Infiltration In Tumor Microenvironment Research Articles

Exploiting tertiary lymphoid structures gene signature to evaluate tumor microenvironment infiltration and immunotherapy response in colorectal cancer

BackgroundTertiary lymphoid structures (TLS) is a particular component of tumor microenvironment (TME). However, its biological mechanisms in colorectal cancer (CRC) have not yet been understood. We desired to reveal the TLS gene signature in CRC and evaluate its role in prognosis and immunotherapy response.MethodsThe data was sourced from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Based on TLS-related genes (TRGs), the TLS related subclusters were identified through unsupervised clustering. The TME between subclusters were evaluated by CIBERSORT and xCell. Subsequently, developing a risk model and conducting external validation. Integrating risk score and clinical characteristics to create a comprehensive nomogram. Further analyses were conducted to screen TLS-related hub genes and explore the relationship between hub genes, TME, and biological processes, using random forest analysis, enrichment and variation analysis, and competing endogenous RNA (ceRNA) network analysis. Multiple immunofluorescence (mIF) and immunohistochemistry (IHC) were employed to characterize the existence of TLS and the expression of hub gene.ResultsTwo subclusters that enriched or depleted in TLS were identified. The two subclusters had distinct prognoses, clinical characteristics, and tumor immune infiltration. We established a TLS-related prognostic risk model including 14 genes and validated its predictive power in two external datasets. The model’s AUC values for 1-, 3-, and 5-year overall survival (OS) were 0.704, 0.737, and 0.746. The low-risk group had a superior survival rate, more abundant infiltration of immune cells, lower tumor immune dysfunction and exclusion (TIDE) score, and exhibited better immunotherapy efficacy. In addition, we selected the top important features within the model: VSIG4, SELL and PRRX1. Enrichment analysis showed that the hub genes significantly affected signaling pathways related to TLS and tumor progression. The ceRNA network: PRRX1-miRNA (hsa-miR-20a-5p, hsa-miR-485–5p) -lncRNA has been discovered. Finally, IHC and mIF results confirmed that the expression level of PRRX1 was markedly elevated in the TLS- CRC group.ConclusionWe conducted a study to thoroughly describe TLS gene signature in CRC. The TLS-related risk model was applicable for prognostic prediction and assessment of immunotherapy efficacy. The TLS-hub gene PRRX1, which had the potential to function as an immunomodulatory factor of TLS, could be a therapeutic target for CRC.

Highly expressed of BID indicates poor prognosis and mediates different tumor microenvironment characteristics in clear cell renal cell carcinoma

BackgroundStudies have found that BH3 interacting domain death agonist (BID) is closely related to the occurrence and development of many kinds of tumors. However, little attention has been paid to the situation of BID in clear cell renal cell carcinoma (ccRCC). So, our aim was to explore the effect of BID in ccRCC.MethodsSurvival analysis, ROC curve, correlation analysis and Cox regression analysis were executed to analyze the prognostic value and clinical correlation of BID in ccRCC. The risk prognosis model was constructed in the training cohort and further validated in the internal testing cohort, ICGC cohort, and GEO cohort. Transcriptome sequencing and immunohistochemical staining of clinical specimens were used to validate the results of bioinformatics analysis. The GSEA, ESTIMATE algorithm, CIBERSORT algorithm, ssGSEA, TIDE score, correlation and difference analysis were used to analyze the effects of BID on immune infiltration in tumor microenvironment (TME).ResultsBID was highly expressed in ccRCC tissues, which was verified by transcriptome sequencing and immunohistochemical staining of clinical specimens. Patients with high expression of BID had a worse prognosis. BID is an independent prognostic factor for ccRCC. The prognostic model based on BID can accurately predict the prognosis of patients in different cohorts. In addition, the expression levels of BID was closely related to immunomodulatory molecules such as PD-1, LAG3, and CTLA4. Enrichment analysis indicated that BID was significantly enriched in immune-related responses and cancer-related pathways. The change of BID expression mediates different characteristics of immune infiltration in TME.ConclusionsBID is highly expressed in ccRCC, which is a reliable biomarker of ccRCC prognosis. It is closely related to TME, and may be a potential target for immunotherapy in patients with ccRCC.

Retraction Note: Molecular subtypes based on PANoptosis-related genes and tumor microenvironment infiltration characteristics in lower-grade glioma.

Retraction Note: Molecular subtypes based on PANoptosis-related genes and tumor microenvironment infiltration characteristics in lower-grade glioma.

