Abstract IA13: Replication stress in cancer pathogenesis: Mechanisms and treatment opportunities
Bibliographic record
Abstract
Abstract Replication stress (RS) induced by activated oncogenes and loss of some tumor suppressors is emerging as one of the hallmarks of cancer. The RS-induced DNA damage and the ensuing activation of cell cycle checkpoints commonly induce cellular senescence or cell death of the nascent tumor cells, providing an inducible intrinsic barrier to cancer progression. On the other hand, this scenario creates an environment that favors outgrowth of tumor cell clones featuring p53 mutations and other checkpoint defects such as those in ATM or Chk2 kinases, events that allow tumor growth at the expense of enhanced genomic instability. The ongoing enhanced RS, on the other hand, unmasks higher dependence of tumor cells on RS-support pathways such as those provided by the ATR-Chk1 axis and replication fork protective mechanisms, a vulnerability that can be targeted by inhibitors of ATR, Chk1, MK2 and Wee1 kinases, for example. The lecture will briefly outline this concept and then focus on our new data relevant for three of the open questions in this field: i) How are the molecular obstacles such as RNA-DNA hybrids and aberrant intermediates resulting from RS-causing collisions between replication and transcription resolved in cells?; ii) What is the impact of RS and the ensuing ATR signaling on the mutation spectra (mutation signatures) in major types of human solid tumors such as breast carcinomas?; and iii) How do human aggressive cancers such as glioblastomas cope with the excessive endogenous RS, how do such mechanisms promote tumor cell survival and how could this knowledge help in designing innovative treatment strategies. Citation Format: Jiri Bartek, Jirina Bartkova. Replication stress in cancer pathogenesis: Mechanisms and treatment opportunities [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 IA13.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".