Abstract PR03: High-resolution detection of fitness genes and genotype-specific cancer vulnerabilities with CRISPR-Cas9 screens
Bibliographic record
Abstract
Abstract Genetic knockouts are a fundamental tool for elucidating gene function in model organisms and hold great potential for finding therapeutic targets for diseases such as cancer. The advance that pooled CRISPR-Cas9 library technology brings to human genetics sets the stage for identifying cellular fitness genes which operate either globally or specifically within a particular genetic background or environmental context. To extend the catalogue of human core and context-dependent fitness genes, we have developed the TKO (Toronto KnockOut) library, a second-generation gRNA library of 176,500 guides targeting 17,661 human protein coding genes. We used the library to screen five human cell lines, representing a cross-section of wild type and cancer tissues, to identify genes whose knockouts induced significant fitness defects. We consistently discover fivefold more fitness genes than were previously observed using systematic RNA interference, including many genes at moderate expression levels that are largely refractory to RNAi methods. We expand the known set of human core fitness genes more than fourfold to 1,580 genes, and identify dozens of essential protein complexes, both known and novel, whose heterozygous copy loss in chromosomally unstable cancers may induce a therapeutic window. We further characterize novel fitness genes of unknown function and find that they all likely exist in protein complexes with other essential genes. TKO screens accurately recapitulate pathway-specific genetic vulnerabilities induced by known oncogenes and reveal cell-type-specific dependencies for specific receptor tyrosine kinases, even in oncogenic KRAS backgrounds. We also identified a surprising and specific dependency on mitochondrial activity, which strongly supports the idea that oxidative phosphorylation dependency - a clear exception to the Warburg effect - is a targetable weakness of some tumors. Our findings demonstrate that the CRISPR-Cas9 system fundamentally alters the landscape for systematic genetics in human cells through rigorous identification of cell line essential genes, affording a high-resolution view of the genetic vulnerabilities of a cell that may represent therapeutic opportunities in cancer and that might conceivably contribute to cell plasticity and tumor progression. Citation Format: Traver Hart, Megha Chandrashekhar, Michael Aregger, Zachary Steinhart, Kevin R. Brown, Graham Macleod, Monika Mis, Michal Zimmermann, Amelie Fradet-Turcotte, Song Sun, Peter Driks, Sachdev Sidhu, Frederick P. Roth, Olivia S. Rissland, Daniel Durocher, Stephane Angers, Jason Moffat. High-resolution detection of fitness genes and genotype-specific cancer vulnerabilities with CRISPR-Cas9 screens. [abstract]. In: Proceedings of the Fourth AACR International Conference on Frontiers in Basic Cancer Research; 2015 Oct 23-26; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2016;76(3 Suppl):Abstract nr PR03.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".