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Published on: February 5, 2020
Noninvasive early identification of durable clinical benefit from immune checkpoint inhibition: a prospective
Xinghao Ai1, Bo Jia2, Zhiyi He3
1Shanghai Lung Cancer Center, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Abstract:
Immune checkpoint inhibitors (ICIs) have changed the treatment landscape for patients with non-small cell lung cancer (NSCLC). In spite of durable responses in some patients, many patients develop early disease progression during the ICI treatment. Thus, early identification of patients with no durable benefit would facilitate the clinical decision for these patients. In this prospective, multicenter study, 101 non-EGFR/ALK patients who received ICI treatment were enrolled after screening 328 stage III-IV NSCLC patients. At the date of cutoff, 83 patients were eligible for ICI efficacy evaluation, with 56 patients having progress-free survival (PFS) over 6 months, which was defined as durable clinical benefit (DCB). A multimodal model was established by integrating normalized bTMB, early dynamic of ctDNA and the first RECIST response. This model could robustly predict DCB with area under the curve (AUC) of 0.878, sensitivity of 79.2% at 86.4% specificity (accuracy = 80.0%). This model was further validated in the independent cohort of the DIREct-On study with AUC of 0.887, sensitivity of 94.7% at 85.3% specificity (accuracy = 90.3%). Patients with higher predict scores had substantially longer PFS than those with lower scores (training cohort: median PFS 13.6 vs 4.2 months, P < 0.001, HR = 0.24; validation cohort: median PFS 11.0 vs 2.2 months, P < 0.001, HR = 0.17). Taken together, these results demonstrate that integrating early changes of ctDNA, normalized bTMB, and the first RECIST response can provide accurate, noninvasive, and early prediction of durable benefits for NSCLC patients treated with ICIs. Further prospective studies are warranted to validate these findings and guide clinical decision-making for optimal immunotherapy in NSCLC patients.
Insights
Identifying non-small cell lung cancer (NSCLC) patients who will not benefit from immune checkpoint inhibitors (ICIs) early is crucial. A new multimodal model integrating ctDNA, bTMB, and RECIST response accurately predicts durable clinical benefit (DCB) in NSCLC patients receiving ICIs.
Area of Science:
- Oncology
- Immunotherapy
- Biomarkers
Background:
- Immune checkpoint inhibitors (ICIs) have revolutionized non-small cell lung cancer (NSCLC) treatment.
- However, many patients experience early disease progression, necessitating methods for early identification of non-responders.
- Predicting durable clinical benefit (DCB) is essential for optimizing treatment strategies.
Purpose of the Study:
- To develop and validate a multimodal model for early prediction of DCB in NSCLC patients treated with ICIs.
- To integrate normalized tumor mutational burden (bTMB), circulating tumor DNA (ctDNA) dynamics, and RECIST response for enhanced predictive accuracy.
- To identify patients unlikely to achieve sustained benefit from ICI therapy.
Main Methods:
- Prospective, multicenter study enrolling 328 stage III-IV NSCLC patients receiving ICIs.
- Development of a multimodal predictive model using normalized bTMB, early ctDNA dynamics, and first RECIST response.
- Validation of the model in an independent cohort (DIREct-On study).
Main Results:
- The multimodal model achieved high predictive performance: AUC of 0.878 (training) and 0.887 (validation).
- High prediction scores correlated with significantly longer progression-free survival (PFS) in both cohorts (P < 0.001).
- The model demonstrated robust accuracy, sensitivity, and specificity in predicting DCB.
Conclusions:
- Integrating ctDNA dynamics, normalized bTMB, and RECIST response provides an accurate, noninvasive, and early prediction of durable benefit from ICIs in NSCLC.
- This multimodal approach can aid in clinical decision-making for NSCLC patients undergoing immunotherapy.
- Further prospective studies are recommended to confirm these findings and guide clinical practice.
