Clinical implementation of genetic testing in adults for hereditary hematologic malignancy syndromes
Bibliographic record
Abstract
PURPOSE: As research on hereditary hematologic malignancy syndromes (HHMS) are accumulating, cancer genetics clinics are identifying more adult hematology patients with an inherited component to their disease. However, investigations for HHMS are complex, and there is no formal consensus on genetic testing criteria. METHODS: We developed genetic testing criteria for adult hematology patients through a comprehensive literature review and our experience at the Princess Margaret Cancer Centre. We validated our criteria by applying them retrospectively to patients referred to our clinic for HHMS assessment. RESULTS: Our genetic testing criteria are comprehensive of myeloid malignancies, lymphoid malignancies, and bone marrow failure, including age at diagnosis, family history, and genetic test results in blood and bone marrow. Of the 104 patients who met the criteria, 26% had at least 1 actionable variant in any gene associated with an increased risk of cancer and 13% had an actionable variant resulting in an HHMS diagnosis. A total of 15 patients had incidental findings, including 11 patients with a pathogenic variant associated with carrier status for an autosomal recessive disorder and 4 patients with a mosaic result. CONCLUSION: Our high gene positivity rate shows the utility of a broad approach to germline testing in an adult hematology population.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".