Full Text] .

Dreyling M, Santoro A, Mollica L, Leppä S, et al. Phosphatidylinositol 3-Kinase Inhibition by Copanlisib in Relapsed or Refractory Indolent Lymphoma. J Clin Oncol . 2017 Dec 10. 35 (35):3898-3905. [QxMD MEDLINE Link] . [Full Text] .

Flinn IW, Miller CB, Ardeshna KM, Tetreault S, et al. DYNAMO: A Phase II Study of Duvelisib (IPI-145) in Patients With Refractory Indolent Non-Hodgkin Lymphoma. J Clin Oncol . 2019 Apr 10. 37 (11):912-922. [QxMD MEDLINE Link] . [Full Text] .

Ukoniq (umbralisib) [package insert]. Edison, NJ: TG Therapeutics, Inc. February 2021. Available at [Full Text] .

Sehn LH, Herrera AF, Matasar MJ, et al. Addition of polatuzumab vedotin to bendamustine and rituximab (BR) improves outcomes in transplant-ineligible patients with relapsed/refractory (r/r) diffuse large B-cell lymphoma (DLBCL) versus BR alone: Results from a randomized phase 2 study. Blood . 2017. 130:2821. [Full Text] .

Tixier F, Ranchon F, Iltis A, Vantard N, Schwiertz V, Bachy E, et al. Comparative toxicities of 3 platinum-containing chemotherapy regimens in relapsed/refractory lymphoma patients. Hematol Oncol . 2016 Jul 5. [QxMD MEDLINE Link] .

Blay J, Gomez F, Sebban C. The International Prognostic Index correlates to survival in patients with aggressive lymphoma in relapse: analysis of the PARMA trial. Parma Group. Blood . 1998 Nov 15. 92(10):3562-8. [QxMD MEDLINE Link] .

Philip T, Guglielmi C, Hagenbeek A, Somers R, Van der Lelie H, Bron D, et al. Autologous bone marrow transplantation as compared with salvage chemotherapy in relapses of chemotherapy-sensitive non-Hodgkin's lymphoma. N Engl J Med . 1995 Dec 7. 333(23):1540-5. [QxMD MEDLINE Link] . [Full Text] .

Majhail NS, Bajorunaite R, Lazarus HM, et al. Long-term survival and late relapse in 2-year survivors of autologous haematopoietic cell transplantation for Hodgkin and non-Hodgkin lymphoma. Br J Haematol . 2009 Jul 1. [QxMD MEDLINE Link] . [Full Text] .

Malik SM, Liu K, Qiang X, Sridhara R, Tang S, McGuinn WD Jr, et al. Folotyn (pralatrexate injection) for the treatment of patients with relapsed or refractory peripheral T-cell lymphoma: U.S. Food and Drug Administration drug approval summary. Clin Cancer Res . 2010 Oct 15. 16(20):4921-7. [QxMD MEDLINE Link] .

Piekarz RL, Frye R, Prince HM, Kirschbaum MH, Zain J, Allen SL, et al. Phase 2 trial of romidepsin in patients with peripheral T-cell lymphoma. Blood . 2011 Jun 2. 117(22):5827-34. [QxMD MEDLINE Link] .

Bristol Myers Squibb Statement on Istodax® (romidepsin) Relapsed/Refractory Peripheral T-cell Lymphoma U.S. Indication. Bristol Myers Squibb. Available at https://news.bms.com/news/details/2021/Bristol-Myers-Squibb-Statement-on-Istodax-romidepsin-Relapsed-Refractory-Peripheral-T-cell-Lymphoma-U.S.-Indication/default.aspx . August 2, 2021; Accessed: August 3, 2021.

FDA approves Beleodaq to treat rare, aggressive form of non-Hodgkin lymphoma. U.S. Food and Drug Administration. Available at https://wayback.archive-it.org/7993/20170112023831/https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm403929.htm . July 3, 2014; Accessed: February 24, 2021.

O’Connor OA, Masszi T, Savage KJ, Pinter-Brown LC, Foss FM, Popplewell L, et al. Belinostat, a novel pan-histone deacetylase inhibitor (HDACi), in relapsed or refractory peripheral T-cell lymphoma (R/R PTCL): Results from the BELIEF trial (abstract 8507). Presented at the 2013 ASCO Annual Meeting. Chicago, IL. May 31-June 4, 2013.

Jacobsen ED, Kim HT, Ho VT, et al. A large single-center experience with allogeneic stem-cell transplantation for peripheral T-cell non-Hodgkin lymphoma and advanced mycosis fungoides/Sezary syndrome. Ann Oncol . 2011 Jul. 22(7):1608-13. [QxMD MEDLINE Link] . [Full Text] .

Bilora F, Pietrogrande F, Campagnolo E, et al. Are Hodgkin and non-Hodgkin patients at a greater risk of atherosclerosis? A follow-up of 3 years. Eur J Cancer Care (Engl) . 2009 Aug 26. [QxMD MEDLINE Link] .

FDA approves CAR-T cell therapy to treat adults with certain types of large B-cell lymphoma. U.S. Food & Drug Administration. Available at https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm581216.htm . October 18, 2017; Accessed: February 24, 2021.

Neelapu SS, Jacobson CA, Ghobadi A, et al. Five-year follow-up of ZUMA-1 supports the curative potential of axicabtagene ciloleucel in refractory large B-cell lymphoma. Blood . 2023 May 11. 141 (19):2307-2315. [QxMD MEDLINE Link] . [Full Text] .

National Cancer Institute sponsored study of classifications of non-Hodgkin's lymphomas: summary and description of a working formulation for clinical usage. The Non-Hodgkin's Lymphoma Pathologic Classification Project. Cancer . 1982 May 15. 49 (10):2112-35. [QxMD MEDLINE Link] .

Harris NL, Jaffe ES, Stein H, Banks PM, Chan JK, Cleary ML, et al. A revised European-American classification of lymphoid neoplasms: a proposal from the International Lymphoma Study Group. Blood . 1994 Sep 1. 84 (5):1361-92. [QxMD MEDLINE Link] . [Full Text] .

Alaggio R, Amador C, Anagnostopoulos I, et al. The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Lymphoid Neoplasms. Leukemia . 2022 Jul. 36 (7):1720-1748. [QxMD MEDLINE Link] . [Full Text] .

[Guideline] Cheson BD, Fisher RI, Barrington SF, et al. Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: the Lugano classification. J Clin Oncol . 2014 Sep 20. 32 (27):3059-68. [QxMD MEDLINE Link] . [Full Text] .

A predictive model for aggressive non-Hodgkin's lymphoma. The International Non-Hodgkin's Lymphoma Prognostic Factors Project. N Engl J Med . 1993 Sep 30. 329 (14):987-94. [QxMD MEDLINE Link] . [Full Text] .

Federico M, Bellei M, Marcheselli L, Luminari S, Lopez-Guillermo A, Vitolo U, et al. Follicular lymphoma international prognostic index 2: a new prognostic index for follicular lymphoma developed by the international follicular lymphoma prognostic factor project. J Clin Oncol . 2009 Sep 20. 27 (27):4555-62. [QxMD MEDLINE Link] . [Full Text] .

Hoster E, Dreyling M, Klapper W, et al. A new prognostic index (MIPI) for patients with advanced-stage mantle cell lymphoma. Blood . 2008 Jan 15. 111 (2):558-65. [QxMD MEDLINE Link] . [Full Text] .

Jaffe ES. The 2008 WHO classification of lymphomas: implications for clinical practice and translational research. Hematology Am Soc Hematol Educ Program . 2009. 523-31. [QxMD MEDLINE Link] . [Full Text] .

[Guideline] Dreyling M, Ghielmini M, Marcus R, Salles G, Vitolo U, Ladetto M, et al. Newly diagnosed and relapsed follicular lymphoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol . 2014 Sep. 25 Suppl 3:iii76-82. [QxMD MEDLINE Link] . [Full Text] .

Brice P, Bastion Y, Lepage E, Brousse N, Haïoun C, Moreau P, et al. Comparison in low-tumor-burden follicular lymphomas between an initial no-treatment policy, prednimustine, or interferon alfa: a randomized study from the Groupe d'Etude des Lymphomes Folliculaires. Groupe d'Etude des Lymphomes de l'Adulte. J Clin Oncol . 1997 Mar. 15 (3):1110-7. [QxMD MEDLINE Link] .

[Guideline] Zucca E, Copie-Bergman C, Ricardi U, Thieblemont C, Raderer M, Ladetto M, et al. Gastric marginal zone lymphoma of MALT type: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol . 2013 Oct. 24 Suppl 6:vi144-8. [QxMD MEDLINE Link] . [Full Text] .

Rohatiner A, d'Amore F, Coiffier B, Crowther D, Gospodarowicz M, Isaacson P, et al. Report on a workshop convened to discuss the pathological and staging classifications of gastrointestinal tract lymphoma. Ann Oncol . 1994 May. 5 (5):397-400. [QxMD MEDLINE Link] .

Ruskoné-Fourmestraux A, Dragosics B, Morgner A, Wotherspoon A, De Jong D. Paris staging system for primary gastrointestinal lymphomas. Gut . 2003 Jun. 52 (6):912-3. [QxMD MEDLINE Link] . [Full Text] .

[Guideline] Dreyling M, Geisler C, Hermine O, Kluin-Nelemans HC, Le Gouill S, Rule S, et al. Newly diagnosed and relapsed mantle cell lymphoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol . 2014 Sep. 25 Suppl 3:iii83-92. [QxMD MEDLINE Link] . [Full Text] .

Rosenwald A, Wright G, Chan WC, et al. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N Engl J Med . 2002 Jun 20. 346 (25):1937-47. [QxMD MEDLINE Link] . [Full Text] .

[Guideline] Tilly H, Gomes da Silva M, Vitolo U, Jack A, Meignan M, Lopez-Guillermo A, et al. Diffuse large B-cell lymphoma (DLBCL): ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol . 2015 Sep. 26 Suppl 5:v116-25. [QxMD MEDLINE Link] . [Full Text] .

Castillo JJ, Winer ES, Olszewski AJ. Population-based prognostic factors for survival in patients with Burkitt lymphoma: an analysis from the Surveillance, Epidemiology, and End Results database. Cancer . 2013 Oct 15. 119 (20):3672-9. [QxMD MEDLINE Link] .

[Guideline] NCCN Clinical Practice Guidelines in Oncology. Primary Cutaneous Lymphomas. National Comprehensive Cancer Network. Available at https://www.nccn.org/professionals/physician_gls/pdf/primary_cutaneous.pdf . Version 2.2024 — May 6, 2024; Accessed: May 28, 2024.

[Guideline] Senff NJ, Noordijk EM, Kim YH, Bagot M, Berti E, Cerroni L, et al. European Organization for Research and Treatment of Cancer and International Society for Cutaneous Lymphoma consensus recommendations for the management of cutaneous B-cell lymphomas. Blood . 2008 Sep 1. 112 (5):1600-9. [QxMD MEDLINE Link] . [Full Text] .

Willemze R, Jaffe ES, Burg G, et al. WHO-EORTC classification for cutaneous lymphomas. Blood . 2005 May 15. 105 (10):3768-85. [QxMD MEDLINE Link] . [Full Text] .

Kim YH, Willemze R, Pimpinelli N, Whittaker S, Olsen EA, Ranki A, et al. TNM classification system for primary cutaneous lymphomas other than mycosis fungoides and Sezary syndrome: a proposal of the International Society for Cutaneous Lymphomas (ISCL) and the Cutaneous Lymphoma Task Force of the European Organization of Research and Treatment of Cancer (EORTC). Blood . 2007 Jul 15. 110 (2):479-84. [QxMD MEDLINE Link] . [Full Text] .

[Guideline] Trautinger F, Knobler R, Willemze R, Peris K, Stadler R, Laroche L, et al. EORTC consensus recommendations for the treatment of mycosis fungoides/Sézary syndrome. Eur J Cancer . 2006 May. 42 (8):1014-30. [QxMD MEDLINE Link] . [Full Text] .

[Guideline] Willemze R, Hodak E, Zinzani PL, Specht L, Ladetto M, ESMO Guidelines Committee. Primary cutaneous lymphomas: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol . 2018 Oct 1. 29 (Suppl 4):iv30-iv40. [QxMD MEDLINE Link] . [Full Text] .

Olsen E, Vonderheid E, Pimpinelli N, Willemze R, Kim Y, Knobler R, et al. Revisions to the staging and classification of mycosis fungoides and Sezary syndrome: a proposal of the International Society for Cutaneous Lymphomas (ISCL) and the cutaneous lymphoma task force of the European Organization of Research and Treatment of Cancer (EORTC). Blood . 2007 Sep 15. 110 (6):1713-22. [QxMD MEDLINE Link] . [Full Text] .

[Guideline] Olsen EA, Whittaker S, Kim YH, et al. Clinical end points and response criteria in mycosis fungoides and Sézary syndrome: a consensus statement of the International Society for Cutaneous Lymphomas, the United States Cutaneous Lymphoma Consortium, and the Cutaneous Lymphoma Task Force of the European Organisation for Research and Treatment of Cancer. J Clin Oncol . 2011 Jun 20. 29 (18):2598-607. [QxMD MEDLINE Link] . [Full Text] .

[Guideline] Kempf W, Pfaltz K, Vermeer MH, et al. EORTC, ISCL, and USCLC consensus recommendations for the treatment of primary cutaneous CD30-positive lymphoproliferative disorders: lymphomatoid papulosis and primary cutaneous anaplastic large-cell lymphoma. Blood . 2011 Oct 13. 118 (15):4024-35. [QxMD MEDLINE Link] . [Full Text] .

Horwitz SM, Kim YH, Foss F, Zain JM, Myskowski PL, Lechowicz MJ, et al. Identification of an active, well-tolerated dose of pralatrexate in patients with relapsed or refractory cutaneous T-cell lymphoma. Blood . 2012 May 3. 119 (18):4115-22. [QxMD MEDLINE Link] . [Full Text] .

Kim YH, Tavallaee M, Sundram U, Salva KA, Wood GS, Li S, et al. Phase II Investigator-Initiated Study of Brentuximab Vedotin in Mycosis Fungoides and Sézary Syndrome With Variable CD30 Expression Level: A Multi-Institution Collaborative Project. J Clin Oncol . 2015 Nov 10. 33 (32):3750-8. [QxMD MEDLINE Link] .

• Posteroanterior (PA) chest radiograph in a man with thoracic non-Hodgkin lymphoma (NHL) shows mediastinal widening due to grossly enlarged right paratracheal and left paratracheal nodes.
• Posteroanterior (PA) chest radiograph in a 16-year-old male adolescent with thoracic non-Hodgkin lymphoma (NHL) shows subtle enlargement of the lower paratracheal lymph nodes.
• Nonenhanced CT scan through the mediastinum shows multiple enlarged lymph nodes in the prevascular space, in the right and left paratracheal region. Nodes in the left paratracheal region cause the trachea to be indented and narrowed on the left side. Note the small, bilateral pleural effusion
• Nonenhanced CT scan through the mediastinum at the level of the carina shows enlarged tracheobronchial and subcarinal nodes. Note the small bilateral pleural effusion.
• Contrast-enhanced axial CT scan in a child shows hypoattenuating, enlarged, subcarinal lymph nodes with splaying of the tracheal bifurcation.
• Posteroanterior (PA) chest radiograph shows a large mass in the right parahilar region extending into the right upper and middle zones, with silhouetting of the right pulmonary artery. Smaller mass is seen in the periphery of the right lower zone. The masses did not respond to a trial of antibiotics. Core-needle biopsy of the larger lesion revealed NHL deposits in the lung.
• Lateral image shows a large mass in the anterior aspect of the right upper lobe of the lung.
• Posterior bone scan shows no abnormally increased uptake in the thoracic vertebrae. Image shows an unusual pattern of non-Hodgkin lymphoma (NHL) of the upper thoracic vertebra.
• This 28-year-old man was being evaluated for fever of unknown origin. Gallium-67 study shows extensive uptake in the mediastinal lymph nodes due to non-Hodgkin lymphoma (NHL).
• T1-weighted coronal MRI of the thorax in a 55-year-old woman with lower dorsal pain. Note the signal-intensity changes in the body of D12; these are associated with a right-sided, large, paravertebral soft-tissue mass involving the psoas muscle. Biopsy confirmed non-Hodgkin lymphoma (NHL).
• T1-weighted coronal MRI of the thorax in a 55-year-old woman with lower dorsal pain (same patient as in the previous image). Note the signal-intensity changes in the body of D12; these are associated with a right-sided, large, paravertebral soft-tissue mass involving the psoas muscle. Biopsy confirmed non-Hodgkin lymphoma (NHL).
• Positron emission tomography (PET) CT in an 80-year-old woman with diffuse, large B-cell NHL of the skin and subcutaneous tissues that recently transformed from previous low-grade non-Hodgkin lymphoma (NHL). PET shows high level of uptake in the anterior subcutaneous nodule in the chest (white arrows). CT scan of similar nodules (arrowheads) on the anterior left chest does not show PET uptake; these may represent regions of lower-grade NHL. PET image of posterior lesions shows only mild uptake (gray arrow).
• Non-Hodgkin lymphoma of the terminal ileum. Note the doughnut sign, ie, intraluminal contrast material surrounded by a grossly thickened bowel wall. This appearance is highly suggestive of small noncleaved cell lymphoma (Burkitt type).
• Computed tomography of the throat in highly-malignant non-hodgkin lymphoma present as lymph node swelling in a child (transverse section with contrast). DE: Computertomographie des Halses bei einem hoch-malignen Non-Hodgkin
• Malignant lymphoma high grade_B_cell
• Ultrasound throat lymphadenopathy non-hodgkin-lymphoma
•   Table 1. Non-Hodgkin lymphoma staging.
• Table 2. Comparison of Lugano and Paris staging systems
• Table 3. International Society for Cutaneous Lymphoma/European Organization for Research and Treatment of Cancer tumor-node-metastasis classification for cutaneous B-cell lymphoma
• Table 4. International Society for Cutaneous Lymphoma/European Organization for Research and Treatment of Cancer tumor-node-metastasis-blood revised classification for mycosis fungoides and Sezary syndrome
• Table 5. Staging classifications for mycosis fungoides and Sezary syndrome

Contributor Information and Disclosures

Sanjay Vinjamaram, MD, MPH Physician in Hematology/Oncology, Essentia/Innovis Health Cancer Center Sanjay Vinjamaram, MD, MPH is a member of the following medical societies: American Association for the Advancement of Science , Sigma Xi, The Scientific Research Honor Society , American Society for Cell Biology Disclosure: Nothing to disclose.

