Biology subjects

Tsherniak, A.

Publications and source records attributed to Tsherniak, A..

15 recordsLinked to original sources

bioRxiv2022

Partial gene suppression improves identification of cancer vulnerabilities when CRISPR-Cas9 knockout is pan-lethal

Hundreds of genome-wide loss-of-function screens have been performed, as part of efforts such as The Cancer Dependency Map, to create a catalog of genetic dependencies in a diverse set of cancer contexts. In recent years, large-scale screening efforts have shifted perturbation technology from RNAi to CRISPR-Cas9, due to the superior efficacy and specificity of CRISPR-Cas9-mediated approaches. However, questions remain about the extent to which partial suppression of gene targets could result in selective dependency across cell lines, potentially revealing a larger set of targetable cancer vulnerabilities than can be identified using CRISPR knockout alone. Here, we use CRISPR-Cas9 and RNAi screening data for more than 400 shared cell lines to represent knockout and partial suppression genetic perturbation modalities and evaluate the utility of each for therapeutic target discovery and the inference of gene function. We find that CRISPR screens identify more dependencies, and yield more accurate predictive models and co-dependency relationships overall. However, RNAi outperforms CRISPR in identifying associations (omics, drug, co-dependencies) with genes that are common dependencies for most cell lines (pan-dependencies). As pan-dependencies occur frequently in the CRISPR dataset (~2,000 genes), using results from both RNAi and CRISPR analyses facilitates the discovery of predictive models and associated co-dependencies for a wider range of gene targets than could be detected using either dataset alone. These findings can aid in the interpretation of contrasting results obtained from CRISPR and RNAi screens and reinforce the importance of partial gene suppression methods in building a cancer dependency map.

cancer biology↗

bioRxiv2021

Chronos: a CRISPR cell population dynamics model

CRISPR loss of function screens are a powerful tool to interrogate cancer biology but are known to exhibit a number of biases and artifacts that can confound the results, such as DNA cutting toxicity, incomplete phenotype penetrance and screen quality bias. Computational methods that more faithfully model the CRISPR biological experiment could more effectively extract the biology of interest than typical current methods. Here we introduce Chronos, an algorithm for inferring gene knockout fitness effects based on an explicit model of the dynamics of cell proliferation after CRISPR gene knockout. Chronos is able to exploit longitudinal CRISPR data for improved inference. Additionally, it accounts for multiple sources of bias and can effectively share information across screens when jointly analyzing large datasets such as Project Achilles and Score. We show that Chronos outperforms competing methods across a range of performance metrics in multiple types of experiments.

bioinformatics↗

bioRxiv2020

Integrated cross-study datasets of genetic dependencies in cancer

CRISPR-Cas9 viability screens are increasingly performed at a genome-wide scale across large panels of cell lines to identify new therapeutic targets for precision cancer therapy. Integrating the datasets resulting from these studies is necessary to adequately represent the heterogeneity of human cancers and to assemble a comprehensive map of cancer genetic vulnerabilities. Here, we integrated the two largest public independent CRISPR-Cas9 screens performed to date (at the Broad and Sanger institutes) by assessing, comparing, and selecting methods for correcting biases due to heterogeneous single guide RNA efficiency, gene-independent responses to CRISPR-Cas9 targeting originated from copy number alterations, and experimental batch effects. Our integrated datasets recapitulate findings from the individual datasets, provide greater statistical power to cancer- and subtype-specific analyses, unveil additional biomarkers of gene dependency, and improve the detection of common essential genes. We provide the largest integrated resources of CRISPR-Cas9 screens to date and the basis for harmonizing existing and future functional genetics datasets.

