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Treatment Research

In a new study in mice, researchers showed they could enhance radiation therapy by boosting levels of the BAMBI protein in MDSC immune cells in the tumor microenvironment. After radiation, T cells flooded into the tumor and killed tumors elsewhere in the body.

In a clinical trial, people being treated for cancer who participated in virtual mind–body fitness classes were less likely to be hospitalized, and had shorter stays when they were hospitalized, than people who did not take the classes.

NCI’s James H. Doroshow, M.D., reflects on the accomplishments of NCI-MATCH, a first-of-its-kind precision medicine cancer trial, and gives an overview of three new successor trials: ComboMATCH, MyeloMATCH, and iMATCH.

A new study, conducted largely in mice, may help explain why a currently used molecular marker—called mismatch repair deficiency—doesn’t always work to predict which patients will respond to immunotherapies called immune checkpoint inhibitors.

New approach may increase the effectiveness of T-cell-based immunotherapy treatments against solid tumors.

A cancer-infecting virus engineered to tamp down a tumor’s ability to suppress the immune system shrank tumors in mice, a new study shows. The modified oncolytic virus worked even better when used along with an immune checkpoint inhibitor.

Despite recommendations, a new analysis shows few people with cancer undergo germline testing to learn if their cancer may have been caused by gene changes inherited from a parent. Germline testing can help doctors determine the best treatments for a patient and help identify people whose family members may be at higher risk of cancer.

ComboMATCH will consist of numerous phase 2 cancer treatment trials that aim to identify promising drug combinations that can advance to larger, more definitive clinical trials.

A new study has compared three formulations of an mRNA vaccine designed to treat cancers caused by human papillomavirus (HPV) infections. All three vaccines showed promise in mice.

Researchers have identified a mechanism by which cancer cells develop specific genetic changes needed to become resistant to targeted therapies. They also showed that this process, called non-homologous end-joining (NHEJ), can potentially be disrupted.

For some people with cancer, is 6 months of immunotherapy the only treatment they might ever need? Or 4 weeks of immunotherapy followed by minor surgery? Results from several small clinical trials suggest these scenarios may be bona fide possibilities.

Two research teams have developed ways of overcoming barriers that have limited the effectiveness of CAR T-cell therapies, including engineering ways to potentially make them effective against solid tumors like pancreatic cancer and melanoma.

In people with cancer treated with immune checkpoint inhibitors, a rare, but often fatal, side effect is inflammation in the heart, called myocarditis. Researchers have now identified a potential chief cause of this problem: T cells attacking a protein in heart cells called α-myosin.

Researchers have modified a chemo drug, once abandoned because it caused serious gut side effects, so that it is only triggered in tumors but not normal tissues. After promising results in mice, the drug, DRP-104, is now being tested in a clinical trial.

Two research teams have developed a treatment approach that could potentially enable KRAS-targeted drugs—and perhaps other targeted cancer drugs—flag cancer cells for the immune system. In lab studies, the teams paired these targeted drugs with experimental antibody drugs that helped the immune system mount an attack.

Inflammation is considered a hallmark of cancer. Researchers hope to learn more about whether people with cancer might benefit from treatments that target inflammation around tumors. Some early studies have yielded promising results and more are on the horizon.

NCI researchers are developing an immunotherapy that involves injecting protein bits from cytomegalovirus (CMV) into tumors. The proteins coat the tumor, causing immune cells to attack. In mice, the treatment shrank tumors and kept them from returning.

FDA has approved the combination of the targeted drugs dabrafenib (Tafinlar) and trametinib (Mekinist) for nearly any type of advanced solid tumor with a specific mutation in the BRAF gene. Data from the NCI-MATCH trial informed the approval.

People with cancer who take immunotherapy drugs often develop skin side effects, including itching and painful rashes. New research in mice suggests these side effects may be caused by the immune system attacking new bacterial colonies on the skin.

Researchers have developed tiny “drug factories” that produce an immune-boosting molecule and can be implanted near tumors. The pinhead-sized beads eliminated tumors in mice with ovarian and colorectal cancer and will soon be tested in human studies.

Women are more likely than men to experience severe side effects from cancer treatments such as chemotherapy, targeted therapy, and immunotherapy, a new study finds. Researchers hope the findings will increase awareness of the problem and help guide patient care.

Research to improve CAR T-cell therapy is progressing rapidly. Researchers are working to expand its use to treat more types of cancer and better understand and manage its side effects. Learn how CAR T-cell therapy works, which cancers it’s used to treat, and current research efforts.

Experts say studies are needed on how to best transition telehealth from a temporary solution during the pandemic to a permanent part of cancer care that’s accessible to all who need it.

Removing immune cells called naive T cells from donated stem cells before they are transplanted may prevent chronic graft-versus-host disease (GVHD) in people with leukemia, a new study reports. The procedure did not appear to increase the likelihood of patients’ cancer returning.

A specific form of the HLA gene, HLA-A*03, may make immune checkpoint inhibitors less effective for some people with cancer, according to an NCI-led study. If additional studies confirm the finding, it could help guide the use of these commonly used drugs.

The success of mRNA vaccines for COVID-19 could help accelerate research on using mRNA vaccine technology to treat cancer, including the development of personalized cancer vaccines.

Aneuploidy—when cells have too many or too few chromosomes—is common in cancer cells, but scientists didn’t know why. Two new studies suggest that aneuploidy helps the cells survive treatments like chemotherapy and targeted therapies.

New research suggests that fungi in the gut may affect how tumors respond to cancer treatments. In mice, when bacteria were eliminated with antibiotics, fungi filled the void and impaired the immune response after radiation therapy, the study found.

FDA has approved belumosudil (Rezurock) for the treatment of chronic graft-versus-host disease (GVHD). The approval covers the use of belumosudil for people 12 years and older who have already tried at least two other therapies.

In lab studies, the antibiotic novobiocin showed promise as a treatment for cancers that have become resistant to PARP inhibitors. The drug, which inhibits a protein called DNA polymerase theta, will be tested in NCI-supported clinical trials.

A drug called avasopasem manganese, which has been found to protect normal tissues from radiation therapy, can also make cancer cells more vulnerable to radiation treatment, a new study in mice suggests.

While doctors are familiar with the short-term side effects of immune checkpoint inhibitors, less is known about potential long-term side effects. A new study details the chronic side effects of these drugs in people who received them as part of treatment for melanoma.

Cholesterol-lowering drugs known as PCSK9 inhibitors may improve the effectiveness of cancer immune checkpoint inhibitors, according to studies in mice. The drugs appear to improve the immunotherapy drugs’ ability to find tumors and slow their growth.

Researchers have developed a nanoparticle that trains immune cells to attack cancer. According to the NCI-funded study, the nanoparticle slowed the growth of melanoma in mice and was more effective when combined with an immune checkpoint inhibitor.

A comprehensive analysis of patients with cancer who had exceptional responses to therapy has revealed molecular changes in the patients’ tumors that may explain some of the exceptional responses.

Researchers are developing a new class of cancer drugs called radiopharmaceuticals, which deliver radiation therapy directly and specifically to cancer cells. This Cancer Currents story explores the research on these emerging therapies.

FDA has recently approved two blood tests, known as liquid biopsies, that gather genetic information to help inform treatment decisions for people with cancer. This Cancer Currents story explores how the tests are used and who can get the tests.

Cancer cells with a genetic feature called microsatellite instability-high (MSI-high) depend on the enzyme WRN to survive. A new NCI study explains why and reinforces the idea of targeting WRN as a treatment approach for MSI-high cancer.

Efforts to contain the opioid epidemic may be preventing people with cancer from receiving appropriate prescriptions for opioids to manage their cancer pain, according to a new study of oncologists’ opioid prescribing patterns.

The gene-editing tool CRISPR is changing the way scientists study cancer, and may change how cancer is treated. This in-depth blog post describes how this revolutionary technology is being used to better understand cancer and create new treatments.

FDA’s approval of pembrolizumab (Keytruda) to treat people whose cancer is tumor mutational burden-high highlights the importance of genomic testing to guide treatment, including for children with cancer, according to NCI Director Dr. Ned Sharpless.

Patients with acute graft-versus-host disease (GVHD) that does not respond to steroid therapy are more likely to respond to the drug ruxolitinib (Jakafi) than other available treatments, results from a large clinical trial show.

NCI is developing the capability to produce cellular therapies, like CAR T cells, to be tested in cancer clinical trials at multiple hospital sites. Few laboratories and centers have the capability to make CAR T cells, which has limited the ability to test them more broadly.

An experimental drug may help prevent the chemotherapy drug doxorubicin from harming the heart and does so without interfering with doxorubicin’s ability to kill cancer cells, according to a study in mice.

In people with blood cancers, the health of their gut microbiome appears to affect the risk of dying after receiving an allogeneic hematopoietic stem cell transplant, according to an NCI-funded study conducted at four hospitals across the globe.

A novel approach to analyzing tumors may bring precision cancer medicine to more patients. A study showed the approach, which analyzes gene expression using tumor RNA, could accurately predict whether patients had responded to treatment with targeted therapy or immunotherapy.

Bone loss associated with chemotherapy appears to be induced by cells that stop dividing but do not die, a recent study in mice suggests. The researchers tested drugs that could block signals from these senescent cells and reverse bone loss in mice.

Some experts believe that proton therapy is safer than traditional radiation, but research has been limited. A new observational study compared the safety and effectiveness of proton therapy and traditional radiation in adults with advanced cancer.

In people with cancer, the abscopal effect occurs when radiation—or another type of localized therapy—shrinks a targeted tumor but also causes untreated tumors in the body to shrink. Researchers are trying to better understand this phenomenon and take advantage of it to improve cancer therapy.

An experimental drug, AMG 510, that targets mutated forms of the KRAS protein completely shrank tumors in cancer mouse models and data from a small clinical trial show that it appears to be active against different cancer types with a KRAS mutation.

Researchers have engineered an oncolytic virus to kill cancer cells and boost the immune response against tumors. In a new study, the virus provided T cells around tumors with a hormone they need for their own cell-killing functions.

FDA has approved entrectinib (Rozlytrek) for the treatment of children and adults with tumors bearing an NTRK gene fusion. The approval also covers adults with non-small cell lung cancer harboring a ROS1 gene fusion.

A new NCI-supported study showed that altering cancer cell metabolism by feeding mice a diet very low in the nutrient methionine improved the ability of chemotherapy and radiation therapy to shrink tumors.

An NCI-funded clinical trial is testing the immunotherapy drug nivolumab (Opdivo) in people who have advanced cancer and an autoimmune disease, such as rheumatoid arthritis, lupus, or multiple sclerosis, who are often excluded from such trials.

Researchers have identified a protein called CD24 that may be a new target for cancer immunotherapy. The protein is a ‘don’t eat me’ signal that prevents immune cells called macrophages from engulfing and eating cells.

Injecting cells undergoing necroptosis, a form of cell death, into tumors in mice kickstarted an immune response against the tumors, researchers have found. When combined with immunotherapy, the treatment was effective at eliminating tumors in mice.

Researchers have identified proteins that may play a central role in transforming T cells from powerful destroyers to depleted bystanders that can no longer harm cancer cells. The findings could lead to strategies for boosting cancer immunotherapies.

Did you know that NCI supports clinical trials of new treatments for pet dogs with cancer? Learn more about NCI’s comparative oncology studies and how they may also help people with cancer.

Researchers have discovered a potential way to turn on one of the most commonly silenced tumor-suppressor proteins in cancer, called PTEN. They also found a natural compound, I3C, that in lab studies could flip the on switch.

