Biology subjects

Smirnov, P.

Publications and source records attributed to Smirnov, P..

16 recordsLinked to original sources

bioRxiv2025

Nucleotide insufficiency induced by p53 deficiency leads to replication stress driving genomic instability

P53 prevents DNA damage by inducing repair processes, cell cycle arrest or apoptosis. P53 loss leads to replication stress and genomic instability, yet the mechanisms underlying these effects and their contribution to catastrophic genomic events such as chromothripsis remain poorly understood. Using patient-derived fibroblasts with germline p53 variants, that spontaneously undergo chromothripsis, and p53-downregulated fibroblasts, we discovered that p53 loss leads to aberrant transcriptional upregulation, increasing nucleotide consumption while simultaneously decreasing nucleotide biosynthesis. This imbalance in production and consumption results in insufficient nucleotide pools, leading to replication stress and genomic instability, which are rescued by nucleoside supplementation or transcription normalization. The replication stress triggers telomere dysfunction, micronuclei formation, and ultimately chromothripsis. Emerging dominant chromothriptic clones exhibit normal DNA replication, telomere stabilization, and ecDNA, highlighting critical features for clonal selection. Hence, p53 coordinates transcription and nucleotide pools, crucial for maintaining genomic stability and preventing early cancer development.

cancer biology↗

bioRxiv2023

Similarity metric learning on perturbational datasets improves functional identification of perturbations

Analysis of high-throughput perturbational datasets, including the Next Generation Connectivity Map (L1000) and the Cell Painting projects, uses similarity metrics to identify perturbations or disease states that induce similar changes in the biological feature space. Similarities among perturbations are then used to identify drug mechanisms of action, to nominate therapeutics for a particular disease, and to construct bio-logical networks among perturbations and genes. Standard similarity metrics include correlations, cosine distance and gene set enrichment methods, but these methods operate on the measured features without refinement by transforming the measurement space. We introduce Perturbational Metric Learning (PeML), a weakly supervised similarity metric learning method to learn a data-driven similarity function that maximizes discrimination of replicate signatures by transforming the biological measurements into an intrinsic, dataset-specific basis. The learned similarity functions show substantial improvement for recovering known biological relationships, like mechanism of action identification. In addition to capturing a more meaningful notion of similarity, data in the transformed basis can be used for other analysis tasks, such as classification and clustering. Similarity metric learning is a powerful tool for the analysis of large biological datasets.

bioinformatics↗

bioRxiv2023

Detection of circular RNAs and their potential as biomarkers predictive of drug response

The introduction of high-throughput sequencing technologies has allowed for comprehensive RNA species detection, both coding and non-coding, which opened new avenues for the discovery of predictive and prognostic biomarkers. However the consistency of the detection of different RNA species depends on the RNA selection protocol used for RNA-sequencing. While preliminary reports indicated that non-coding RNAs, in particular circular RNAs, constitute a rich source of biomarkers predictive of drug response, the reproducibility of this novel class of biomarkers has not been rigorously investigated. To address this issue, we assessed the inter- lab consistency of circular RNA expression in cell lines profiled in large pharmacogenomic datasets. We found that circular RNA expression quantified from rRNA-depleted RNA-seq data is stable and yields robust prognostic markers in cancer. On the other hand, quantification of the expression of circular RNA from poly(A)-selected RNA-seq data yields highly inconsistent results, calling into question results from previous studies reporting their potential as predictive biomarkers in cancer. We have also identified median expression of transcripts and transcript length as potential factors influencing the consistency of RNA detection. Our study provides a framework to quantitatively assess the stability of coding and non-coding RNA expression through the analysis of biological replicates within and across independent studies.

bioinformatics↗

bioRxiv2022

Meta-analysis of preclinical pharmacogenomic studies to discover robust and translatable biomarkers of drug response

