Abstract A021: Hunting for synthetic lethal partners in DNA damage response-altered endometrial cancer
Bibliographic record
Abstract
Abstract DNA damage response (DDR) genes are frequently altered in cancer, which may lead to protein loss-of-function (LoF) and result in particular DDR defects that can be therapeutically targeted via the concept of synthetic lethality (SL). There is a high prevalence of DDR alterations in endometrial cancer, a gynecologic malignancy with increased incidence and mortality in recent decades, and an unmet clinical need for a wider range of treatment options to reduce adverse effects and combat therapy resistance. In this study, a large-scale, agnostic bioinformatic screening approach was employed to discover novel potential SL targets specific to DDR LoF biomarkers in endometrial cancer for subsequent experimental validation in vitro. SL analysis was performed with the in-house Target / Biomarker / Lineage (TBL) bioinformatic pipeline (Division of Cancer Therapeutics, The Institute of Cancer Research) using datasets obtained from the Cancer Dependency Map (DepMap). The TBL pipeline statistically analyzed cancer cell line target gene dependency probabilities, inferred from Project Achilles and Project Score genome-wide CRISPR-Cas9 dropout screens, and integrated this with mutation and copy number variation genomic datasets. Potential interactions were filtered for target-biomarker mutual exclusivity using patient data from The Cancer Genome Atlas (TCGA). The ability of the TBL pipeline to identify known SL interactions was demonstrated through in silico identification of ARID1B/ARID1A in ovarian cancer and SMARCA2/SMARCA4 in lung cancer as significant hits. In addition, the identification of WRN/ARID1A as a significant hit in uterine cancer, given that ARID1A loss has been significantly associated with microsatellite instability (MSI) in endometrial cancer, is consistent with previous findings of WRN helicase as a SL partner with MSI in endometrial cancer. Analysis and downstream refinement of endometrial cancer significant hits led to the identification of seven final candidate target-biomarker gene pairs. These putative SL partners comprise novel potential endometrial cancer targets, with various functions ranging from nucleotide biosynthesis to epigenetic remodeling, and include novel potential and established LoF biomarkers relevant to endometrial cancer biology, namely DNA mismatch repair. In vitro validation of the final candidates by genetic or pharmacologic approaches to assess target gene dependency in endometrial cancer cell lines with altered or wildtype biomarker status will be discussed. These experiments will provide key experimental data to validate our bioinformatic approach to SL target-biomarker discovery, and may identify novel SL partners involving DDR LoF alterations in endometrial cancer that could lead to the development of new molecular targeted therapies and improved treatment options for this patient subpopulation. Citation Format: Julie Zhou, Konstantinos Mitsopoulos, Olivia Rossanese. Hunting for synthetic lethal partners in DNA damage response-altered endometrial cancer [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 A021.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".