Translating Patient Data into Clinical Use with AI in Cancer Care

•Why/Biological Discovery : We can discover new drug targets, understand why a disease occurs, why it becomes resistant to the drugs we give it, then how we can intervene.

•For Whom/Clinical Guidance : We can personalize treatment plans for cancer patients: who should get what drug, where should that decision-making be done, and how algorithms can guide them.

•How/Equitable Implementation : We can do this so that everyone, near and far, can experience it, and not just a select few patients who happen to lock into a clinical trial or happen to be living near a big quaternary care cancer center. We can bridge the gap between data/AI/computational tools and clinical applications in the long term. Medical oncologist and computational biologist Dr. Eli Van Allen is uniquely qualified to describe the landscape of patient data repositories and translating that data into clinical use. He is an institute member at the Broad Institute of MIT and Harvard, Scientific Champion for Count Me In, an associate professor of medicine at Harvard Medical School, and chief of the Division of Population Sciences at the Dana-Farber Cancer Institute. His areas of focus are computational cancer genomics, the application of new molecular technologies to advance precision cancer medicine, and studying resistance to cancer therapeutics using biologically guided artificial intelligence. Van Allen’s current research includes integrative studies of genitourinary (prostate, bladder, kidney, and testicular) cancers to determine the appropriate treatment for individual patients. He earned a B.S. from the Symbolic Systems Program at Stanford University and his M.D. from the David Geffen School of Medicine at UCLA. He completed his residency in internal medicine at the University of California, San Francisco, and served as a fellow in medical oncology at Dana-Farber. Why/Biological Discovery: How are cancer patient data and AI guiding clinical innovations in cancer care and uncovering new drug targets?

•Case Example: AI was used to predict which tumors are lethal . To understand which genes are lethal and which are not in prostate cancer, 1000 prostate cancer patient whole genome sequences (20,000 genes) were fed into an AI model (a biologically- informed interpretable neural network) with genes mapped to molecular pathways mapped to processes. This enabled stratification, prediction, and interpretation – you can look at the answers from the model and understand why it predicted what it predicted. For Whom/Clinical Guidance: How are cancer patient data and AI personalizing treatment plans for cancer patients?

•Computer vision algorithms can analyze medical images and predict which cancer patients are most likely to benefit from immunotherapy.

•AI can match patients to clinical trials based on genetics and generate longitudinal data on disease evolution.

•An algorithm was used to figure out which patients with prostate cancer or melanoma have inherited genetic events that are actionable. How/Equitable Implementation: What changes are needed to bridge the gap between data/AI/computational tools and clinical applications in the long term?

•Gather training data that represents humanity, where any patient from anywhere can participate in research and contribute their data.

•Simplify data access rules, e.g., HIPAA regulations, to facilitate collaboration among researchers for more data sharing and overcome challenges in sharing clinical trial data.

•Develop better tools for interpreting data.

•Develop better tools for inclusion of patient-reported outcomes.

•Evaluate personalized medicine algorithms to avoid bias and ensure equity, understanding both "for whom" and "why" a drug will be effective.

•Ease access for patients to their genomic data.

•Ease access for p patients to experimental therapies. For example, implement the Promising Pathway Act, a new law that would change the clinical trial system to allow everybody to access experimental treatments through a conditional approval, within an observational clinical trial where experience is tracked to get enough proof to see whether it should graduate to full approval.

•Manage the exposure risk of a provider in one place guiding the clinical care of local patients, which could then be impacting patients around the world who are following those treatments and outcomes, creating possible problems of access, equity, and bias, and whether these models are generalized and be translatable.

•Find new drug targets that already have existing drugs or clinical trials to accelerate time to patient access.

•Inform patients of their rights under HIPAA to demand all of your raw data from every hospital you've ever been to or every commercial sequencing site – not just a little .pdf from a portal. (Then patients can share their data with whomever they want.) The information and opinions expressed on this website or platform, or during discussions and presentations (both verbal and written) are not intended as health care recommendations or medical advice by Cancer Patient Lab, its principals, presenters, participants, or representatives for any medical treatment, product, or course of action. You should always consult a doctor about your specific situation before pursuing any health care program, treatment, product or other course of action that might affect your health. Meeting Notes SUMMARY KEYWORDS patients, data, work, question, ellie, algorithms, tumor, cancer, point, called, ai, prostate cancer, information, clinical, drug, access, biology, models, snowballed, clinical trials SPEAKERS Eli Van Allen (45:31), Brad Power (3:29), Brian McCloskey (2:55), Frank Nothaft (2:20), Rick Stanton (1:40), Al Musella (1:36), Jeff Krolick (1:30), David Plunkett (0:14), Mike Donohoo (via chat), Eric Hall (via chat) OUTLINE 1.Using patient data for medical research and personalized insights. (0:00) 2.Patient cancer data and its use in clinical innovations. (2:15) 3.Personal background, cancer research, and data-driven approaches in oncology. (4:12) 4.Using AI in cancer research and treatment. (9:14) 5.Using AI for cancer diagnosis and treatment. (14:27) 6.Using AI to personalize cancer treatment. (19:35) 7.AI modeling in cancer research and liquid biopsy results. (24:29) 8.Analyzing cancer DNA in blood samples. (28:24) 9.Personalized cancer treatment and patient data. (34:08) 10. AI for personalized medicine. (39:04) 11.AI for drug discovery and regulatory barriers. (44:06) 12.Personalized cancer treatment options and the challenges of interpreting data. (52:49) SUMMARY

•Eli Van Allen, a medical oncologist and computational biologist, discusses how Count Me In can help patients with personalized clinical insights.