New findings from a clinical trial suggest that a single dose of radiation therapy may control painful bone metastases as effectively as multiple lower doses of radiation therapy.

The expanding use of cancer immunotherapy has revealed a variety of side effects associated with this treatment approach. Researchers are now trying to better understand how and why these side effects occur and develop strategies for better managing them.

The investigational immunotherapy drug bintrafusp alfa (also called M7824), a bifunctional fusion protein, shrank the tumors of some patients with advanced HPV-related cancers, according to results from a phase 1 clinical trial.

A new study provides insight into how cancer immunotherapy works and suggests ways to enhance the treatment’s effectiveness. The NCI-led study, published in Science, examined the effect of high potassium levels on T cells.

Pain is a common and much-feared symptom among people with cancer and long-term survivors. As more people survive cancer for longer periods, there is a renewed interest in developing new, nonaddictive approaches for managing their chronic pain.

• Article Information

Shown are the trends in use of oncology drugs that received AA and were subsequently withdrawn between 2016 and 2022. Observed quarterly prescription rate of AA therapies is a proportion of all treatment initiations for an indication, and estimated prescription rate is the daily percentage of treatment-eligible patients who used an AA drug based on logistic regression with restricted cubic splines. ODAC indicates the US Food and Drug Administration Oncology Drug Advisory Committee.

Data Sharing Statement

• Clinical Benefit and Regulatory Outcomes of Cancer Drugs Receiving Accelerated Approval JAMA Original Investigation April 7, 2024 This study aims to determine whether cancer drugs granted accelerated approval from the US Food and Drug Administration ultimately demonstrate clinical benefit and to evaluate the basis of conversion to regular approval. Ian T. T. Liu, MD, JD, MPH, MS; Aaron S. Kesselheim, MD, JD, MPH; Edward R. Scheffer Cliff, MBBS, MPH

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Parikh RB , Hubbard RA , Wang E, et al. Exposure to US Cancer Drugs With Lack of Confirmed Benefit After US Food and Drug Administration Accelerated Approval. JAMA Oncol. 2023;9(4):567–569. doi:10.1001/jamaoncol.2022.7770

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Exposure to US Cancer Drugs With Lack of Confirmed Benefit After US Food and Drug Administration Accelerated Approval

• 1 Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
• 2 Flatiron Health, New York, New York
• Original Investigation Clinical Benefit and Regulatory Outcomes of Cancer Drugs Receiving Accelerated Approval Ian T. T. Liu, MD, JD, MPH, MS; Aaron S. Kesselheim, MD, JD, MPH; Edward R. Scheffer Cliff, MBBS, MPH JAMA

Between 2009 and 2022, the US Food and Drug Administration (FDA) approved 48 drugs for 66 oncology-related indications under the Accelerated Approval (AA) program. 1 Indications granted AA based on surrogate end points are subsequently required to confirm clinical benefit. 2 Since 2009, 15 indications (23%) have been withdrawn due to lack of benefit over standard of care. 3 We estimate the proportion of patients who received treatment for 5 oncology-related indications later withdrawn after failure to confirm efficacy under the AA program.

This cross-sectional study included patients with advanced or recurrent breast, bladder, hepatocellular, gastric, or small cell lung cancer between May 18, 2016, and March 8, 2022, treated with at least 1 line of systemic therapy. We used deidentified, electronic health record (EHR)–derived, patient-level data curated via technology-enabled abstraction. 4 The Copernicus Group and University of Pennsylvania institutional review boards approved this study and granted waivers of informed consent owing to use of deidentified data. This study followed the STROBE reporting guideline.

We studied 5 disease-specific AA indications with subsequent published negative phase III confirmatory trials and indication withdrawal ( Table ). These drugs had additional biomarker-specific, cancer-agnostic indication approvals, such that they were available for use before AA and continued to be available after AA withdrawal. Outcomes were calculated for patients aged 18 years or older who initiated therapy and were eligible for the AA indication. Race and ethnicity were identified from the EHR and included as covariates because minority status is associated with decreased access to novel therapies. We excluded patients with 1 or more cancers or, for biomarker-specific indications, missing biomarker data. The primary outcome was initiation of AA therapies as a proportion of all indication-specific treatment initiations. We calculated the primary outcome at 3 intervals: AA to indication withdrawal, AA to negative confirmatory trial, and negative trial to indication withdrawal. Because 4 indications were reviewed at the April 2021 FDA Oncology Drug Advisory Committee meeting, we included this date as a discrete point. Logistic regression–estimated time trends with restricted cubic splines were used to estimate the daily percentage of treatment-eligible patients who initiated a withdrawn AA drug. Analyses were performed using SAS, version 9.4 (SAS Institute Inc).

The cohort included 4342 patients who received 6560 eligible lines of therapy (median age, 70 [range, 24-85] years; female, 1639 [39%]; male, 2649 [61%]; Asian, 93 [2%]; Black, 348 [8%]; Hispanic, 248 [6%]; White, 2909 [67%]; Other, 637 [15%]), and 3709 (85%) received care at community practices. The median time from AA to indication withdrawal was 46 (range, 12-58) months. Between AA and subsequent withdrawal, 1361 oncology treatment initiations (26.1%) involved an AA therapy that was subsequently withdrawn (triple-negative breast, 23.1% [113 of 490]; bladder, 22.5% [695 of 3096]; hepatocellular, 38.8% [323 of 832]; gastric, 41.4% [101 of 244]; small cell lung, 23.6% [129 of 546]) (breast and bladder shown in the Figure ). Prevalence of AA drug initiations was higher between AA and negative trial publication (overall, 35.5%; triple-negative breast, 24.1%; bladder, 39.7%; hepatocellular, 55.6%; gastric, 71.4%; small cell lung, 21.0%) than between negative trial publication and withdrawal (15.7%, 8.6%, 12.8%, 20.3%, 38.6%, 2.5%, respectively) ( Table ).

Among 5 oncology indications, 26.1% of eligible treatment initiations involved an AA indication subsequently withdrawn due to lack of benefit. An expected trade-off exists between expediting access to promising cancer drugs and withdrawal of some indications. 5 Given the growth of withdrawals due to negative confirmatory trials and emerging evidence on the high spending associated with AA drugs, it is critical to balance early access against population-level exposure to cancer therapies with no benefit over standard of care. 6 Limitations included an inability to assess population-level exposure because only 5 withdrawn AA indications had sufficient sample and follow-up for analysis. Earlier access and more rapid FDA responses to negative confirmatory trial data, a key proposal of the Accelerated Approval Integrity Act proposed in March 2022, may minimize exposure to AA therapies with lack of benefit.

Accepted for Publication: December 8, 2022.

Published Online: February 23, 2023. doi:10.1001/jamaoncol.2022.7770

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Parikh RB et al. JAMA Oncology .

Corresponding Author: Ravi B. Parikh, MD, MPP, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Blockley 1102, Philadelphia, PA 19104 ( [email protected] ).

Author Contributions: Dr Parikh and Mr Wang had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Parikh, Royce, Cohen, Mamtani.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Parikh, Wang, Royce, Clark.

Critical revision of the manuscript for important intellectual content: Parikh, Hubbard, Royce, Cohen, Clark, Mamtani.

Statistical analysis: Parikh, Hubbard, Wang, Cohen.

Obtained funding: Parikh.

Administrative, technical, or material support: Parikh, Royce.

Supervision: Parikh, Royce, Cohen, Clark, Mamtani.

Conflict of Interest Disclosures: Dr Parikh reported receiving grants from the National Institutes of Health, Prostate Cancer Foundation, National Palliative Care Research Center, National Comprehensive Cancer Network Foundation, Conquer Cancer Foundation, Humana, Emerson Collective, and Veterans Health Administration; personal fees and equity from GNS Healthcare, Thyme Care, and Onc.AI; personal fees from Cancer Study Group, ConcertAI, Biofourmis, Humana, and Nanology; and honoraria from Flatiron Health and Medscape, as well as board membership (unpaid) at the Coalition to Transform Advanced Care and American Cancer Society and leadership consortium membership (unpaid) at the National Quality Forum outside the submitted work. Dr Hubbard reported receiving grants from Merck, Pfizer, and Johnson & Johnson outside the submitted work. Dr Royce reported employment with and stock ownership in Roche for which Flatiron Health is an independent subsidiary during the conduct of the study. Dr Cohen reported stock ownership in Roche and receiving equity from Flatiron Health, which is an independent subsidiary of Roche, outside the submitted work. Dr Clark reported receiving grants from Eli Lilly outside the submitted work. Dr Mamtani reported receiving personal fees from Flatiron Health, Bristol Myers Squibb, and Astellas Pharma and grants from Merck and Astellas Pharma outside the submitted work. No other disclosures were reported.

Data Sharing Statement: See the Supplement .

Additional Contributions: The authors thank Flatiron Health for access to the data and technical assistance and Kara Berwanger, BS, University of Pennsylvania, for assistance in editing the manuscript. She received no compensation for this contribution.

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

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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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New approaches and procedures for cancer treatment: Current perspectives

Dejene tolossa debela.

1 Center for Innovative Drug Development and Therapeutic Trials for Africa (CDT-Africa), College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia

Seke GY Muzazu

2 Enteric Diseases and Vaccines Research Unit, Centre for Infectious Disease Research in Zambia (CIDRZ), Lusaka, Zambia

Kidist Digamo Heraro

3 Wachemo University, Hossana, Ethiopia

Maureen Tayamika Ndalama

Betelhiem woldemedhin mesele.

4 Kotebe Metropolitan University, Addis Ababa, Ethiopia

Dagimawi Chilot Haile

5 University of Gondar, Gondar, Ethiopia

Sophia Khalayi Kitui

Tsegahun manyazewal.

Cancer is a global health problem responsible for one in six deaths worldwide. Treating cancer has been a highly complex process. Conventional treatment approaches, such as surgery, chemotherapy, and radiotherapy, have been in use, while significant advances are being made in recent times, including stem cell therapy, targeted therapy, ablation therapy, nanoparticles, natural antioxidants, radionics, chemodynamic therapy, sonodynamic therapy, and ferroptosis-based therapy. Current methods in oncology focus on the development of safe and efficient cancer nanomedicines. Stem cell therapy has brought promising efficacy in regenerating and repairing diseased or damaged tissues by targeting both primary and metastatic cancer foci, and nanoparticles brought new diagnostic and therapeutic options. Targeted therapy possessed breakthrough potential inhibiting the growth and spread of specific cancer cells, causing less damage to healthy cells. Ablation therapy has emerged as a minimally invasive procedure that burns or freezes cancers without the need for open surgery. Natural antioxidants demonstrated potential tracking down free radicals and neutralizing their harmful effects thereby treating or preventing cancer. Several new technologies are currently under research in clinical trials, and some of them have already been approved. This review presented an update on recent advances and breakthroughs in cancer therapies.

Introduction

Cancer is a global health problem responsible for one in six deaths worldwide. In 2020, there were an estimated 19.3 million new cancer cases and about 10 million cancer deaths globally. Cancer is a very complicated sequence of disease conditions progressing gradually with a generalized loss of growth control. 1 – 3 There were only a few options of cancer treatment for patients for many decades which include surgery, radiation therapy, and chemotherapy as single treatments or in combination. 4 , 5 But recently, many pathways involved in cancer therapy progression and how they can be targeted has improved dramatically, with combinatorial strategies, involving multiple targeted therapies or “traditional” chemotherapeutics, such as the taxanes and platinum compounds, being found to have a synergistic effect. 6 New approaches, such as drugs, biological molecules, and immune-mediated therapies, are being used for treatment even if the excepted therapy level has not reached that resists the mortality rate and decreases the prolonged survival time for metastatic cancer.

