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Abstract A16: Potent and selective ATR inhibitors for the treatment of homologous-recombination deficient and PARPi-resistant cancers

2017· article· en· W2604304527 on OpenAlexaboutno aff
Laura Butler, Ryan L. Ragland, Hank Breslin, Erin George, Tina Gill, Matthew Scheiwer, Nicolas Gordon, Karen E. Knudsen, Fiona Simpkins, Oren Gilad, Eric J. Brown

Bibliographic record

VenueMolecular Cancer Research · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA Repair Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsHomologous recombinationSynthetic lethalityCancer researchDNA damageCHEK1BiologyG2-M DNA damage checkpointRAD51CancerKinaseDNA repairNeuroblastoma RAS viral oncogene homologMutationKRASChemistryDNAGeneticsCell cycleCell cycle checkpointGene

Abstract

fetched live from OpenAlex

Abstract Ataxia Telangiectasia and Rad3-related (ATR) and Checkpoint kinase 1 (CHK1) stabilize stalled replication forks and prevent their collapse into DNA double strand breaks (DSBs). Inhibition of ATR in cells experiencing oncogenic stress or harboring other cancer-associated defects synergistically increases the formation of DSBs and causes synthetic lethality. Thus specific targeting of ATR represents an emerging strategy to treat a broad spectrum of cancers, most notably those that currently lack effective treatments. We and others have shown that inhibition of the ATR checkpoint kinase is synthetically lethal with multiple distinct cancer-associated mutations, including p53 loss, oncogenic stress (HRAS-G12V, KRAS-G12D, NRAS-G12D, MYC and CCNE1), deficiency in homologous recombination (BRCA1/2, PALB2, ATM loss), alternative lengthening of telomeres (ALT), chromatin modification (SETD2 loss), and others. The specificity of ATR inhibitors is vital to their successful clinical application, since off-targeting increases toxicity to normal cells and limits value in personalized treatments. We report the generation of a novel class of highly potent and specific ATR inhibitors (ATRN series) that exhibit low nanomolar activity in cultured cells (IC50 = 2-8 nM) and do not detectably inhibit ATM, DNA-PK or mTOR (IC50 > 10 µM). In side-by-side comparisons, both the potency and selectivity of the ATRN series is superior to previously reported ATR inhibitors (VE821, VE822, AZD20, AZD6738, ETP-46464). In addition, the ATRN series has sufficient bioavailability and stability for in vivo application. Our lead compound (ATRN-119) slows progression of human BRCA2-deficient PDAC (CAPAN1) and RAS oncogene-driven p53-null colon tumors in mice with minimal toxicity to tissues under normal proliferative control, including the bone marrow and intestine. Additionally, mice engrafted with BRCA2-mutant patient-derived xenograft (PDX) ovarian tumors show a significant reduction in tumor progression after 5 weeks treatment of ATRN-119, and display no toxicity or significant weight loss. In addition, cancers that maintain or reacquire HR function, and thus are resistant to treatment with PARPi or cisplatin, remain responsive to ATRN series inhibitors. Thus, ATRN-119 is highly efficacious in suppressing tumor growth in multiple murine models, including suppression of patient-derived BRCA2-mutant ovarian, and PARP resistant cancers, suggesting that the clinical application of the ATRN series will provide a new and effective treatment for human malignancies with fewer side effects than conventional chemotherapies. In summary, the ATRN series is a highly selective and potent class of ATR inhibitors with therapeutic potential for treating a broad range of cancers. The potentially identified biomarkers, in particular HR-deficiency, will inform patient selection (a critical component for delivering medical benefit) for treatment with our agent and could reduce future clinical risk and side-effects. Thus targeting patients with HR-deficient cancers is a particularly promising strategy in treating a broad range of cancers. Citation Format: Laura R. Butler, Ryan L. Ragland, Hank J. Breslin, Erin George, Tina Gill, Matthew Scheiwer, Nicolas Gordon, Karen Knudsen, Fiona Simpkins, Oren Gilad and Eric J. Brown. Potent and selective ATR inhibitors for the treatment of homologous-recombination deficient and PARPi-resistant cancers [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 A16.

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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)0.000
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 score0.039
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.036
GPT teacher head0.359
Teacher spread0.323 · 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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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