Biology subjects

Michaut, M.

Publications and source records attributed to Michaut, M..

4 recordsLinked to original sources

bioRxiv2018

iTOP: Inferring the Topology of Omics Data

MotivationIn biology, we are often faced with multiple datasets recorded on the same set of objects, such as multi-omics and phenotypic data of the same tumors. These datasets are typically not independent from each other. For example, methylation may influence gene expression, which may, in turn, influence drug response. Such relationships can strongly affect analyses performed on the data, as we have previously shown for the identification of biomarkers of drug response. Therefore, it is important to be able to chart the relationships between datasets.\n\nResultsWe present iTOP, a methodology to infera topology of relationships between datasets. We base this methodology on the RV coefficient, a measure of matrix correlation, which can be used to determine how much information is shared between two datasets. We extended the RV coefficient for partial matrix correlations, which allows the use of graph reconstruction algorithms, such as the PC algorithm, to infer the topologies. In addition, since multi-omics data often contain binary data (e.g. mutations), we also extended the RV coefficient for binary data. Applying iTOP to pharmacogenomics data, we found that gene expression acts as a mediator between most other datasets and drug response: only proteomics clearly shares information with drug response that is not present in gene expression. Based on this result, we used TANDEM, a method for drug response prediction, to identify which variables predictive of drug response were distinct to either gene expression or proteomics.\n\nAvailabilityAn implementation of our methodology is available in the R package iTOP on CRAN. Additionally, an R Markdown document with code to reproduce all figures is provided as Supplementary Material.\n\nContacta.k.smilde@uva.nl and l.wessels@nki.nl\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

Identifying biomarkers of anti-cancer drug synergy using multi-task learning

Combining anti-cancer drugs has the potential to increase treatment efficacy. Because patient responses to drug combinations are highly variable, predictive biomarkers of synergy are required to identify which patients are likely to benefit from a drug combination. To aid biomarker identification, the DREAM challenge consortium has recently released data from a screen containing 85 cell lines and 167 drug combinations. The main challenge of these data is the low sample size: per drug combination, a median of 14 cell lines have been screened. We found that widely used methods in single drug response prediction, such as Elastic Net regression per drug, are not predictive in this setting. Instead, we propose to use multi-task learning: training a single model simultaneously on all drug combinations, which we show results in increased predictive performance. In contrast to other multi-task learning approaches, our approach allows for the identification of biomarkers, by using a modified random forest variable importance score, which we illustrate using artificial data and the DREAM challenge data. Notably, we find that mutations in MYO15A are associated with synergy between ALK / IGFR dual inhibitors and PI3K pathway inhibitors in triple-negative breast cancer.\n\nAuthor summaryCombining drugs is a promising strategy for cancer treatment. However, it is often not known which patients will benefit from a particular drug combination. To identify patients that are likely to benefit, we need to identify biomarkers, such as mutations in the tumors DNA, that are associated with favorable response to the drug combination. In this work, we identified such biomarkers using the drug combination data released by the DREAM challenge consortium, which contain 85 tumor cell lines and 167 drug combinations. The main challenge of these data is the extremely low sample size: a median of 14 cell lines have been screened per drug combination. We found that traditional methods to identify biomarkers for monotherapy response, which analyze each drug separately, are not suitable in this low sample size setting. Instead, we used a technique called multi-task learning to jointly analyze all drug combinations in a single statistical model. In contrast to existing multi-task learning algorithms, which are black-box methods, our method allows for the identification of biomarkers. Notably, we find that, in a subset of breast cancer cell lines, MYO15A mutations associate with response to the combination of ALK / IGFR dual inhibitors and PI3K pathway inhibitors.

bioinformatics↗

bioRxiv2017

A screen for combination therapies in BRAF/NRAS wild type melanoma identifies nilotinib plus MEK inhibitor as a synergistic combination

Despite recent therapeutic advances in the management of BRAFV600-mutant melanoma, there is still a compelling need for more effective treatments for patients who developed BRAF/NRAS wild type disease. Since the activity of single targeted agents is limited by innate and acquired resistance, we performed a high-throughput drug screen using 180 drug combinations to generate over 18,000 viability curves, with the aim of identifying agents that synergise to kill BRAF/NRAS wild type melanoma cells. From this screen we observed strong synergy between the tyrosine kinase inhibitor nilotinib and MEK inhibitors and validated this combination in an independent cell line collection. We found that AXL expression was associated with synergy to the nilotinib/MEK inhibitor combination, and that both drugs work in concert to suppress pERK. This finding was supported by genome-wide CRISPR screening which revealed that resistance mechanisms converge on regulators of the MAPK pathway. Finally, we validated the synergy of nilotinib/trametinib combination in vivo using patient-derived xenografts. Our results indicate that a nilotinib/MEK inhibitor combination may represent an effective therapy in BRAF/NRAS wild type melanoma patients.

cancer biology↗

bioRxiv2016

Genomic determinants of protein abundance variation in colorectal cancer cells

Assessing the extent to which genomic alterations compromise the integrity of the proteome is fundamental in identifying the mechanisms that shape cancer heterogeneity. We have used isobaric labelling and tribrid mass spectrometry to characterize the proteomic landscapes of 50 colorectal cancer cell lines and to decipher the relationships between genomic and proteomic variation. The robust quantification of 12,000 proteins and 27,000 phosphopeptides revealed how protein symbiosis translates to a co-variome which is subjected to a hierarchical order and exposes the collateral effects of somatic mutations on protein complexes. Targeted depletion of key chromatin modifiers confirmed the transmission of variation and the directionality as characteristics of protein interactions. Protein level variation was leveraged to build drug response predictive models towards a better understanding of pharmacoproteomic interactions in colorectal cancer. Overall, we provide a deep integrative view of the molecular structure underlying the variation of colorectal cancer cells.\n\nHighlightsO_LIThe cancer cell functional \"co-variome\" is a strong attribute of the proteome.\nC_LIO_LIMutations can have a direct impact on protein levels of chromatin modifiers.\nC_LIO_LITransmission of genomic variation is a characteristic of protein interactions.\nC_LIO_LIPharmacoproteomic models are strong predictors of response to DNA damaging agents.\nC_LI\n\nAbbreviations

systems biology↗