Cancer Treatment Matching: Finding the Best Therapy for Your Tumor
Featuring: Istvan Petak, MD, PhD
In short
Precision oncology researcher Istvan Petak, MD, PhD, explains why matching a cancer patient to the right targeted therapy is so hard — and how computational tools that analyze a tumor's full molecular profile may improve those decisions. He walks through the real barriers patients face, from incomplete biomarker testing to inconsistent recommendations from different tumor boards, and describes how AI-based systems are being developed and validated to help oncologists choose more effectively.
- •Ask your oncologist about comprehensive molecular testing (DNA sequencing, RNA sequencing, liquid biopsy, and tissue staining) — more complete test data gives any decision-support tool more to work with.
- •If your tumor board or oncologist recommends a targeted therapy, it's fair to ask how that recommendation accounts for all your genetic alterations together, not just a single biomarker.
- •Know that two different tumor boards reviewing the same molecular results may agree only 44–63% of the time, so getting a second opinion on complex molecular findings is reasonable.
- •Off-label targeted therapies — approved drugs used for a different cancer type based on your molecular profile — may be an option worth discussing, though insurance coverage can be a hurdle your care team may need to help navigate.
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1Brad Power July 17, 2024 “The software becomes the new device. We need to implement the real concept of precision oncology and solve the paradigm that we want to provide personalized therapy, and we want to select the targeted therapy based on the molecular profile of the patient. But we want to do this in a way that is evidence-based.
” – Istvan Petak, MD, PhD “I am most excited about how to shorten the 14 million years we theoretically would need to do all the clinical trials to match the right therapy for every cancer patient. In a review paper in 2019, the authors envisioned that by 2039 we will have clinical trials that do not compare drugs, but AI-based treatment assignment algorithms.
This is how we can shift the paradigm in medicine and test the personalized treatment selection methods, instead of individual therapies. We want to make sure that we don't have to wait until 2039.
Meeting Summary
Advanced cancer patients want access to therapies that are uniquely selected to them, based on their medical history and genomic and molecular profile. For many cancers, an array of molecularly-targeted agents are approved and available to patients. The complexity of cancer, with its numerous types and genetic mutations, makes the decision on treatments difficult.
Each cancer is caused by a unique combination of over six million potential mutations of 700+ cancer genes. The targeted therapies that currently exist are focused on only some of the most frequent cancer genes... and often fail to work due to the complex, unique molecular background of each tumor.
Comprehensive molecular testing is key in treatment decisions and interpretation of complex molecular profiles is essential, but can be challenging and subjective. Istvan Petak, MD, PhD, is uniquely qualified to discuss the challenges of matching a patient's profile with their best treatment plan. Dr. Petak is a biomedical scientist with over 25 years of experience in precision oncology.
He is an adjunct professor of molecular pharmacology at the University of Illinois at Chicago (UIC), author of over 150 scientific publications focusing on precision medicine, and founder of the medical technology companies Oncompass Medicine and Genomate Health .
He pioneered the molecular pharmacology of programmed cell death in 1998, predictive molecular diagnostics of lung cancer in 2003, and next generation sequencing in molecular profiling of solid tumors in 2008. He led the development of a novel computational method that successfully implemented cognitive computing in precision oncology in 2021.
Genomate® helps physicians find the right targeted therapy for every cancer patient based on the individual molecular profile of their tumor.
For example, in a recently published study they demonstrated that their solution, an algorithmic computational reasoning model that ranks associated targeted therapies based on the totality of individual tumor genomic data, and using thousands of evidence rather than matching one drug to one biomarker with one evidence, was predictive of relative benefit of the agents.
They also collected real-world clinical outcome data 2from lung cancer patients who received decision support where digital drug assignment was integrated to aid a molecular tumor board and found higher effectiveness of administered therapies supported by the computational model. These results have been published in peer- reviewed journals and presented at professional meetings.
What are the potential benefits of using your genomic profile and software tools to guide your treatment decisions? If researchers can identify the molecular mechanism and a target of malignant transformations that create cancer cells, they can often develop an effective therapy. Then you need a diagnostic assay that will identify if you have the target biomarker which will predict if you will respond to that therapy.
You get therapies that have the highest probability that they should work for you. What are the challenges that you may face when you want to implement precision oncology in clinical practice that can be addressed with treatment guidance software?
