The stimulation of fibroblast growth factor receptors (FGFRs) with distinct FGF ligands generates specific cellular responses. However, the mechanisms underlying this paradigm have remained elusive. Here, we show that FGF-7 stimulation leads to FGFR2b degradation and, ultimately, cell proliferation, whereas FGF-10 promotes receptor recycling and cell migration. By combining mass-spectrometry-based quantitative proteomics with fluorescence microscopy and biochemical methods, we find that FGF-10 specifically induces the rapid phosphorylation of tyrosine (Y) 734 on FGFR2b, which leads to PI3K and SH3BP4 recruitment. This complex is crucial for FGFR2b recycling and responses, given that FGF-10 stimulation of either FGFR2b_Y734F mutant- or SH3BP4-depleted cells switches the receptor endocytic route to degradation, resulting in decreased breast cancer cell migration and the inhibition of epithelial branching in mouse lung explants. Altogether, these results identify an intriguing ligand-dependent mechanism for the control of receptor fate and cellular outputs that may explain the pathogenic role of deregulated FGFR2b, thus offering therapeutic opportunities.

Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from eleven clear cell renal cell carcinoma (ccRCC) patients. COSMOS was able to capture relevant crosstalks within and between multiple omics layers, such as known ccRCC drug targets. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies.

HIGHLIGHTS Outline of EGF dependent in-vivo phosphotyrosine signaling in lung tissue  A peptide based screen identifies proteins recruited to regulated phosphotyrosine sites in lung tissue  Several somatic mutations affect proline residues within position +3 of regulated phosphotyrosine sites and introduce molecular switches  A lung cancer oncogenic mutation in EGFR causes aberrant activation of phosphorylation signaling pathways by switching of a recruited protein complex  Cancer mutations in vicinity of phosphotyrosine sites induce molecular switches that alter protein signaling networks  Introduction of cutting-edge LC-MS instrumentation and DIA enables scalable, rapid and highthroughput analysis of phosphotyrosine site interactions

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SH-SY5Y neuroblastoma cells respond to nerve growth factor (NGF)-mediated activation of the tropomyosin-related kinase A (TrkA) with neurite outgrowth, thereby providing a model to study neuronal differentiation. We performed a time-resolved analysis of NGF-TrkA signaling in neuroblastoma cells using mass spectrometry-based quantitative proteomics. The combination of interactome, phosphoproteome, and proteome data provided temporal insights into the molecular events downstream of NGF binding to TrkA. We showed that upon NGF stimulation, TrkA recruits the E3 ubiquitin ligase Cbl-b, which then becomes phosphorylated and ubiquitylated and decreases in abundance. We also found that recruitment of Cbl-b promotes TrkA ubiquitylation and degradation. Furthermore, the amount of phosphorylation of the kinase ERK and neurite outgrowth increased upon Cbl-b depletion in several neuroblastoma cell lines. Our findings suggest that Cbl-b limits NGF-TrkA signaling to control the length of neurites.

ReseaRch aRticle abstRactTriple-negative breast cancers (TNBC) are genetically characterized by aberrations in TP53 and a low rate of activating point mutations in common oncogenes, rendering it challenging in applying targeted therapies. We performed whole-exome sequencing (WES) and RNA sequencing (RNA-seq) to identify somatic genetic alterations in mouse models of TNBCs driven by loss of Trp53 alone or in combination with Brca1. Amplifications or translocations that resulted in elevated oncoprotein expression or oncoprotein-containing fusions, respectively, as well as frameshift mutations of tumor suppressors were identified in approximately 50% of the tumors evaluated. Although the spectrum of sporadic genetic alterations was diverse, the majority had in common the ability to activate the MAPK/PI3K pathways. Importantly, we demonstrated that approved or experimental drugs efficiently induce tumor regression specifically in tumors harboring somatic aberrations of the drug target. Our study suggests that the combination of WES and RNA-seq on human TNBC will lead to the identification of actionable therapeutic targets for precision medicine-guided TNBC treatment. SIGNIFICANCE:Using combined WES and RNA-seq analyses, we identified sporadic oncogenic events in TNBC mouse models that share the capacity to activate the MAPK and/or PI3K pathways. Our data support a treatment tailored to the genetics of individual tumors that parallels the approaches being investigated in the ongoing NCI-MATCH, My Pathway Trial, and ESMART clinical trials. Cancer Discov; 8(3);[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]

Multi-omics datasets can provide molecular insights beyond the sum of individual omics.Diverse tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from nine renal cell carcinoma patients. We used COSMOS to generate novel hypotheses such as the impact of Androgen Receptor on nucleoside metabolism and the influence of the JAK-STAT pathway on propionyl coenzyme A production. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies.

Therapeutic options for the treatment of glioblastoma remain inadequate despite concerted research efforts in drug development. Therapeutic failure can result from poor permeability of the blood-brain barrier, heterogeneous drug distribution, and development of resistance. Elucidation of relationships among such parameters could enable the development of predictive models of drug response in patients and inform drug development. Complementary analyses were applied to a glioblastoma patient-derived xenograft model in order to quantitatively map distribution and resulting cellular response to the EGFR inhibitor erlotinib. Mass spectrometry images of erlotinib were registered to histology and magnetic resonance images in order to correlate drug distribution with tumor characteristics. Phosphoproteomics and immunohistochemistry were used to assess protein signaling in response to drug, and integrated with transcriptional response using mRNA sequencing. This comprehensive dataset provides simultaneous insight into pharmacokinetics and pharmacodynamics and indicates that erlotinib delivery to intracranial tumors is insufficient to inhibit EGFR tyrosine kinase signaling.