Using Artificial Intelligence to Match Combination Targeted Therapies in Oncology
A Q&A with Razelle Kurzrock, MD, Director of the Center for Personalized Cancer Therapy and the Rare Tumor Clinic at U.C. San Diego, and Co-Founder and Board Member of CureMatch, Inc. Email: email@example.com
Q: The new understanding of many cancers brought about by molecular testing has led to a whole new field: precision oncology, which emphasizes targeted and immunotherapy. While promising, and sometimes spectacularly successful, targeted monotherapy has limitations. The evolution of targeted and immunotherapy by combinations of drugs offers new scientific options for cancer patients. But there are so many new molecular findings, so many new investigational drugs or drugs newly approved by the U.S. Food and Drug Administration (FDA), and so few appropriate patients, that matching patients to best drug combinations can be a mathematical nightmare. What have you and your company CureMatch to offer to help with this dilemma?
A: Thank you for this excellent question. As you correctly noted, tumors, even those that share the same histologic origin, are highly heterogenous and unique at the molecular level. Therefore, the existing paradigm of treating all cancer patients based on their tumor’s tissue of origin, even by adding minimal biomarker stratification criteria, has proven largely inadequate. The advent of molecular diagnostics allows for improved patient stratification during therapy selection; however, most patients are still treated with monotherapies, which ultimately perform poorly.
Early data show that individualized matched combination therapies targeting most of a patient’s druggable aberrations are associated with improved outcome. However, selecting the “right” combination in routine oncology practice could be challenging. The average oncologist is pressed for time, seeing approximately 350 new patients annually and up to 100 patients per week. To complicate matters, even if an oncologist wanted to rationally combine only the approximately 300 FDA-approved “oncology-specific” drugs, there would be estimated 45,000 possible two-drug and approximately 4.5M three-drug combinations. Even molecular tumor boards found in some academic centers rely largely upon expert knowledge and experience to tailor personalized combination treatment strategies for hundreds of patients with unique molecular profiles. Clearly, the drug selection process is rapidly outpacing human capabilities, and software tools are needed to help with data analytics.
Bionov™, a rule-based artificial intelligence platform developed by CureMatch, utilizes the latest data available on targeted, immuno-oncology, hormone therapy, and cytotoxic agents. Bionov™ employs an algorithm that matches patients with monotherapy and multidrug regimens based on their available tumor “omic” profile. Drug regimens provided in the Bionov™ report are ranked using a predictive “Bionov™ score” that reflects the degree to which a given regimen matches the patient’s molecular profile.
To generate our database, we curated all FDA-approved drugs relevant to oncology for their biological impact on their targets. Recently, we added oncology drugs that have been approved by the European Medicines Agency (EMA) to our database, and FDA/EMA-approved drugs are kept up-to-date based on their respective labeling changes. Further, we researched and curated preclinical and clinical literature pertaining to the efficacy of these drugs, including drug toxicities and contraindications. The CureMatch scientific team conducts literature reviews on a routine basis to ensure drug efficacy is kept current.
Our methodology has been validated in several studies, and I will highlight two of them here. First, in a retrospective meta-analysis of 70 exceptional responders for whom molecular profiling data was available, Bionov™ correctly ranked the response to all treatment regimens (including failed regimens) with 84% sensitivity and 77% specificity. This analysis demonstrates how the Bionov™ algorithm is able to discriminate, solely on the basis of the molecular fingerprints of a patient’s cancer, treatment regimens that favor a positive outcome from those that are more likely to be associated with an unsuccessful response. The second study I want to highlight is a prospective clinical trial: our group found that a higher matching score (similar to the Bionov™ score) was an independent predictor of increased disease control rate, prolonged progression-free (PFS), and overall survival rates. Furthermore, PFS was significantly improved in 75% of patients treated with combination therapies based on high matching scores.
We live in the “big data” generation. As the patient progresses through their treatment journey, massive amounts of actionable molecular information are generated, and clinical oncologists may not be entirely prepared to effectively utilize it. We believe that predictive analytics models—such as Bionov™—can provide an alternative framework for modern clinical practice, collaborating with and empowering oncologists in their decision-making process.
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