Abstract IA14: Mechanisms of the ATR-dependent replication stress response
Bibliographic record
Abstract
Abstract Replication stress can be caused by DNA damage, difficult to replicate DNA sequences, collisions between replication and transcriptional machineries, and aberrations in the replication timing or other regulatory mechanisms. Cancer cells often have elevated levels of replication stress driven by oncogenes. Replication stress response pathways are important in these contexts to allow cells to complete replication, maintain genome stability, and remain viable. Drugs that increase the replication stress burden or inactivate components of the replication stress response pathway can be useful as cancer therapeutics. For example, inhibitors of the replication checkpoint kinase ATR are currently being tested in clinical trials. To better understand how replication stress response pathways operate, we have taken both genetic and proteomic approaches. These approaches include purifying active and stressed replication fork proteomes using isolation of proteins on nascent DNA (iPOND). iPOND can monitor changes in the replication fork proteome and when combined with mass spectrometry provides an unbiased analysis. These approaches have allowed us characterize the consequences of replication stress and functions of ATR. For example, we found that ATR does not regulate the stability of the replisome itself in response to stress, but it does direct the action of a number of fork remodeling enzymes including SMARCAL1 that act to protect and repair damaged forks. In addition, iPOND identified new replication stress response proteins including ETAA1—a new regulator of the ATR checkpoint kinase. I will present our latest data on the replication stress response emphasizing the results from iPOND proteomic analyses. Citation Format: David Cortez. Mechanisms of the ATR-dependent replication stress response [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 IA14.
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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 | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".