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Cancer Treatments and Research

Learn more about the progress made in improving cancer survival rates

Cancer Treatment Development

Radiotherapy, immunotherapy, targeted therapy.

• Combination Therapies

Diagnostics

Considerable progress has been made in reducing cancer rates and improving cancer survival in the United States since the 1990s. A greater understanding of the immune system , genetics , and cancer pathology has opened the doors to an ever-increasing range of new cancer treatments and diagnostic tools.

Advances in cancer care have been highly specific in terms of the diagnostic and treatment modalities that are recommended for each type of cancer. This article will describe these key treatments as well as the process of cancer treatment development.

sanjeri / Getty Images

Throughout the years, there have been discoveries of drugs and treatment methods that prove to be more successful or reliable than previous ones. These treatment methods are discovered in different ways.

Some are found in nature through the testing and studying of plants, fungi, and animals. Others are found through the study of cancer cells and existing drugs or procedures. But before any type of treatment method is used on patients, there is an important process that ensures its safety and effectiveness.

New cancer drugs typically go through stages of clinical research. These stages are:

• Preclinical research : Preclinical research aims to ensure a form of treatment is safe for human use. Laboratory studies that include animal research and in vitro studies , or experiments usually done in test tubes and Petri dishes, are common in this research stage.
• Clinical research : After preclinical research is successful, clinical research focuses on testing the form of therapy on humans. This clinical research stage can be lengthy (up to 10 years or more) as the discovered treatment goes through phases of clinical trials .
• Post-clinical research : Post-clinical research involves studying a therapy that has gone through the clinical research phase and received approval for human use. This involves collecting data on effectiveness and safety in real-world use.

Advances in and refinement of cancer surgery—including the use of targeted drugs and other medications before and after surgery—that can improve outcomes for cancer patients continue to emerge.

Studies comparing the outcomes of different surgical methods have helped guide doctors in selecting the technique that is most likely to result in a better long-term prognosis.

Video-Assisted Thoracoscopic Surgery (VATS) Lobectomy for Lung Cancer

During a lobectomy , a portion of a lobe of a lung that is affected by cancer is removed.

The minimally invasive technique known as VATS lobectomy, done with general anesthesia , often involves a shorter recovery time than open surgery for lung cancer . The American College of Chest Physicians identifies VATS lobectomy as the preferred method for treating early-stage lung cancer.

During the procedure, a thoracoscope, which is a small tube with a light and camera attached to the end, is inserted between the ribs through a small incision. The affected lung tissue is then removed using special tools.

Open Surgery for Cervical Cancer

In a clinical trial between 2008 and 2013, 631 women were enrolled to compare the efficacy of open surgery with that of minimally invasive surgery for the treatment of cervical cancer .

Postoperative quality of life for both groups was similar. But open surgery resulted in lower rates of cancer recurrence and higher disease-free survival.

Another study found that patients with early-stage cervical cancer who had minimally invasive surgery experienced higher recurrence rates than those who had open surgery, making open surgery a better option for some patients.

Radiation therapy is used as an adjunct to cancer treatment. More effective and targeted radiotherapies are being used to treat early and advanced cancers.

Stereotactic Ablative Radiotherapy (SABR) for Metastatic Cancer

A study demonstrated that patients receiving SABR in addition to standard of care showed improved survival compared with patients receiving palliative standard of care.  

SABR for Inoperable Early-Stage Lung Cancer

For patients who are not surgical candidates, SABR offers an alternative. This approach was shown to have excellent local control and well tolerated in a cohort of 273 patients.

Immunotherapy uses the body's immune system to fight cancer. Immunotherapy can boost or change how the immune system works so it can find and attack cancer cells.  

Molecular testing, which can help select patients most suitable for immunotherapy, has opened the door to this newer form of treatment. Some of the early and commonly used immunotherapy agents are vaccines, including the first FDA-approved cancer vaccine, sipuleucel-T, for prostate cancer .

Below are some breakthrough agents grouped by category:

• Monoclonal antibodies , such as Trodelvy for metastatic triple-negative breast cancer
• Oncolytic virus therapy , including Imlygic for inoperable melanoma
• CAR T-cell therapy , such as CD22 for acute lymphoblastic leukemia relapse
• Cancer vaccines , such as Provenge for prostate cancer

Targeted therapy is when drugs are directed at specific proteins or genes that promote cancer cell growth. It is designed to attack cancer cells directly.

Some of the targeted drugs commonly used to treat cancer are Tagrisso (osimertinib), Tarceva (erlotinib), and Iressa (gefitinib) for lung cancer, and Kadcyla (ado-trastuzumab), Tykerb (lapatinib), and Afinitor (everolimus) for breast cancer.

Kinase Inhibitors

Dysregulation of protein kinases is involved in many types of cancer, and this protein is the target of several cancer drugs.

Drugs like Rozlytrek (entrectinib) and Tabrecta (capmatinib) are used to treat metastatic non-small cell lung cancer .

• Rozlytrek (entrectinib) is used to treat non-small cell lung cancer that is positive for ROS1 and the neurotrophic receptor tyrosine kinases (NTRK) fusion-positive solid tumors. It inhibits cell-proliferation while targeting ROS1, a receptor tyrosine kinase.
• Tabrecta (capmatinib) is a tyrosine kinase inhibitor that can help to shrink tumors involving a MET mutation. The MET gene produces a receptor tyrosine kinase, which is involved in cell proliferation and cell survival.

Kinase Inhibitor

Our bodies contain enzymes called kinases, which help to regulate functional processes such as cell signaling and cell division. A kinase inhibitor blocks the action of kinases.

PARP Inhibitors

Drugs, such as Zejula, are used to treat ovarian cancer . The drug inhibits the enzymatic activity of enzyme poly (ADP-ribose) polymerase (PARP). In a study of 533 patients who had recurring ovarian cancer, Zejula increased the time experienced without symptoms compared with standard therapy.

Combination Therapies 

Combination therapy means using two forms of cancer therapy in conjunction. Newer classes of drugs are being combined with traditional chemotherapy to improve outcomes. This approach becoming the standard of care for treating some types of cancer.

One recent example is the combination of Tecentriq and Avastin in the treatment of liver cancer.

It is an ongoing area of critical research to develop better and more accurate diagnostic and screening techniques. Below are some next-generation technologies that are being developed. However, keep in mind these techniques (aside from ctDNA) have yet to be approved by the FDA.

Artificial Intelligence Mammograms

In a study that involved 28,296 independent interpretations, AI performance was comparable to radiologists' diagnostic ability for detecting breast cancer.

Liquid Biopsy for Breast Cancer

A liquid biopsy can detect circulating levels of cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA).

In a meta-analysis that included 69 published research studies. with 5,736 breast cancer patients, researchers determined that the status of ctDNA mutation predicts disease recurrence and adverse survival results. They also found that the levels of cfDNA can predict metastasis of the axillary lymph node.

Monarch Robotic Endoscopy for Lung Cancer

This may be advantageous for patients with external lung lesions that need biopsy prior to surgery, radiation, targeted therapies, or immunotherapy.  

Genomic Cancer Screening in Embryos

A polygenic risk score used by genomic prediction accurately distinguished which person in a set of siblings will inherit a medical condition. The accuracy was cited between 70% and 90%, depending upon the condition.  

At-Home Urine Test for Prostate Cancer

A convenient, at-home urine test can be used to detect extracellular vesicle-derived RNA to provide prognostic information for men under active surveillance for prostate cancer.   

A Word From Verywell

Cancer research that is investigating better treatments and diagnostic tools is ongoing. Even if you have advanced metastatic cancer, it may be comforting to know that newer treatments are being studied and approved every year. As treatments become better and better, your chances of survival and remission will also improve. If you have been diagnosed with cancer, it may also help to seek a cancer support group to boost your mental well-being and resilience.

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Cancer.net Improvements in Surgery for Cancer: The 2020 Advance of the Year.

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Frumovitz M, Obermair A, Coleman RL, Pareja R, Lopez A, Ribero R. Quality of life in patients with cervical cancer after open versus minimally invasive radical hysterectomy (Lacc): a secondary outcome of a multicentre, randomised, open-label, phase 3, non-inferiority trial . Lancet Oncol . 2020 Jun;21(6):851-860. doi: 10.1016/S1470-2045(20)30081-4

Kim SI, Cho JH, Seol A, et al. Comparison of survival outcomes between minimally invasive surgery and conventional open surgery for radical hysterectomy as primary treatment in patients with stage IB1-IIA2 cervical cancer .  Gynecol Oncol . 2019;153(1):3-12. doi:10.1016/j.ygyno.2019.01.008

Palma DA, Olson R, Harrow S, Gaede S, Louie A, Haasbeek C. Stereotactic ablative radiotherapy versus standard of care palliative treatment in patients with oligometastatic cancers (Sabr-comet): a randomised, phase 2, open-label trial. Lancet. 2019 May 18;393(10185):2051-2058. doi: 10.1016/S0140-6736(18)32487-5

Murray L, Ramasamy S, Lilley J, et al. Stereotactic Ablative Radiotherapy (SABR) in Patients with Medically Inoperable Peripheral Early Stage Lung Cancer: Outcomes for the First UK SABR Cohort .  Clin Oncol (R Coll Radiol) . 2016;28(1):4-12. doi:10.1016/j.clon.2015.09.007

American Cancer Society. Immunotherapy .

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Vansteenkiste JF, Van De Kerkhove C, Wauters E, Van Mol P. Capmatinib for the treatment of non-small cell lung cancer.   Expert Rev Anticancer Ther . 2019;19(8):659-671. doi:10.1080/14737140.2019.1643239

Matulonis UA, Walder L, Nøttrup TJ, et al. Niraparib Maintenance Treatment Improves Time Without Symptoms or Toxicity (TWiST) Versus Routine Surveillance in Recurrent Ovarian Cancer: A TWiST Analysis of the ENGOT-OV16/NOVA Trial .  J Clin Oncol . 2019;37(34):3183-3191. doi:10.1200/JCO.19.00917

Breast Cancer Research Foundation. How Combination Therapies Are Changing the Landscape of Breast Cancer Care .

Finn RS, Qin S, Ikeda M, et al. Atezolizumab plus Bevacizumab in Unresectable Hepatocellular Carcinoma .  N Engl J Med . 2020;382(20):1894-1905. doi:10.1056/NEJMoa1915745

Rodriguez-Ruiz A, Lång K, Gubern-Merida A, et al. Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists .  J Natl Cancer Inst . 2019;111(9):916-922. doi:10.1093/jnci/djy222

Alimirzaie S, Bagherzadeh M, Akbari MR. Liquid biopsy in breast cancer: A comprehensive review . Clin Genet . 2019 Jun;95(6):643-660. doi: 10.1111/cge.13514

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Home > Cancer Research Catalyst > Experts Forecast Cancer Research and Treatment Advances in 2022

Experts Forecast Cancer Research and Treatment Advances in 2022

The year 2021 defied our expectations in a variety of ways. 

The delta and omicron COVID-19 variants imposed unprecedented challenges on the health care system and threatened our hopes of an end to the pandemic, but widespread vaccine distribution provided protection, preventing an estimated 36 million cases and 1 million deaths in the United States. As omicron called into question the efficacy of existing vaccines, tests, and treatments, the U.S. Food and Drug Administration (FDA) provided new options, in the form of emergency use authorizations for the first two oral COVID-19 drugs, nirmatrelvir/ritonavir (Paxlovid) and molnupiravir (Lagevrio). 

Aside from the pandemic, supply chain delays and worker shortages sparked frustration, but the national unemployment rate gradually fell to its lowest percentage since February 2020. Through a year of harsh weather conditions ranging from ice storms to wildfires to hurricanes and tornadoes, the United States doubled down on initiatives to battle climate change . 

In spite of the year’s setbacks, the field of cancer research also made progress. The FDA approved 16 new oncology drugs —including two to treat genetic conditions that cause high rates of tumor formation—as well as two cancer detection agents that help physicians better identify certain tumors during imaging or surgery. We celebrated the 50th anniversary of the National Cancer Act , saw marked progress in many areas of cancer research , and helped provide cancer patients with reliable information about their COVID-19 risks and vaccine efficacy . 

As in previous years , we have asked a panel of experts to reflect on the progress made in 2021 and forecast their predictions for cancer research in the year 2022. We spoke with AACR President-Elect Lisa Coussens, PhD, FAACR , about basic research; AACR board member and co-editor-in-chief of Cancer Discovery Luis Diaz Jr., MD , about precision immunotherapy; co-editor-in-chief of Cancer Prevention Research Michael Pollak, MD , and deputy editor of Cancer Prevention Research Avrum Spira, MD , about cancer prevention; and AACR board member and former Annual Meeting Program Chair John Carpten, PhD, FAACR , about cancer disparities. 

Priorities for Basic Research in 2022 

“There isn’t a drug on the market that doesn’t have its origins in a basic science discovery,” said Lisa Coussens, PhD, FAACR , chair of the department of Cell Development and Cancer Biology at Oregon Health and Science University, when asked about the ways that laboratory science has shaped the landscape of cancer care. “We can’t lose sight of the importance of basic research at any step in the pipeline toward advancing cancer medicine and improving outcomes for our patients.” 

