Biology subjects

Dempster, J. M.

Publications and source records attributed to Dempster, J. M..

13 recordsLinked to original sources

bioRxiv2026

Dissecting context-dependent cancer vulnerabilities using Perturb-seq

Background CRISPR-mediated viability assays in diverse cancer cell lines have informed cancer biology and precision medicine, but cell fitness is not the only cancer-relevant phenotype. Gene expression profiling provides insight into cellular stress, inflammation, and differential state, while still identifying activation of cell-death pathways. Perturb-seq allows scalable functional genomics screening of expression phenotypes at single-cell resolution, however existing datasets cover only a small number of work-horse cell lines. Results We produced a proof-of-concept Perturb-seq dataset targeting 100 genes in 16 diverse cancer cell lines. In the process, we established methods to address single-cell technical artifacts, identified Cas9-mediated chromosomal aberrations and assessed screen quality. Even with a limited library, we observed common signatures of deleting essential genes as well as context-specific responses based on intrinsic genomic properties of the models. For example, we inferred a previously undescribed relationship between dependence on the ER-golgi transport gene immediate early response 3 interacting protein 1 (IER3IP1) and oxidative stress, demonstrating the potential of integrated Perturb-seq for hypothesis generation. Conclusions We established a framework for building a comprehensive map of post-perturbational transcriptional phenotypes using parallel Perturb-seq experiments across multiple cell lines. We demonstrated that integrated Perturb-seq experiments spanning diverse contexts enable hypotheses about gene function specific to tissue types or cancer subtypes - suggesting large-scale, genome-wide datasets would offer invaluable insight into the highly context-dependent nature of cancer biology.

cancer biology↗

bioRxiv2025

Natural language processing of gene descriptions for overrepresentation analysis with GeneTEA

Overrepresentation analysis (ORA) is used to identify the biological relationships in a list of genes by testing gene sets for enrichment in the query. However, the inconsistent definition and highly overlapping nature of gene set databases can make interpreting ORA results difficult. Here, we introduce GeneTEA, a model that takes in free-text gene descriptions and incorporates several natural language processing methods to learn a sparse gene-by-term embedding, which can be treated as a de novo gene set database. We benchmark performance against other popular ORA tools and find that only GeneTEA properly controls false discovery while consistently surfacing relevant biology. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/646026v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@145a76aorg.highwire.dtl.DTLVardef@1f221d3org.highwire.dtl.DTLVardef@18acdacorg.highwire.dtl.DTLVardef@1c5106a_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2022

Mapping the landscape of genetic dependencies in chordoma

Identifying the spectrum of genes required for cancer cell survival can reveal essential cancer circuitry and therapeutic targets, but such a map remains incomplete for many cancer types. We applied genome-scale CRISPR-Cas9 loss-of-function screens to map the landscape of selectively essential genes in chordoma, a bone cancer with few validated targets. This approach confirmed a known chordoma dependency, TBXT (T; brachyury), and identified a range of additional dependencies, including PTPN11, ADAR, PRKRA, LUC7L2, SRRM2, SLC2A1, SLC7A5, FANCM, and THAP1. CDK6, SOX9, and EGFR, genes previously implicated in chordoma biology, were also recovered. We found genomic and transcriptomic features that predict specific dependencies, including interferon-stimulated gene expression, which correlates with ADAR dependence and is elevated in chordoma. Validating the therapeutic relevance of dependencies, small-molecule inhibitors of SHP2, encoded by PTPN11, had potent preclinical efficacy against chordoma. Our results generate an emerging map of chordoma dependencies to enable biological and therapeutic hypotheses.

cancer biology↗

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

Transcriptional plasticity drives leukemia immune escape

Relapse of acute myeloid leukemia (AML) after allogeneic bone marrow transplantation (alloSCT) has been linked to immune evasion due to reduced expression of major histocompatibility complex class II (MHC-II) proteins through unknown mechanisms. We developed CORENODE, a computational algorithm for genome-wide transcription network decomposition, that identified the transcription factors (TFs) IRF8 and MEF2C as positive regulators and MYB and MEIS1 as negative regulators of MHC-II expression in AML cells. We show that reduced MHC-II expression at relapse is transcriptionally driven by combinatorial changes in the levels of these TFs, acting both independently and through the MHC-II coactivator CIITA. Beyond the MHC-II genes, MYB and IRF8 antagonistically regulate a broad genetic program responsible for cytokine signaling and T-cell stimulation that displays reduced expression at relapse. A small number of cells with altered TF levels and silenced MHC-II expression are present at the time of initial leukemia diagnosis, likely contributing to eventual relapse. Our findings reveal an adaptive transcriptional mechanism of AML evolution after allogenic transplantation whereby combinatorial fluctuations of TF levels under immune pressure result in selection of cells with a silenced T-cell stimulation program.

genomics↗

bioRxiv2021

A distinct core regulatory module enforces oncogene expression in KMT2A-rearranged leukemia

A small set of lineage-restricted transcription factors (TFs), termed core regulatory circuitry (CRC), control cell identity and malignant transformation. Here, we integrated gene dependency, chromatin architecture and TF perturbation datasets to characterize 31 core TFs in acute myeloid leukemia (AML). Contrary to a widely accepted model, we detected a modular CRC structure with hierarchically organized, partially redundant and only sparsely interconnected modules of core TFs controlling distinct genetic programs. Rapid TF degradation followed by measurement of genome-wide transcription rates revealed that core TFs directly regulate dramatically fewer genes than previously assumed. Leukemias carrying KMT2A (MLL) rearrangements depend on the IRF8/MEF2 axis to directly enforce expression of the key oncogenes MYC, HOXA9 and BCL2. Our datasets provide an evolving model of CRC organization in human cells, and a resource for further inquiries into and therapeutic targeting of aberrant transcriptional circuits in cancer.

genomics↗

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

Non-canonical open reading frames encode functional proteins essential for cancer cell survival

A key question in genome research is whether biologically active proteins are restricted to the [~]20,000 canonical, well-annotated genes, or rather extend to the many non-canonical open reading frames (ORFs) predicted by genomic analyses. To address this, we experimentally interrogated 553 ORFs nominated in ribosome profiling datasets. Of these 553 ORFs, 57 (10%) induced a viability defect when the endogenous ORF was knocked out using CRISPR/Cas9 in 8 human cancer cell lines, 257 (46%) showed evidence of protein translation when ectopically expressed in HEK293T cells, and 401 (73%) induced gene expression changes measured by transcriptional profiling following ectopic expression across 4 cell types. CRISPR tiling and start codon mutagenesis indicated that the biological effects of these non-canonical ORFs required their translation as opposed to RNA-mediated effects. We selected one of these ORFs, G029442--renamed GREP1 (Glycine-Rich Extracellular Protein-1)--for further characterization. We found that GREP1 encodes a secreted protein highly expressed in breast cancer, and its knock-out in 263 cancer cell lines showed preferential essentiality in breast cancer derived lines. Analysis of the secretome of GREP1-expressing cells showed increased abundance of the oncogenic cytokine GDF15, and GDF15 supplementation mitigated the growth inhibitory effect of GREP1 knock-out. Taken together, these experiments suggest that the non-canonical ORFeome is surprisingly rich in biologically active proteins and potential cancer therapeutic targets deserving of further study.

cancer biology↗

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

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↗