Perturbation-response genes reveal signaling footprints in cancer gene expression

Abstract

Aberrant cell signaling can cause cancer and other diseases and is a focal point of drug research. A common approach is to infer signaling activity of pathways from gene expression. However, mapping gene expression to pathway components disregards the effect of post-translational modifications, and downstream signatures represent very specific experimental conditions. Here we present PROGENy, a method that overcomes both limitations by leveraging a large compendium of publicly available perturbation experiments to yield a common core of Pathway RespOnsive GENes. Unlike pathway mapping methods, PROGENy can (i) recover the effect of known driver mutations, (ii) provide or improve strong markers for drug indications, and (iii) distinguish between oncogenic and tumor suppressor pathways for patient survival. Collectively, these results show that PROGENy accurately infers pathway activity from gene expression in a wide range of conditions.

Dysregulated mesenchymal PDGFR‐β drives kidney fibrosis

“…(F) PAS staining showed more prominent mesangial expansion and intratubular protein casts indicative of proteinuria only in hypertensive Foxd1Cre::Pdgfrb +/J mice. (G) Hypertension did not induce mesangial expansion and activation in wt mice, but led to a significant increase in hypertensive PROGENy (Schubert et al, 2018), a tool to estimate pathway activities by evaluating changes in expression levels of genes affected by perturbation of pathways. PROGENy showed the highest activity in the JAK/STAT pathway, but also high activity in other central pathways known to be active in kidney diseases and fibrosis, like TNFa, NFjB, MAPK, p53, and TGFb.…”

Stratification and prediction of drug synergy based on target functional similarity

“…(i) First, we compute activities scores for 11 pathways from gene expression of cancer cell lines. It consists in multiplying the transcriptomics data by a loading matrix using PROGENy (12). (ii) We then use Macau algorithm (14) to predict multiple drugs' responses simultaneously by uncovering the common (latent) features that can benefit each individual learning task.…”

“…For this, we extend the notion of compound similarity to target similarity: the functional similarity of a pair of target proteins is defined as the correlation between the drug response upon perturbation of those proteins, as a function of the activity of a set of essential pathways. Pathway activities are computed from data-derived gene sets, that have been demonstrated to be more predictive than pathway-based gene sets (11,12). Different cancer types may be driven by different cancer pathways.…”

An Inter-Species Translation Model Implicates Integrin Signaling in Infliximab-Resistant Colonic Crohn’s Disease

“…Direct comparison of ileal CD biopsies between Infliximab responders and non-responders revealed no statistically significant differences. We next assessed pathway level dysregulation between CD biopsies and controls and between Infliximab resistant and sensitive patients using PROGENy (Pathway RespOnsive Genes) ( Figure 1C) (15). PROGENy infers differences in pathway activity based on high confidence signatures of downstream differentially regulated genes indicative of pathway activity rather than other approaches that use expression of pathway members to infer activity (16).…”