Dolores A Estrada-Garcia, MD Consulting Staff in Hematology-Oncology, Cancer Care Specialists of Central Illinois Dolores A Estrada-Garcia, MD is a member of the following medical societies: American Society of Clinical Oncology , American Society of Hematology Disclosure: Nothing to disclose.

Francisco J Hernandez-Ilizaliturri, MD Professor of Medicine, Department of Medical Oncology, Associate Professor of Immunology, Department of Immunology, Chief, Lymphoma and Myeloma Section, Director, The Lymphoma Translational Research Program, Roswell Park Cancer Institute, University of Buffalo State University of New York School of Medicine and Biomedical Sciences Francisco J Hernandez-Ilizaliturri, MD is a member of the following medical societies: American Association for Cancer Research , American Society of Hematology Disclosure: Nothing to disclose.

Emmanuel C Besa, MD Professor Emeritus, Department of Medicine, Division of Hematologic Malignancies and Hematopoietic Stem Cell Transplantation, Kimmel Cancer Center, Jefferson Medical College of Thomas Jefferson University Emmanuel C Besa, MD is a member of the following medical societies: American Association for Cancer Education , American Society of Clinical Oncology , American College of Clinical Pharmacology , American Federation for Medical Research , American Society of Hematology , New York Academy of Sciences Disclosure: Nothing to disclose.

Koyamangalath Krishnan, MD, FRCP, FACP Paul Dishner Endowed Chair of Excellence in Medicine, Professor of Medicine and Chief of Hematology-Oncology, James H Quillen College of Medicine at East Tennessee State University

Koyamangalath Krishnan, MD, FRCP, FACP is a member of the following medical societies: Alpha Omega Alpha , American College of Physicians-American Society of Internal Medicine , American Society of Hematology , and Royal College of Physicians

Disclosure: Nothing to disclose.

Lakshmi Rajdev, MD Site Director, Jacobi Medical Center; Assistant Professor, Department of Radiation Oncology, Albert Einstein College of Medicine

Joseph A Sparano, MD Professor of Medicine, Albert Einstein College of Medicine/Cancer Center; Program Director, Director of Breast Medical Oncology, Department of Internal Medicine, Division of Oncology, Montefiore Medical Center

Joseph A Sparano, MD is a member of the following medical societies: American Association for Cancer Research , American College of Physicians , and American Society of Hematology

Francisco Talavera, PharmD, PhD Adjunct Assistant Professor, University of Nebraska Medical Center College of Pharmacy; Editor-in-Chief, Medscape Drug Reference

Disclosure: Medscape Salary Employment

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• Pathology of Primary Bone Lymphoma
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• Hodgkin lymphoma (Hodgkin disease)

Parts of the immune system

The lymphatic system is part of the body's immune system, which protects against infection and disease. The lymphatic system includes the spleen, thymus, lymph nodes and lymph channels, as well as the tonsils and adenoids.

Lymph node clusters

Lymph nodes are bean-sized collections of cells called lymphocytes. Hundreds of these nodes cluster throughout the lymphatic system, for example, near the knee, groin, neck and armpits. The nodes are connected by a network of lymphatic vessels.

Hodgkin lymphoma is a type of cancer that affects the lymphatic system. The lymphatic system is part of the body's germ-fighting and disease-fighting immune system. Hodgkin lymphoma begins when healthy cells in the lymphatic system change and grow out of control.

The lymphatic system includes lymph nodes. They are found throughout the body. Most lymph nodes are in the abdomen, groin, pelvis, chest, underarms and neck.

The lymphatic system also includes the spleen, thymus, tonsils and bone marrow. Hodgkin lymphoma can affect all these areas and other organs in the body.

Hodgkin lymphoma, which used to be called Hodgkin disease, is one of two broad types of lymphoma. The other is non-Hodgkin lymphoma.

Advances in diagnosis and treatment of Hodgkin lymphoma have helped give people with this disease the chance for a full recovery.

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Swollen lymph nodes

One of the most common places to find swollen lymph nodes is in the neck. The inset shows three swollen lymph nodes below the lower jaw.

Signs and symptoms of Hodgkin lymphoma may include:

• Painless swelling of lymph nodes in the neck, armpits or groin.
• Feeling very tired.
• Night sweats.
• Weight loss that happens without trying.
• Itchy skin.

When to see a doctor

Make an appointment with a doctor or other healthcare professional if you have ongoing symptoms that worry you. Hodgkin lymphoma symptoms are like those of many more-common conditions, such as infections. The healthcare professional may check for those causes first.

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Healthcare professionals aren't sure what causes Hodgkin lymphoma. It begins with changes in the DNA of a disease-fighting blood cell called a lymphocyte. A cell's DNA contains the instructions that tell the cell what to do.

The DNA changes tell the cells to multiply quickly and live when other cells would naturally die. The Hodgkin lymphoma cells attract many healthy immune system cells to protect them and help them grow. The extra cells crowd into the lymph nodes and cause swelling and other symptoms.

There are multiple types of Hodgkin lymphoma. The type of lymphoma you have is based on the characteristics of the cells involved in your disease and their behavior. The type of lymphoma you have helps determine your treatment options.

Classical Hodgkin lymphoma

Classical Hodgkin lymphoma is the more common type of this disease. People diagnosed with this type have large lymphoma cells called Reed-Sternberg cells in their lymph nodes.

Subtypes of classical Hodgkin lymphoma include:

• Nodular sclerosis Hodgkin lymphoma.
• Mixed cellularity Hodgkin lymphoma.
• Lymphocyte-depleted Hodgkin lymphoma.
• Lymphocyte-rich Hodgkin lymphoma.

Nodular lymphocyte-predominant Hodgkin lymphoma

This type of Hodgkin lymphoma is much rarer. It involves lymphoma cells sometimes called popcorn cells because of how they look. Usually, it is diagnosed early and may need less intensive treatments than the classical type of Hodgkin lymphoma.

Risk factors

Factors that can increase the risk of Hodgkin lymphoma include:

• Your age. Hodgkin lymphoma is most often diagnosed in people in their 20s and 30s and those over age 65.
• A family history of Hodgkin lymphoma. Having a blood relative with Hodgkin lymphoma increases the risk of Hodgkin lymphoma.
• Being male. People who are assigned male at birth are slightly more likely to develop Hodgkin lymphoma than are those who are assigned female at birth.
• Past Epstein-Barr infection. People who have had illnesses caused by the Epstein-Barr virus are at higher risk of Hodgkin lymphoma than are those who haven't. One example is infectious mononucleosis.
• HIV infection. People who are infected with HIV have an increased risk of Hodgkin lymphoma.

There's no way to prevent Hodgkin lymphoma.

Hodgkin lymphoma (Hodgkin disease) care at Mayo Clinic

• Lymphoma — Non-Hodgkin. Cancer.Net. https://www.cancer.net/cancer-types/41246/view-all. Accessed Dec. 18, 2023.
• Hodgkin lymphoma treatment (PDQ) — Patient version. National Cancer Institute. https://www.cancer.gov/types/lymphoma/patient/adult-hodgkin-treatment-pdq. Accessed Feb. 2, 2024.
• Lymphoma — Patient version. National Cancer Institute. https://www.cancer.gov/types/lymphoma. Accessed Feb. 2, 2024.
• Aster JC, et al. Pathogenesis of Hodgkin lymphoma. https://www.uptodate.com/contents/search. Accessed Feb. 5, 2024.
• Hoffman R, et al. Hodgkin lymphoma. In: Hematology: Basic Principles and Practice. 8th ed. Elsevier; 2023. https://www.clinicalkey.com. Accessed Feb. 5, 2024.
• Ansell SM. Hodgkin lymphoma: 2023 update on diagnosis, risk-stratification and management. American Journal of Hematology. 2022; doi:10.1002/ajh.26717.
• Side effects of chemotherapy. Cancer.Net. https://www.cancer.net/navigating-cancer-care/how-cancer-treated/chemotherapy/side-effects-chemotherapy. Accessed Feb. 6, 2024.
• Side effects of radiation therapy. Cancer.Net. https://www.cancer.net/navigating-cancer-care/how-cancer-treated/radiation-therapy/side-effects-radiation-therapy. Accessed Feb. 6, 2024.
• Side effects of a bone marrow transplant (stem cell transplant). Cancer.Net. https://www.cancer.net/navigating-cancer-care/how-cancer-treated/bone-marrowstem-cell-transplantation/side-effects-bone-marrow-transplant-stem-cell-transplant. Accessed Feb. 6, 2024.
• Distress management. National Comprehensive Cancer Network. https://www.nccn.org/guidelines/guidelines-detail?category=3&id=1431. Accessed Jan. 3, 2024.
• Laurent C, et al. Impact of expert pathologic review of lymphoma diagnosis: Study of patients from the French Lymphopath Network. Journal of Clinical Oncology. 2017; doi:10.1200/JCO.2016.71.2083.
• Goyal G, et al. Association between facility volume and mortality of patients with classic Hodgkin lymphoma. Cancer. 2020; doi:10.1002/cncr.32584.
• Member institutions. Alliance for Clinical Trials in Oncology. https://www.allianceforclinicaltrialsinoncology.org/main/public/standard.xhtml?path=%2FPublic%2FInstitutions. Accessed Feb. 7, 2024.
• Membership institution lists. NRG Oncology. https://www.nrgoncology.org/About-Us/Membership/Member-Institution-Lists. Accessed Feb. 7, 2024.
• Hodgkin lymphoma. National Comprehensive Cancer Network. https://www.nccn.org/guidelines/guidelines-detail?category=1&id=1439. Accessed Feb. 5, 2024.
• Hodgkin's vs. non-Hodgkin's lymphoma: What's the difference?

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Clinical presentation and characteristics of lymphoma in the head and neck region

Katharina storck.

1 Department of ENT, Head and Neck Surgery, Klinikum Rechts der Isar, Technical University of Munich, Ismaninger Strasse 22, 81675 Munich, Germany

Markus Brandstetter

Ulrich keller.

2 Third Department of Internal Medicine, Haematology and Oncology, Klinikum rechts der Isar, Technische Universität München, Ismaningerstr. 22, 81675 Munich, Germany

Andreas Knopf

Associated data.

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

The study analyses clinical characteristics of histologically defined head and neck (H&N) lymphoma to raise the awareness of ENT specialists to the leading symptoms.

From 2003 to 2011, all patients with histologically defined H&N lymphoma from our clinic were evaluated.

This study identified 221 patients with H&N lymphoma comprising 193 non-Hodgkin lymphomas (NHL) and 28 Hodgkin lymphomas (HL). Among NHL there were 77 indolent (iNHL), 110 aggressive (aNHL), six highly aggressive NHL and further 28 HL. Patients with highly aggressive NHL and HL were significantly younger ( p  < 0.0001). Corresponding to the leading symptoms, we found nodal and extranodal involvement. NHL demonstrated manifestation in neck lymph nodes, tonsils, major salivary glands, sinonasal-system and hypopharynx/larynx. HL showed exclusive manifestation in lymph nodes of the neck and the tonsils ( p  < 0.0001). The mean time from first symptoms to diagnosis ranged from 1.5 ± 0.7 months in highly aggressive lymphoma to 7.5 ± 11.5 months in iNHL.

Conclusions

The variable clinical presentation of lymphoma is a challenge for the ENT specialist. Fast diagnosis is crucial for rapid treatment, especially in highly aggressive NHL like the Burkitt-lymphoma and HL. A standardized medical history, clinical examination and imaging evaluations paired with patient’s signs, symptoms and demographic knowledge might indicate lymphoma. Biopsies in the H&N region should always be immediately performed in suspicious findings.

Lymphomas are a heterogeneous group of malignant tumours of the haematopoietic system and are characterized by the aberrant proliferation of mature lymphoid cells or their precursors [ 1 ]. Lymphomas can be divided into two major entities: Hodgkin’s lymphoma (HL) and non-Hodgkin’s lymphoma (NHL). Over 20 different subtypes of NHL have been classified according to the specific subtype of lymphoid cells involved.

Several classifications have been developed over the years for lymphomas. The currently used classification is that of the World Health Organization (WHO) and is based on the principles of the Revised European-American Classification of Lymphoid Neoplasm (REAL) from 1994 [ 2 ]. The latest update of the classification was published in two reviews in Blood in 2016 [ 3 – 5 ]. The subtype of the lymphomas is defined based on the cell of origin: B-cell lymphomas, T-cell and natural killer-cell lymphomas (T/NK-NHL) and HL [ 6 , 7 ]. The two recent WHO classifications from 2008 and 2016 include and encompass (as previously stated in previous classifications) morphology, immunophenotype, genetic and clinical features in order to define “real” diseases [ 3 , 4 ]. HLs frequently involve lymph nodes of the neck and mediastinum, whereas extranodal sites account for only 5% of HLs for example in the tonsils. In contrast, approximately 30% of NHLs show heterogeneous extranodal manifestations, such as in the major salivary glands, paranasal sinuses, mandible, maxilla and Waldeyer’s ring (largely depending and often characteristic for the specific NHL subtype) [ 3 , 8 ]. Other than the gastrointestinal tract, the head and neck region is frequently involved as an extranodal site in NHL, affecting 11–33% of patients [ 9 ]. The clinical behaviour and manifestations of lymphomas in the head and neck region usually lack specific characteristics that would enable attribution to a specific lymphoma entity without biopsy and histological evidence. In particular, with regard to lymphomas having an aggressive course, immediate histological evidence is crucial for patient management, early treatment initiation and often for the outcome [ 10 , 11 ]. Available imaging techniques (ultrasound, computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET)) fail to distinguish HL from NHL and cannot differentiate their various subtypes, necessitating pathological diagnosis [ 8 ]. Sometimes, clinical parameters and the various sites within the head and neck can help to distinguish between the two categories as they each have predilections as mentioned above [ 3 ]. Typical symptoms can include an indolent lymphadenopathy (here, we concentrate on the cervical lymph nodes), fatigue, occasionally B-symptoms such as fever > 38 °C, night sweats and weight loss (> 10% within 6 month), susceptibility to infections and changes in the haemogram. Especially with respect to the differential blood count, iNHL presents cytopenia more often than aNHL but it does not lead to the diagnosis as a single parameter. In chronic lymphatic leukemia (CLL), for example, the frequency of lymphocytes in the differential blood count is elevated as a characteristic sign. Further symptoms of lymphoma might include anaemia, leucopenia/leucocytosis and thrombopenia, although specific serum and blood parameters might sometimes also suggest indolent vs aggressive lymphoma, e.g. elevated lactate dehydrogenase (LDH) in cases of highly proliferative disease or increased β2-microglobulin. The most important differential diagnosis for head and neck lymphadenopathy is infection or lymph-node metastasis from regional or distant primaries being affected by solid cancer.

Our retrospective study includes 221 patients who were suffering from NHL and HL and who were consecutively diagnosed in the Department of Otorhinolaryngology, Head and Neck surgery. Histologically confirmed lymphomas were classified according to the clinical system defined below. Thorough analysis of epidemiological data, leading symptomatology, clinical disease presentation and laboratory testing were carried out to identify clinical parameters in order to expedite diagnostic regimes/work-up.

Patients and methods

All patients presenting with head and neck symptoms that resulted in the histologically established diagnosis of lymphoma from January 2003 to December 2011 ( n  = 221) were included in this retrospective study. The study has been approved by the ethic committee of the Technical University of Munich (Permit Number: 493/17).

A standardized medical history was obtained from all patients: clinical examination, age at diagnosis, gender, location in the head and neck region, imaging evaluations (especially ultrasound), leading symptoms (B-symptoms; fever, night sweats, weight loss), time to diagnosis, known risk factors (HIV, EBV), histological findings and survival outcome (Munich cancer centre). All patients underwent clinical examination and a high-resolution B-mode ultrasound of the neck (S2000, tissue harmonic imaging, 9 MHz linear array, Siemens, Germany). Blood chemistry (including LDH and C-reactive protein (CRP)) and a complete blood count with a leucocyte differential count were undertaken. During diagnostic work-up (staging), all patients also underwent contrast CT-scans of the neck, chest, abdomen and pelvic cavity and bone marrow biopsy. If central nervous system involvement was suspected, staging also included contrast MR imaging of the brain and/or the spine.

Lymphomas were classified according to the lymphoma classification effective at the time: up until 2008, we used the Revised European American Lymphoma Classification (REAL) [ 2 ], and starting from 2008, we employed the WHO classification [ 3 , 4 ]. For practical purposes and because the observation period spanned two classifications, we refer here to lymphomas in two main categories, namely Hodgkin lymphomas (HL) and non-Hodgkin lymphomas (NHL). NHL were further clinically subgrouped into 1. indolent lymphoma (iNHL) (including follicular lymphomas and margional zone lymphomas), 2. aggressive lymphoma (aNHL) (e.g. DLBCL) and 3. highly aggressive lymphoma (Burkitt lymphoma and lymphoblastic lymphomas) (Fig. ​ (Fig.1 1 ).

