Abstract IA03: Targeting the DNA damage response to generate new medicines for cancer treatment
Bibliographic record
Abstract
Abstract Cancers are characterized by high levels of genomic instability and endogenous DNA damage. Traditional cancer therapies include radiotherapy and DNA-damaging chemotherapy, but these treatments are associated with significant damage to normal tissue. These unwanted side effects may be reduced by using targeted treatment approaches that preferentially affect tumor cells with specific mutations. The DNA damage response (DDR) in cancer cells differs in at least three aspects to those of normal cells, namely the loss of one or more DDR pathway or capability, increased levels of replication stress and higher levels of endogenous DNA damage. In addition, an analysis of DDR associated genes suggests that there are more than 450 gene products involved in various aspects of DDR, many of which are performing enzymatic activities that could be targeted by small molecule inhibitors. DDR therefore represents both a hallmark of cancer and a weakness that can be exploited for new cancer therapies. Both the opportunities and challenges associated with translating inhibitors of DDR into new medicines for cancer patients will be presented. Citation Format: Mark J. O'Connor. Targeting the DNA damage response to generate new medicines for cancer treatment [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 IA03.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".