Abstract 7335: Characterizing germline variants in patients with small cell lung cancer
Bibliographic record
Abstract
Abstract Introduction: Small cell lung cancer (SCLC) has traditionally been considered to arise from environmental and lifestyle factors. Recent evidence has shown that germline mutations may also impact the development of SCLC, however, remains understudied. We sought to identify novel germline mutations in SCLC including unexplored copy-number variations (CNVs) in our cohort of patients. Methods: We applied a custom hybrid-capture gene panel of 191 cancer predisposition and commonly mutated genes in SCLC to a pre-treatment cohort of 67 patients with SCLC. Variant calls were performed using VarScan2 (2.3.8), Pindel (0.2.5b8) and CNVs were assessed using DeCON 1.0.2. Calls were annotated using the American College of Medical Genetics (ACMG) criteria and classified using gnomeAD (release 2.11), ESP6500 (ESP6500SI-V2), 1000Genomes, ExAC (release 0.3), COSMIC (v70), dbSNP (build 147), and ClinVar (NCBI ClinVar 20190305). We annotated variants of uncertain significance (VUS) with several in silico tools including MutationTaster, LRT, Polyphen2 HDIV, Polyphen2 HVAR, SIFT, PROVEAN, and Deepmind AlphaMissense. Kaplan-Meier survival analysis and log-rank tests were done to examine differences in overall survival (OS) and progression-free survival (PFS) from the date of diagnosis. Continuous variables were tested using Kruskal-Wallis H test and categorical variables using Fisher’s exact test. Results: The median age of our cohort was 68 years old, 64% (43/67) were male, 89% (60/67) were current/former smokers, and 58% (39/67) were Extensive-Stage. 64.6% (42/67) of patients self-reported a family history of cancer and 16% of the patients also self-reported personal history of cancer. In terms of self-reported ancestral background, 76% of were Caucasian, 1.5% were Middle-Eastern, 3% were Latin American, 14% were Asian, and 4.5% were Black. We identified pathogenic/likely pathogenic alterations in 7/67 patients. Five (71%) were novel alterations (BCORL1, FANCC, ATR, and BBC3) and a novel CNV (SLFN11) with the remaining two (29%) previously described mutations (CHEK1 and BRIP1). We identified 191 VUS in 60/67 patients. 4.7-14.1% of VUS were in silico predicted to be pathogenic. Patients with the 7 pathogenic alterations had a numerically larger OS (HR = 0.50, 95%CI = 0.18-1.39, p = 0.18) and PFS (HR = 0.45, 95%CI = 0.16 - 1.24, p = 0.11) compared to the rest of the cohort. We identified 20 variants at low variant allele fractions (0.05 to 0.24), likely somatic mutations. Some genes identified are implicated with clonal hematopoiesis such as GNAS, STAG, TET2, and MGA. Conclusion: In summary, we have identified several novel germline alterations (mutations and CNVs) in patients with SCLC. In addition, we utilized in silico prediction models to categorize potential high-risk VUS. Our findings suggest that beyond tobacco exposure germline alterations may also modulate development of tumorigenesis in SCLC patients. Citation Format: Sami Ul Haq, Gregory Downs, Luna Jia Zhan, Danielle Sacdalan, Janice J. Li, Vivek Philip, Raymond H. Kim, Geoffrey Liu, Scott V. Bratman, Peter J. Sabatini, Benjamin H. Lok. Characterizing germline variants in patients with small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7335.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".