Background: Anti-programmed death-1 (PD-1) antibody changed the treatment of non-small cell lung cancer (NSCLC), however, reliable predictive markers were lacking. We aimed to explore factors associated with response and survival, and develop predictive models.
Methods: This multicenter retrospective study included a training cohort (n = 92) and validation cohort (n = 111) with NSCLC patients received anti-PD-1 antibody monotherapy in eight Chinese hospitals, and a control cohort (n = 124) with NSCLC patients received chemotherapy. Logistic and Cox models were used to identify factors associated with response and survival respectively. Nomograms were developed based on significant factors, and evaluated by Concordance-index (C-index), area under the curve (AUC) and calibration curve.
Result: In training cohort, smoking history (P = 0.027) and higher absolute lymphocyte count (P = 0.038) were associated with response. Female (P < 0.001), age ≥ 65 years (P = 0.004) and higher lactate dehydrogenase (LDH, P < 0.001) were associated with shorter progression-free survival (PFS). Higher LDH (P < 0.001) and derived neutrophil-to-lymphocyte ratio (P = 0.035) were associated with poorer overall survival (OS). While these factors were nonsignificant in chemotherapy cohort. Three nomograms to predict response at 6-week after treatment, PFS and OS at 6-, 12- and 18-months were developed, and validated in validation cohort. The C-indices of each nomogram in both cohorts were as follow (training vs validation): 0.706 vs 0.701; 0.728 vs 0.701; 0.741 vs 0.709; respectively. AUC showed a good discriminative ability. Calibration curves demonstrated a consistence between actual results and predictions.
Conclusion: We developed predictive nomograms based on easily available factors to help clinicians early assess response and prognosis for NSCLC patients received anti-PD-1 antibody.
Keywords: Anti-PD-1 antibody; Immunotherapy; Nomogram; Non-small cell lung cancer; Predictive factors.