Biology subjects

Perron, U.

Publications and source records attributed to Perron, U..

8 recordsLinked to original sources

bioRxiv2026

CRISPR-enhanced assessment of variants of unknown significance nominates oncology therapeutic targets and drug repositioning opportunities

Interpreting infrequent somatic variants remains a challenge in cancer genomics. We developed CRISPR-VUS, a framework that uses public Cancer Dependency Map data to identify Dependency-Associated Mutations (DAMs) - variants linked to increased host-gene dependency - with resolution extending to singleton events. Analysis of 977 cell lines across 36 cancer types identified 2,376 DAMs in 1,383 genes, including 1,260 not established as cancer drivers. DAM-bearing genes converge on oncogenic networks, while recurrence in histology-matched tumours, functional-impact predictions, tractability and pharmacological associations enable systematic prioritisation. Prime editing showed that the prioritised NSCLC-specific RTN4IP1-A80T DAM conferred a significant competitive growth advantage in a lung epithelial model, nominating a candidate driver allele. Exploratory pharmacological testing showed a greater maximal response to istaroxime in ATP1B3-I189M-bearing than in ATP1B3-wild-type cells. CRISPR-VUS combines dependency-based rare-variant discovery with evidence-guided prioritisation to nominate candidate drivers, therapeutic targets and drug-repositioning hypotheses. Interactive results are available at https://vus-portal.fht.org/.

bioinformatics↗

bioRxiv2023

Integrative ensemble modelling of cetuximab sensitivity in colorectal cancer PDXs

AO_SCPLOWBSTRACTC_SCPLOWPatient-derived xenografts (PDXs) are tumour fragments engrafted into mice for preclinical studies. PDXs offer clear advantages over simpler in vitro cancer models - such as cancer cell lines (CCLs) and organoids - in terms of structural complexity, heterogeneity, and stromal interactions. We characterised 231 colorectal cancer PDXs at the genomic, transcriptomic, and epigenetic level and measured their response to cetuximab, an EGFR inhibitor in clinical use for metastatic colorectal cancer. After assessing PDXs quality, stability, and molecular concordance with publicly available patient cohorts, we trained, interpreted, and validated an integrated ensemble classifier (CeSta) which takes in input the PDXs multi-omic characterisation and predicts their sensitivity to cetuximab treatment (AUROC > 0.9). Our study shows that large PDX collections can be used to train accurate, interpretable models of drug sensitivity, which 1) better recapitulate patient-derived therapeutic biomarkers than other models trained on CCL data, 2) can be robustly validated across independent PDX cohorts, and 3) can be used for the development of novel therapeutic biomarkers.

bioinformatics↗

bioRxiv2022

CRISPRcleanR WebApp: an interactive web application for processing genome-wide pooled CRISPR-Cas9 viability screen

A limitation of pooled CRISPR-Cas9 viability screens is the high false-positive rate in detecting essential genes arising from copy number-amplified (CNA) regions of the genome. To solve this issue, we developed CRISPRcleanR: a computational method implemented as R/python package and in a dockerized version. CRISPRcleanR detects and corrects biased responses to CRISPR-Cas9 targeting in an unsupervised fashion, accurately reducing false-positive signals, while maintaining sensitivity in identifying relevant genetic dependencies. Here, we present CRISPRcleanRWebApp, a web-based application enabling access to CRISPRcleanR through an intuitive graphical web-interface. CRISPRcleanRWebApp removes the complexity of low-level R/python-language user interactions; it provides a user-friendly access to a complete analytical pipeline, not requiring any data pre-processing, and generating gene-level summaries of essentiality with associated statistical scores; it offers a range of interactively explorable plots, while supporting a wider range of CRISPR guide RNAs libraries with respect to the original package. CRISPRcleanRWebApp is freely available at: https://crisprcleanr-webapp.fht.org/. HighlightsO_LICRISPR-Cas9 screens are widely used for the identification of cancer dependencies C_LIO_LIIn such screens, false-positives arise from targeting copy number amplified genes C_LIO_LICRISPRcleanR corrects this bias in an unsupervised fashion C_LIO_LICRISPRcleanRWebApp is a web user-friendly front-end for CRISPRcleanR C_LI O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/483924v2_ufig1.gif" ALT="Figure 1"> View larger version (56K): org.highwire.dtl.DTLVardef@739480org.highwire.dtl.DTLVardef@1a6c0aorg.highwire.dtl.DTLVardef@ab2b8eorg.highwire.dtl.DTLVardef@1b8a623_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

Reduced gene templates for supervisedanalysis of scale-limited CRISPR-Cas9 fitnessscreens

Pooled genome-wide CRISPR-Cas9 screens are furthering our mechanistic understanding of human biology and have allowed us to identify new oncology therapeutic targets. Scale-limited CRISPR-Cas9 screens - typically employing guide RNA libraries targeting subsets of functionally related genes, individual biological pathways, or portions of the druggable genome - constitute an optimal setting for investigating narrow hypotheses and they are easier to execute on complex models, such as organoids and in vivo models. Different supervised methods are used for the computational analysis of genome-wide CRISPR-Cas9 screens; most are not well suited for scale-limited screens as they require large sets of positive/negative control genes (gene templates) to be included among the screened ones. We have developed a computational framework identifying optimal subsets of known essential and nonessential genes (at different subsampling percentages) that can be used as templates for supervised analyses of scale-limited CRISPR-Cas9 screens, while having a reduced impact on the size of the employed library. HighlightsO_LIScale-limited CRISPR-Cas9 screens are experimentally easier than genome-wide screens C_LIO_LIReference gene templates are used for supervised analyses of genome-wide screens C_LIO_LIReduced templates allow supervised analyses of scale-limited CRISPR-Cas9 screens C_LIO_LIWe present optimal reduced templates and a computational method to assemble them C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=199 SRC="FIGDIR/small/482271v2_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@1dcf4corg.highwire.dtl.DTLVardef@1149e3borg.highwire.dtl.DTLVardef@a04788org.highwire.dtl.DTLVardef@b84fc9_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2021

