Longitudinal genomic profiling using liquid biopsies in metastatic nonsquamous NSCLC following first line immunotherapy

Abstract

Tumor genomic profiling is often limited to one or two timepoints due to the invasiveness of tissue biopsies, but longitudinal profiling may provide deeper clinical insights. Using ctDNA data from IMpower150 study, we examined genetic changes in metastatic non-squamous NSCLC post-first-line immunotherapy. Mutations were most frequently detected in TP53, KRAS, SPTA1, FAT3, and LRP1B at baseline and during treatment. Mutation levels rose prior to radiographic progression in most progressing patients, with specific mutations (SPTA1, STK11, KEAP1, SMARCA4, TBX3, CDH2, and MLL3) significantly enriched in those with progression or nondurable response. However, ctDNA's role in detecting hyperprogression and pseudoprogression remains uncertain. STK11, SMARCA4, KRAS, SLT2, and KEAP1 mutations showed the strongest correlation with poorer overall survival, while SMARCA4, STK11, SPTA1, TBX3, and KEAP1 mutations correlated with shorter progression-free survival. Overall, longitudinal liquid biopsy profiling provided valuable insights into lung cancer biology post-immunotherapy, potentially guiding personalized therapies and future drug development.

Fig. 1. Overview of the evolution of genomic landscape in 1 L NSCLC following immunotherapy.

A Oncoprints of the top 10 most prevalent mutations in ctDNA at Baseline, C2D1, C3D1, C4D1, and C8D1. B Longitudinal change in frequencies of top mutations over time. C Longitudinal change in ctDNA levels (log-transformed tumor molecules per milliliter of plasma (Log(TMPMP))) over time for top gene mutations.

Fig. 2. Dissecting the molecular features in radiographic progressions.

A Proportions of molecular progression for gene mutations in different treatment arms. B Comparison of molecular progression proportions between patients with durable response and progressive or nondurable response. C Comparison of molecular progression proportions between patients with progressive disease and nondurable response. D The trajectories of tumor molecules per milliliter of plasma of mutations of some progressive and nondurable response patients. The dotted line represents the time before the first PD, and the solid line represents the time after the first PD.

Fig. 3. Molecular kinetics in potential radiographical hyperprogressive disease (HPD) and pseudoprogressors.

A Dynamics of the sum of long diameter of tumors in HPD at baseline and week 6 and dynamics of the median of TMPMP in the HPD patients. B Changes in individual mutations for the HPD patients at baseline and week 3. C The trajectories of individual mutations and ctDNA concentrations according to the median of TMPMP in the pseudoprogression patient.

Fig. 4. Association between molecular progression and survival outcomes (overall survival (OS) and progression-free survival (PFS)) based on landmark analysis.

A All significant genes (P < 0.05) with mutations present in at least 3 patients. B Kaplan–Meier (KM) plots for OS for the top 5 significant genes (P < 0.05) with mutations present in at least 5 patients. The genes in the KM plots are ordered by their P-values, from lowest to highest. C KM plots for PFS for the top 5 significant genes (P < 0.05) with mutations present in at least 5 patients. D Forest plot of the OS hazard ratios (HR) for the top 10 significant genes (P < 0.05) with mutations present in at least 5 patients. The genes in the forest plot are ordered by their estimated HR, from highest to lowest. E Forest plot of the PFS HR for top 10 significant genes (P < 0.05) with mutations present in at least 5 patients.