Abstract B07: Learning from human tumors: Modeling of Mre11 complex patient mutations in yeast
Bibliographic record
Abstract
Abstract The Mre11 complex plays a central role in the eukaryotic DNA damage response (DDR) in which it acts as a sensor of DNA double strand breaks (DSBs), and thereby governs DNA damage signaling as well as DSB repair. The complex is comprised of Mre11, Rad50, and Nbs1 (or Xrs2 in budding yeast), with dimers of Rad50 and Mre11 forming its core and a Nbs1/Xrs2 protomer binding to each Mre11. The Mre11 complex globular domain harbors all enzymatic and DNA binding functions of the Mre11 complex and is a very dynamic domain regulated by conformational changes induced by Rad50 ATP binding and hydrolysis cycles. It was suggested that the ATP-bound closed form of the complex promotes Mre11 complex role in ATM/Te1l signaling, whereas ATP hydrolysis opens the globular domain, and renders the Mre11 nuclease domain accessible to the DNA substrate to promote its role in DSB repair. We have recently shown that Mre11 complex role in ATM/Te1l signaling and DSB repair are genetically separable. The curative response of a patient with metastatic small cell cancer to combined checkpoint kinase 1 (Chk1) inhibition and DNA damaging chemotherapy was promoted in part by the simultaneous inhibition of both the ATR and the ATM axes of the DDR, as the tumor harbored a missense mutation within the Rad50 ATPase D-loop domain attenuating ATM signaling. To date, targeted tumor sequencing identified more than 800 missense mutations within Mre11 complex genes spanning various types of tumors, with some mutations occurring in highly conserved residues in several patients. Modeling of these alleles in yeast revealed a strong bias towards attenuating Mre11 complex's role in Tel1/ATM signaling, while leaving its DSB repair function intact. Using a combination of yeast and mouse genetic studies along with biochemical analysis, we aim to get mechanistic insights into Mre11 complex-ATM/Tel1 signaling. Learning from tumor samples deficient in Mre11 complex ATM signaling could not only be valuable for guiding treatment decisions in a synthetic lethal approach with ATR inhibitors, but also potentiate the design of drugs that specifically block Mre11 complex ATM signaling. Citation Format: Marcel Hohl, Akiko Inagaki, John H.J. Petrini. Learning from human tumors: Modeling of Mre11 complex patient mutations in yeast [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 B07.
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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 | 0.000 | 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".