Preclinical pharmacogenomic studies provide an opportunity to discover novel biomarkers for drug response. However, pharamcogenomic studies linking gene expression profiles to drug response do not always agree on the significance or strength of biomarkers. We apply a statistical meta-analysis approach to 7 large independent pharmacogenomic studies, testing for tissue-specific gene expression markers predictive of response among cancer cell lines. We found 4,338 statistically-significant biomarkers across 8 tissue types and 34 drugs. Significant biomarkers were found to be closer than random to drug targets in a gene network built on pathway co-membership (average distance of 2 vs 2.9). However, functional relationships with the drug target did not predict reproducibility across studies. To validate these biomarkers, we utilized 10 clinical datasets, allowing 42/4338 biomarkers to be assessed for clinical translation. Of the 42 candidate biomarkers, the expression of ODC1 was found to be significantly predictive of Paclitaxel response as a neoadjuvant treatment of breast carcinoma across 2 independent clinical studies of >200 patients each. We expect that as more clinical transcriptomics data matched with response are available, our results can be used to prioritize which genes to evaluate as clinical biomarkers of drug response.

cancer biology↗

bioRxiv2022

Insulin Receptor Loss Impairs Mammary Tumorigenesis in Mice

Breast cancer (BC) prognosis and outcome are adversely affected by obesity. Hyperinsulinemia, common in obese state, is associated with higher risk of death and recurrence in BC. Up to 80% of breast cancers overexpress the insulin receptor (INSR), which correlates with worse prognosis. INSR role in mammary tumorigenesis was tested by generating MMTV-driven polyoma middle T (PyMT) and ErbB2/Her2 BC mouse models, respectively, with coordinate mammary epithelium-restricted deletion of INSR. In both models, deletion of either one or both copies of INSR led to a marked delay in tumor onset and burden. Longitudinal phenotypic characterization of mouse tumours and cells revealed that INSR deletion impacted tumour initiation, not progression and metastasis. INSR upheld a bioenergetic phenotype in non-transformed mammary epithelial cells, independent of its kinase activity. Similarity of phenotypes elicited by deletion of one or both copies of INSR suggest a dose-dependent threshold for INSR impact on mammary tumorigenesis.

cancer biology↗

bioRxiv2021

PharmacoDB 2.0 : Improving scalability and transparency of in vitro pharmacogenomics analysis

Cancer pharmacogenomics studies provide valuable insights into disease progression and associations between genomic features and drug response. PharmacoDB integrates multiple cancer pharmacogenomics datasets profiling approved and investigational drugs across cell lines from diverse tissue types. The web-application enables users to efficiently navigate across datasets, view and compare drug dose-response data for a specific drug-cell line pair. In the new version of PharmacoDB (version 2.0, https://pharmacodb.ca/), we present: (i) new datasets such as NCI-60, the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) dataset, as well as updated data from the Genomics of Drug Sensitivity in Cancer (GDSC) and the Genentech Cell Line Screening Initiative (gCSI); (ii) implementation of FAIR data pipelines using ORCESTRA and PharmacoDI; (iii) enhancements to drug response analysis such as tissue distribution of dose-response metrics and biomarker analysis; (iv) improved connectivity to drug and cell line databases in the community. The web interface has been rewritten using a modern technology stack to ensure scalability and standardization to accommodate growing pharmacogenomics datasets. PharmacoDB 2.0 is a valuable tool for mining pharmacogenomics datasets, comparing and assessing drug response phenotypes of cancer models. HIGHLIGHTSO_LIPharmacoDB 2.0 includes new and updated large pharmacogenomic datasets. The data processing for PharmacoDB is made fully reproducible through the use of the ORCESTRA platform and automated data ingestion pipelines C_LIO_LIThe new release contains enriched annotations for drugs and cell lines via connectivity to external databases, as well as new analytical methods for tissue-specific and pan-cancer biomarker discovery C_LIO_LIThe new version of PharmacoDB incorporates a scalable and reproducible framework that can accelerate the implementation of analytical pipelines including machine learning/AI for biomarker discovery in the future C_LI

bioinformatics↗

bioRxiv2021

Drug Sensitivity Prediction From Cell Line-Based Pharmacogenomics Data: Guidelines for Developing Machine Learning Models

