“Translating Patient Data into Clinical Use”

“Translating Patient Data into Clinical Use” (Eli Van Allen) [#81] Brad Power January 10, 2024 “The goals of what we're trying to do are to (1) do some biological discovery…, (2) make better ways of integrating this into clinical care, and then (3) doing it in a way that everyone can access it.

” – Eli Van Allen “The patient is the model system that we want to study, instead of cell lines in a petri dish, not mice, but rather human experiences… Applying algorithms to the high dimensional data we can generate from our patients to learn as much as we can from them.” – Eli Van Allen “For me, it can be mentally jarring flipping between my clinical and research brains.

In my research brain, I'm looking at spatial transcriptomics and single cell data of like, millions, billions, or one day trillions of data points from all these high dimensional sources. My clinical brain is like, “Alright, I'm doing Lupron and docetaxel, or Lupron or Abi (abiraterone).” It's like, this or that, and what do I have to guide us on those things? It's a completely different scope.

So the goal, where the data comes in, is you build the bridge, and you also build new bridges.” - Eli Van Allen Meeting Summary In an ideal world, patients , caregivers, and physicians (and AI working on their behalf) would be able to search a database of the experiences of patients similar to them and help them in their decision-making process about treatment.

And researchers would be able to develop more complex biomarkers and processes to predict disease progression and drug response. To achieve these insights we can harness “bioinformatics” – tons of cancer patient data and technological advances, especially artificial intelligence.

●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.

“Translating Patient Data into Clinical Use” (Eli Van Allen) [#81] Population Sciences at the Dana-Farber Cancer Institute. His areas of focus are computational

rvard, Scientific Champion for Count Me In, an associate professor of medicine at Harvard Medical School, and chief of the Division of

“Translating Patient Data into Clinical Use” (Eli Van Allen) [#81] 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.

“Translating Patient Data into Clinical Use” (Eli Van Allen) [#81]

clusion 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.

“Translating Patient Data into Clinical Use” (Eli Van Allen) [#81] ●Ease access for 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.

“Translating Patient Data into Clinical Use” (Eli Van Allen) [#81] 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.

atment. (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.Using 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 ●Using patient data for medical research and personalized insights. 0:00 ○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. ●Personal background, cancer research, and data-driven approaches in oncology. 4:12 ○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.

“Translating Patient Data into Clinical Use” (Eli Van Allen) [#81] ○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. ●Using AI in cancer research and treatment.

9:14 ○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. ●Using AI for cancer diagnosis and treatment. 14:27 ○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.