FRMD6 has tumor suppressor functions in prostate cancer.

Available tools for prostate cancer (PC) prognosis are suboptimal but may be improved by better knowledge about genes driving tumor aggressiveness. Here, we identified FRMD6 (FERM domain-containing protein 6) as an aberrantly hypermethylated and significantly downregulated gene in PC. Low FRMD6 expression was associated with postoperative biochemical recurrence in two large PC patient cohorts. In overexpression and CRISPR/Cas9 knockout experiments in PC cell lines, FRMD6 inhibited viability, proliferation, cell cycle progression, colony formation, 3D spheroid growth, and tumor xenograft growth in mice. Transcriptomic, proteomic, and phospho-proteomic profiling revealed enrichment of Hippo/YAP and c-MYC signaling upon FRMD6 knockout. Connectivity Map analysis and drug repurposing experiments identified pyroxamide as a new potential therapy for FRMD6 deficient PC cells. Finally, we established orthotropic Frmd6 and Pten, or Pten only (control) knockout in the ROSA26 mouse prostate. After 12 weeks, Frmd6/Pten double knockouts presented high-grade prostatic intraepithelial neoplasia (HG-PIN) and hyperproliferation, while Pten single-knockouts developed only regular PIN lesions and displayed lower proliferation. In conclusion, FRMD6 was identified as a novel tumor suppressor gene and prognostic biomarker candidate in PC.

From Big Data to Precision Medicine.

For over a decade the term "Big data" has been used to describe the rapid increase in volume, variety and velocity of information available, not just in medical research but in almost every aspect of our lives. As scientists, we now have the capacity to rapidly generate, store and analyse data that, only a few years ago, would have taken many years to compile. However, "Big data" no longer means what it once did. The term has expanded and now refers not to just large data volume, but to our increasing ability to analyse and interpret those data. Tautologies such as "data analytics" and "data science" have emerged to describe approaches to the volume of available information as it grows ever larger. New methods dedicated to improving data collection, storage, cleaning, processing and interpretation continue to be developed, although not always by, or for, medical researchers. Exploiting new tools to extract meaning from large volume information has the potential to drive real change in clinical practice, from personalized therapy and intelligent drug design to population screening and electronic health record mining. As ever, where new technology promises "Big Advances," significant challenges remain. Here we discuss both the opportunities and challenges posed to biomedical research by our increasing ability to tackle large datasets. Important challenges include the need for standardization of data content, format, and clinical definitions, a heightened need for collaborative networks with sharing of both data and expertise and, perhaps most importantly, a need to reconsider how and when analytic methodology is taught to medical researchers. We also set "Big data" analytics in context: recent advances may appear to promise a revolution, sweeping away conventional approaches to medical science. However, their real promise lies in their synergy with, not replacement of, classical hypothesis-driven methods. The generation of novel, data-driven hypotheses based on interpretable models will always require stringent validation and experimental testing. Thus, hypothesis-generating research founded on large datasets adds to, rather than replaces, traditional hypothesis driven science. Each can benefit from the other and it is through using both that we can improve clinical practice.