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Method may inform personalized cancer treatments

Physicians treating patients with early-stage lung cancer face a conundrum: choosing potentially helpful yet toxic therapies such as chemotherapy, radiation or immunotherapy to knock out the cancer and lessen the risk of it spreading to the brain, or waiting to see if lung surgery alone proves sufficient. When up to 70% of such patients do not experience brain metastasis — the spread of cancer to the brain — the question arises: Who should receive additional aggressive treatments, and who can safely wait?

A new study led by Washington University School of Medicine in St. Louis could help physicians strike the right balance between proactive intervention and cautious monitoring for patients with early-stage lung cancer. The study, published March 4 in The Journal of Pathology, uses an artificial intelligence (AI) method to study patients’ lung biopsy images and predict whether the cancer will spread to the brain.

“There are no predictive tools available to help physicians when treating patients with lung cancer,” said Richard J. Cote, MD, the Edward Mallinckrodt Professor and head of the Department of Pathology & Immunology. “We have risk predictors that tell us which population is more likely to progress to more advanced stages, but we lack the ability to predict individual patient outcomes. Our study is an indication that AI methods may be able to make meaningful predictions that are specific and sensitive enough to impact patient management.”

Lung cancer is the leading cause of cancer death in the U.S. and worldwide. Most lung cancers are characterized as non-small cell lung cancers, which are largely, but not exclusively, caused by smoking. For early-stage cancer patients, tumors are confined to the lung, and surgery is recommended as a first line of treatment. Roughly 30% of such patients progress to advanced stages, when the cancer spreads to the lymph nodes and other organs. With the brain often affected first, such patients require additional treatments, including chemotherapy, targeted drug therapy, radiation therapy and/or immunotherapy. However, physicians have no way of knowing whose cancer will progress, so they frequently treat patients with aggressive therapies out of caution.

Cote worked with Ramaswamy Govindan, MD, the Anheuser Busch Endowed Chair in Medical Oncology and associate director of the oncology division at Washington University; Mark Watson, MD, PhD, the Margaret Gladys Smith Professor in the Department of Pathology & Immunology; and Changhuei Yang, PhD, a professor of electrical engineering, bioengineering, and medical engineering at the California Institute of Technology, to determine if AI could predict whether cancer will spread to the brain.

In diagnostic testing, a pathologist examines biopsied tissues under a microscope to identify cellular abnormalities that may hint at disease. Advanced technologies – such as AI – are being explored to replicate what a pathologist sees when making diagnoses but with greater accuracy, Cote explained.

A key question: Can AI detect abnormal features that a pathologist cannot?

The researchers trained a machine-learning algorithm to predict brain metastasis using 118 lung biopsy samples from early-stage non-small cell lung cancer patients. Some of the patients developed brain cancer during a five-year monitoring period, and some did not and were in remission. Then the researchers tested the AI method on its ability to predict brain metastasis, and to identify patients who develop no metastasis, using 40 other patients’ lung biopsy samples.

The algorithm was able to predict the eventual development of brain cancer with 87% accuracy. In comparison, four pathologists who participated in the study were an average of 57.3% accurate. Importantly, the algorithm was highly accurate in predicting which patients would not develop brain metastasis.

“Our results need to be validated in a larger study, but we think there is great potential for AI to make accurate predictions and impact care decisions,” said Govindan, who treats lung cancer patients at Siteman Cancer Center, based at Barnes-Jewish Hospital and Washington University School of Medicine. “Systemic treatments such as chemotherapy, while effective in killing cancer cells, can also harm healthy cells and are not always the preferred treatment method for all early-stage cancer patients. Identification of patients who are likely to relapse in the brain may help us develop strategies to intercept cancer early in the process of metastasis. We think AI-based predictions could, one day, inform personalized treatments.”

The AI system evaluates tumors’ and healthy cells’ features, similar to how the human brain allows us to scan facial features for quick recognition of familiar faces. However, what the algorithm sees is unknown; the scientists are working to understand the molecular and cellular features that AI uses for its predictions. This knowledge could lead to the development of novel therapeutics and influence the design of imaging instruments optimized for the collection of data for AI.

“This study started as an attempt to find predictive biomarkers,” said Yang. “But we couldn’t find any. Instead, we found that AI has the potential to make predictions about cancer progression using biopsy samples that are already being collected for diagnosis. If we can get to a prediction accuracy that will allow us to use this algorithm clinically and not have to resort to expensive biomarkers, we are talking about significant ramifications in cost-effectiveness.”

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Zhou H, Watson M, Bernadt CT, Lin S, Lin CY, Ritter JH, Wein A, Mahler S, Rawal S, Govindan R, Yang C and Cote RJ. AI-guided histopathology predicts brain metastasis in lung cancer patients. The Journal of Pathology. March 4, 2024. DOI: 10.1002/path.6263

This work was supported by the National Cancer Institute of the National Institutes of Health (NIH), grant numbers 5R01CA182746 and U01CA233363; Washington University in St. Louis School of Medicine Personalized Medicine Initiative; Sensing to Intelligence, grant number 13520296; and the Heritage Research Institute for the Advancement of Medicine and Science at Caltech, grant number HMRI-15-09-01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Test can quickly identify treatable cancer mutations, guide precision therapy

A genetic test that identifies cancer mutations in solid tumors and that was developed by researchers at Washington University School of Medicine in St. Louis has been approved for reimbursement by the Centers for Medicare & Medicaid Services (CMS). The test, known as GatewaySeq, identifies mutations that can be targeted with available drugs, helping advance precision medicine treatment strategies for patients with solid tumors, such as those of the lung, colon and pancreas.

