Abstract PR07: Identifying factors mediating response and resistance to chemotherapy through a chemical-genetic interaction map
Bibliographic record
Abstract
Abstract Nearly every cancer patient, especially those with highly lethal and aggressive tumor types, is treated with cytotoxic chemotherapy. Currently, the selection of chemotherapy is based on average responses over a large number of patients. This belies our understanding of DNA repair that has demonstrated that the inability of a tumor cell to properly repair particular types of DNA damage has a dramatic influence on cell survival. Here, we report the generation of a quantitative chemical-genetic interaction map to chart the influence of knockdown of 625 genes on sensitivity to 30 FDA approved chemotherapeutic agents in breast epithelial cells. The resulting map highlights key genes which, when mutated or deleted in tumor cells, can dramatically induce sensitivity or resistance to particular DNA damaging agents. Interrogation of this map reveals new DNA repair factors that are recurrently deleted in breast and ovarian cancers, provides a platform for prediction of cancer cell line responses from genomic data and provides the basis for the prioritization of new drug combinations. Our data reveal that the loss of ARID1A, a key component of the SWI/SNF chromatin remodeling complex, drives resistance to cisplatin as well as PARP inhibition. Mechanistically, we uncover that ARID1A functions as a suppressor of DNA repair by homologous recombination (HR) and that its loss via mutation or deletion rescues HR in a BRCA1 independent fashion. Together, our data indicates a plethora of potential biomarkers for chemotherapy response and provides an opportunity for computational integration of chemical-genetic networks with patient genomic data to predict optimal chemotherapeutic regimes. This abstract is also being presented as Poster A14. Citation Format: Hsien-Ming Hu, Sourav Bandyopadhyay. Identifying factors mediating response and resistance to chemotherapy through a chemical-genetic interaction map [abstract]. In: Proceedings of the AACR Special Conference on DNA Repair: Tumor Development and Therapeutic Response; 2016 Nov 2-5; Montreal, QC, Canada. Philadelphia (PA): AACR; Mol Cancer Res 2017;15(4_Suppl):Abstract nr PR07.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".