The goal of precision oncology is to tailor treatment for patients individually using the genomic profile of their tumors. Pharmacogenomics datasets such as cancer cell lines are among the most valuable resources for drug sensitivity prediction, a crucial task of precision oncology. Machine learning methods have been employed to predict drug sensitivity based on the multiple omics data available for large panels of cancer cell lines. However, there are no comprehensive guidelines on how to properly train and validate such machine learning models for drug sensitivity prediction. In this paper, we introduce a set of guidelines for different aspects of training gene expression-based predictors using cell line datasets. These guidelines provide extensive analysis of the generalization of drug sensitivity predictors, and challenge many current practices in the community including the choice of training dataset and measure of drug sensitivity. Application of these guidelines in future studies will enable the development of more robust preclinical biomarkers.

bioinformatics↗

bioRxiv2020

ORCESTRA: a platform for orchestrating and sharing high-throughput pharmacogenomic analyses

Reproducibility is essential to open science, as there is limited relevance for findings that can not be reproduced by independent research groups, regardless of its validity. It is therefore crucial for scientists to describe their experiments in sufficient detail so they can be reproduced, scrutinized, challenged, and built upon. However, the intrinsic complexity and continuous growth of biomedical data makes it increasingly difficult to process, analyze, and share with the community in a FAIR (findable, accessible, interoperable, and reusable) manner. To overcome these issues, we created a cloud-based platform called ORCESTRA (orcestra.ca), which provides a flexible framework for the reproducible processing of multimodal biomedical data. It enables processing of clinical, genomic and perturbation profiles of cancer samples through automated processing pipelines that are user-customizable. ORCESTRA creates integrated and fully documented data objects with persistent identifiers (DOI) and manages multiple dataset versions, which can be shared for future studies.

bioinformatics↗

bioRxiv2020

Bimodality of gene expression in cancer patient tumors as interpretable biomarkers for drug sensitivity

Identifying biomarkers predictive of cancer cells response to drug treatment constitutes one of the main challenges in precision oncology. Recent large-scale cancer pharmacogenomic studies have boosted the research for finding predictive biomarkers by profiling thousands of human cancer cell lines at the molecular level and screening them with hundreds of approved drugs and experimental chemical compounds. Many studies have leveraged these data to build predictive models of response using various statistical and machine learning methods. However, a common challenge in these methods is the lack of interpretability as to how they make the predictions and which features were the most associated with response, hindering the clinical translation of these models. To alleviate this issue, we develop a new machine learning pipeline based on the recent LOBICO approach that explores the space of bimodally expressed genes in multiple large in vitro pharmacogenomic studies and builds multivariate, nonlinear, yet interpretable logic-based models predictive of drug response. Using our method, we used a compendium of three of the largest pharmacogenomic data sets to build robust and interpretable models for 101 drugs that span 17 drug classes with high validation rate in independent datasets.

cancer biology↗

bioRxiv2019

Creating reproducible pharmacogenomic analysis pipelines

The field of Pharmacogenomics presents great challenges for researchers that are willing to make their studies reproducible and shareable. This is attributed to the generation of large volumes of high-throughput multimodal data, and the lack of standardized workflows that are robust, scalable, and flexible to perform large-scale analyses. To address this issue, we developed pharmacogenomic workflows in the Common Workflow Language to process two breast cancer datasets in a reproducible and transparent manner. Our pipelines combine both pharmacological and molecular profiles into a portable data object that can be used for future analyses in cancer research. Our data objects and workflows are shared on Harvard Dataverse and Code Ocean where they have been assigned a unique Digital Object Identifier, providing a level of data provenance and a persistent location to access and share our data with the community.

bioinformatics↗

bioRxiv2019

Karyotypic divergence confounds cellular phenotypes in large pharmacogenomic studies

