We report herein an exploratory biomarker analysis of refractory tumors collected from pediatric patients before atezolizumab therapy (iMATRIX-atezolizumab, NCT02541604). Elevated levels of CD8+ T cells and PD-L1 were associated with progression-free survival and a diverse baseline infiltrating T-cell receptor repertoire was prognostic. Differential gene expression analysis revealed elevated expression of CALCA (preprocalcitonin) and CCDC183 (highly expressed in testes) in patients who experienced clinical activity, suggesting that tumor neoantigens from these genes may contribute to immune response. In patients who experienced partial response or stable disease, elevated Igα2 expression correlated with T- and B-cell infiltration, suggesting that tertiary lymphoid structures existed in these patients’ tumors. Consensus gene co-expression network analysis identified core cellular pathways that may play a role in antitumor immunity. Our study uncovers features associated with response to immune-checkpoint inhibition in pediatric patients with cancer and provides biological and translational insights to guide prospective biomarker profiling in future clinical trials.
Background
Lung cancer is the leading cause of cancer mortality globally. Early detection through risk-based screening can markedly improve prognosis. However, most risk models were developed in North American cohorts of smokers, while less is known about risk profiles for never-smokers, which represent a growing proportion of lung cancers, particularly in Asian populations.
Methods
Based on the China Kadoorie Biobank, a population-based prospective-cohort of 512,639 adults with up to 12 years of follow-up, we built Asian Lung Cancer Absolute Risk Models (ALARM) for lung cancer mortality using flexible parametric survival models, separately for never (ALARM-NS) and ever-smokers (ALARM-ES), accounting for competing risks of mortality. Model performance was evaluated in a 25% hold-out test set using the time-dependent area under the curve (AUC) and by comparing model-predicted and observed risks for calibration.
Results
Predictors assessed in the never-smoker lung cancer mortality model were demographics, BMI, lung function, history of emphysema/bronchitis, personal or family history of cancer, passive smoking, and indoor air pollution. The ever-smoker model additionally assessed smoking history. The 5-year AUCs in the test set were 0.77 (95% CI: 0.73–0.80) and 0.81 (95% CI: 0.79–0.84) for ALARM-NS and ALARM-ES, respectively. The maximum 5-year risk for never and ever-smokers were 2.6% and 12.7%, respectively.
Conclusions
This study is among the first to develop risk models specifically for Asian populations, separately for never and ever-smokers. Our models accurately identify Asians at high risk of lung cancer death and may identify those with risks exceeding common eligibility thresholds who may benefit from screening.
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