Predisposition to hematopoietic malignancies by deleterious germline CHEK2 variants
Bibliographic record
Abstract
Abstract The role of germline CHEK2 variants in hematopoietic malignancies (HMs) is poorly understood. We examined pathogenic/likely pathogenic (P/LP) CHEK2 variants in patients with hereditary HMs (HHMs), a solid tumor risk cohort, public datasets, and a knock-in mouse model. In the HHM cohort, 57 probands had germline P/LP CHEK2 variants, mostly p.I157T (53%, 30/57). Among CHEK2 p.I157T carriers, 43% (19/44) had myeloid malignancies, 32% (14/44) had lymphoid malignancies, and 2% (1/44) had both. Among those with other germline P/LP CHEK2 alleles, 36% (13/36) had myeloid malignancies, 28% (10/36) had lymphoid malignancies, and 6% (2/36) had both. CHEK2 p.I157T was enriched in HM patients (OR 6.44, 95%CI 3.68–10.73, P < 0.001). In a solid tumor risk cohort, 36% (15/42) of CHEK2 p.I157T patients had a HM family history. A genome wide association study showed enrichment of CHEK2 loss-of-function variants with myeloid leukemia ( P = 5.78e −7 ). In public acute myeloid leukemia (AML) datasets, 1% (16/1348) of patients had P/LP CHEK2 variants. In a public myelodysplastic neoplasms (MDS) dataset, 2% (5/214) had P/LP CHEK2 variants. Chek2 p.I161T mice, homologous to human p.I157T, had worse survival as heterozygotes ( P = 0.037) or homozygotes ( P = 0.005), with fewer Lin-CD34+ and Lin-cKit+ cells. Our data suggest P/LP CHEK2 variants are HHM risk alleles.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".