The creation of a new revolution in neoplastic cancer or targeting drugs depends on the pathways and characteristics of different tumor entities. 7 Chemotherapy is considered the most effective and widely used modality in treating cancers as used alone or in combination with radiotherapy. Genotoxicity is how chemotherapy drugs target the tumor cells mainly producing reactive oxygen species that largely destroy tumor cells. 8 Hormonal treatments are also widely used for cancer malignancies and considered as cytostatic because it restricts tumor development by limiting the hormonal growth factors acting through the direction of hypothalamic–pituitary–gonadal axis (HPGA), hormone receptor blockage, and limiting of adrenal steroid synthesis. 9

In this narrative review, a general overview of the most advanced and novel cancer therapies was provided. In addition, also new strategies currently under investigation at the research stage that should overwhelm the drawbacks of standard therapies; different strategies to cancer diagnosis and therapy; and their current status in the clinical context, underlining their impact as innovative anti-cancer approaches.

Cancer treatment modalities

We can see cancer treatment modalities by dividing them into conventional (traditional) and advanced or novel or modern categories. In this era worldwide, over half of all ongoing medical treatment trials are focusing on cancer treatments. 7 Entities, such as the type of cancer, its site, and severity, guide to select treatment options and its progress. The most widely used traditional treatment methods are surgery, chemotherapy, and radiotherapy, while modern modalities include hormone therapy, anti-angiogenic, stem cell therapies, immunotherapy, and dendritic cell-based immunotherapy. 10

Conventional cancer therapies

The most recommended conventional cancer treatment strategies include surgical resection of the tumors followed by radiotherapy with x-rays and/or chemotherapy. 11 Of these modalities, surgery is most effective at an early stage of disease progression. Radiation therapy can damage healthy cells, organs, and tissues. Although chemotherapy has reduced morbidity and mortality, virtually all chemotherapeutic agents damage healthy cells, especially rapidly dividing and growing cells. 12 Drug resistance, a major problem with chemotherapy, is a phenomenon wherein cancer cells that initially were suppressed by an anti-cancer drug develop resistance to the drug. This is caused primarily by reduced drug uptake and increased drug efflux. 13 Limitations of conventional chemotherapeutic modality, such as dosage selection difficulty, lack of specificity, rapid drug metabolism, and mainly harmful side effects. 14

Advanced and innovative cancer therapies

Among the obstacles of cancer, drug resistance and its delivery systems are the most problem in cancer cure and decreasing signs and symptoms; but currently, there are many approved treatment approaches and drugs. The efficiency of conventional cancer is reduced due to tumor pathology and architectural abnormality of tumor tissue blood vessels. 15 The following are the advanced and innovative cancer therapy types with their benefits and challenges.

Stem cells therapy

Stem cells are undifferentiated cells present in the bone marrow (BM) with an ability to differentiate into any type of body cell. Stem cell therapeutic strategy is also one of the treatment options for cancer which are considered to be safe and effective. Application of stem cell is yet in the experimental clinical trial; for example, their use in the regeneration of other damaged tissue is being explored. Mesenchymal stem cells (MSCs) are currently being used in trials that are delivered from the BM, fat tissues, and connective tissues. 16

Pluripotent stem cells

Embryonic stem cells (ESCs) isolated from the uniform inner mass cells of the embryo possess the flexibility to administer rise to any or all kinds of cells except those within the placenta. In 2006, the invention of Yamanaka factors to induce pluripotent stem cells (iPSCs) from physical cells in a culture marked a breakthrough in cell biology. 17 Avoiding ethical issues from embryo destruction, iPSCs and ESCs have the same characteristics. Hematopoietic embryonic stem cells (hESCs) and iPSCs are currently used for the induction of effector T cells and natural killer (NK) cells, 18 and anti-tumor vaccine preparation. 19

Adult stem cells

Adult stem cells (ASCs) groups often used in tumor therapy include hematopoietic stem cells (HSCs), MSCs, and neural stem cells (NSCs). HSCs, located in BM, can form all mature blood cells in the body. Currently, only approved by the Food and Drug Administration (FDA) is the infusion of HSCs derived from cord blood to treat multiple myeloma and leukemia. 20 MSCs are found in many tissues and organs, playing important roles in tissue repair and regeneration into cells, such as osteocytes, adipocytes, and chondrocytes. MSCs have special biological characteristics and are used as complimentary with other approaches in treating tumors. 21 NSCs can self-renew and generate new neurons and glial cells and are used for treating both primary and metastatic breast and other tumors. 22

Cancer stem cells

Cancer stem cells (CSCs) are generated in normal stem cells or precursor/progenitor cells by the epigenetic mutations process. Their role in tumor treatment includes cancer growth, metastasis, and recurrence, so that it could give promise in the treatment of solid tumors. 23 Stem cells have several action mechanisms in treating the tumor. The homing process is one mechanism which is a rapid migration of HSCs into defined stem cell niches in BM after that the transplants undergo the engraftment process before giving rise to specialized blood cells. This mechanism is dependent on the active interaction between stem cell CXCR4 receptors and requires their interaction with endothelial cells through LFA-1, VLA-4/5, CD44, and the secretion of matrix degradable enzyme MMP-2/9. 22 The second mechanism is the tumor-tropic effect in which the migration of MSCs toward tumor microenvironment (TM) after attraction by CXCL16, SDF-1, CCL-25, and IL-6 secreted by tumor cells and differentiation of MSCs within the tumor cells which contributes to tumor stromal development. 24 Stem cells also act by paracrine factor secretion, including extracellular vesicles (EVs) and soluble materials, 25 and their differentiation capacity, such as transplanted HSCs, can give rise to all blood cell types. 26

Generally, cancer treatment using stem cell therapy by various strategies, including transplantation of HSC, 27 MSC infusion, 28 therapeutic carriers, 29 generation of immune effector cells, 30 and vaccine production. 31 The stem cell cancer therapy approach confronted the following side effects: (1) tumorigenesis, (2) adverse events in allogeneic HSC transplantation, (3) drug toxicity and drug resistance, (4) increased immune responses and autoimmunity, and (5) viral infection. 22 Despite several successes, there are challenges, such as therapeutic dose control, low cell targeting, and retention in tumor sites, that should be investigated and overcome in the future. In addition, existing results from stem cell technologies are highly encouraging for tumor treatment but it still needs further efforts to improve the safety and efficacy before they could enter clinical trials. Table 1 summarized the licensed list of stem cell therapies.

Licensed stem cell therapies.

AML: acute myelogenous leukemia; FDA: The US Food and Drug Administration; iPSC: induced pluripotent stem cell; MSC-INβ: mesenchymal stem cells with interferon beta.

Targeted drug therapy

Targeted cancer therapies are drugs or other substances which are sometimes interchangeably used as “molecularly targeted drugs,” “molecularly targeted therapies,” and “precision medicines.” Those drugs’ mechanism of action is by interfering with growth molecules which leads to blocking the growth and spreading of cancer. 34 Tumor initiation and progression are determined by the TM of an atypical tumor which comprises endothelial cells, pericytes, smooth muscle cells, fibroblasts, various inflammatory cells, dendritic cells, and CSCs. There are various signaling mechanisms and pathways that TM-forming cells dynamically interact with the cancerous cells which are suitable for sustaining a reasonably high cellular proliferation. So, it is the area of research interest using TM conditions to mediate effective targeting measures for cancer therapy. 35

Selectively treating cancer cells with conventional chemotherapy is difficult since it is similar to normal cells. So those problems are intervened by cellular mechanisms, such as cell cycle arrest, apoptosis induction, proliferation prevention, and interfering with metabolic reprogramming by targeted drug therapy agents. 36 Modifying TM and targeting TM for drug delivery for effective treatment are two strategies that can be used for the treatment of cancer. 37 Targeted therapy drugs do work in different ways from standard chemotherapy drugs treatment like attacking cancer cells while doing less damage to normal cells which is a programming that sets them apart from normal, healthy cells. 38

Using targeted therapy markedly increased the survival rate for some diseases, for example, from 17% to 24% in patients with advanced pancreatic cancer, the addition of erlotinib to standard chemotherapy. Imatinib has had a dramatic effect on chronic myeloid leukemia, and rituximab, sunitinib, and trastuzumab have revolutionized the treatment of renal cell carcinoma and breast cancer, respectively. 39

We can classify the targeted cell agents based on the mechanism of their work or their target site. Some enzymes serve as signals for cancer cells to grow. Some targeted therapies inhibit enzymes that are signals for cancer cells to grow. These drugs are called enzyme inhibitors. Blocking these cell signals can inhibit cancer from getting bigger and spreading. 40

Some targeted therapies are called apoptosis-inducing drugs because they are aimed right at the parts of the cell that control whether cells live or die. The examples are serine/threonine kinase, protein kinase B (PKB/Akt), which promotes cell survival, and inhibitors of this protein are in the preclinical phase. 41

These agents stop the tumors from making new blood vessels which helps cut off the tumors’ blood supply so that tumors cannot grow. In addition, they arrest tumor growth that involves by curtailing blood supply to the tumor by inhibiting angiogenic factors, such as vascular endothelial growth factor (VEGF) or its receptors. The study showed the survival of patients with advanced colorectal carcinoma extended by months after the use of Avastin (bevacizumab) in combination with 5-fluorouracil-based chemotherapy. 42

Types of target agents

Monoclonal antibodies.

Antibody drugs are man-made versions of immune system proteins administered intravenously to attack certain targets on cancer cells. They contain a more proportion of human components than murine components. 43 Their attack mechanisms of action are recruiting host immune functions to attack the target cell, binding to ligands or receptors thereby interrupting essential cancer cell processes, and carrying a lethal payload, such as radioisotope or toxin, to the target cell. 44 Gemtuzumab is an example of a CD-33-specific monoclonal antibody currently used for AML treatment by conjugating with calicheamicin. 45 In addition, ibritumomab tiuxetan is an anti-CD20, a 90Y metal isotope-based is developed in clinical therapy. 46 Delivery of active therapeutics, prodrug activation enzymes, and chemotherapy toxins are also another use of target agents of monoclonal antibodies. 47

Small molecule inhibitors

These are smaller protein in size (⩽500 Da) than monoclonal antibodies, so that they can simply translocate through plasma membranes and can be taken orally. Their main function is interrupting cellular processes by interfering with the intracellular signaling of tyrosine kinases which leads to the inhibition of tyrosine kinase signaling and initiates a molecular cascade that can lead to the inhibition of cell growth, proliferation, migration, and angiogenesis in malignant tissues. 48 Examples of small molecule inhibitors are gefitinib and erlotinib which inhibit epidermal growth factor receptor (EGFR) kinase and EGFR in non-small cell lung cancer (NSCLC) patients, respectively. There are also lapatinib and sorafenib which act on the inhibition of EGFR/Erb-B2 Receptor Tyrosine Kinase 2 (ERBB2) for ERBB2-positive breast cancer and VEGFR kinase, in renal cancer. 49

Ablation cancer therapy

Ablation is a treatment technique that destroys tumors without removing them mostly indicated for small-size tumors of less than 3 cm and the surgical option is contraindicated. Ablation is also used with embolization for larger tumors. However, this technique might not be indicated for treating tumors near major blood vessels, the diaphragm, or major bile ducts due to destroying some of the normal tissue around the tumor. 50

