Biology subjects

Vandin, F.

Publications and source records attributed to Vandin, F..

3 recordsLinked to original sources

bioRxiv2019

Network-based approaches elucidate differences within APOBEC and clock-like signatures in breast cancer

Studies of cancer mutations typically focus on identifying cancer driving mutations. However, in addition to the mutations that confer a growth advantage, cancer genomes accumulate a large number of passenger somatic mutations resulting from normal DNA damage and repair processes as well as mutations triggered by carcinogenic exposures or cancer related aberrations of DNA maintenance machinery. These mutagenic processes often produce characteristic mutational patterns called mutational signatures. Understanding the etiology of the mutational signatures shaping a cancer genome is an important step towards understanding tumorigenesis. Considering mutational signatures as phenotypes, we asked two complementary questions (i) what are functional pathways whose gene expression profiles are associated with mutational signatures, and (ii) what are mutated pathways (if any) that might underlie specific mutational signatures? We have been able to identify pathways associated with mutational signatures on both expression and mutation levels. In particular, our analysis provides novel insights into mutagenic processes in breast cancer by capturing important differences in the etiology of different APOBEC related signatures and the two clock-like signatures. These results are important for understanding mutagenic processes in cancer and for developing personalized drug therapies.

bioinformatics↗

bioRxiv2019

Identifying Drug Sensitivity Subnetworks with NETPHLIX

Phenotypic heterogeneity in cancer is often caused by different patterns of genetic alterations. Understanding such phenotype-genotype relationships is fundamental for the advance of personalized medicine. One of the important challenges in the area is to predict drug response on a personalized level. The pathway-centric view of cancer significantly advanced the understanding of genotype-phenotype relationships. However, most of network identification methods in cancer focus on identifying subnetworks that include general cancer drivers or are associated with discrete features such as cancer subtypes, hence cannot be applied directly for the analysis of continuous features like drug response. On the other hand, existing genome wide association approaches do not fully utilize the complex proprieties of cancer mutational landscape. To address these challenges, we propose a computational method, named NETPHLIX (NETwork-to-PHenotpe mapping LeveragIng eXlusivity), which aims to identify mutated subnetworks that are associated with drug response (or any continuous cancer phenotype). Utilizing properties such as mutual exclusivity and interactions among genes, we formulate the problem as an integer linear program and solve it optimally to obtain a set of genes satisfying the constraints. NETPHLIX identified gene modules significantly associated with many drugs, including interesting response modules to MEK1/2 inhibitors in both directions (increased and decreased sensitivity to the drug) that the previous method, which does not utilize network information, failed to identify. The genes in the modules belong to MAPK/ERK signaling pathway, which is the targeted pathway of the drug.

bioinformatics↗

bioRxiv2018

Quantitative behavior of protein complexes in human cells

Translational and post-translational control mechanisms in the cell result in widely observable differences between measured gene transcription and protein abundances. Herein, protein complexes are among the most tightly controlled entities by selective degradation of their individual proteins. They furthermore act as control hubs that regulate highly important processes in the cell and exhibit a high functional diversity due to their ability to change their composition and their structure. To better understand and predict these functional states, extensive characterization of complex composition, behavior, and abundance is necessary. Mass spectrometry provides an unbiased approach to directly determine protein abundances across cell populations and thus to profile a comprehensive abundance map of proteins. We investigated the behavior of protein subunits in known complexes by comparing their abundance profiles across up to 140 cell types available in ProteomicsDB. After thorough assessment of different randomization methods and statistical scoring algorithms, we developed a computational tool to quantify the significance of concurrent profiles within a complex, therefore providing insights into the conservation of their composition across human cell types. We identified the intrinsic structures in complex behavior that allow to determine which proteins orchestrate complex function. This analysis can be extended to investigate common profiles within arbitrary protein groups. With the CoExpresso web service, we offer a potent scoring scheme to assess proteins for their co-regulation and thereby offer insight into their potential for forming functional groups like protein complexes. CoExpresso can be accessed through http://computproteomics/Apps/CoExpresso. Source code and R scripts for database generation are available at https://bitbucket.org/veitveit/coexpresso.\n\nAuthor summaryMany proteins form multi-functional assemblies called protein complexes instead of working as singly units. These complexes control most processes in the cell making the full characterization of their behavior inevitable to understand cellular control mechanisms. Detailed knowledge about complex behavior will elucidate biomarkers and drug targets that exhibit and correct aberrant cell states, respectively. We investigated abundance changes of the protein complex components over more than 100 different human cell types. By using statistical scoring models, we estimated the evidence for the co-regulation of the proteins and revealed which proteins form subunits with impact on complex function and composition. By providing the interactive web service CoExpresso, any combination of proteins can be tested for their co-regulation in human cells.

bioinformatics↗