Development of CDK4/6 Inhibitors in Gastrointestinal Cancers: Biomarkers to Move Forward
Bibliographic record
Abstract
Targeting the cell cycle has become a focus of cancer research bearing impressive results with the introduction of CDK4/6 inhibitors in the treatment of ER-positive/HER2-negative breast cancers. However, no definitive benefit in other cancers has been observed. In gastrointestinal cancers, despite preclinical studies pinpointing positive effects on cancer inhibition in pre-clinical models, no positive clinical trials have been published with CDK4/6 inhibitors. Several biomarkers have been proposed in breast cancers, where the field is more advanced, and include up-regulations of the inhibited kinases CDK4 and CDK6 and their partner cyclin D as well as the main target of phosphorylation, RB. Up-regulation of Cyclin E, an E2F1/RB regulated gene, also arises as a marker of CDK4/6 inhibition resistance. Signaling from receptor tyrosine kinase pathways through KRAS/BRAF/MEK and PI3K/AKT/mTOR are also implicated in feedback CDK4/6 activation and inhibitors resistance. In gastrointestinal cancers, some of these biomarkers have also proven valuable in predicting sensitivity to CDK4/6 inhibitors and would lead markers to guide clinical development. Modulation of the tumor microenvironment, where immune cells are prominent components, arises as a feature of CDK4/6 inhibition and could be harnessed in therapeutic combinations.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".