Basic science—fundamental research about the way cells and molecules function and interact—spans applications from protein chemistry to cell genomics to animal models. Such discoveries help researchers determine, for example, which proteins can be targeted with drugs to fight a disease, or which biomarkers might help determine a patient’s prognosis or course of treatment.  

An important priority for improving our knowledge of cancer cell biology, Coussens explained, is to better understand how cells shift between different states, especially in response to a disease or therapy. 

“We need to understand nuances between different tissue states within our body, and how they respond to changes in their environment,” Coussens said, noting that this is true in healthy organs as well as in evolving tumors, where single cell types typically steer disease processes but are dependent on cues from the multiple cell types surrounding them. 

“Understanding those nuances will lead to bigger discoveries about how to target cell state changes so we can return cells back to normal control mechanisms,” she continued. 

Tumor cells are not the only cells that might change their patterns of gene expression and metabolism during the course of cancer progression and treatment, however. Other cells that surround and interact with the tumor, such as fibroblasts and immune cells, play a vital role in determining how the tumor behaves. 

“A full understanding of tumor ecosystems includes the neoplastic cells—the ‘bad guys’ with mutations—as well as the normal host cells that are recruited or co-opted to help tumor cells survive and disseminate,” Coussens said. 

Emerging classes of therapies, such as immune checkpoint inhibitors, leverage elements of the tumor microenvironment to kill cancer cells. In order to develop more drugs targeting these cancer support systems, researchers need to learn more about how tumors interact with their surroundings. 

“I think the next years will bring a major focus on understanding communication networks between all the different types of cells in tumor ecosystems,” Coussens said, adding that a basic understanding of cell communications could produce benefits beyond the scope of cancer. “Basic discoveries about tumor ecosystems can have far-reaching impacts on autoimmune diseases, chronic inflammatory diseases, and how individuals respond to therapies that are designed to treat Alzheimer’s, for example,” she explained. 

Coussens believes that many of these discoveries will be driven by the expanded use of technology and data science. Since the turn of the century, rapid advances in genomics, proteomics, and metabolomics have created an abundance of biological data from patients, animal models, and cell lines. Designing computational programs capable of integrating these data and determining how to analyze them in meaningful ways has been a constant source of innovation over the past 20 years. 

Coussens emphasized that continued progress in this area could significantly shape basic research in the coming years. 

“The biggest impact we’re seeing right now is with the emergence of technology development and computational data sciences,” Coussens said. “I think the greatest advances we will see over the next several years will be emerging out of team science embracing technology, data science, and biology.” 

As technological advances spur more integration between different disciplines, Coussens predicts that collaboration will become more crucial than ever.  

“Science has changed—we no longer do science in isolation,” she said. “The best science today, I think, comes out of multidisciplinary team science. I’m a biologist, but I now need to be able to communicate with data scientists, epidemiologists, and chemists.” 

Coussens expressed that young investigators entering the field should consider this new paradigm when planning their training. “The more you can round out your education in a multidisciplinary way, the better. You need to be able to communicate your science with people who don’t necessarily speak your field’s language.” 

Part of her advice hinged on trainees finding strong mentors who can help guide them toward these opportunities, especially as they recover from lost time and funding resulting from the COVID-19 pandemic. “Invest your time and energy in identifying mentors who care about who you are and the trajectory of your career. Find mentors who you will grow to respect and love,” she said. 

Overall, Coussens was optimistic about the state of basic research moving forward. 

“The basic science discoveries we’re going to see in the next five years will reshape the medical landscape for years to come,” she said. 

PRIORITIES FOR Precision Immunotherapy IN 2022 

The art of deciding which cancer therapies to give a patient, based on their individual tumor characteristics, has evolved over the past several decades, according to Luis Diaz Jr., MD , head of Solid Tumor Oncology at Memorial Sloan Kettering Cancer Center and a member of the National Cancer Advisory Board. Such decisions were first made based on protein markers expressed by the tumor, then by genetic changes in the tumor’s DNA. Now, Diaz said, a precise understanding of tumor characteristics can predict which patients may benefit most from immunotherapy. 

“One example has been PD-L1 overexpression, either on the tumors themselves or on the surrounding cells,” Diaz said. “Another is mismatch repair deficiency, which seems to prime cells to become very sensitive to immunotherapy.” 

This is just one of the ways that the fields of precision medicine and immunotherapy have grown to complement each other in recent years. As Diaz noted, antibodies targeting PD-1 or PD-L1 have become an effective therapy for patients whose tumors express these immunosuppressive markers. 

The treatment of patients with CAR T cells—immune cells which are harvested from a patient’s body, engineered to target tumors, and returned to the patient’s bloodstream—represents an even more patient-specific approach to immunotherapy. 

But these therapies are not appropriate for all cancer types, and many patients who receive these therapies eventually relapse, creating a need for the expansion of immunotherapy types and indications. 

Diaz believes researchers can improve the efficacy of immunotherapy by offering it earlier in a patient’s course of treatment. 

“In many cases, we’re testing new therapies on patients for whom all standard therapies have already failed,” he said. “As we move forward, we need to begin to treat earlier in the diagnosis.” 

Diaz emphasized that treating advanced cancer poses far more challenges than intervening in early-stage disease or preventing tumor formation altogether. “If we can begin to bring targeted therapy and immunotherapy into the prevention space, I think we’ll see a profound impact,” he said. 

A different approach to improving immunotherapy efficiency is to reach more patients by making cell-based immunotherapies, such as CAR T, effective against a broader range of tumor types, including solid tumors.  

To overcome these hurdles, Diaz said, “The priority needs to be in maximizing specificity and minimizing toxicity.” 

Solid tumors, Diaz explained, are often heterogeneous. An immune response against a single target may kill some of the tumor, but cancer cells that don’t express the target may continue to grow and evade the immune system. Researchers have designed CAR T cells that target multiple tumor cell markers, but more targets also increase the likelihood of harmful side effects.  

“It’s a mathematical problem we can’t solve very easily,” Diaz said. “We need some clever new ideas.” 

Boosting the number of people who receive immunotherapy also involves addressing accessibility issues, especially for patients in rural or underresourced communities. Diaz speculated that the increase in remote care options resulting from the COVID-19 pandemic might provide a blueprint for the decentralization of clinical trials, paving the way for large cancer centers to collaborate with community hubs. 

He emphasized that one way to promote decentralization is to encourage more clinical trial ownership from clinicians rather than pharmaceutical companies. “I’d like to see our investigators becoming the initiators of more trials to be run at large cancer centers and elsewhere,” Diaz said.  

He noted that clinical trial decentralization will pose some challenges, such as standardizing procedures and supplies and ensuring that quality does not suffer. However, he was optimistic that it would eventually improve care. “I think it will make clinical development move faster than it ever has before,” he said. 

Targeting new populations and tumor types with immunotherapy, however, will only benefit patients whose tumors mount an immune response. Some tumors—deemed immunologically “cold”—expertly evade the immune system, and the mechanisms underlying that process are complex. 

“We need a better understanding of what makes tumors immunogenic so we can harness that knowledge to make cancers more immunogenic,” Diaz said. 

He noted that research into the interface between immune cells and cancer cells has done a great job of producing the therapies on the market today, but that advancing precision immunotherapy will require those efforts to continue. 

“As exciting as everything is that we’re doing, we need to do so much more,” Diaz said. “What’s popular right now is probably only the tip of the iceberg.” 

Priorities for Cancer Prevention in 2022 

“The most transformative impact we could have on cancer care would be to prevent cancer from happening in the first place,” said Avrum Spira, MD , a professor of medicine, pathology and laboratory medicine, and bioinformatics at the Boston University School of Medicine and global head of the Lung Cancer Initiative at Johnson & Johnson. 

Spira and his colleagues study how physicians can better detect early-stage lung cancer or signs of precancerous changes in the lungs. He also studies how to intervene in these early stages to prevent disease progression. 

“Researchers have found molecular alterations in late-stage cancer and used that information to develop new targeted therapies and immunotherapies that are transforming the treatment of advanced-stage disease,” Spira said. “It’s absolutely critical to move that fundamental molecular understanding to early-stage and even premalignant disease.” 

Understanding what drives benign cells into a tumorigenic state is an important component of this process, Spira emphasized. Drawing on the success of large-scale programs such as The Cancer Genome Atlas , the Human Cell Atlas , and the Human Tumor Atlas Network , dedicated to fully characterizing the blueprints of the human body, researchers have embarked on the development of a Pre-cancer Atlas . 

“Within the Human Tumor Atlas Network, researchers are forming large coalitions for multiple different cancer types to develop a temporal and spatial atlas of the cellular and molecular changes associated with the transition of a premalignant lesion to a fully-blown invasive cancer,” Spira said. “I think, in 2022, we’re going to see a proliferation of those types of studies, generating a vast amount of cellular and molecular data from premalignant tissue across many cancer types.” 

Spira believes such an atlas will benefit patients in two key ways: the development of biomarkers that can help predict which precancerous lesions will advance to cancer, and the identification of drug targets to stop the progression. 

“For most cancer types, we don’t know what those early events are, and therefore, we have no effective way to intercept the disease process,” he said. “I think in 2022, we will begin to understand these events and gain unprecedented insight into targeted approaches aimed at intercepting premalignancy.” 

Spira elaborated more on the need for biomarkers, which may not only identify patients at an elevated cancer risk but may also determine which patients with abnormal imaging results may need a biopsy. The most effective biomarkers, he stressed, would be the ones detectable via noninvasive tests. 

“I’m excited about the future of blood-based tests looking at nucleic acids,” Spira said. “The technologies are evolving very rapidly to the point where they can now detect very small amounts of DNA or epigenetic changes that are circulating in the blood, and they can screen people across multiple cancer types.” 

While blood-based liquid biopsies have attracted a great deal of attention in recent years, Spira also drew attention to other emerging noninvasive tests with the potential to have a significant impact on early cancer detection, such as urine markers of urologic cancer, stool markers of colon cancer, and nasal brushings to assess lung cancer risk. 

Spira hopes these noninvasive tests can be integrated with each other and with imaging results to give the best possible assessment of a patient’s risk. “That’s a complicated space, but I think this convergent approach is one that will advance significantly in 2022,” he said. 

Even noninvasive tests, however, can only benefit patients who are able to access them. Spira pointed out a few ways the field adapted during the COVID-19 pandemic that could continue to be leveraged moving forward. 

“We need to find ways to get screening to patients as opposed to them having to come to the hospital,” Spira said. He highlighted advances such as remote clinical trial management, as well as mobile CT and radiology units, set up in large vans or trucks that can drive to various neighborhoods to perform screening. Used during COVID-19 to promote social distancing and minimize virus exposure, such units could be used in the future to help people catch up on screenings missed during the pandemic, especially in areas with poor health care access. 

Spira also noted that the pandemic placed a spotlight on behavioral risk factors that increased COVID-19 susceptibility and the risk for severe disease, such as smoking, alcohol consumption, obesity, and physical inactivity. He pointed out that, often, these same behaviors contribute to cancer risk. 

“This has become a teachable moment,” Spira said. “I think we can encourage the public to alter some cancer-causing behaviors that are also related to virus susceptibility.” 

Michael Pollak, MD , a professor of oncology and medicine at McGill University in Montreal, Canada, who studies cancer prevention through the lens of reducing risk, also emphasized addressing lifestyle behaviors that affect multiple health conditions. 

“An important trend for 2022 may be the concept of healthy lifestyle behaviors integrated across diseases,” Pollak said. “We have to recognize that some of the activities and lifestyles and approaches to cancer risk just contribute to overall good health.” 

While many behavioral factors are known to broadly increase risk of several cancers, Pollak noted that risks vary in unique ways among different individuals.  

“Oncologists are used to personalization of treatments,” he said. “We try to find out what treatment would be particularly useful for one patient as compared to their neighbor. In prevention, we may discover an analogy to that customization.” 

He explained that an individual assessment of risk may make the message of behavioral intervention more personal. “If you hear your doctor saying that, in your particular case, the way your body is put together, your weight especially increases your risk for cancer, it may help motivate some people.” 

Pollak believes risk assessment can be further personalized beyond the level of the individual, down to the level of discrete cell types. “We’re used to thinking of a person’s cancer risk as if the person was homogeneous, but carcinogenesis takes place at the cellular level,” he said. “We need to know what’s going on differently in the different cells that might determine risk on a per-cell basis.” 

Pollak mentioned the Pre-cancer Atlas as an important vehicle for realizing this goal. “With the Pre-cancer Atlas, we’ll learn more about the cellular composition and subcellular features that lead to carcinogenesis,” he said, noting that such a granular understanding of tumor formation could pave the way for improved therapies. 

“We really won’t be able to prevent every cancer, but even if we confine our goals to preventing the subset of cancers that are preventable, that’s estimated to be about half of all cancers,” Pollak concluded. “Even acknowledging the limitations, the potential gains are absolutely enormous.” 

Priorities for Cancer Disparities in 2022 

The past two years have presented health care challenges beyond COVID-19, encompassing financial and access-related struggles that affected many facets of medicine, including cancer care. Many individuals have had to delay routine cancer screenings, alter the course of treatment, or miss follow-up appointments as a result of the pandemic. 

