PD-1 and PD-L1 inhibitors in cold colorectal cancer: challenges and strategies
Bibliographic record
Abstract
Colorectal cancer (CRC) is the second most common cause of cancer mortality, with mismatch repair proficient (pMMR) and/or microsatellite stable (MSS) CRC making up more than 80% of metastatic CRC. Programmed death-ligand 1 (PD-L1) and programmed death 1 (PD-1) immune checkpoint inhibitors (ICIs) are approved as monotherapy in many cancers including a subset of advanced or metastatic colorectal cancer (CRC) with deficiency in mismatch repair (dMMR) and/or high microsatellite instability (MSI-H). However, proficient mismatch repair and microsatellite stable (pMMR/MSS) cold CRCs have not shown clinical response to ICIs alone. To potentiate the anti-tumor response of PD-L1/PD-1 inhibitors in patients with MSS cold cancer, combination strategies currently being investigated include dual ICI, and PD-L1/PD-1 inhibitors in combination with chemotherapy, radiotherapy, vascular endothelial growth factor (VEGF) /VEGF receptor (VEGFR) inhibitors, mitogen-activated protein kinase (MEK) inhibitors, and signal transducer and activation of transcription 3 (STAT3) inhibitors. This paper will review the mechanisms of PD-1/PD-L1 ICI resistance in pMMR/MSS CRC and potential combination strategies to overcome this resistance, summarize the published clinical experience with different combination therapies, and make recommendations for future avenues of research.
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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 | 0.001 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".