Biology subjects

Luna, A.

Publications and source records attributed to Luna, A..

25 records · Page 2Linked to original sources

bioRxiv2019

Pathway Commons: 2019 Update

Pathway Commons (https://www.pathwaycommons.org) is an integrated resource of publicly available information about biological pathways including biochemical reactions, assembly of biomolecular complexes, transport and catalysis events and physical interactions involving proteins, DNA, RNA, and small molecules (e.g., metabolites and drug compounds). Data is collected from multiple providers in standard formats, including the Biological Pathway Exchange (BioPAX) language and the Proteomics Standards Initiative Molecular Interactions format, and then integrated. Pathway Commons provides biologists with (1) tools to search this comprehensive resource, (2) a download site offering integrated bulk sets of pathway data (e.g., tables of interactions and gene sets), (3) reusable software libraries for working with pathway information in several programming languages (Java, R, Python, and Javascript), and (4) a web service for programmatically querying the entire dataset. Visualization of pathways is supported using the Systems Biological Graphical Notation (SBGN). Pathway Commons currently contains data from 22 databases with 4,794 detailed human biochemical processes (i.e., pathways) and [~]2.3 million interactions. To enhance the usability of this large resource for end-users, we develop and maintain interactive web applications and training materials that enable pathway exploration and advanced analysis.

bioinformatics↗

bioRxiv2019

Interpretable Machine Learning for Perturbation Biology

Systematic perturbation of cells followed by comprehensive measurements of molecular and phenotypic responses provides an informative data resource for constructing computational models of cell biology. Models that generalize well beyond training data can be used to identify combinatorial perturbations of potential therapeutic interest. Major challenges for machine learning on large biological datasets are to find global optima in an enormously complex multi-dimensional solution space and to mechanistically interpret the solutions. To address these challenges, we introduce a hybrid approach that combines explicit mathematical models of dynamic cell biological processes with a machine learning framework, implemented in Tensorflow. We tested the modeling framework on a perturbation-response dataset for a melanoma cell line after drug treatments. The models can be efficiently trained to accurately describe cellular behavior, as tested by cross-validation. Even though completely data-driven and independent of prior knowledge, the resulting de novo network models recapitulate some known interactions. The main predictive application is the identification of combinatorial candidates for cancer therapy. The approach is readily applicable to a wide range of kinetic models of cell biology.

systems biology↗

bioRxiv2019

BET inhibition induces an anti-apoptotic adaptive response and therapeutic vulnerability to MCL1 inhibitors in breast cancer

The development of effective targeted therapies for the treatment of basal-like breast cancers remains challenging. Here, we demonstrate that BET inhibition induces a multi-faceted adaptive response program leading to MCL1 protein-driven evasion of apoptosis in breast cancers. Consequently, co-targeting MCL1 and BET is highly synergistic in in vitro and in vivo breast cancer models. Drug response and genomics analyses revealed that MCL1 copy number alterations, including low-level gains, are selectively enriched in basal-like breast cancers and associated with effective BET and MCL1 co-targeting. The mechanism of adaptive response to BET inhibition involves upregulation of critical lipid metabolism enzymes including the rate-limiting enzyme stearoyl-CoA desaturase (SCD). Changes in the lipid metabolism are associated with increases in cell motility and membrane fluidity as well as transitions in cell morphology and adhesion. The structural changes in the cell membrane leads to re-localization and activation of HER2/EGFR which can be interdicted by inhibiting SCD activity. Active HER2/EGFR, in turn, induces accumulation of MCL1 protein and therapeutic vulnerability to MCL1 inhibitors. The BET protein, lipid metabolism and receptor tyrosine kinase activation cascade is observed in patient cohorts of basal-like and HER2-amplified breast cancers. The high frequency of MCL1 chromosomal amplifications (>30%) and gains (>50%) in basal-like breast cancers suggests that BET and MCL1 co-inhibition may have therapeutic utility in this aggressive subtype.

cancer biology↗

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↗