BackgroundSomatic copy-number alterations that affect large genomic regions are a major source of genomic diversity in cancer and can impact cellular phenotypes. Clonal heterogeneity within cancer cell lines can affect phenotypic presentation, including drug response.\n\nMethodsWe aggregated and analyzed SNP and copy number profiles from six pharmacogenomic datasets encompassing 1,691 cell lines screened for 13 molecules. To look for sources of genotype and karyotype discordances, we compared SNP genotypes and segmental copy-ratios across 5 kb genomic bins. To assess the impact of genomic discordances on pharmacogenomic studies, we assessed gene expression and drug sensitivity data for compared discordant and concordant lines.\n\nResultsWe found 6/1,378 (0.4%) cell lines profiled in two studies to be discordant in both genotypic and karyotypic identity, 51 (3.7%) discordant in genotype, 97 (7.0%) discordant in karyotype, and 125 (9.1%) potential misidentifications. We highlight cell lines REH, NCI-H23 and PSN1 as having drug response discordances that may hinge on divergent copy-number q\n\nConclusionsOur study highlights the low level of misidentification as evidence of effective cell line authentication standards in recent pharmacogenomic studies. However, the proclivity of cell lines to acquire somatic copy-number variants can alter the cellular phenotype, resulting in a biological and predictable effects on drug sensitivity. These findings highlight the need for verification of cell line copy number profiles to inform interpretation of drug sensitivity data in biomedical studies.

cancer biology↗

bioRxiv2018

Integrative Pharmacogenomics Analysis of Patient Derived Xenografts

One of the key challenges in cancer precision medicine is finding robust biomarkers of drug response. Patient-derived tumor xenografts (PDXs) have emerged as reliable preclinical models since they better recapitulate tumor response to chemo- and targeted therapies. However, the lack of standard tools poses a challenge in the analysis of PDXs with molecular and pharmacological profiles. Efficient storage, access and analysis is key to the realization of the full potential of PDX pharmacogenomic data. We have developed Xeva (XEnograft Visualization & Analysis), an open-source software package for processing, visualization and integrative analysis of a compendium of in vivo pharmacogenomic datasets. The Xeva package follows the PDX minimum information (PDX-MI) standards and can handle both replicate-based and 1x1x1 experimental designs. We used Xeva to characterize the variability of gene expression and pathway activity across passages. We found that only a few genes and pathways have passage specific alterations (median intraclass correlation of 0.53 for genes and positive enrichment score for 92.5% pathways). For example, activity of the mRNA 3-end processing and elongation arrest and recovery pathways were strongly affected by model passaging (gene set enrichment analysis false discovery rate [FDR] <5%). We then leveraged our platform to link the drug response and the pathways whose activity is consistent across passages by mining the Novartis PDX Encyclopedia (PDXE) data containing 1,075 PDXs spanning 5 tissue types and 62 anticancer drugs. We identified 87 pathways significantly associated with response to 51 drugs (FDR < 5%), including associations such as erlotinib response and signaling by EGFR in cancer pathways and MAP kinase activation in TLR cascade and binimetinib response. Among the significant pathway-drug associations, we found novel biomarkers based on gene expressions, Copy Number Aberrations (CNAs) and mutations predictive of drug response (concordance index > 0.60; FDR < 0.05). Xeva provides a flexible platform for integrative analysis of preclinical in vivo pharmacogenomics data to identify biomarkers predictive of drug response, a major step toward precision oncology.

bioinformatics↗

bioRxiv2018

Modeling cellular response in large-scale radiogenomic databases to advance precision radiotherapy

Radiotherapy is integral to the care of a majority of cancer patients. Despite differences in tumor responses to radiation (radioresponse), dose prescriptions are not currently tailored to individual patients. Recent large-scale cancer cell line databases hold the promise of unravelling the complex molecular arrangements underlying cellular response to radiation, which is critical to novel predictive biomarker discovery. Here, we present RadioGx, a computational platform for integrative analyses of radioresponse using radiogenomic databases. We first used RadioGx to investigate the robustness of radioresponse assays and indicators. We then combined radioresponse and genome-wide molecular data with established radiobiological models to predict molecular pathways that are relevant for individual tissue types and conditions. We also applied RadioGx to pharmacogenomic data to identify several classes of drugs whose effects correlate with radioresponse. RadioGx provides a unique computational toolbox to advance preclinical research for radiation oncology and precision medicine.

bioinformatics↗

bioRxiv2017

PharmacoDB: an integrative database for mining in vitro anticancer drug screening studies

