INHIBITING DNA MISMATCH REPAIR IN COLORECTAL CANCER
Bibliographic record
Abstract
Colorectal cancer (CRC) remains one of the most prevalent cancers in the US with a high mortality rate. Locally advanced and metastatic CRC have poor prognosis and only a small subset (5- 15%) of patient tumors with DNA mismatch repair deficiency (dMMR) respond to immunotherapy. Low numbers of tumor infiltrating lymphocytes (TIL) remain an obstacle to effective immunotherapy for the majority of mismatch repair proficient (pMMR) CRC. dMMR CRCs have increased tumor mutational burden and show a robust inflammatory multicellular network of stromal and immune cells, including TIL, within the tumor. To investigate whether disrupting MMR in pMMR CRC could improve immune responsiveness, we designed a method of tumor-targeted gene knockdown by constructing EpCAM aptamer-linked siRNAs (called aptamer- siRNA chimeras or AsiCs) to target the MMR gene Mlh1 and convert pMMR CRC to dMMR cancers sensitive to immune checkpoint blockade. The MLH1 AsiC bound to EpCAM+ CRC cell lines and induced tumor-specific MLH1 knockdown in vitro and in vivo with no apparent toxicity. Mice bearing subcutaneous mouse pMMR CRC cell line (SL4) tumors overexpressing EpCAM treated with EpCAM AsiCs against Mlh1 showed decreased tumor growth and enhanced survival. These findings suggest that tumor-targeted knockdown of MMR genes might provide a promising strategy to expand the range of CRC tumors that respond to immunotherapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".