GatewaySeq is the most recent genetic assessment tool developed by Washington University to receive CMS approval. Other recent such tests are ChromoSeq, which is a whole-genome sequencing test specifically for initial diagnosis of the blood cancers acute myeloid leukemia and myelodysplastic syndrome; and MyeloSeq, which is used to monitor the effectiveness of ongoing treatment for blood and bone marrow cancers, including acute myeloid leukemia, myelodysplastic syndrome and others.

Via Washington University Pathology Services, GatewaySeq testing is available at Siteman Cancer Center, based at Barnes-Jewish Hospital and Washington University School of Medicine. With the new approval of the test, doctors across the country who provide care for Medicare patients diagnosed with solid tumors also can order the test through Washington University Pathology Services. With Medicare approval for the test, Medicare will cover the cost.

“We designed this test to return results as quickly as possible to inform initial treatment decisions for most solid tumors,” said Eric Duncavage, MD, a professor of pathology & immunology and one of the test’s developers. “The test focuses on identifying known gene mutations that can help direct treatment when a patient is first diagnosed. This panel captures all of the gene mutations an oncologist needs to form a treatment plan for a newly diagnosed patient. If there is a drug approved by the Food and Drug Administration (FDA) that targets a specific gene mutation, that gene is on our test. We want to make this as accessible as possible so that every patient gets molecular testing to guide treatment from the beginning.”

The testing will be performed by the School of Medicine’s McDonnell Genome Institute, which has extensive expertise in genome sequencing and analysis. Scientists at the Genome Institute were the first in the world to demonstrate the validity of whole-genome sequencing in identifying genetic errors responsible for the development and progression of cancer.

The test is designed to run on minimal DNA, according to Duncavage, making it easier to get a sufficiently sized tumor sample to return accurate results. By focusing on gene mutations that are treatable with FDA-approved drugs, the researchers could make the test return results faster and more cost effectively than other genomic tests, increasing accessibility. The researchers also prioritized speed in returning results so that patients whose tumors have druggable mutations can begin targeted therapy as soon as possible. Test results are available in 10 to 14 days, but Duncavage said the team plans later this year to reduce that time to seven to 10 days.

If the GatewaySeq test doesn’t find treatable mutations, doctors have the option of ordering additional genomic test panels that include more genes but may take longer to return results. Rather than identify specific drugs, other genomic test panels that include far more genes can inform care in other ways. For example, such tests can help determine how aggressive the cancer is likely to be, which can guide choices about surgery, radiation therapy and chemotherapy.

GatewaySeq can help guide treatment because of effective drugs now available for specific mutations that are common in solid tumors. For example, in certain types of lung cancer, a sizable number of tumors have mutations in a gene called EGFR, which can be treated with widely available EGFR inhibitors. A specific mutation in another gene called KRAS that is also common in lung cancer can be targeted with a drug approved by the Food and Drug Administration (FDA) in 2021. There is also a therapy available for gene fusions — where two genes fuse together and drive cancer — in the NTRK gene. The GatewaySeq test also can identify these fusions.

“NTRK fusions are not super common, but there are inhibitors that target that mutation, and they work pretty well, so it’s important to identify those patients,” Duncavage said. “In the past, because of limitations in time and cost, we might run molecular testing for only a handful of patients with solid tumors. We focused on those who had already received standard care but their cancers had returned or progressed. It was a last-ditch effort. Today, we can run this test on everyone in advance so we can identify the best treatment strategies as early as possible.”

Some of the tumors the GatewaySeq test can be used to analyze include cancers of the lung, breast, skin, brain, thyroid, gastrointestinal tract, genitourinary tract, gynecologic tract, hepatobiliary tract, bone and soft tissue, and lymphomas.

“The development of a genetic assessment tool for solid tumors represents a major step forward in our efforts to provide the most complete and up-to-date information crucial in a precision approach to treating these cancers,” said Richard J. Cote, MD, the Edward Mallinckrodt Professor and head of the Department of Pathology & Immunology, which runs the pathology service in collaboration with the clinical sequencing laboratory at the School of Medicine’s McDonnell Genome Institute.

“These CMS approvals are a testament to the leadership of our department in developing tests that will specify the most precise treatments leading to the most optimal outcomes for our patients with cancer.”

Other co-developers of GatewaySeq include Lulu Sun, MD, PhD, an assistant professor of pathology & immunology, Drew Hughes, MD, PhD, an assistant professor of pathology & immunology, and David Spencer, MD, PhD, an associate professor of medicine and of pathology & immunology.

About Washington University School of Medicine

WashU Medicine is a global leader in academic medicine, including biomedical research, patient care and educational programs with 2,900 faculty. Its National Institutes of Health (NIH) research funding portfolio is the second largest among U.S. medical schools and has grown 56% in the last seven years. Together with institutional investment, WashU Medicine commits well over $1 billion annually to basic and clinical research innovation and training. Its faculty practice is consistently within the top five in the country, with more than 1,900 faculty physicians practicing at 130 locations and who are also the medical staffs of Barnes-Jewish and St. Louis Children’s hospitals of BJC HealthCare. WashU Medicine has a storied history in MD/PhD training, recently dedicated $100 million to scholarships and curriculum renewal for its medical students, and is home to top-notch training programs in every medical subspecialty as well as physical therapy, occupational therapy, and audiology and communications sciences.

Author: Julia Evangelou Strait, Senior Medical Science Writer, 314-286-0141 | [email protected]

Media Contact: James Goodwin, Associate Director of Strategic Communication, Siteman Cancer Center

314-680-8251 | [email protected]