Frequency of histologic subtypes within the iNHL, aNHL and highly aggressive lymphoma and HL

Statistical analyses were performed by using unpaired t-tests and one-way ANOVA testing (SPSS Inc., Chicago, IL). Post-hoc analysis was carried out with Tukey’s test. A p value of < 0.05 was considered statistically significant and a p value < 0.001 was defined as highly significant.

Epidemiology and characteristics of the head and neck cohort

A total of 221 patients were included in this study: 193 with NHL and 28 with HL. With respect to the clinical classification system, we included 77 indolent NHL (iNHL), 110 aggressive lymphomas (aNHL), 6 highly aggressive lymphomas and 28 HL (Fig. ​ (Fig.1). 1 ). The median age for indolent and for aggressive lymphoma was 70 years, for highly aggressive lymphoma 34 years and for HL 33 years. Patients with highly aggressive lymphoma and HL were significantly younger than their counterparts with less aggressive types ( p  < 0.0001; Table ​ Table1). 1 ). The study comprised 114 [52%] males and 107 [48%] females without differences between the groups (Table ​ (Table1 1 ).

Frequency of histologic types of head and neck lymphoma including also epidemiology, symptomatology, disease manifestations, localization and laboratory findings

aNHL Aggressive non-Hodgkin lymphoma, haNHL Highly aggressive non-Hodgkin lymphoma, HL Hodgkin lymphoma, iNHL Indolent non-Hodgkin lymphoma

The mean time from first symptoms to diagnosis ranged from 1.5 ± 0.7 months in highly aggressive lymphoma to 7.5 ± 11.5 months in indolent lymphoma. This difference was not statistically significant (Table ​ (Table1 1 ).

Independent from the classification the vast majority of lymphoma patients ( n  = 168) suffered from cervical masses as the leading symptom. Fifty-nine patients complained of odyno−/dysphagia. Globus pharyngis, dysphonia and dyspnea occurred infrequently. Occult lymphoma without clinical symptoms was diagnosed in five patients during sonographic procedure for another disease (Table ​ (Table1). 1 ). The distribution of leading symptoms differed significantly between the groups ( p  < 0.05, Table ​ Table1). 1 ). Whereas patients with highly aggressive lymphoma and HL usually presented with a cervical mass and/or odyno/dysphagia, patients with indolent and aggressive lymphoma demonstrated a broad variety of leading symptoms (Table ​ (Table1). 1 ). B-symptoms occurred in 28 (13%) patients (NHL, n  = 25; HL, n  = 3).

Disease manifestation

Corresponding to the diverse leading symptoms, we found a nodal and an extranodal involvement of the head and neck organs. NHL demonstrated manifestation in neck lymph nodes ( n  = 83), tonsils ( n  = 60), major salivary glands ( n  = 32), the sinonasal system ( n  = 6) and the hypopharynx/larynx ( n  = 7) whereas HL showed exclusive manifestation in neck lymph nodes ( n  = 27) and the tonsils ( n  = 1). Although highly aggressive NHL and HL exclusively originated in indolent neck lymph nodes and the tonsils, indolent and aggressive lymphomas showed a distinct disease heterotopia ( p  < 0.0001; Table ​ Table1). 1 ). In our study, extranodal head and neck manifestation occurred in 57% NHL and in 4% HL. NHL presented a unilateral localization in 84% of cases, and HLs were unilateral in 79% of cases. We found n  = 12 cases (20%) of NHL with bilaterally affected tonsils. Systemic involvement was seen in n  = 43 [56%] patients with iNHL, in n  = 75 [68%] patients with aNHL, in n = 6 [100%] patients with highly aggressive lymphoma, and in n  = 22 [79%] patients with HL.

Laboratory findings

Basic laboratory testing included blood counts, C-reactive protein (CRP) and lactate dehydrogenase (LDH). The level of leucocytes (normal range: 4.0–9.0 [G/l]) differed significantly between the groups ( p  < 0.05; Table ​ Table1). 1 ). However, all levels ranged within the norm. Patients with iNHL exhibited a leucocyte level of 11.8 ± 12.0 (mean ± SD), patients with aNHL 7.7 ± 3.6 and patients with HL 8.0 ± 3.0. In patients with highly aggressive lymphoma, the leucocyte level was significantly decreased at 4.5 ± 3.2. Similar results were seen in haemoglobin levels, which showed a normal level in all the groups, with the lowest level in highly aggressive lymphomas at 10.5 ± 6.1 (mean ± SD). Differences between haemoglobin levels were not statistically significant. CRP (normal range: < 0.5 [mg/dl]) was slightly elevated in all groups, with the highest level in HL at 3.30 ± 4.30 (mean ± SD). LDH levels (normal range: < 244 [U/l]) were elevated in aNHL at 278 ± 291[U/l] (mean ± SD) and in HL at 271 ± 121[U/l] (Table ​ (Table1 1 ).

Survival outcome

Available overall survival data for the previously defined subgroups are shown in Fig. ​ Fig.2 2 .

Overall survival data for the previously defined subgroups

Using the cox’s regression for forward selection, we also evaluated the survival rate depending on the laboratory findings. We could not find any significant differences in the survival rate depending on (pathological) laboratory findings (Fig. ​ (Fig.3 3 ).

Survival rate depending on the laboratory findings

Lymphoma is the third most common malignancy worldwide representing 3% of all malignant tumours. With 12% of all malignant tumours of the head and neck region, lymphomas are the third most frequent malignancy after squamous cell carcinoma (46%) and thyroid carcinoma (33%) [ 12 , 13 ] and should thus always be taken into consideration in cases of unknown cervical or oral masses. Misinterpretation of the clinical appearance and of the radiological findings (ultrasound, CT-scan, MRI) can lead to delay in diagnosis, delayed treatment initiation and impairment of the patient’s prognosis. In the current study, we have analysed clinical and epidemiological data of the entire lymphoma cohort diagnosed within an eight-year time frame at our ENT Department in order to bring to the attention of ENT specialists the specific clinical symptoms that allow the early diagnosis of lymphomas. In agreement with recent publications, we saw no differences in the gender distribution. Patients with HL (33 years) and highly aggressive lymphomas (34 years) were significantly younger than patients having other lymphoma subtypes (70 years) [ 14 , 15 ]. In our study, we found 193 NHL and 28 HL. Concordant with the present literature, diffuse large B-cell NHL (DLBCL) comprised the largest percentage of NHL in the head and neck region with 36.7% of cases [ 16 , 17 ]. Cervical lymphadenopathy (syn. nodal) is the most common site for both NHL and HL in the head and neck region. Differentiation from other causes of pathological lymph node enlargement caused by infectious diseases (CMV, EBV) or metastatic squamous cell carcinoma is crucial and often difficult and requires histopathological assessment. Certain differences, including the history of alcohol and / or tobacco use, the age of the patient, abnormalities in the clinical ENT examination, constitutional symptoms and systemic lymphadenopathy, may increase the probability of one versus the other. Associated mediastinal adenopathy is more common in HL and abdominal adenopathy in NHL [ 15 ]. In our series, we found n  = 110 (50%) patients presenting with cervical lymphadenopathy. Of these, 90 were unilateral and only 20 presented with a bilateral cervical lymphadenopathy. In HL, 22/27 were unilateral. An important aspect for ENT and maxillofacial specialists is the variety of extranodal sites. Taking all lymphomas together, we found 111 (50%) lymphomas in extranodal sites. In particular, NHL contributed to this high proportion. With n  = 110 of 193 NHL (57%), NHL presented an extranodal site in a surprisingly large number of cases. In the literature, 25–30% NHL occur in extranodal sites [ 18 ]. In HL, we found an extranodal site in just one case (palatine tonsils), whereas all other patients presented with cervical lymph nodes (96%). The literature also describes > 90% manifestations of HL occurring in the lymph nodes and only 1–4% involving extranodal areas [ 8 , 14 , 19 ]. The extranodal sites included in this study were the major salivary glands ( n  = 33), sinonasal system ( n  = 6), palatinal tonsils/nasopharynx ( n  = 60) and hypopharynx/larynx ( n  = 7). The literature also describes extranodal sites such as the palate, buccal mucosa, maxilla and mandible [ 8 , 20 ]. In our clinic, the patients with lymphomas in bone regions usually attend the Department of Maxillofacial Surgery; thus, patients with extranodal sites are partially preselected. The leading symptoms were correlated with the localization of the tumour mass, the majority of patients presenting with a cervical mass (76%) followed by odyno−/dysphagia, globus pharyngis, dysphonia and dyspnea.

Taking all patients together ( n  = 221), we found that only n  = 8 (13%) of the patients presented with constitutional symptoms or specific B-symptoms. Only n = 3 patients with HL suffered from B-symptoms. This low percentage agrees with the data in the literature [ 21 , 22 ]. Concentration on the presence or absence of B-symptoms might thus mislead the physician, as the rate of patients without such symptoms is high. The same also applies to results from blood and serum testing, as the majority of all of our patients had normal haemoglobin and leucocyte counts and only a few had slightly elevated levels of LDH and CRP. According to the WHO classification, two major subtypes of NHL (DLBCL 70–80% and Burkitt 7–20%) are related to HIV [ 23 ]. In our cohort, we found two HIV-positive patients. One of them was diagnosed with a nodal DLBCL and the other with nodal plasmablastic lymphoma (PBL), an aggressive and rare DLBCL subtype that is commonly found in patients with HIV [ 24 ].

Burkitt lymphoma (BL) is listed in the WHO’s classification of lymphoid tumours as an “aggressive B-cell non-Hodgkin’s lymphoma” characterized by a high degree of proliferation of malignant cells and deregulation of the MYC gene [ 25 ]. In our study, we found 5 cases of BL and all were male, as reported in the literature [ 20 ]; none of them were associated with HIV or EBV [ 26 ]. With a median age of 34 years, these patients were significantly younger ( p  < 0.0001) than patients suffering from iNHL or aNHL. Only 1.2% of BL are of extranodal origin in the head and neck [ 27 ]. We found three to be extranodal in the tonsils. The median time to diagnosis was 1.5 months. Despite its highly aggressive nature, BL is a curable lymphoma. Patients have a better prognosis when the diagnosis is established rapidly and if they present with a limited stage [ 28 ]. BL patients exhibited the lowest leucocyte and haemoglobin levels but the levels still ranged within the norm and the number of patients was too low to make a statistically valid statement. Patients with highly aggressive lymphomas and HL all presented with either a cervical mass as a sign of a nodal lymphoma or odynophagia / dysphagia, with the tonsils as the extranodal site in all cases, reflecting their admission to the ENT Department. These findings were highly significant compared with iNHL and aNHL aggressive lymphomas with a larger variety of localizations and leading symptoms. All six highly aggressive lymphomas showed a unilateral cervical mass but a systemic dissemination.

All Patients received standard therapies through the hematology department or associated hematologists/oncologist.

The focus of this report is to describe the different clinical presentations of lymphomas in the head and neck region and to raise awareness on the wide variety of symptoms. As we included all types of lymphoma that may manifest in the head and neck region, there is a large variety standard therapy approaches which sometimes were also adapted to comorbidity. Thus, the intend of this report was not to focus on this clearly important issue which is covered by numerous publications and results from prospective studies. Furthermore, treatment standards have evolved during the observation time of the patient groups described herein.

Concerning the survival outcome, we could see significant differences between the groups as expected. In the blood and serum testings we did not see any differences concerning the survival rate, which strengthens our assumption that we can not identify lymphomas in the head and neck region only by laboratory findings.

Lymphomas comprise 12% of all head and neck malignancies. The variable clinical presentation of lymphoma, in addition to the nodal involvement, is sometimes a challenge for the ENT specialist. A rapid diagnosis is crucial for early treatment initiation, especially in cases of BL and HL, which mostly affect younger patients. A standardized medical history, clinical examination and imaging evaluations (especially ultrasound) paired with patient’s signs, symptoms and demographic knowledge (e.g. age, gender, HIV, EBV) may lead to a correct diagnosis and accelerated the decision for a biopsy.

Tumours in the head and neck are easily accessible and a biopsy should immediately be performed following suspicious findings. In particular, for NHL with extranodal involvement in the head and neck occurring at a frequency of 20–30%, biopsy should always be part of the diagnosis in any head and neck lesion, including those in the oral cavity, major salivary glands, oropharynx, nasopharynx, paranasal sinus and larynx. Unilaterality, the absence of EBV or other acute viruses, the absence of an obvious tumour and a systemic involvement (if previously noted at the first presentation) should alert the ENT specialist to lymphomas, even in the absence of B-symptoms or blood disturbances.

Acknowledgments

This work was supported by the German Research Foundation (DFG) and the Technical University of Munich within the funding programme Open Access Publishing.

The corresponding author states no financial or other relationships with other people or organizations, which may lead to a conflict of interest.

Availability of data and materials

Authors’ contributions.

Conceived and designed the study: KS, AK, MB. Performed the study and analysed the data: KS, MB, UK, AK. Wrote the paper: KS. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The study has been approved by the ethic committee of the Technical University of Munich (Permit Number: 493/17).

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

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Jan 03, 2020

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Lymphoma. Farjah Hassan AlGahtani Assistant Professor, Consultant Hematology Director of Transfusion Medicine and Blood Bank. Overview. Concepts, classification, biology Epidemiology Clinical presentation Diagnosis Staging Three important types of lymphoma. Conceptualizing lymphoma.

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Lymphoma Farjah Hassan AlGahtani Assistant Professor, Consultant Hematology Director of Transfusion Medicine and Blood Bank

Overview • Concepts, classification, biology • Epidemiology • Clinical presentation • Diagnosis • Staging • Three important types of lymphoma

Conceptualizing lymphoma • neoplasms of lymphoid origin, typically causing lymphadenopathy • leukemia vs lymphoma • lymphomas as clonal expansions of cells at certain developmental stages

ALL CLL Lymphomas MM Neutrophils AML Myeloproliferative disorders Myeloid progenitor Eosinophils Hematopoietic stem cell Basophils Monocytes Platelets Red cells naïve germinal center B-lymphocytes Plasma cells Lymphoid progenitor T-lymphocytes

CLL MM ALL DLBCL, FL, HL B-cell development memory B-cell germinal center B-cell stem cell mature naive B-cell lymphoid progenitor progenitor-B pre-B immature B-cell plasma cell Bone marrow Lymphoid tissue

Clinically useful classification Biologically rational classification • Diseases that have distinct • morphology • immunophenotype • genetic features • clinical features • Diseases that have distinct • clinical features • natural history • prognosis • treatment Classification

Lymphoma classification(2001 WHO) • B-cell neoplasms • precursor • mature • T-cell & NK-cell neoplasms • precursor • mature • Hodgkin lymphoma Non- Hodgkin Lymphomas

Category Survival of untreated patients Curability To treat or not to treat Non-Hodgkin lymphoma Indolent Years Generally not curable Generally defer Rx if asymptomatic Aggressive Months Curable in some Treat Very aggressive Weeks Curable in some Treat Hodgkin lymphoma All types Variable – months to years Curable in most Treat A practical way to think of lymphoma

Mechanisms of lymphomagenesis • Genetic alterations • Infection • Antigen stimulation • Immunosuppression

Epidemiology of lymphomas • 5th most frequently diagnosed cancer in both sexes • males > females • incidence • NHL increasing • Hodgkin lymphoma stable

Incidence of lymphomas in comparison with other cancers in Canada

Age distribution of new NHL cases in Canada

Age distribution of new Hodgkin lymphoma cases in Canada

Risk factors for NHL • immunosuppression or immunodeficiency • connective tissue disease • family history of lymphoma • infectious agents • ionizing radiation

Clinical manifestations • Variable • severity: asymptomatic to extremely ill • time course: evolution over weeks, months, or years • Systemic manifestations • fever, night sweats, weight loss, anorexia, pruritis • Local manifestations • lymphadenopathy, splenomegaly most common • any tissue potentially can be infiltrated

Other complications of lymphoma • bone marrow failure (infiltration) • CNS infiltration • immune hemolysis or thrombocytopenia • compression of structures (eg spinal cord, ureters) • pleural/pericardial effusions, ascites

Diagnosis requires an adequate biopsy • Diagnosis should be biopsy-proven before treatment is initiated • Need enough tissue to assess cells and architecture • open bx vs core needle bx vs FNA

Stage I Stage II Stage III Stage IV Staging of lymphoma A: absence of B symptoms B: fever, night sweats, weight loss

Three common lymphomas • Follicular lymphoma • Diffuse large B-cell lymphoma • Hodgkin lymphoma

Relative frequencies of different lymphomas Non-Hodgkin Lymphomas Diffuse large B-cell Hodgkin lymphoma NHL Follicular Other NHL ~85% of NHL are B-lineage

Follicular lymphoma • most common type of “indolent” lymphoma • usually widespread at presentation • often asymptomatic • not curable (some exceptions) • associated with BCL-2 gene rearrangement [t(14;18)] • cell of origin: germinal center B-cell

defer treatment if asymptomatic (“watch-and-wait”) • several chemotherapy options if symptomatic • median survival: years • despite “indolent” label, morbidity and mortality can be considerable • transformation to aggressive lymphoma can occur

Diffuse large B-cell lymphoma • most common type of “aggressive” lymphoma • usually symptomatic • extranodal involvement is common • cell of origin: germinal center B-cell • treatment should be offered • curable in ~ 40%

Hodgkin lymphoma Thomas Hodgkin (1798-1866)

Epidemiology • ~ 20 000 new cases in North America and Europe every year • Annual incidence 2.7/100 000 per year • Annual mortality only 0.5/100 000 per year • North American lifetime risk – 1/250 to 1/300 • Young adults • 90% in adults 16-65 • Median Age 35 • Slight male predominance • Much less frequent in eastern Asian populations

Associated (etiological?) factors • EBV infection • smaller family size • higher socio-economic status • caucasian > non-caucasian • possible genetic predisposition • other: HIV? occupation? herbicides?