Recent pharmacogenomic studies profiled large panels of cancer cell lines against hundreds of approved drugs and experimental chemical compounds. The overarching goal of these screens is to measure sensitivity of cell lines to chemical perturbation, correlate these measures to genomic features, and thereby develop novel predictors of drug response. However, leveraging this valuable data is challenging due to the lack of standards for annotating cell lines and chemical compounds, and quantifying drug response. Moreover, it has been recently shown that the complexity and complementarity of the experimental protocols used in the field result in high levels of technical and biological variation in the in vitro pharmacological profiles. There is therefore a need for new tools to facilitate rigorous comparison and integrative analysis of large-scale drug screening datasets. To address this issue, we have developed PharmacoDB (pharmacodb.pmgenomics.ca), a database integrating the largest pharmacogenomic studies published to date. Here, we describe how the curation of cell line and chemical compound identifiers maximizes the overlap between datasets and how users can leverage such data to compare and extract robust drug phenotypes. PharmacoDB provides a unique resource to mine a compendium of curated pharmacogenomic datasets that are otherwise disparate and difficult to integrate.\n\nKey pointsO_LICuration of cell line and drug identifiers in the largest pharmacogenomic studies published to date\nC_LIO_LIUniform processing of drug sensitivity data to reduce heterogeneity across studies\nC_LIO_LIMultiple drug response summary metrics enabling visual comparison and integrative analysis\nC_LI

bioinformatics↗

bioRxiv2017

Gene isoforms as expression-based biomarkers predictive of drug response in vitro

BackgroundOne of the main challenges in precision medicine is the identification of molecular features associated to drug response to provide clinicians with tools to select the best therapy for each individual cancer patient. The recent adoption of next-generation sequencing technologies enables accurate profiling of not only gene expression but also alternatively-spliced transcripts in large-scale pharmacogenomic studies. Given that altered mRNA splicing has been shown to be prominent in cancers, linking this feature to drug response will open new avenues of research in biomarker discovery.\n\nMethodsTo address the lack of reproducibility of drug sensitivity measurements across studies, we developed a meta-analytical framework combining the pharmacological data generated within the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC). Predictive models are fitted with CCLE RNA-seq data as predictor variables, controlled for tissue type, and combined GDSC and CCLE drug sensitivity values as dependent variables.\n\nResultsWe first validated the biomarkers identified from GDSC and CCLE using an existing pharmacogenomic dataset of 70 breast cancer cell lines. We further selected four drugs with the most promising biomarkers to test whether their predictive value is robust to change in pharmacological assay. We successfully validated 10 isoform-based biomarkers predictive of drug response in breast cancer, including TGFA-001 for the MEK tyrosine kinase inhibitor (TKI) AZD6244, DUOX-001 for the EGFR inhibitor erlotinib, and CPEB4-001 transcript expression associated with lack of sensitivity to paclitaxel.\n\nConclusionThe results of our meta-analysis of pharmacogenomic data suggest that isoforms represent a rich resource for biomarkers predictive of response to chemo- and targeted therapies. Our study also showed that the validation rate for this type of biomarkers is low (<50%) for most drugs, supporting the requirements for independent datasets to identify reproducible predictors of response to anticancer drugs.

bioinformatics↗

bioRxiv2016

Tissue specificity of in vitro drug sensitivity

Research in oncology traditionally focuses on specific tissue type from which the cancer develops. However, advances in high-throughput molecular profiling technologies have enabled the comprehensive characterization of molecular aberrations in multiple cancer types. It was hoped that these large-scale datasets would provide the foundation for a paradigm shift in oncology which would see tumors being classified by their molecular profiles rather than tissue types, but tumors with similar genomic aberrations may respond differently to targeted therapies depending on their tissue of origin. There is therefore a need to reassess the potential association between pharmacological response and tissue of origin for therapeutic drugs, and to test how these associations translate from preclinical to clinical settings.\n\nIn this paper, we investigate the tissue specificity of drug sensitivities in large-scale pharmacological studies and compare these associations to those found in clinical trial descriptions. Our meta-analysis of the four largest in vitro drug screening datasets indicates that tissue of origin is strongly associated with drug response. We identify novel tissue-drug associations, which may present exciting new avenues for drug repurposing. One caveat is that the vast majority of the significant associations found in preclinical settings do not concur with clinical observations. Accordingly, our results call for more testing to find the root cause of the discrepancies between preclinical and clinical observations.

bioinformatics↗