Deep multiomics profiling of brain tumors identifies signaling networks downstream of cancer driver genes

Abstract

High throughput omics approaches provide an unprecedented opportunity for dissecting molecular mechanisms in cancer biology. Here we present deep profiling of whole proteome, phosphoproteome and transcriptome in two high-grade glioma (HGG) mouse models driven by mutated RTK oncogenes, PDGFRA and NTRK1 , analyzing 13,860 proteins and 30,431 phosphosites by mass spectrometry. Systems biology approaches identify numerous master regulators, including 41 kinases and 23 transcription factors. Pathway activity computation and mouse survival indicate the NTRK1 mutation induces a higher activation of AKT downstream targets including MYC and JUN, drives a positive feedback loop to up-regulate multiple other RTKs, and confers higher oncogenic potency than the PDGFRA mutation. A mini-gRNA library CRISPR-Cas9 validation screening shows 56% of tested master regulators are important for the viability of NTRK -driven HGG cells, including TFs (Myc and Jun) and metabolic kinases (AMPK a 1 and AMPK a 2), confirming the validity of the multiomics integrative approaches, and providing novel tumor vulnerabilities.

Genetic characterization of stem cell-like cancer cell in the peri-tumoral regions and proliferative lymphocyte in the peripheral blood of patients with glioblastoma

“…We found that JUN played role in four CNV patterns, and MYC in CNV patterns 2, 3 and 4, while TP53 and E2F1 were specific to CNV pattern 1, SMAD2 specific to CNV patterns 3, and FOS specific to CNV pattern 4 (Figure 3B, C). Similarly, Hong Wang and colleagues also identified JUN and MYC as centered TFs in high-grade glioma (HGG) mouse models by combinatory profiling of proteome, phosphoproteome, and transcriptome 29 , which proved the effectiveness of our analysis strategy. On the other hand, we identified differentially expressed TFs and TCFs with fold changes larger than two compared to normal cells.…”

“…In particular, the core TFs-TCFs gene set of CNV pattern 4 was negatively correlated with both OS and PFS of GBM patients. Despite prominent heterogeneity, we figured out some common TFs among four CNV patterns, such as JUN and MYC, which were also identified as central TFs by recently published study using multiomics to construct kinase-TF centered network in HGG 29 .…”

Argonaut: A Web Platform for Collaborative Multi-omic Data Visualization and Exploration

“…PCA is commonly used to segregate groups of samples or explore origins of variance in a dataset. 41 , 42 Veling et al used PCA (2B) to demonstrate clear separation between the respiration-deficient and respiration-competent deletion strains. Biomolecule abundance correlation analysis is often used in gene deletion experiments to (2C) demonstrate a functional relationship between two gene deletion strains.…”

Advances in the Knowledge of the Molecular Biology of Glioblastoma and Its Impact in Patient Diagnosis, Stratification, and Treatment

“…Recently, Wang et al identified signaling networks downstream of cancer driver genes in brain tumors by multi‐omics profiling. [ 158 ] It has been shown that pathways are more relevant than individual genes to cancer progression, [ 159 ] which makes it worth studying mutations at pathway level. Identification of multi‐omics signatures of GBM has also allowed to establish prognostic subtypes, namely invasive (poor), mitotic (favorable), and intermediate.…”