Abstract 7335: Characterizing germline variants in patients with small cell lung cancer
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».