Biology subjects

Iorio, F.

Publications and source records attributed to Iorio, F..

8 recordsLinked to original sources

bioRxiv2018

Tandem duplications lead to loss of fitness effects in CRISPR-Cas9 data

CRISPR-Cas9 gene-editing is widely used to study gene function and is being advanced for therapeutic applications. Structural rearrangements are a ubiquitous feature of cancers and their impact on CRISPR-Cas9 gene-editing has not yet been systematically assessed. Utilising CRISPR-Cas9 knockout screens for 163 cancer cell lines, we demonstrate that targeting tandem amplified regions is highly detrimental to cellular fitness, in contrast to amplifications caused by chromosomal duplications which have little to no effect. Genomically clustered Cas9 double-strand DNA breaks are associated with a strong gene-independent decrease in cell fitness. We systematically identified collateral vulnerabilities in 25% of cancer cells, introduced by tandem amplifications of tissue non-expressed genes. Our analysis demonstrates the importance of structural rearrangements in mediating the effect of CRISPR-Cas9-induced DNA damage, with implications for the use of CRISPR-Cas9 gene-editing technology, and how resulting collateral vulnerabilities are a generalisable strategy to target cancer cells.

genomics↗

bioRxiv2018

JACKS: joint analysis of CRISPR/Cas9 knock-out screens

Genome-wide CRISPR/Cas9 knockout screens are revolutionizing mammalian functional genomics. Their range of applications remains limited by signal variability from different guide RNAs targeting the same gene, which confounds analysis, and dictates large experiment sizes. To address this problem, we report JACKS, a Bayesian method that jointly analyses screens performed with the same guide RNA library. Modeling the variable guide efficacies greatly improves hit identification, and allows a 2.5-fold reduction in required cell numbers without sacrificing performance compared to current analysis standards.

genomics↗

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

CELLector: Genomics Guided Selection of Cancer in vitro Models

The selection of appropriate cancer models is a key prerequisite for maximising translational potential and clinical relevance of in-vitro oncology studies. We developed CELLector: a computational method (implemented in an open source R Shiny application and R package) allowing researchers to select the most relevant cancer cell lines in a patient-genomic guided fashion. CELLector leverages tumour genomics data to identify recurrent sub-types with associated genomic signatures. It then evaluates these signatures in cancer cell lines to rank them and prioritise their selection. This enables users to choose appropriate models for inclusion/exclusion in retrospective analyses and future studies. Moreover this allows bridging data from cancer cell line screens to precisely defined sub-cohorts of primary tumours. Here, we demonstrate usefulness and applicability of our method through example use cases, showing how it can be used to prioritise the development of new in-vitro models and to effectively unveil patient-derived multivariate prognostic and therapeutic markers. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=161 SRC="FIGDIR/small/275032v3_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@11f46b1org.highwire.dtl.DTLVardef@5a207aorg.highwire.dtl.DTLVardef@10a57edorg.highwire.dtl.DTLVardef@12b332_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2017

Unsupervised correction of gene-independent cell responses to CRISPR-Cas9 targeting

Background: Genome editing by CRISPR-Cas9 technology allows large-scale screening of gene essentiality in cancer. A confounding factor when interpreting CRISPR-Cas9 screens is the high false-positive rate in detecting essential genes within copy number amplified regions of the genome. We have developed the computational tool CRISPRcleanR which is capable of identifying and correcting gene-independent responses to CRISPR-Cas9 targeting. CRISPRcleanR uses an unsupervised approach based on the segmentation of single-guide RNA fold change values across the genome, without making any assumption about the copy number status of the targeted genes.\n\nResults Applying our method to existing and newly generated genome-wide essentiality profiles from 15 cancer cell lines, we demonstrate that CRISPRcleanR reduces false positives when calling essential genes, correcting biases within and outside of amplified regions, while maintaining true positive rates. Established cancer dependencies and essentiality signals of amplified cancer driver genes are detectable post-correction. CRISPRcleanR reports sgRNA fold changes and normalised read counts, is therefore compatible with downstream analysis tools, and works with multiple sgRNA libraries.\n\nConclusions CRISPRcleanR is a versatile open-source tool for the analysis of CRISPR-Cas9 knockout screens to identify essential genes.

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↗

bioRxiv2017

GDSCTools for Mining Pharmacogenomic Interactions in Cancer

MotivationLarge pharmacogenomic screenings integrate heterogeneous cancer genomic data sets as well as anti-cancer drug responses on thousand human cancer cell lines. Mining this data to identify new therapies for cancer sub-populations would benefit from common data structures, modular computational biology tools and user-friendly interfaces.\n\nResultsWe have developed GDSCTools: a software aimed at the identification of clinically relevant genomic markers of drug response. The Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerRxgene.org) integrates heterogeneous cancer genomic data sets as well as anti-cancer drug responses on a thousand cancer cell lines. Including statistical tools (ANOVA) and predictive methods (Elastic Net), as well as common data structures, GDSCTools allows users to reproduce published results from GDSC, to analyse their own drug responses or genomic datasets, and to implement new analytical methods.\n\nContactthomas.cokelaer@pasteur.fr

cancer biology↗

bioRxiv2017

Transcription factor activities enhance markers of drug response in cancer

Transcriptional dysregulation is a key feature of cancer. Transcription factors (TFs) are the main link between signalling pathways and the transcriptional regulatory machinery of the cell, positioning them as key oncogenic inductors and therefore potential targets of therapeutic intervention. We implemented a computational pipeline to infer TF regulatory activities from basal gene expression and applied it to publicly available and newly generated RNA-seq data from a collection of 1,010 cancer cell lines and 9,250 primary tumors. We show that the predicted TF activities recapitulate known mechanisms of transcriptional dysregulation in cancer and dissect mutant-specific effects in driver genes. Importantly, we show the potential for predicted TF activities to be used as markers of sensitivity to the inhibition of their upstream regulators. Furthermore, combining these inferred activities with existing pharmacogenomic markers significantly improves the stratification of sensitive and resistant cell lines for several compounds. Our approach provides a framework to link driver genomic alterations with transcriptional dysregulation that helps to predict drug sensitivity in cancer and to dissect its mechanistic determinants.

cancer biology↗