Predicting Disease Progression in Inoperable Localized NSCLC Patients Using ctDNA Machine Learning Model

Abstract

IntroductionThere is an urgent clinical need to accurately predict the risk for disease progression in post‐treatment NSCLC patients, yet current ctDNA mutation profiling approaches are limited by low sensitivity. We represent a non‐invasive liquid biopsy assay utilizing cfDNA neomer profiling for predicting disease progression in 44 inoperable localized NSCLC patients.MethodsA total of 97 plasma samples were collected at various time points during or post‐treatments (TP1: 39, TP2: 33, TP3: 25). cfDNA neomer profiling, generated based on target sequencing data, was used to fit survival support vector machine models for each time point. Leave‐one‐out cross‐validation (LOOCV) was performed to evaluate the models' predictive performances.ResultsOur cfDNA neomer profiling assay showed excellent performance in detecting patients with a high risk for disease progression. At TP1, the high‐risk patients detected by our model showed an increased risk of 3.62 times (hazard ratio [HR] = 3.62, p = 0.0026) for disease progression, compared to 3.91 times (HR = 3.91, p = 0.0022) and 4.00 times (HR = 4.00, p = 0.019) for TP2 and TP3. These neomer profiling determined HRs were higher than the ctDNA mutation‐based results (HR = 2.08, p = 0.074; HR = 1.49, p = 0.61) at TP1 and TP3. At TP1, the predictive model reached 40% sensitivity at 92.9% specificity, outperforming the mutation‐based method (40% sensitivity at 78.6% specificity), while the combination results reached a higher sensitivity (60%). Finally, the longitudinal analysis showed that the combination of neomer and ctDNA mutation‐based results could predict disease progression with an excellent sensitivity of 88.9% at 80% specificity.ConclusionIn conclusion, we developed a cfDNA neomer profiling assay for predicting disease progression in inoperable NSCLC patients. This assay showed increased predicting power during and post‐treatment compared to the ctDNA mutation‐based method, thus illustrating a great clinical potential to guide treatment decisions in inoperable NSCLC patients.Trial RegistrationClinicalTrials.gov: NCT04014465