Such problems were more pronounced in some communities than others. 

“The pandemic has definitely impacted our opportunities to move forward toward eliminating disparities in all areas of cancer research,” said John Carpten, PhD, FAACR , chair of the department of Translational Genomics at the University of Southern California Keck School of Medicine and chair of the National Cancer Advisory Board. “As we consider gaps in cancer screening and cancer diagnosis, many challenges were further exacerbated in underrepresented minority communities during the pandemic.”  

Carpten also pointed out the disproportionate challenges minority cancer researchers faced during COVID-19. “Many underrepresented minority investigators, who may have already had challenges in terms of access to funding, were also impacted severely by the pandemic,” he said. “This is especially true for early-stage investigators and postdoctoral fellows who were unable to be in their laboratories to perform research.” 

Although the issue of lost time and funding due to the pandemic may be difficult to solve, Carpten believes that other initiatives to support underrepresented minority researchers—especially trainees and early-career investigators—will positively influence health disparities research in 2022. 

Carpten specifically listed diversifying the biomedical workforce as a key priority for tackling health disparities. “Increasing underrepresented minority faculty members will increase the number of mentors who will then be able to train more underrepresented minorities and fellows,” he said. 

He mentioned the National Institutes of Health (NIH) FIRST program , a funding opportunity provided to institutions to promote the hiring of early-career investigator cohorts from diverse backgrounds in support of their career development. Providing a supportive environment and sufficient resources to these investigators, Carpten said, can make significant strides toward ensuring a successful career trajectory in academic research. 

“We believe that this is going to be a huge component in the growth of underrepresented minorities in the area of biomedical research, specifically cancer research,” he said. 

Encouraging diversity of researchers, however, is only one step where meaningful interventions can occur. Another is the broader inclusion of diverse patients and samples in cancer research, especially of patients recruited into clinical trials. 

“We need to understand the broader impact of new therapies for all people, preferably prior to approvals, to ensure that we have the most accurate picture relative to effectiveness and toxicity profiles across representative groups of patients,” Carpten said. 

Diversity in preclinical studies, including patient-derived samples, genetic data, and model systems, is also key to understanding the biological basis of cancer health disparities. 

“Whether it’s understanding the influence of genetic factors on cancer risk or understanding how collections of mutations that occur in cancer cells differ across individuals from different groups, it will be very important for us to continue increasing representation of the reagents, models, and data that we use,” Carpten said. 

“Ensuring that we understand how biological changes impact cancer initiation, progression, and growth across an array of models will provide additional information so that we can really capture the full complexity of cancer,” he added. 

Carpten also encourages working to address the cultural, social, and access-related issues underlying cancer health disparities by striving harder to engage with the community. 

“We need to advance our relationships with various stakeholders, especially in terms of community engagement, outreach, and involvement,” Carpten said. “If we don’t build better relationships with the community, get their feedback, understand their issues, and work together to address them, I think we’ll continue to have challenges.” 

As observed during the pandemic , improving community engagement can help health care providers build trust with their patients, bring care to broader geographic areas, and better understand the needs of the populations disparities researchers are working to serve. 

“I really look forward to working with my colleagues in academia, industry, and the government, but most importantly, with our colleagues in the community,” Carpten concluded. “Their voice really needs to be heard and will be key in achieving cancer health equity.” 

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How CRISPR Is Changing Cancer Research and Treatment

July 27, 2020 , by NCI Staff

CRISPR is a highly precise gene editing tool that is changing cancer research and treatment.

Ever since scientists realized that changes in DNA cause cancer , they have been searching for an easy way to correct those changes by manipulating DNA . Although several methods of gene editing have been developed over the years, none has really fit the bill for a quick, easy, and cheap technology.

But a game-changer occurred in 2013, when several researchers showed that a gene-editing tool called CRISPR could alter the DNA of human cells like a very precise and easy-to-use pair of scissors. 

The new tool has taken the research world by storm, markedly shifting the line between possible and impossible. As soon as CRISPR made its way onto the shelves and freezers of labs around the world, cancer researchers jumped at the chance to use it.

“CRISPR is becoming a mainstream methodology used in many cancer biology studies because of the convenience of the technique,” said Jerry Li, M.D., Ph.D., of NCI’s Division of Cancer Biology .

Now CRISPR is moving out of lab dishes and into trials of people with cancer. In a small study, for example, researchers tested a cancer treatment involving immune cells that were CRISPR-edited to better hunt down and attack cancer. 

Despite all the excitement, scientists have been proceeding cautiously, feeling out the tool’s strengths and pitfalls, setting best practices, and debating the social and ethical consequences of gene editing in humans. 

How Does CRISPR Work?

Like many other advances in science and medicine, CRISPR was inspired by nature. In this case, the idea was borrowed from a simple defense mechanism found in some microbes, such as bacteria. 

To protect themselves against invaders like viruses, these microbes capture snippets of the intruder’s DNA and store them away as segments called CRISPRs, or clustered regularly interspersed short palindromic repeats. If the same germ tries to attack again, those DNA segments (turned into short pieces of RNA ) help an enzyme called Cas find and slice up the invader’s DNA. 

After this defense system was discovered, scientists realized that it had the makings of a versatile gene-editing tool. Within a handful of years, multiple groups had successfully adapted the system to edit virtually any section of DNA, first in the cells of other microbes, and then eventually in human cells.

CRISPR consists of a guide RNA (RNA-targeting device, purple) and the Cas enzyme (blue). When the guide RNA matches up with the target DNA (orange), Cas cuts the DNA. A new segment of DNA (green) can then be added.

In the laboratory, the CRISPR tool consists of two main actors: a guide RNA and a DNA-cutting enzyme, most commonly one called Cas9. Scientists design the guide RNA to mirror the DNA of the gene to be edited (called the target). The guide RNA partners with Cas and—true to its name—leads Cas to the target. When the guide RNA matches up with the target gene's DNA, Cas cuts the DNA. 

What happens next depends on the type of CRISPR tool that’s being used. In some cases, the target gene's DNA is scrambled while it's repaired, and the gene is inactivated . With other versions of CRISPR, scientists can manipulate genes in more precise ways such as adding a new segment of DNA or editing single DNA letters . 

Scientists have also used CRISPR to detect specific targets, such as DNA from cancer-causing viruses and RNA from cancer cells . Most recently, CRISPR has been put to use as an experimental test to detect the novel coronavirus .

Why Is CRISPR a Big Deal?

Scientists consider CRISPR to be a game-changer for a number of reasons. Perhaps the biggest is that CRISPR is easy to use, especially compared with older gene-editing tools. 

“Before, only a handful of labs in the world could make the proper tools [for gene editing]. Now, even a high school student can make a change in a complex genome ” using CRISPR, said Alejandro Chavez, M.D., Ph.D., an assistant professor at Columbia University who has developed several novel CRISPR tools.

CRISPR is also completely customizable. It can edit virtually any segment of DNA within the 3 billion letters of the human genome, and it’s more precise than other DNA-editing tools. 

And gene editing with CRISPR is a lot faster. With older methods, “it usually [took] a year or two to generate a genetically engineered mouse model , if you’re lucky,” said Dr. Li. But now with CRISPR, a scientist can create a complex mouse model within a few months, he said. 

Another plus is that CRISPR can be easily scaled up. Researchers can use hundreds of guide RNAs to manipulate and evaluate hundreds or thousands of genes at a time. Cancer researchers often use this type of experiment to pick out genes that might make good drug targets . 

And as an added bonus, “it’s certainly cheaper than previous methods,” Dr. Chavez noted.

What Are CRISPR’s Limitations?

With all of its advantages over other gene-editing tools, CRISPR has become a go-to for scientists studying cancer. There’s also hope that it will have a place in treating cancer, too. But CRISPR isn’t perfect, and its downsides have made many scientists cautious about its use in people.

A major pitfall is that CRISPR sometimes cuts DNA outside of the target gene—what’s known as “off-target” editing. Scientists are worried that such unintended edits could be harmful and could even turn cells cancerous , as occurred in a 2002 study of a gene therapy . 

“If [CRISPR] starts breaking random parts of the genome, the cell can start stitching things together in really weird ways, and there’s some concern about that becoming cancer,” Dr. Chavez explained. But by tweaking the structures of Cas and the guide RNA, scientists have improved CRISPR’s ability to cut only the intended target, he added. 

Another potential roadblock is getting CRISPR components into cells. The most common way to do this is to co-opt a virus to do the job. Instead of ferrying genes that cause disease, the virus is modified to carry genes for the guide RNA and Cas. 

Slipping CRISPR into lab-grown cells is one thing; but getting it into cells in a person's body is another story. Some viruses used to carry CRISPR can infect multiple types of cells, so, for instance, they may end up editing muscle cells when the goal was to edit liver cells. 

Researchers are exploring different ways to fine-tune the delivery of CRISPR to specific organs or cells in the human body. Some are testing viruses that infect only one organ, like the liver or brain. Others have created tiny structures called  nanocapsules that are designed to deliver CRISPR components to specific cells.

Because CRISPR is just beginning to be tested in humans, there are also concerns about how the body—in particular, the immune system —will react to viruses carrying CRISPR or to the CRISPR components themselves. 

Some wonder whether the immune system could attack Cas (a bacterial enzyme that is foreign to human bodies) and destroy CRISPR-edited cells. Twenty years ago, a patient died after his immune system launched a massive attack against the viruses carrying a gene therapy he had received. However, newer CRISPR-based approaches rely on viruses that appear to be safer than those used for older gene therapies.

Another major concern is that editing cells inside the body could accidentally make changes to sperm or egg cells that can be passed on to future generations. But for almost all ongoing human studies involving CRISPR, patients’ cells are removed and edited outside of their bodies. This “ ex vivo ” approach is considered safer because it is more controlled than trying to edit cells inside the body, Dr. Chavez said.

However, one ongoing study is testing CRISPR gene editing directly in the eyes of people with a genetic disease that causes blindness, called Leber congenital amaurosis.

The First Clinical Trial of CRISPR for Cancer

The first trial in the United States to test a CRISPR-made cancer therapy was launched in 2019 at the University of Pennsylvania. The study, funded in part by NCI, is testing a type of immunotherapy in which patients’ own immune cells are genetically modified to better “see” and kill their cancer. 

The therapy involves making four genetic modifications to T cells , immune cells that can kill cancer. First, the addition of a synthetic gene gives the T cells a claw-like protein (called a receptor ) that “sees” NY-ESO-1, a molecule on some cancer cells.

Then CRISPR is used to remove three genes: two that can interfere with the NY-ESO-1 receptor and another that limits the cells’ cancer-killing abilities. The finished product, dubbed NYCE T cells, were grown in large numbers and then infused into patients. 

The first trial of CRISPR for patients with cancer tested T cells that were modified to better "see" and kill cancer. CRISPR was used to remove three genes: two that can interfere with the NY-ESO-1 receptor and another that limits the cells’ cancer-killing abilities. 

“We had done a prior study of NY-ESO-1–directed T cells and saw some evidence of improved response and low toxicity ,” said the trial’s leader, Edward Stadtmauer, M.D., of the University of Pennsylvania. He and his colleagues wanted to see if removing the three genes with CRISPR would make the T cells work even better, he said. 

The goal of this study was to first find out if the CRISPR-made treatment was safe. It was tested in two patients with advanced multiple myeloma and one with metastatic sarcoma . All three had tumors that contained NY-ESO-1, the target of the T-cell therapy. 

Initial findings suggest that the treatment is safe . Some side effects did occur, but they were likely caused by the chemotherapy patients received before the infusion of NYCE cells, the researchers reported. There was no evidence of an immune reaction to the CRISPR-edited cells. 

Only about 10% of the T cells used for the therapy had all four of the desired genetic edits. And off-target edits were found in the modified cells of all three patients. However, none of the cells with off-target edits grew in a way that suggested they had become cancer, Dr. Stadtmauer noted.

The treatment had a small effect on the patients’ cancers. The tumors of two patients (one with multiple myeloma and one with sarcoma) stopped growing for a while but resumed growing later. The treatment didn't work at all for the third patient. 

It's exciting that the treatment initially worked for the sarcoma patient because “ solid tumors have been a much more difficult nut to crack with cellular therapy," Dr. Stadtmauer said. "Perhaps [CRISPR] techniques will enhance our ability to treat solid tumors with cell therapies.”

Although the trial shows that CRISPR-edited cell therapy is possible, the long-term effects still need to be monitored, Dr. Stadtmauer continued. The NYCE cells are “safe for as long as we’ve been watching [the study participants]. Our plan is to keep monitoring them for years, if not decades,” he said. 

More Studies of CRISPR Treatments to Come 

While the study of NYCE T cells marked the first trial of a CRISPR-based cancer treatment, there are likely more to come. 

“This [trial] was really a proof-of-principle, feasibility, and safety thing that now opens up the whole world of CRISPR editing and other techniques of [gene] editing to hopefully make the next generation of therapies,” Dr. Stadtmauer said. 

Other clinical studies of CRISPR-made cancer treatments are already underway. A few trials are testing CRISPR-engineered CAR T-cell therapies , another type of immunotherapy. For example, one company is testing CRISPR-engineered CAR T cells in people with B cell cancers and people with multiple myeloma .

