Mark A. Rubin

The paper identifies Aurora kinase A as a synthetic lethal target in FANCA-deficient cancers and shows AURKA inhibition induces chromosomal instability and G2/M arrest in these cells.

The paper uses a forward genetic screen in a mouse model to identify Sirtuin1 as a driver of neuroendocrine prostate cancer, showing its role in promoting NEPC and potential as a therapeutic target.

This study identifies a SWI/SNF-targeting therapy effective against a double-negative prostate cancer subtype, showing it reduces viability in both AR-dependent and WNT-dependent models.

This study shows that targeting the minor spliceosome, specifically U6atac snRNA, disrupts DNA repair and overcomes therapy resistance in prostate and breast cancers.

The paper shows that loss of PI5P4K slows cancer progression in prostate basal cells by disrupting lipid metabolism in a Pten mutant model.

PipeIT2 is a somatic variant calling workflow for Ion Torrent sequencing data that identifies mutations without germline control, achieving >95% recall for variants with >10% allele fraction.

This study uses PRO-cap to identify drug-responsive enhancers in prostate cancer, finding many not detected by ChIP-seq or ATAC-seq.

The paper introduces the Cancer LncRNA Census (CLC), a curated resource of lncRNAs with causative roles in cancer, and shows their genomic and evolutionary features.

ActivePathways integrates multivariate omics data to identify enriched pathways, revealing cancer driver genes and prognostic pathways in breast cancer.

This study identifies molecular subgroups of BPH and suggests mTOR inhibitors as a potential therapy for specific subtypes.

GRAM predicts the molecular effect of non-coding variants in a cell-type specific manner using SELEX and expression profiles, achieving high performance on MPRA and luciferase assays.

The paper analyzes non-coding mutations across 2,583 cancer genomes to identify genes and pathways affected, finding clusters of interacting proteins and insights into cancer mechanisms.

The paper identifies and characterizes coding and non-coding driver mutations across 2,500 cancer genomes, revealing new candidates and questioning previous findings.

This study reports that 17% of advanced cancer patients have clinically relevant germline variants, with higher rates of cancer-associated variants compared to control cohorts.

This paper develops a deep learning model that predicts SPOP mutation status in prostate cancer from H&E-stained whole slide images, achieving high accuracy across different slide types and demonstrating content-based image retrieval for genetic-histologic relationships.