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Abstract Background Tertiary lymphoid structures (TLSs) affect the prognosis and efficacy of immunotherapy in patients with non‐small cell lung cancer (NSCLC), but the underlying mechanisms are not well understood. Methods TLSs were identified and categorized online from the Cancer Digital Slide Archive (CDSA). Overall survival (OS) and disease‐free survival (DFS) were analyzed. GSE111414 and GSE136961 datasets were downloaded from the GEO database. GSVA, GO and KEGG were used to explore the signaling pathways. Immune cell infiltration was analyzed by xCell, ssGSEA and MCP‐counter. The analysis of WGCNA, Lasso and multivariate cox regression were conducted to develop a gene risk score model based on the SU2C‐MARK cohort. Results TLS‐positive was a protective factor for OS according to multivariate cox regression analysis (p = 0.029). Both the TLS‐positive and TLS‐mature groups exhibited genes enrichment in immune activation pathways. The TLS‐mature group showed more activated dendritic cell infiltration than the TLS‐immature group. We screened TLS‐related genes using WGCNA. Lasso and multivariate cox regression analysis were used to construct a five‐genes (RGS8, RUF4, HLA‐DQB2, THEMIS, and TRBV12‐5) risk score model, the progression free survival (PFS) and OS of patients in the low‐risk group were markedly superior to those in the high‐risk group (p < 0.0001; p = 0.0015, respectively). Calibration and ROC curves indicated that the combined model with gene risk score and clinical features could predict the PFS of patients who have received immunotherapy more accurately than a single clinical factor. Conclusions Our data suggested a pivotal role of TLSs formation in survival outcome and immunotherapy response of NSCLC patients. Tumors with mature TLS formation showed more activated immune microenvironment. In addition, the model constructed by TLS‐related genes could predict the response to immunotherapy and is meaningful for clinical decision‐making.

Abstract Background The effects of gut microbiota and metabolites on the responses to immune checkpoint inhibitors (ICIs) in advanced epidermal growth factor receptor (EGFR) wild-type non-small cell lung cancer (NSCLC) have been studied. However, their effects on EGFR-mutated (EGFR +) NSCLC remain unknown. Methods We prospectively recorded the clinicopathological characteristics of patients with advanced EGFR + NSCLC and assessed potential associations between the use of antibiotics or probiotics and immunotherapy efficacy. Fecal samples were collected at baseline, early on-treatment, response and progression status and were subjected to metagenomic next-generation sequencing and ultra-high-performance liquid chromatography-mass spectrometry analyses to assess the effects of gut microbiota and metabolites on immunotherapy efficacy. Results The clinical data of 74 advanced EGFR + NSCLC patients were complete and 18 patients’ fecal samples were dynamically collected. Patients that used antibiotics had shorter progression-free survival (PFS) (mPFS, 4.8 vs. 6.7 months; P = 0.037); probiotics had no impact on PFS. Two dynamic types of gut microbiota during immunotherapy were identified: one type showed the lowest relative abundance at the response time point, whereas the other type showed the highest abundance at the response time point. Metabolomics revealed significant differences in metabolites distribution between responders and non-responders. Deoxycholic acid, glycerol, and quinolinic acid were enriched in responders, whereas L-citrulline was enriched in non-responders. There was a significant correlation between gut microbiota and metabolites. Conclusions The use of antibiotics weakens immunotherapy efficacy in patients with advanced EGFR + NSCLC. The distribution characteristics and dynamic changes of gut microbiota and metabolites may indicate the efficacy of immunotherapy in advanced EGFR + NSCLC.

ABSTRACTMetabolism reprogramming within the tumor microenvironment (TME) can have a profound impact on immune cells. Identifying the association between metabolic phenotypes and immune cells in lung adenocarcinoma (LUAD) may reveal mechanisms of resistance to immune checkpoint inhibitors (ICIs). Metabolic phenotypes were classified by expression of metabolic genes. Somatic mutations and transcriptomic features were compared across the different metabolic phenotypes. The metabolic phenotype of LUAD is predominantly determined by reductase-oxidative activity and is divided into two categories: redoxhigh LUAD and redoxlow LUAD. Genetically, redoxhigh LUAD is mainly driven by mutations in KEAP1, STK11, NRF2, or SMARCA4. These mutations are more prevalent in redoxhigh LUAD (72.5%) compared to redoxlow LUAD (17.4%), whereas EGFR mutations are more common in redoxlow LUAD (19.0% vs. 0.7%). Single-cell RNA profiling of pre-treatment and post-treatment samples from patients receiving neoadjuvant chemoimmunotherapy revealed that tissue-resident memory CD8+ T cells are responders to ICIs. However, these cells are significantly reduced in redoxhigh LUAD. The redoxhigh phenotype is primarily attributed to tumor cells and is positively associated with mTORC1 signaling. LUAD with the redoxhigh phenotype demonstrates a lower response rate (39.1% vs. 70.8%, p = 0.001), shorter progression-free survival (3.3 vs. 14.6 months, p = 0.004), and overall survival (12.1 vs. 31.2 months, p = 0.022) when treated with ICIs. The redoxhigh phenotype in LUAD is predominantly driven by mutations in KEAP1, STK11, NRF2, and SMARCA4. This phenotype diminishes the number of tissue-resident memory CD8+ T cells and attenuates the efficacy of ICIs.

Background: COVID-19 has been ravaging the globe for more than three years. Due to systemic immunosuppression of anti-tumor therapy, application of chemotherapy and adverse effects of surgery, the short- and long-term prognosis of cancer patients to COVID-19 are of significant concern. Method: This research included three parts of data. The first part of the data came from the public database that covered Veneto residents. The second part of the data included participants in Guangzhou. The third part of the data was used for MR analysis. We assessed the associations by logistic, linear or Cox regression when appropriate. Result: Lung cancer patients with COVID-19 had shorter progression-free survival (PFS) after COVID-19 (Model II: HR: 3.28, 95% CI: 1.6~6.72; Model III: HR: 3.39, 95% CI: 1.45~7.95), compared with lung cancer patients without COVID-19. Targeted therapy patients recovered from SARS-CoV-2 infection more quickly (Model I: β: −0.58, 95% CI: −0.75~−0.41; Model II: β: −0.59, 95% CI: −0.76~−0.41; Model III: β: −0.57; 95% CI: −0.75~−0.40). Conclusions: PFS in lung cancer patients is shortened by COVID-19. The outcome of COVID-19 in lung cancer patients was not significantly different from that of the healthy population. In lung cancer patients, targeted therapy patients had a better outcome of COVID-19, while chemotherapy patients had the worst.