Thermal ablation

This technique uses extreme hyperthermia or hypothermia to destruct tumor tissue concentrating on a focal zone in and around the tumor. Similar to surgery, thermal ablation removes the tumor and a 5–10 mm thick margin of seemingly normal tissue but the tissue is killed in situ and then absorbed by the body later. The procedure is similar to surgery using an open, laparoscopic, or endoscopic approach but is commonly applied using a percutaneous or non-invasive approach. The type of tumor, site, physician’s choice, and health status determine the approach. 51

Radiofrequency ablation (RFA), microwave ablation, high-intensity focused ultrasound, and cryoablation are currently being used in the clinical setting. Cryoablation uses a hypothermic modality to induce tissue damage by a freeze-thaw process against others. All these treatments operate on the principle of hyperthermia except cryoablation. Of all the ablation techniques, cryoablation demonstrated the highest potential to elicit a post-ablative immunogenic response. 52

Recent studies showed additional to tissue disruption RFA and cryoablation can modulate the immune system that they were applied as therapy on TM and in the systematic circulation. Evidence has shown that ablation procedures affect carcinogenesis due to its local inflammatory response leading to an immunogenic gene signature. 53

The advantage of this procedure over surgery is that it provides a minimal (e.g. percutaneously or laparoscopically) or non-invasive approach to cancer therapy and gets attention as an alternative to standard surgical therapies. 54

Cryoablation

Cryoablation is one of the ablation techniques which ablates the extensive tissue by freezing to lethal temperatures followed by liquid formation, causing extensive tissue. Benign and malignant primary tumors are mostly treated by this therapy. 55 James Arnott reported that the freezing temperatures can impair cancer cell viability after he attempted the usage of cold temperatures by salt and ice solutions for the generation of local numbness before surgical operations in the nineteenth century. He suggested cryoablation as an attractive therapeutic option and increased a patient’s survival. 56

Cryoablation techniques are based on the principle of the Joule–Thomson effect which was studied in the 1930s by many researchers and concluded using liquid CO 2 under high pressure, liquid air, and liquid oxygen to achieve the cooling effect and the subsequent formation of ice crystals so employed to treat lesions, warts, and keratosis. However, after 1950, Allington replaced liquid N 2 for the treatment of various skin lesion disorders. 47

RFA therapy

RFA is a minimally invasive procedure and an image-guided technique using hyperthermic (high-frequency electrical currents) conditions to destroy cancer cells. Imaging techniques, such as ultrasound, computed tomography (CT), or magnetic resonance imaging (MRI), guide needle electrodes into a tumor cell. Generally, RFA is the most effective approach for treating small-size tumors of less than 3 cm in diameter. RFA can be used in combination with other conventional cancer treatment options. 57 After starting the use of deployable devices or multiple-electrode systems, RFA can treat medium tumors (up to 5 cm diameter). 58

Gene therapy

Gene therapy is the insertion of a normal copy of a defective gene in the genome to cure a specific disorder. The first application dates back to 1990 when a retroviral vector was exploited to deliver the adenosine deaminase (ADA) gene to T cells in patients with severe combined immunodeficiency (SCID). Approximately, about 2900 gene therapy clinical trials are currently ongoing, two-third of which are related to cancer. Strategies, such as expression of proapoptotic and chemosensitizing genes, expression of wild-type tumor suppressor genes, expression of genes able to solicit specific anti-tumor immune responses, and targeted silencing of oncogenes, are under evaluation for cancer gene therapy. 47

Thymidine kinase (TK) gene delivery is effective for the administration of prodrug ganciclovir to activate its expression and induce specific cytotoxicity. 59 The p53 tumor suppressor gene which is vectors carrying has been assessed for the clinical purpose very recently. ONYX-015 has been tested in NSCLC patients and gave a high response rate when given alone or combined with chemotherapy. 60 Gendicine, a recombinant adenovirus carrying wild-type p53-induced complete disease regression in head and neck squamous cell cancer had similar success when combined with radiotherapy. 61

Some challenges that have been faced with gene therapy are the selection of the right conditions and the choice of the best delivery mechanism. Identified drawbacks of this therapy are genome integration, limited efficacy in specific subsets of patients, and high chances of being neutralized by the immune system. Basic research and medical translation used RNA interference (RNAi) as an efficient technology that able to produce targeted gene silencing. 62 RNA-induced silencing complex (RISC) mediates the targeted gene silencing process by cleaving the messenger RNA (mRNA) and interference with protein synthesis. 63 A siRNAs can be designed to block desired targets, involving cell proliferation and metastatic invasion; hence, precise molecular mechanisms are a triggering factor for tumor formation. This method relies on siRNA-mediated gene silencing of anti-apoptotic proteins, transcription factors (i.e. c-myc gene), 64 , 65 or cancer mutated genes (i.e. K-RAS). 66

Advantages of siRNA-based drugs are safety, high efficacy, specificity, few side effects, and low costs of production. 67 However, occasionally, they can induce off-target effects or elicit innate immune responses, followed by specific inflammation. 68 Delivery methods currently under study are chemical modification (insertion of a phosphorothioate at 3’ end, introduction of a 2’ O-methyl group, and modification by 2,4-dinitrophenol) and lipid encapsulation, or conjugation with organic molecules (polymers, peptides, lipids, antibodies, small molecules) efficiently target to spontaneously cross cell membranes of naked siRNAs. 69 Interaction of cationic liposomes with negatively charged nucleic acids facilitates easy transfection by simple electrostatic interactions. 70 They can be constituted by 1,2-dioleoyl-3-trimethylammonium propane (DOTAP) and N-[1-(2,3-dioleoyloxy) propyl]-N, Ntrimethylammonium methyl sulfate (DOTMA). 71 Currently, a Phase I clinical trial is recruiting patients for evaluating the safety of Eph receptor A2 (EphA2) targeting 1,2-dioleoyl-sn-glycero-3-phosphocholine (DOPC) encapsulated siRNA (siRNA-EphA2- DOPC) in patients with advanced and recurrent cancer. 72 siRNAs can be concentrated in cationic polymers, such as chitosan, cyclodextrin, and polyethyleneimine (PEI). 73 CALAA-01 is one of the cyclodextrin polymers conjugated with human transferrin is being entered a Phase I clinical trial. PEI has been used as an anti-cancer by forming small cationic nanoparticles and loading with human epidermal growth factor receptor 2 (HER-2 receptor)-specific siRNA. 74 Phase II clinical trial has been started to evaluate Local Drug EluteR (siG12D LODER) directed to mutated Kirsten rat sarcoma (K-RAS) oncogene for the treatment of advanced pancreatic cancer. Conjugating to peptides, antibodies, and aptamers improves stability during circulation and enhances cellular uptake of siRNAs. 75 The introduction of nanocarriers has largely improved siRNAs stability, pharmacokinetics and biodistribution properties, and targeting specificity. Polyallylamine phosphate nanocarriers have been developed to release siRNAs in the cytoplasm after disassembly at low endosomal pH. 76

Dose correction and variabilities between individuals and different stages of disease are challenging issues on clinical translation of the siRNA-based approach. In the future, the needed research is on setting up the best-personalized therapy and toward controlled release to reach only specific targets on treating the tumor. Table 2 summarizes the gene therapy drugs based on their mechanism of action and induction.

Summary of gene therapy approaches.

Natural antioxidants

Day to day, the anatomy undergoes many exogenous insults, such as ultraviolet (UV) rays, pollution, and tobacco smoke, that end in the assembly of reactive species, particularly oxidants and free radicals, liable for the onset of many diseases, together with cancer. These molecules can even be made as a consequence of clinical administration of medication; however, they are additionally naturally created within our cells and tissues by mitochondria and peroxisomes, and from macrophages metabolism, throughout traditional physiological aerobic processes. 47

Oxidative stress and radical oxygen species can significantly change the regulation of transcription factors by damaging the DNA and other bio-macromolecule. 77

Vitamins, polyphenols, and plant-derived bioactive compounds are natural antioxidants used as preventive and therapeutic drugs against these molecules that damage the body due to their anti-inflammatory and antioxidant properties. 78 Studies added to cancer therapy after appreciating their anti-proliferative and proapoptotic properties. Compounds, such as vitamins, alkaloids, flavonoids, carotenoids, curcumin, berberine, quercetin, and others, are examples of natural antioxidants screened in vitro and in vivo. 79

Limited bioavailability and/ or toxicity is one of the challenges of natural drugs while their translation into clinical practice. 47 Curcumin has cytotoxic effects in different kinds of tumors, such as the brain, lung, leukemia, pancreatic, and hepatocellular carcinoma, 80 while sparing normal cells at effective therapeutic doses. The curcumin’s biological properties, treatment duration and efficient therapeutic doses are under study. 80 This day, about 27 clinical trials are done, while 40 are under study on curcumin.

Berberine is an alkaloid compound that has been studied to be effective against different cancers as a chemopreventive agent, modulating many signaling pathways. Different nanotechnological strategies have been developed to facilitate its delivery across cell membranes due to their poorly soluble in water. 81 Six clinical trials are under study and two have been completed.

Quercetin is another natural plant origin agent that is effective alone and also in combination with chemotherapeutic agents in treating many cancers, such as lung, prostate, liver, colon, and breast cancers. 82 Quercetin’s mechanism of action is by binding to cellular receptors and the interference of several signaling pathways. 83 Currently, six clinical trials are under study and seven studies have been completed.

Current clinical trials

In recent years, analysis of cancer medication has taken outstanding steps toward more practical, precise, and fewer invasive cancer treatments in the research of clinical trials ( Figure 1 ).

Current status of clinical trials of innovative and novel strategies of cancer therapy.

Currently, the most frequent entries focusing on cancer therapies in the database of clinical trials ( www.clinicalTrials.gov ) include the terminologies stem cell, targeted therapy, immunotherapy, and gene therapy because they are very promising and effective. Table 3 summarizes the potential advantages and disadvantages of the new treatment approaches.

Comparison of advantageous and disadvantageous of new cancer therapies.

MRI: magnetic resonance imaging.

Table 4 summarizes the approaches to advanced cancer therapies and their respective delivery systems with examples.

Advanced therapy approaches and delivery systems.

Current methods in oncology focus on the development of safe and efficient cancer nanomedicines. Targeted medical care helped rising the biodistribution of recent or already tested chemotherapeutical agents around the specific tissue to be treated; different methods, such as sequence medical care, siRNAs delivery, therapy, and inhibitor molecules, supply new potentialities to cancer patients. Gene therapy acts by direct in situ insertion of exogenous genes into benign tumors. Noticeably, stem cells can be used as regenerative medicine, therapeutic carriers, drug targeting, and generation of immune cells because of having unique biological actions on other cells. 22 On the opposite hand, thermal ablation and magnetic hyperthermia are promising alternatives to the growth surgical process. Finally, radionics and pathomics approaches facilitate the management of huge knowledge sets from cancer patients to enhance prognosis and outcomes. Much progress has been made, but many others are likely to come soon, producing more and more ad hoc personalized therapies. Further development and refinement of drug delivery systems are essential for improving therapeutic outcomes.

Acknowledgments

The authors thank the Center for Innovative Drug Development and Therapeutic Trials for Africa (CDT-Africa), Addis Ababa University, for the support rendered.

Author contributions: D.T.D. is a major contributor in writing the manuscript. All others reviewed and approved the manuscript.

Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.