bioinformatics↗

bioRxiv2020

Global computational alignment of tumor and cell line transcriptional profiles

Cell lines are key tools for preclinical cancer research, but it remains unclear how well they represent patient tumor samples. Identifying cell line models that best represent the features of particular tumor samples, as well as tumor types that lack in vitro model representation, remain important challenges. Gene expression has been shown to provide rich information that can be used to identify tumor subtypes, as well as predict the genetic dependencies and chemical vulnerabilities of cell lines. However, direct comparisons of tumor and cell line transcriptional profiles are complicated by systematic differences, such as the presence of immune and stromal cells in tumor samples and differences in the cancer-type composition of cell line and tumor expression datasets. To address these challenges, we developed an unsupervised alignment method (Celligner) and applied it to integrate several large-scale cell line and tumor RNA-Seq datasets. While our method aligns the majority of cell lines with tumor samples of the same cancer type, it also reveals large differences in tumor/cell line similarity across disease types. Furthermore, Celligner identifies a distinct group of several hundred cell lines from diverse lineages that present a more mesenchymal and undifferentiated transcriptional state and which exhibit distinct chemical and genetic dependencies. This method could thus be used to guide the selection of cell lines that more closely resemble patient tumors and improve the clinical translation of insights gained from cell line models.

cancer biology↗

bioRxiv2020

Post-perturbational transcriptional signatures of cancer cell line vulnerabilities

While chemical and genetic viability screens in cancer cell lines have identified many promising cancer vulnerabilities, simple univariate readouts of cell proliferation fail to capture the complex cellular responses to perturbations. Complementarily, gene expression profiling offers an information-rich measure of cell state that can provide a more detailed account of cellular responses to perturbations. Relatively little is known, however, about the relationship between transcriptional responses to per-turbations and the long-term cell viability effects of those perturbations. To address this question, we integrated thousands of post-perturbational transcriptional profiles from the Connectivity Map with large-scale screens of cancer cell lines viability response to genetic and chemical perturbations. This analysis revealed a generalized transcriptional signature associated with reduced viability across perturbations, which was consistent across post-perturbation time-points, perturbation types, and viability datasets. At a more granular level, we lay out the landscape of treatment-specific expression-viability relationships across a broad panel of drugs and genetic reagents, and we demonstrate that these post-perturbational expression signatures can be used to infer long-term viability. Together, these results help unmask the transcriptional changes that are associated with perturbation-induced viability loss in cancer cell lines.

bioinformatics↗

bioRxiv2020

Gene expression has more power for predicting in vitro cancer cell vulnerabilities than genomics

Achieving precision oncology requires accurate identification of targetable cancer vulnerabilities in patients. Generally, genomic features are regarded as the state-of-the-art method for stratifying patients for targeted therapies. In this work, we conduct the first rigorous comparison of DNA- and expression-based predictive models for viability across five datasets encompassing chemical and genetic perturbations. We find that expression consistently outperforms DNA for predicting vulnerabilities, including many currently stratified by canonical DNA markers. Contrary to their perception in the literature, the most accurate expression-based models depend on few features and are amenable to biological interpretation. This work points to the importance of exploring more comprehensive expression profiling in clinical settings.

cancer biology↗

bioRxiv2019

Multiplexed single-cell profiling of post-perturbation transcriptional responses to define cancer vulnerabilities and therapeutic mechanism of action

Assays to study cancer cell responses to pharmacologic or genetic perturbations are typically restricted to using simple phenotypic readouts such as proliferation rate or the expression of a marker gene. Information-rich assays, such as gene-expression profiling, are generally not amenable to efficient profiling of a given perturbation across multiple cellular contexts. Here, we developed MIX-Seq, a method for multiplexed transcriptional profiling of post-perturbation responses across a mixture of samples with single-cell resolution, using SNP-based computational demultiplexing of single-cell RNA-sequencing data. We show that MIX-Seq can be used to profile responses to chemical or genetic perturbations across pools of 100 or more cancer cell lines, and combine it with Cell Hashing to further multiplex additional experimental conditions, such as multiple post-treatment time points or drug doses. Analyzing the high-content readout of scRNA-seq reveals both shared and context-specific transcriptional response components that can identify drug mechanism of action and can be used to predict long-term cell viability from short-term transcriptional responses to treatment.

cancer biology↗

bioRxiv2019

Pan-cancer single cell RNA-seq uncovers recurring programs of cellular heterogeneity