•He discusses patient cancer data and its use in guiding clinical innovations.

•Eli Van Allen shares his origin story, from boredom in school to discovering computers and eventually starting a nonprofit for kids whose parents have cancer.

•He is now an M.D. clinician at Dana Farber, seeing mostly prostate cancer patients, and previously worked in technology companies before pivoting to cancer medicine.

•He shares a personal story of a patient with metastatic kidney cancer who was able to go into remission through a clinical trial, highlighting the potential of data- driven cancer care.

•He emphasizes the importance of harnessing large amounts of cancer patient data to find new drug targets and understand disease resistance, with the goal of developing personalized treatment plans.

•Eli Van Allen discusses the potential of AI in oncology, particularly in prostate cancer, to predict which tumors are lethal and identify genetic lesions for drug development.

•He highlights the limitations of current AI approaches, including the lack of understanding of why certain predictions are made, and the need for more research to integrate patient data and advance equitable cancer treatment.

•He discusses using a biologically informed neural network to analyze cancer patient data and identify new drug targets.

•He also highlights the importance of understanding "for whom" the drug will be effective, in addition to "why" it will be effective.

•Eli Van Allen discusses using computer vision algorithms to analyze medical images and predict which cancer patients are most likely to benefit from immunotherapy.

•The study aims to develop ethical frameworks for using these algorithms in clinical care, while avoiding potential harm to patients.

•He discusses the potential of using AI models like ChatGPT to analyze cancer cells and generate hypotheses for new discoveries.

•He demonstrates how ChatGPT can be used to write a song in the style of Britney Spears about the top five men's singles tennis players, showing its ability to generate novel content.

•Dana Farber is using AI to match patients to clinical trials based on their genetics, with algorithms already in deployment.

•Eli Van Allen is involved in the Metastatic Prostate Cancer Project, which invites patients from anywhere to participate in research by clicking a "Count me in" button and completing a survey and consent form.

•He discusses a research project aimed at generating longitudinal data on the course of disease, with a focus on understanding tumor evolution and developing new biology that could help everyone.

•He thanks his team and acknowledges the unique opportunity to work with a talented group of individuals, including those on the Zoom call, to make a meaningful impact in the field of oncology.

•Eli Van Allen acknowledges limitations of toy model that snowballed into Nature paper.

•He questions whether AI models can accurately analyze liquid biopsy data due to limitations in prior knowledge.

•Rick Stanton is curious about a high VAF (variant allele fraction) in a tumor sample, suggesting a possible tumor suppressor mutation.

•Eli Van Allen explains that high VAF can occur if the mutation is needed to turn off a gene, and provides an example of men with de novo metastatic prostate cancer having a high shed tumor burden in their blood.

•Eli Van Allen discusses challenges in sharing data for AI research, citing regulatory issues and a lack of access to data from the Promise Program.

•Eli Van Allen advocates for simplifying data access rules to make it easier for researchers to collaborate and share data.

•Sequencing vendors struggle to provide personalized treatment recommendations due to limited data and lack of patient context.

•Eli Van Allen discusses the challenges of integrating computational tools into clinical care, including the need for better data sharing and implementation.

•He also highlights the importance of including patient-reported outcomes in cancer research, such as supportive care and other factors not captured in medical notes.

•Using AI for personalized medicine. 39:04

•Brad Power expresses interest in using AI for personalized medicine, specifically finding patients with similar profiles and understanding treatment outcomes.

•Eli Van Allen explains that within their organization, there is a project focused on returning results to patients as part of a national cance cancer institute consortia, but acknowledges the challenges of implementing personalized medicine, including uncertainty and complexity for providers.

•He discusses the challenges of developing patient similarity algorithms that can generalize across different populations and medical environments.

•He expresses concerns about bias and equity in the development and implementation of these algorithms, and the need for careful consideration and evaluation to ensure they are effective and ethical.

•Eli Van Allen highlights the potential for short-term patient gains from using a data asset like the one being developed, such as the work on TRL prediction from slides.

•He notes that there are other examples of research that could be more quickly translated to the clinic, such as digital pathology and precision oncology.

•Frank Nothaft discusses the challenges of sharing clinical trial data, including regulatory issues and concerns about patient privacy.

•Eli Van Allen highlights the complexity of HIPAA regulations and the challenges of managing identifiable information in clinical trials.

•He advocates for patients' right to access their genomic data and encourages them to demand their data through HIPAA right of access.

•His call to action has faced resistance from some companies, which have intentionally violated HIPAA or refused to provide data, highlighting the challenges in scaling up this approach.

•Al Musella discusses a project to provide experimental treatments to patients outside of clinical trials, including the Promising Pathway Act, which would give access to experimental therapies through a distributed clinical trial model.

•Eli Van Allen shares a personal experience with a patient with metastatic bladder cancer who faced difficulties in accessing early access to pembrolizumab through Merck, highlighting the need for more efficient and equitable access to treatments.

•Eli Van Allen shares a personal story of a patient who was cured of a metastatic solid tumor.

•Brian McCloskey raises a concern about the lack of concordance in treatment options provided by different service providers, highlighting the need for more predictive and targeted approaches in cancer treatment.

•Eli Van Allen expresses skepticism about the immediate practicality of some