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This paper is in the following e-collection/theme issue:

Published on 16.4.2024 in Vol 26 (2024)

Adverse Event Signal Detection Using Patients’ Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models

Authors of this article:

Original Paper

• Satoshi Nishioka 1 , PhD   ; 
• Satoshi Watabe 1 , BSc   ; 
• Yuki Yanagisawa 1 , PhD   ; 
• Kyoko Sayama 1 , MSc   ; 
• Hayato Kizaki 1 , MSc   ; 
• Shungo Imai 1 , PhD   ; 
• Mitsuhiro Someya 2 , BSc   ; 
• Ryoo Taniguchi 2 , PhD   ; 
• Shuntaro Yada 3 , PhD   ; 
• Eiji Aramaki 3 , PhD   ; 
• Satoko Hori 1 , PhD  

1 Division of Drug Informatics, Keio University Faculty of Pharmacy, Tokyo, Japan

2 Nakajima Pharmacy, Hokkaido, Japan

3 Nara Institute of Science and Technology, Nara, Japan

Corresponding Author:

Satoko Hori, PhD

Division of Drug Informatics

Keio University Faculty of Pharmacy

1-5-30 Shibakoen

Tokyo, 105-8512

Phone: 81 3 5400 2650

Email: [email protected]

Background: Early detection of adverse events and their management are crucial to improving anticancer treatment outcomes, and listening to patients’ subjective opinions (patients’ voices) can make a major contribution to improving safety management. Recent progress in deep learning technologies has enabled various new approaches for the evaluation of safety-related events based on patient-generated text data, but few studies have focused on the improvement of real-time safety monitoring for individual patients. In addition, no study has yet been performed to validate deep learning models for screening patients’ narratives for clinically important adverse event signals that require medical intervention. In our previous work, novel deep learning models have been developed to detect adverse event signals for hand-foot syndrome or adverse events limiting patients’ daily lives from the authored narratives of patients with cancer, aiming ultimately to use them as safety monitoring support tools for individual patients.

Objective: This study was designed to evaluate whether our deep learning models can screen clinically important adverse event signals that require intervention by health care professionals. The applicability of our deep learning models to data on patients’ concerns at pharmacies was also assessed.

Methods: Pharmaceutical care records at community pharmacies were used for the evaluation of our deep learning models. The records followed the SOAP format, consisting of subjective (S), objective (O), assessment (A), and plan (P) columns. Because of the unique combination of patients’ concerns in the S column and the professional records of the pharmacists, this was considered a suitable data for the present purpose. Our deep learning models were applied to the S records of patients with cancer, and the extracted adverse event signals were assessed in relation to medical actions and prescribed drugs.

Results: From 30,784 S records of 2479 patients with at least 1 prescription of anticancer drugs, our deep learning models extracted true adverse event signals with more than 80% accuracy for both hand-foot syndrome (n=152, 91%) and adverse events limiting patients’ daily lives (n=157, 80.1%). The deep learning models were also able to screen adverse event signals that require medical intervention by health care providers. The extracted adverse event signals could reflect the side effects of anticancer drugs used by the patients based on analysis of prescribed anticancer drugs. “Pain or numbness” (n=57, 36.3%), “fever” (n=46, 29.3%), and “nausea” (n=40, 25.5%) were common symptoms out of the true adverse event signals identified by the model for adverse events limiting patients’ daily lives.

Conclusions: Our deep learning models were able to screen clinically important adverse event signals that require intervention for symptoms. It was also confirmed that these deep learning models could be applied to patients’ subjective information recorded in pharmaceutical care records accumulated during pharmacists’ daily work.

Introduction

Increasing numbers of people are expected to develop cancers in our aging society [ 1 - 3 ]. Thus, there is increasing interest in how to detect and manage the side effects of anticancer therapies in order to improve treatment regimens and patients’ quality of life [ 4 - 8 ]. The primary approaches for side effect management are “early signal detection and early intervention” [ 9 - 11 ]. Thus, more efficient approaches for this purpose are needed.

It has been recognized that patients’ voices concerning adverse events represent an important source of information. Several studies have indicated that the number, severity, and time of occurrence of adverse events might be underevaluated by physicians [ 12 - 15 ]. Thus, patient-reported outcomes (PROs) have recently received more attention in the drug evaluation process, reflecting patients’ real voices. Various kinds of PRO measures have been developed and investigated in different disease populations [ 16 , 17 ]. Health care authorities have also encouraged the pharmaceutical industry to use PROs for drug evaluation [ 18 , 19 ], and it is becoming more common to take PRO assessment results into consideration for drug marketing approval [ 20 , 21 ]. Similar trends can be seen in the clinical management of individual patients. Thus, health care professionals have an interest in understanding how to appropriately gather patients’ concerns in order to improve safety management and clinical decisions [ 22 - 24 ].

The applications of deep learning for natural language processing have expanded dramatically in recent years [ 25 ]. Since the development of a high-performance deep learning model in 2018 [ 26 ], attempts to apply cutting-edge deep learning models to various kinds of patient-generated text data for the evaluation of safety events or the analysis of unscalable subjective information from patients have been accelerating [ 27 - 31 ]. Most studies have been conducted to use patients’ narrative data for pharmacovigilance [ 27 , 32 - 35 ], while few have been aimed at improvement of real-time safety monitoring for individual patients. In addition, there have been some studies on adverse event severity grading based on health care records [ 36 - 39 ], but none has yet aimed to extract clinically important adverse event signals that require medical intervention from patients’ narratives. It is important to know whether deep learning models could contribute to the detection of such important adverse event signals from concern texts generated by individual patients.

To address this question, we have developed deep learning models to detect adverse event signals from individual patients with cancer based on patients’ blog articles in online communities, following other types of natural language processing–related previous work [ 40 , 41 ]. One deep learning model focused on the specific symptom of hand-foot syndrome (HFS), which is one of the typical side effects of anticancer treatments [ 42 ], and another focused on a broad range of adverse events that impact patients’ activities of daily living [ 43 ]. We showed that our models can provide good performance scores in targeting adverse event signals. However, the evaluation relied on patients’ narratives from the patients’ blog data used for deep learning model training, so further evaluation is needed to ensure the validity and applicability of the models to other texts regarding patients’ concerns. In addition, the blog data source did not contain medical information, so it was not feasible to assess whether the models could contribute to the extraction of clinically important adverse event signals.

To address these challenges, we focused on pharmaceutical care records written by pharmacists at community pharmacies. The gold standard format for pharmaceutical care records in Japan is the SOAP (subjective, objective, assessment, plan)-based document that follows the “problem-oriented system” concept proposed by Weed [ 44 ] in 1968. Pharmacists track patients’ subjective concerns in the S column, provide objective information or observations in the O column, give their assessment from the pharmacist perspective in the A column, and suggest a plan for moving forward in the P column [ 45 , 46 ]. We considered that SOAP-based pharmaceutical care records could be a unique data source suitable for further evaluation of our deep learning models because they contain both patients’ concerns and professional health care records by pharmacists, including the medication prescription history with time stamps. Therefore, this study was designed to assess whether our deep learning models could extract clinically important adverse event signals that require intervention by medical professionals from these records. We also aimed to evaluate the characteristics of the models when applied to patients’ subjective information noted in the pharmaceutical care records, as there have been only a few studies on the application of deep learning models to patients’ concerns recorded during pharmacists’ daily work [ 47 - 49 ].

Here, we report the results of applying our deep learning models to patients’ concern text data in pharmaceutical care records, focusing on patients receiving anticancer treatment.

Data Source

The original data source was 2,276,494 pharmaceutical care records for 303,179 patients, created from April 2020 to December 2021 at community pharmacies belonging to the Nakajima Pharmacy Group in Japan [ 50 ]. To focus on patients with cancer, records of patients with at least 1 prescription for an anticancer drug were retrieved by sorting individual drug codes (YJ codes) used in Japan (YJ codes starting with 42 refer to anticancer drugs). Records in the S column (ie, S records) were collected from the patients with cancer as the text data of patients’ concerns for deep learning model analysis.

Deep Learning Models

The deep learning models used for this research were those that we constructed based on patients’ narratives in blog articles posted in an online community and that showed the best performance score in each task in our previous work (ie, a Bidirectional Encoder Representations From Transformers [BERT]–based model for HFS signal extraction [ 42 ] and a T5-based model for adverse event signal extraction [ 43 ]). BERT [ 26 ] and T5 [ 51 ] both belong to a type of deep learning model that has recently shown high performance in several studies [ 29 , 52 ]. Hereafter, we refer to the deep learning model for HFS signals as the HFS model, the model for any adverse event signals as All AE (ie, all or any adverse events) model, and the model for adverse event signals limited to patients’ activities of daily living as the AE-L (adverse events limiting patients’ daily lives) model. It was also confirmed that these deep learning models showed similar or higher performance scores for the HFS, All AE, or AE-L identification tasks using 1000 S records randomly extracted from the data source of this study compared to the values obtained in our previous work [ 42 , 43 ] (the performance scores of sentence-level tasks from our previous work are comparable, as the mean number of words in the sentences in the data source in our previous work was 32.7 [SD 33.9], which is close to that of the S records used in this study, 38.8 [SD 29.4]). The method and results of the performance-level check are described in detail in Multimedia Appendix 1 [ 42 , 43 ]. We applied the deep learning models to all text data in this study without any adjustment in setting parameters from those used in constructing them based on patient-authored texts in our previous work [ 42 , 43 ].

Evaluation of Extracted S Records by the Deep Learning Models

In this study, we focused on the evaluation of S records that our deep learning models extracted as HFS or AE-L positive. Each positive S record was assessed as if it was a true adverse event signal, a sort of adverse event symptom, whether or not an intervention was made by health care professionals. We also investigated the kind of anticancer treatment prescription in connection with each adverse event signal identified in S records.

To assess whether an extracted positive S record was a true adverse event signal, we used the same annotation guidelines as in our previous work [ 43 ]. In brief, each S record was treated as an “adverse event signal” if any untoward medical occurrence happened to the patient, regardless of the cause. For the AE-L model only, if a positive S record was confirmed as an adverse event signal, it was further categorized into 1 or more of the following adverse event symptoms: “fatigue,” “nausea,” “vomiting,” “diarrhea,” “constipation,” “appetite loss,” “pain or numbness,” “rash or itchy,” “hair loss,” “menstrual irregularity,” “fever,” “taste disorder,” “dizziness,” “sleep disorder,” “edema,” or “others.”

For the assessment of interventions by health care professionals and anticancer treatment prescriptions, information from the O, A, and P columns and drug prescription history in the data source were investigated for the extracted positive S records. The interventions by health care professionals were categorized in any of the following: “adding symptomatic treatment for the adverse event signal,” “dose reduction or discontinuation of causative anticancer treatment,” “consultation with physician,” “others,” or “no intervention (ie, just following up the adverse event signal).” The actions categorized in “others” were further evaluated individually. For this assessment, we also randomly extracted 200 S records and evaluated them in the same way for comparison with the results from the deep learning model. Prescription history of anticancer treatment was analyzed by primary category of mechanism of action (MoA) with subcategories if applicable (eg, target molecule for kinase inhibitors).

Applicability Check to Other Text Data Including Patients’ Concerns

To check the applicability of our deep learning models to data from a different source, interview transcripts from patients with cancer were also evaluated. The interview transcripts were created by the Database of Individual Patient Experiences-Japan (DIPEx-Japan) [ 53 ]. DIPEx-Japan divides the interview transcripts into sections for each topic, such as “onset of disease” and “treatment,” and posts the processed texts on its website. Processing is conducted by accredited researchers based on qualitative research methods established by the University of Oxford [ 54 ]. In this study, interview text data created from interviews with 52 patients with breast cancer conducted from January 2008 to October 2018 were used to assess whether our deep learning models can extract adverse event signals from this source. In total, 508 interview transcripts were included with the approval of DIPEx-Japan.

