Pan-Cancer landscape of protein activities identifies drivers of signalling dysregulation and patient survival.

Genetic alterations in cancer cells trigger oncogenic transformation, a process largely mediated by the dysregulation of kinase and transcription factor (TF) activities. While the mutational profiles of thousands of tumours have been extensively characterised, the measurements of protein activities have been technically limited until recently. We compiled public data of matched genomics and (phospho)proteomics measurements for 1,110 tumours and 77 cell lines that we used to estimate activity changes in 218 kinases and 292 TFs. Co-regulation of kinase and TF activities reflects previously known regulatory relationships and allows us to dissect genetic drivers of signalling changes in cancer. We find that loss-of-function mutations are not often associated with the dysregulation of downstream targets, suggesting frequent compensatory mechanisms. Finally, we identified the activities most differentially regulated in cancer subtypes and showed how these can be linked to differences in patient survival. Our results provide broad insights into the dysregulation of protein activities in cancer and their contribution to disease severity.

PHONEMeS: Efficient Modeling of Signaling Networks Derived from Large-Scale Mass Spectrometry Data.

Post-translational modifications of proteins play an important role in the regulation of cellular processes. The mass spectrometry analysis of proteome modifications offers huge potential for the study of how protein inhibitors affect the phosphosignaling mechanisms inside the cells. We have recently proposed PHONEMeS, a method that uses high-content shotgun phosphoproteomic data to build logical network models of signal perturbation flow. However, in its original implementation, PHONEMeS was computationally demanding and was only used to model signaling in a perturbation context. We have reformulated PHONEMeS as an Integer Linear Program (ILP) that is orders of magnitude more efficient than the original one. We have also expanded the scenarios that can be analyzed. PHONEMeS can model data upon perturbation on not only a known target but also deregulated pathways upstream and downstream of any set of deregulated kinases. Finally, PHONEMeS can now analyze data sets with multiple time points, which helps us to obtain better insight into the dynamics of the propagation of signals. We illustrate the value of the new approach on various data sets of medical relevance, where we shed light on signaling mechanisms and drug modes of action.

Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses.

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.

Gender Differential Transcriptome in Gastric and Thyroid Cancers.

Cancer has an important and considerable gender differential susceptibility confirmed by several epidemiological studies. Gastric (GC) and thyroid cancer (TC) are examples of malignancies with a higher incidence in males and females, respectively. Beyond environmental predisposing factors, it is expected that gender-specific gene deregulation contributes to this differential incidence. We performed a detailed characterization of the transcriptomic differences between genders in normal and tumor tissues from stomach and thyroid using Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) data. We found hundreds of sex-biased genes (SBGs). Most of the SBGs shared by normal and tumor belong to sexual chromosomes, while the normal and tumor-specific tend to be found in the autosomes. Expression of several cancer-associated genes is also found to differ between sexes in both types of tissue. Thousands of differentially expressed genes (DEGs) between paired tumor-normal tissues were identified in GC and TC. For both cancers, in the most susceptible gender, the DEGs were mostly under-expressed in the tumor tissue, with an enrichment for tumor-suppressor genes (TSGs). Moreover, we found gene networks preferentially associated to males in GC and to females in TC and correlated with cancer histological subtypes. Our results shed light on the molecular differences and commonalities between genders and provide novel insights in the differential risk underlying these cancers.

Gene Expression Analyses in Non Muscle Invasive Bladder Cancer Reveals a Role for Alternative Splicing and Tp53 Status.

Non-muscle invasive bladder cancer (NMIBC) represents a crucial problem for the national health care systems due to its high rates of recurrence and the consequent need of frequent follow-ups. Here, gene expression analyses in patients diagnosed as NMIBC were performed to determine those molecular pathways involved in tumor initiation, finding that both MYC and E2F are up regulated and helps to tumor initiation and progression. Our results also support an important involvement of alternative splicing events, modifying key pathways to favour bladder tumor evolution. Finally, since MDM2 showed differential exon usage, mutations in TP53 and its protein expression have been also studied in the same patients. Our data support that recurrence is epigenetically mediated and favoured by an increase protein expression of TP53, which appears more frequently mutated in advanced stages and grades, being associated to a worse prognosis. Therefore, TP53 mutational status could be used as a potential biomarker in the first stages of NMIBC to predict recurrence and prognosis.

