CRISPR-Cas9 viability screens are being 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 that might be exploited therapeutically. Here, we integrated the two largest independent CRISPR-Cas9 screens performed to date (at Broad and Sanger institutes), by assessing and selecting methods for correcting technology-specific biases and batch effects arising from differences in the underlying experimental protocols. Our integrated datasets recapitulate findings from the individual ones, provide larger statistical power allowing novel cancer- and subtype-specific analyses, unveil additional biomarkers of gene dependency, and improve the detection of common essential genes. Finally, we provide the largest integrated resources of CRISPR-Cas9 screens to date and the basis for harmonizing existing and future functional genetics datasets and assembling large cross-study cancer dependency maps.

https://biorxiv.org/content/10.1101/2020.05.22.110247v1.full.pdf

Integrated cross-study datasets of genetic dependencies in cancer

Exciting therapeutic targets are emerging from CRISPR-based screens of high mutational-burden adult cancers. A key question, however, is whether functional genomic approaches will yield new targets in pediatric cancers, known for remarkably few mutations, which often encode proteins considered challenging drug targets. To address this, we created a first-generation pediatric cancer dependency map representing 13 pediatric solid and brain tumor types. Eighty-two pediatric cancer cell lines were subjected to genome-scale CRISPR–Cas9 loss-of-function screening to identify genes required for cell survival. In contrast to the finding that pediatric cancers harbor fewer somatic mutations, we found a similar complexity of genetic dependencies in pediatric cancer cell lines compared to that in adult models. Findings from the pediatric cancer dependency map provide preclinical support for ongoing precision medicine clinical trials. The vulnerabilities observed in pediatric cancers were often distinct from those in adult cancer, indicating that repurposing adult oncology drugs will be insufficient to address childhood cancers. A pediatric cancer dependency map generated with genome-scale CRISPR–Cas9 loss-of-function screens in 82 pediatric cancer cell lines highlights genetic dependencies across a range of tumor types.

A first-generation pediatric cancer dependency map.

http://www.cell.com/article/S1535610822002744/pdf

Pan-cancer proteomic map of 949 human cell lines

Abstract Genomic analyses have revealed mutational footprints associated with DNA maintenance gone awry, or with mutagen exposures. Because cancer therapeutics often target DNA synthesis or repair, we asked if mutational signatures make useful markers of drug sensitivity. We detect mutational signatures in cancer cell line exomes (where matched healthy tissues are not available) by adjusting for the confounding germline mutation spectra across ancestries. We identify robust associations between various mutational signatures and drug activity across cancer cell lines; these are as numerous as associations with established genetic markers such as driver gene alterations. Signatures of prior exposures to DNA damaging agents – including chemotherapy – tend to associate with drug resistance, while signatures of deficiencies in DNA repair tend to predict sensitivity towards particular therapeutics. Replication analyses across independent drug and CRISPR genetic screening data sets reveal hundreds of robust associations, which are provided as a resource for drug repurposing guided by mutational signature markers. 

/pdf/mutational-signatures-are-markers-of-drug-sensitivity-of-8zvkjhci.pdf

Mutational signatures are markers of drug sensitivity of cancer cells

As the fourth leading cause of cancer-related death in the world, liver cancer poses a major threat to human health. Although a growing number of therapies have been approved for the treatment of hepatocellular carcinoma in the past few years, most of them only provide a limited survival benefit. Therefore, an urgent need exists to identify novel targetable vulnerabilities and powerful drug combinations for the treatment of liver cancer. The advent of functional genetic screening has contributed to the advancement of liver cancer biology, uncovering many novel genes involved in tumorigenesis and cancer progression in a high-throughput manner. In addition, this unbiased screening platform also provides an efficient tool for the exploration of the mechanisms involved in therapy resistance as well as identifying potential targets for therapy. In this Review, we describe how functional screens can help to deepen our understanding of liver cancer and guide the development of new therapeutic strategies. This Review describes how functional genetic screens can shed light on various outstanding questions in liver cancer biology, including which genes can drive hepatocarcinogenesis, how to improve the efficacy of existing therapies and how to find novel therapeutic targets for liver cancer.

Exploring liver cancer biology through functional genetic screens.

Low success rates during drug development are due, in part, to the difficulty of defining drug mechanism-of-action and molecular markers of therapeutic activity Here, we integrated 199,219 drug sensitivity measurements for 397 unique anti-cancer drugs with genome-wide CRISPR loss-of-function screens in 484 cell lines to systematically investigate cellular drug mechanism-of-action We observed an enrichment for positive associations between the profile of drug sensitivity and knockout of a drug's nominal target, and by leveraging protein-protein networks, we identified pathways underpinning drug sensitivity This revealed an unappreciated positive association between mitochondrial E3 ubiquitin-protein ligase MARCH5 dependency and sensitivity to MCL1 inhibitors in breast cancer cell lines We also estimated drug on-target and off-target activity, informing on specificity, potency and toxicity Linking drug and gene dependency together with genomic data sets uncovered contexts in which molecular networks when perturbed mediate cancer cell loss-of-fitness and thereby provide independent and orthogonal evidence of biomarkers for drug development This study illustrates how integrating cell line drug sensitivity with CRISPR loss-of-function screens can elucidate mechanism-of-action to advance drug development

https://www.embopress.org/doi/pdf/10.15252/msb.20199405

Drug mechanism-of-action discovery through the integration of pharmacological and CRISPR screens.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Iman Mali

Author Tools

Chat about Author

Papers

Drug mechanism-of-action discovery through the integration of pharmacological and CRISPR screens.