CoRe: A robustly benchmarked R package for the identification of core fitness genes in genome-wide pooled CRISPR knock-out screens

CRISPR-Cas9 genome-wide screens are being increasingly performed, allowing systematic explorations of cancer dependencies at unprecedented accuracy and scale. Identifying from these screens the genes that are essential for cell survival invariantly across tissues, conditions, and genomic-contexts (core-fitness genes), is of paramount importance to assess the safety profile of candidate therapeutic targets and for elucidating mechanisms involved in tissue-specific genetic diseases. We present CoRe: An R package implementing novel methods for identifying core-fitness genes from joint analyses of multiple CRISPR-Cas9 screens. We demonstrate that CoRe outperforms state-of-the-art tools, yielding more reliable sets of core-fitness genes than existing and widely used reference sets.

bioinformatics↗

bioRxiv2020

Sampling bias and model choice in continuous phylogeography: getting lost on a random walk

Phylogeographic inference allows reconstruction of past geographical spread of pathogens or living organisms by integrating genetic and geographic data. A popular model in continuous phylogeography -- with location data provided in the form of latitude and longitude coordinates -- describes spread as a Brownian motion (Brownian Motion Phylogeography, BMP) in continuous space and time, akin to similar models of continuous trait evolution. Here, we show that reconstructions using this model can be strongly affected by sampling biases, such as the lack of sampling from certain areas. As an attempt to reduce the effects of sampling bias on BMP, we consider the addition of sequence-free samples from under-sampled areas. While this approach alleviates the effects of sampling bias, in most scenarios this will not be a viable option due to the need for prior knowledge of an outbreaks spatial distribution. We therefore consider an alternative model, the spatial {Lambda}-Fleming-Viot process ({Lambda}FV), which has recently gained popularity in population genetics. Despite the {Lambda}FVs robustness to sampling biases, we find that the different assumptions of the {Lambda}FV and BMP models result in different applicabilities, with the {Lambda}FV being more appropriate for scenarios of endemic spread, and BMP being more appropriate for recent outbreaks or colonizations.

evolutionary biology↗

bioRxiv2019

Ambiguity coding allows accurate inference of evolutionary parameters from alignments in an aggregated state-space

How can we best learn the history of a proteins evolution? Ideally, a model of sequence evolution should capture both the process that generates genetic variation and the functional constraints determining which changes are fixed. However, in practical terms the most suitable approach may simply be the one that combines the convenience of easily available input data with the ability to return useful parameter estimates. For example, we might be interested in a measure of the strength of selection (typically obtained using a codon model) or an ancestral structure (obtained using structural modelling based on inferred amino acid sequence and side chain configuration).\n\nBut what if data in the relevant state-space are not readily available? We show that it is possible to obtain accurate estimates of the outputs of interest using an established method for handling missing data. Encoding observed characters in an alignment as ambiguous representations of characters in a larger state-space allows the application of models with the desired features to data that lack the resolution that is normally required. This strategy is viable because the evolutionary path taken through the observed space contains information about states that were likely visited in the \"unseen\" state-space. To illustrate this, we consider two examples with amino acid sequences as input.\n\nWe show that{omega} , a parameter describing the relative strength of selection on non-synonymous and synonymous changes, can be estimated in an unbiased manner using an adapted version of a standard 61-state codon model. Using simulated and empirical data, we find that ancestral amino acid side chain configuration can be inferred by applying a 55-state empirical model to 20-state amino acid data. Where feasible, combining inputs from both ambiguity-coded and fully resolved data improves accuracy. Adding structural information to as few as 12.5% of the sequences in an amino acid alignment results in remarkable ancestral reconstruction performance compared to a benchmark that considers the full rotamer state information. These examples show that our methods permit the recovery of evolutionary information from sequences where it has previously been inaccessible.

evolutionary biology↗

bioRxiv2019

Modelling structural constraints on protein evolution via side-chain conformational states

Few models of sequence evolution incorporate parameters describing protein structure, despite its high conservation, essential functional role and increasing availability. We present a structurally-aware empirical substitution model for amino acid sequence evolution in which proteins are expressed using an expanded alphabet that relays both amino acid identity and structural information. Each character specifies an amino acid as well a rotamer state: the discrete geometric pattern of permitted side-chain atomic positions. By assigning rotamer states in 251,194 protein structures and identifying 4,508,390 substitutions between closely related sequences, we generate a 55-state model that shows that the evolutionary properties of amino acids depend strongly upon side-chain geometry. The model performs as well as or better than traditional 20-state models for divergence time estimation, tree inference and ancestral state reconstruction. We conclude that the concomitant evolution of sequence and structure is a valuable source of phylogenetic information.

evolutionary biology↗