Multi-omics indicators of long-term survival benefits after immune checkpoint inhibitor therapy

Association of prognostic nutritional index with long-term survival in lung cancer receiving immune checkpoint inhibitors: A meta-analysis

BACKGROUND

This meta-analysis aimed to identify the association of prognostic nutritional index (PNI) with long-term survival in lung cancer patients who received the immune checkpoint inhibitors.

METHODS

The Medline, CNKI, EMBASE, and Web of Science databases were searched up to August 20, 2023. Progression-free survival (PFS) and overall survival (OS) were main outcomes and hazard ratios (HRs) and 95% confidence intervals were combined. Subgroup analysis stratified by the pathological type [non-small cell lung cancer (NSCLC) vs small cell lung cancer (SCLC)], previous treatment history and combination of other treatment was performed.

RESULTS

Twenty-two available studies with 2550 patients were included. Pooled results demonstrated that lower PNI was related to worse PFS (HR = 0.51, P<.001) and OS (HR = 0.43, P<.001). Furthermore, subgroup analysis based on the pathological type (non-small cell lung cancer: HR = 0.52, P<.001 for PFS, HR = 0.41, P<.001 for OS; small cell lung cancer: HR = 0.41, P = .007 for PFS, HR = 0.44, P = .007 for OS), previous treatment history (first-line treatment: HR = 0.67, P<.001 for PFS, HR = 0.52, P<.001 for OS) and combination of other treatment (No: HR = 0.54, P<.001 for PFS, HR = 0.43, P<.001 for OS; Yes: HR = 0.63, P<.001 for PFS, HR = 0.51, P<.001) showed similar findings.

CONCLUSION

PNI is significantly associated with long-term survival in immune checkpoint inhibitors treated lung cancer and patients with lower PNI are more likely to experience poorer prognosis.

Integrative analysis of pan-cancer single-cell data reveals a tumor ecosystem subtype predicting immunotherapy response

Tumor ecosystem shapes cancer biology and potentially influence the response to immunotherapy, but there is a lack of direct clinical evidence. In this study, we utilized EcoTyper and publicly available scRNA-Seq cohorts from ICI-treated patients. We found a ecosystem subtype (ecotype) was linked to improved responses to immunotherapy. Then, a novel immunotherapy-responsive ecotype signature (IRE.Sig) was established and validated through the analysis of pan-cancer data. Utilizing IRE.Sig, machine learning models successfully predicted ICI responses in both validation and testing cohorts, achieving area under the curve (AUC) values of 0.72 and 0.71, respectively. Furthermore, using 5 CRISPR screening cohorts, we identified several potential drugs that may augment the efficacy of ICI. We also elucidated the candidate cellular biomarkers of response to the combined treatment of pembrolizumab plus eribulin in breast cancer. This signature has the potential to serve as a valuable tool for patients in selecting appropriate immunotherapy treatments.

Prediction performance comparison of biomarkers for response to immune checkpoint inhibitors in advanced non-small cell lung cancer

BACKGROUND

The aim of the present study was to compare the predictive accuracy of PD-L1 immunohistochemistry (IHC), tissue or blood tumor mutation burden (tTMB, bTMB), gene expression profile (GEP), driver gene mutation, and combined biomarkers for immunotherapy response of advanced non-small cell lung cancer (NSCLC).

METHODS

In part 1, clinical trials involved with predictive biomarker exploration for immunotherapy in advanced NSCLC were included. The area under the curve (AUC) of the summary receiver operating characteristic (SROC), sensitivity, specificity, likelihood ratio and predictive value of the biomarkers were evaluated. In part 2, public datasets of immune checkpoint inhibitor (ICI)-treated NSCLC involved with biomarkers were curated (N = 871). Odds ratio (OR) of the positive versus negative biomarker group for objective response rate (ORR) was measured.

RESULTS

In part 1, the AUC of combined biomarkers (0.75) was higher than PD-L1 (0.64), tTMB (0.64), bTMB (0.68), GEP (0.67), and driver gene mutation (0.51). Combined biomarkers also had higher specificity, positive likelihood ratio and positive predictive value than single biomarkers. In part 2, the OR of combined biomarkers of PD-L1 plus TMB (PD-L1 cutoff 1%, 0.14; cutoff 50% 0.13) was lower than that of PD-L1 (cutoff 1%, 0.33; cutoff 50% 0.24), tTMB (0.28), bTMB (0.48), EGFR mutation (0.17) and KRAS mutation (0.47), for distinguishing ORR of patients after immunotherapy. Furthermore, positive PD-L1, tTMB-high, wild-type EGFR, and positive PD-L1 plus TMB were associated with prolonged progression-free survival (PFS).

CONCLUSION

Combined biomarkers have superior predictive accuracy than single biomarkers for immunotherapy response of NSCLC. Further investigation is warranted to select optimal biomarkers for various clinical settings.