High Probability of Lynch Syndrome Among Colorectal Cancer Patients Is Associated With Higher Occurrence of KRAS and PIK3CA Mutations
Bibliographic record
Abstract
Background: In Indonesia, early-onset colorectal cancer (EOCRC) rates are higher in patients < 50 years old compared to Western populations, possibly due to a higher frequency of Lynch syndrome (LS) in CRC patients. We aimed to examine the association of KRAS and PIK3CA mutations with LS. Methods: In this retrospective cross-sectional single-center study, the PCR-HRM-based test was used for screening of microsatellite instability (MSI) mononucleotide markers (BAT25, BAT26, BCAT25, MYB, EWSR1), MLH1 promoter methylation, and oncogene mutations of BRAF (V600E), KRAS (exon 2 and 3), and PIK3CA (exon 9 and 20) in FFPE DNA samples. Results: All the samples (n = 244) were from Dr. Sardjito General Hospital Yogyakarta, Indonesia. KRAS and PIK3CA mutations were found in 151/244 (61.88%) and 107/244 (43.85%) of samples, respectively. KRAS and PIK3CA mutations were significantly associated with MSI status in 32/42 (76.19%) and 25/42 (59.52%) of samples, respectively. KRAS mutation was significantly associated with LS status in 26/32 (81.25%) of samples. The PIK3CA mutation was present in a higher proportion in LS samples of 19/32 (59.38%), but not statistically significant. Clinicopathology showed that KRAS mutation was significantly associated with right-sided CRC and higher histology grade in 39/151 (25.83%) and 24/151 (16.44%) samples, respectively. PIK3CA mutation was significantly associated with female sex and lower levels of tumor-infiltrating lymphocytes in 62/107 (57.94%) and 26/107 (30.23%) samples, respectively. KRAS and PIK3CA mutations did not significantly affect overall survival (120 months) in LS and non-LS patients. Conclusions: The high probability of LS in Indonesian CRC patients is associated with KRAS and PIK3CA mutations.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".