The EBV Association • 3x increased risk Hodgkins with serologically confirmed infectious mononucleosis • EBV genomes detected in ~ 1/3 of Hodgkin lymphoma tissues (developed countries) • Highest proportion mixed cellularity • Population study showed high pre-diagnostic titres of EBV in patients later diagnosed with Hodgkin’s • ?causative – especially in younger patients

Pathology • B cell neoplasm • Unique due to the relative paucity of clonal malignant cells in a background of reactive inflammatory cells • 2 distinct entities • Nodular Lymphocyte predominant HL • L&H cell “popcorn cell” • Classical HL • Reed Sternberg cell • 4 subtypes

Classical Hodgkin Lymphoma

Hodgkin lymphoma • cell of origin: germinal centre B-cell • Reed-Sternberg cells (or RS variants) in the affected tissues • most cells in affected lymph node are polyclonal reactive lymphoid cells, not neoplastic cells

Reed-Sternberg cell

Reed Sternberg Cell • “owl’s eye” • 2 nuclear lobes with large inclusion like nucleoli (eosinophilic) • Clear halo around nucleolus (chromatin condensed to nuclear membrane) • Abundant cytoplasm – usually eosinophilic • Lymphocytic and Histiocytic Cell • “popcorn cell” • Polylobated nucleus • Lack of prominent eosinophilic nucleoli • Lack of halo

RS cell and variants classic RS cell lacunar cell popcorn cell (lymphocyte predominance) (mixed cellularity) (nodular sclerosis)

A possible model of pathogenesis loss of apoptosis transforming event(s) EBV? cytokines germinal centre B cell RS cell inflammatory response

Nodular Lymphocyte Predominant Hodgkin’s Lymphoma • 5-10% of patients • “popcorn cell” • Positive for CD 45 • express B-cell associated antigens CD19, CD20, CD22, CD79a, EMA • lack CD15 and CD30 • Background of primarily B lymphocytes +/- histiocytes • Commonly presents early stage (~80%) • 4:1 M:F • slightly higher risk of development of NHL (2% to 5%) • Usually DLBCL • Some treatment differences compared with classical Hodgkin’s

Classical Hodgkin’s Lymphoma • Nodular Sclerosis • Mixed Cellularity • Lymphocyte-depleted • Lymphocyte-rich • CD 15 and CD 30 positive +/- CD 20

Nodular Sclerosis • partially nodular pattern with fibrous bands separating the nodules • lacunar type RS cells - multilobated nuclei and small nucleoli with abundant pale cytoplasm that retracts in formalin-fixed sections producing an empty space • 40%-70% of patients • Commonly present early stage (~70%) • Often confined above the diaphragm • Slight female predominance • Commonly adolescents and young adults

Mixed Cellularity • Classic RS cells common • Background of lymphocytes, eosinophils, plasma cells and histiocytes • 30%-50% of patients • More commonly presents with advanced stage disease, B symptoms • Pediatric and older patients

Lymphocyte-depleted • Classic RS cells with hypocellular fibrotic or reticular background • Presents more commonly in older patients • Commonly advanced stage • Less common involvement of peripheral nodes and mediastinum • Lymphocye-rich • Similar to NLPHL but has classical immunophenotype

Clinical Presentation • Painless lymphadenopathy • Contiguous spread between lymphoid regions • Usually begins supra diaphragmatically • Regional sub diaphragmatic disease < 10% • Symptoms associated with compressive effect • *mediastinal mass • Abdominal/inguinal • “B symptoms” • Wt loss > 10% over 6 months • Persistent fever >38.2 • Drenching night sweats • Puritis

Weird and wonderful… • Alcohol induced pain • Nephrotic syndrome • paraneoplastic secondary to lymphokines • Dermatologic • ichthyosis, acrokeratosis (Bazex syndrome), urticaria, erythema multiforme, erythema nodosum, necrotizing lesions, hyperpigmentation, and skin infiltration

Neurologic • cerebellar degeneration, chorea, neuromyotonia, limbic encephalitis, subacute sensory neuronopathy, subacute lower motor neuronopathy, and the stiff man syndrome • Cholestatic liver disease • Hypercalcemia

Modified Ann Arbour Staging System • I • Single lymph node region (I) • or one extralymphatic site (IE) • II • Two or more lymph node regions, same side of the diaphragm (II) • or local extralymphatic extension plus one or more lymph node regions same side of the diaphragm (IIE)

III • Lymph node regions on both sides of the diaphragm (III) • Which may be accompanied by local extralymphatic extension (IIIE) • IV • Diffuse involvement of one or more extralymphatic organs or sites

A = no B symptoms • B = atleast one of • Unexplained weight loss > 10% during preceding 6 months • Recurrent unexplained fever > 38 • Recurrent night sweats • Bulky disease • Single mass > 10 cm largest diameter

Staging Evaluation • Pathology Review • History looking for B symptoms or other symptoms suggesting systemic disease • Physical for lymphadenopathy and organomegaly • CBC and ESR • Cr, ALP, LDH, bili, Ca, AST, albumin, SPEP • CXR – PA and lat • CT neck, thorax, abdomen and pelvis

Bone marrow aspirate and biopsy if • B symptoms • WBC < 4 • Hgb <120 (women) 130 (men) • Platelets < 125 • ENT examination if • Stage IA or IIA disease with upper cervical lymph node involvement (supra-hyoid)

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Early autologous and/or allogeneic stem cell transplantation for adult patients with advanced stage T- lymphoblastic leukemia/lymphoma or Burkitt lymphoma. A retrospective single-centre analysis

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• Published: 03 September 2024

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• N. Steiner 1 ,
• K. Baier 1 ,
• D. Ritter 1 ,
• J. Rudzki 1 ,
• G. Hetzenauer 1 ,
• S. Köck 1 ,
• B. Kircher 1 ,
• E. Gunsilius 1 ,
• D. Wolf 1 &
• D. Nachbaur 1  

T-cell acute lymphoblastic leukemia/lymphoma (T-ALL/LBL) and Burkitt lymphoma (BL) are uncommon, highly aggressive diseases originating either from immature precursor T cells or from mature B cells in BL. We retrospectively analyzed the outcome of an early autologous and/or allogeneic stem cell transplantation (SCT) concept in 28 patients with advanced stage T-ALL/LBL and BL after three to four remission induction/consolidation chemotherapy cycles. Considering only patients in first complete remission (CR), the 5-year overall survival (OS) and event-free survival (EFS) was 91% in patients with BL and 73% in patients with T-ALL/LBL with a 5-year relapse incidence (RI) of 9% in patients with BL and 27% in patients with T-ALL/LBL. All relapsing patients finally succumbed to the disease ( n  = 10) or complications/toxicity after having received a salvage allogeneic transplant ( n  = 5). Despite the low patient number our retrospective single-centre analysis by incorporating an early intensive high-dose chemo-/radiotherapy strategy with either autologous or allogeneic stem cell transplantation, although preliminary, show promising long-term outcome. Further studies are highly warranted to better define those patients who might benefit most from such a treatment approach.

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Introduction

T lymphoblastic leukemia/lymphoma (T-ALL/LBL) and Burkitt lymphoma (BL) are uncommon, highly aggressive B and T cell neoplasms [ 1 , 2 ]. T-ALL/LBL is committed to immature precursor T lymphoblasts, clinically characterized by bone marrow or blood involvement with or without primary involvement of the thymus and nodal or extranodal sites. It is more common in male adolescents with a male to female ratio of 2.5. A bone marrow involvement of > 25% blasts is used as the threshold for defining leukemia [ 2 , 3 , 4 , 5 , 6 , 7 ].

Burkitt lymphoma originates from mature B cells located in the germinal center or post germinal center and can be divided into three distinct entities (i) endemic, which is associated with the Epstein-Barr virus occurring mainly in equatorial Africa and South America and mainly affecting children under the age of 18 with an incidence of 3–6/100.000 per year (ii) immunodeficiency-related, typically associated with HIV with an incidence of 22/100.000 per year in the United States and (iii) sporadic BL, which occurs mainly in Europe, East Asia and North America, with a median age at diagnosis of 45 years and an incidence of 2.5/ per million per year in adults [ 1 , 8 , 9 , 10 ]. The clinical presentation is characterized by an extremely short doubling time often presenting in extra nodal (e.g. abdominal) sites or as an acute leukemia (1,10). Translocations involving the myc oncogene on chromosome 8 are highly characteristic but not specific [ 11 ].

Current treatment approaches using ALL-based regimen with or without autologous stem cell transplantation or DA-EPOCH give survival rates of 90% for BL and 70% for adult T-ALL/LBL [ 12 , 13 ].

Herein, we report our single-centre approach using a short and intensive ALL-like induction/consolidation according to the respective GMALL protocols followed by autologous and/or allogeneic SCT for the treatment of advanced T-ALL/LBL and BL.

Patients and methods

Patient demographics.

Between Dec 2007 and May 2023, 28 patients with either advanced stage T-ALL/LBL ( n  = 13) or advanced BL (Ann Arbor III-IV, n  = 15), received a first autologous ( n  = 22) or first allogeneic ( n  = 6) SCT whenever possible in first CR after 3–4 ALL-based induction/consolidation according to the GMALL protocol 07/2002 amendment 6 for T-ALL/LBL and according to the GMALL-B-ALL/NHL protocol 2002 Amend IX 03/2010 for patients with BL. Patient demographics are shown in Table  1 . Five patients (39%) in the T-precursor cohort initially presented as T-ALL. Eight patients with T-ALL/LBL and 11 patients with BL achieved a first CR before stem cell transplantation. All patients gave written informed consent. All autologous stem cell grafts were analyzed by clonality testing by TCR or IG gene rearrangement, next generation flow cytometry, conventional cytogenetics, and myc -FISH analysis in patients with BL. Standard BEAM ( n  = 22) conditioning was used for autologous transplants. Patients with a very high-risk constellation according to the respective GMALL protocol were undergoing a first allogeneic transplant, either received mainly TBI-based MAC or BUFLU- or FBM-based RIC conditioning regimen.

Study endpoints

The primary endpoint was overall survival (OS). Secondary endpoints were event-free survival (EFS), non-relapse mortality (NRM), and relapse incidence (RI).

Statistical methods

Data were retrospectively analyzed as of December 2022. All statistics were computed using NCSS Statistical Software version 19.0.5. The probabilities of OS were calculated using the Kaplan–Meier method from the date of the first transplant until death. EFS was calculated from the date of the first transplant until relapse or death whatever occurred first. The cumulative RI was calculated from the date of the first transplant until relapse with death without relapse as competing risk. The cumulative incidence of NRM was calculated from the date of the first transplant to the date of death with death from relapse as competing risk.

According to the EBMT statistical guidelines patients receiving a second allogeneic transplant because of relapse after first transplant were neglected and considered only once for calculations of OS, EFS, and NRM [ 14 ].

Overall and event-free survival

The 5-year and 10-year OS of patients with BL was 67%. The OS of patients with T-ALL/LBL was 65 and 48% after 10 years.

In patients in first CR the 5-year and 10-year OS in patients with BL was 91%, and 73% and 49% in patients with T-ALL/LBL, respectively. (Fig.  1 ) .

Overall survival of stem cell transplant patients with T-ALL/LBL and BL in CR1

The 5- and 10-year EFS for patients with BL was constantly 67%. In patients with T-ALL/LBL the 5- and 10-year EFS was 65% and 52%. In patients in CR1, the 5- and 10-year EFS was 91% in patients with BL and 73% and 55% in patients with T-ALL/LBL (Fig.  2 ).

Event-free survival of stem cell transplant patients with T-ALL/LBL and BL in CR1

One patient suffered life-threatening multi-organ failure during induction phase 1 and was only able to receive an autologous SCT 20 months after diagnosis in partial remission 2.

Non-relapse mortality and relapse incidence

Overall, 10/28 patients died, all of them having documented disease relapse after their first SCT. Of these, five pts. received a second allogeneic SCT either from a matched ( n  = 3) or mismatched unrelated/family donor ( n  = 2). Eight patients died either of disease relapse/progression and two from treatment related causes (one due to septicMOF with CT scan-documented disease progression on the day of death and another due to progressive multifocal JC virus-negative leukoencephalopathy). Of the remaining 18 patients without relapse after their first autologous ( n  = 13) or allogeneic ( n  = 4) transplant all were alive and in ongoing complete remission resulting in a non-relapse mortality after the first procedure of 0%.

The incidence of relapse in patients with BL was 33% and 35% and 48% after five and ten years for patients with T-ALL/LBL. If only the patients in first CR were considered the overall RI was 9% for patients with BL and 45% for patients with T-ALL/LBL (Fig.  3 ).

Cumulative incidence of relapse of stem cell transplant patients with T-ALL/LBL and BL in CR1

This retrospective single-center analysis shows clear benefits of an early SCT approach to patients with advanced stage BL and T-ALL/LBL. It results in shorter treatment duration and surprisingly good tolerability of early intensification.

Nearly all patients underwent remission induction/consolidation according to the GMALL 07/2003 or the GMALL B-ALL/NHL 2002 protocol.

Patients with BL in first CR achieved a 10-year OS and EFS of 91%. Compared to a study analyzing the results of the GMALL B-ALL/NHL 2002 protocol in patients with BL, the 5-year OS was similar to our study, except for the longer duration of conventional treatment, namely 94% in patients < 55 years and 64% in patients > 55 years [ 15 ]. Similar results were found in a large study by Evens et al. of patients with BL treated at 30 different US cancer centers. The majority of these patients were treated according to the CODOX-M/IVAC regimen in combination with rituximab in 90% of patients [ 16 ]. This shows again that early SCT can achieve outcomes superior to those of previously used regimens, but with a dramatic reduction in treatment duration, namely a median time of four months from diagnosis to SCT. In addition, the improved treatment outcome is demonstrated when CR is achieved prior to SCT.

T-ALL/LBL patients in the first CR1 achieved a 5-year OS and EFS rate of identical 73%. In addition, the entire patient cohort reached a 5-year OS and EFS of 65%. Compared to a study published by Fredman et al. using the GMALL 07/2003 protocol in ALL and LBL in Israel in a cohort for 127 patients, a 5-year OS of 68% was achieved in the T-ALL group, similar to our study [ 17 ]. In an update of the GMALL study 08/2013, a 3-year OS rate of 78% was described in 208 T-ALL patients. In high-risk T-ALL/LBL patients in CR1 who received allogeneic SCT, a 3-year OS rate of 68% was observed with CIR and TRM rates of 26% and 15%, respectively [ 18 ].

In our study, we could demonstrate that comparable results can be achieved with a significant reduction in the median treatment duration of three months after diagnosis. The limitating factor in this comparison is the smaller number of patients enrolled.

A multicenter phase II study published in 2005 by the Dutch-Belgian Hemato-Oncology Cooperative Group (HOVON) analyzed the outcome of autologous SCT after short course of chemotherapy in patients with BL and LBL with a 5-year OS of 81% in patients with BL and 40% in patients with LBL. The reason for these rather unsatisfactory results could be the advanced stage of patients; namely 37% of patients with BL and 53% of patients with LBL belonged to the high-intermediate or high-risk group after aa-IPI [ 19 ]. In our study, we could nicely demonstrate that achieving prior CR is of more prognostic value than the advanced stage at the beginning of diagnosis. However, due to the small number of treated patients, every firm conclusion will remain limited in its full strength.

In addition, a meta-analysis by Hoelzer et al. of early autologous and allogeneic SCT for T-ALL/LBL showed that patients who received autologous SCT in CR1 achieved a DFS of 61%. However, patients who did not undergo autologous SCT in CR had a shorter DFS of only 47% [ 13 ]. This again indicates the clear benefit of SCT in CR1.

Furthermore, all patients in our study had an NRM rate of zero. This can be attributed to the short treatment duration with early SCT and the good tolerability of SCT. It highlights the value of implementing SCT and an intensified protocol from the very far beginning for a limited time in the treatment approach of BL and T-ALL/LBL patients to omit long-lasting chemotherapeutic-based regimens.

Nevertheless, our study has several limitations, such as limited patient number, single center analysis, retrospective nature of the study with missing data, and lack of MRD status including unestablished modalities and experience with assessing MRD years ago. All these factors may influence the analyses.

In conclusion, the results of our single-center study with a long overall observation period of 15 years stresses a clear benefit of early SCT in patients with advanced-stage BL and T-LBL in CR1 without major toxicity/mortality rates. Further research is necessary to better define those patients who might benefit most from such an approach incorporating better molecular subtyping, early MRD and PET diagnostics and to identify those patients who are at high risk of early relapse or with refractory disease requiring front-line allogeneic SCT.

Data availability

No datasets were generated or analysed during the current study.