•There has been slow progress in cancer research due to the large number of mutations that need to be validated if they are really driver mutations.
•Only a fraction of patients have a biomarker that can be derived from a companion diagnostic that is actionable.
•If you have an actionable biomarker, it’s not sure that you will respond to a treatment which targets that biomarker because one of your co-occurring other mutations can alter your response to a therapy.
•If you have multiple tests which identify multiple gene alterations, each can be linked to a specific possible therapy, but there is no way to figure out which one to choose for your specific combination of alterations. If you have multiple options you can choose from, you may not know which one to choose.
•Personalized cancer therapy is often supported by only low level statistical evidence and is associated with low level reproducibility and scalability.
•Molecular tumor boards are one possible solution, but if you send the same molecular diagnostic test results to two boards, concordance on treatment recommendations is only 44-63% according to published investigations.
•Treatment options that come out as being best, but are “off-label” (not approved for this indication), are hard to get reimbursed.
•Your test data inputs may be old and not reflect the current state of your disease. What do you need as inputs to treatment guidance software? All test results can be used: DNA sequencing, RNA sequencing, liquid biopsies, immunohistochemistry (staining of tissue slides), FISH (Fluorescence in Situ Hybridization, a test that uses fluorescent molecules to visualize and map the genetic material in a cell's chromosomes) What’s next in the development of treatment guidance software? 3
•More and better algorithmic companion diagnostics identify molecularly-targeted therapies personalized to you, including identification of new indications for therapies (“off label uses”) that will become on-label use of targeted therapies based on your unique molecular profile if FDA approves these new indications.
•Payers will hopefully reimburse algorithmic tests using new codes
•Payers will hopefully automatically approve therapies with a threshold level of certainty and evidence showing a very high correlation to outcomes (even off-label)
•The software can also help accelerate clinical development of novel targeted therapies How can you learn more?
•See the notes, transcript, and recording from our discussion with Dr. Michael Castro on using AI for treatment selection based on molecular pathways.
•Contact Istvan Petak at istvan.petak@genomate.health. 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. 4Meeting Notes KEYWORDS patient, therapy, genetic alterations, test, targeted therapy, molecular, gene, mutations, based, cancer, question, oncologists, model, today, treatment options, target, biomarker, information, alterations, companion diagnostics SPEAKERS Istvan Petak (86%), Richard Anders (5%), Roger Royse (4%), Brad Power (3%), Mark Stoner (1%), Adrien Sipos (1%) CHAT CONTRIBUTORS Stratis Telloglou, Alina Luchian, Richard Anders, Brad Power, Saed Sayad, Rick Davis, Ari Akerstein SUMMARY For personalized cancer treatment, cancer patients, caregivers, and physicians must identify genetic causes and match patients with effective treatments. AI has the potential to revolutionize cancer treatment by providing personalized therapies based on a patient's molecular profile. Regulators and researchers need to review AI-based diagnosis and treatment assignment algorithms, as well as the importance of explainability and transparency in AI-driven diagnostics. OUTLINE Introductions and background in using software and technology to match cancer patients with their best treatment options and identifying the genetic causes of cancer and developing targeted therapies.
•Dr. Petak started in pediatric oncology in 1995.
•He was studying why a 5% subset of pediatric leukema patients that harbored the BCR- ABL translocation that were resistant to chemotherapy, that led him to dedicate his life to find the right targeted therapy, that targets the genetic cause, for every cancer patients based on the individual mulecular profile of their tumor.
•Between 1998-2003 he did research on potential molecular targets that regulate cell growth and cell death at St. Jude.
•In 2003, he had a leading role in the first documented successful targeted therapy of a metastatic lung cancer patient based on the presence of a specific mutation published in Journal of Clinical Oncology in 2005. This patint survived more then five years and died of an unrelated cause.
•In 2010, he was the invited author of Nature Reviews Drug Discovery where he predicted that all targeted therapies would need companion diagnostic tests to identify genetic alterations in cancer patients. 5
•In 2020, a Nature paper reported that whole genome sequencing could identify genetic causes of cancer in 95% of cancer patients, marking the beginning of the post-cancer genomic era.
•In 2021, Dr. Petak and his team published in Nature Partner Journal Precision Oncology the first successful clinical validation of their computational method that enables oncologists to make treatment decisions based on the totality of genetic alterations. Challenges in personalized cancer treatment.