There are still a lot of questions about all the ways that CRISPR might be put to use in cancer research and treatment. But one thing is for certain: The field is moving incredibly fast and new applications of the technology are constantly popping up. 

“People are still improving CRISPR methods,” Dr. Li said. “It’s quite an active area of research and development. I’m sure that CRISPR will have even broader applications in the future.”

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New cancer treatment may reawaken the immune system

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Immunotherapy is a promising strategy to treat cancer by stimulating the body’s own immune system to destroy tumor cells, but it only works for a handful of cancers. MIT researchers have now discovered a new way to jump-start the immune system to attack tumors, which they hope could allow immunotherapy to be used against more types of cancer.

Their novel approach involves removing tumor cells from the body, treating them with chemotherapy drugs, and then placing them back in the tumor. When delivered along with drugs that activate T cells, these injured cancer cells appear to act as a distress signal that spurs the T cells into action.

“When you create cells that have DNA damage but are not killed, under certain conditions those live, injured cells can send a signal that awakens the immune system,” says Michael Yaffe, who is a David H. Koch Professor of Science, the director of the MIT Center for Precision Cancer Medicine, and a member of MIT’s Koch Institute for Integrative Cancer Research.

In mouse studies, the researchers found that this treatment could completely eliminate tumors in nearly half of the mice.

Yaffe and Darrell Irvine, who is the Underwood-Prescott Professor with appointments in MIT’s departments of Biological Engineering and Materials Science and Engineering, and an associate director of the Koch Institute, are the senior authors of the study, which appears today in Science Signaling . MIT postdoc Ganapathy Sriram and Lauren Milling PhD ’21 are the lead authors of the paper.

T cell activation

One class of drugs currently used for cancer immunotherapy is checkpoint blockade inhibitors, which take the brakes off of T cells that have become “exhausted” and unable to attack tumors. These drugs have shown success in treating a few types of cancer but do not work against many others.

Yaffe and his colleagues set out to try to improve the performance of these drugs by combining them with cytotoxic chemotherapy drugs, in hopes that the chemotherapy could help stimulate the immune system to kill tumor cells. This approach is based on a phenomenon known as immunogenic cell death, in which dead or dying tumor cells send signals that attract the immune system’s attention.

Several clinical trials combining chemotherapy and immunotherapy drugs are underway, but little is known so far about the best way to combine these two types of treatment.

The MIT team began by treating cancer cells with several different chemotherapy drugs, at different doses. Twenty-four hours after the treatment, the researchers added dendritic cells to each dish, followed 24 hours later by T cells. Then, they measured how well the T cells were able to kill the cancer cells. To their surprise, they found that most of the chemotherapy drugs didn’t help very much. And those that did help appeared to work best at low doses that didn’t kill many cells.

The researchers later realized why this was so: It wasn’t dead tumor cells that were stimulating the immune system; instead, the critical factor was cells that were injured by chemotherapy but still alive.

“This describes a new concept of immunogenic cell injury rather than immunogenic cell death for cancer treatment,” Yaffe says. “We showed that if you treated tumor cells in a dish, when you injected them back directly into the tumor and gave checkpoint blockade inhibitors, the live, injured cells were the ones that reawaken the immune system.”

The drugs that appear to work best with this approach are drugs that cause DNA damage. The researchers found that when DNA damage occurs in tumor cells, it activates cellular pathways that respond to stress. These pathways send out distress signals that provoke T cells to leap into action and destroy not only those injured cells but any tumor cells nearby.

“Our findings fit perfectly with the concept that ‘danger signals’ within cells can talk to the immune system, a theory pioneered by Polly Matzinger at NIH in the 1990s, though still not universally accepted,” Yaffe says.  

Tumor elimination

In studies of mice with melanoma and breast tumors, the researchers showed that this treatment eliminated tumors completely in 40 percent of the mice. Furthermore, when the researchers injected cancer cells into these same mice several months later, their T cells recognized them and destroyed them before they could form new tumors.

The researchers also tried injecting DNA-damaging drugs directly into the tumors, instead of treating cells outside the body, but they found this was not effective because the chemotherapy drugs also harmed T cells and other immune cells near the tumor. Also, injecting the injured cells without checkpoint blockade inhibitors had little effect.

“You have to present something that can act as an immunostimulant, but then you also have to release the preexisting block on the immune cells,” Yaffe says.

Yaffe hopes to test this approach in patients whose tumors have not responded to immunotherapy, but more study is needed first to determine which drugs, and at which doses, would be most beneficial for different types of tumors. The researchers are also further investigating the details of exactly how the injured tumor cells stimulate such a strong T cell response.

The research was funded, in part, by the National Institutes of Health, the Mazumdar-Shaw International Oncology Fellowship, the MIT Center for Precision Cancer Medicine, and the Charles and Marjorie Holloway Foundation.

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Researchers develop a new way to safely boost immune cells to fight cancer

by Virginia Tech

Last year alone, more than 600,000 people in the United States died from cancer, according to the American Cancer Society. The relentless pursuit of understanding this complex disease has shaped medical progress in developing treatment procedures that are less invasive while still highly effective.

Immunotherapy is on the rise as a possible solution. Immunotherapy involves harnessing the power of the body's immune system to fight against cancer cells. Researchers in the College of Engineering have found a way to revamp a treatment procedure into a groundbreaking practice.

Rong Tong, associate professor in chemical engineering, has teamed up with Wenjun "Rebecca" Cai, associate professor in materials science and engineering, to explore a cancer immunotherapy treatment that has long been of interest to researchers.

In their newly published article in the journal Science Advances, Tong and Cai detailed their approach, which involves activating the immune cells in the body and reprogramming them to attack and destroy the cancer cells. This therapeutic method is frequently implemented with the protein cytokine . Cytokines are small protein molecules that act as intercellular biochemical messengers and are released by the body's immune cells to coordinate their response.

"Cytokines are potent and highly effective at stimulating the immune cells to eliminate cancer cells," Tong said. "The problem is they're so potent that if they roam freely throughout the body, they'll activate every immune cell they encounter, which can cause an overactive immune response and potentially fatal side effects."

Tong and Cai, in collaboration with chemical engineering and materials science and engineering graduate students, have developed an innovative approach to employ cytokine proteins as a potential immunotherapy treatment. Unlike previous methods, their technique ensures that the immune cell stimulating cytokines effectively localize within the tumors for weeks while preserving the cytokine's structure and reactivity levels.

Combining forces to take down cancer cells

Current cancer treatments, such as chemotherapy, cannot distinguish between healthy cells and cancer cells. When someone with cancer is treated with chemotherapy, the treatment attacks all of the cells in their body, which can lead to side effects such as hair loss and fatigue.

Stimulating the body's immune system to attack tumors is a promising alternative to treat cancer. The delivery of cytokines can jump-start immune cells in the tumor , but overstimulating healthy cells can cause severe side effects.

"Scientists determined a while ago that cytokines can be used to activate and fight against tumors, but they didn't know how to localize them inside the tumor while not exposing toxicity to the rest of the body," said Tong. "Chemical engineers can look at this from an engineering approach and use their knowledge to help refine and elevate the effectiveness of the cytokines so they can work inside the body effectively."

The research team's goal is to find a balance between killing cancer cells in the body while sparing healthy cells.

To accomplish this goal, Tong and his students used their expertise to create specialized particles with distinctive sizes that help determine where the drug is going. These microparticles are designed to stay within the tumor environment after being injected into the body. Cai and her students worked on measuring these particles' surface properties.

"In the field of materials science and engineering, we study the surface chemistry and mechanical behavior of materials, such as the specialized particle created for this project," Cai said. "Surface engineering and characterization, along with particle size , play important roles in controlled drug delivery , ensuring prolonged drug presence and sustained therapeutic effectiveness."

To ensure successful drug delivery, Tong and his chemical engineering students designed a novel strategy that:

• Anchors cytokines to these new microparticles, limiting the harm of cytokines to healthy cells
• Allows the newly particle-anchored cytokines to jump-start immune systems and recruit immune cells to attack cancer cells

"Our strategy not only minimizes cytokine-induced harm to healthy cells, but also prolongs cytokine retention within the tumor," Tong said. "This helps facilitate the recruitment of immune cells for targeted tumor attack."

The next step in the process involves combining the new, localized cytokine therapy method with commercially available, Food and Drug Administration (FDA)-approved checkpoint blockade antibodies, which reactivate the tumor immune cells that have been silenced so they can fight back the cancer cells.

"When there is a tumor inside the body, the body's immune cells are being deactivated by the cancer cells," Tong explained. "The FDA-approved checkpoint blocking antibody helps 'take off the brakes' that tumors put on immune cells, while the cytokine molecules 'step on the gas' to jump-start the immune system and get an immune cell army to fight cancer cells. These two approaches work together to activate immune cells."

Combining the checkpoint antibodies with the particle-anchored cytokine proved to successfully eliminate many tumors in their study.

Engineering an impact on cancer treatment

Team members hope their impact on immunotherapy treatment is part of a greater movement toward cancer treatment approaches that are harmless to healthy cells. The new approach of attaching cytokines to particles also could be used in the future to deliver other types of immunostimulatory drugs, according to the team.

"Researchers are still looking for safer and more effective cancer treatments," said Tong. "This motivation is what drives us to develop new technologies in the field. The whole class of drugs that are employed to jump-start the immune system to fight cancer cells has largely not yet succeeded.

"Our goal is to create novel solutions that allow researchers to test these drugs with existing FDA-approved therapeutics, ensuring both safety and enhanced efficacy."

Cai said the nature of cancer treatment research requires expertise across engineering disciplines.

"I view this project as a perfect marriage between chemical engineering and materials science," Cai said. "The former focuses on the synthesis and drug delivery part, the latter on applying advanced materials characterization. This collaboration not only accelerates immunotherapy research, but also has the ability to transform cancer treatment."

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Personalized cancer treatments based on testing drugs quickly leads to faster treatment, better outcomes

Assistant Professor of Environmental Health Sciences, Florida International University

Disclosure statement

Diana Azzam is a co-founder and owns shares in First Ascent Biomedical. She receives funding from Florida Department of Health and National Institute of Health.

Florida International University provides funding as a member of The Conversation US.

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Despite many efforts to find better, more effective ways to treat cancer, it remains a leading cause of death by disease among children in the U.S.

Cancer patients are also getting younger. Cancer diagnoses among those under 50 has risen by about 80% worldwide over the past 30 years. As of 2023, cancer is the second-leading cause of death both in the U.S. and around the world. While death rates from cancer have decreased over the past few decades, about 1 in 3 patients in the U.S. and 1 in 2 patients worldwide still die from cancer.

Despite advances in standard cancer treatments, many cancer patients still face uncertain outcomes when these treatments prove ineffective. Depending on the stage and location of the cancer and the patient’s medical history, most cancer types are treated with a mix of radiation, surgery and drugs. But if those standard treatments fail, patients and doctors enter a trial-and-error maze where effective treatments become difficult to predict because of limited information on the patient’s cancer.

My mission as a cancer researcher is to build a personalized guide of the most effective drugs for every cancer patient. My team and I do this by testing different medications on a patient’s own cancer cells before administering treatment, tailoring therapies that are most likely to selectively kill tumors while minimizing toxic effects.

In our newly published results of the first clinical trial combining drug sensitivity testing with DNA testing to identify effective treatments in children with cancer, an approach called functional precision medicine , we found this approach can help match patients with more FDA-approved treatment options and significantly improve outcomes.

What is functional precision medicine?

Even though two people with the same cancer might get the same medicine, they can have very different outcomes. Because each patient’s tumor is unique , it can be challenging to know which treatment works best.

To solve this problem, doctors analyze DNA mutations in the patient’s tumor, blood or saliva to match cancer medicines to patients. This approach is called precision medicine . However, the relationship between cancer DNA and how effective medicines will be against them is very complex. Matching medications to patients based on a single mutation overlooks other genetic and nongenetic mechanisms that influence how cells respond to drugs.

How to best match medicines to patients through DNA is still a major challenge. Overall, only 10% of cancer patients experience a clinical benefit from treatments matched to tumor DNA mutations.

Functional precision medicine takes a different approach to personalizing treatments. My team and I take a sample of a patient’s cancer cells from a biopsy, grow the cells in the lab and expose them to over 100 drugs approved by the Food and Drug Administration. In this process, called drug sensitivity testing , we look for the medications that kill the cancer cells.

New clinical trial results

Providing functional precision medicine to cancer patients in real life is very challenging. Off-label use of drugs and financial restrictions are key barriers. The health of cancer patients can also deteriorate rapidly, and physicians may be hesitant to try new methods.

But this is starting to change. Two teams in Europe recently showed that functional precision medicine could match effective treatments to about 55% of adult patients with blood cancers such as leukemia and lymphoma that did not respond to standard treatments.

Most recently, my team’s clinical trial focused on childhood cancer patients whose cancer came back or didn’t respond to treatment. We applied our functional precision medicine approach to 25 patients with different types of cancer.