Saleh K, Michot JM, Camara-Clayette V, Vassetsky Y, Ribrag V (2020) Burkitt and Burkitt-Like Lymphomas: a Systematic Review. Curr Oncol Rep 22(4):33

Article   PubMed   Google Scholar  

Mehta AA, Shafi TM, Nambiar A, Pavithran K (2021) Diagnostic utility of medical thoracoscopy in T cell lymphoblastic lymphoma presenting with pleural effusion. Respir Med Case Rep 32:101322

PubMed   Google Scholar  

Burkhardt B, Hermiston ML (2019) Lymphoblastic lymphoma in children and adolescents: review of current challenges and future opportunities. Br J Haematol 185(6):1158–1170

You MJ, Medeiros LJ, Hsi ED (2015) T-lymphoblastic leukemia/lymphoma. Am J Clin Pathol 144(3):411–422

Article   PubMed   CAS   Google Scholar  

Baleydier F, Decouvelaere AV, Bergeron J, Gaulard P, Canioni D, Bertrand Y et al (2008) T cell receptor genotyping and HOXA/TLX1 expression define three T lymphoblastic lymphoma subsets which might affect clinical outcome. Clin Cancer Res 14(3):692–700

Lai C, Dunleavy K (2013) NK/T-cell lymphomas in children. Best Pract Res Clin Haematol 26(1):33–41

Article   PubMed   PubMed Central   CAS   Google Scholar  

Bigas A, Rodriguez-Sevilla JJ, Espinosa L, Gallardo F (2022) Recent advances in T-cell lymphoid neoplasms. Exp Hematol 106:3–18

King RL, Hsi ED, Chan WC, Piris MA, Cook JR, Scott DW et al (2023) Diagnostic approaches and future directions in Burkitt lymphoma and high-grade B-cell lymphoma. Virchows Arch 482(1):193–205

Molyneux EM, Rochford R, Griffin B, Newton R, Jackson G, Menon G et al (2012) Burkitt’s lymphoma. Lancet 379(9822):1234–1244

Graham BS, Lynch DT (2023) Burkitt Lymphoma: StatPearls

Said J, Lones M, Yea S (2014) Burkitt lymphoma and MYC: what else is new? Adv Anat Pathol 21(3):160–165

Dunleavy K, Pittaluga S, Shovlin M, Steinberg SM, Cole D, Grant C et al (2013) Low-intensity therapy in adults with Burkitt’s lymphoma. N Engl J Med 369(20):1915–1925

Hoelzer D, Gökbuget N (2009) T-cell lymphoblastic lymphoma and T-cell acute lymphoblastic leukemia: a separate entity? Clin Lymphoma Myeloma 9(Suppl 3):S214–S221

Iacobelli S (2013) Suggestions on the use of statistical methodologies in studies of the European Group for Blood and Marrow Transplantation. Bone Marrow Transplant 48(Suppl 1):S1-37

Pohlen M, Gerth HU, Liersch R, Koschmieder S, Mesters RM, Kessler T et al (2011) Efficacy and toxicity of a rituximab and methotrexate based regimen (GMALL B-ALL/NHL 2002 protocol) in Burkitt’s and primary mediastinal large B-cell lymphoma. Am J Hematol 86(12):E61–E64

Evens AM, Danilov A, Jagadeesh D, Sperling A, Kim SH, Vaca R et al (2021) Burkitt lymphoma in the modern era: real-world outcomes and prognostication across 30 US cancer centers. Blood 137(3):374–386

Fredman D, Moshe Y, Wolach O, Heering G, Shichrur K, Goldberg I et al (2022) Evaluating outcomes of adult patients with acute lymphoblastic leukemia and lymphoblastic lymphoma treated on the GMALL 07/2003 protocol. Ann Hematol 101(3):581–593

Goekbuget N, Fiedler W, Alakel N, Topp MS, Hanoun M, Steffen B et al (2022) Results of the Risk-Adapted, MRD-Stratified GMALL Trial 08/2013 in 281 T-ALL / T-Lbl Patients: Excellent Outcome of Standard Risk Thymic T-ALL. Blood 140(Supplement 1):115–117

Article   Google Scholar  

van Imhoff GW, van der Holt B, MacKenzie MA, Ossenkoppele GJ, Wijermans PW, Kramer MH et al (2005) Short intensive sequential therapy followed by autologous stem cell transplantation in adult Burkitt. Burkitt-like Lymphoblastic Lymphoma Leukemia 19(6):945–952

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The authors would like to acknowledge all patients as well as the nursing team for the excellent clinical care given to the patients

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N.S. and D.N. wrote the paper All authors (N.S., K.B., D.R., J.R., G.H., S.K., B.K., E.G., D.W., D.N.) reviewed the manuscript.

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Steiner, N., Baier, K., Ritter, D. et al. Early autologous and/or allogeneic stem cell transplantation for adult patients with advanced stage T- lymphoblastic leukemia/lymphoma or Burkitt lymphoma. A retrospective single-centre analysis. Ann Hematol (2024). https://doi.org/10.1007/s00277-024-05979-3

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DOI : https://doi.org/10.1007/s00277-024-05979-3

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Materials and methods, authors’ disclosures, authors’ contributions, acknowledgments, multiplexed spatial profiling of hodgkin reed–sternberg cell neighborhoods in classic hodgkin lymphoma.

Clin Cancer Res 2024;30:3881–93

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Maryam Pourmaleki , Caitlin J. Jones , Sabrina D. Mellinghoff , Brian D. Greenstein , Priyadarshini Kumar , Miguel Foronda , Daniel A. Navarrete , Carl Campos , Mikhail Roshal , Nikolaus Schultz , Sohrab P. Shah , Andrea Schietinger , Nicholas D. Socci , Travis J. Hollmann , Ahmet Dogan , Ingo K. Mellinghoff; Multiplexed Spatial Profiling of Hodgkin Reed–Sternberg Cell Neighborhoods in Classic Hodgkin Lymphoma. Clin Cancer Res 1 September 2024; 30 (17): 3881–3893. https://doi.org/10.1158/1078-0432.CCR-24-0942

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Classic Hodgkin lymphoma (cHL) is a B-cell lymphoma that occurs primarily in young adults and, less frequently, in elderly individuals. A hallmark of cHL is the exceptional scarcity (1%–5%) of the malignant Hodgkin Reed–Sternberg (HRS) cells within a network of nonmalignant immune cells. Molecular determinants governing the relationship between HRS cells and their proximal microenvironment remain largely unknown.

We performed spatially resolved multiplexed protein imaging and transcriptomic sequencing to characterize HRS cell states, cellular neighborhoods, and gene expression signatures of 23.6 million cells from 36 newly diagnosed Epstein–Barr virus (EBV)-positive and EBV-negative cHL tumors.

We show that MHC-I expression on HRS cells is associated with immune-inflamed neighborhoods containing CD8 + T cells, MHC-II + macrophages, and immune checkpoint expression (i.e., PD1 and VISTA). We identified spatial clustering of HRS cells, consistent with the syncytial variant of cHL, and its association with T-cell–excluded neighborhoods in a subset of EBV-negative tumors. Finally, a subset of both EBV-positive and EBV-negative tumors contained regulatory T-cell–high neighborhoods harboring HRS cells with augmented proliferative capacity.

Our study links HRS cell properties with distinct immunophenotypes and potential immune escape mechanisms in cHL.

Classic Hodgkin lymphoma (cHL) is a B-cell lymphoma that occurs primarily in young adults and, less frequently, in elderly individuals. Cure rates with radiation therapy and multiagent chemotherapy exceed 90%. However, treatment is associated with a life-long risk of secondary malignancies, cardiac dysfunction, and other complications. Immune-directed therapies with antibody–drug conjugates and immune checkpoint inhibitors have been shown to be effective as second-line therapies, but further improvements will require a deeper understanding of the tumor-immune architecture of cHL. We performed an in situ analysis of malignant Hodgkin Reed–Sternberg cells and their proximal immune microenvironment in newly diagnosed and previously untreated cHL. We identified differences in the tumor microenvironment of Epstein–Barr virus (EBV)-positive and EBV-negative cHL that may be relevant for future immunotherapy approaches if confirmed in larger patient cohorts.

Hodgkin Reed–Sternberg (HRS) cells, the malignant cells in classic Hodgkin lymphoma (cHL), constitute only 1% of cells in the tumor mass and are surrounded by an extraordinarily heterogeneous tumor microenvironment (TME; refs. 1 , 2 ). The rarity of HRS cells and their inability to grow in culture ( 3 ) has posed a considerable technical challenge in characterizing the molecular pathogenesis of cHL. The application of single-cell technologies to study cHL has enabled the identification of specific immune cell populations within the cHL TME, including PD1 + T helper 1 (Th1)-polarized T effector cells ( 4 ), PD1 − CD4 + regulatory T cells (T reg ; ref. 4 ), and immunosuppressive LAG3 + T cells ( 5 ), and recently, identification of cell–cell interactions, including PDL1 + tumor-associated macrophages with PD1 + CD4 + helper T cells ( 6 ) and CTLA4 + T cells with CD86 + (CTLA4 ligand) HRS cells and tumor-associated macrophages ( 7 , 8 ). Most recently, sequencing of circulating tumor DNA in patients with cHL has revealed two distinct cHL genomic subtypes ( 9 ). Much of this work has been accomplished through the ex-vivo characterization of isolated cells or tumor cell products, and thus, spatial interactions between HRS cells and their proximal immune microenvironment remain incompletely understood.

Approximately 30% of cHL cases occur in the setting of prior Epstein–Barr virus (EBV), a virus that infects nearly 95% of the world’s population. However, only a small proportion of these individuals will develop an EBV-associated malignancy, including cHL ( 10 – 13 ). Essentially all malignant cells in EBV-positive cancers express EBV-related genes, including Epstein–Barr nuclear antigen 1, latent membrane protein (LMP) 1 and 2, EBV-encoded small RNAs (EBER), and EBV BART-region micro RNAs ( 14 ). Interestingly, EBV not only potently transforms B cells, but it also hyperactivates the cellular immune response more than any other tumor-associated virus. This perhaps explains why only a very small number of EBV-infected individuals develop EBV-associated cancers ( 15 ), raising the question of how tumor cells survive within a particularly hostile TME.

Several molecular mechanisms may enable HRS cells to escape lethal attacks from surrounding cytotoxic CD8 + T cells and natural killer (NK) cells, including loss of MHC class I (MHC-I) expression, expression of cell surface molecules that impair CD8 + T-cell or NK-cell function (e.g., PDL1 and CD95L), attraction of immunosuppressive T regs and macrophages, and secretion of immunosuppressive molecules ( 16 ). To better understand the relationship between HRS cells and their neighboring immune cells, we generated an integrated dataset of multiplexed protein imaging and transcriptomic data, spanning over 23 million cells from 36 newly diagnosed cHLs, including both EBV-positive and EBV-negative cHL.

Patients and tissue

This study includes newly diagnosed specimens from patients with cHL who were admitted to Memorial Sloan Kettering Cancer Center (MSKCC) from 2012 to 2017 ( n = 36; Supplementary Table S1). All patients signed statements of informed consent under protocols approved by the MSKCC Institutional Review Board (IRB; IRB number 21-269). The study was conducted in accordance with the Declaration of Helsinki. Informed written consent was obtained from each participant. All tumors were surgically resected and immediately formalin-fixed, paraffin-embedded (FFPE) with standard tissue processing in the MSKCC surgical pathology lab (Clinical Laboratory Improvement Amendments accredited). FFPE blocks were maintained in the MSKCC Department of Pathology temperature-controlled storage units. Adjacent FFPE tissue sections were freshly cut for hematoxylin and eosin (H&E; one section, 5 microns), multiplexed immunofluorescence (mpIF; one section, 3 microns), IHC (three sections, 5 microns), and NanoString (five sections, 10 microns). H&Es were reviewed by a board-certified pathologist (Ahmet Dogan). EBV status for each patient was determined through EBER in situ hybridization and IHC for latent membrane protein 1 (LMP1). The normal human tissue microarrays for antibody validation were processed and consented to as described above.

H&E staining

H&E staining was performed using the Ventana Symphony automated H&E stainer with standard clinical protocol. Tissue sections were baked for 1 hour at 60°C, hydrated, stained with hematoxylin (Leica catalog #3801560), stained with bluing reagent (Leica catalog #3802918), stained with eosin counterstain (Leica catalog #3801600), rinsed, dehydrated, and coverslipped.

Multiplexed immunofluorescence: antibody conjugation, staining, and data acquisition

Selection of primary antibody clones for the mpIF panel of 29 proteins; conjugation of primary antibodies to Cy2, Cy3, or Cy5 Bis NHS Ester dyes (GE catalog #PA22000, PA13000, or PA25000, respectively); validation of the conjugated primary antibody; and testing of epitope stability to alkaline H 2 O 2 -based signal inactivation on normal human tissue microarrays was performed using previously described methods (Supplementary Fig. S1; ref. 17 ). CD15 and Pax5 were not included for HRS cell identification due to poor antibody conjugation. The antibody clones, dye/protein ratios, and mpIF staining concentrations are listed in Supplementary Table S2. mpIF (Cell Dive) was performed for 30 out of 36 tumors based on tissue availability using previously described methods ( 17 ). A normal human tissue microarray containing tonsil, placenta, colon, skin, and spleen (at least one positive and one negative control for each of the 29 proteins) was included on each cHL mpIF slide to assess the quality and specificity of each marker for each slide. Fields of view (FOV) were placed evenly throughout the tumor tissue to capture intratumor heterogeneity. The order of the markers in the Cell Dive panel was determined based on epitope stability to hydrogen peroxide signal deactivation. Tissue sections underwent 14 cycles of background imaging, staining, imaging, and signal inactivation. Images were acquired using the Cytell Cell Imaging System (Cytiva, Issaquah, WA). Image App software was used for image acquisition and registration (using DAPI). An acquired background image following each cycle of dye inactivation was used to subtract autofluorescence from the subsequent stain round.

Multiplexed immunofluorescence analysis

Image analysis.

Indica Labs’ HALO Image Analysis software (RRID:SCR_018350) was used for image visualization, low-level image annotation, cell segmentation, and marker thresholding. For each FOV, images for the 29 markers and DAPI were stacked. Markers with technical issues or unspecific staining (based on comparison to the on-slide normal human tissue microarray) were excluded from analysis for all FOVs of the sample. Regions within each marker channel of all FOVs containing high-intensity artifacts (e.g., folded tissue and dust spots) were annotated for downstream exclusion. A custom nuclear segmentation algorithm using HALO Image Analysis software was pretrained using 10 manually annotated cHL FOVs each containing approximately 10,000 cells to overcome the difficulty of cell segmentation in this high-density tumor type. Nuclear segmentation quality was assessed and optimized in a minimum of two FOVs from each sample (apart from HL_17, which had one FOV). Manual thresholds for marker positivity were set for each marker in each sample on the mean cytoplasm (for cytoplasmic markers) or mean nucleus (for nuclear markers) pixel intensity in reference to marker expression on the on-slide normal human tissue microarray and again assessed and optimized in a minimum of two FOVs from each sample. Cells from additional FOVs were visually assessed for samples with larger numbers of FOVs. In each sample, a random sampling of cells across different regions of the tissue defined as positive and negative for each marker was examined to ensure that the thresholds were representative.

Cell loss computation

DAPI images from each cycle were processed with intensity normalization by histogram matching. The sum of the squared differences was used to generate a pixel-level bit mask image indicating areas of cell loss/drift between the first DAPI image and each subsequent DAPI image from all cycles. This bit mask and cell coordinates were used to calculate a loss/drift percentage for each cell in every cycle of imaging. Cells with greater than 10% loss/drift of pixels were flagged for removal from the dataset, and the cell loss percentage for each sample was calculated.

Initial data processing

Each cell was assigned a unique ID. Cells in regions with artifacts, within the 20-micron border region of each FOV, or with greater than 10% loss/drift of pixels were removed from all analyses. For all pairs of cells within a 50-micron radius, the pairwise distance between the cells’ cellular centroids (determined following nuclear segmentation) was calculated for downstream spatial analyses. Intensity values for each marker were normalized at the sample level by dividing the intensities for each marker by the value of the threshold. Intensity values below the threshold (less than 1) were set to 1. The log of the intensity values for each marker was divided by the value of the 97.5 percentile computed across each FOV.

Cell type assignment

Cells were assigned to a cell type using a two-step method. First, positive and negative combinations of cell identity markers were used to label cell types, which are hierarchically grouped by “Category” (Tumor vs. Immune), “Cell type,” and “Cell subtype” (Supplementary Table S3). Second, for recovery of HRS cells, recovery of unknown cells that did not fit a cell type definition, reassignment of T cell/null phenotype cells, and reassignment of CD4 + /CD8 + T cells that occurred at higher than biologically expected percentages, a cell reassignment hierarchy was designed ( https://github.com/mskcc/cHL-spatial-profiling/blob/main/reassignment_rules.md ). For the rank comparison, we first computed the rank for each marker based on its single-cell intensities and then normalized to 0 to 1 using NormRank = (Rank + 1)/(nCells + 2). Next, in the case in which the pair of markers of interest were both positive (e.g., CD4 + /CD8 + T cells), we compared the ranks and switched the marker with the lower rank value to negative. In the case in which the pair of markers of interest were both negative (e.g., T cell/null phenotype), we compared the ranks of CD4 and CD8 and switched the marker with the higher rank to positive. Lastly, in the case in which we wanted to preserve a subset of the cell type being reassigned (e.g., we expect 5% of the T cells to be CD4 + /CD8 + T cells), we selected the cells to reassign by computing the log odds ratio (OR) of the normalized ranks and selected the cells with the largest difference in their log OR for reassignment/marker positivity “flipping.” Following cell type labeling, because our mpIF panel was not sufficiently extensive for labeling every immune cell type in the body, 4.7% of total cells were labeled “other leukocyte” based on positivity for only CD45. Additionally, 5.6% of total cells were labeled “negative” based on positivity for no cell identity markers. Again, our mpIF panel does not include the full range of stromal markers explaining this “negative” population. Finally, because of the difficulty in cell segmentation of the lymph node given the high density of cells, 17.7% of cells were labeled “unknown” based on positivity for markers signifying at least two cell lineages.

