Pan-cancer analysis of chromothripsis-related gene expression patterns indicates an association with tumor immune and therapeutic agent responses.

Chromothripsis is a catastrophic event involving numerous chromosomal rearrangements in confined genomic regions of one or a few chromosomes, causing complex effects on cells via the extensive structural variation. The development of whole-genome sequencing (WGS) has promoted great progress in exploring the mechanism and effect of chromothripsis. However, the gene expression characteristics of tumors undergone chromothripsis have not been well characterized. In this study, we found that the transcriptional profile of five tumor types experiencing chromothripsis is associated with an immune evasion phenotype. A gene set variation analysis (GSVA) was used to develop a CHP score, which is based on differentially expressed gene sets in the TCGA database, revealing that chromothripsis status in multiple cancers is consistent with an abnormal tumor immune microenvironment and immune cell cytotoxicity. Evaluation using four immunotherapy datasets uncovered the ability of the CHP score to predict immunotherapy response in diverse tumor types. In addition, the CHP score was found to be related to resistance against a variety of anti-tumor drugs, including anti-angiogenesis inhibitors and platinum genotoxins, while EGFR pathway inhibitors were found to possibly be sensitizers for high CHP score tumors. Univariate COX regression analysis indicated that the CHP score can be prognostic for several types of tumors. Our study has defined gene expression characteristics of tumors with chromothripsis, supporting the controversial link between chromothripsis and tumor immunity. We also describe the potential value of the CHP score in predicting the efficacy of immunotherapy and other treatments, elevating chromothripsis as a tool in clinical practice.

Prospect of Tumor Treatment in the Post-PD-1/PD-L1 Inhibitor Era / 肿瘤防治研究

Immune checkpoint inhibitors restart and maintain cancer-immunity circulation to normalize the anti-tumor immunity. Currently, anti-PD-1/PD-L1 antibodies, as new milestone in immunotherapy, have significantly improved the prognosis of patients with various malignant tumors. However, anti-PD-1/PD-L1 antibody alone exhibited a low response rate, and the combination of anti-PD-1/PD-L1 antibody with traditional therapies such as surgery, chemotherapy, radiotherapy and targeted therapy have shown great potential. As new immune checkpoint inhibitors or in combination therapy are on the way, tumor immunotherapy is entering the era of post-anti-PD-1/PD-L1 antibody. The methodology of combination therapy and biomarker screening remain the focus. This paper reviews the current status of immune checkpoint inhibitor therapy and makes a perspective for the future of post-anti-PD-1/PD-L1 antibody era.

Classification of stomach adenocarcinoma based on fatty acid metabolism-related genes frofiling.

Background: Fatty acid metabolism (FAM)-related genes play a key role in the development of stomach adenocarcinoma (STAD). Although immunotherapy has led to a paradigm shift in STAD treatment, the overall response rate of immunotherapy for STAD is low due to heterogeneity of the tumor immune microenvironment (TIME). How FAM-related genes affect TIME in STAD remains unclear. Methods: The univariate Cox regression analysis was performed to screen prognostic FAM-related genes using transcriptomic profiles of the Cancer Genome Atlas (TCGA)-STAD cohort. Next, the consensus clustering analysis was performed to divide the STAD cohort into two groups based on the 13 identified prognostic genes. Then, gene set enrichment analysis (GSEA) was carried out to identify enriched pathways in the two groups. Furthermore, we developed a prognostic signature model based on 7 selected prognostic genes, which was validated to be capable in predicting the overall survival (OS) of STAD patients using the univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analyses. Finally, the "Estimation of STromal and Immune cells in MAlignant Tumours using Expression data" (ESTIMATE) algorithm was used to evaluate the stromal, immune, and ESTIMATE scores, and tumor purity of each STAD sample. Results: A total of 13 FAM-related genes were identified to be significantly associated with OS in STAD patients. Two molecular subtypes, which we named Group 1 and Group 2, were identified based on these FAM-related prognostic genes using the consensus clustering analysis. We showed that Group 2 was significantly correlated with poor prognosis and displayed higher programmed cell death ligand 1 (PD-L1) expressions and distinct immune cell infiltration patterns. Furthermore, using GSEA, we showed that apoptosis and HCM signaling pathways were significantly enriched in Group 2. We constructed a prognostic signature model using 7 selected FAM-related prognostic genes, which was proven to be effective for prediction of STAD (HR = 1.717, 95% CI = 1.105-1.240, p < 0.001). After classifying the patients into the high- and low-risk groups based on our model, we found that patients in the high-risk group tend to have more advanced T stages and higher tumor grades, as well as higher immune scores. We also found that the risk scores were positively correlated with the infiltration of certain immune cells, including resting dendritic cells (DCs), and M2 macrophages. We also demonstrated that elevated expression of gamma-glutamyltransferase 5 (GGT5) is significantly associated with worse OS and disease-free survival (DFS), more advanced T stage and higher tumor grade, and increased immune cell infiltration, suggesting that STAD patients with high GGT5 expression in the tumor tissues might have a better response to immunotherapy. Conclusion: FAM-related genes play critical roles in STAD prognosis by shaping the TIME. These genes can regulate the infiltration of various immune cells and thus are potential therapeutic targets worthy of further investigation. Furthermore, GGT5 was a promising marker for predicting immunotherapeutic response in STAD patients.