Our trial showed that we could provide treatment options for almost all patients in less than two weeks. My colleague Arlet Maria Acanda de la Rocha was instrumental in helping return drug sensitivity data to patients as fast as possible. We were able to provide test results within 10 days of receiving a sample, compared with the roughly 30 days that standard genomic testing results that focus on identifying specific cancer mutations typically take to process.

Most importantly, our study showed that 83% of cancer patients who received treatments guided by our approach had clinical benefit, including improved response and survival.

Expanding into the real world

Functional precision medicine opens new paths to understanding how cancer drugs can be better matched to patients. Although doctors can read any patient’s DNA today, interpreting the results to understand how a patient will respond to cancer treatment is much more challenging. Combining drug sensitivity testing with DNA analysis can help personalize cancer treatments for each patient.

I, along with colleague Noah E. Berlow , have started to add artificial intelligence to our functional precision medicine program. AI enables us to analyze each patient’s data to better match them with tailored treatments and drug combinations. AI also allows us to understand the complex relationships between DNA mutations within tumors and how different treatments will affect them.

My team and I have started two clinical trials to expand the results of our previous studies on providing treatment recommendations through functional precision medicine. We’re recruiting a larger cohort of adults and children with cancers that have come back or are resistant to treatment.

The more data we have, the easier it will become to understand how to best treat cancer and ultimately help more patients access personalized cancer treatments.

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April 16, 2024

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New Prostate Cancer Treatments Offer Hope for Advanced Cases

Major discoveries during the past 10 years have transformed prostate cancer treatment, enabling it to proceed even for the most advanced form of the disease

By Marc B. Garnick

David Cheney

D eciding how to diagnose and treat prostate cancer has long been the subject of controversy and uncertainty. A prime example involves prostate-specific antigen (PSA) testing, a blood test for a telltale protein that can reveal cancer even when the patient has no symptoms. After its introduction in the early 1990s, PSA testing was widely adopted—millions of tests are done in the U.S. every year. In 2012, however, a government task force indicated that this test can lead to overtreatment of cancers that might have posed little danger to patients and so might have been best left alone.

While arguments for and against PSA testing continue to seesaw back and forth, the field has achieved a better grasp on what makes certain prostate cancers grow quickly, and those insights have paved the way for better patient prognoses at every stage of the disease, even for the most advanced cases. A prostate cancer specialist today has access to an enhanced tool set for treatment and can judge when measures can be safely deferred.

The importance of these advances cannot be overstated. Prostate cancer is still one of the most prevalent malignancies. Aside from some skin cancers, prostate cancers are the most common cancers among men in the U.S. Nearly 270,000 people in America will be diagnosed with prostate cancer this year, and it is the fourth most common cancer worldwide. Fortunately, the vast majority of patients will live for years after being diagnosed and are more likely to die of causes unrelated to a prostate tumor.

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At its most basic level, prostate cancer is a malignancy that occurs in the prostate gland, which produces fluid that mixes with sperm from the testicles to make semen. The prostate is located in front of the rectum, below the bladder and above the penis, and cancer in the gland has four major stages.

Early on, localized tumors show no evidence of extension beyond the prostate gland. A second, “regionally advanced” form of the disease remains close to the prostate. Then there are metastatic prostate cancers, which spread outside the gland to other parts of the body. Treatment of tumors in this category has benefited from improved diagnostic imaging tests. In fact, with these tests, cancer specialists have characterized the fourth category, oligometastatic prostate cancer, a disease stage on a continuum between localized prostate cancer and more broadly dispersed metastatic disease. Major discoveries in the past 10 years have transformed the way we approach each type of prostate cancer, and these advances are likely to continue for decades to come.

The first treatment steps for people with localized cancer involve risk stratification. Through this process, a physician gauges the likelihood of a cancer’s being eliminated or cured by local treatment (usually surgery or radiation) and, if it does abate, of its returning. A physician determines the risk based on PSA results, physical examination of the prostate gland and inspection of cells from the biopsied tumor.

The right course of action for a patient with elevated PSA levels continues to undergo constant revision. Until five to seven years ago, a physician evaluated a person with high PSA by feeling their prostate gland for potentially cancerous abnormalities. Invariably, the next step would be a needle biopsy—an uncomfortable procedure in which the physician obtains snippets of prostate tissue through the rectum.

But we now have a way to biopsy through the perineum—the area between the back of the scrotum and the anal-rectal area. Thanks to technical improvements, it can be done in an outpatient setting without general anesthesia or sedation. The technique reduces the patient’s risk of infection and need for antibiotics because it doesn’t disrupt the bacterial flora in the rectum. In a recent study, researchers compared outcomes in patients who underwent a trans­rectal biopsy and received antibiotics with those for people who had a transperineal biopsy with minimal to no antibiotics. They found the two approaches comparable in terms of complications from infections.

Even more exciting is the prospect of eliminating biopsies altogether. When a patient has an abnormal PSA value but their rectal examination shows no obvious evidence of cancerous deposits, physicians can now use magnetic resonance imaging (MRI) to look at the prostate and surrounding tissue. MRI scans are best for identifying clinically significant cancers—those that, if left untreated or undiagnosed, could eventually spread. MRI can also uncover more extensive cancer spread or tumors in unusual locations such as the front of the prostate.

Another benefit of MRI procedures is that they identify fewer clinically insignificant cancers—those that are unlikely to cause problems and might best be left alone. In this case, failure to detect certain cancers is a good thing because it spares people unnecessary treatment. In some medical centers in the U.S. and many in Europe, a physician will perform a biopsy only if the MRI scan does reveal evidence of clinical significance. Studies that have compared the two diagnostic approaches—routine biopsy for all patients with elevated PSA levels versus biopsies based on abnormal MRI findings—found they are similarly effective at detecting clinically significant cancers.

Once a patient is diagnosed with prostate cancer, what happens next? For decades the debate over treatment has been just as contentious as the debate over diagnosis. Fortunately, new research from the U.K. has provided some clarity. Investigators there studied several thousand people with elevated PSA levels whose prostate biopsies showed cancer. These patients were randomized to receive surgical removal of the cancerous gland, radiation treatments or no active treatment at all. At the end of 15 years of comprehensive follow-up, about 3 percent of patients in each group had died of prostate cancer, and nearly 20 percent in each group had died of unrelated causes.

Based on the results of this study and others, more people are now being offered “active surveillance” after a prostate cancer diagnosis, in which treatment is either delayed or avoided altogether. Careful monitoring of patients who have not undergone surgery or radiation is becoming more common; it is now being extended even to those with more worrisome tumors. The monitoring involves a range of measures: PSA testing every three to six months, physical examination of the prostate gland and assessment of the patient’s urinary symptoms. Those tests are followed by repeat biopsies at increasing intervals, as long as there are no significant pathological changes.

If a cancer is identified as having either intermediate- or high-risk features, doctors need to track its progression, usually with bone scans using radio­­pharma­ceut­i­cals and with abdominal-pelvic computed tomography (CT) scans, which may show any spread in the areas to which prostate cancer most often metastasizes. Unfortunately, these techniques are not sensitive enough to reliably detect cancer in structures less than a centimeter in diameter, such as lymph nodes. Consequently, small areas of metastatic disease may go undetected. These cases are said to be “understaged.”

Understaging can now be studied through more precise diagnostic testing. Typically patients whose disease is understaged are not treated until the cancer becomes detectable through symptoms such as urination problems or pain. The disease then may require intensive therapies, and there is less of a chance of long-term remission. One technology that can help address understaging is advanced scanning that combines radiodiagnostic positron-emission tomography (PET) with CT.

These scans can detect molecules commonly found in prostate cancer cells, such as prostate-specific membrane antigen (PSMA). If PSMA is present outside the prostate gland, such as in pelvic lymph nodes, the affected areas can be identified, and a plan can be made for targeted radiation treatments or surgical removal.

Let’s consider how PET-CT scanning can be used in clinical practice. One of my patients, a 68-year-old man, was diagnosed with prostate cancer that was localized but had high-risk features. The traditional diagnostic bone and CT scans did not show any evidence of cancer spread outside the prostate. A PET-CT scan for PSMA, however, did reveal the presence of several small deposits of cancer cells in well-defined areas of the pelvis, indicating the cancer had spread to the lymph nodes. This finding prompted treatment that included radiation therapy in the prostate gland and the cancerous lymph nodes, as well as androgen-deprivation therapy (ADT), a treatment that reduces levels of testosterone, the hormone that enables prostate cancer to grow and progress.

The more precise identification of small tumor deposits in a limited number of pelvic lymph nodes—diagnosed as oligometastatic prostate cancer—enabled a new use for an old technology in oncology called metastasis-directed therapy (MDT), which targets cancer-containing lymph nodes or bony areas with radiation. At times, surgical removal of the abnormal lymph nodes may also be incorporated into MDT. Recently published studies on the use of MDT in conjunction with conventional treatments show, in some cases, long-term remission lasting through years of follow-up. Until recently, such a scenario was unthinkable for people whose prostate cancer had spread to their lymph nodes. My patient had the PSMA scan and MDT, as well as a relatively short course of ADT. He is cancer-free for now.

Precise identification of small metastatic deposits has other positive benefits. ADT has for decades been the mainstay for treating many forms of prostate cancer. Patients must continue the therapy for years, sometimes for the rest of their lives. Side effects of ADT are similar to those experienced during menopause. In fact, “andropause” is the term that captures the effects of ADT. Lower levels of testosterone are accompanied by a multitude of symptoms, including but not limited to loss of libido, erectile dysfunction, weight gain, hot flashes, bone loss, cognitive impairment, mood changes, diminished energy, and worsening of preexisting heart and vascular problems.

Studies of MDT for oligometastatic prostate cancer have raised the question of whether ADT could be delayed, administered for a shorter duration or even omitted in patients who otherwise would have required it. By strategically deploying traditional forms of localized treatment—usually surgery to remove the prostate gland or radiation—with added MDT for oligometastatic disease, doctors can significantly shorten the duration of ADT or potentially eliminate it. Such an approach would have been difficult to imagine five years ago. Longer-term follow-up studies will help scientists determine whether some people diagnosed in this fashion can go into an extended remission.

F or advanced forms of prostate cancer that have spread to other parts of the body, ADT has been the main treatment. Physicians historically have generally recommended surgical removal of the testicles—the primary source of testosterone—or the administration of other hormones that block the production and action of testosterone. In the mid-1980s I was involved with research on drugs called luteinizing hormone–releasing hormone analogues that lowered testosterone by shutting off the signal in the brain that instructs the testicles to make testosterone. Today newer agents have been added that further lower and block testosterone’s action.

The goal of prostate cancer treatment at later stages is to eliminate multiple sources of testosterone. As noted earlier, testosterone in the body comes predominantly from the testicles; the adrenal glands also produce a small amount. But prostate cancer cells can evolve to produce their own androgens. Testosterone and its active form, dihydrotestosterone (DHT), traverse the membranes of prostate cancer cells and interact with androgen receptors in the cytoplasm, a cell’s liquid interior. The receptors then transport DHT to the nucleus, where it instructs the cancer cell to grow, replicate and spread.

Traditional ADT does little to affect either the production of testosterone by the adrenal glands or androgen-producing prostate cancer cells, and it doesn’t block the activity of androgen receptors. But new approaches to ADT may address these shortcomings. Drug combinations that affect all these processes have substantially improved survival in people with metastatic prostate cancer—and, more important, patients are able to tolerate these more intensive treatment programs.

Instead of just one drug to decrease testosterone, new standards for treatment prescribe combinations of two or even three drugs. In addition to traditional ADT, there are medications such as do­cetaxel, a chemotherapy, and other new drugs that can block the production of testosterone by the adrenal glands or cancer cells or stop it by interfering with the activity of androgen receptors. All these drug combinations have resulted in meaningful improvements in survival.

Yet another therapy for advanced disease involves the identification of PSMA-expressing cancer cells that can be targeted with pharmaceuticals designed to deliver radioactive bombs. An injectable radiopharmaceutical can be delivered selectively to these cells, leaving healthy cells mostly unaffected. This therapy, lutetium-177-­PSMA-617 (marketed as Pluvicto), has been approved by the U.S. Food and Drug Administration for the treatment of prostate cancer that has become resistant to other forms of ADT and chemotherapy. It is likely to become an important therapy for even earlier stages of prostate cancer.

Genetics and genomic testing of patients and cancers have also helped in the quest for improvement of symptoms and longer survival. Some genetic mutations that are known to increase the risk of breast and ovarian cancer have also been associated with a heightened risk of prostate cancer. Testing for such mutations is becoming much more common, and patients who have them can be treated with specific therapies that block their deleterious effects, leading to better outcomes.

An understanding of the type of mutation is also critical—for both patients and their family members. Germline mutations are inherited from a patient’s biological parents by every cell in the body. These mutations can be passed along to the patient’s children. A somatic mutation, in contrast, is not inherited but develops in the cancer itself. Targeted therapies designed specifically to correct the effects of either germline or somatic mutations have produced significant improvements in patient longevity. Some of the most commonly recognized cancer mutations—either somatic or germline—are those in BRCA genes, which have been associated with early-onset breast and ovarian cancer.