Ethical Considerations

This study was conducted with anonymized data following approval by the ethics committee of the Keio University Faculty of Pharmacy (210914-1 and 230217-1) and in accordance with relevant guidelines and regulations and the Declaration of Helsinki. Informed consent specific to this study was waived due to the retrospective observational design of the study with the approval of the ethics committee of the Keio University Faculty of Pharmacy. To respect the will of each individual stakeholder, however, we provided patients and pharmacists of the pharmacy group with an opportunity to refuse the sharing of their pharmaceutical care records by posting an overview of this study at each pharmacy store or on their web page regarding the analysis using pharmaceutical care records. Interview transcripts from DIPEx-Japan were provided through a data sharing arrangement for using narrative data for research and education. Consent for interview transcription and its sharing from DIPEx-Japan was obtained from the participants when the interviews were recorded.

From the original data source of 2,180,902 pharmaceutical care records for 291,150 patients, S records written by pharmacists for patients with a history of at least 1 prescription of an anticancer drug were extracted. This yielded 30,784 S records for 2479 patients with cancer ( Table 1 ). The mean and median number of words in the S records were 38.8 (SD 29.4) and 32 (IQR 20-50), respectively. We applied our deep learning models, HFS, All AE, and AE-L, to these 30,784 S records for the evaluation of the deep learning models for adverse event signal detection.

For interview transcripts created by DIPEx-Japan, the mean and median number of words were 428.9 (SD 160.9) and 416 (IQR 308-526), respectively, in the 508 transcripts for 52 patients with breast cancer.

a SOAP: subjective, objective, assessment, plan.

b S: subjective.

Application of the HFS Model

First, we applied the HFS model to the S records for patients with cancer. The BERT-based model was used for this research as it showed the best performance score in our previous work [ 42 ].

S Records Extracted as HFS Positive

The S records extracted as HFS positive by the HFS model ( Table 2 ) amounted to 167 (0.5%) records for 119 (4.8%) patients. A majority of the patients had 1 HFS-positive record in their S records (n=91, 76.5%), while 2 patients had as many as 6 (1.7%) HFS-positive records. When we examined whether the extracted S records were true adverse event signals or not, 152 records were confirmed to be adverse event signals, while the other 15 records were false-positives. All the false-positive S records were descriptions about the absence of symptoms or confirmation of improving condition (eg, “no diarrhea, mouth ulcers, or limb pain so far” or “the skin on the soles of my feet has calmed down a lot with this ointment”). Some examples of S records that were predicted as HFS positive by the model are shown in Table S1 in Multimedia Appendix 2 .

The same examination was conducted with interview transcripts from DIPEx-Japan. Only 1 (0.2%) transcript was extracted as HFS positive by the HFS model, and it was a true adverse event signal (100%). The actual transcript extracted as HFS positive is shown in Table S2 in Multimedia Appendix 2 .

a S: subjective.

b HFS: hand-foot syndrome.

c All false-positive S records were denial of symptoms or confirmation of improving condition.

Interventions by Health Care Professionals

The 167 S records extracted as HFS positive as well as 200 randomly selected records were checked for interventions by health care professionals ( Figure 1 ). The proportion showing any action by health care professionals was 64.1% for 167 HFS-positive S records compared to 13% for the 200 random S records. Among the actions taken for HFS positives, “adding symptomatic treatment” was the most common, accounting for around half (n=79, 47.3%), followed by “other” (n=18, 10.8%). Most “other” actions were educational guidance from pharmacists, such as instructions on moisturizing, nail care, or application of ointment and advice on daily living (eg, “avoid tight socks”).

Anticancer Drugs Prescribed

The types of anticancer drugs prescribed for HFS-positive patients are summarized based on the prescription histories in Table 3 . For the 152 adverse event signals identified by the HFS model in the previous section, the most common MoA class of anticancer drugs used for the patients was antimetabolite (n=62, 40.8%), specifically fluoropyrimidines (n=59, 38.8%). Kinase inhibitors were next (n=49, 32.2%), with epidermal growth factor receptor (EGFR) inhibitors and multikinase inhibitors as major subgroups (n=28, 18.4% and n=14, 9.2%, respectively). The third and fourth most common MoAs were aromatase inhibitors (n=24, 15.8%) and antiandrogen or estrogen drugs (n=7, 4.6% each) for hormone therapy.

a EGFR: epidermal growth factor receptor.

b VEGF: vascular endothelial growth factor.

c HER2: human epidermal growth factor receptor-2.

d CDK4/6: cyclin-dependent kinase 4/6.

Application of the All AE or AE-L model

The All AE and AE-L models were also applied to the same S records for patients with cancer. The T5-based model was used for this research as it gave the best performance score in our previous work [ 43 ].

S Records Extracted as All AE or AE-L positive

The numbers of S records extracted as positive were 7604 (24.7%) for 1797 patients and 196 (0.6%) for 142 patients for All AE and AE-L, respectively. In the case of All AE, patients tended to have multiple adverse event positives in their S records (n=1315, 73.2% of patients had at least 2 positives). In the case of AE-L, most patients had only 1 AE-L positive (n=104, 73.2%), and the largest number of AE-L positives for 1 patient was 4 (2.8%; Table 4 ).

We focused on AE-L evaluation due to its greater importance from a medical viewpoint and lower workload for manual assessment, considering the number of positive S records. Of the 197 AE-L–positive S records, it was confirmed that 157 (80.1%) records accurately extracted adverse event signals, while 39 (19.9%) records were false-positives that did not include any adverse event signals ( Table 4 ). The contents of the 39 false-positives were all descriptions about the absence of symptoms or confirmation of improving condition, showing a similar tendency to the HFS false-positives (eg, “The diarrhea has calmed down so far. Symptoms in hands and feet are currently fine” and “No symptoms for the following: upset in stomach, diarrhea, nausea, abdominal pain, abdominal pain or stomach cramps, constipation”). Examples of S records that were predicted as AE-L positive are shown in Table S3 in Multimedia Appendix 2 .

The deep learning models were also applied to interview transcripts from DIPEx-Japan in the same manner. The deep learning models identified 84 (16.5%) and 18 (3.5%) transcripts as All AE or AE-L positive, respectively. Of the 84 All AE–positive transcripts, 73 (86.9%) were true adverse event signals. The false-positives of All AE (n=11, 13.1%) were categorized into any of the following 3 types: explanations about the disease or its prognosis, stories when their cancer was discovered, or emotional changes that did not include clear adverse event mentions. With regard to AE-L, all the 18 (100%) positives were true adverse event signals (Table S4 in Multimedia Appendix 2 ). Examples of actual transcripts extracted as All AE or AE-L positive are shown in Table S5 in Multimedia Appendix 2 .

b All AE: all (or any of) adverse event.

c AE-L: adverse events limiting patients’ daily lives.

d All false-positive S records were denial of symptoms or confirmation of improving condition.

Whether or not interventions were made by health care professionals was investigated for the 196 AE-L–positive S records. As in the HFS model evaluation, data from 200 randomly selected S records were used for comparison ( Figure 2 ). In total, 91 (46.4%) records in the 196 AE-L–positive records were accompanied by an intervention, while the corresponding figure in the 200 random records was 26 (13%) records. The most common action in response to adverse event signals identified by the AE-L model was “adding symptomatic treatment” (n=71, 36.2%), followed by “other” (n=11, 5.6%). “Other” included educational guidance from pharmacists, inquiries from pharmacists to physicians, or recommendations for patients to visit a doctor.

The types of anticancer drugs prescribed for patients with adverse event signals identified by the AE-L model were summarized based on the prescription histories ( Table 5 ). In connection with the 157 adverse event signals, the most common MoA of the prescribed anticancer drug was antimetabolite (n=62, 39.5%) and fluoropyrimidine (n=53, 33.8%), which accounted for the majority. Kinase inhibitor (n=31, 19.7%) was the next largest category with multikinase inhibitor (n=14, 8.9%) as the major subgroup. These were followed by antiandrogen (n=27, 17.2%), antiestrogen (n=10, 6.4%), and aromatase inhibitor (n=10, 6.4%) for hormone therapy.

b JAK: janus kinase.

c VEGF: vascular endothelial growth factor.

d BTK: bruton tyrosine kinase.

e FLT3: FMS-like tyrosine kinase-3.

f PARP: poly-ADP ribose polymerase.

g CDK4/6: cyclin-dependent kinase 4/6.

h CD20: cluster of differentiation 20.

Adverse Event Symptoms

For the 157 adverse event signals identified by the AE-L model, the symptoms were categorized according to the predefined guideline in our previous work [ 43 ]. “Pain or numbness” (n=57, 36.3%) accounted for the largest proportion followed by “fever” (n=46, 29.3%) and “nausea” (n=40, 25.5%; Table 6 ). Symptoms classified as “others” included chills, tinnitus, running tears, dry or peeling skin, and frequent urination. When comparing the proportion of the symptoms associated with or without interventions by health care professionals, a trend toward a greater proportion of interventions was observed in “fever,” “nausea,” “diarrhea,” “constipation,” “vomiting,” and “edema” ( Figure 3 , black boxes). On the other hand, a smaller proportion was observed in “pain or numbness,” “fatigue,” “appetite loss,” “rash or itchy,” “taste disorder,” and “dizziness” ( Figure 3 , gray boxes).

This study was designed to evaluate our deep learning models, previously constructed based on patient-authored texts posted in an online community, by applying them to pharmaceutical care records that contain both patients’ subjective concerns and medical information created by pharmacists. Based on the results, we discuss whether these deep learning models can extract clinically important adverse event signals that require medical intervention, and what characteristics they show when applied to data on patients’ concerns in pharmaceutical care records.

Performance for Adverse Event Signal Extraction

The first requirement for the deep learning models is to extract adverse event signals from patients’ narratives precisely. In this study, we evaluated the proportion of true adverse event signals in positive S records extracted by the HFS or AE-L model. True adverse event signals amounted to 152 (91%) and 157 (80.1%) for the HFS and AE-L models, respectively ( Tables 2 and 4 ). Given that the proportion of true adverse event signals in 200 randomly extracted S records without deep learning models was 54 (27%; categories other than “no adverse event” in Figures 1 and 2 ), the HFS and AE-L models were able to concentrate S records with adverse event mentions. Although 15 (9%) for the HFS model and 39 (19.9%) for the AE-L model were false-positives, it was confirmed all of the false-positive records described a lack of symptoms or confirmation of improving condition. We considered that such false-positives are due to the unique feature of pharmaceutical care records, where pharmacists might proactively interview patients about potential side effects of their medications. As the data set of blog articles we used to construct the deep learning models included few such cases (especially comments on lack of symptoms), our models seemed unable to exclude them correctly. Even though we confirmed that the proportion of true “adverse event” signals extracted from the S records by the HFS or AE-L model was more than 80%, the performance scores to extract true “HFS” or “AE-L” signals were not so high based on the performance check using 1000 randomly extracted S records ( F 1 -scores were 0.50 and 0.22 for true HFS and AE-L signals, respectively; Table S1 in Multimedia Appendix 1 ). It is considered that the performance to extract true HFS and AE-L signals was relatively low due to the short length of texts in the S records, providing less context to judge the impact on patients’ daily lives, especially for the AE-L model (the mean word number of the S records was 38.8 [SD 29.4; Table 1 ], similar to the sentence-level tasks in our previous work [ 42 , 43 ]). However, we consider a true adverse event signal proportion of more than 80% in this study represents a promising outcome, as this is the first attempt to apply our deep learning models to a different source of patients’ concern data, and the extracted positive cases would be worthy of evaluation by a medical professional, as the potential adverse events could be caused by drugs taken by the patients.

