Dissecting the Genome for Drug Response Prediction.

The prediction of the cancer cell lines sensitivity to a specific treatment is one of the current challenges in precision medicine. With omics and pharmacogenomics data being available for over 1000 cancer cell lines, several machine learning and deep learning algorithms have been proposed for drug sensitivity prediction. However, deciding which omics data to use and which computational methods can efficiently incorporate data from different sources is the challenge which several research groups are working on. In this review, we summarize recent advances in the representative computational methods that have been developed in the last 2 years on three public datasets: COSMIC, CCLE, NCI-60. These methods aim to improve the prediction of the cancer cell lines sensitivity to a given treatment by incorporating drug's chemical information in the input or using a priori feature selection. Finally, we discuss the latest published method which aims to improve the prediction of clinical drug response of real patients starting from cancer cell line molecular profiles.

Variation in the co-expression profile highlights a loss of miRNA-mRNA regulation in multiple cancer types.

Recent research provides insight into the ability of miRNA to regulate various pathways in several cancer types. Despite their involvement in the regulation of the mRNA via targeting the 3'UTR, there are relatively few studies examining the changes in these regulatory mechanisms specific to single cancer types or shared between different cancer types. We analyzed samples where both miRNA and mRNA expression had been measured and performed a thorough correlation analysis on 7494 experimentally validated human miRNA-mRNA target-gene pairs in both healthy and tumoral samples. We show how more than 90% of these miRNA-mRNA interactions show a loss of regulation in the tumoral samples compared with their healthy counterparts. As expected, we found shared miRNA-mRNA dysregulated pairs among different tumors of the same tissue. However, anatomically different cancers also share multiple dysregulated interactions, suggesting that some cancer-related mechanisms are not tumor-specific. 2865 unique miRNA-mRNA pairs were identified across 13 cancer types, ≈ 40% of these pairs showed a loss of correlation in the tumoral samples in at least 2 out of the 13 analyzed cancers. Specifically, miR-200 family, miR-155 and miR-1 were identified, based on the computational analysis described below, as the miRNAs that potentially lose the highest number of interactions across different samples (only literature-based interactions were used for this analysis). Moreover, the miR-34a/ALDH2 and miR-9/MTHFD2 pairs show a switch in their correlation between healthy and tumor kidney samples suggesting a possible change in the regulation exerted by the miRNAs. Interestingly, the expression of these mRNAs is also associated with the overall survival. The disruption of miRNA regulation on its target, therefore, suggests the possible involvement of these pairs in cell malignant functions. The analysis reported here shows how the regulation of miRNA-mRNA interactions strongly differs between healthy and tumoral cells, based on the strong correlation variation between miRNA and its target that we obtained by analyzing the expression data of healthy and tumor tissue in highly reliable miRNA-target pairs. Finally, a go term enrichment analysis shows that the critical pairs identified are involved in cellular adhesion, proliferation, and migration.

Phosphorylation within Intrinsic Disordered Region Discriminates Histone Variant macroH2A1 Splicing Isoforms-macroH2A1.1 and macroH2A1.2.

Background: Gene expression in eukaryotic cells can be governed by histone variants, which replace replication-coupled histones, conferring unique chromatin properties. MacroH2A1 is a histone H2A variant containing a domain highly similar to H2A and a large non-histone (macro) domain. MacroH2A1, in turn, is present in two alternatively exon-spliced isoforms: macroH2A1.1 and macroH2A1.2, which regulate cell plasticity and proliferation in a remarkably distinct manner. The N-terminal and the C-terminal tails of H2A histones stem from the nucleosome core structure and can be target sites for several post-translational modifications (PTMs). MacroH2A1.1 and macroH2A1.2 isoforms differ only in a few amino acids and their ability to bind NAD-derived metabolites, a property allegedly conferring their different functions in vivo. Some of the modifications on the macroH2A1 variant have been identified, such as phosphorylation (T129, S138) and methylation (K18, K123, K239). However, no study to our knowledge has analyzed extensively, and in parallel, the PTM pattern of macroH2A1.1 and macroH2A1.2 in the same experimental setting, which could facilitate the understanding of their distinct biological functions in health and disease. Methods: We used a mass spectrometry-based approach to identify the sites for phosphorylation, acetylation, and methylation in green fluorescent protein (GFP)-tagged macroH2A1.1 and macroH2A1.2 expressed in human hepatoma cells. The impact of selected PTMs on macroH2A1.1 and macroH2A1.2 structure and function are demonstrated using computational analyses. Results: We identified K7 as a new acetylation site in both macroH2A1 isoforms. Quantitative comparison of histone marks between the two isoforms revealed significant differences in the levels of phosphorylated T129 and S170. Our computational analysis provided evidence that the phosphorylation status in the intrinsically disordered linker region in macroH2A1 isoforms might represent a key regulatory element contributing to their distinct biological responses. Conclusions: Taken together, our results report different PTMs on the two macroH2A1 splicing isoforms as responsible for their distinct features and distribution in the cell.

