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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16 Clinical OMICs March/April 2019 www.clinicalomics.com When Foundation Medicine and Flatiron Health (both owned by Roche) set out to connect their genomic and clini- cal data, they faced yet a different conundrum—how to inte- grate privacy-protected genomic data held by Foundation Medicine with privacy-protected clinical information held by Flatiron Health. First, they needed to make the connec- tion between Jean Doe with a particular date of birth in one dataset and Jean Doe in the other dataset. For this, they used a third-party entity, which "tokenized" the identity infor- mation into a scrambled 32-character key that cannot be reverse engineered. "If both Flatiron and Foundation Medi- cine have seen the same case, both will have the same key," said Guarav Singal, chief data officer. While companies are making progress toward building deep datasets, there remains the problem of structuring, standardizing, and democratizing the data—as well as its analysis—so that healthcare providers can benefit from the wisdom they contain. "There's a big data normal- ization problem," Cour- noyer said. "Big data sits in silos across any hospital sys- tem." Even when healthcare providers own and have access to both genomic data and patient treatment and outcomes data, they aren't integrating basic information such as stage of cancer or prior treatments into the decision support workflow along with the genetic report. "These are things that can fundamentally change the interpretation and the targeted therapy choice for that patient," Cournoyer said. Data silos are not the only problem. Too much data is unstructured, residing in pathology reports, lab reports, and physician's text notes. And there is a lack of standard- ization across EHRs. To help address that problem, ASCO has launched a project called mCODE (minimal Common Oncology Data Elements) to identify the minimal standard data that oncology EHRs should include and that every EHR vendor should support. The goal is to create a com- mon vocabulary so at least that part of every EHR will be interoperable with other EHRs. An additional challenge going forward will be the growth of different kinds of genetic and molecular testing of tumors and cancer patients. For example, today a patient might have one genetic test to identify late-stage treatment options. In the future, more patients will undergo early screening and early detection testing, and cancer patients will have not just one genetic test, but potentially multiple tests over time to look for tumor changes, residual disease, and response rates. "So the datasets are going to grow exponentially," said Sean Scott, head of business development for Qiagen (which now owns N-of-One). Moreover, the types of assays and samples will likely expand as well, including test types with little standardization, Scott added. Learning from Real-World Data: Demonstrated Value Remarkably, despite the data challenges, several compa- nies are already demonstrating that analyzing integrated genomic and clinical datasets can yield valuable insights. CancerLinQ has created an app (currently under revision) called "Patients Like This." Clinicians often face a patient who has a complicated treatment history, or for whom the guidelines don't quite apply. Patients Like This allows clini- cians to filter the EHR by age, cancer stage, comorbidities, or prior treatment to find patients similar to the one in front of them. They can then look at how those similar patients have been treated and what their outcomes have been. Currently, it difficult to do this well, but "clinicians express a desire to look at the data in this way," Rubinstein said. Project GENIE conducted two breast cancer natural his- tory studies using GENIE's unified genomic database. The projects identified cohorts of metastatic breast cancer patients who had particularly rare variants, then collected treatment history and outcomes data from their medi- cal records. The goal: To determine whether patients with these variants do better or worse on certain treatments than someone who doesn't have them. "We can now answer that question," said Sweeney. Studies such as this can provide a synthetic control arm for single-arm clinical trials, which are becoming commonplace for the treatment of rare cancers. "If you have 50 patients on the single arm trial, it is neces- sary to know if the treatment actually helped by comparison to a natural history cohort," Sweeney said. "That's one way of using real-world clinical data." Foundation Medicine's success story goes further. They set out to see if the clinical-genomic database they had (continued from previous page) Shawn Sweeney, director, AACR Project GENIE "There's a big data normalization problem. Big data sits in silos across any hospital system." —Chris Cournoyer, former CEO, N-of-One

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