The S100 family is a prognostic biomarker and correlated with immune cell infiltration in pan-cancer

BackgroundThe S100 protein family is a group of small molecular EF-hand calcium-binding proteins that play critical roles in various biological processes, including promotion of growth, metastasis and immune evasion of tumor. However, the potential roles of S100 protein family expression in tumor microenvironment (TME) cell infiltration in pan-cancer remain elusive.MethodsHerein, we conducted a comprehensive assessment of the expression patterns of the S100 protein family in pan-cancer, meticulously examining their correlation with characteristics of TME cell infiltration. The S100 score was constructed to quantify S100 family expression patterns of individual tumors.ResultsThe S100 family was a potent risk factor in many cancers. Clustering analysis based on the transcriptome patterns of S100 protein family identified two cancer clusters with distinct immunophenotypes and clinical characteristics. Cluster A, with lower S100 expression, exhibited lower immune infiltration, whereas, Cluster B, with higher S100 expression, featured higher immune infiltration. Interestingly, Cluster B had a poorer prognosis, likely due to an immune-excluded phenotype resulting from stromal activation. The analysis revealed robust enrichment of the TGFb and EMT pathways in the cohort exhibiting high S100 score, alongside a positive correlation between the S100 score and Treg levels, suggesting the manifestation of an immune-excluded phenotype in this group. Moreover, S100 families were associated with the prognosis of 22 different cancers and a noteworthy association was observed between high S100 score and an unfavorable response to anti-PD-1/L1 immunotherapy. Consistent findings across two independent immunotherapy cohorts substantiated the advantageous therapeutic outcomes and clinical benefits in patients displaying lower S100score.ConclusionOur analysis demonstrated the role of S100 family in formation of TME diversity and complexity, enabling deeper cognition of TME infiltration characterization and the development of personalized immunotherapy strategies targeting S100 family for unique tumor types.

Identification of prognostic m6A modification patterns and score system in melanoma patients.

N6-methyladenosine (m6A) is the most common modification on RNAs and LncRNAs. It plays an important role in cancer stem cell differentiation, T cell differentiation, and immune homeostasis. In this study, we explored the potential roles of m6A modification of RNA in melanoma and investigated the immune cell infiltration in tumor microenvironment in diverse m6Aclusters and different m6Ascore groups. A consensus clustering algorithm determined m6A modification patterns based on 14 m6A regulators, and further explored the biological functions and the connection with TME. An m6A-related gene signature (m6Ascore) was constructed based on m6A-related genes using principal component analysis. Three m6A modification patterns were identified based on 14 m6A regulators, named as m6Aclusters A-C. The prognosis of m6Acluster A was more favorable than m6Aclusters B and C, and it was more closely associated with immune regulation. To quantify the m6A modification patterns of individual tumor, an m6Ascore was constructed, and patients were classified into high and low m6Ascore groups. The low m6Ascore group, which had a favorable prognosis, was more relevant to immunology. The expression of PD-L1 was higher and the immunophenoscore (IPS) revealed stronger response to immunotherapy in the low m6Ascore group. This study identified 3 m6A modification patterns with different immune characteristics and constructed an m6Ascore system to predict prognosis and immunogenicity of patients, which is conducive to clinical prognosis judgment and individual treatment.

DNA methylation regulator-based molecular subtyping and tumor microenvironment characterization in hepatocellular carcinoma.

Although recent studies have reported the regulation of the immune response in hepatocellular carcinoma (HCC) through DNA methylation, the comprehensive impact methylation modifications on tumor microenvironment characteristics and immunotherapy efficacy has not been fully elucidated. In this research, we conducted a comprehensive assessment of the patterns of DNA methylation regulators and the profiles of the tumor microenvironment (TME) in HCC, focusing on 21 specific DNA methylation regulators. We subsequently developed a unique scoring system, a DNA methylation score (DMscore), to assess the individual DNA methylation modifications among the three distinct methylation patterns for differentially expressed genes (DEGs). Three distinct methylation modification patterns were identified with distinct TME infiltration characteristics. We demonstrated that the DMscore could predict patient subtype, TME infiltration, and patient prognosis. A low DMscore, characterized by an elevated tumor mutation burden (TMB), hepatitis B virus (HBV)/hepatitis C virus (HCV) infection, and immune activation, indicates an inflamed tumor microenvironment phenotype with a 5-year survival rate of 7.8%. Moreover, a low DMscore appeared to increase the efficacy of immunotherapy in the anti-CTLA-4/PD-1/PD-L1 cohort. In brief, this research has enhanced our understanding of the correlation between modifications in DNA methylation patterns and the profile of the tumor microenvironment in individuals diagnosed with HCC. The DMscore may serve as an alternative biomarker for survival and efficacy of immunotherapy in patients with HCC.

