The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures

Motivation: Biomarker discovery from high-dimensional data is a crucial problem with enormous applications in biology and medicine. It is also extremely challenging from a statistical viewpoint, but surprisingly few studies have investigated the relative strengths and weaknesses of the plethora of existing feature selection methods. Methods: We compare 32 feature selection methods on 4 public gene expression datasets for breast cancer prognosis, in terms of predictive performance, stability and functional interpretability of the signatures they produce. Results: We observe that the feature selection method has a significant influence on the accuracy, stability and interpretability of signatures. Simple filter methods generally outperform more complex embedded or wrapper methods, and ensemble feature selection has generally no positive effect. Overall a simple Student's t-test seems to provide the best results. Availability: Code and data are publicly available at http://cbio.ensmp.fr/~ahaury/

EMA - A R package for Easy Microarray data analysis

<p>Abstract</p> <p>Background</p> <p>The increasing number of methodologies and tools currently available to analyse gene expression microarray data can be confusing for non specialist users.</p> <p>Findings</p> <p>Based on the experience of biostatisticians of Institut Curie, we propose both a clear analysis strategy and a selection of tools to investigate microarray gene expression data. The most usual and relevant existing R functions were discussed, validated and gathered in an easy-to-use R package (EMA) devoted to gene expression microarray analysis. These functions were improved for ease of use, enhanced visualisation and better interpretation of results.</p> <p>Conclusions</p> <p>Strategy and tools proposed in the EMA R package could provide a useful starting point for many microarrays users. EMA is part of Comprehensive R Archive Network and is freely available at <url>http://bioinfo.curie.fr/projects/ema/</url>.</p

Cdc42 controls the dilation of the exocytotic fusion pore by regulating membrane tension.

Membrane fusion underlies multiple processes, including exocytosis of hormones and neurotransmitters. Membrane fusion starts with the formation of a narrow fusion pore. Radial expansion of this pore completes the process and allows fast release of secretory compounds, but this step remains poorly understood. Here we show that inhibiting the expression of the small GTPase Cdc42 or preventing its activation with a dominant negative Cdc42 construct in human neuroendocrine cells impaired the release process by compromising fusion pore enlargement. Consequently the mode of vesicle exocytosis was shifted from full-collapse fusion to kiss-and-run. Remarkably, Cdc42-knockdown cells showed reduced membrane tension, and the artificial increase of membrane tension restored fusion pore enlargement. Moreover, inhibiting the motor protein myosin II by blebbistatin decreased membrane tension, as well as fusion pore dilation. We conclude that membrane tension is the driving force for fusion pore dilation and that Cdc42 is a key regulator of this force.journal articleresearch support, non-u.s. gov't2014 Oct 152014 08 20importe

Combination Therapies Targeting Alk-Aberrant Neuroblastoma in Preclinical Models.

BACKGROUND: ALK activating mutations are identified in approximately 10% of newly diagnosed neuroblastomas and ALK amplifications in a further 1-2% of cases. Lorlatinib, a third generation ALK inhibitor, will soon be given alongside induction chemotherapy for children with ALK-aberrant neuroblastoma. However, resistance to single agent treatment has been reported and therapies that improve the response duration are urgently required. We studied the preclinical combination of lorlatinib with chemotherapy, or with the MDM2 inhibitor, idasanutlin, as recent data has suggested that ALK inhibitor resistance can be overcome through activation of the p53-MDM2 pathway. AIMS: To study the preclinical activity of ALK inhibitors alone and combined with chemotherapy or idasanutlin. METHODS: We compared different ALK inhibitors in preclinical models prior to evaluating lorlatinib in combination with chemotherapy or idasanutlin. We developed a triple chemotherapy (CAV: cyclophosphamide, doxorubicin and vincristine) in vivo dosing schedule and applied this to both neuroblastoma genetically engineered mouse models (GEMM) and patient derived xenografts (PDX). RESULTS: Lorlatinib in combination with chemotherapy was synergistic in immunocompetent neuroblastoma GEMM. Significant growth inhibition in response to lorlatinib was only observed in the ALK-amplified PDX model with high ALK expression. In this PDX lorlatinib combined with idasanutlin resulted in complete tumor regression and significantly delayed tumor regrowth. CONCLUSION: In our preclinical neuroblastoma models, high ALK expression was associated with lorlatinib response alone or in combination with either chemotherapy or idasanutlin. The synergy between MDM2 and ALK inhibition warrants further evaluation of this combination as a potential clinical approach for children with neuroblastoma

Genetic markers and phosphoprotein forms of beta-catenin pβ-Cat552 and pβ-Cat675 are prognostic biomarkers of cervical cancer

BACKGROUND: Cervical cancer (CC) remains a leading cause of gynaecological cancer-related mortality world wide and constitutes the third most common malignancy in women. The RAIDs consortium (http://www. raids-fp7.eu/) conducted a prospective European study [BioRAIDs (NCT02428842)] with the objective to stratify CC patients for innovative treatments. A “metagene” of genomic markers in the PI3K pathway and epigenetic regulators had been previously associated with poor outcome [2]. METHODS: To detect new, more specific, targets for treatment of patients who resist standard chemo-radiation, a high-dimensional Cox model was applied to define dominant molecular variants, copy number variations, and reverse phase protein arrays (RPPA). FINDINGS: Survival analysis on 89 patients with all omics data available, suggested loss-of-function (LOF) or activating molecular alterations in nine genes to be candidate biomarkers for worse prognosis in patients treated by chemo-radiation while LOF of ATRX, MED13 as well as CASP8 were associated with better prognosis. When protein expression data by RPPA were factored in, the supposedly low molecula

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe