*August 2022* From WCLC Abstract presented by Dr. Jie Huang
Introduction: Small cell lung cancer (SCLC) transformation remains one of the unsettled resistant mechanisms for lung adenocarcinoma (LUAD), of which co-occurring TP53 and RB1 mutations defined a subgroup of patients with increased risk of SCLC transformation. Our study aimed to uncover genomic features and identify clinically high-risk patients of SCLC-transformed LUAD with EGFR/TP53/RB1 alterations.
Methods: The study was performed in 58 LUAD patients harboring concurrent mutations in EGFR/TP53/RB1. Capture-based targeted sequencing covering 76 genes related to LUAD were performed at baseline and after SCLC transformation to obtain genomic profiles. A decision tree integrating clinical and genomic features was constructed to predict SCLC transformation.
Results: Of 58 patients, 30 were pathologically confirmed as SCLC-transformed LUAD while 28 remained LUAD. In addition to TP53 and RB1, we also identified alterations in PIK3CA (12%), PTEN (12%), and copy number variations (CNVs) in CCNE1 (10%), IL7R (10%), MET (10%), MYC (10%) and TERT (10%). Comparable mutation counts were observed between SCLC transformed group and control group but EGFR 19del mutation and CNVs were more frequently presented in transformed group (p<0.05). Patients with SCLC transformation were diagnosed with LUAD at younger age than control group (p<0.05). The overall median time to SCLC transformation was 27.43 months. In transformed group, less mutation count at LUAD diagnosis was independently associated with less time to transformation (p<0.05). The presence of PTEN mutation was also associated with less time to transformation (p<0.05) while CNVs in IL7R and TERT were associated with longer time to transformation (p<0.05). From baseline to SCLC transformation, the overall trends of mutation counts tended to increase in transformed group but decrease in control group (p<0.001). The second sequencing after SCLC transformation observed that gain of CNVs in genes including PIK3CA, AKT1 and MYC was the key factor underlying increased mutation counts. In addition, EGFR mutations featured by absence of CNVs occurred more frequently in transformed group than control (p<0.05). A decision-tree based model was constructed to predict SCLC transformation using CNV≥2, age≤56.5 years and positive EGFR 19del mutation with sensitivity of 76% and specificity of 71%.
Conclusions: In LUAD patients with concurrent EGFR/TP53/RB1 mutations, SCLC transformed patients showed different mutation profiles featured by gain of CNVs. The combination of clinical and molecular features might be used to predict SCLC transformation of LUAD.