Cellular State Transformations Using Deep Learning for Precision Medicine Applications.

We introduce the Transcriptome State Perturbation Generator (TSPG) as a novel deep-learning method to identify changes in genomic expression that occur between tissue states using generative adversarial networks. TSPG learns the transcriptome perturbations from RNA-sequencing data required to shift from a source to a target class. We apply TSPG as an effective method of detecting biologically relevant alternate expression patterns between normal and tumor human tissue samples.

Recommendations for the Management of Rare Kidney Cancers.

Context: The European Association of Urology Renal Cell Carcinoma Guideline Panel recently conducted a systematic review of treatment options for patients with advanced non-clear-cell renal cell carcinomas (RCCs), which showed a substantial lack of evidence for management recommendations.

Objective: To improve the outcomes of patients with rare kidney cancers (RKCs), we performed a subsequent unstructured review to determine current treatment strategies and druggable pathways, involving key stakeholders with a global perspective to generate recommendations.

Evidence Acquisition: Based on the systematic review, literature was queried in Pubmed, Medline, and abstracts from proceedings of European Society for Medical Oncology and American Society of Clinical Oncology, in addition to consulting key opinion leaders and stakeholders.