A longitudinal circulating tumor DNA-based model associated with survival in metastatic non-small-cell lung cancer - Assaf, Zoe June F.; Zou, Wei; Fine, Alexander D.; Socinski, Mark A.; Young, Amanda; Lipson, Doron; Freidin, Jonathan F.; Kennedy, Mark; Polisecki, Eliana; Nishio, Makoto; Fabrizio, David; Oxnard, Geoffrey R.; et al. (Nature medicine, 2023)
One of the great challenges in therapeutic oncology is determining who might achieve survival benefits from a particular therapy. Studies on longitudinal circulating tumor DNA (ctDNA) dynamics for the prediction of survival have generally been small or nonrandomized. We assessed ctDNA across 5 time points in 466 non-small-cell lung cancer (NSCLC) patients from the randomized phase 3 IMpower150 study comparing chemotherapy-immune checkpoint inhibitor (chemo-ICI) combinations and used machine learning to jointly model multiple ctDNA metrics to predict overall survival (OS). ctDNA assessments through cycle 3 day 1 of treatment enabled risk stratification of patients with stable disease (hazard ratio (HR) = 3.2 (2.0–5.3), P < 0.001; median 7.1 versus 22.3 months for high- versus low-intermediate risk) and with partial response (HR = 3.3 (1.7–6.4), P < 0.001; median 8.8 versus 28.6 months). The model also identified high-risk patients in an external validation cohort from the randomized phase 3 OAK study of ICI versus chemo in NSCLC (OS HR = 3.73 (1.83–7.60), P = 0.00012). Simulations of clinical trial scenarios employing our ctDNA model suggested that early ctDNA testing outperforms early radiographic imaging for predicting trial outcomes. Overall, measuring ctDNA dynamics during treatment can improve patient risk stratification and may allow early differentiation between competing therapies during clinical trials. A machine learning model that uses longitudinal ctDNA metrics robustly predicts survival in two phase 3 trials of patients with metastatic NSCLC, which may improve therapy selection and risk stratification.