The Feasibility of Using Biomarkers Derived from Circulating Tumor DNA Sequencing as Predictive Classifiers in Patients with Small-Cell Lung Cancer

Purpose This study aimed to investigate the feasibility of biomarkers based on dynamic circulating tumor DNA (ctDNA) to classify small cell lung cancer (SCLC) into different subtypes. Materials and Methods Tumor and longitudinal plasma ctDNA samples were analyzed by next-generation sequencing of 1,021 genes. PyClone was used to infer the molecular tumor burden index (mTBI). Pre-treatment tumor tissues (T1) and serial plasma samples were collected (pre-treatment [B1], after two [B2], six [B3] cycles of chemotherapy and at progression [B4]). Results Overall concordance between T1 and B1 sequencing (n=30) was 66.5%, and 89.5% in the gene of RB1. A classification method was designed according to the changes of RB1 mutation, named as subtype I (both positive at B1 and B2), subtype II (positive at B1 but negative at B2), and subtype III (both negative at B1 and B2). The median progressive-free survival for subtype I patients (4.5 months [95% confidence interval (CI), 2.6 to 5.8]) was inferior to subtype II (not reached, p < 0.001) and subtype III (10.8 months [95% CI, 6.0 to 14.4], p=0.002). The median overall survival for subtype I patients (16.3 months [95% CI, 5.3 to 22.9]) was inferior to subtype II (not reached, p=0.01) and subtype III (not reached, p=0.02). Patients with a mTBI dropped to zero at B2 had longer median overall survival (not reached vs. 19.5 months, p=0.01). The changes of mTBI from B4 to B1 were sensitive to predict new metastases, with a sensitivity of 100% and a specificity of 85.7%. Conclusion Monitoring ctDNA based RB1 mutation and mTBI provided a feasible tool to predict the prognosis of SCLC.

Automated summary

This study investigated the feasibility of using dynamic circulating tumor DNA as biomarkers to classify small cell lung cancer into different subtypes and predict prognosis. The results showed that monitoring RB1 mutation and molecular tumor burden index provided a feasible tool in predicting the prognosis of SCLC.