Abstract LB077: CSF-1R inhibition with Pimicotinib (ABSK021) enhanced anti-tumor efficacy of KRASG12C inhibitors in preclinical non-small cell lung cancer mouse models

Abstract Background: KRAS mutations are highly prevalent oncogenic drivers in human cancers and KRASG12C is the most frequent mutations found in KRAS-mutant non-small lung cancer (NSCLC). It is reported that tumor associated macrophages (TAMs) are enriched in KRAS-driven transgenic lung cancer models. Depletion of macrophage results in a significant decrease in tumor burden and increased survival. Sotorasib (AMG510), the first KRASG12C inhibitor approved by FDA, has been found to modulate tumor immunity, impacting the recruitment of macrophages. CSF-1R signaling is critical for macrophage proliferation and survival. CSF-1R inhibition depletes TAMs and reprograms tumor microenvironment (TME), resulting in enhancement of anti-tumor immunity. Therefore, the combination of KRASG12C and CSF-1R inhibitors may synergize to enhance anti-tumor efficacy. Pimicotinib (ABSK021) is a potential best-in-class small molecule inhibitor of CSF-1R in clinical development of multiple indications. Here, we conducted in vivo experiments to explore the combined effects of KRASG12C and CSF-1R inhibitors. Methods: In vivo efficacy studies were performed to evaluate the combined treatment of KRASG12C inhibitors (AMG510 and ABSK071, a next-generation inhibitor targeting KRASG12C with better inhibitory activity) and CSF-1R inhibitor (ABSK021) in NCI-H2122 PBMC humanized mice and LLC syngeneic mice. RNA sequencing was employed to investigate the mechanism of action underlying the synergistic anti-tumor effect. Immunohistochemistry (IHC) was utilized to examine the changes of immune cell populations in TME. Results: The combinatory regimen of ABSK021 and KRASG12C inhibitors showed improved anti-tumor effects than each single agent alone in both mouse NSCLC models. Bioinformatics and follow-up analysis revealed a reduction in immune suppressive TAMs in tumors treated with ABSK021 alone or in combination with KRASG12C inhibitors. Furthermore, modulation of TME related to anti-tumor immunity, such as activated CD4 T, CD8 T and NK cells, was increased, and pro-tumor immunity, such as MDSC, regulatory T cells, was decreased in combination group. IHC results confirmed that ABSK021 addition led to macrophage depletion and more CD8 T cell infiltration in TME. Conclusions: For the first time, we demonstrate that the combined inhibition of KRASG12C and CSF-1R leads to superior therapeutic efficacy in preclinical NSCLC mouse models. This results suggests a potential novel therapeutic regimen that could yield improved clinical benefit to patients. Citation Format: Nannan Zhang, Bin Shen, Shenyan Liu, Cheng Dai, Haiyan Ying, Fushen Guo, Zhixuan Zhu, Wan Shi, Min Tu, Jie Wang, Jie Zhang, Manqi Liu, Qi Zhang, Zhui Chen. CSF-1R inhibition with Pimicotinib (ABSK021) enhanced anti-tumor efficacy of KRASG12C inhibitors in preclinical non-small cell lung cancer mouse models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB077.

Signature Construction and Disulfidptosis-Related Molecular Cluster Identification for Better Prediction of Prognosis in Glioma.