When researchers studied cancer in families with BRCA mutations, they uncovered many cases of prostate cancer. This finding led to the discovery that BRCA mutations appeared in both men and women in these families. The mutations change the way DNA is repaired, introducing defects that can result in cancer formation. Drugs have now been developed that treat cancers linked to the BRCA mutations. Several such drugs—those in a class called poly­(ADP-ribose) polymerase (PARP) inhibitors—have recently received FDA approval for use as a treatment in people with these mutations. This research has led to more widespread genetic testing of patients with prostate cancer and, when germline mutations are found, family genetic counseling.

All these advances have occurred over the past decade—an incredibly short interval in the context of cancer oncology. Current options for early-stage prostate cancer enable physicians and patients to feel more at ease with conservative choices rather than immediate interventions with negative side effects. For patients whose cancers are advanced at initial diagnosis or progress and become metastatic, the treatment of oligometastases now often leads to long-term remission and requires fewer treatments with harmful systemic side effects. For those with more widespread metastatic disease, their cancer can now be managed with improved therapeutics based on a better understanding of disease biology. These new strategies have begun to transform this once rapidly fatal disease into a chronic condition that people can live with for years or even for their full life expectancy.

Marc B. Garnick is Gorman Brothers Professor of Medicine at Harvard Medical School and Beth Israel Deaconess Medical Center in Boston. He is editor in chief of Harvard Medical School’s 2024–2025 Report on Prostate Diseases.

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Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach

1 Jinling Institute of Science and Technology, Nanjing City, Jiangsu Province, People’s Republic of China

Huiping Shi

Hongtao wang.

2 School of Life Science, Tonghua Normal University, Tonghua City, Jilin Province, People’s Republic of China

Cancer is a leading cause of morbidity and mortality worldwide. While progress has been made in the diagnosis, prognosis, and treatment of cancer patients, individualized and data-driven care remains a challenge. Artificial intelligence (AI), which is used to predict and automate many cancers, has emerged as a promising option for improving healthcare accuracy and patient outcomes. AI applications in oncology include risk assessment, early diagnosis, patient prognosis estimation, and treatment selection based on deep knowledge. Machine learning (ML), a subset of AI that enables computers to learn from training data, has been highly effective at predicting various types of cancer, including breast, brain, lung, liver, and prostate cancer. In fact, AI and ML have demonstrated greater accuracy in predicting cancer than clinicians. These technologies also have the potential to improve the diagnosis, prognosis, and quality of life of patients with various illnesses, not just cancer. Therefore, it is important to improve current AI and ML technologies and to develop new programs to benefit patients. This article examines the use of AI and ML algorithms in cancer prediction, including their current applications, limitations, and future prospects.

Introduction

Cancer is a significant public health issue globally, marked by an elevated incidence and mortality rate. 1 According to the GLOBOCAN 2020 database, approximately 19.3 million new cases and 10 million deaths have been reported annually. 2 Lung cancer remains the most common cause of cancer-related mortality, with expected 1.8 million fatalities, followed by stomach, liver, colorectal, and breast cancer. 2 The prevention and treatment of cancer remain difficult. 3 After heart disease, cancer remains the second leading cause of death in the United States. In 2023, there are projected to be 1.9 million new cancer cases (equivalent to around 5370 cases per day) and 609,820 deaths from cancer (equivalent to around 1670 deaths per day) in the US. The International Agency for Research on Cancer (IARC) has released a poster on known causes and prevention by organ site of human cancer. A description of known causes and prevention by organ site is provided in Figure S1 .

The “Global Cancer Observatory” reports indicate that on a global scale, 37 individuals are diagnosed with cancer and over 19 individuals succumb to the disease every minute. Figure 1A shows the number of new cases in 2020 and Figure 1B shows the number of deaths in 2020 due to all cancers.

The number of new cancer cases reported in 2020 and the number of deaths caused by these cancers. ( A ). Estimated number of new cases worldwide in 2020 among both sexes. ( B ) Estimated number of deaths worldwide in 2020 among both sexes. Reprinted from World Health Organization. © International Agency for Research on Cancer, 2020. Cancer Today. Available from: https://gco.iarc.fr/today/online-analysis-pie?v=2020&mode=cancer&mode_population=continents&population=900&populations=900&key=total&sex=0&cancer=39&type=0&statistic=5&prevalence=0&population_group=0&ages_group%5B%5D=0&ages_group%5B%5D=17&nb_items=7&group_cancer=1&include_nmsc=1&include_nmsc_other=1&half_pie=0&donut=0 . [Accessed June 20, 2023]. 4

However, the introduction of machine learning and artificial intelligence positively supports cancer prevention and management. 5 Artificial intelligence is commonly defined as a set of computer-coded programs or algorithms that use data analysis and pre-programmed instructions to make predictions and decisions about various aspects of a disease. Machine learning is a specialized field within AI that refers to a group of algorithms designed to automatically learn and improve from experience. In other words, machine learning is an AI subset that focuses on developing algorithms capable of learning from data and refining their performance over time. 6 , 7 Deep Learning is a subfield of “Machine Learning” that employs neural network-based models to imitate the human brain’s capacity to analyze huge amounts of complicated data in areas such as language processing, drug discovery, and image recognition. 8 An overview of AI, ML, and DL is provided in Figure 2 . 9

( a ) The concept of the AI, ML and DL. ( b ) Timescale of major breakthrough ( c ). Schematic of workflow of ML and ( d ). of DL.

All of these computer algorithms use data such as investigations performed, scans conducted, patients’ medical histories, and other information to forecast or diagnose a cancerous condition. During surgical procedures, the use of cutting-edge imaging technology and artificial intelligence (AI) has enabled the real-time detection and diagnosis of brain tumors. 10 Moreover, this approach demonstrated an exceptional ability to differentiate between malignant and normal tissue by accurately identifying cancerous tissue. The use of artificial intelligence (AI) to analyze the expression of specific genes enabled successful classification of cancer based on their activity levels, distinguishing between active, hyperactive, and quiet genes in both malignant and normal tissue. 11 Similarly, machine learning algorithms have been applied to identify mismatch repair deficit (dMMR) in colorectal screening. 12 , 13

AI techniques such as CS-SVM have been used on liver cancer rehabilitation groups and discovered that it can predict the timing and site of cancer recurrence. 14 Studies have also reported that different ML and AI techniques serve as models for global practices allowing big data and cognition-capable computers to aid cancer research specialists in revolutionizing medicine by performing multifaceted tasks to improve clinical workflow, diagnostic accuracy, reduce human resource cost, increase efficiency of data, and enhance treatment. 15 , 16 Therefore, researchers are increasingly discussing the use of AI and ML for cancer prognosis, diagnosis, and rehabilitation; assessing the broad scope, development, and accomplishments may serve as a benchmark for future studies and applications of breakthrough technology. 17

Although AI has been quickly integrated into cancer research, artificial intelligence-based solutions are still in their early stages. Only a few applications based on AI have been authorized for usage in the real world, such as in drug firms, hospitals, and so on. It is still a matter of debate whether AI can replace medical practitioners as professionals.

Much of the popular discourse on artificial intelligence centers on AI’s use in cancer clinical research. As a result, research into AI technologies has accelerated and achieved performance similar to that of human biological experts. Moreover, AI will provide human decision-makers with more knowledge and may become an integral part of the health-care team. Figure 3 shows schematic representation of machine learning representative fields of health-care services. This article presents an introduction to AI and ML in cancer clinical research including its prediction, prognosis, and limitations.

Machine learning representative fields of health care services.

Machine Learning- An Introduction

Machine Learning (ML) is a subfield of artificial intelligence (AL) that allows computers to “learn” from training data and enhance their performance with time without being supervised learning. 18 Machine learning can recognize patterns in data and obtain information from them in order to develop their own predictions. 19 Generally, machine learning models and algorithms acquire knowledge through experience. These models and algorithms retrieve patterns in data and link those patterns to compact classes of samples in the data. 20 For example, given a set of features discussing a person, an ML model with experience can predict whether that person is ill or healthy; given a set of parameters describing an animal, an ML model predicts whether that animal is being treated or under control; or given a set of features describing molecules, an ML model predicts whether those molecules are likely to interact or not. Such patterns may also be discovered using ML methods in an agnostic way, that is, without knowledge of the classes. These techniques are referred to as supervised and unsupervised machine learning, respectively. 21 Reinforcement learning is a third form of ML that looks for a series of actions that help achieve a certain objective. All of these techniques are gaining popularity in biomedical research across a wide range of disciplines, including treatment outcome prediction, drug development, medical imaging analysis, patient stratification, molecular interactions, and many more. 21

Furthermore, researchers describe ML as an academic field of study that incorporates computer science, statistics, and mathematics. Machine learning is the fundamental engine that is driving the development of artificial intelligence forward. 22 It is remarkably being used in both business and academia to promote the creation of “intelligent products” that can generate accurate predictions from varied data sources. 23 To date, the primary benefactors of the twenty-first-century surge in the availability of big data, data science, and machine learning have been companies that were able to acquire these data and pay the required personnel to transform their products. The learning methods created in and for these businesses have the potential to significantly improve clinical treatment and medical research, particularly as more and more providers adopt electronic health records. For example, the medical sector may benefit from the deployment of machine learning techniques in the areas of diagnostics and outcome prediction using electronic health records. This includes the prospect of identifying those at high risk for medical problems such as relapse or progression to a different illness state. Recently, ML algorithms have been used successfully in prognosis of skin cancer with equivalent accuracy to a skilled dermatologist, 24 and to predict the development of pre-diabetes type 2 diabetes using routinely obtained electronic health record data of the patients. 25 In medical sciences, one of the biggest advantages of using machine learning is that it is an automated technique that lets robots solve issues with minimal or no human input and respond based on previous observations.

An Introduction to AI in the Medical Field

In healthcare, artificial intelligence refers to the use of software, or “machine-learning algorithms” to simulate human “cognition” in the examination, presentation, and understanding of complicated health and medical care data. 26 In particular, artificial intelligence can provide results based on input data alone. The basic objective of AI applications in the field of health care is to examine associations between patient outcomes and clinical procedures. But what differentiates artificial intelligence from previous traditional methods is its ability to collect data, process it, interpret it, and provide a definite output. 27 This is accomplished by AI using deep learning methods and machine learning techniques. These systems can identify behavioral patterns and generate their own reasoning. Currently, AI-based information is being used in diagnostic procedures, medication development, customized medicine, patient monitoring, and treatment protocol formation. 28

For example, it is being used in precisely identifying and risk-stratifying individuals with “coronary artery disease.” In terms of diagnostic accuracy of coronary artery disease, artificial intelligence algorithms have demonstrated potential as an early triage tool. 29 Several aspects of gastroenterology are also taking advantage of the use of AI. For instance, endoscopic examinations, including colonoscopies and esophagogastroduodenoscopies rely on the quick diagnosis of abnormal tissue. Researchers are now believing that by incorporating AI into these endoscopic procedures, practitioners may diagnose illnesses, estimate their severity, and view blind regions more quickly. 30

Additionally, AI has demonstrated great potential in the clinical and laboratory settings of “infectious disease medicine.” 31 As the novel coronavirus infests the world, the United States is expected to spend more than $2 billion in AI-linked research studies by 2025, quadrupling the amount spent in 2019 ($463 million). 32 AI-based Neural networks have been introduced to quickly and precisely identify a host response to Coronavirus on the basis of mass spectrometry samples. Other uses of AI in infectious diseases include “support-vector machines” for discovering antibiotic resistance, ML investigation of blood smears for malaria diagnosis, and enhanced point-of-care diagnostics for Lyme disease on the basis of antigen detection. AI has also been studied for refining the diagnosis of tuberculosis, meningitis, and sepsis for predicting treatment problems in hepatitis C and B patients. 31

Uses of Machine Learning in Cancer Prediction

The ability to accurately predict which treatment regimens are best suited for each patient based on their distinct molecular, genetic, and tumor-based features is a challenging task in oncologic care that AI is intended to solve. 33 To assess whether AI and its subfield including machine learning can help in oncology care, a large number of studies investigated the applications of AI in cancer risk stratification, diagnoses, cancer medication development, and molecular tumor characterization. 34–36 According to these researches, ML can help in cancer prediction and diagnosis by analyzing pathology profiles, imaging studies, and its ability to convert pictures to “mathematical sequences.” In January 2020, researchers developed an artificial intelligence system based on a “Google DeepMind algorithm” capable of outperforming human “breast cancer” detection specialists. 36 , 37 In July 2020, the University of Pittsburgh developed an AI system-based machine learning technique with the highest accuracy in diagnosing prostate cancer, with a specificity of 98% and sensitivity of 98%. 38 A very recent study used an improved ViT (Vision Transformer) architecture, which they called ViT-Patch, is validated on a publicly available dataset, and the results of the experiments reveal that it is effective for both malignant detection and tumor localization. 39

A study used machine learning techniques to classify data relevant to cancer and generated a diagnosis for breast cancer. 40 In this study, different classification techniques were examined and applied to certain feature subsets, including support vector machine classifiers, probabilistic neural networks, and K-nearest neighbors. Support vector machine classifier models showed the maximum overall accuracy for the diagnosis of breast cancer. Rana et al used machine learning classification algorithms, which use stored historical data to learn from and forecast new input categories, benign and malignant tumors. 41 According to this study, the random forest model demonstrated the highest accuracy of 96% to detect different cancers. This study served as the foundation for a thorough understanding of the random forest model and served as the basis for the suggested AI system’s implementation.

