Scaffold coupling: ERK activation by trans-phosphorylation across different scaffold protein species.

RAS-ERK (extracellular signal-regulated kinase) pathway signals are modulated by scaffold proteins that assemble the components of different kinase tiers into a sequential phosphorylation cascade. In the prevailing model scaffold proteins function as isolated entities, where the flux of phosphorylation events progresses downstream linearly, to achieve ERK phosphorylation. We show that different types of scaffold proteins, specifically KSR1 (kinase suppressor of Ras 1) and IQGAP1 (IQ motif-containing guanosine triphosphatase activating protein 1), can bind to each other, forming a complex whereby phosphorylation reactions occur across both species. MEK (mitogen-activated protein kinase kinase) bound to IQGAP1 can phosphorylate ERK docked at KSR1, a process that we have named "trans-phosphorylation." We also reveal that ERK trans-phosphorylation participates in KSR1-regulated adipogenesis, and it also underlies the modest cytotoxicity exhibited by KSR-directed inhibitors. Overall, we identify interactions between scaffold proteins and trans-phosphorylation as an additional level of regulation in the ERK cascade, with broad implications in signaling and the design of scaffold protein-aimed therapeutics.

CiberAMP: An R Package to Identify Differential mRNA Expression Linked to Somatic Copy Number Variations in Cancer Datasets.

Somatic copy number variations (SCNVs) are genetic alterations frequently found in cancer cells. These genetic alterations can lead to concomitant perturbations in the expression of the genes included in them and, as a result, promote a selective advantage to cancer cells. However, this is not always the case. Due to this, it is important to develop in silico tools to facilitate the accurate identification and functional cataloging of gene expression changes associated with SCNVs from pan-cancer data. Here, we present a new R-coded tool, designated as CiberAMP, which utilizes genomic and transcriptomic data contained in the Cancer Genome Atlas (TCGA) to identify such events. It also includes information on the genomic context in which such SCNVs take place. By doing so, CiberAMP provides clues about the potential functional relevance of each of the SCNV-associated gene expression changes found in the interrogated tumor samples. The main features and advantages of this new algorithm are illustrated using glioblastoma data from the TCGA database.

A hotspot mutation targeting the R-RAS2 GTPase acts as a potent oncogenic driver in a wide spectrum of tumors.

A missense change in RRAS2 (Gln72 to Leu), analogous to the Gln61-to-Leu mutation of RAS oncoproteins, has been identified as a long-tail hotspot mutation in cancer and Noonan syndrome. However, the relevance of this mutation for in vivo tumorigenesis remains understudied. Here we show, using an inducible knockin mouse model, that R-Ras2Q72L triggers rapid development of a wide spectrum of tumors when somatically expressed in adult tissues. These tumors show limited overlap with those originated by classical Ras oncogenes. R-Ras2Q72L-driven tumors can be classified into different subtypes according to therapeutic susceptibility. Importantly, the most relevant R-Ras2Q72L-driven tumors are dependent on mTORC1 but independent of phosphatidylinositol 3-kinase-, MEK-, and Ral guanosine diphosphate (GDP) dissociation stimulator. This pharmacological vulnerability is due to the extensive rewiring by R-Ras2Q72L of pathways that orthogonally stimulate mTORC1 signaling. These findings demonstrate that RRAS2Q72L is a bona fide oncogenic driver and unveil therapeutic strategies for patients with cancer and Noonan syndrome bearing RRAS2 mutations.

Cancer-associated mutations in VAV1 trigger variegated signaling outputs and T-cell lymphomagenesis.

