Identifying Novel Germline Mutations and Copy Number Variations in Patients With SCLC
Bibliographic record
Abstract
Introduction: SCLC has traditionally been considered to arise from toxic exposure factors, such as smoking. Recent evidence has revealed that germline mutations may also affect the development of SCLC; however, these alterations remain understudied. We sought to identify novel germline mutations in SCLC including germline copy number variations (CNVs) in our cohort of patients. Methods: We designed a custom hybrid-capture gene panel to evaluate germline alterations in 192 cancer-predisposition and frequently mutated genes in SCLC. We applied this panel to germline analysis of a treatment-naive cohort of 67 patients with SCLC at our institution. Subsequently, we annotated the variants using the American College of Medical Genetics criteria and further classified variants of uncertain significance using a set of in silico tools, including DeepMind AlphaMissense, MutationTaster, SIFT, and Polyphen2. Results: ). We also identified 191 variants of uncertain significance in 60 of 67 patients, of which, depending on the in silico tool, 5% to 14% were predicted to be pathogenic. Patients with SCLC with the seven pathogenic alterations were observed to have a numerically longer overall survival (hazard ratio = 0.50) and progression-free survival (hazard ratio = 0.45) though not statistically significant compared with the remaining cohort. Conclusions: Our study identifies novel germline alterations, including a CNV, and provides additional evidence that germline factors could be important contributing factors to the development of SCLC.
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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 | 0.003 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".