rScudo: an R package for classification of molecular profiles using rank-based signatures.

SUMMARY: The classification of biological samples by means of their respective molecular profiles is a topic of great interest for its potential diagnostic, prognostic and investigational applications. rScudo is an R package for the classification of molecular profiles based on a radically new approach consisting in the analysis of the similarity of rank-based sample-specific signatures. The validity of rScudo unconventional approach has been validated through direct comparison with current methods in the international SBV IMPROVER Diagnostic Signature Challenge. Due to its novelty, there is ample room for conceptual improvements and for exploring additional applications. The rScudo package has been specifically designed to facilitate experimenting with the rank-based signature approach, to test its application to different types of molecular profiles and to simplify direct comparison with existing methods. AVAILABILITY AND IMPLEMENTATION: The package is available as part of the Bioconductor suite at https://bioconductor.org/packages/rScudo.

Bladder cancer organoids as a functional system to model different disease stages and therapy response.

Bladder Cancer (BLCa) inter-patient heterogeneity is the primary cause of treatment failure, suggesting that patients could benefit from a more personalized treatment approach. Patient-derived organoids (PDOs) have been successfully used as a functional model for predicting drug response in different cancers. In our study, we establish PDO cultures from different BLCa stages and grades. PDOs preserve the histological and molecular heterogeneity of the parental tumors, including their multiclonal genetic landscapes, and consistently share key genetic alterations, mirroring tumor evolution in longitudinal sampling. Our drug screening pipeline is implemented using PDOs, testing standard-of-care and FDA-approved compounds for other tumors. Integrative analysis of drug response profiles with matched PDO genomic analysis is used to determine enrichment thresholds for candidate markers of therapy response and resistance. Finally, by assessing the clinical history of longitudinally sampled cases, we can determine whether the disease clonal evolution matched with drug response.

Fast mutual exclusivity algorithm nominates potential synthetic lethal gene pairs through brute force matrix product computations.

Mutual Exclusivity analysis of genomic aberrations contributes to the exploration of potential synthetic lethal (SL) relationships thus guiding the nomination of specific cancer cells vulnerabilities. When multiple classes of genomic aberrations and large cohorts of patients are interrogated, exhaustive genome-wide analyses are not computationally feasible with commonly used approaches. Here we present Fast Mutual Exclusivity (FaME), an algorithm based on matrix multiplication that employs a logarithm-based implementation of the Fisher's exact test to achieve fast computation of genome-wide mutual exclusivity tests; we show that brute force testing for mutual exclusivity of hundreds of millions of aberrations combinations can be performed in few minutes. We applied FaME to allele-specific data from whole exome experiments of 27 TCGA studies cohorts, detecting both mutual exclusivity of point mutations, as well as allele-specific copy number signals that span sets of contiguous cytobands. We next focused on a case study involving the loss of tumor suppressors and druggable genes while exploiting an integrated analysis of both public cell lines loss of function screens data and patients' transcriptomic profiles. FaME algorithm implementation as well as allele-specific analysis output are publicly available at https://github.com/demichelislab/FaME.