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

Issue link: https://clinicalomics.epubxp.com/i/1093879

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AlisaRut / iStock / Getty Images www.clinicalomics.com March/April 2019 Clinical OMICs 43 easy-to-interpret picture of a patient's immune response, with improved quantitation and limit of detection beyond individual markers used currently. For example, a Health Expression Model of CD4+ cells is used to define the quan- titative presence of Helper T cells in each sample. With Health Expression Models of many different immune cell types, the immune response for a patient sample can be fully characterized. The good news is that early versions of these techniques are already being put to use in clinical research, and are proving effective. Leaning on molecular assays that seek to identify more and more subsets, researchers recently produced immu- notherapy response rates of 48 percent for patients with BRAF-mutant colorectal cancer (CRC). As Scott Kopetz, an associate professor in the Department of Gastrointestinal Medical Oncology and the Division of Cancer Medicine at The University of Texas MD Anderson Cancer Center, puts it: "The theme in all of this is that we're looking for smaller and smaller subsets." The more dimensions to the model, the better. In the case of invasive breast cancer, the Oncotype DX genomic test is helping to usher in a major shift in the treatment of early-stage-estrogen-receptive breast cancer. Studies show that utilizing the test makes treatment more cost effective, and "will spare a large proportion of patients with unnecessary over-treatment, while assuring that those patients with unfavorable biology will receive the whole gamut of available therapies to try to provide most survival benefit as possible." Predictive immune modeling: Predictive immune mod- eling leverages RNA-based health expression models of immune response to more accurately predict clinical outcomes in the context of disease and treatment. In this approach, patient cohorts with clinical outcomes (e.g., responders and non-responders to therapy) are evalu- ated via machine learning to create a Predictive Immune Model. This model captures the salient characteristics that most accurately differentiate the two cohorts. A predictive immune model for responders and non responders can be used to determine which cohort a new patient is most likely to belong and their clinical outcome. Evidence of the benefits to adopting predictive immune modeling keep piling up, yet we're still facing the same challenges we've been facing for the last decade. We need both to pay attention to the data and adopt a foundation built on health expression models for immuno-oncology to flourish, and we need to share in our collective successes. When Amazon built a robust, yet plastic IT infrastruc- ture for its e-commerce website, it didn't tuck its findings away. Instead, it opened up its technology as Amazon Web Services (AWS), revolutionizing cloud computing. As a result, the industry and technology in many fields has improved exponentially. As immuno-oncology stands on the cusp of a similar revolution, standardizing processes and technologies isn't going to be easy. We can turn precision medicine into pre- dictive medicine and vice versa, creating models and mul- tidimensional markers which truly allow us to reach the patient with the right treatment at the right timeā€”but we'll only get there through shared insight and going through the hard work of building a new platform that is better, faster and more cost effective than the last one. David Messina, Ph.D., is COO of Cofactor Genomics, based in San Francisco. "Will spare a large proportion of patients with unnecessary over-treatment, while assuring that those patients with unfavorable biology will receive the whole gamut of available therapies to try to provide most survival benefit as possible" SolStock / E+ / Getty Images

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