Cultured cell lines are the workhorse of cancer research, but it is unclear to what extent they recapitulate the cellular heterogeneity observed among malignant cells in tumors, given the absence of a native tumor microenvironment. Here, we used multiplexed single cell RNA-seq to profile ~200 cancer cell lines. We uncovered expression programs that are recurrently heterogeneous within many cancer cell lines and are largely independent of observed genetic diversity. These programs of heterogeneity are associated with diverse biological processes, including cell cycle, senescence, stress and interferon responses, epithelial-to-mesenchymal transition, and protein maturation and degradation. Notably, some of these recurrent programs recapitulate those seen in human tumors, suggesting a prominent role of intrinsic plasticity in generating intra-tumoral heterogeneity. Moreover, the data allowed us to prioritize specific cell lines as model systems of cellular plasticity. We used two such models to demonstrate the dynamics, regulation and drug sensitivities associated with a cancer senescence program also observed in human tumors. Our work describes the landscape of cellular heterogeneity in diverse cancer cell lines, and identifies recurrent patterns of expression heterogeneity that are shared between tumors and specific cell lines and can thus be further explored in follow up studies.

cancer biology↗

bioRxiv2019

Non-oncology drugs are a source of previously unappreciated anti-cancer activity

Anti-cancer uses of non-oncology drugs have been found on occasion, but such discoveries have been serendipitous and rare. We sought to create a public resource containing the growth inhibitory activity of 4,518 drugs tested across 578 human cancer cell lines. To accomplish this, we used PRISM, which involves drug treatment of molecularly barcoded cell lines in pools. Relative barcode abundance following treatment thus reflects cell line viability. We found that an unexpectedly large number of non-oncology drugs selectively inhibited subsets of cancer cell lines. Moreover, the killing activity of the majority of these drugs was predictable based on the molecular features of the cell lines. Follow-up of several of these compounds revealed novel mechanisms. For example, compounds that kill by inducing PDE3A-SLFN12 complex formation; vanadium-containing compounds whose killing is dependent on the sulfate transporter SLC26A2; the alcohol dependence drug disulfiram, which kills cells with low expression of metallothioneins; and the anti-inflammatory drug tepoxalin, whose killing is dependent on high expression of the multi-drug resistance gene ABCB1. These results illustrate the potential of the PRISM drug repurposing resource as a starting point for new oncology therapeutic development. The resource is available at https://depmap.org.

cancer biology↗

bioRxiv2019

Extracting Biological Insights from the Project Achilles Genome-Scale CRISPR Screens in Cancer Cell Lines

One of the main goals of the Cancer Dependency Map project is to systematically identify cancer vulnerabilities across cancer types to accelerate therapeutic discovery. Project Achilles serves this goal through the in vitro study of genetic dependencies in cancer cell lines using CRISPR/Cas9 (and, previously, RNAi) loss-of-function screens. The project is committed to the public release of its experimental results quarterly on the DepMap Portal (https://depmap.org), on a pre-publication basis. As the experiment has evolved, data processing procedures have changed. Here we present the current and projected Achilles processing pipeline, including recent improvements and the analyses that led us to adopt them, spanning data releases from early 2018 to the first quarter of 2020. Notable changes include quality control metrics, calculation of probabilities of dependency, and correction for screen quality and other biases. Developing and improving methods for extracting biologically-meaningful scores from Achilles experiments is an ongoing process, and we will continue to evaluate and revise data processing procedures to produce the best results.

cancer biology↗

bioRxiv2019

Agreement between two large pan-cancer CRISPR-Cas9 gene dependency datasets

Genome-scale CRISPR-Cas9 viability screens performed in cancer cell lines provide a systematic approach to identify cancer dependencies and new therapeutic targets. As multiple large-scale screens become available, a formal assessment of the reproducibility of these experiments becomes necessary. We analyzed data from recently published pan-cancer CRISPR-Cas9 screens performed at the Broad and Sanger institutes. Despite significant differences in experimental protocols and reagents, we found that the screen results are highly concordant across multiple metrics with both common and specific dependencies jointly identified across the two studies. Furthermore, robust biomarkers of gene dependency found in one dataset are recovered in the other. Through further analysis and replication experiments at each institute, we found that batch effects are driven principally by two key experimental parameters: the reagent library and the assay length. These results indicate that the Broad and Sanger CRISPR-Cas9 viability screens yield robust and reproducible findings.

