Selective tumor killing based on specific DNA-damage response deficiencies
Bibliographic record
Abstract
Organisms constantly undergo various stresses within their life span, which can damage their DNA. In order to maintain genomic stability and counteract the development of unwanted genomic mutations, organisms have evolved a DNA-damage response (DDR) to protect their genome. Due to the critical roles played by DDR in genomic stability, its defects can lead to cellular transformation and potentially tumorigenesis. Consequently, this also provides the opportunity to specifically target tumor cells due to a weakened ability to tolerate genotoxic stresses. In this lies a treatment strategy in which the inhibition of remaining DDR pathways can hyper-sensitize tumors to chemotherapeutic agents while minimizing deleterious effects to healthy cells. Therefore it is important to understand the genotypic background of specific tumors to determine which DDR pathways remain and can be targeted for inhibition. Tumor therapies based on the DDR are ideal not only as a means of increasing the effectiveness of current chemotherapies but also as a means to selectively target tumor cells while leaving healthy cells unharmed. Thus, targeting DDR components as a means of increasing effectiveness and discrimination of current chemotherapeutic tumor treatments is currently the focus of many studies and clinical trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".