Transcriptome and Gene Fusion Analysis of Synchronous Lesions Reveals lncMRPS31P5 as a Novel Transcript Involved in Colorectal Cancer.

Fusion genes and epigenetic regulators (i.e., miRNAs and long non-coding RNAs) constitute essential pieces of the puzzle of the tumor genomic landscape, in particular in mechanisms behind the adenoma-to-carcinoma progression of colorectal cancer (CRC). In this work, we aimed to identify molecular signatures of the different steps of sporadic CRC development in eleven patients, of which synchronous samples of adenomas, tumors, and normal tissues were analyzed by RNA-Seq. At a functional level, tumors and adenomas were all characterized by increased activity of the cell cycle, cell development, cell growth, and biological proliferation functions. In contrast, organic survival and apoptosis-related functions were inhibited both in tumors and adenomas at different levels. At a molecular level, we found that three individuals shared a tumor-specific fusion named MRPS31-SUGT1, generated through an intra-chromosomal translocation on chromosome 13, whose sequence resulted in being 100% identical to the long non-coding RNA (lncRNA) MRPS31P5. Our analyses suggest that MRPS31P5 could take part to a competitive endogenous (ce)RNA network by acting as a miRNA sponge or/and as an interactor of other mRNAs, and thus it may be an important gene expression regulatory factor and could be used as a potential biomarker for the detection of early CRC events.

Modeling cancer drug response through drug-specific informative genes.

Recent advances in pharmacogenomics have generated a wealth of data of different types whose analysis have helped in the identification of signatures of different cellular sensitivity/resistance responses to hundreds of chemical compounds. Among the different data types, gene expression has proven to be the more successful for the inference of drug response in cancer cell lines. Although effective, the whole transcriptome can introduce noise in the predictive models, since specific mechanisms are required for different drugs and these realistically involve only part of the proteins encoded in the genome. We analyzed the pharmacogenomics data of 961 cell lines tested with 265 anti-cancer drugs and developed different machine learning approaches for dissecting the genome systematically and predict drug responses using both drug-unspecific and drug-specific genes. These methodologies reach better response predictions for the vast majority of the screened drugs using tens to few hundreds genes specific to each drug instead of the whole genome, thus allowing a better understanding and interpretation of drug-specific response mechanisms which are not necessarily restricted to the drug known targets.

Discovering sequence and structure landscapes in RNA interaction motifs.

RNA molecules are able to bind proteins, DNA and other small or long RNAs using information at primary, secondary or tertiary structure level. Recent techniques that use cross-linking and immunoprecipitation of RNAs can detect these interactions and, if followed by high-throughput sequencing, molecules can be analysed to find recurrent elements shared by interactors, such as sequence and/or structure motifs. Many tools are able to find sequence motifs from lists of target RNAs, while others focus on structure using different approaches to find specific interaction elements. In this work, we make a systematic analysis of RBP-RNA and RNA-RNA datasets to better characterize the interaction landscape with information about multi-motifs on the same RNAs. To achieve this goal, we updated our BEAM algorithm to combine both sequence and structure information to create pairs of patterns that model motifs of interaction. This algorithm was applied to several RNA binding proteins and ncRNAs interactors, confirming already known motifs and discovering new ones. This landscape analysis on interaction variability reflects the diversity of target recognition and underlines that often both primary and secondary structure are involved in molecular recognition.