When the deep learning models were applied to DIPEx-Japan interview transcripts, including patients’ concerns, the proportion of true adverse event signals was also more than 80% (for All AE: n=73, 86.9% and for HFS and AE-L: n=18, 100%). The difference in the results between pharmaceutical care S records and DIPEx-Japan interview transcripts was the features of false-positives, descriptions about lack of symptoms or confirmation of improving condition in S records versus explanations about disease or its prognosis, stories about when their cancer was discovered, or emotional changes in interview transcripts. This is considered due to the difference in the nature of the data source; the pharmaceutical care records were generated in a real-time manner by pharmacists through their daily work, where adverse event signals are proactively monitored, while the interview transcripts were purely based on patients’ retrospective memories. Our deep learning models were able to extract true adverse event signals with an accuracy of more than 80% from both text data sources in spite of the difference in their nature. When looking at future implementation of the deep learning models in society (discussed in the Potential for Deep Learning Model Implementation in Society section), it may be desirable to further adjust deep learning models to reduce false-positives depending upon the features of the data source.

Identification of Important Adverse Events Requiring Medical Intervention

To assess whether the models could extract clinically important adverse event signals, we investigated interventions by health care professionals connected with the adverse event signals that are identified by our deep learning models. In the 200 randomly extracted S records, only 26 (13%) consisted of adverse event signals, leading to any intervention by health care professionals. On the other hand, the proportion of signals associated with interventions was increased to 107 (64.1%) and 91 (46.4%) in the S records extracted as positive by the HFS and AE-L models, respectively ( Figures 1 and 2 ). These results suggest that both deep learning models can screen clinically important adverse event signals that require intervention from health care professionals. The performance level in screening adverse event signals requiring medical intervention was higher in the HFS model than in the AE-L model (n=107, 64.1% vs n=91, 46.4%; Figures 1 and 2 ). Since the target events were specific and adverse event signals of HFS were narrowly defined, which is one of the typical side effects of some anticancer drugs, we consider that health care providers paid special attention to HFS-related signals and took action proactively. In both deep learning models, similar trends were observed in actions taken by health care professionals in response to extracted adverse event signals; common actions were attempts to manage adverse event symptoms by symptomatic treatment or other mild interventions, including educational guidance from pharmacists or recommendations for patients to visit a doctor. More direct interventions focused on the causative drugs (ie, “dose reduction or discontinuation of anticancer treatment”) amounted to less than 5%; 7 (4.2%) for the HFS model and 6 (3.1%) for the AE-L model ( Figures 1 and 2 ). Thus, it appears that our deep learning models can contribute to screening mild to moderate adverse event signals that require preventive actions such as symptomatic treatments or professional advice from health care providers, especially for patients with less sensitivity to adverse event signals or who have few opportunities to visit clinics and pharmacies.

Ability to Catch Real Side Effect Signals of Anticancer Drugs

Based on the drug prescription history associated with S records extracted as HFS or AE-L positive, the type and duration of anticancer drugs taken by patients experiencing the adverse event signals were investigated. For the HFS model, the most common MoA of anticancer drug was antimetabolite (fluoropyrimidine: n=59, 38.8%), followed by kinase inhibitors (n=49, 32.2%, of which EGFR inhibitors and multikinase inhibitors accounted for n=28, 18.4% and n=14, 9.2%, respectively) and aromatase inhibitors (n=24, 15.8%; Table 3 ). It is known that fluoropyrimidine and multikinase inhibitors are typical HFS-inducing drugs [ 55 - 58 ], suggesting that the HFS model accurately extracted HFS side effect signals derived from these drugs. Note that symptoms such as acneiform rash, xerosis, eczema, paronychia, changes in the nails, arthralgia, or stiffness of limb joints, which are common side effects of EGFR inhibitors or aromatase inhibitors [ 59 , 60 ], might be extracted as closely related expressions to those of HFS signals. When looking at the MoA of anticancer drugs for patients with adverse event signals identified by the AE-L model, antimetabolite (fluoropyrimidine) was the most common one (n=53, 33.8%), as in the case of those identified by the HFS model, followed by kinase inhibitors (n=31, 19.7%) and antiandrogens (n=27, 17.2%; Table 5 ). Since the AE-L model targets a broad range of adverse event symptoms, it is difficult to rationalize the relationship between the adverse event signals and types of anticancer drugs. However, the type of anticancer drugs would presumably closely correspond to the standard treatments of the cancer types of the patients. Based on the prescribed anticancer drugs, we can infer that a large percentage of the patients had breast or lung cancer, indicating that our study results were based on data from such a population. Thus, a possible direction for the expansion of this research would be adjusting the deep learning models by additional training with expressions for typical side effects associated with standard treatments of other cancer types. To interpret these results correctly, it should be noted that we could not investigate anticancer treatments conducted outside of the pharmacies (eg, the time-course relationship with intravenously administered drugs would be missed, as the administration will be done at hospitals). To further evaluate how useful this model is in side effect signal monitoring for patients with cancer, comprehensive medical information for the eligible patients would be required.

Suitability of the Deep Learning Models for Specific Adverse Event Symptoms

Among the adverse event signals identified by the AE-L model, the type of symptom was categorized according to a predefined annotation guideline that we previously developed [ 43 ]. The most frequently recorded adverse event signals identified by the AE-L model were “pain or numbness” (n=57, 36.3%), “fever” (n=46, 29.3%), and “nausea” (n=40, 25.5%; Table 6 ). Since the pharmaceutical care records had information about interventions by health care professionals, the frequency of the presence or absence of the interventions for each symptom was examined. A trend toward a greater proportion of interventions was observed in “fever,” “nausea,” “diarrhea,” “constipation,” “vomiting,” and “edema” ( Figure 3 , black boxes). There seem to be 2 possible explanations for this: these symptoms are of high importance and require early medical intervention or effective symptomatic treatments are available for these symptoms in clinical practice so that medical intervention is an easy option. On the other hand, a trend for a smaller proportion of adverse event signals to result in interventions was observed for “pain or numbness,” “fatigue,” “appetite loss,” “rash or itchy,” “taste disorder,” and “dizziness” ( Figure 3 , gray boxes). The reason for this may be the lack of effective symptomatic treatments or the difficulty of judging whether the severity of these symptoms justifies medical intervention by health care providers. In either case, there may be room for improvement in the quality of medical care for these symptoms. We expect that our research will contribute to a quality improvement in safety monitoring in clinical practice by supporting adverse event signal detection in a cost-effective manner.

Potential for Deep Learning Model Implementation in Society

Although we evaluated our deep learning models using pharmaceutical care records in this study, the main target of future implementation of our deep learning models in society would be narrative texts that patients directly write to record their daily experiences. For example, the application of these deep learning models to electronic media where patients record their daily experiences in their lives with disease (eg, health care–related e-communities and disease diary applications) could enable information about adverse event signal onset that patients experience to be provided to health care providers in a timely manner. Adverse event signals can automatically be identified and shared with health care providers based on the concern texts that patients post to any platform. This system will have the advantage that health care providers can efficiently grasp safety-related events that patients experience outside of clinic visits so that they can conduct more focused or personalized interactions with patients at their clinic visits. However, consideration should be given to avoid an excessive burden on health care providers. For instance, limiting the sharing of adverse event signals to those of high severity or summarizing adverse event signals over a week rather than sharing each one in a real-time manner may be reasonable approaches for medical staff. We also need to think about how to encourage patients to record their daily experiences using electronic tools. Not only technical progress and support but also the establishment of an ecosystem where both patients and medical staff can feel benefit will be required. Prospective studies with deep learning models to follow up patients in the long term and evaluate outcomes will be needed. We primarily looked at patient-authored texts as targets of implementation, but our deep learning models may also be worth using medical data including patients’ subjective concerns, such as pharmaceutical care S records. As this study confirmed that our deep learning models are applicable to patients’ concern texts tracked by pharmacists, it should be possible to use them to analyze other “patient voice-like” medical text data that have not been actively investigated so far.

Limitations

First, the major limitation of this study was that we were not able to collect complete medical information of the patients. Although we designed this study to analyze patients’ concerns extracted by the deep learning models and their relationship with medical information contained in the pharmaceutical care records, some information could not be tracked (eg, missing history of medical interventions or anticancer treatment at hospitals as well as diagnosis of patients’ primary cancers). Second, there might be a data creation bias in S records for patients’ concerns by pharmacists. For example, symptoms that have little impact on intervention decisions might less likely be recorded by them. It should be also noted that the characteristics of S records may not be consistent at different community pharmacies.

Conclusions

Our deep learning models were able to screen clinically important adverse event signals that require intervention by health care professionals from patients’ concerns in pharmaceutical care records. Thus, these models have the potential to support real-time adverse event monitoring of individual patients taking anticancer treatments in an efficient manner. We also confirmed that these deep learning models constructed based on patient-authored texts could be applied to patients’ subjective information recorded by pharmacists through their daily work. Further research may help to expand the applicability of the deep learning models for implementation in society or for analysis of data on patients’ concerns accumulated in professional records at pharmacies or hospitals.

Acknowledgments

This work was supported by Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research (KAKENHI; grant 21H03170) and Japan Science and Technology Agency, Core Research for Evolutional Science and Technology (CREST; grant JPMJCR22N1), Japan. Mr Yuki Yokokawa and Ms Sakura Yokoyama at our laboratory advised SN about the structure of pharmaceutical care records. This study would not have been feasible without the high quality of pharmaceutical care records created by many individual pharmacists at Nakajima Pharmacy Group through their daily work.

Data Availability

The data sets generated and analyzed during this study are available from the corresponding author on reasonable request.

Authors' Contributions

SN and SH designed the study. SN retrieved the subjective records of patients with cancer from the data source for the application of deep learning models and organized other data for subsequent evaluations. SN ran the deep learning models with the support of SW. SN, YY, and KS checked the adverse event signals for each subjective record that was extracted as positive by the models for hand-foot syndrome or adverse events limiting patients’ daily lives and evaluated the adverse event signal symptoms, details of interventions taken by health care professionals, and types of anticancer drugs prescribed for patients based on available data from the data source. HK and SI advised on the study concept and process. MS and RT provided pharmaceutical records at their community pharmacies along with advice on how to use and interpret them. SY and EA supervised the natural language processing research as specialists. SH supervised the study overall. SN drafted and finalized the paper. All authors reviewed and approved the paper.

Conflicts of Interest

SN is an employee of Daiichi Sankyo Co, Ltd. All other authors declare no conflicts of interest.

Performance evaluation of deep learning models.

Examples of S records and sample interview transcripts.

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Abbreviations

Edited by G Eysenbach; submitted 25.12.23; peer-reviewed by CY Wang, L Guo; comments to author 24.01.24; revised version received 14.02.24; accepted 09.03.24; published 16.04.24.

©Satoshi Nishioka, Satoshi Watabe, Yuki Yanagisawa, Kyoko Sayama, Hayato Kizaki, Shungo Imai, Mitsuhiro Someya, Ryoo Taniguchi, Shuntaro Yada, Eiji Aramaki, Satoko Hori. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 16.04.2024.

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April 11, 2024

Scientists Found a Way to Supercharge Cancer-Fighting Cells

The bioengineered immune players called CAR T cells last longer and work better if pumped up with a large dose of a protein that makes them resemble stem cells

By Sara Reardon & Nature magazine

T cell attacking cancer cell.

