“Matching Patients with Treatments”

lid 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.

“Matching Patients with Treatments” (Istvan Petak, MD, PhD) [#107] 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.

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?

“Matching Patients with Treatments” (Istvan Petak, MD, PhD) [#107] 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.

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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.

“Matching Patients with Treatments” (Istvan Petak, MD, PhD) [#107] 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

, 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.

“Matching Patients with Treatments” (Istvan Petak, MD, PhD) [#107] 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.