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Abstract IA07: Genetic determinants of tumor development, therapy response and resistance in mouse models of BRCA-deficient breast cancer

2017· article· en· W2604825924 on OpenAlexaboutno aff
Jos Jonkers

Bibliographic record

VenueMolecular Cancer Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsnot available
Fundersnot available
KeywordsCancer researchBreast cancerCancerPARP inhibitorDNA repairSynthetic lethalityGenome instabilityCisplatinOvarian cancerMammary tumorBiologyHomologous recombinationDrug resistanceDNA damagePoly ADP ribose polymeraseMedicineGenePolymeraseGeneticsChemotherapyDNA

Abstract

fetched live from OpenAlex

Abstract Heterozygous germline mutations in BRCA1 or BRCA2 strongly predispose to development of breast and ovarian cancer (as well as other cancer types) via loss of the remaining wildtype allele. BRCA1/2-deficient cancers are defective in DNA double-strand break (DSB) repair via homologous recombination (HR) and therefore hypersensitive to DNA-damaging agents, including platinum drugs and poly(ADP-ribose) polymerase (PARP) inhibitors. However, these treatments do not result in tumor eradication and eventually resistance develops. To maximize therapeutic efficacy of these drugs and achieve durable remissions, it is important to unravel the mechanisms by which these tumors acquire resistance to platinum drugs and PARP inhibitors, in order to develop combination therapies that prevent development of resistance or re-sensitize resistant tumors. To study therapy response and resistance in a realistic in vivo setting, we have established several genetically engineered mouse models (GEMMs) and patient-derived tumor xenograft (PDX) models for BRCA-deficient breast cancer. These mice develop mammary tumors that are characterized by genomic instability and hypersensitivity to DNA-damaging agents, including platinum drugs and PARP inhibitors. Using cross-species oncogenomics and reverse genetics, we have identified several cancer genes including p53, MYC and RB as critical drivers in BRCA1-associated breast cancer. In addition, we have used these mammary tumor models for preclinical evaluation of therapy response and elucidation of mechanisms of acquired drug resistance. Using functional genetic screens, reverse genetics and genomic analysis of therapy-resistant tumors, we found that therapy response and resistance of BRCA1-deficient mammary tumors to cisplatin and the clinical PARP inhibitor olaparib is affected by several factors, including drug efflux transporter activity, type of BRCA1 founder mutation and restoration of HR repair via loss of 53BP1 or REV7. Also BRCA1 re-activation via genetic or epigenetic mechanisms contributes to therapy resistance in PDX models of BRCA1-deficient breast cancer. Importantly, pharmacokinetic or HR-related mechanisms underlie olaparib-resistance in only a fraction of BRCA1/2-deficient mammary tumors, indicating the existence of additional, unknown mechanisms. Citation Format: Jos Jonkers. Genetic determinants of tumor development, therapy response and resistance in mouse models of BRCA-deficient breast cancer [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 IA07.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.234
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.056
GPT teacher head0.383
Teacher spread0.328 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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