An oncogene regulating chromatin favors response to immunotherapy: Oncogene CHAF1A and immunotherapy outcomes.

Many biological processes related to cell function and fate begin with chromatin alterations, and many factors associated with the efficacy of immune checkpoint inhibitors (ICIs) are actually downstream events of chromatin alterations, such as genome changes, neoantigen production, and immune checkpoint expression. However, the influence of genes as chromatin regulators on the efficacy of ICIs remains elusive, especially in gastric cancer (GC). In this study, thirty out of 1593 genes regulating chromatin associated with a favorable prognosis were selected for GC. CHAF1A, a well-defined oncogene, was identified as the highest linkage hub gene. High CHAF1A expression were associated with microsatellite instability (MSI), high tumor mutation burden (TMB), high tumor neoantigen burden (TNB), high expressions of PD-L1 and immune effector genes, and live infiltration of immune cells. High CHAF1A expression indicated a favorable response and prognosis in immunotherapy of several cohorts, which was independent of MSI, TMB, TNB, PD-L1 expression, immune phenotype and transcriptome scoring, and improved patient selection based on these classic biomarkers. In vivo, CHAF1A knockdown alone inhibited tumor growth but it impaired the effect of an anti-PD-1 antibody by increasing the relative tumor proliferation rate and decreasing the survival benefit, potentially through the activation of TGF-ß signaling. In conclusion, CHAF1A may be a novel biomarker for improving patient selection in immunotherapy.

A novel biomarker associated with EBV infection improves response prediction of immunotherapy in gastric cancer.

BACKGROUND: Novel biomarkers are required in gastric cancer (GC) treated by immunotherapy. Epstein-Barr virus (EBV) infection induces an immune-active tumor microenvironment, while its association with immunotherapy response is still controversial. Genes underlying EBV infection may determine the response heterogeneity of EBV + GC. Thus, we screened hub genes associated with EBV infection to predict the response to immunotherapy in GC. METHODS: Prognostic hub genes associated with EBV infection were screened using multi-omic data of GC. EBV + GC cells were established and confirmed by EBV-encoded small RNA in situ hybridization (EBER-ISH). Immunohistochemistry (IHC) staining of the hub genes was conducted in GC samples with EBER-ISH assay. Infiltrating immune cells were stained using immunofluorescence. RESULTS: CHAF1A was identified as a hub gene in EBV + GC, and its expression was an independent predictor of overall survival (OS). EBV infection up-regulated CHAF1A expression which also predicted EBV infection well. CHAF1A expression also predicted microsatellite instability (MSI) and a high tumor mutation burden (TMB). The combined score (CS) of CHAF1A expression with MSI or TMB further improved prognostic stratification. CHAF1A IHC score positively correlated with the infiltration of NK cells and macrophages M1. CHAF1A expression alone could predict the immunotherapy response, but its CS with EBV infection, MSI, TMB, or PD-L1 expression showed better effects and improved response stratification based on current biomarkers. CONCLUSIONS: CHAF1A could be a novel biomarker for immunotherapy of GC, with the potential to improve the efficacy of existing biomarkers.

Association of genes in hereditary metabolic diseases with diagnosis, prognosis, and treatment outcomes in gastric cancer.

Background: Aberrant metabolism is a major hallmark of cancers and hereditary diseases. Genes associated with inborn metabolic errors may also play roles in cancer development. This study evaluated the overall impact of these genes on gastric cancer (GC). Methods: In total, 162 genes involved in 203 hereditary metabolic diseases were identified in the Human Phenotype Ontology database. Clinical and multi-omic data were acquired from the GC cohort of the Affiliated Hospital of Jiangsu University and other published cohorts. A 4-gene and 32-gene signature was established for diagnosis and prognosis or therapeutic prediction, respectively, and corresponding abnormal metabolism scores (AMscores) were calculated. Results: The diagnostic AMscore showed high sensitivity (0.88-1.00) and specificity (0.89-1.00) to distinguish between GC and paired normal tissues, with area under the receiver operating characteristic curve (AUC) ranging from 0.911 to 1.000 in four GC cohorts. The prognostic or predictive AMscore was an independent predictor of overall survival (OS) in five GC cohorts and a predictor of the OS and disease-free survival benefit of postoperative chemotherapy or chemoradiotherapy in one GC cohort with such data. The AMscore adversely impacts immune biomarkers, including tumor mutation burden, tumor neoantigen burden, microsatellite instability, programmed death-ligand 1 protein expression, tumor microenvironment score, T cell receptor clonality, and immune cell infiltration detected by multiplex immunofluorescence staining. The AUC of the AMscore for predicting immunotherapy response ranging from 0.780 to 0.964 in four cohorts involving GC, urothelial cancer, melanoma, and lung cancer. The objective response rates in the low and high AMscore subgroups were 78.6% and 3.2%, 40.4% and 7%, 52.6% and 0%, and 72.7% and 0%, respectively (all p<0.001). In cohorts with survival data, a high AMscore was hazardous for OS or progression-free survival, with hazard ratios ranged from 5.79 to 108.59 (all p<0.001). Importantly, the AMscore significantly improved the prediction of current immune biomarkers for both response and survival, thus redefining the advantaged and disadvantaged immunotherapy populations. Conclusions: Signatures based on genes associated with hereditary metabolic diseases and their corresponding scores could be used to guide the diagnosis and treatment of GC. Therefore, further validation is required.

Lipid metabolism reprogramming in tumor-associated macrophages and implications for therapy.