Dimensionality reduction

Based on marker data availability in the greatest number of FOVs, the normalized intensities of 23 out of the 29 markers were used in the Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) embedding (ICOS, B7H3, VISTA, CD40, CD40L, and MHC-I were excluded). The cells containing information on these 23 markers represent 24 out of 30 patients. A weighted-down sampling of cell subtypes was performed using the function sample_n from the package dplyr . The weight for each cell is 1/sqrt (n_type) where n_type is the count for the cell subtype. A total of 1,276,712 cells were used in the total cell UMAP embedding and 200,012 cells were used in the HRS cell UMAP embedding.

Statistical analysis

Fractions of each cell state (Supplementary Table S4) were transformed to log odds at the FOV level and R function wilcox.test () with the default two-sided option was used to compute the significance of the differences and effect sizes as log ORs. P values were adjusted for multiple testing with Bonferroni adjustment separately for immune cell states and tumor cell states. A cell state fraction was considered statistically significant if the adjusted P value was less than 0.05 and biologically significant if the median fraction difference between the two groups was greater than 0.1, the minimum cell state (numerator) count was 300 cells, and the minimum population (denominator) count was 1,000 cells. For spatially restricted statistics, fractions were computed at the FOV level for cells within 30 microns of the cell of interest (e.g., HRS cell).

Hodgkin Reed–Sternberg cell neighborhoods

Immune cells were grouped by being in a specific HRS neighborhood (e.g., syncytial HRS cell neighborhood) based on neighborhoods (30-micron radius) having a minimum of 95% HRS cells of that specific category (e.g., 95% of HRS cells in a syncytial HRS cell neighborhood are HRS cells in an aggregate). The specific HRS neighborhoods characterized were EBV-positive, EBV-negative, syncytial, non-syncytial, and HRS cells positive and/or negative for the following combinations: B2M + /MHC-I + , B2M − /MHC-I − , B2M + /MHC-I + /MHC-II + /PDL1 + , and B2M − /MHC-I − /MHC-II − /PDL1 − .

Hodgkin Reed–Sternberg cell spatial topology classification

The Delaunay triangulation was run on all cellular coordinates in each FOV using the function triangulate from package RTriangle . Two HRS cells were considered neighbors either if they shared an edge in the triangulation graph or if there was at least one path with just one non-HRS cell between them. We then constructed a new graph connecting neighboring HRS cells as just defined. This graph was then split into its connected components using the function components from the package igraph . This resulted in disjoint sub-graphs of connected HRS cells (a minimum of two HRS cells per connected component). In total, there were 40,680 HRS connected components of which the majority contained <20 HRS cells (specifically 39,611 or 97.2% of the connected components). Given this, we then defined an HRS aggregate to be any sub-graph of 20 or more HRS cells.

Coefficient of variation

Ihc staining and analysis.

IHC for B2M, MHC-I, and MHC-II was performed for all tumors using an automated staining system (Leica Bond RX) and previously described methods ( 17 ). The same primary antibody clones used for mpIF were used for IHC (Supplementary Table S2). The staining was scored exclusively in HRS cells as positive, cytoplasmic, or negative for B2M and MHC-I and positive or negative for MHC-II. Slides were scored blindly by a board-certified hematopathologist (Ahmet Dogan).

NanoString targeted RNA sequencing and analysis

Antigen processing/presentation: KEGG_Antigen_processing_and_presentation

Interaction lymphoid/non-lymphoid: REACTOME_Immunoregulatory_interactions_between_a_lymphoid_and_a_non_lymphoid_cell

NK cell–mediated toxicity: KEGG_Natural_killer_cell_mediated_cytotoxicity

Interferon alpha/beta/gamma: union of

HALLMARK_Interferon_gamma_response

HALLMARK_Interferon_alpha_response

REACTOME_Interferon_signaling

REACTOME_Interferon_gamma_signaling

REACTOME_Interferon_alpha_beta_signaling

Hepatitis B virus (HBV) infection: WIELAND_UP_BY_HBV_INFECTION ( 19 )

Epithelial–mesenchymal transition: HALLMARK_Epithelial_mesenchymal_transition

Code availability

All code and detailed computational methods are publicly available at https://github.com/mskcc/cHL-spatial-profiling .

Data availability

All data supporting the findings of this study are publicly available at https://zenodo.org/records/10659311 .

Patient cohort and study design

We assembled a retrospective case series encompassing 36 patients with newly diagnosed cHL. All patients signed statements of informed consent under protocols approved by the MSKCC IRB. Most tumors were histologically classified as nodular sclerosis (NS), the most common subtype of cHL ( Fig. 1A ; Supplementary Table S1). Seven of 36 tumors expressed EBER RNAs and LMP1 protein. As expected, a larger fraction of EBV-positive tumors (3/7), compared only with 1/29 EBV-negative tumors, were of the mixed cellularity (MC) morphologic variant.

Multidimensional molecular profiling of newly diagnosed cHL. A, Clinical characteristics of patients with cHL and their data availability. Tumors are grouped by EBV status. Gray (clinical annotation) indicates unknown characteristics. The bar graph (right) indicates the cohort-level summary. B, Study design. Derivation of cell types and cell states from cell identity and cell function antigens using mpIF. For cell type abbreviations, see Supplementary Table S3. C, Summary of FOV counts per patient. D, UMAP of all cells ( n = 1,276,712 subsampled from 23,678,036) profiled by mpIF colored by cell subtype. HRS clusters are circled in the UMAP. Bar plot indicating the percentage of each cell type over total cells. E, Bar plot indicating the percentage of HRS cells over total cells in each patient. F, Representative mpIF FOVs overlaying five markers delineating major cell types from 2 patients (HL_15 FOV 14, HL_21 FOV 5). Scale bar, 100 microns. ( A, Adapted from images by GraphicsRF/stock.adobe.com and wowow/stock.adobe.com ). F, female; LR, lymphocyte rich; M, male.

We performed mpIF on 30 patient samples based on tissue availability, examining at single-cell resolution the co-expression of 29 proteins, of which 13 were cell identity–related and 17 were cell function–related ( Fig. 1B ; Supplementary Table S2).

For each protein, we validated the primary antibody clone, epitope stability to hydrogen peroxide signal inactivation, and conjugated primary antibody on normal human tissue microarrays (Supplementary Fig. S1; “Materials and Methods”). Additionally, each mpIF slide contained an adjacent section of the normal human tissue microarray for further quality control (“Materials and Methods”).

HALO Image Analysis software and a pretrained segmentation algorithm on 10 manually annotated cHL images each containing an average of 10,000 cells were used for cell segmentation (“Materials and Methods”).

Marker expression was categorized as positive and negative using per-marker thresholds and intensities for positive markers were normalized (“Materials and Methods”). We used a minimum of six different protein markers to identify each cell type. Cell type curation was based on the combination of positive cell staining for some markers and negative staining for other markers. For example, a CD8 + T cell was defined by positive staining for both CD8 and CD3 and the absence of staining with antibodies against CD4, FOXP3, CD20, CD56, CD30, and MUM1. We labeled 18 distinct cell types, including HRS cells and subpopulations of T cells, B cells, NK cells, macrophages, monocytes, and plasma cells ( Fig. 1B ; Supplementary Table S3; “Materials and Methods”).

Using combinations of cell types and positive and negative cell function antigens, we were able to label 1,012 unique cell states ( Fig. 1B ; Supplementary Table S4).

We examined an average of 20 FOVs per patient (median: 16 FOVs, range 1–43; Fig. 1C ; Supplementary Table S5) with the total tumor area ranging from 6.4 to 153.3 mm 2 (mean: 63.1 mm 2 , median: 34.6 mm 2 ). Our method enabled us to examine a much larger tumor area than is typically examined in multiplexed imaging studies using tissue microarray cores.

Following the exclusion of a small percentage of cells that were displaced during mpIF staining (Supplementary Fig. S2; Supplementary Table S5), we determined in situ protein expression for 23,678,036 single cells within 587 high-dimensional FOVs (Supplementary Fig. S3).

To assess the global expression of functional markers and patient specificity of cell states, we used dimensionality reduction to visualize the mpIF data. The major cell lineages were well resolved in the UMAP ( Fig. 1D ) with intermixing of immune cells from all patients (Supplementary Fig. S4A). The percentage of HRS cells in each tumor ranged from 0.1% to 5.2%, with an overall percentage of about 1% HRS cells across all examined tumors (266,757/23,678,036 cells; Fig. 1E ). HRS cells were embedded within an immune-rich TME with T cells and B cells being most abundant ( Fig. 1D and F ; Supplementary Table S6). We detected very rare populations of CD8 + regulatory T cells (<1% of the total cell population) and confirmed their existence in the mpIF images (Supplementary Fig. S4B). We observed extensive heterogeneity present in the composition of immune cell populations between both different tumors (Supplementary Fig. S4C) and within different FOVs of the same tumor (Supplementary Fig. S4D).

A subset of “other” cells could not be resolved into cell types and were classified as either “other leukocyte” based on positivity for only CD45, “negative” based on negativity for all cell identity markers, or “unknown” based on positivity for several cell identity markers of multiple lineages (Supplementary Table S6; “Materials and Methods”). The former category included cell types that could not be identified using our preselected panel of markers (e.g., eosinophils, mast cells, and granulocytes) and the latter category included cells that could not be unambiguously separated from other cells within a densely packed stroma.

Among co-inhibitory and co-stimulatory checkpoint receptors, CD27 and PD1 expression was limited to T cells and B cells, TIM3 was ubiquitously expressed in all cell types, and LAG3 and CD40L expression was rare (Supplementary Fig. S4E). Variability in PDL1 expression within the HRS cell population explained the separation of HRS cells into multiple clusters ( Fig. 1D ). We found a proliferating (Ki67 + ) subset within each cell population. Among antigen presentation proteins, beta-2 microglobulin (B2M) and MHC-I were expressed in all leukocytes.

Characterization of HRS cell states

The large number of cells in our cohort (>23 million) and the large tumor area captured by our spatial profiling platform provided an opportunity to characterize the protein expression of HRS cells, subsequently referred to as “HRS cell states,” and the spatial distribution of HRS cell states within their native TME.

The UMAP projection of all HRS cells showed widespread expression of B2M, MHC-I, MHC-II, CD40, TIM3, and PDL1, whereas expression of other cell function markers was restricted to smaller subsets of HRS cells ( Fig. 2A ). Although patient-specific clustering of immune cells was not observed in the UMAP projection, we did identify patient-specific clustering of HRS cells (Supplementary Fig. S5A).

Characterization of HRS cell states and their spatial heterogeneity. A, UMAP of HRS cells profiled by multiplexed immunofluorescence colored by normalized intensity of cell function antigens. B, Heatmap indicating the fraction of HRS cell states (over total HRS cells) at the patient level grouped by EBV status and ordered by decreasing cohort fraction depicted by the bar graph (left). Cell states with a minimum cohort fraction of 3% are included (except IDO1 + ). Gray indicates missing data. The bar graph (right) indicates the maximum CoV for each cell state for patients with a minimum of five FOVs ( n = 29). See also Supplementary Table S7. C, Forest plot indicating effect size and 95% CI of statistically and biologically significant (see “Materials and Methods”) HRS cell fractions colored by P -adjusted (two-sided Wilcoxon test adjusted by Bonferroni correction) in the comparison of EBV-positive ( n = 150 FOVs) and EBV-negative ( n = 437 FOVs) patients. For exact P values, see Supplementary Table S8. D, Density plot indicating normalized intensity of B2M, MHC-I, and MHC-II. E, Stacked bar plot indicating percent of HRS cells positive for all combinations of B2M, MHC-I, and MHC-II. MHC-I with or without MHC-II refers to HRS cells positive for only MHC-I or both MHC-I and MHC-II. Patients with data for all three markers ( n = 26) are shown. F, Box plot indicating the percent of HRS cells in three antigen presentation machinery states (two-sided Wilcoxon test). Each point represents one patient (box plot represents minimum, first quartile, median, third quartile, and maximum). G, B2M, MHC-I, and MHC-II IHC of an EBV-positive (HL_33) and EBV-negative (HL_14) tumor. Scale bar, 100 microns. H, Bar graph indicating the fraction of tumors expressing B2M, MHC-I, and MHC-II on HRS cells based on pathologist quantification of IHC staining (two-sided Fisher’s exact test). Ag, antigen; CI, confidence interval.

In total, we characterized 92 HRS cell states based on positivity for different combinations of 15 cell function markers. More than one-third of the 92 HRS cell states were very rare, each representing less than 3% of the HRS cells in the entire cohort, of uncertain biological significance, and likely only detectable due to the large number of cells profiled in our study (Supplementary Fig. S5B; Supplementary Table S7). Even some of the more common HRS cell states (e.g., MHC-II + HRS cells) showed considerable heterogeneity between tumors (i.e., inter -patient heterogeneity; Fig. 2B ).

The extraordinary depth of spatial profiling in our study provided an opportunity to characterize intratumoral heterogeneity in established cancer immunotherapy biomarkers such as PDL1 and MHC-I. For the subset of tumors for which were able to image a minimum of five FOVs ( n = 29), we examined the spatial distribution of HRS cell states within each tumor (i.e., intra -patient heterogeneity). Some HRS cell states were distributed very unevenly throughout the tumor (i.e., high intratumor heterogeneity), whereas other HRS cell states were distributed very evenly (i.e., low intratumor heterogeneity; Supplementary Fig. S5C). For example, the fraction of MHC-II + HRS cells in patient HL_01 ranged from 7.5% to 89.7% across 18 FOVs whereas the fraction of CD40 + HRS cells in patient HL_04 exhibited a much tighter range of 93.2% to 99.9% across 15 FOVs. Using CoV as a metric for intratumor heterogeneity ( Fig. 2B ; Supplementary Fig. S5B and S5D), we found CD40 + and Ki67 + HRS cells to be least spatially variable and IDO1 + /Ki67 + and IDO1 + HRS cells to be most spatially variable within tumors (Supplementary Fig. S5E). PDL1, a common immunotherapy biomarker, exhibited variable intratumor heterogeneity among patients ( Fig. 2B ).

HRS cells in EBV-positive cHL maintain expression of MHC-I

Previous studies have reported decreased or absent expression of B2M and MHC-I in the majority of patients with cHL (up to 79%; ref. 20 ). Given that our initial assessment of HRS cell states demonstrated preserved MHC-I expression in most HRS cells from EBV-positive cHLs ( Fig. 2B ), we compared the co-expression of B2M, MHC-I, and MHC-II and all other HRS cell states across all HRS cells in EBV-positive and EBV-negative cHLs in our dataset.

Compared with HRS cells from EBV-negative cHLs, HRS cells from EBV-positive tumors exhibited increased Ki67 positivity, increased PDL1 positivity, and decreased TIM3 positivity ( Fig. 2C ; Supplementary Table S8). Most strikingly, HRS cells from EBV-positive tumors exhibited increased “triple-positive” expression of B2M + /MHC-I + /MHC-II + [OR, 12.8; CI 8.86–18.5; P -adjusted 1.57E-29]. Even within the subgroup of B2M + /MHC-I + /MHC-II + HRS cells, HRS cells from EBV-positive cHL tumors showed higher levels of B2M, MHC-I, and MHC-II protein expression ( Fig. 2D ).

We also examined various combinations of B2M, MHC-I, and MHC-II marker positivity at single-cell resolution in each tumor. HRS cells from EBV-positive tumors more frequently expressed MHC-I or both MHC-I and MHC-II, whereas HRS cells from EBV-negative tumors, in contrast, expressed only MHC-II or showed loss of both MHC-I and MHC-II antigen presentation machinery ( Fig. 2E and F ).

We performed immunohistochemical staining of our cHL cohort with antibodies against B2M, MHC-I, and MHC-II as an orthogonal approach for measuring these proteins. This further allowed us to closely evaluate the membranous expression of these markers in HRS cells, a feature that is not discernable using mpIF. Quantification by a hematopathologist blinded to the results of our mpIF analysis showed that all EBV-positive tumors, compared with less than 25% of EBV-negative tumors, exhibited membranous positivity for B2M and MHC-I in HRS cells ( Fig. 2G and H ). We did not observe distinct phenotypic features of the HRS cell states that lacked MHC-I expression.

Immune inflamed immunotype in MHC-I–positive cHL

Given the prominent role of MHC-I in regulating adaptive immunity ( 21 ) and the apparent loss of MHC-I expression in HRS cells from EBV-negative cHLs, we expected considerable differences between the proximal TME of HRS cells in EBV-positive versus EBV-negative cHLs. To further examine this question, we quantified cell states within a 30-micron radius of HRS cells, referred to as “HRS neighborhoods.” We specifically focused all neighborhood analyses on a 30-micron radius to restrict the number of cells within any given neighborhood to a maximum of 10 cells (if lymphocytes) across the diameter of the neighborhood given the overcrowded cellular landscape in cHL.

Compared with HRS neighborhoods in EBV-negative cHL, HRS neighborhoods in EBV-positive cHL contained increased CD8 + T cells (OR, 2.77; P -adjusted 1.24E-21), increased MHC-II + macrophages (OR, 3.39; P -adjusted 4.98E-13), and decreased CD4 + helper T cells (OR, 0.403; P -adjusted 4.45E-26; Fig. 3A ; Supplementary Table S8). Additionally, a larger fraction of CD8 + T cells in HRS neighborhoods in EBV-positive cHL were proliferating (Ki67 + ).

