Abstract LB-234: A comparative genomic approach for identifying synthetic lethal interactions in human cancer.
Bibliographic record
Abstract
Abstract Synthetic lethal interactions enable a novel approach for discovering specific genetic vulnerabilities in cancer cells that can be exploited for the development of therapeutics. This concept has recently been exploited in the development of PARP inhibitors as novel chemotherapeutics for breast cancer. While PARP is not essential in normal cells, BRCA mutant cells are dependent on it for their survival. This led to the successful treatment of BRCA mutant tumors of the breast, ovary, and prostate with the oral PARP inhibitor, olaparib. Therapeutic strategies based on such synthetic-lethal (SL) interactions can enable drug targeting of cancer-specific alterations for otherwise undruggable tumor suppressors. Large-scale analyses of genetic interaction networks in model organisms like yeast suggest that disease-specific networks should not only give rise to destructive phenotypes (e.g. uncontrolled cell growth), but will also present unique vulnerabilities that can be exploited for novel therapies. Despite successes in model organisms such as yeast, discovering synthetic lethal interactions on a large scale in human cells remains a significant challenge. We have used a combination of computational and experimental approach whereby yeast interactions between human orthologs are filtered by cancer association and prioritized by genomic features, and candidates from the prioritized list are then validated in human cell lines using an RNA interference approach. As a proof of principle, we utilized this approach to discover two previously unknown synthetic lethal/sick interactions, one between SMARCB1 (yeast SNF5) and PSMA4 (yeast PRE9), and another between ASPSCR1 (yeast UBX4) and PSMC2 (yeast RPT1) based on shRNA double knock-down in normal human fibroblast cells. These interactions suggest potentially new therapeutic targets for SMARCB1 and ASPSCR1 mutated cancers, and more broadly, illustrate the potential of this cross-species approach. Citation Format: Michael K. Asiedu, Raamesh Deshpande, Mitchell Klebig, Shari Sutor, Elena Kuzmin, Jeff Piotrowski, Seung Ho Shin, Michael Costanzo, Charles Boone, Dennis A. Wigle, Chad L Myers. A comparative genomic approach for identifying synthetic lethal interactions in human cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr LB-234. doi:10.1158/1538-7445.AM2013-LB-234
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".