Disulfidptosis is a newly discovered form of regulatory cell death. However, the identification of disulfidptosis-related molecular subtypes and potential biomarkers in gliomas and their prognostic predictive potential need to be further elucidated. RNA sequencing profiles and the relevant clinical data were obtained from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Disulfidptosis-related clusters were identified by unsupervised clustering analysis. Immune cell infiltration analysis and drug sensitivity analysis were used to explore the differences between clusters. Gene set enrichment analysis (GSEA) of differential genes between clusters was performed to explore the potential biological functions and signaling. A disulfidptosis-related scoring system (DRSS) was constructed based on a combined COX and LASSO analysis. Mendelian randomization (MR) analyses were used to further explore the causal relationship between levels of genes in DRSS and an increased risk of glioma. A prognosis nomogram was constructed based on the DRSS and 3 clinical features (age, WHO stage, and IDH status). The accuracy and stability of the prognosis nomogram were also validated in different cohorts. We identified two clusters that exhibited different prognoses, drug sensitivity profiles, and tumor microenvironment infiltration profiles. The overall survival (OS) of Cluster2 was significantly better than Cluster1. Cluster1 had an overall greater infiltration of immune cells compared to Cluster2. However, the Monocytes, activated B cells had higher infiltration abundance in Cluster2. GSEA results showed significant enrichment of immune-related biological processes in Cluster1, while Cluster2 was more enriched for functions related to neurotransmission and regulation. PER3, RAB34, NKX3-2, GPX7, FRA10AC1, and TGIF1 were finally included to construct DRSS. DRSS was independently related to prognosis. There was a significant difference in overall survival between the low-risk score group and the high-risk score group. Among six genes in DRSS, GPX7 levels were demonstrated to have a causal relationship with an increased risk of glioma. GPX7 may become a more promising biomarker for gliomas. The prognosis nomogram constructed based on the DRSS and three clinical features has considerable potential for predicting the prognosis of patients with glioma. Free online software for implementing this nomogram was established: https://yekun-zhuang.shinyapps.io/DynNomapp/ . Our study established a novel glioma classification based on the disulfidptosis-related molecular subtypes. We constructed the DRSS and the prognosis nomogram to accurately stratify the prognosis of glioma patients. GPX7 was identified as a more promising biomarker for glioma. We provide important insights into the treatment and prognosis of gliomas.

Identification of Key Genes Associated with Tumor Microenvironment Infiltration and Survival in Gastric Adenocarcinoma via Bioinformatics Analysis.

Gastric carcinoma (GC) is the fifth most commonly diagnosed cancer and the third leading cause of cancer-related deaths globally. The tumor microenvironment plays a significant role in the pathogenesis, prognosis, and response to immunotherapy. However, the immune-related molecular mechanisms underlying GC remain elusive. Bioinformatics analysis of the gene expression of GC and paracancerous healthy tissues from the same patient was performed to identify the key genes and signaling pathways, as well as their correlation to the infiltration of the tumor microenvironment (TME) by various immune cells related to GC development. We employed GSE19826, a gene expression profile from the Gene Expression Omnibus (GEO), for our analysis. Functional enrichment analysis of Differentially Expressed Genes (DEGs) was conducted using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes database. Cytoscape software facilitated the identification of nine hub DEGs, namely, FN1, COL1A1, COL1A2, THBS2, COL3A1, COL5A1, APOE, SPP1, and BGN. Various network analysis algorithms were applied to determine their high connectivity. Among these hub genes, FN1, COL1A2, THBS2, COL3A1, COL5A1, and BGN were found to be associated with a poor prognosis for GC patients. Subsequent analysis using the TIMER database revealed the infiltration status of the TME concerning the overexpression of these six genes. Specifically, the abovementioned genes demonstrated direct correlations with cancer-associated fibroblasts, M1 and M2 macrophages, myeloid-derived suppressor cells, and activated dendritic cells. Our findings suggest that the identified hub genes, particularly BGN, FN1, COL1A2, THBS2, COL3A1, and COL5A1, play crucial roles in GC prognosis and TME cell infiltration. This comprehensive analysis enhances our understanding of the molecular mechanisms underlying GC development and may contribute to the identification of potential therapeutic targets and prognostic markers for GC patients.

M6A-mediated molecular patterns and tumor microenvironment infiltration characterization in nasopharyngeal carcinoma