EBV-positive cHL exhibits an immune-inflamed immunotype. A, Analysis approach to defining EBV-positive and EBV-negative HRS neighborhoods. The forest plot shows the effect size and 95% CI of immune cell fractions colored by P -adjusted (two-sided Wilcoxon test adjusted by Bonferroni correction) in the comparison of EBV-positive ( n = 150 FOVs) and EBV-negative ( n = 437 FOVs) HRS neighborhoods. For cell type abbreviations, see Supplementary Table S3. For exact P values, see Supplementary Table S8. B, Analysis approach to defining B2M/MHC-I positive and negative HRS cell neighborhoods. The forest plot shows the effect size and 95% CI of immune cell fractions colored by P -adjusted in the comparison of B2M + /MHC-I + and B2M − /MHC-I − HRS neighborhoods in EBV-negative tumors. For exact P values, see Supplementary Table S10. C, Representative mpIF FOVs overlaying five markers from an EBV-positive (HL_19 FOV 3) and an EBV-negative tumor (HL_28 FOV 13). Scale bar, 500 microns. D, Heatmap indicating the scaled RNA expression values for differentially expressed genes ( P -adjusted < 0.01) in EBV-positive ( n = 7) vs. EBV-negative ( n = 25) tumors sorted by fraction of samples with a Z -score in the same direction within each EBV group. Genes in red are in the mpIF panel. The annotated pathways represent pathways with the highest count of differentially expressed genes. See also Supplementary Table S11. EMT, epithelial–mesenchymal transition; IFN, interferon; Lymph, lymphoid.

In terms of immune checkpoint expression within each immune cell type, HRS neighborhoods in EBV-positive cHL contained increased fractions of (i) PD1 + and VISTA + CD4 + helper T cells, CD8 + T cells, T regs , and B cells (Supplementary Fig. S6A–S6D), and (ii) PDL1 + macrophages (MHC-II + and MHC-II − macrophages; Fig. 3A ). HRS neighborhoods in EBV-negative cHLs contained increased fractions of (i) TIM3 + T regs , MHC-II + macrophages, and MHC-II − macrophages, and (ii) CD40 + macrophages (MHC-II + and MHC-II − macrophages; Fig. 3A ).

Within the subgroup of EBV-positive cHLs, MC cHLs contained increased CD8 + T cells and decreased NK cells compared with NS cHLs (Supplementary Fig. S7; Supplementary Table S9). MC cHLs also harbored increased fractions of PD1 + CD4 + helper T cells, CD8 + T cells, B cells, NK, NKT, and CD8 + NKT cells, whereas NS cHLs harbored increased fractions of ICOS + CD4 + helper T cells and CD4 + T regs and proliferating CD4 + T regs .

Our results thus far indicated a strong association between EBV status, expression of MHC-I in HRS cells, and the immune-inflamed immunotype. However, we also identified MHC-I–positive HRS cells in EBV-negative cHLs ( Fig. 2E ), which provided an opportunity to compare the relationship between MHC-I expression on HRS cells and HRS neighborhoods within the subgroup of EBV-negative cHLs. We observed a striking similarity between the neighborhoods of MHC-I–positive HRS cells in EBV-negative cHLs ( Fig. 3B ; Supplementary Table S10) and the neighborhoods of EBV-positive HRS cells ( Fig. 3A ). This suggests that MHC-I–positive HRS cells engender an inflamed tumor architecture even in the absence of EBV positivity.

We also compared the neighborhoods of HRS cells that were “quadruple-positive” for B2M + /MHC-I + /MHC-II + /PDL1 + with the neighborhoods of HRS cells that were “quadruple-negative” for all four markers in EBV-negative tumors (Supplementary Fig. S8; Supplementary Table S10). We again found increased CD8 + T cells, B cells, and MHC-II + macrophages and decreased CD4 + helper T cells in “quadruple-positive” HRS neighborhoods, indicating that positivity for B2M/MHC-I is driving these differences in immune cell populations. Likewise, we found increased PD1 + and VISTA + CD4 + helper T cells, CD8 + T cells, T regs , and B cells, and decreased “triple-negative” PD1 − /LAG3 − /TIM3 − CD4 + helper T cells, CD8 + T cells, T regs , and B cells in the “quadruple-positive” HRS neighborhood. Unlike the B2M + /MHC-I + HRS neighborhood ( Fig. 3B ), the “quadruple-positive” HRS neighborhood harbored increased fractions of TIM3 + CD4 + helper T cells, CD8 + T cells, T regs , and B cells, and PDL1 + macrophages (both MHC-II + macrophages and MHC-II − macrophages; Supplementary Fig. S8), indicating that positivity for B2M/MHC-I alone is not responsible for these immunophenotypes. Taken together, these neighborhood analyses suggest that expression of B2M/MHC-I, MHC-II, and PDL1 on HRS cells have independent and synergistic effects on their cellular neighborhoods.

Our data thus far demonstrated that HRS cells in EBV-positive cHL maintain expression of antigen presentation pathway proteins (B2M, MHC-I, and MHC-II) and are surrounded by activated CD8 + T cells and macrophages ( Fig. 3C ). To further characterize differences in the TME of EBV-positive and EBV-negative cHL, we next examined our transcriptomic data, specifically the expression of 750 immune-related genes in immediately adjacent tissue sections of the same tumors ( Fig. 1B ). One hundred and eleven genes were differentially expressed in EBV-positive and EBV-negative tumors ( Fig. 3D ; Supplementary Fig. S9A; Supplementary Table S11).

Among the differentially expressed genes, 22/111 are commonly used to distinguish mature human hematopoietic populations (so-called LM22 genes) in a computational method for quantifying cell fractions from bulk tissue gene expression profiles (CIBERSORT; ref. 18 ) and represented CD4 memory resting, CD8, Macrophage M1, and dendritic cell activated cells (Supplementary Fig. S9B and S9C). The association of EBV positivity with increased CD8 + T cells and MHC-II + “M1” macrophages, as inferred using CIBERSORT, was consistent with our mpIF findings (Supplementary Fig. S9D).

The most consistently upregulated genes in EBV-positive cHL included: (i) the gene encoding delta-like canonical Notch ligand 1 ( DLL1 ); (ii) all three CXCR3 receptor ligands ( CXCL9 , CXCL10 , and CXCL11 ), which mediate the recruitment of CD8 + T cells, Th1 cells, and NK cells into tumors ( 22 ); (iii) genes encoding the complement component 1q ( C1QA and C1QB ); (iv) SLAMF7 , which regulates effector function of NK cells ( 23 ); (v) antiviral genes IFIH1 and TLR8 ; (vi) several interferon-inducible GTPases ( GBP1 , GBP2 , and GBP4 ; ref. 24 ); and (vii) IFNγ itself, which is known to activate the JAK/STAT signaling pathway and is typically secreted by CD8 + T cells, CD4 + Th1 cells, and NK cells ( Fig. 3D ). Genes that were downregulated in EBV-positive cHL included CXCL6 and CXCL8 , which are chemo-attractants for neutrophilic granulocytes and interact with the chemokine receptors CXCR1 and CXCR2 .

At the pathway level, upregulated genes in EBV-positive tumors associated with gene sets related to antigen processing and presentation, the interaction between a lymphoid and non-lymphoid cell, NK cell–mediated toxicity, IFNα/IFNβ/IFNγ signaling, and HBV infection ( Fig. 3D ; ref. 19 ). In EBV-negative tumors, several of the upregulated genes overlapped with an epithelial–mesenchymal transition gene set. Interestingly, patients over the age of 45 had a more pronounced EBV-negative–like transcriptome.

Identification of additional immune escape mechanisms in EBV-negative cHL

Pathologists have described a “syncytial variant” ( 1 ) of cHL in which HRS cells form cellular islands. To understand the relationship between this histologic variant and EBV, we first quantified HRS cellular islands by constructing graphs from the segmented mpIF images (Supplementary Fig. S10A) and identifying HRS cellular islands or “syncytial HRS cells” with a minimum of 20 neighboring HRS cells ( Fig. 4A ; Supplementary Fig. S10B; Supplementary Table S12; “Materials and Methods”). We observed near-perfect overlap between non-syncytial HRS cells and EBV-positive tumors in the UMAP projection ( Fig. 4B ) and a significantly larger fraction of syncytial HRS cells (i.e., HRS cells in cellular islands) in EBV-negative tumors compared with EBV-positive tumors (Supplementary Fig. S10C). EBV-positive tumors rarely contained syncytial HRS aggregates (Supplementary Fig. S10D), and the few EBV-positive HRS aggregates were all smaller than 30 HRS cells compared with the HRS aggregate size in EBV-negative tumors, which had an upper range of more than 1,000 HRS cells (Supplementary Fig. S10E). This indicates that syncytial cHL predominately occurs in EBV-negative tumors. To identify contributors of syncytial cHL or the effect of syncytial HRS cells on the TME, we characterized the cellular neighborhood of syncytial HRS cells versus non-syncytial HRS cells in EBV-negative tumors ( Fig. 4C ; Supplementary Table S10). Among HRS cell states, syncytial HRS neighborhoods harbored increased MHC-II + HRS cells and decreased Ki67 + HRS cells. Among immune cell states, syncytial HRS neighborhoods were enriched for CD4 + helper T cells, CD8 + T cells, T regs , and MHC-II − macrophages positive for TIM3 and MHC-II + macrophages positive for PDL1, whereas non-syncytial HRS neighborhoods were enriched for naïve CD4 + helper T cells, CD8 + T cells, T regs , and B cells “triple-negative” for PD1 − /LAG3 − /TIM3 − . Most notably, non-syncytial HRS neighborhoods contained increased fractions of T cells indicating non-syncytial cHL exemplifies a T-cell infiltrated tumor, whereas syncytial cHL exemplifies a T-cell excluded tumor.

Determinants of spatial neighborhoods in EBV-negative cHL. A, FOV showing two patterns of HRS cell spatial arrangement. B, UMAP of HRS cells colored by HRS cell subtype and EBV status. Stacked bar plot indicates the percent of syncytial and non-syncytial HRS cells by EBV status. C, Analysis approach to defining syncytial and non-syncytial HRS cell neighborhoods. The forest plot shows the effect size and 95% CI of cell fractions colored by P -adjusted in the comparison of syncytial and non-syncytial HRS neighborhoods in EBV-negative tumors. D, Analysis approach to defining CD4 + T reg high and low neighborhoods. The forest plot shows the effect size and 95% CI cell fractions colored by P -adjusted in the comparison of CD4 + T reg high and CD4 + T reg low neighborhoods in EBV-negative tumors. For cell type abbreviations, see Supplementary Table S3. For exact P -values, see Supplementary Table S10.

cHL often exhibits an HRS/lymphocyte “rosette” characteristic where HRS cells are in close contact with clusters of T cells, mainly composed of CD4 + helper T cells and immunosuppressive T regs ( 3 ). To investigate the role of T regs in cHL, we first explored whether their abundance, measured by the percentage of T regs over immune cells, differs across FOVs or tumors. We found a considerable range of nearly 0 to more than 30% in the fraction of T regs over immune cells across all FOVs in the cohort (Supplementary Fig. S11A). At the patient level, we found spatial heterogeneity in T reg abundance within some tumors (Supplementary Fig. S11B). Specifically, some tumors had low T reg abundance throughout (e.g., HL_27), others contained a mix of low and high T reg abundance FOVs (e.g., HL_19, HL_13), and three tumors (all EBV-negative) contained exclusively high T reg abundance FOVs. To determine the effect of T regs on HRS cells and other cells in the TME, we compared high-abundance T reg cellular neighborhoods to low-abundance T reg cellular neighborhoods ( Fig. 4D ; Supplementary Table S10). T reg high neighborhoods contained increased proliferating HRS cells and HRS cells positive for PDL1, B7H3, and CD40, whereas T reg low neighborhoods contained increased MHC-II + HRS cells. Among cells in the TME, T reg low neighborhoods contained increased fractions of CD8 + T cells, NK cells, and MHC-II + macrophages, whereas T reg high neighborhoods contained increased proliferating T regs and T and B cells positive for ICOS and CD27. Interestingly, T reg high neighborhoods harbored increased fractions of MHC-II + and MHC-II − macrophages positive for B7H3 and CD40, mimicking expression patterns of HRS cells in these neighborhoods.

Our study represents a detailed in situ analysis of HRS cells and their proximal immune microenvironment in newly diagnosed and previously untreated cHL. We show that HRS cells in EBV-positive cHL consistently express MHC-I (or both MHC-I and MHC-II), reside in neighborhoods containing activated (PD1 + and VISTA + ) T cells and B cells, shield themselves from immune attack through expression of PDL1 and habitation in a niche of PDL1 + macrophages, and proliferate in a cytokine milieu that is characterized by upregulation of IFNγ and CXCR3 receptor ligands ( CXCL9 , CXCL10 , and CXCL11 ) and downregulation of CXCR1 / CXCR2 receptor ligands ( CXCL6 and CXCL8 ; Fig. 5 ). These data are consistent with experimental models of EBV infection, which have shown that LMP1, an EBV protein expressed during the EBV “latency program” of cHL, upregulates antigen presentation, regulates the expression of various co-stimulatory ligands, and induces potent T-cell responses that include not only CD8 + T cells but also CD4 + helper T cells ( 25 ). Functional studies in experimental models are required to provide mechanistic details of how EBV licenses the coexistence of MHC-positive cells with locally activated CD8 + T cells. In EBV-negative cHL, HRS cells seemed to use multiple different mechanisms of immune escape including downregulation of MHC-I, formation of a syncytial architecture, and attraction of T reg high cellular neighborhoods. Further work is required to identify molecular determinants driving these differences.

A model of HRS cells and their TME in EBV-positive and EBV-negative cHL. Distinguishing features of EBV-positive and EBV-negative tumors. An FOV is characterized as syncytial cHL if it contains a minimum of one HRS aggregate.

Previous studies have used IHC or flow-based approaches to examine the relationship between EBV status and individual protein members of the antigen presentation machinery, in particular B2M, MHC-I, and MHC-II ( 4 , 20 , 26 ). Our study expands this prior work by characterizing the co-expression of all three proteins (B2M, MHC-I, and MHC-II) at single-cell resolution in a large number of HRS cells and by linking distinct co-expression patterns to specific HRS tumor neighborhoods. We confirm prior findings that EBV-positive cHLs harbor MHC-I expressing HRS cells and CD8 + T cells, and we also show that HRS neighborhoods in EBV-positive cHLs harbor increased fractions of CD8 + T cells and activated (PD1 + and VISTA + ) T cells and B cells. Most importantly, we identified similar neighborhoods surrounding B2M + /MHC-I + HRS cells in EBV-negative cHL, suggesting that HRS cell expression of MHC-I, and not EBV positivity, is responsible for these TME differences.

Overall, our data suggest that there is a fundamental difference between EBV-positive and EBV-negative cHL. Although our study was limited by a lack of genomic profiling of the HRS cells, our findings are consistent with a recently published study describing two genomic subtypes of cHL, H1 and H2, characterized by mutations in NF-κB, JAK-STAT, and PI3K pathways, or TP53 and KMT2D , respectively ( 9 ). The H2 subtype was enriched for EBV-positive tumors and exhibited increased CD8 + T cells and upregulation of T-cell activation genes, consistent with our data. Furthermore, we found an overlap between our list of EBV-related differentially expressed genes and the genomic subtype-related differentially expressed genes, specifically upregulation of TBX21 in EBV-positive H2 cHL and CCR4 and CXCL1 in EBV-negative H1 cHL. These parallels between EBV and genomic subtypes further support EBV-related cHL as a distinct subtype of the disease.

An important goal of our work was to develop an integrated approach using multiplex protein imaging and transcriptomics to evaluate the TME in routinely collected clinical cancer biospecimens. Our mpIF platform allowed us to examine large areas of each tumor and generate a single-cell proteomic dataset that exceeds prior studies ( 5 , 7 ) by several orders of magnitude (>23 million cells). The size of our dataset enabled the characterization of otherwise rare HRS cells and their cellular neighborhoods. The large number of cell states in our dataset is a reflection of the very large number of cells that were profiled in our study and of our goal to provide an unbiased evaluation of protein co-expression patterns without preconceived cell states. Many of these states were observed in only a small fraction of cells and their biological significance warrants further study and validation using an independent method such as flow cytometry. It is unlikely that these patterns of protein co-expression or cell states can be attributed to technical issues or batch artifacts as we included replicates of a normal human tissue microarray on each tumor slide to serve as a positive staining control and minimize batch artifacts. The additional collection of gene expression data from immediately adjacent tumor sections allowed us to further interrogate functional differences between these tumors, develop a multidimensional portrait of the cHL tumor-immune architecture, and link our findings to prior studies focusing on protein ( 4 , 6 , 7 , 26 , 27 ) or gene expression analyses ( 28 ). The methods presented here provide a framework for future unbiased evaluation of spatial neighborhoods with the goal of identifying specific defects within the cancer immunity cycle ( 29 ).

Our study provides new insights into the architecture of the TME of cHL but is largely descriptive. Further studies are needed to validate the functional consequences for disease biology and potential implications for immunotherapy approaches. Clinical responses in cHL to antibodies against the PD1/PDL1 signaling axis, as defined by progression-free survival, have been associated with HRS cell expression of MHC-II ( 30 ) and a peripheral blood immune signature consistent with the expansion of clonally diverse CD4 + helper T cells ( 31 ). Our findings that HRS cells in EBV-positive cHL express not only MHC-II but also MHC-I and are surrounded by PD1 + immune effector cells and PDL1 + macrophages raise the question of whether EBV status might be a predictor of clinical response to PD1/PDL1 blockade. Interestingly, recent studies have reported encouraging responses to immune checkpoint blockade in patients with EBV-positive metastatic gastric cancer and non-Hodgkin lymphoma ( 32 , 33 ).

