Functional variants in the cystic fibrosis transmembrane conductance regulator (<i>CFTR</i>) gene are associated with increased risk of colorectal cancer
Bibliographic record
Abstract
BACKGROUND: Individuals with cystic fibrosis (CF; a recessive disorder) have an increased risk of colorectal cancer (CRC). Evidence suggests individuals with a single CFTR variant may also have increased CRC risk. METHODS: Using population-based studies (GECCO, CORECT, CCFR, and ARIC; 53 785 CRC cases and 58 010 controls), we tested for an association between the most common CFTR variant (Phe508del) and CRC risk. For replication, we used whole exome sequencing data from UK Biobank (UKB; 5126 cases and 20 504 controls matched 4:1 based on genetic distance, age, and sex), and extended our analyses to all other heterozygous CFTR variants annotated as CF-causing. RESULTS: In our meta-analysis of GECCO-CORECT-CCFR-ARIC, the odds ratio (OR) for CRC risk associated with Phe508del was 1.11 (P = 0.010). In our UKB replication, the OR for CRC risk associated with Phe508del was 1.28 (P = 0.002). The sequencing data from UKB also revealed an association between the presence of any other single CF-causing variant (excluding Phe508del) and CRC risk (OR = 1.33; P = 0.030). When stratifying CFTR variants by functional class, class I variants (no protein produced) had a stronger association (OR = 1.77; p = 0.002), while class II variants (misfolding and retention of the protein in the endoplasmic reticulum) other than Phe508del (OR = 1.75; p = 0.107) had similar effect size as Phe508del, and variants in classes III-VI had non-significant ORs less than 1.0 and/or were not present in cases. CONCLUSIONS: CF-causing heterozygous variants, especially class I variants, are associated with a modest but statistically significant increased CRC risk. More research is needed to explain the biology underlying these associations.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".