Observational research compared the accuracy of the support vector machine, artificial neural networks (ANN), Naive Bayes classifier, and AdaBoost tree to identify a potent model for breast cancer prediction. 42 Principal component analysis was used to reduce dimensionality. The study found that, when compared to techniques like decision trees, regression trees, and so on, artificial neural network (ANN) was found to be the most popular one. The ANN method offered a reliable approach to making real-time predictions and prognoses.

Pulse-Coupled Neural Networks have been utilized in the field especially for image processing. 43 A survey investigated the disadvantages and advantages of various neural network designs. According to the survey, multilayer auto-encoders probabilistic and neural networks both delivered a 96% accuracy for a given cancer dataset. 44 On the basis of the Wisconsin Diagnostic Breast Cancer dataset, the research examined a variety of machine learning techniques, including support vector machines, linear regression, multilayer perceptron, and SoftMax regression. The results demonstrated that all of the machine language algorithms given completed the classification job successfully and with high test accuracy in the prediction of cancer. This study also proposed that more accurate feature selection techniques, which were used in the proposed model, can increase prediction accuracy. 45

Use of AI in Cancer Prediction

Over the past few decades, caregivers from all fields, from experts to paramedics, have been inquired to predict cancer prognoses based on their professional experience. Clinicians realize the need to use AI innovations such as DL and ML as a result of the emergence of the digital data era. 9 They believe that due to the complex and vast nature of statistical analysis it is difficult to anticipate how cancer will progress. 6 Health-care experts are also concerned about the risk that a patient may contract a disease, can have a tumor recurrence after treatment, or die. These considerations have a substantial influence on treatment options and results. In reality, a large body of evidence on clinical cancer is concerned with predicting patient response to therapy or establishing prognosis. Patients with more accurate prognoses can get more effective therapies; in fact, these treatment options typically include personalizing or individualizing care for each patient. AI can evaluate and understand “multi-factor” data from several patient assessments and provide more precise information about the patient survival, prognosis, and disease progression predictions in order to predict cancer. 9 Enshaei et al evaluated many strategies, integrating classifiers with traditional logistic regression analytic techniques to demonstrate that AI has a role in providing forecasting and predicting information to ovarian cancer patients. 46

Algorithms based on artificial intelligence have been shown to be capable of analyzing unstructured data and correctly estimating the likelihood of patients getting different illnesses, including cancer. 47 Accurately, agnostic AI algorithms can improve risk stratification criteria and influence cancer screening recommendation outcomes. 48–52 For instance, an artificial “neural network model” for “colorectal cancer risk stratification” demonstrated the highest accuracy than “current screening guidelines. 49

These AI algorithms might be used for a whole population. These algorithms can benefit those people who are at high risk of developing cancer or High-risk persons who are not covered in the existing screening criteria. Although traditional screening procedures for individuals with “early-onset sporadic colorectal cancer” are restricted, patients can benefit from the rigorous risk-based screening guidelines. 49

For tumors without an established screening method that is primarily asymptomatic in their first stages, individualized risk prediction might assist in early detection and perhaps increase treatment rates. For instance, an “artificial neural network model” for predicting “pancreatic cancer” risk has attained an area under the “receiver operating characteristic curve” of 85%. 53 In low-resource settings, individualized risk calculation algorithms can assist prioritize “screening for high-risk” people.

Which Types of Cancer are Easily Predicted?

Cancer is a genetic disorder and several types of cancers have been identified; 54 , 55 therefore, it is no surprise that AI advancements have helped oncology in particular. For example, “DNA methylation analysis” in cancer has been shown to be useful for cancer categorization and prognosis. 56 The “machine-determined DNA methylation” technique can reclassify more than 70% of human-labeled cancers, potentially leading to dramatically altered prognosis and therapy options. 57 In a research MethylationEPIC (850 k) and Illumina HumanMethylation450 showed the highest 93% accuracy in categorizing 82 kinds of “brain tumors.” 58 The authors’ reported the highest accuracy even greater than pathologists.

Deep Learning technique is effective in different industries and is used to detect a variety of chronic conditions and aid clinicians in making medical decisions. 59 According to a review, the deep learning algorithm was able to classify five different forms of cancer, including prostate and colon adenocarcinoma, breast invasive carcinoma, kidney renal clear cell carcinoma (KIRC), and lung adenocarcinoma (LUAD). 60

Dwivedi et al used microarray gene expression patterns to propose a system 61 of supervised machine-learning approaches for differentiating acute lymphoblastic leukemia from acute myeloid leukemia. This classification was achieved using an “artificial neural network” (ANN). 62 In 2020, Gupta et al predicted prostate cancer using multi-layer perceptrons and discovered multiple data-balancing procedures. 63 Another recent study in 2021 using ANN predicted mesothelioma with 96% accuracy and provided a method for classifying cancer depending on gene expression data. 64 In these studies, the logarithmic transformation was used to preprocess gene expression data in order to lessen the classification’s complexity, while the Bhattacharya distance was used to identify the most informative genes. In another research, An ML technique predicted blood and colon cancer on the basis of gene expression with a detection rate of 0.9666 and an accuracy of 0.9534. 65

Using machine learning (ML), researchers achieved a 97% accuracy rate in diagnosing two common types of lung cancer by analyzing tissue sample slides. The algorithm studied cancer tissue imaging and detected genetic changes associated with the disease. It successfully distinguished between normal lung tissue and the two most prevalent types of lung cancer, adenocarcinomas, and squamous cell carcinomas, which are often challenging to differentiate even for experienced pathologists due to their distinct cell origins and different treatment requirements. 66

Recently, a technique called radiomics has been introduced, a part of “Deep learning” techniques that can be “applied” to medical images in order to obtain a huge number of features that are invisible by humans and perhaps reveal disease-related patterns and characteristics. 67 In medical field, radiomics is the study of these characteristics, and there is a rising interest in merging them with clinical genomic data. 68 Radiomic techniques can inform models that accurately anticipate the treatment effectiveness or adverse effects of cancer therapy. Radiomics was found to be useful in predicting three different types of cancer such as lung, brain, and liver cancer 69 , 70 Additionally, deep learning exploiting radiomic brain features MRI can distinguish between brain gliomas and brain metastases with the same accuracy as expert neuroradiologists. 71

Currently, Al-based “cancer survival prediction” has been introduced for numerous cancer types, such as lung, prostate, and breast carcinomas. 72–74 The survival prediction accuracy of AI-based systems is superior to that of conventional analytic methodologies. 75 This could be due to their greater accuracy for variables having nonlinear relationships, making them more applicable to real-world situations. Predicting cancer survival can assist in customizing treatment plans. For patients at high risk, treatment planning can be strengthened, while therapies with limited effect can be avoided. 72 Moreover, AI models can predict the risk of illness recurrence following a therapeutic option. The applications of AI for the prediction of cancer recurrence have demonstrated greater accuracy than standard statistical models, 76 , 77 which will further facilitate the optimization of clinical follow-up plans.

Limitations

The use of artificially intelligent systems in any industry, including healthcare, has its limitations and obstacles. The moment has come to shift our perspective from being reactive to proactive in the face of emerging technology flaws. Here, we address various limitations of AI and machine learning with an emphasis on those that are particularly relevant to healthcare.

Data Privacy

Data accessibility and data gathering is the first step in developing an artificially intelligent system after problem selection and solution approach development. For the creation of effective models, it is necessary to have access to a large quantity of high-quality data. Due to patient privacy concerns and data breaches by prominent organizations, the issue of data collection is still in controversy. For example, patient confidentiality restricts the availability of data, which in turn limits model training; hence, a model’s full potential is not explored.

Fragmented Data

Another limitation of the deployment of artificial intelligence is that models that one organization designs and deploys for a specific job such as natural language processing, regression, classification, clustering, NLP cannot be effortlessly transferred to another organization for immediate use without recalculation. Due to privacy issues, data sharing between health-care organizations is frequently inaccessible or restricted, resulting in fragmented data that reduces the model’s dependability. 78 A group of researchers have described a four-tier model; access, transitions, quality, and socioeconomic/environmental impact, which offers a pragmatic framework to establish measurements that may be helpful to advance equity for both the patients and the staff of the health-care provider organization. 79 The use of blockchain technology in healthcare may help in reducing fragmentation. 80 Data silos is another problem facing by the ML in health care. Researchers found that using mobile phones apps may help in data silos, 81 while others focused on cloud computing. 82 Similarly, the application of FAIR principles (Findable, Accessible, Interoperable, and Reusable) in health research data is a hot topic of the day. There have been several research groups working this aspects. 83 The European Union has released a special project. 84 An editorial has been published which is based on the challenges of the effectual implementation of FAIR Principles in biomedical research. 85 Similarly, a Hybrid Hierarchical K-means (HHK) clustering machine learning algorithm was applied to group the data into homogeneous subgroups and ascertain the underlying structure of the data using a Nigerian-based FAIR dataset. The data contained economic factors, health-care facilities, and coronavirus occurrences in all the 36 states of the country. The model successfully interpreted the research data and it was obvious that the ML pipeline can be FAIRified, shared, and reused by implementing the proposed FAIRification workflow and the technical architecture. 86

Knowledge Graphs

A knowledge Graph (KG) is a structured representation of facts that defines a set of interconnected entity descriptions, relationships, and entity semantic descriptions. 87 Stokman FN and de Vries PH first proposed the concept of organized knowledge in a graph in 1988 88 , and it gained popularity in 2012 after being used in Google’s search engine. A Knowledge Graph is generally defined as “A multi-relational graph composed of entities as nodes and relations as different types of edges.” 89 Besides its uses in other data sciences and social sciences, 90 the knowledge graphs have been used in medical field and health-care science. Traditional graphs and networks used for biomedical data integration only contain one type of relation (eg, interactions between proteins), whereas the Knowledge Graphs provides diverse information, including multiple entities (eg, proteins, targets, and drugs) and multiple types of relations (eg, interactions between drugs or drug-target pairs). 91 Complex relations between entities in biological systems can be easily modeled by a Knowledge Graphs. 92 The knowledge graph-based works that instrument drug repurposing and hostile drug reaction projection for drug discovery has been reviewed somewhere else. 93 A knowledge graph-based approach was used in organizing cancer registry data. This knowledge graph approach semantically augments the data, and easily enables linking with third-party data, which can help explain variation in cancer incidence patterns, disparities, and outcomes. 94 Another team have used the knowledge graphs based approach to discover unobvious genes that drive drug resistance in lung cancer. 95 Medical Knowledge Graph Deep Learning for cancer phenotyping has been established. 96

The volume of data created by and about patients is expanding exponentially and is becoming increasingly challenging to manage within the necessary timeframes for therapeutic usefulness. Patient data are being generated at a rate that is beyond our capacity to collect and analyze them. 97 However, what may be more troublesome is the fact that practically all medical data are unavailable for analysis, regardless of how many are generated. Medical imaging systems are exclusive and prohibit interaction with other systems, rendering the information they contain relatively useless and incapable of being used for machine learning.

Data Normalization

The initial step in data analysis is the processing of a specified data set beforehand (s). Combining several datasets requires feature selection, noise filtering, and normalization. Merging various data sets necessitates normalization to reduce bias while studying the resulting data set. The pattern recognition method is the selection of defined features important to the effectiveness of classification and regression. Integrating data obtained from many types of “omics” and assorted information sources to foresee clinical conclusions and biomarkers is an additional significant problem in precision oncology. 98

Complicated Data

Due to the complexity of the mathematical methods used, Artificial Intelligence systems have a reputation for being black boxes. Models should be made more accessible and easier to interpret. While there has been significant effort in this area, there is still a need for improvement. 99

What are the Future Prospects?

As soon as obstacles are resolved and “AI algorithms” are confirmed by future research, AI-based models will be integrated into all aspects of healthcare. In the coming years, “oncology AI applications” will be realized through “data intelligence”, a better knowledge of tumors, more accurate therapy alternatives, and enhanced “decision-making processes.” The field of Oncology will become a more specialized field, and individuals will receive treatment more frequently than ever.

Additionally, risk assessment tools integrated into smartphone applications will give the general public an immediate cancer risk estimate. The provision of high-risk estimates can drive patients to receive medical help and comply with medical advice. In addition, risk reduction estimates might drive individuals to adopt healthier behaviors, such as stopping smoking or being physically active. Algorithms will assist physicians in determining whether to refer people to high-complexity healthcare centers in a primary care setting. Algorithm integration with EHR systems can help health-care facilities by providing an alternative for improved resource allocation based on the information of the individual subgroups with a greater risk of cancer growth or cancer-related consequences. 100

The advent of ChatGPT by OpenAI has been recognized for its expansive knowledge of varied subjects. It is a Large Language Model that has become the fastest growing consumer application. Researchers found that ChatGPT has the potential to revolutionize the field of colorectal surgery by providing personalized and precise medical information, reducing errors and complications, and improving patient outcomes. 101 Besides, the neuro-oncology was taken as an example to study the ChatGPT responses. 102 A quick analysis of radiographic imaging and predictive outcome based on tumor genomics are some of the possible aspects where ChatGPT may help the physicians in diagnostic processing.

