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 16 of 51 March/April 2019 Clinical OMICs 15 a "Patients Like This" app (CancerLinQ); the use of real- world data to generate a natural history study of breast can- cer patients (Project GENIE); the recapitulation of known findings from real-world data (Foundation Medicine); and the use of machine learning (ML) on an aggregated dataset of colon cancer patients (Jintel and Intermountain Health). Ultimately, all of the companies in this space share the dream of a learning healthcare system built on big data that is constantly growing and incorporating new information. In cancer, each patient's journey would be recorded into the health record in such a way that it would help inform preci- sion treatments for future patients. "That's the full learning system and we're in the early, early stages of that," said Chris Cournoyer, strategic advisor and former CEO of N-of-One. Building Deep Datasets In the field of precision-guided genomics medicine, useful data is found in two big pots: (1) genomic and other molec- ular datasets, and (2) clinical "real world" datasets derived from electronic health records (EHRs). The companies and organizations working in this space face a host of challenges. For example, CancerLinQ, a not-for-profit subsidiary of the American Society of Clinical Oncology (ASCO), is good at extracting clinical data from a variety of EHRs but strug- gles to obtain computable genomics data. "Genomic testing labs don't have to share their data in electronic format," said Wendy Rubinstein, M.D., Ph.D., division director, clinical data management and curation at CancerLinQ. "Largely, they view the electronic format as proprietary." This means CancerLinQ must go through several more steps to make the genomic data computable, which can sometimes com- promise quality. AACR's Project GENIE faces the opposite challenge. It recently released a publicly available genomic dataset con- sisting of next generation sequencing data for about 60,000 tumors from patients at 19 different institutions. Many of the institutions generate their own genomic data (in com- putable form) and for institutions that send their tumor samples to outside labs (such as Foundation Medicine), Project GENIE has requested and received the genomic data electronically. When it comes to clinical data, however, Project GENIE has a much smaller amount, and what they have has been primarily retrieved through manual methods. They dig into EHRs of individual patients to gather the clinical data needed to answer specific questions, link the data to the appropriate genomic profile, and de-identify the data prior to use. For specific cohorts of interest, for example, they collected a very detailed dataset—meaning it included any fields that could be remotely relevant to the questions at hand. Going forward, they've found a way to define a slightly less detailed and more pragmatic dataset to collect. "You have to wrestle down what data you need to answer the questions you want to ask," Sweeney said. The prag- matic dataset will include each patient's status at the time of genetic testing, treatment history and outcomes, pan-cancer data, and certain unique values for specific cancers. "This will provide a fairly complete picture of each patient's jour- ney with cancer going forward," Sweeney said. (continued on next page) Tetiana Lazunova / iStock / Getty Images

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