BackgroundDurable efficacy of immune checkpoint blockade (ICB) occurred in a small number of patients with metastatic gastric cancer (mGC) and the determinant biomarker of response to ICB remains unclear.MethodsWe developed an open-source TMEscore R package, to quantify the tumor microenvironment (TME) to aid in addressing this dilemma. Two advanced gastric cancer cohorts (RNAseq, N=45 and NanoString, N=48) and other advanced cancer (N=534) treated with ICB were leveraged to investigate the predictive value of TMEscore. Simultaneously, multi-omics data from The Cancer Genome Atlas of Stomach Adenocarcinoma (TCGA-STAD) and Asian Cancer Research Group (ACRG) were interrogated for underlying mechanisms.ResultsThe predictive capacity of TMEscore was corroborated in patient with mGC cohorts treated with pembrolizumab in a prospective phase 2 clinical trial (NCT02589496, N=45, area under the curve (AUC)=0.891). Notably, TMEscore, which has a larger AUC than programmed death-ligand 1 combined positive score, tumor mutation burden, microsatellite instability, and Epstein-Barr virus, was also validated in the multicenter advanced gastric cancer cohort using NanoString technology (N=48, AUC=0.877). Exploration of the intrinsic mechanisms of TMEscore with TCGA and ACRG multi-omics data identified TME pertinent mechanisms including mutations, metabolism pathways, and epigenetic features.ConclusionsCurrent study highlighted the promising predictive value of TMEscore for patients with mGC. Exploration of TME in multi-omics gastric cancer data may provide the impetus for precision immunotherapy.

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Tyrosine kinase inhibitors (TKI) play a pivotal role in the treatment of non-small-cell lung cancer (NSCLC) with mutations in epidermal growth factor receptor (EGFR) and rearrangements in anaplastic lymphoma kinase (ALK). However, the influences of TKIs on the tumor immune microenvironment (TIM), especially dynamic changes of responders, have not yet been fully elucidated. Therefore, RNA sequencing and whole-exome sequencing were performed on EGFR/ALK-positive NSCLC samples before and after TKI treatment. In combination with neoantigen and mutational-load estimations, xCell and single-sample gene set enrichment analysis (ssGSEA) were used to assess tumor immune-cell infiltration and activity. Furthermore, weighted-gene correlation network analysis and the bottleneck method were used to identify the hub genes that affected treatment-related immune responses. We found that TKI treatment remodeled the TIM in treatment-responsive samples. Profound increases in the rate of anti-tumor cell infiltration and cytotoxicity was observed following TKI treatment, while antigen presentation was limited in ALK-rearranged samples. However, no significant change in anti-tumor cell infiltration or cytotoxicity was found between pre-treatment and post-progression samples. Subsequently, we found that neurofilament heavy (NEFH) mutations were enriched in samples after TKI treatment and were associated with reduced neutrophil infiltration. The cytotoxicity of EGFR-mutant NSCLCs with co-driver TP53 mutation and ALK-rearranged samples with wild-type TP53 seems to be more easily induced by TKI. Finally, the immuneassociated score generated by hub genes was positively correlated with immune infiltration, immune activation, and a favorable prognosis. In conclusion, the dynamic changes in the TIM provide clues to drug selection and timing for TKI-immunotherapy combinations.