The tumormicroenvironment (TME) plays a key role in tumor progression. Tumor-associated macrophages (TAMs), which are natural immune cells abundantin the TME, are mainly divided into the anti-tumor M1 subtype and pro-tumor M2 subtype. Due to the high plasticity of TAMs, the conversion of the M1 to M2 phenotype in hypoxic and hypoglycemic TME promotes cancer progression, which is closely related to lipid metabolism. Key factors of lipid metabolism in TAMs, including peroxisome proliferator-activated receptor and lipoxygenase, promote the formation of a tumor immunosuppressive microenvironment and facilitate immune escape. In addition, tumor cells promote lipid accumulation in TAMs, causing TAMs to polarize to the M2 phenotype. Moreover, other factors of lipid metabolism, such as abhydrolase domain containing 5 and fatty acid binding protein, have both promoting and inhibiting effects on tumor cells. Therefore, further research on lipid metabolism in tumors is still required. In addition, statins, as core drugs regulating cholesterol metabolism, can inhibit lipid rafts and adhesion of tumor cells, which can sensitize them to chemotherapeutic drugs. Clinical studies on simvastatin and lovastatin in a variety of tumors are underway. This article provides a comprehensive review of the role of lipid metabolism in TAMs in tumor progression, and provides new ideas for targeting lipid metabolism in tumor therapy.

m6A methylation modification and immune cell infiltration: implications for targeting the catalytic subunit m6A-METTL complex in gastrointestinal cancer immunotherapy.

N6-methyladenosine (m6A) methylation modification is a ubiquitous RNA modification involved in the regulation of various cellular processes, including regulation of RNA stability, metabolism, splicing and translation. Gastrointestinal (GI) cancers are some of the world's most common and fatal cancers. Emerging evidence has shown that m6A modification is dynamically regulated by a complex network of enzymes and that the catalytic subunit m6A-METTL complex (MAC)-METTL3/14, a core component of m6A methyltransferases, participates in the development and progression of GI cancers. Furthermore, it has been shown that METTL3/14 modulates immune cell infiltration in an m6A-dependent manner in TIME (Tumor immune microenvironment), thereby altering the response of cancer cells to ICIs (Immune checkpoint inhibitors). Immunotherapy has emerged as a promising approach for treating GI cancers. Moreover, targeting the expression of METTL3/14 and its downstream genes may improve patient response to immunotherapy. Therefore, understanding the role of MAC in the pathogenesis of GI cancers and its impact on immune cell infiltration may provide new insights into the development of effective therapeutic strategies for GI cancers.

ACSL3 and ACSL4, Distinct Roles in Ferroptosis and Cancers.

The long-chain fatty acyl CoA synthetase (ACSLs) family of enzymes contributes significantly to lipid metabolism and produces acyl-coenzyme A by catalyzing fatty acid oxidation. The dysregulation of ACSL3 and ACSL4, which belong to the five isoforms of ACSLs, plays a key role in cancer initiation, development, metastasis, and tumor immunity and may provide several possible therapeutic strategies. Moreover, ACSL3 and ACSL4 are crucial for ferroptosis, a non-apoptotic cell death triggered by the accumulation of membrane lipid peroxides due to iron overload. Here, we present a summary of the current knowledge on ACSL3 and ACSL4 and their functions in various cancers. Research on the molecular mechanisms involved in the regulation of ferroptosis is critical to developing targeted therapies for cancer.

A Genomic Signature Reflecting Fibroblast Infiltration Into Gastric Cancer Is Associated With Prognosis and Treatment Outcomes of Immune Checkpoint Inhibitors.

Background: The immunotherapy efficacy in gastric cancer (GC) is limited. Cancer-associated fibroblasts (CAFs) induce primary resistance to immunotherapy. However, CAF infiltration in tumors is difficult to evaluate due to the lack of validated and standardized quantified methods. This study aimed to investigate the impact of infiltrating CAFs alternatively using fibroblast-associated mutation scoring (FAMscore). Methods: In a GC cohort from Affiliated Hospital of Jiangsu University (AHJU), whole exon sequencing of genomic mutations, whole transcriptome sequencing of mRNA expression profiles, and immunofluorescence staining of tumor-infiltrating immune cells were performed. GC data from The Cancer Genome Atlas were used to identify genetic mutations which were associated with overall survival (OS) and impacted infiltrating CAF abundance determined by transcriptome-based estimation. FAMscore was then constructed through a least absolute shrinkage and selection operator Cox regression model and further validated in AHJU. The predictive role of FAMscore for immunotherapy outcomes was tested in 1 GC, one melanoma, and two non-small-cell lung cancer (NSCLC-1 and -2) cohorts wherein participants were treated by immune checkpoint inhibitors. Results: FAMscore was calculated based on a mutation signature consisting of 16 genes. In both TCGA and AHJU, a high FAMscore was an independent predictor for poor OS of GC patients. FAMscore was associated with immune-associated genome biomarkers, immune cell infiltration, and signaling pathways of abnormal immunity. Importantly, patients with high FAMscore presented inferiority in the objective response rate of immunotherapy compared to those with low FAMscore, with 14.6% vs. 66.7% (p<0.001) in GC, 19.6% vs. 68.2% (p<0.001) in NSCLC-1, 23.1% vs 75% (p = 0.007) in NSCLC-2, and 40.9% vs 75% (p = 0.037) in melanoma. For available survival data, a high FAMscore was also an independent predictor of poor progression-free survival in NSCLC-1 (HR = 2.55, 95% CI: 1.16-5.62, p = 0.02) and NSCLC-2 (HR = 5.0, 95% CI: 1.13-22.19, p = 0.034) and poor OS in melanoma (HR = 3.48, 95% CI: 1.27-9.55, p = 0.015). Conclusions: Alternative evaluation of CAF infiltration in GC by determining the FAMscore could independently predict prognosis and immunotherapy outcomes. The FAMscore may be used to optimize patient selection for immunotherapy.