Several targeted immunotherapies have recently showed significant advances in treatment of non-small cell lung cancer (NSCLC), including antibodies and inhibitors targeting programmed death-1 (PD-1) and its ligand (PD-L1). Tumor tissue samples were prospectively collected from 183 patients with NSCLC including lung adenocarcinoma (ADC) and squamous cell carcinoma (SQCC). PD-L1 expression level was measured by immunohistochemistry assay and tumor mutational burden (TMB) status was assessed by next generation sequencing. Correlations between PD-L1 expressions, TMB status with clinicopathological characteristics were analyzed. PD-L1 expression was detected in 37% of ADC group and 55% in SQCC group while all clinicopathological characteristics were found comparable between these two groups. PD-L1 expression was negatively associated with overall survival in ADC group (P < 0.0001) but not in SQCC group (P = 0.418). In consistent with PD-L1 expression level, TMB status was significantly lower in ADC subjects as compared to SQCC subjects (P = 0.024) while PD-L1 positive subgroup and TMB high subgroup shared less subjects within ADC group than SQCC group. More importantly, the combination of TMB status and PD-L1 expression successfully identified responders, who showed significant longer median overall survival than non-responders (32 months vs. 8.5 months) in ADC subjects (P < 0.0001) but not in SQCC subjects. Here we tested the hypothesis that monitoring TMB, in addition to the existing PD-L1 expression level, could represent valuable non-invasive biomarkers for the chemotherapy and targeted therapy. Further analyses are in need to further assess the prognostic value of TMB for ADC and SQCC patients receiving immunotherapy.

Background Despite the great achievements made in immune-checkpoint-blockade (ICB) in cancer therapy, there are no effective predictive biomarkers in gastrointestinal (GI) cancer. Methods This study included 93 metastatic GI patients treated with ICBs. The first cohort comprising 73 GI cancer patients were randomly assigned into discovery (n=44) and validation (n=29) cohorts. Comprehensive genomic profiling was performed on all samples to determine tumor mutational burden (TMB) and copy-number alterations (CNAs). A subset of samples was collected for RNA immune oncology (IO) panel sequencing, microsatellite instability (MSI)/mismatch repair and program death ligand 1 (PD-L1) expression evaluation. In addition, 20 gastric cancer (GC) patients were recruited as the second validation cohort. Results In the first cohort of 73 GI cancer patients, a lower burden of CNA was observed in patients with durable clinical benefit (DCB). In both the discovery (n=44) and validation (n=29) subsets, lower burden of CNA was associated with an improved clinical benefit and better overall survival (OS). Efficacy also correlated with a higher TMB. Of note, a combinatorial biomarker of TMB and CNA may better stratify DCB patients from ICB treatment, which was further confirmed in the second validation cohort of 20 GC patients. Finally, patients with lower burden of CNA revealed increased immune signatures in our cohort and The Cancer Genome Atlas data sets as well. Conclusions Our results suggest that the burden of CNA may have superior predictive value compared with other signatures, including PD-L1, MSI and TMB. The joint biomarker of CNA burden and TMB may better stratify DCB patients, thereby providing a rational choice for GI patients treated with ICBs.

Immune checkpoint inhibitors (ICIs) have revolutionized the therapeutic landscape of gastrointestinal cancer. However, biomarkers correlated with the efficacy of ICIs in gastrointestinal cancer are still lacking. In this study, we performed 395-plex immune oncology (IO)-related gene target sequencing in tumor samples from 96 patients with metastatic gastrointestinal cancer patients treated with ICIs, and a linear support vector machine learning strategy was applied to construct a predictive model. ResultsAll 96 patients were randomly assigned into the discovery (n=72) and validation (n=24) cohorts. A 24-gene RNA signature (termed the IO-score) was constructed from 395 immune-related gene expression profiling using a machine learning strategy to identify patients who might benefit from ICIs. The durable clinical benefit rate was higher in patients with a high IO-score than in patients with a low IO-score (discovery cohort: 92.0% vs 4.3%, p<0.001; validation cohort: 85.7% vs 17.6%, p=0.004). The IO-score may exhibit a higher predictive value in the discovery (area under the receiver operating characteristic curve (AUC)=0.97)) and validation (AUC=0.74) cohorts compared with the programmed death ligand 1 positivity (AUC=0.52), tumor mutational burden (AUC=0.69) and microsatellite instability status (AUC=0.59) in the combined cohort. Moreover, patients with a high IO-score also exhibited a prolonged overall survival compared with patients with a low IO-score (discovery cohort: HR, 0.29; 95% CI 0.15 to 0.56; p=0.003; validation cohort: HR, 0.32; 95% CI 0.10 to 1.05; p=0.04). Taken together, our results indicated the potential of IO-score as a biomarker for immunotherapy in patients with gastrointestinal cancers.

Background This study was designed to screen potential biomarkers in plasma cell-free DNA (cfDNA) for predicting the clinical outcome of immune checkpoint inhibitor (ICI)-based therapy in advanced hepatobiliary cancers. Methods Three cohorts including 187 patients with hepatobiliary cancers were recruited from clinical trials at the Peking Union Medical College Hospital. Forty-three patients received combination therapy of programmed cell death protein 1 (PD-1) inhibitor with lenvatinib (ICI cohort 1), 108 patients received ICI-based therapy (ICI cohort 2) and 36 patients received non-ICI therapy (non-ICI cohort). The plasma cfDNA and blood cell DNA mutation profiles were assessed to identify efficacy biomarkers by a cancer gene-targeted next-generation sequencing panel. Results Based on the copy number variations (CNVs) in plasma cfDNA, the CNV risk score model was constructed to predict survival by using the least absolute shrinkage and selection operator Cox regression methods. The results of the two independent ICI-based therapy cohorts showed that patients with lower CNV risk scores had longer overall survival (OS) and progression-free survival (PFS) than those with high CNV risk scores (log-rank p<0.01). In the non-ICI cohort, the CNV risk score was not associated with PFS or OS. Furthermore, the results indicated that 53% of patients with low CNV risk scores achieved durable clinical benefit; in contrast, 88% of patients with high CNV risk scores could not benefit from combination therapy (p<0.05). Conclusions The CNVs in plasma cfDNA could predict the clinical outcome of the combination therapy of PD-1 inhibitor with lenvatinib and other ICI-based therapies in hepatobiliary cancers.