PD-1 and PD-L1 inhibitors in cold colorectal cancer: challenges and strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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