cancer biology↗

bioRxiv2018

Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration

The availability of multiple datasets together comprising hundreds of genome-scale RNAi viability screens across a diverse range of cancer cell lines presents new opportunities for understanding cancer vulnerabilities. Integrated analyses of these data to assess differential dependency across genes and cell lines are challenging due to confounding factors such as batch effects and variable screen quality, as well as difficulty assessing gene dependency on an absolute scale. To address these issues, we incorporated estimation of cell line screen quality parameters and hierarchical Bayesian inference into an analytical framework for analyzing RNAi screens (DEMETER2; https://depmap.org/R2-D2). We applied this model to individual large-scale datasets and show that it substantially improves estimates of gene dependency across a range of performance measures, including identification of gold-standard essential genes as well as agreement with CRISPR-Cas9-based viability screens. This model also allows us to effectively integrate information across three large RNAi screening datasets, providing a unified resource representing the most extensive compilation of cancer cell line genetic dependencies to date.

genomics↗

bioRxiv2018

Inhibition of mitochondrial ferredoxin 1 (FDX1) prevents adaptation to proteotoxic stress

The mechanisms used by cancer cells to resist the severe disruption in protein homeostasis caused by proteasome inhibitors remain obscure. Here, we show this resistance correlates with a metabolic shift from glycolysis to oxidative phosphorylation (OXPHOS). Employing small molecule screens, we identified a striking overlap between compounds that preferentially impede the growth of proteasome inhibitor-resistant cancer cells and those that block the growth of high OXPHOS cells. Elesclomol potently exhibits both characteristics. Using genome-wide CRISPR/Cas9-based screening, in vitro validation and NMR spectroscopy we identify mitochondrial protein ferredoxin 1 (FDX1), a critical component of mitochondrial iron-sulfur (Fe-S) cluster biosynthesis, as the primary target of elesclomol. In a mouse model of multiple myeloma, inhibition of FDX1 with elesclomol significantly attenuated the emergence of proteasome inhibitor-resistance and markedly prolonged survival. Our work reveals that the mitochondrial Fe-S cluster pathway is a targetable vulnerability in cancers that are resistant to increased proteotoxic burden.

cancer biology↗

bioRxiv2017

A unified web platform for network-based analyses of genomic data

Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to quantitatively compare the signal-to-noise ratio of different networks, the biology they describe, and to identify the optimal network to interpret a particular genetic dataset. Via GeNets users can train a machine-learning model (Quack) to make such comparisons; and they can execute, store, and share analyses of genetic and RNA sequencing datasets.

genomics↗

bioRxiv2017

Computational correction of copy-number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells

The CRISPR-Cas9 system has revolutionized gene editing both on single genes and in multiplexed loss-of-function screens, enabling precise genome-scale identification of genes essential to proliferation and survival of cancer cells. However, previous studies reported that an anti-proliferative effect of Cas9-mediated DNA cleavage confounds such measurement of genetic dependency, particularly in the setting of copy number gain1-4. We performed genome-scale CRISPR-Cas9 essentiality screens on 342 cancer cell lines and found that this effect is common to all lines, leading to false positive results when targeting genes in copy number amplified regions. We developed CERES, a computational method to estimate gene dependency levels from CRISPR-Cas9 essentiality screens while accounting for the copy-number-specific effect, as well as variable sgRNA activity. We applied CERES to sets of screens performed with different sgRNA libraries and found that it reduces false positive results and provides meaningful estimates of sgRNA activity. As a result, the application of CERES improves confidence in the interpretation of genetic dependency data from CRISPR-Cas9 essentiality screens of cancer cell lines.

genomics↗