Clinical OMICS

MAR-APR 2019

Healthcare magazine for research scientists, labs, pathologists, hospitals, cancer centers, physicians and biopharma companies providing news articles, expert interviews and videos about molecular diagnostics in precision medicine

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Page 43 of 51

42 Clinical OMICs March/April 2019 ical trials become more complex, require more patients. According to some studies, drug candidates experience higher failure rates in drugs tested on human subjects. And it's the patient who pays the price. The cost of new chimeric antigen receptor (CAR) T-cell therapies, for exam- ple, approach $500,000 for a one-time treatment. While this is a new therapy and cost will surely eventually go down, many studies peg the average cost of immunotherapy treat- ments for cancer patients at $100,000 per patient, per year— all for treatments that may or may not work. The cost of developing market-approved drugs, espe- cially in a growing field like immuno-oncology, is not only prohibitive, it's unsustainable. The Problem with Single-Analyte Markers in Immuno-Oncology Make no mistake: Discovering how gene mutations affect the potential development of cancers is a major break- through. But when you consider the wealth of data at our fingertips, we may as well be looking at a single tree in a massive forest. The fact is response rates to immunotherapy for cancer patients are still relatively low. The most successful rates seen with immune-checkpoint blockade therapies average around 30 percent. Put another way, even at its most suc- cessful, 7 out of 10 cancer patients see no response from their prescribed immunotherapy. This is partly due to the fact that, while we've learned a lot, we're still typically only looking at single markers when developing therapy models. Essentially, we have a "yes" or "no" proposition—either a mutation is there or not—and we're throwing Hail Marys based on those isolated markers. We're far more precise than we used to be, but we're also far from being as precise as we can be. Our approach to immunotherapy should mimic what we understand about biology itself. Disease is not the result of one single, large change in a biological system, but rather the culmination of multiple small changes. Recognizing this will help us reach the next level of precision in med- icine. But to get there, we need to start developing therapies and studying disease with a bigger, wider lens, where we can consider every tree in the forest in a meaningful way. What's needed is a new predictive system that will reduce costs for precision medicine and allow newer, better stan- dards of treatment to be widely adopted. We need to know which patients will respond sooner and faster, hone in on populations that will benefit the most from targeted ther- apies, be able to use routine clinical samples and deliver easy-to-interpret reports. What's needed is predictive immune modeling. How predictive immune modeling works: Predictive immune modeling helps to answer a critical question: Which patients will or won't respond to immunotherapy? Using molecular diagnostics, we can start answering this question before the patient ever receives treatment. This can bring us to a new level of targeted, precision medicine: but it requires key changes to how therapies are developed. RNA over DNA: Complex biological systems can't be understood solely through DNA. RNA provides a mean- ingful, dynamic snapshot that better represents the patient at that moment in time. Using RNA, we can build multi- dimensional models that take into account not just the presence or absence of a single marker, but the overall pro- gression of the disease, patient lifestyle, and other dynamic factors that allow us to better predict a patient's response to precision treatments. Develop health expression models: Health expression mod- els represent multiple facets of biology—looking at both the presence or absence of RNA, as well as the dynamic expres- sion levels that can be influenced by the state of the disease, environmental effects, therapy, and much more. Rather than focusing on a single analyte in isolation to define a cell type, patient, or therapy response, health expression models are a multidimensional representation. In the case of immu- no-oncology, immune cells known to be important to and predictive of drug responses are modeled to generate an David Messina, Ph.D. (continued from previous page) Westend61 / Getty Images

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