Multi-omics Characterization of Interaction-mediated Control of Human Protein Abundance levels.

Proteogenomic studies of cancer samples have shown that copy-number variation can be attenuated at the protein level for a large fraction of the proteome, likely due to the degradation of unassembled protein complex subunits. Such interaction-mediated control of protein abundance remains poorly characterized. To study this, we compiled genomic, (phospho)proteomic and structural data for hundreds of cancer samples and find that up to 42% of 8,124 analyzed proteins show signs of post-transcriptional control. We find evidence of interaction-dependent control of protein abundance, correlated with interface size, for 516 protein pairs, with some interactions further controlled by phosphorylation. Finally, these findings in cancer were reflected in variation in protein levels in normal tissues. Importantly, expression differences due to natural genetic variation were increasingly buffered from phenotype differences for highly attenuated proteins. Altogether, this study further highlights the importance of posttranscriptional control of protein abundance in cancer and healthy cells.

CDH1 somatic alterations in Mexican patients with diffuse and mixed sporadic gastric cancer.

BACKGROUND: Diffuse gastric cancer (DGC) is associated with the reduction or absence of the expression of the cell adhesion protein E-cadherin (encoded by the CDH1 gene). Molecular characteristics are less well described for mixed gastric cancer (MGC). The main somatic alterations that have been described in the CDH1 gene are mutations, loss of heterozygosity (LOH) and promoter methylation. The aim was to analyze CDH1 somatic alterations in Mexican patients with diffuse and mixed gastric cancer. METHODS: We searched for mutations in the CDH1 gene in tumor DNA from DGC (n = 13) and MGC (n = 7) patients by next generation sequencing (NGS). Validation of findings was performed using Sanger sequencing. LOH was analyzed using dinucleotide repeat markers surrounding the CDH1 gene, and methylation was investigated by DNA bisulfite conversion and sequencing. E-cadherin protein deficiency was analyzed by immunohistochemistry. RESULTS: Seventeen point variants were identified by NGS, 13 of them were validated by Sanger sequencing. Only 1/13 had not been previously reported (c.-137C > A), and 12/13 were already reported as polymorphisms. Two DGC cases presented LOH at the locus 16q22.1 (13.3%). CDH1 promoter methylation was positive in (7/11) 63.6% and (4/6) 66.6% of the cases with DGC and MGC, respectively. E-cadherin protein deficiency was observed in 58.3% of DGC cases while 100% in MGC cases. CONCLUSIONS: While no pathogenic somatic mutations were found that could explain the diffuse histology of gastric cancer in DGC and MGC, methylation was the most common somatic inactivation event of the CDH1 gene, and LOH was rare. The previously unreported c.-137C > A variant modify the CDH1 gene expression since it alters the binding sites for transcription factors.

The effects of death and post-mortem cold ischemia on human tissue transcriptomes.

Post-mortem tissues samples are a key resource for investigating patterns of gene expression. However, the processes triggered by death and the post-mortem interval (PMI) can significantly alter physiologically normal RNA levels. We investigate the impact of PMI on gene expression using data from multiple tissues of post-mortem donors obtained from the GTEx project. We find that many genes change expression over relatively short PMIs in a tissue-specific manner, but this potentially confounding effect in a biological analysis can be minimized by taking into account appropriate covariates. By comparing ante- and post-mortem blood samples, we identify the cascade of transcriptional events triggered by death of the organism. These events do not appear to simply reflect stochastic variation resulting from mRNA degradation, but active and ongoing regulation of transcription. Finally, we develop a model to predict the time since death from the analysis of the transcriptome of a few readily accessible tissues.