M. Roshal reports personal fees from Auron, grants from AstraZeneca, and grants and nonfinancial support from Genentech outside the submitted work. S.P. Shah reports grants from AstraZeneca and Bristol Myers Squibb during the conduct of the study. N.D. Socci reports grants from NIH during the conduct of the study. A. Dogan reports grants from Roche and AstraZeneca outside the submitted work. I.K. Mellinghoff reports grants from General Electric, Lilly, and Roche; grants and other support from Amgen, Kazia Therapeutics, Servier, and Agios; and other support from AstraZeneca outside the submitted work. No disclosures were reported by the other authors.

M. Pourmaleki: Conceptualization, resources, formal analysis, supervision, funding acquisition, investigation, methodology, writing–original draft, writing–review and editing. C.J. Jones: Conceptualization, resources, formal analysis, supervision, investigation, methodology, writing–original draft, writing–review and editing. S.D. Mellinghoff: Formal analysis, investigation. B.D. Greenstein: Formal analysis, investigation. P. Kumar: Resources, data curation, formal analysis, investigation. M. Foronda: Resources, data curation, investigation, writing–review and editing. D.A. Navarrete: Formal analysis, investigation, writing–review and editing. C. Campos: Formal analysis, supervision, investigation. M. Roshal: Resources, formal analysis, supervision, investigation. N. Schultz: Resources, data curation, supervision. S.P. Shah: Data curation, supervision. A. Schietinger: Data curation, supervision. N.D. Socci: Data curation, formal analysis, supervision, investigation, methodology. T.J. Hollmann: Resources, data curation, formal analysis, supervision, investigation, methodology. A. Dogan: Resources, data curation, formal analysis, supervision. I.K. Mellinghoff: Conceptualization, resources, supervision, funding acquisition, writing–original draft, writing–review and editing.

We acknowledge the use of the Integrated Genomics Operation Core, funded by the NCI Cancer Center Support Grant (CCSG, P30 CA08748; all authors) and Cycle for Survival (I.K. Mellinghoff). Funding for this project was provided by the National Institutes of Health grant R35 NS105109 04 (I.K. Mellinghoff), Geoffrey Beene Cancer Research Center (I.K. Mellinghoff), Cycle for Survival (I.K. Mellinghoff), and National Institutes of Health/National Cancer Institute grant P30 CA008748 (all authors). M. Pourmaleki was also funded through the National Institutes of Health grant F31 CA271778 01.

Note: Supplementary data for this article are available at Clinical Cancer Research Online ( http://clincancerres.aacrjournals.org/ ).

Supplementary data

Supplementary Figure S1. Validation of mpIF antibody panel.

Supplementary Figure S2. Tracking of cell loss across staining cycles.

Supplementary Figure S3. FOV placement for mpIF.

Supplementary Figure S4. cHL tumors exhibit heterogeneity in immune cell composition and states.

Supplementary Figure S5. Heterogeneity in HRS cell states and their spatial distribution.

Supplementary Figure S6. EBV-positive tumors exhibit increased expression of VISTA.

Supplementary Figure S7. EBV-positive MC tumors contain a T cell rich activated TME.

Supplementary Figure S8. B2M/MHCI/MHCII/PDL1-related HRS cell neighborhoods.

Supplementary Figure S9. Gene expression landscape of EBV-related cHL.

Supplementary Figure S10. Quantification of HRS cell patterns of spatial organization.

Supplementary Figure S11. Intertumoral heterogeneity in regulatory T cell abundance.

Supplementary Table S1. Patient characteristics.

Supplementary Table S2. mpIF panel and antibody information.

Supplementary Table S3. Cell type definitions.

Supplementary Table S4. List of cell states.

Supplementary Table S5. Cell count, cell loss, and field of view count for each patient.

Supplementary Table S6. Cell type counts for each patient.

Supplementary Table S7. Summary of HRS cell states for each patient.

Supplementary Table S8. Comparison of EBV-positive and EBV-negative cHL.

Supplementary Table S9. Comparison of EBV-positive NS and EBV-positive MC cHL.

Supplementary Table S10. Comparison of EBV-negative neighborhoods.

Supplementary Table S11. Differential expression of genes and cell types in EBV-positive and EBV-negative cHL.

Supplementary Table S12. HRS cell aggregates.

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Clinical and histologic variants of cd8+ cutaneous t-cell lymphomas.

Simple Summary

1. introduction, 2. cd8+ mycosis fungoides (mf), 3. lymphomatoid papulosis (lyp), type d, 4. subcutaneous panniculitis-like t-cell lymphoma (sptcl), 5. primary cutaneous gamma/delta t-cell lymphoma (pcgdtl), 6. cd8+ aggressive epidermotropic cutaneous t-cell lymphoma (aectcl), 7. acral cd8+ t-cell lymphoproliferative disorder (acral cd8+ tclpd), 8. other diagnostic considerations: peripheral t-cell lymphomas not otherwise specified (ptcl-nos) and natural killer t-cell (nk/t) lymphoma and lymphoproliferative disorders, 9. conclusions, author contributions, conflicts of interest, abbreviations.

• Girardi, M.; Heald, P.W.; Wilson, L.D. The pathogenesis of mycosis fungoides. N. Engl. J. Med. 2004 , 350 , 1978–1988. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Alaggio, R.; Amador, C.; Anagnostopoulos, I.; Attygalle, A.D.; Araujo, I.B.O.; Berti, E.; Bhagat, G.; Borges, A.M.; Boyer, D.; Calaminici, M.; et al. The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Lymphoid Neoplasms. Leukemia 2022 , 36 , 1720–1748. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Carter, J.B.; Goyal, A.; McDivitt Duncan, L. Atlas of Cutaneous Lymphomas ; Springer International Publishing: Cham, Switzerland, 2015. [ Google Scholar ]
• Kempf, W.; Petrella, T.; Willemze, R.; Jansen, P.; Berti, E.; Santucci, M.; Geissinger, E.; Cerroni, L.; Maubec, E.; Battistella, M.; et al. Clinical, histopathological and prognostic features of primary cutaneous acral CD8 + T-cell lymphoma and other dermal CD8 + cutaneous lymphoproliferations: Results of an EORTC Cutaneous Lymphoma Group workshop. Br. J. Dermatol. 2022 , 186 , 887–897. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Guitart, J.; Martinez-Escala, M.E.; Subtil, A.; Duvic, M.; Pulitzer, M.P.; Olsen, E.A.; Kim, E.; Rook, A.H.; Samimi, S.S.; Wood, G.S.; et al. Primary cutaneous aggressive epidermotropic cytotoxic T-cell lymphomas: Reappraisal of a provisional entity in the 2016 WHO classification of cutaneous lymphomas. Mod. Pathol. 2017 , 30 , 761–772. [ Google Scholar ] [ CrossRef ]
• Kalay Yildizhan, I.; Sanli, H.; Akay, B.N.; Surgun, E.; Heper, A. CD8 + cytotoxic mycosis fungoides: A retrospective analysis of clinical features and follow-up results of 29 patients. Int. J. Dermatol. 2020 , 59 , 127–133. [ Google Scholar ] [ CrossRef ]
• Ahn, H.J.; Shin, E.J.; Gwak, M.J.; Jeong, K.H.; Shin, M.K. Sudden aggravated CD8 + mycosis fungoides accompanied by hidden adenocarcinoma of the colon. JAAD Case Rep. 2017 , 3 , 83–86. [ Google Scholar ] [ CrossRef ]
• Martinez-Escala, M.E.; Kantor, R.W.; Cices, A.; Zhou, X.A.; Kaplan, J.B.; Pro, B.; Choi, J.; Guitart, J. CD8 + mycosis fungoides: A low-grade lymphoproliferative disorder. J. Am. Acad. Dermatol. 2017 , 77 , 489–496. [ Google Scholar ] [ CrossRef ]
• Song, S.X.; Willemze, R.; Swerdlow, S.H.; Kinney, M.C.; Said, J.W. Mycosis fungoides: Report of the 2011 Society for Hematopathology/European Association for Haematopathology workshop. Am. J. Clin. Pathol. 2013 , 139 , 466–490. [ Google Scholar ] [ CrossRef ]
• Cao, S.; Kruglov, O.; Akilov, O.E. CD8+ T Lymphocytes in Hypopigmented Mycosis Fungoides: Malignant Cells or Reactive Clone? J. Investig. Dermatol. 2023 , 143 , 521–524.e3. [ Google Scholar ] [ CrossRef ]
• Mangold, A.R.; Thompson, A.K.; Davis, M.D.; Saulite, I.; Cozzio, A.; Guenova, E.; Hodak, E.; Amitay-Laish, I.; Pujol, R.M.; Pittelkow, M.R.; et al. Early clinical manifestations of Sezary syndrome: A multicenter retrospective cohort study. J. Am. Acad. Dermatol. 2017 , 77 , 719–727. [ Google Scholar ] [ CrossRef ]
• Mehrany, K.; El-Azhary, R.A.; Bouwhuis, S.A.; Pittelkow, M.R. Cutaneous T-cell lymphoma and atopy: Is there an association? Br. J. Dermatol. 2003 , 149 , 1013–1017. [ Google Scholar ] [ CrossRef ]
• Cordel, N.; Tressieres, B.; D’Incan, M.; Machet, L.; Grange, F.; Esteve, E.; Dalac, S.; Ingen-Housz-Oro, S.; Bagot, M.; Beylot-Barry, M.; et al. Frequency and Risk Factors for Associated Lymphomas in Patients With Lymphomatoid Papulosis. Oncologist 2016 , 21 , 76–83. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wieser, I.; Oh, C.W.; Talpur, R.; Duvic, M. Lymphomatoid papulosis: Treatment response and associated lymphomas in a study of 180 patients. J. Am. Acad. Dermatol. 2016 , 74 , 59–67. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wakumoto, K.; Sugita, K.; Yamamoto, O. Subcutaneous Panniculitis-like T-Cell Lymphoma Without Erythema and Subcutaneous Tumors. Yonago Acta Med. 2021 , 64 , 126–128. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ukishima, S.; Miyagami, T.; Arikawa, M.; Kushiro, S.; Takaku, T.; Naito, T. Subcutaneous panniculitis-like T-cell lymphoma post-mRNA-1273 COVID-19 vaccination. Clin. Case Rep. 2023 , 11 , e7143. [ Google Scholar ] [ CrossRef ]
• Kreher, M.A.; Ahn, J.; Werbel, T.; Motaparthi, K. Subcutaneous panniculitis-like T-cell lymphoma after COVID-19 vaccination. JAAD Case Rep. 2022 , 28 , 18–20. [ Google Scholar ] [ CrossRef ]
• Ou, W.; Zhao, Y.; Wei, A.; Ma, H.; Zhang, L.; Lian, H.; Zhang, Q.; Wang, D.; Li, Z.; Wang, T.; et al. Subcutaneous panniculitis-like T-cell lymphoma associated with hemophagocytic lymphohistiocytosis: A systematic review of 63 patients reported in the literature. Clin. Exp. Med. 2023 , 23 , 4575–4583. [ Google Scholar ] [ CrossRef ]
• Willemze, R.; Jansen, P.M.; Cerroni, L.; Berti, E.; Santucci, M.; Assaf, C.; Canninga-van Dijk, M.R.; Carlotti, A.; Geerts, M.L.; Hahtola, S.; et al. Subcutaneous panniculitis-like T-cell lymphoma: Definition, classification, and prognostic factors: An EORTC Cutaneous Lymphoma Group Study of 83 cases. Blood 2008 , 111 , 838–845. [ Google Scholar ] [ CrossRef ]
• Guenova, E.; Schanz, S.; Hoetzenecker, W.; DeSimone, J.A.; Mehra, T.; Voykov, B.; Urosevic-Maiwald, M.; Berneburg, M.; Dummer, R.; French, L.E.; et al. Systemic corticosteroids for subcutaneous panniculitis-like T-cell lymphoma. Br. J. Dermatol. 2014 , 171 , 891–894. [ Google Scholar ] [ CrossRef ]
• Mehta, N.; Wayne, A.S.; Kim, Y.H.; Hale, G.A.; Alvarado, C.S.; Myskowski, P.; Jaffe, E.S.; Busam, K.J.; Pulitzer, M.; Zwerner, J.; et al. Bexarotene is active against subcutaneous panniculitis-like T-cell lymphoma in adult and pediatric populations. Clin. Lymphoma Myeloma Leuk. 2012 , 12 , 20–25. [ Google Scholar ] [ CrossRef ]
• Jothishankar, B.; Espinosa, M.L.; Zain, J.; Parekh, V.; Di Raimondo, C.; Abdulla, F. Complete response to romidepsin as monotherapy in treatment-resistant subcutaneous panniculitis-like T-cell lymphoma. JAAD Case Rep. 2020 , 6 , 1245–1247. [ Google Scholar ] [ CrossRef ]
• Torres-Cabala, C.A.; Huen, A.; Iyer, S.P.; Miranda, R.N. Gamma/Delta Phenotype in Primary Cutaneous T-cell Lymphomas and Lymphoid Proliferations: Challenges for Diagnosis and Classification. Surg. Pathol. Clin. 2021 , 14 , 177–194. [ Google Scholar ] [ CrossRef ]
• Onkarappa Mangala, Y.; Onukogu, I.D.; Breen, C.M.; Colvin, G.A. Primary Cutaneous Gamma-Delta T-cell Lymphoma: A Case Report and Review of Literature. Cureus 2023 , 15 , e35442. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Guitart, J.; Weisenburger, D.D.; Subtil, A.; Kim, E.; Wood, G.; Duvic, M.; Olsen, E.; Junkins-Hopkins, J.; Rosen, S.; Sundram, U.; et al. Cutaneous γδ T-cell lymphomas: A spectrum of presentations with overlap with other cytotoxic lymphomas. Am. J. Surg. Pathol. 2012 , 36 , 1656–1665. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Alberti-Violetti, S.; Maronese, C.A.; Venegoni, L.; Merlo, V.; Berti, E. Primary Cutaneous Gamma-Delta T Cell Lymphomas: A Case Series and Overview of the Literature. Dermatopathology 2021 , 8 , 515–524. [ Google Scholar ] [ CrossRef ]
• Oh, Y.; Stoll, J.R.; Moskowitz, A.; Pulitzer, M.; Horwitz, S.; Myskowski, P.; Noor, S.J. Primary cutaneous T-cell lymphomas other than mycosis fungoides and Sezary syndrome. Part II: Prognosis and management. J. Am. Acad. Dermatol. 2021 , 85 , 1093–1106. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Nofal, A.; Abdel-Mawla, M.Y.; Assaf, M.; Salah, E. Primary cutaneous aggressive epidermotropic CD8+ T-cell lymphoma: Proposed diagnostic criteria and therapeutic evaluation. J. Am. Acad. Dermatol. 2012 , 67 , 748–759. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ormerod, E.; Murigu, T.; Pawade, J.; Beasley, M.; Dunnill, G. Primary cutaneous acral CD8+ T-cell lymphoma of the ear: A case report. J. Cutan. Pathol. 2019 , 46 , 790–793. [ Google Scholar ] [ CrossRef ]
• Oluwasanjo, A.; Kartan, S.; Johnson, W.; Alpdogan, O.; Gru, A.; Mishra, A.; Haverkos, B.M.; Gong, J.; Porcu, P. Peripheral T-Cell Lymphoma, not Otherwise Specified (PTCL-NOS). Cancer Treat. Res. 2019 , 176 , 83–98. [ Google Scholar ] [ CrossRef ]
• Cardwell, L.A.; Majerowski, J.; Chiu, Y.E.; Harrington, A.M.; Sokumbi, O. Post-transplant primary cutaneous peripheral T-cell lymphoma not otherwise specified in a pediatric patient. J. Cutan. Pathol. 2021 , 48 , 706–712. [ Google Scholar ] [ CrossRef ]
• de Matos, P.R.; Amoedo, P.; Nogueira, A.; Lisboa, C.; Carvalhais, I.; Fonseca, E.; Azevedo, F. Primary cutaneous peripheral T-cell lymphoma not otherwise specified (PTCL-NOS) simulating periorbital cellulitis. Int. J. Dermatol. 2023 , 62 , e125–e128. [ Google Scholar ] [ CrossRef ]
• García-Herrera, A.; Calonje, E. Cutaneous Lymphomas with Cytotoxic Phenotype. Surg. Pathol. Clin. 2017 , 10 , 409–427. [ Google Scholar ] [ CrossRef ]
• Geller, S.; Myskowski, P.L.; Pulitzer, M. NK/T-cell lymphoma, nasal type, γδ T-cell lymphoma, and CD8-positive epidermotropic T-cell lymphoma-clinical and histopathologic features, differential diagnosis, and treatment. Semin. Cutan. Med. Surg. 2018 , 37 , 30–38. [ Google Scholar ] [ CrossRef ]
• Schukow, C.; Ahmed, A. Dermatopathology, Cutaneous Lymphomas. In StatPearls ; StatPearls Publishing: Treasure Island, FL, USA, 2023. [ Google Scholar ]

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Swallow, M.A.; Micevic, G.; Zhou, A.; Carlson, K.R.; Foss, F.M.; Girardi, M. Clinical and Histologic Variants of CD8+ Cutaneous T-Cell Lymphomas. Cancers 2024 , 16 , 3087. https://doi.org/10.3390/cancers16173087

Swallow MA, Micevic G, Zhou A, Carlson KR, Foss FM, Girardi M. Clinical and Histologic Variants of CD8+ Cutaneous T-Cell Lymphomas. Cancers . 2024; 16(17):3087. https://doi.org/10.3390/cancers16173087

Swallow, Madisen A., Goran Micevic, Amanda Zhou, Kacie R. Carlson, Francine M. Foss, and Michael Girardi. 2024. "Clinical and Histologic Variants of CD8+ Cutaneous T-Cell Lymphomas" Cancers 16, no. 17: 3087. https://doi.org/10.3390/cancers16173087

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