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Updated: Jan 8, 2026
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Comparative predictive value of immunotherapy biomarkers: a systematic review and network meta-analysis
Nuerye Tuerhong1,2, Yang Yang1,2, Junhao Feng3
1Department of Medical Oncology, Cancer Center, West China Hospital, Sichuan University, Sichuan, China.
Background:
Immunotherapy efficacy remains limited in over 60% of cancer patients, necessitating reliable predictive biomarkers. This network meta-analysis (NMA) compared the performance of 13 biomarkers to identify optimal predictors.
Methods:
We searched PubMed, OVID, Embase, Cochrane Trials, Web of Science, and trial registries (ClinicalTrials.gov, WHO ICTRP) from inception to 1 September 2025, for a comprehensive NMA evaluating 13 biomarkers (circulating tumor DNA [ctDNA], programmed cell death ligand 1 [PD-L1; at varying thresholds], tumor mutational burden [TMB], et al.). Subgroup analyses were performed for various cancers. Heterogeneity and publication bias were assessed.
Results:
This analysis included 54 634 patients from 194 clinical studies worldwide. ctDNA demonstrated the highest sensitivity (0.82, 95% CI: 0.72-0.89) and overall discriminative ability (DOR = 9.75, 95% CI: 5.20-16.73; AUC = 0.769). PD-L1 exhibited threshold-dependent performance: the ≥ 50% cutoff showed the highest specificity (0.78, 95% CI: 0.73-0.81) and diagnostic accuracy (DOR = 2.60, 95% CI: 1.86-3.52; AUC = 0.661) but the lowest sensitivity (0.42, 95% CI: 0.36-0.49), while the ≥ 1% cutoff achieved the highest sensitivity (0.68, 95% CI: 0.65-0.71) at the cost of the lowest specificity (0.48, 95% CI: 0.45-0.51). TMB provided a moderate balance of sensitivity (0.56, 95% CI: 0.50-0.60) and specificity (0.69, 95% CI: 0.65-0.73). MSI demonstrated the highest specificity overall (0.89, 95% CI: 0.85-0.93), but had limited sensitivity (0.36, 95% CI: 0.27-0.46). irAEs displayed relatively higher sensitivity (0.69, 95% CI: 0.60-0.77) with moderate specificity (0.59, 95% CI: 0.50-0.67). Among inflammatory markers, PLR (AUC = 0.623) showed slightly better predictive power than NLR (AUC = 0.613), while LIPI and LDH exhibited the least overall effectiveness (AUC = 0.585 and 0.544, respectively).
Conclusion:
Biomarker performance varies by cancer type and clinical context. ctDNA, PD-L1 (high thresholds, as ≥50%), and TMB are leading predictors, with combinations potentially optimizing performance. Future research must address heterogeneity and standardization to refine individualized immunotherapy strategies.