Mutations in VAV1, a gene that encodes a multifunctional protein important for lymphocytes, are found at different frequencies in peripheral T-cell lymphoma (PTCL), non-small cell lung cancer, and other tumors. However, their pathobiological significance remains unsettled. After cataloguing 51 cancer-associated VAV1 mutations, we show here that they can be classified in five subtypes according to functional impact on the three main VAV1 signaling branches, GEF-dependent activation of RAC1, GEF-independent adaptor-like, and tumor suppressor functions. These mutations target new and previously established regulatory layers of the protein, leading to quantitative and qualitative changes in VAV1 signaling output. We also demonstrate that the most frequent VAV1 mutant subtype drives PTCL formation in mice. This process requires the concurrent engagement of two downstream signaling branches that promote the chronic activation and transformation of follicular helper T cells. Collectively, these data reveal the genetic constraints associated with the lymphomagenic potential of VAV1 mutant subsets, similarities with other PTCL driver genes, and potential therapeutic vulnerabilities.

Genomic and Functional Regulation of TRIB1 Contributes to Prostate Cancer Pathogenesis.

Prostate cancer is the most frequent malignancy in European men and the second worldwide. One of the major oncogenic events in this disease includes amplification of the transcription factor cMYC. Amplification of this oncogene in chromosome 8q24 occurs concomitantly with the copy number increase in a subset of neighboring genes and regulatory elements, but their contribution to disease pathogenesis is poorly understood. Here we show that TRIB1 is among the most robustly upregulated coding genes within the 8q24 amplicon in prostate cancer. Moreover, we demonstrate that TRIB1 amplification and overexpression are frequent in this tumor type. Importantly, we find that, parallel to its amplification, TRIB1 transcription is controlled by cMYC. Mouse modeling and functional analysis revealed that aberrant TRIB1 expression is causal to prostate cancer pathogenesis. In sum, we provide unprecedented evidence for the regulation and function of TRIB1 in prostate cancer.

HERC Ubiquitin Ligases in Cancer.

HERC proteins are ubiquitin E3 ligases of the HECT family. The HERC subfamily is composed of six members classified by size into large (HERC1 and HERC2) and small (HERC3-HERC6). HERC family ubiquitin ligases regulate important cellular processes, such as neurodevelopment, DNA damage response, cell proliferation, cell migration, and immune responses. Accumulating evidence also shows that this family plays critical roles in cancer. In this review, we provide an integrated view of the role of these ligases in cancer, highlighting their bivalent functions as either oncogenes or tumor suppressors, depending on the tumor type. We include a discussion of both the molecular mechanisms involved and the potential therapeutic strategies.

CANCERTOOL: A Visualization and Representation Interface to Exploit Cancer Datasets.

With the advent of OMICs technologies, both individual research groups and consortia have spear-headed the characterization of human samples of multiple pathophysiologic origins, resulting in thousands of archived genomes and transcriptomes. Although a variety of web tools are now available to extract information from OMICs data, their utility has been limited by the capacity of nonbioinformatician researchers to exploit the information. To address this problem, we have developed CANCERTOOL, a web-based interface that aims to overcome the major limitations of public transcriptomics dataset analysis for highly prevalent types of cancer (breast, prostate, lung, and colorectal). CANCERTOOL provides rapid and comprehensive visualization of gene expression data for the gene(s) of interest in well-annotated cancer datasets. This visualization is accompanied by generation of reports customized to the interest of the researcher (e.g., editable figures, detailed statistical analyses, and access to raw data for reanalysis). It also carries out gene-to-gene correlations in multiple datasets at the same time or using preset patient groups. Finally, this new tool solves the time-consuming task of performing functional enrichment analysis with gene sets of interest using up to 11 different databases at the same time. Collectively, CANCERTOOL represents a simple and freely accessible interface to interrogate well-annotated datasets and obtain publishable representations that can contribute to refinement and guidance of cancer-related investigations at all levels of hypotheses and design.Significance: In order to facilitate access of research groups without bioinformatics support to public transcriptomics data, we have developed a free online tool with an easy-to-use interface that allows researchers to obtain quality information in a readily publishable format. Cancer Res; 78(21); 6320-8. ©2018 AACR.