Clinical OMICS

MAY-JUN 2017

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 34 of 47 May/June 2017 Clinical OMICs 33 New Algorithm Predicts Treatment Outcome in Multiple Myeloma Patients SkylineDX, a Netherlands- based biotech company, pre- sented a new predictive algo- rithm, TOPSPIN (Treatment Outcome Prediction using Similarity between Patients) at the BioSB 2017 conference in April. The new computa- tional tool, which the group developed in collaboration with the Erasmus Medical Center in Rotterdam and the University Medical Center Utrecht, uses genomic data to identity subgroups of cancer patients that will benefit most from a specific treatment. TOPSPIN is based on MMprofiler, an existing SkylineDX genetic pro- filing tool. "One of the big problems [in oncology] is that many of the cancer drugs do work, but generally not for everybody," said Martin van Vliet, executive vice pres- ident of bioinformatics and product development at SkylineDX. To find genetic signatures associated with treatment outcome, the company applied a machine learning approach to gene expression datasets from multiple myeloma patients collected from three Phase III clinical trials. "We've come up with a new algorithm that enables us to [identify] signatures that would, in the context of selecting the treatment for multiple myeloma patients, [provide] a prediction, where you could say that this patient will benefit from a particular treatment," van Vliet added. To demonstrate the utility of their new tool, SkylineDX tested TOPSPIN on a multiple myeloma dataset containing 910 patients, where 396 received borte- zomib (Velcade), a proteasome inhibitor anticancer drug, and 514 did not. The algorithm pinpointed a subset of patients (27 percent) who experienced a longer period of progression-free survival after receiving the drug, while in the remain- ing 73 percent, there was no benefit from bortezomib over other treatments. The company now plans to do a further validation study with another cohort of mul- tiple myeloma patients. TOPSPIN could be adapted to identify gene expression signatures for other cancer treatments, and could potentially be applied beyond the oncology space, van Vliet noted. "As long as you have a group of patients with differ- ent treatments and a large dataset where we measure gene expression levels of those patients, [then] you can then apply this algorithm to try and [find] such a signature."—Diana Kwon Lab technician working with Skyline's MMprofiler, a genetic profiling tool. Participants will also be asked to self-administer the cold pressor test, designed to determine pain tolerance. The combined dataset of survey answers and genetic information will be studied by researchers from 23andMe and Grü- nenthal for insights into how and why different people experience pain differ- ently, and how best to manage pain with more targeted treatments. NSF Grant to Bolster UCSD Bioinformatics A grant from the National Science Foun- dation (NSF) will allow the San Diego Supercomputer Center (SDSC) at UC San Diego to augment its existing com- puting cluster by providing targeted capabilities in bioinformatics, specifi- cally aimed for researchers to conduct de-multiplexing, mapping, and variant calling of a single human genome in less than one hour. " This new award illustrates SDSC's increasing role in providing high-per- formance campus cyberinfrastructure in addition to its ongoing national super- computing role," said SDSC Director Michael Norman. "Through the capabil- ities enabled by this award, we expect to see substantial gains in productivity that should be of benefit to many UC San Diego researchers using bioinformatics tools and techniques for life sciences research." The grant, part of the NSF's Campus Cyberinfrastructure program, invests in coordinated campus-level cyberin- frastructure (CI) components of data, networking, computing infrastructure, capabilities, and integrated services that lead to higher levels of performance, reliability, and predictability for science applications and distributed research projects. Learning and workforce devel- opment in CI is explicitly addressed in the program, and science-driven require- ments are the primary motivation for any proposed activity. n (continued from previous page)

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