Tumour suppressor genes in chemotherapeutic drug response
Why this work is in the frame
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Bibliographic record
Abstract
Since cancer is one of the leading causes of death worldwide, there is an urgent need to find better treatments. Currently, the use of chemotherapeutics remains the predominant option for cancer therapy. However, one of the major obstacles for successful cancer therapy using these chemotherapeutics is that patients often do not respond or eventually develop resistance after initial treatment. Therefore identification of genes involved in chemotherapeutic response is critical for predicting tumour response and treating drug-resistant cancer patients. A group of genes commonly lost or inactivated are tumour suppressor genes, which can promote the initiation and progression of cancer through regulation of various biological processes such as cell proliferation, cell death and cell migration/invasion. Recently, mounting evidence suggests that these tumour suppressor genes also play a very important role in the response of cancers to a variety of chemotherapeutic drugs. In the present review, we will provide a comprehensive overview on how major tumour suppressor genes [Rb (retinoblastoma), p53 family, cyclin-dependent kinase inhibitors, BRCA1 (breast-cancer susceptibility gene 1), PTEN (phosphatase and tensin homologue deleted on chromosome 10), Hippo pathway, etc.] are involved in chemotherapeutic drug response and discuss their applications in predicting the clinical outcome of chemotherapy for cancer patients. We also propose that tumour suppressor genes are critical chemotherapeutic targets for the successful treatment of drug-resistant cancer patients in future applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it