Spatial Tissue Proteomics Quantifies Inter- and Intratumor Heterogeneity in Hepatocellular Carcinoma (HCC).

The interpatient variability of tumor proteomes has been investigated on a large scale but many tumors display also intratumoral heterogeneity regarding morphological and genetic features. It remains largely unknown to what extent the local proteome of tumors intrinsically differs. Here, we used hepatocellular carcinoma as a model system to quantify both inter- and intratumor heterogeneity across human patient specimens with spatial resolution. We defined proteomic features that distinguish neoplastic from the directly adjacent nonneoplastic tissue, such as decreased abundance of NADH dehydrogenase complex I. We then demonstrated the existence of intratumoral variations in protein abundance that re-occur across different patient samples, and affect clinically relevant proteins, even in the absence of obvious morphological differences or genetic alterations. Our work demonstrates the suitability and the benefits of using mass spectrometry-based proteomics to analyze diagnostic tumor specimens with spatial resolution. Data are available via ProteomeXchange with identifier PXD007052.

Spatiotemporal variation of mammalian protein complex stoichiometries.

BACKGROUND: Recent large-scale studies revealed cell-type specific proteomes. However, protein complexes, the basic functional modules of a cell, have been so far mostly considered as static entities with well-defined structures. The co-expression of their members has not been systematically charted at the protein level. RESULTS: We used measurements of protein abundance across 11 cell types and five temporal states to analyze the co-expression and the compositional variations of 182 well-characterized protein complexes. We show that although the abundance of protein complex members is generally co-regulated, a considerable fraction of all investigated protein complexes is subject to stoichiometric changes. Compositional variation is most frequently seen in complexes involved in chromatin regulation and cellular transport, and often involves paralog switching as a mechanism for the regulation of complex stoichiometry. We demonstrate that compositional signatures of variable protein complexes have discriminative power beyond individual cell states and can distinguish cancer cells from healthy ones. CONCLUSIONS: Our work demonstrates that many protein complexes contain variable members that cause distinct stoichometries and functionally fine-tune complexes spatiotemporally. Only a fraction of these compositional variations is mediated by changes in transcription and other mechanisms regulating protein abundance contribute to determine protein complex stoichiometries. Our work highlights the superior power of proteome profiles to study protein complexes and their variants across cell states.

Histone Deacetylase Inhibitors (HDACi) Cause the Selective Depletion of Bromodomain Containing Proteins (BCPs).

Histone deacetylases (HDACs) and acetyltransferases control the epigenetic regulation of gene expression through modification of histone marks. Histone deacetylase inhibitors (HDACi) are small molecules that interfere with histone tail modification, thus altering chromatin structure and epigenetically controlled pathways. They promote apoptosis in proliferating cells and are promising anticancer drugs. While some HDACi have already been approved for therapy and others are in different phases of clinical trials, the exact mechanism of action of this drug class remains elusive. Previous studies have shown that HDACis cause massive changes in chromatin structure but only moderate changes in gene expression. To what extent these changes manifest at the protein level has never been investigated on a proteome-wide scale. Here, we have studied HDACi-treated cells by large-scale mass spectrometry based proteomics. We show that HDACi treatment affects primarily the nuclear proteome and induces a selective decrease of bromodomain-containing proteins (BCPs), the main readers of acetylated histone marks. By combining time-resolved proteome and transcriptome profiling, we show that BCPs are affected at the protein level as early as 12 h after HDACi treatment and that their abundance is regulated by a combination of transcriptional and post-transcriptional mechanisms. Using gene silencing, we demonstrate that the decreased abundance of BCPs is sufficient to mediate important transcriptional changes induced by HDACi. Our data reveal a new aspect of the mechanism of action of HDACi that is mediated by an interplay between histone acetylation and the abundance of BCPs. Data are available via ProteomeXchange with identifier PXD001660 and NCBI Gene Expression Omnibus with identifier GSE64689.