•There has been slow progress in cancer research due to the limited number of validated driver mutations and the limited actionability of genetic alterations.
•Personalized cancer therapy is hard, due to the low level of evidence and scalability.
•Molecular tumor boards are one possible solution, but they can to scale to address the need of all cancer patients that are treated at community oncologists and there is low concordance between opinions. Developing a computational reasoning system for identifying targeted therapies for cancer patients based on their molecular profile.
•Researchers analyzed 10,000 cancer cases to develop a mathematical model linking driver genes to targeted therapies.
•Developed an algorithm to predict targeted therapy effectiveness based on individual patient molecular profile.
•The system automates and augments evidence and guideline-based decision making process. Instead of making a decision based on one biomarker and one evidence, the system uses on average 1000 published evidence to generate treatment recommendations based on the totality of alterations. Using computational models to improve personalized cancer treatment decisions based on molecular profiling and AI.
•Treatment decisions supported with Genomate’s computational clinical decision system had 4x higher response rate and longer progression-free survival based on the clinical trial data of SHIVA01. But more research is ongoing to provide further evidence on the clinical performance of the method to become an FDA-approved diagnostic device in the future.
•There are advancements in personalized cancer treatment using existing diagnostic tests also for immunotherapies.
•AI-based treatment assignment algorithms will be in clinical trials before 2039.
•Oncologists and oncology practices will use AI solutions to personalize cancer treatment.
•Genomate’s AI-powered platform for personalized cancer treatment has used anonymized data to validate their method and increase trust among doctors.
•Doctors will accept the use of algorithms to make better decisions, but they still need to work together with AI developers to create better solutions. Using machine learning to predict cancer treatment response, with focus on database and algorithm updates, validation, and effectiveness in different tumor types.
•Richard Anders questions the feasibility of analyzing quintillions of datasets with complex drug interactions and toxicity. 6
•Dr. Petak in his response tells that researchers update AI model's database and algorithm after testing on validation data sets to improve prediction of therapy response. Real-world evidence and existing clinical trial data can also validate novel software algorithms. Timing of molecular profiling in cancer treatment.
•Mark Stoner connects with NASA and Institute Curie, discusses microgravity research opportunities.
•Timing of biopsy vs. precision of therapy in cancer treatment.
•Dr. Petak explains that while doing the tests from new biopsy is biologically advantageous he is concerned about delay in starting the next line of therapy. He advises to test as soon as possible and use the information to plan the sequence of therapies.
•Higher clinical utilisation of molecular diagnostics aided by computational tools will increase oncologists' willingness to order tests before starting treatments. 7TRANSCRIPT Brad Power This is the Cancer Patient Lab. We're honored to have Dr. Istvan Petak broadcasting from Budapest. I always like it when we're International in our reach. Some quick housekeeping. This is not medical advice. This is for information purposes only, so that you can take information you get to your medical team. The Cancer Patient Lab is a patient-led, volunteer-led nonprofit. We would appreciate any donations you might make, which you can do on our website. Dr. Petak and I had a nice conversation when he was passing through Cambridge on his way to ASCO. I learned of his work in using software and technology to help match patients with their best treatment options, which is of course very central to what we are interested in at the Cancer Patient Lab. I'm sure he'll do a lot more to introduce himself and his topic. Roger will be moderating. Istvan Petak 2:02 Thank you very much for having me and inviting me, Brad, and all of you. I am very excited about sharing what you have been working on and trying to achieve. 8Just a few words about myself and the journey. I graduated as an MD 29 years ago in 1995. As a young doctor, I decided to go into pediatric oncology. I was working with pediatric patients, I treated patients with leukemia, In 1995 this was before the human genome era, and it was before the targeted therapy era. It was basically identifying the use of chemotherapy. At that time, we noticed that a fraction of the 5% of acute leukemia patients had a worse prognosis than the average, and we found out that they carried the BCR-ABL translocation , the “Philadelphia chromosome”, that was really the first of the genetic events which link to cancer. We already knew in 1995 that that gene is actually the cause of cancer. This was very exciting to start to understand the molecular mechanism of the disease. We knew that if we took this fusion gene to transgenic mice (genetically modified mice that have had DNA from another source added to their genome), the mice developed leukemia. We could knock it out and silence this gene and actually stop the cancer cells from growing.
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