ABSTRACT N6-methyladenosine (m6A) is the most predominant RNA epigenetic regulation in eukaryotic cells. Numerous evidence revealed that m6A modification exerts a crucial role in the regulation of tumor microenvironment (TME) cell infiltration in several tumors. Nevertheless, the potential role and mechanism of m6A modification in nasopharyngeal carcinoma (NPC) remains unknown. mRNA expression data and clinical information from GSE102349, and GSE53819 datasets obtained from Gene Expression Omnibus (GEO) was used for differential gene expression and subsequent analysis. Consensus clustering was used to identify m6A-related molecular patterns of 88 NPC samples based on prognostic m6A regulators using Univariate Cox analysis. The TME cell-infiltrating characteristics of each m6A-related subclass were explored using single-sample gene set enrichment (ssGSEA) algorithm and CIBERSORT algotithm. DEGs between two m6A-related subclasses were screened using edgeR package. The prognostic signature and predicated nomogram were constructed based on the m6A-related DEGs. The cell infiltration and expression of prognostic signature in NPC was determined using immunohistochemistry (IHC) analysis. Chi-square test was used to analysis the significance of difference of the categorical variables. And survival analysis was performed using Kaplan–Meier plots and log-rank tests. The NPC samples were divided into two m6A-related subclasses. The TME cell-infiltrating characteristics analyses indicated that cluster 1 is characterized by immune-related and metabolism pathways activation, better response to anit-PD1 and anti-CTLA4 treatment and chemotherapy. And cluster 2 is characterized by stromal activation, low expression of HLA family and immune checkpoints, and a worse response to anti-PD1 and anti-CTLA4 treatment and chemotherapy. Furthermore, we identified 1558 DEGs between two m6A-related subclasses and constructed prognostic signatures to predicate the progression-free survival (PFS) for NPC patients. Compared to non-tumor samples, REEP2, TMSB15A, DSEL, and ID4 were upregulated in NPC samples. High expression of REEP2 and TMSB15A showed poor survival in NPC patients. The interaction between REEP2, TMSB15A, DSEL, ID4, and m6A regulators was detected. Our finding indicated that m6A modification plays an important role in the regulation of TME heterogeneity and complexity.

A Novel Gene Signature Based on Immune Cell Infiltration Landscape Predicts Prognosis in Lung Adenocarcinoma Patients.

The tumor microenvironment (TME) is created by the tumor and dominated by tumor-induced interactions. Long-term survival of lung adenocarcinoma (LUAD) patients is strongly influenced by immune cell infiltration in TME. The current article intends to construct a gene signature from LUAD ICI for predicting patient outcomes. For the initial phase of the study, the TCGA-LUAD dataset was chosen as the training group for dataset selection. We found two datasets named GSE72094 and GSE68465 in the Gene Expression Omnibus (GEO) database for model validation. Unsupervised clustering was performed on the training cohort patients using the ICI profiles. We employed Kaplan-Meier estimators and univariate Cox proportional-hazard models to identify prognostic differentially expressed genes in immune cell infiltration (ICI) clusters. These prognostic genes are then used to develop a LASSO Cox model that generates a prognostic gene signature. Validation was performed using Kaplan-Meier estimation, Cox, and ROC analysis. Our signature and vital immune-relevant signatures were analyzed. Finally, we performed gene set enrichment analysis (GSEA) and immune infiltration analysis on our finding gene signature to further examine the functional mechanisms and immune cellular interactions. Our study found a sixteen-gene signature (EREG, HPGDS, TSPAN32, ACSM5, SFTPD, SCN7A, CCR2, S100P, KLK12, MS4A1, INHA, HOXB9, CYP4B1, SPOCK1, STAP1, and ACAP1) to be prognostic based on data from the training cohort. This prognostic signature was certified by Kaplan-Meier, Cox proportional-hazards, and ROC curves. 11/15 immune-relevant signatures were related to our signature. The GSEA results indicated our gene signature strongly correlates with immune-related pathways. Based on the immune infiltration analysis findings, it can be deduced that a significant portion of the prognostic significance of the signature can be attributed to resting mast cells. We used bioinformatics to determine a new, robust sixteen-gene signature. We also found that this signature's prognostic ability was closely related to the resting mast cell infiltration of LUAD patients.

Abstract 6428: Transcriptome analysis of tissues and peripheral blood revealed potential biomarkers for chemo/radio-immunotherapy in non-small cell lung cancer