AI-ML has flourished in this era due to the technical advances of the time. Previously, these novel innovations were exclusively used for non-medical purposes, but they are now starting to be implemented for the improvement of healthcare around the world. AI and ML have a substantial impact on healthcare and will continue to reshape this field. The potential in the field of oncology is tremendous and has applications in almost every aspect of cancer research including, diagnosis, prognosis, and treatment.

Cancer, one of the life-threatening diseases, may soon have a treatment, but as prevention is preferable to treatment, early and rapid detection, cancer prediction, and disease prognosis prediction are vital. In this article, the most recent deep learning, ML, and AI developments are discussed. Incorporating DL, ML, and AI into many types of cancer prognosis, diagnosis and prediction may one day result in a more effective treatment for cancer. It has the potential to improve the quality of hospital environments for all diseases, not only cancer. The obstacles associated with this debilitating disease will undoubtedly be overcome one day with the help of these increasing algorithms. In light of these aims, additional studies are required to continue to ensure clinical value and analytical and clinical validity.

Funding Statement

Jinling Institute of Science Technology Research Initiation Project (jit-b-202030).

The authors report no conflicts of interest in this work.

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• 10 April 2024

How to supercharge cancer-fighting cells: give them stem-cell skills

• Sara Reardon 0

Sara Reardon is a freelance journalist based in Bozeman, Montana.

You can also search for this author in PubMed   Google Scholar

A CAR T cell (orange; artificially coloured) attacks a cancer cell (green). Credit: Eye Of Science/SPL

You have full access to this article via your institution.

Bioengineered immune cells have been shown to attack and even cure cancer , but they tend to get exhausted if the fight goes on for a long time. Now, two separate research teams have found a way to rejuvenate these cells: make them more like stem cells .

Both teams found that the bespoke immune cells called CAR T cells gain new vigour if engineered to have high levels of a particular protein. These boosted CAR T cells have gene activity similar to that of stem cells and a renewed ability to fend off cancer . Both papers were published today in Nature 1 , 2 .

The papers “open a new avenue for engineering therapeutic T cells for cancer patients”, says Tuoqi Wu, an immunologist at the University of Texas Southwestern in Dallas who was not involved in the research.

Reviving exhausted cells

CAR T cells are made from the immune cells called T cells, which are isolated from the blood of person who is going to receive treatment for cancer or another disease. The cells are genetically modified to recognize and attack specific proteins — called chimeric antigen receptors (CARs) — on the surface of disease-causing cells and reinfused into the person being treated.

But keeping the cells active for long enough to eliminate cancer has proved challenging, especially in solid tumours such as those of the breast and lung. (CAR T cells have been more effective in treating leukaemia and other blood cancers.) So scientists are searching for better ways to help CAR T cells to multiply more quickly and last longer in the body.

Cutting-edge CAR-T cancer therapy is now made in India — at one-tenth the cost

With this goal in mind, a team led by immunologist Crystal Mackall at Stanford University in California and cell and gene therapy researcher Evan Weber at the University of Pennsylvania in Philadelphia compared samples of CAR T cells used to treat people with leukaemia 1 . In some of the recipients, the cancer had responded well to treatment; in others, it had not.

The researchers analysed the role of cellular proteins that regulate gene activity and serve as master switches in the T cells. They found a set of 41 genes that were more active in the CAR T cells associated with a good response to treatment than in cells associated with a poor response. All 41 genes seemed to be regulated by a master-switch protein called FOXO1.

The researchers then altered CAR T cells to make them produce more FOXO1 than usual. Gene activity in these cells began to look like that of T memory stem cells, which recognize cancer and respond to it quickly.

The researchers then injected the engineered cells into mice with various types of cancer. Extra FOXO1 made the CAR T cells better at reducing both solid tumours and blood cancers. The stem-cell-like cells shrank a mouse’s tumour more completely and lasted longer in the body than did standard CAR T cells.

Master-switch molecule

A separate team led by immunologists Phillip Darcy, Junyun Lai and Paul Beavis at Peter MacCallum Cancer Centre in Melbourne, Australia, reached the same conclusion with different methods 2 . Their team was examining the effect of IL-15, an immune-signalling molecule that is administered alongside CAR T cells in some clinical trials. IL-15 helps to switch T cells to a stem-like state, but the cells can get stuck there instead of maturing to fight cancer.

The team analysed gene activity in CAR T cells and found that IL-15 turned on genes associated with FOXO1. The researchers engineered CAR T cells to produce extra-high levels of FOXO1 and showed that they became more stem-like, but also reached maturity and fought cancer without becoming exhausted. “It’s the ideal situation,” Darcy says.

Stem-cell and genetic therapies make a healthy marriage

The team also found that extra-high levels of FOXO1 improved the CAR T cells’ metabolism, allowing them to last much longer when infused into mice. “We were surprised by the magnitude of the effect,” says Beavis.

Mackall says she was excited to see that FOXO1 worked the same way in mice and humans. “It means this is pretty fundamental,” she says.

Engineering CAR T cells that overexpress FOXO1 might be fairly simple to test in people with cancer, although Mackall says researchers will need to determine which people and types of cancer are most likely to respond well to rejuvenated cells. Darcy says that his team is already speaking to clinical researchers about testing FOXO1 in CAR T cells — trials that could start within two years.

And Weber points to an ongoing clinical trial in which people with leukaemia are receiving CAR T cells genetically engineered to produce unusually high levels of another master-switch protein called c-Jun, which also helps T cells avoid exhaustion. The trial’s results have not been released yet, but Mackall says she suspects the same system could be applied to FOXO1 and that overexpressing both proteins might make the cells even more powerful.

Nature 628 , 486 (2024)

doi: https://doi.org/10.1038/d41586-024-01043-2

Doan, A. et al. Nature https://doi.org/10.1038/s41586-024-07300-8 (2024).

Article   Google Scholar  

Chan, J. D. et al. Nature https://doi.org/10.1038/s41586-024-07242-1 (2024).

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After 40 years of smoking, she survived lung cancer thanks to new treatments

Yuki Noguchi

Denise Lee on her last day of chemo. In addition to chemo and surgery, she was treated with immunotherapy. She's currently in remission. Denise Lee hide caption

Denise Lee on her last day of chemo. In addition to chemo and surgery, she was treated with immunotherapy. She's currently in remission.

Denise Lee grew up in Detroit in the mid-1970s and went to an all-girls Catholic high school. She smoked her first cigarette at age 14 at school, where cigarettes were a popular way of trying to lose weight.

Instead, her nicotine addiction lasted four decades until she quit in her mid-50s.

"At some point it got up as high as 2.5 packs a day," Lee, 62, recalls.

Yet she didn't think about lung cancer risk — until she saw a billboard urging former smokers to get screened. Lee, a retired lawyer living in Fremont, Calif., used to drive past it on her way to work.

"The thing that caught my attention was the fact that it was an African American female on the front," she recalls.

Shots - Health News

The american cancer society says more people should get screened for lung cancer.

She eventually got the low-dose CT scan recommended for current and former smokers. When doctors found an early, but dangerous, tumor, Lee cried and panicked. Her mother had cared for her father, who'd died of prostate cancer. "My biggest concern was telling my mom," she says.

But that was six years ago, and Lee is cancer free today. Surgery removed the 2-inch tumor in her lung, then new treatments also boosted her immune system, fighting off any recurrence.

Lung cancer remains the most lethal form of the disease, killing about 135,000 Americans a year – more than breast, prostate and colon cancer combined – which is why many people still think of a diagnosis as synonymous with a death sentence. But with new treatments and technology, the survival rates from lung cancer are dramatically improving, allowing some patients with relatively late-stage cancers to live for years longer.

"If you're gonna have lung cancer, now is a good time," Lee says of the advances that saved her.

Denise Lee has been cancer-free for six years. She says she's grateful she got screened and caught her lung cancer early enough that treatment has been effective. Denise Lee hide caption

Denise Lee has been cancer-free for six years. She says she's grateful she got screened and caught her lung cancer early enough that treatment has been effective.

The key breakthrough, says Robert Winn, a lung cancer specialist at Virginia Commonwealth University, is the ability to better pinpoint the mutations of a patient's particular form of cancer. In the past, treatments were blunt tools that caused lots of collateral damage to healthy parts of the body while treating cancer.

"We've gone from that to molecular characterization of your lung cancer, and it has been a game changer," Winn says. "This is where science and innovation has an impact."

One of those game-changing treatments is called targeted therapy . Scientists identify genetic biomarkers in the mutated cancer cells to target and then deliver drugs that attack those targets, shrinking tumors.

CRISPR gene-editing may boost cancer immunotherapy, new study finds

Another is immunotherapy, usually taken as a pill, which stimulates the body's own defense system to identify foreign cells, then uses the immune system's own power to fight the cancer as if it were a virus.

As scientists identify new cancer genes, they're creating an ever-broader array of these drugs.

Combined, these treatments have helped increase national survival rates by 22% in the past five years – a rapid improvement over a relatively short time, despite the fact that screening rates are very slow to increase. Winn says as these treatments get cheaper and readily available, the benefits are even reaching rural and Black populations with historic challenges accessing health care.

The most remarkable thing about the drugs is their ability to, in some cases, reverse late-stage cancers. Chi-Fu Jeffrey Yang, a thoracic surgeon at Massachusetts General Hospital and faculty at Harvard Medical School, recalls seeing scans where large dark shadows of tumor would disappear: "It was remarkable to see the lung cancer completely melting away."

To Yang, such progress feels personal. He lost his beloved grandfather to the disease when Yang was in college. If he were diagnosed today, he might still be alive.

"Helping to take care of him was a big reason why I wanted to be a doctor," Yang says.

But the work of combating lung cancer is far from over; further progress in lung cancer survival hinges largely on getting more people screened.

Low-dose CT scans are recommended annually for those over 50 who smoked the equivalent of a pack a day for 20 years. But nationally, only 4.5% of those eligible get those scans , compared to rates of more than 75% for mammograms.

Andrea McKee, a radiation oncologist and spokesperson for the American Lung Association, says part of the problem is that lung cancer is associated with the stigma of smoking. Patients often blame themselves for the disease, saying: "'I know I did this to myself. And so I don't I don't think I deserve to get screened.'"

McKee says that's a challenge unique to lung cancer. "And it just boggles my mind when I hear that, because, of course, nobody deserves to die of lung cancer."

Denise Lee acknowledges that fear. "I was afraid of what they would find," she admits. But she urges friends and family to get yearly scans, anyway.

"I'm just so grateful that my diagnosis was early because then I had options," she says. "I could have surgery, I could have chemotherapy, I could be a part of a clinical trial."

And all of that saved her life.

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• lung cancer

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Innovation in lung cancer screening, treatment

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By Jason Howland

Lung cancer  is the largest cancer killer of adult men and women worldwide. One of the reasons is that by the time symptoms develop, the cancer is too advanced for a cure.

The goal of  lung cancer screening  is to detect cancer at an earlier stage and save more lives. Smoking is the No. 1 cause of lung cancer, and it's recommended that anyone over 55 who has a significant history of smoking, even if they no longer smoke, should be screened.

Lung cancer screening, for those eligible, should be done annually, and it involves a low-dose CT scan of the lungs. Janani Reisenauer, M.D. , a thoracic surgeon and interventional pulmonologist, says Mayo Clinic is one of the pioneers in using new technology to fight the disease.

Watch this video to hear Dr. Reisenauer discuss innovation in lung cancer screening and treatment:

A lung nodule or lesion is an abnormality revealed on a  CT scan  that looks like a cancer. And to find out if it is cancer, doctors may recommend a technique called  bronchoscopy .

"Very similar to having a  colonoscopy  performed where somebody is holding a scope. They're twisting the dials on that scope as they're maneuvering that scope through your airway or through your intestines for example," says Dr. Reisenauer.

That's a traditional manual bronchoscopy. Dr. Reisenauer says Mayo Clinic is one of only a handful of medical centers approved by the Food and Drug Administration to use robotic bronchoscopy.

"I would think about robotic bronchoscopy as a remote-controlled car. It doesn't go anywhere without you driving it and telling it where to go. But rather than me holding a scope that's maneuvered through a patient, the scope is docked to a robotic instrument," says Dr. Reisenauer.

Offering more precision, flexibility and control than a traditional bronchoscopy. She says it improves the ability to not only diagnose lung cancer, but now, in some cases, treat it.

"Mayo Clinic is participating in many cancer trials now, where we are treating lung nodules with a variety of ablative mechanisms. So we're either using extreme heat or cold, or even in some cases electricity. Or viruses injecting into tumors to try to reduce tumor burden or kill tumors," says Dr. Reisenauer.

Learn more about lung cancer .

Also, read these articles:

• Mayo Clinic expert on the importance of lung cancer screening
• Reducing your risk of lung cancer
• Does smoking marijuana increase lung cancer risk?
• Era of hope for patients with lung cancer

A version of this article was originally published on the Mayo Clinic News Network .

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