Comprehensive analysis of tumor immune microenvironment and prognosis of m6A-related lncRNAs in gastric cancer.

BACKGROUND: N6-methyladenosine (m6A) modification and long non-coding RNAs (lncRNAs) play pivotal roles in gastric cancer (GC) progression. The emergence of immunotherapy in GC has created a paradigm shift in the approaches of treatment, whereas there is significant heterogeneity with regard to degree of treatment responses, which results from the variability of tumor immune microenvironment (TIME). How the interplay between m6A and lncRNAs enrolling in the shaping of TIME remains unclear. METHODS: The RNA sequencing and clinical data of GC patients were collected from TCGA database. Pearson correlation test and univariate Cox analysis were used to screen out m6A-related lncRNAs. Consensus clustering method was implemented to classify GC patients into two clusters. Survival analysis, the infiltration level of immune cells, Gene set enrichment analysis (GSEA) and the mutation profiles were analyzed and compared between two clusters. A competing endogenous RNA (ceRNA) network and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied for the identification of pathways in which m6A-related lncRNAs enriched. Then least absolute shrinkage and selection operator (LASSO) COX regression was implemented to select pivotal lncRNAs, and risk model was constructed accordingly. The prognosis value of the risk model was explored. In addition, the response to immune checkpoint inhibitors (ICIs) therapy were compared between different risk groups. Finally, we performed qRT-PCR to detect expression patterns of the selected lncRNAs in the 35 tumor tissues and their paired adjacent normal tissues, and validated the prognostic value of risk model in our cohort (N = 35). RESULTS: The expression profiles of 15 lncRNAs were included to cluster patients into 2 subtypes. Cluster1 with worse prognosis harbored higher immune score, stromal score, ESTIMATE score and lower mutation rates of the genes. Different immune cell infiltration patterns were also displayed between the two clusters. GSEA showed that cluster1 preferentially enriched in tumor hallmarks and tumor-related biological pathways. KEGG pathway analysis found that the target mRNAs which m6A-related lncRNAs regulated by sponging miRNAs mainly enriched in vascular smooth muscle contraction, cAMP signaling pathway and cGMP-PKG signaling pathway. Next, eight lncRNAs were selected by LASSO regression algorithm to construct risk model. Patients in the high-risk group had poor prognoses, which were consistent in our cohort. As for predicting responses to ICIs therapy, patients from high-risk group were found to have lower tumor mutation burden (TMB) scores and account for large proportion in the Microsatellite Instability-Low (MSI-L) subtype. Moreover, patients had distinct immunophenoscores in different risk groups. CONCLUSION: Our study revealed that the interplay between m6A modification and lncRNAs might have critical role in predicting GC prognosis, sculpting TIME landscape and predicting the responses to ICIs therapy.

Deep Learning Algorithm Trained with COVID-19 Pneumonia Also Identifies Immune Checkpoint Inhibitor Therapy-Related Pneumonitis.

BACKGROUND: Coronavirus disease 2019 (COVID-19) pneumonia and immune checkpoint inhibitor (ICI) therapy-related pneumonitis share common features. The aim of this study was to determine on chest computed tomography (CT) images whether a deep convolutional neural network algorithm is able to solve the challenge of differential diagnosis between COVID-19 pneumonia and ICI therapy-related pneumonitis. METHODS: We enrolled three groups: a pneumonia-free group (n = 30), a COVID-19 group (n = 34), and a group of patients with ICI therapy-related pneumonitis (n = 21). Computed tomography images were analyzed with an artificial intelligence (AI) algorithm based on a deep convolutional neural network structure. Statistical analysis included the Mann-Whitney U test (significance threshold at p < 0.05) and the receiver operating characteristic curve (ROC curve). RESULTS: The algorithm showed low specificity in distinguishing COVID-19 from ICI therapy-related pneumonitis (sensitivity 97.1%, specificity 14.3%, area under the curve (AUC) = 0.62). ICI therapy-related pneumonitis was identified by the AI when compared to pneumonia-free controls (sensitivity = 85.7%, specificity 100%, AUC = 0.97). CONCLUSIONS: The deep learning algorithm is not able to distinguish between COVID-19 pneumonia and ICI therapy-related pneumonitis. Awareness must be increased among clinicians about imaging similarities between COVID-19 and ICI therapy-related pneumonitis. ICI therapy-related pneumonitis can be applied as a challenge population for cross-validation to test the robustness of AI models used to analyze interstitial pneumonias of variable etiology.