Additional germline findings from a tumor profiling program
Bibliographic record
Abstract
BACKGROUND: Matched tumor-normal sequencing, applied in precision cancer medicine, can identify unidentified germline Medically Actionable Variants (gMAVS) in cancer predisposition genes. We report patient preferences for the return of additional germline results, and describe various gMAV scenarios delivered through a clinical genetics service. METHODS: Tumor profiling was offered to 1960 advanced cancer patients, of which 1556 underwent tumor-normal sequencing with multigene hotspot panels containing 20 cancer predisposition genes. All patients were provided with an IRB-approved consent for return of additional gMAVs. RESULTS: Of the whole cohort 94% of patients consented to be informed of additional germline results and 5% declined, with no statistically significant differences based on age, sex, race or prior genetic testing. Eight patients were found to have gMAVs in a cancer predisposition gene. Five had previously unidentified gMAVs: three in TP53 (only one fulfilled Chompret's Revised criteria for Li-Fraumeni Syndrome), one in SMARCB1 in the absence of schwannomatosis features and one a TP53 variant at low allele frequency suggesting an acquired event in blood. CONCLUSION: Interest in germline findings is high among patients who undergo tumor profiling. Disclosure of previously unidentified gMAVs present multiple challenges, thus supporting the involvement of a clinical genetics service in all tumor profiling programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.009 | 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".