Abstract PR014: Combinatorial genetic screens to map synthetic lethal interactions and identify new cancer drug targets in <i>KRAS</i> mutant cancers
Bibliographic record
Abstract
Abstract The success of precision oncology depends on our ability to translate accumulating genomic data into actionable treatment options in a personalized manner. The first step requires the identification of a specific genomic signature, then matching this signature with the most effective therapy. Unfortunately, more often than not, we do not have a specific cancer drug that is effective for the mutation found in a specific tumor. We desperately need to identify new targets so that we can develop new drugs to deliver on the promise of precision cancer medicine. In addition, we need to develop a rational way to combine drugs that does not depend on trial and error. Combinatorial genetic screening CRISPR-Cas9 now allows us to identify synthetic lethal interactions in which simultaneous perturbation of two genes leads to cell death. However, multiplex gene editing to reveal these synergies between genes is a major challenge. For model systems like yeast, high-throughput methods and technologies have made it possible to create genetic networks with around 23 million double mutants (6000 genes x 6000 genes) and has resulted in the most detailed genetic interaction network to date consisting of ∼900,000 genetic interactions. This landscape of genetic interaction network of yeast has taken the scientific research community 15 years. In human cells, there are ∼20,000 genes and 400 million combinations, making this very complex network impossible to screen and test all these 400M combinations. To systematically interrogate such genetic interactions, we designed a dual CRISPR-Cas9 perturbation library targeting the top 1000 genes upregulated in KRAS mutant cancers (lung, pancreas and colon cancers). We performed these combinatorial double knockout screens on 100K (100x1000) unique gene pairs and identified 27 pairs whose co-disruption results in a loss of cellular fitness. We next validated those top synthetic lethal pairs of genes by performing secondary screens using CRISPR-Cas12a on more KRAS mutant and KRAS WT cell lines. Overall, our findings will provide insight into potential combinational targets in KRAS mutant tumors and will highlight the synthetic lethality effects that occurs among the novel targets, and other proliferation, metastasis, immune and metabolism modules. Citation Format: Rand Arafeh, Laura Chang, Lydia Sawyer, Helen Wang, James McFarland, Joshua Dempster, Peter DeWeirdt, John Doench, William C. Hahn. Combinatorial genetic screens to map synthetic lethal interactions and identify new cancer drug targets in KRAS mutant cancers [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr PR014.
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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 | 0.000 |
| 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) | 0.004 | 0.001 |
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".