Abstract 549: Genome-wide CRISPR-Cas9 screen and RNAseq analysis identify new candidate synthetic lethality partners to PARP inhibitor in triple-negative breast cancer
Bibliographic record
Abstract
Abstract As monotherapy, Poly (ADP-ribose) polymerase inhibitors (PARPi) have achieved remarkable success in treating tumors with germline BRCA1/2 mutation due to the synthetic lethality in DNA damage response. The efficacy of PARPi in combination with other therapeutic agents is still under investigation. However, BRCA mutant tumors constitute only 5-10% of total breast cancer diagnoses. Therefore, to extend the benefit of PARPi beyond BRCA mutant tumors, it is imperative to identify other genetic determinants that also contribute to PARPi sensitivity. Such knowledge also provides valuable insight into the development of combination therapies involving PARPi. To identify genes and pathways that are essential for cell survival under PARPi treatment, we performed genome-wide CRISPR-Cas9 knockout screens in a BRCA-functional, triple-negative breast cancer cell line MDA-MB-231 treated with talazoparib at a low dose (IC20, 20nM). Toronto CRISPR human knockout library TKOV3 was introduced into cells using lentivirus at MOI of 0.3~0.4. After puromycin selection, the surviving cells were treated with talazoparib or DMSO for 20 days before harvesting. The sgRNAs were sequenced at ~30 million reads per sample to achieve a 300x coverage over the TKOV3 library. To increase the accuracy of the CRISPR-Cas9 screen result, and to capture the transcriptomic changes during a relative long-term PARPi treatment as used in the screening process, we also did RNAseq profiling in MDA-MD-231 cells cultured in the conditions matched with the CRISPR-Cas9 screen. Using mRNA level as a filter to remove non-expressed genes, we were able to generate a list of candidate genes that have the potential to form synthetic lethal partnership with talazoparib. Our negative selection screen confirmed that loss of key components of DNA damage repair and DNA replication pathways such as ATM, RNASEH2C, ESCO2, EME1, and several components of Fanconi Anemia (FA) core complex sensitizes cells to talazoparib, as has been reported in similar screens using other PARP inhibitors. Meanwhile, our analysis also revealed several previously unrecognized partner genes involved in subcellular trafficking, RNA splicing, and microRNA biogenesis. In addition, the RNAseq profile depicted a transcriptomic response to long-term talazoparib treatment that has many distinctions from the essential pathways shown in the CRISPR-Cas9 screen, suggesting a complex, multi-layer regulation system in cell response to PARP inhibition. Our study identified a set of genes that have potential synthetic lethal interaction with PARPi. The status of these genes can be used to identify subset of triple-negative breast cancer patients who could potentially benefit from PARPi treatment. Strategies targeting these genes represent new opportunities for PARPi combination therapies. Citation Format: Xue Wu, Yue Zhao, Xiaoyu Xie, Xiaoling Xuei, Yunlong Liu, Lijun Cheng, Lang Li. Genome-wide CRISPR-Cas9 screen and RNAseq analysis identify new candidate synthetic lethality partners to PARP inhibitor in triple-negative breast cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 549.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".