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Abstract PR07: Identifying factors mediating response and resistance to chemotherapy through a chemical-genetic interaction map

2017· article· en· W2605207776 on OpenAlexaboutno aff
Hsien‐Ming Hu, Sourav Bandyopadhyay

Bibliographic record

VenueMolecular Cancer Research · 2017
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsnot available
Fundersnot available
KeywordsARID1ABiologyDNA repairCisplatinCancer researchDNA damageSynthetic lethalityGene knockdownChromatin remodelingGenome instabilityHomologous recombinationCancerChromatinMutationGeneChemotherapyGeneticsDNA

Abstract

fetched live from OpenAlex

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.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.121
GPT teacher head0.474
Teacher spread0.353 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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