Biology subjects

Luna, A.

Publications and source records attributed to Luna, A..

4 recordsLinked to original sources

bioRxiv2018

Integrative analysis of pharmacogenomics in major cancer cell line databases using CellMinerCDB

As precision medicine demands molecular determinants of drug response, CellMinerCDB provides (https://discover.nci.nih.gov/cellminercdb/) a web-based portal for multiple forms of pharmacological, molecular, and genomic analyses, unifying the richest cancer cell line datasets (NCI-60, NCI-SCLC, Sanger/MGH GDSC, and Broad CCLE/CTRP). CellMinerCDB enables genomic and pharmacological data queries for identifying pharmacogenomic determinants, drug signatures, and gene regulatory networks for researchers without requiring specialized bioinformatics support. It leverages overlaps of cell lines and tested drugs to allow assessment of data reproducibility. It builds on the complementarity and strength of each dataset. A panel of 41 drugs evaluated in parallel in the NCI-60 and GDSC is reported, supporting drug reproducibility across databases, repositioning of bisacodyl and acetalax for triple negative breast cancer, and identifying novel drug response determinants and genomic signatures for topoisomerase inhibitors and schweinfurthins in development. CellMinerCDB also allowed the identification of LIX1L as a novel mesenchymal gene regulating cellular migration and invasiveness.

bioinformatics↗

bioRxiv2018

AlignmentViewer: Sequence Analysis of Large Protein Families

SummaryAlignmentViewer is multiple sequence alignment viewer for protein families with flexible visualization, analysis tools and links to protein family databases. It is directly accessible in web browsers without the need for software installation, as it is implemented in JavaScript, and does not require an internet connection to function. It can handle protein families with tens of thousands of sequences and is particularly suitable for evolutionary coupling analysis, facilitating the computation of protein 3D structures and the detection of functionally constrained interactions.\n\nAvailability and ImplementationAlignmentViewer is open source software under the MIT license. The viewer is at http://alignmentviewer.org and the source code, documentation and issue tracking, for co-development, are at https://github.com/dfci/alignmentviewer\n\nContactalignment.viewer@gmail.com, reaches all authors

bioinformatics↗

bioRxiv2018

Rapid proteotyping reveals cancer biology and drug response determinants in the NCI-60 cells

We describe the rapid and reproducible acquisition of quantitative proteome maps for the NCI-60 cancer cell lines and their use to reveal cancer biology and drug response determinants. Proteome datasets for the 60 cell lines were acquired in duplicate within 30 working days using pressure cycling technology and SWATH mass spectrometry. We consistently quantified 3,171 proteotypic proteins annotated in the SwissProt database across all cell lines, generating a data matrix with 0.1% missing values, allowing analyses of protein complexes and pathway activities across all the cancer cells. Systematic and integrative analysis of the genetic variation, mRNA expression and proteomic data of the NCI-60 cancer cell lines uncovered complementarity between different types of molecular data in the prediction of the response to 240 drugs. We additionally identified novel proteomic drug response determinants for clinically relevant chemotherapeutic and targeted therapies. We anticipate that this study represents a significant advance toward the translational application of proteotypes, which reveal biological insights that are easily missed in the absence of proteomic data.

systems biology↗

bioRxiv2018

Causal interactions from proteomic profiles: molecular data meets pathway knowledge

Measurement of changes in protein levels and in post-translational modifications, such as phosphorylation, can be highly informative about the phenotypic consequences of genetic differences or about the dynamics of cellular processes. Typically, such proteomic profiles are interpreted intuitively or by simple correlation analysis. Here, we present a computational method to generate causal explanations for proteomic profiles using prior mechanistic knowledge in the literature, as recorded in cellular pathway maps. To demonstrate its potential, we use this method to analyze the cascading events after EGF stimulation of a cell line, to discover new pathways in platelet activation, to identify influential regulators of oncoproteins in breast cancer, to describe signaling characteristics in predefined subtypes of ovarian and breast cancers, and to highlight which pathway relations are most frequently activated across 32 cancer types. Causal pathway analysis, that combines molecular profiles with prior biological knowledge captured in computational form, may become a powerful discovery tool as the amount and quality of cellular profiling rapidly expands. The method is freely available at http://causalpath.org.

systems biology↗