Thom Leach/Science Photo Library/Getty Images

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 .

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.

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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.

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. 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. 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.

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.

This article is reproduced with permission and was first published on April 10, 2024 .

Opening a new front against pancreatic cancer

A new type of investigational therapeutic in development for pancreatic cancer has shown unprecedented tumor-fighting abilities in preclinical models of the disease, suggesting it has the potential to offer novel treatment options for nearly all pancreatic tumors, a comprehensive study has found.

The inhibitors in this new class of oral medications, being developed by Revolution Medicines Inc., target the oncogenic or active cancer-causing form of RAS proteins (such as KRAS, NRAS, and HRAS). These RAS "oncoproteins" drive up to a third of all human cancers. The research findings -- conducted by a consortium of academic researchers led by Columbia scientists and the scientific team at Revolution Medicines -- were published in a paper appearing today in Nature .

Currently the third leading cause of death from cancer, pancreatic cancer kills about 50,000 people annually in the United States alone. Despite decades of research, the disease continues to stymie drug developers and oncologists. What's especially frustrating is that scientists know exactly what causes most cases at the cellular level. "For over four decades, we have known that there's one particular RAS protein, called KRAS, that's mutated and drives about 95% of all pancreatic ductal adenocarcinoma cases, and we've had no direct tools to attack it for most of that time," says Kenneth Olive, PhD, associate professor of medicine at Columbia University's Vagelos College of Physicians and Surgeons and Herbert Irving Comprehensive Cancer Center, one of the study's senior authors.

When the study's co-senior author, Mallika Singh, PhD, vice president for translational research at Revolution Medicines, told Olive the company had invented a class of inhibitors that had the potential to target all RAS mutations, he was incredulous. "My immediate reaction was skepticism," says Olive. "But I was curious, and we quickly established a collaboration."

Preclinical studies soon launched in the Olive lab at Columbia, led by Urszula Wasko, a PhD student in the molecular pharmacology graduate program. Early pilot experiments with RMC-7977 were remarkably effective. "We immediately knew we were working with something entirely different," says Olive. At the same time, Olive and Revolution Medicines worked to bring together pancreatic cancer experts from other academic institutions, including the University of Pennsylvania, Dana-Farber Cancer Institute, University of North Carolina at Chapel Hill, and Memorial Sloan Kettering. "Rather than compete against one another, we established a consortium and agreed to share data in real time. That was transformative," says Olive.

Pancreatic cancer researchers have developed many different preclinical models of the disease over the years, each with its own strengths and weaknesses. Rather than pick one, the expanded team tested RMC-7977 in all of them. "By unleashing a consortium of scientists on this problem, we were able to examine active RAS inhibition in every major class of model for pancreatic cancer, and this inhibitor performed really well in all," says Olive.

The preclinical tumor model Olive's lab has long favored is widely recognized for its broad resistance to treatment. "RMC-7977 as a single agent outperformed the best combination regimen that has ever been reported in the literature in that model system," he says, adding that it's the first time he's ever seen tumors routinely get smaller in those systems. Other models the consortium tested yielded similar results.

Because RMC-7977 also inhibits wild-type RAS proteins essential to the health of many normal cells, the scientists also carefully examined normal tissues in the treated animals. This work showed that tumor cells are uniquely sensitive to the inhibitor, while the impact in normal cells was minimal.

Though the initial responses in preclinical tumor models to the inhibitor were impressive, Olive hastens to point out that the tumors were not eliminated.

"In almost every case, the tumor came back," he says. In tissue culture, the investigators identified another oncogene, called MYC, that was altered in most of the resistant tumors, then developed a combination treatment that was effective against tumor cells that had developed resistance to the active RAS inhibitor. Those results suggest a combinatorial approach that is worth exploring in patients in the future.

In a field with a long history of failed drug development efforts, the new results are cause for optimism, Olive says. "I've been working on pancreatic cancer for almost 20 years, and I've never seen preclinical results like these. I think there is a real chance this approach will help change the standard of care for pancreatic cancer patients, but only clinical trials can determine that. I'm excited that Columbia is one of many institutions participating in the clinical development of these new agents."

• Pancreatic Cancer
• Brain Tumor
• Lung Cancer
• Breast Cancer
• Ovarian Cancer
• Colon Cancer
• Prostate Cancer
• Renal cell carcinoma
• Monoclonal antibody therapy
• Breast cancer
• Prostate cancer
• Drug discovery
• Tumor suppressor gene

Story Source:

Materials provided by Columbia University Irving Medical Center . Note: Content may be edited for style and length.

Journal Reference :

• Urszula N. Wasko, Jingjing Jiang, Tanner C. Dalton, Alvaro Curiel-Garcia, A. Cole Edwards, Yingyun Wang, Bianca Lee, Margo Orlen, Sha Tian, Clint A. Stalnecker, Kristina Drizyte-Miller, Marie Menard, Julien Dilly, Stephen A. Sastra, Carmine F. Palermo, Marie C. Hasselluhn, Amanda R. Decker-Farrell, Stephanie Chang, Lingyan Jiang, Xing Wei, Yu C. Yang, Ciara Helland, Haley Courtney, Yevgeniy Gindin, Karl Muonio, Ruiping Zhao, Samantha B. Kemp, Cynthia Clendenin, Rina Sor, William P. Vostrejs, Priya S. Hibshman, Amber M. Amparo, Connor Hennessey, Matthew G. Rees, Melissa M. Ronan, Jennifer A. Roth, Jens Brodbeck, Lorenzo Tomassoni, Basil Bakir, Nicholas D. Socci, Laura E. Herring, Natalie K. Barker, Junning Wang, James M. Cleary, Brian M. Wolpin, John A. Chabot, Michael D. Kluger, Gulam A. Manji, Kenneth Y. Tsai, Miroslav Sekulic, Stephen M. Lagana, Andrea Califano, Elsa Quintana, Zhengping Wang, Jacqueline A. M. Smith, Matthew Holderfield, David Wildes, Scott W. Lowe, Michael A. Badgley, Andrew J. Aguirre, Robert H. Vonderheide, Ben Z. Stanger, Timour Baslan, Channing J. Der, Mallika Singh, Kenneth P. Olive. Tumor-selective activity of RAS-GTP inhibition in pancreatic cancer . Nature , 2024; DOI: 10.1038/s41586-024-07379-z

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Many cancer drugs remain unproven 5 years after accelerated approval, study finds

The U.S. Food and Drug Administration’s  accelerated approval program  is meant to give patients early access to promising drugs. But how often do these drugs actually improve or extend patients’ lives?

In a new study , researchers found that most cancer drugs granted accelerated approval do not demonstrate such benefits within five years.

“Five years after the initial accelerated approval , you should have a definitive answer,” said Dr. Ezekiel Emanuel, a cancer specialist and bioethicist at the University of Pennsylvania who was not involved in the research. “Thousands of people are getting those drugs. That seems a mistake if we don’t know whether they work or not.”

The program was created in 1992 to speed access to HIV drugs. Today, 85% of accelerated approvals go to cancer drugs.

It allows the FDA to grant early approval to drugs that show promising initial results for treating debilitating or fatal diseases . In exchange, drug companies are expected to do rigorous testing and produce better evidence before gaining full approval.

Patients get access to drugs earlier, but the tradeoff means some of the medications don’t pan out. It’s up to the FDA or the drugmaker to withdraw disappointing drugs, and sometimes the FDA has decided that less definitive evidence is good enough for a full approval.

The new study found that between 2013 and 2017, there were 46 cancer drugs granted accelerated approval. Of those, 63% were converted to regular approval even though only 43% demonstrated a clinical benefit in confirmatory trials.

The research was published in the Journal of the American Medical Association and discussed at the American Association for Cancer Research annual meeting in San Diego on Sunday.

It’s unclear how much cancer patients understand about drugs with accelerated approval, said study co-author Dr. Edward Cliff of Harvard Medical School.

“We raise the question: Is that uncertainty being conveyed to patients?” Cliff said.

Drugs that got accelerated approval may be the only option for patients with rare or advanced cancers , said Dr. Jennifer Litton of MD Anderson Cancer Center in Houston, who was not involved in the study.

It’s important for doctors to carefully explain the evidence, Litton said.

“It might be shrinking of tumor. It might be how long the tumor stays stable,” Litton said. “You can provide the data you have, but you shouldn’t overpromise.”

Congress recently updated the program, giving the FDA more authority and streamlining the process for withdrawing drugs when companies don’t meet their commitments.

The changes allow the agency “to withdraw approval for a drug approved under accelerated approval, when appropriate, more quickly,” FDA spokesperson Cherie Duvall-Jones wrote in an email. The FDA can now require that a confirmatory trial be underway when it grants preliminary approval, which speeds up the process of verifying whether a drug works, she said.

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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.

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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.

• lung cancer screening
• immunotherapy
• lung cancer

Many cancer drugs remain unproven 5 years after accelerated approval, a study finds

Researchers have found that most cancer drugs granted accelerated approval by the U.S. Food and Drug Administration do not deliver on their early promise

The U.S. Food and Drug Administration’s accelerated approval program is meant to give patients early access to promising drugs. But how often do these drugs actually improve or extend patients’ lives?

In a new study, researchers found that most cancer drugs granted accelerated approval do not demonstrate such benefits within five years.

“Five years after the initial accelerated approval, you should have a definitive answer,” said Dr. Ezekiel Emanuel, a cancer specialist and bioethicist at the University of Pennsylvania who was not involved in the research. “Thousands of people are getting those drugs. That seems a mistake if we don’t know whether they work or not."

The program was created in 1992 to speed access to HIV drugs. Today, 85% of accelerated approvals go to cancer drugs.

It allows the FDA to grant early approval to drugs that show promising initial results for treating debilitating or fatal diseases. In exchange, drug companies are expected to do rigorous testing and produce better evidence before gaining full approval.

Patients get access to drugs earlier, but the tradeoff means some of the medications don’t pan out. It's up to the FDA or the drugmaker to withdraw disappointing drugs, and sometimes the FDA has decided that less definitive evidence is good enough for a full approval.

The new study found that between 2013 and 2017, there were 46 cancer drugs granted accelerated approval. Of those, 63% were converted to regular approval even though only 43% demonstrated a clinical benefit in confirmatory trials.

The research was published in the Journal of the American Medical Association and discussed at the American Association for Cancer Research annual meeting in San Diego on Sunday.

It's unclear how much cancer patients understand about drugs with accelerated approval, said study co-author Dr. Edward Cliff of Harvard Medical School.

“We raise the question: Is that uncertainty being conveyed to patients?” Cliff said.

Drugs that got accelerated approval may be the only option for patients with rare or advanced cancers, said Dr. Jennifer Litton of MD Anderson Cancer Center in Houston, who was not involved in the study.

It’s important for doctors to carefully explain the evidence, Litton said.

“It might be shrinking of tumor. It might be how long the tumor stays stable,” Litton said. “You can provide the data you have, but you shouldn’t overpromise.”

Congress recently updated the program, giving the FDA more authority and streamlining the process for withdrawing drugs when companies don’t meet their commitments.

The changes allow the agency “to withdraw approval for a drug approved under accelerated approval, when appropriate, more quickly,” FDA spokesperson Cherie Duvall-Jones wrote in an email. The FDA can now require that a confirmatory trial be underway when it grants preliminary approval, which speeds up the process of verifying whether a drug works, she said.

The Associated Press Health and Science Department receives support from the Howard Hughes Medical Institute’s Science and Educational Media Group. The AP is solely responsible for all content.

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