SIGN: similarity identification in gene expression.

Motivation: High-throughput molecular profiles of human cells have been used in predictive computational approaches for stratification of healthy and malignant phenotypes and identification of their biological states. In this regard, pathway activities have been used as biological features in unsupervised and supervised learning schemes.

Results: We developed SIGN (Similarity Identification in Gene expressioN), a flexible open-source R package facilitating the use of pathway activities and their expression patterns to identify similarities between biological samples.

Spatially distinct tumor immune microenvironments stratify triple-negative breast cancers.

Understanding the tumor immune microenvironment (TIME) promises to be key for optimal cancer therapy, especially in triple-negative breast cancer (TNBC). Integrating spatial resolution of immune cells with laser capture microdissection gene expression profiles, we defined distinct TIME stratification in TNBC, with implications for current therapies including immune checkpoint blockade. TNBCs with an immunoreactive microenvironment exhibited tumoral infiltration of granzyme B+CD8+ T cells (GzmB+CD8+ T cells), a type 1 IFN signature, and elevated expression of multiple immune inhibitory molecules including indoleamine 2,3-dioxygenase (IDO) and programmed cell death ligand 1 (PD-L1), and resulted in good outcomes.

Tissue specificity of in vitro drug sensitivity.

Objectives: We sought to investigate the tissue specificity of drug sensitivities in large-scale pharmacological studies and compare these associations to those found in drug clinical indications.

Materials And Methods: We leveraged the curated cell line response data from PharmacoGx and applied an enrichment algorithm on drug sensitivity values' area under the drug dose-response curves (AUCs) with and without adjustment for general level of drug sensitivity.

Results: We observed tissue specificity in 63% of tested drugs, with 8% of total interactions deemed significant (false discovery rate <0.

Predictive approaches for drug combination discovery in cancer.

Drug combinations have been proposed as a promising therapeutic strategy to overcome drug resistance and improve efficacy of monotherapy regimens in cancer. This strategy aims at targeting multiple components of this complex disease. Despite the increasing number of drug combinations in use, many of them were empirically found in the clinic, and the molecular mechanisms underlying these drug combinations are often unclear.