Solid Organ Transplantation Is Associated with an Increased Rate of Mismatch Repair Deficiency and PIK3CA Mutations in Colorectal Cancer
Bibliographic record
Abstract
Solid organ transplants are associated with a modestly increased risk of colorectal cancers (CRC). However, the molecular profile of these cancers has not been described. We hypothesized that transplant-related immunosuppression may promote development of more immunogenic tumors as suggested by a high tumor mutation burden or mismatch repair deficiency. We performed an electronic medical record search for patients seen in the Johns Hopkins University Health System (JHHS) between 2017 and 2022 who developed CRC following solid organ transplantation. A comparator cohort of patients treated for CRC at JHHS with molecular profiling data was also identified. In this case, 29 patients were identified that developed post-transplant CRC (renal transplant, n = 18; liver transplant, n = 8; kidney-liver transplantation, n = 3). Compared to the JHHS general population CRC cohort, patients who developed post-transplant CRC had a higher rate of mismatch repair deficiency (41% versus 12%, p-value = 0.0038), and elevated tumor mutation burden (median of 22 mut/Mb versus 3.5 mut/Mb, p-value = 0.033) (range 3.52–53.65). Post-transplant tumors were enriched for PIK3CA mutations (43% versus 24%, p-value = 0.042). Post-Transplant CRCs are associated with clinical and molecular features of immune sensitivity, supporting a potential role for impaired immune surveillance in shaping the landscape of CRCs. These results may help inform the management of patients with post-transplant CRC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".