Abstract Background: Combination of anti-PD-(L)1 antibody with chemotherapy has shown promising efficacy in advanced/metastatic non-small cell lung cancer (NSCLC). Dynamics of peripheral blood mononuclear cells (PBMC) might have impact on repertore of immune cells further resulting different immune infiltration in tumor microenvironment. Thus, monitoring changes in PBMC during therapy or measuring tumor microenvironment provides the possibility to predict the efficacy of tumor immunotherapy. Methods: Patients diagnosed with NSCLC (Stage III-IV)were enrolled in this study. Anti-PD-L1 and chemo/radio combination therapy was performed as adjuvant therapy. Tumor samples were obtained prior to adjuvant therapy PBMC samples were obtaind at multiple time points, including prior adjuvant therapy, 3 months after adjuvant therapy, and 3 months after adjuvant therapy. Tumor and PBMC mRNA sequencing were performed by Amoy Diagnostics Co Ltd. (Xiamen, China). Patients who achieved CR/PR/SD and PFS > 6 months were defined as well responders. Pathway enrichment and GSEA analyses were performed by clusterProfiler (version 4.0.5) on GO, KEGG, and from six different gene sets. Correlations between immune cellular signature ES scores and gene expression were estimated using Pearson correlation. Results: 28 patients were enrolled; mean age was 60.38±7.86 years, 27 (96.4%) patients were male. 21 (75.0%) patients were identified as responder and 7 (25.0%) were non-responder. In the baseline tumor samples, immune infiltration was more extensive in responders compared to non-responders. Cytotoxic lymphocytes, activated CD8+ T cell, and NK cells were significantly enriched in responders. High expression of CCL17 and down regulation of CTSG/PLK1/PRTN3, a signaling axis that inhibits histone H3 hydrolysis and promotes monocyte to macrophage differentiation, was significant in responders. Dynamic monitoring of PBMC before and after treatment showed significant upregulation of immune infiltration-related pathways in responders, including effector cells, NK cells and CD8+ T cells. In the non-responder group, the up-regulated pathways after treatment were mainly enriched in tumor growth rate, EMT, and matrix remodeling. Multi-time point analysis showed that CEACAM5/MKI67 level and tumor proliferation rate in non-responders were higher than those in responders at each time point after treatment. Conclusion: Baseline tumor transcripton signatures and dynamic changes in PBMC could be applied to develop novel biomarkers for immune-related therapy. Patients with stronger immune infiltration signals in baseline tumor samples and PBMC after treatment had better immune response. The upregulation of proliferation-related signals in PBMC predicts poor immune response. Citation Format: Jing Hu, Jianguo Zhang, Peng Cheng, Yanhua Chen, Changbin Zhu, Feilin Yi, Conghua Xie. Transcriptome analysis of tissues and peripheral blood revealed potential biomarkers for chemo/radio-immunotherapy in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6428.

Abstract 3734: Tumoral and systemic immune modulation with neoadjuvant (NeoAd) intratumoral (IT) TAVO-EP (plasmid IL-12 electro gene transfer) and nivolumab (NIVO) in patients (pts) with operable locoregionally advanced melanoma

Abstract Introduction: IT TAVO-EP (tavokinogene telseplasmid delivered by electroporation) results in localized expression of IL12 in the tumor microenvironment (TME). This study (NCT04526730) evaluated NeoAd TAVO-EP and NIVO. All patients provided a written informed consent (Advarra IRB Pro00041794). Biomarkers of tumoral and systemic immune responses were conducted and correlated with clinical outcomes. Methods: NeoAd phase comprised up to 3 × 4-week cycles of IT TAVO-EP and iv NIVO followed by surgery. Endpoints included pathologic complete response (pCR), near pCR, major response (pMR; pCR + near pCR) and nonresponse (pNR). Biospecimens were collected at screening, C1D8, C2D1 (~30 days from treatment start), pre-surgery (~90 days). Slides cut from FFPE tissue were analyzed by chromogenic immunohistochemistry (IHC) for CD8+ tumor infiltrating lymphocytes (TIL) and PD-L1 (22C3 CDx assay). RNA extracted from FFPE tissue was subjected to NanoString’s IO360 transcriptomic analysis, including Tumor Inflammation Signature (TIS). PBMCs were stained and analyzed via flow cytometry on an LSR Fortessa X-20. Serum samples were analyzed for cytokine levels using Luminex® MAGPIX® platform (15-plex). Results: 16 pts were treated; 1 with PR declined surgery, 1 with early distant PD did not have surgery, 14 had surgery: 2 pNR, 3 near pCR, 9 pCR; pMR rate 12/15 (80%). In tumor, at baseline, 9/11 pts tested had <20% CD8+ TIL, 7/11 <10% PD-L1 tumor proportion score (TPS) and 6/10 TIS of ≤0, predicting non-response to anti-PD-1. In 5 pts with evaluable matched tissue at baseline and C2D1: 5/5 had increased peritumoral CD8+ T cells, 4/5 increased CD8+ TIL, 2/5 increased PD-L1, 4/5 increased TIS at C2D1 compared to baseline. Tumoral transcriptome profile revealed significant upregulation of genes involved in innate and adaptive immune responses (IFN-gamma, APM, granzymes, CD8, PD-L1, JAK1, chemokines, others) at C2D1. In blood, proliferating Ki-67+/PD-1+/CD8+ T cells expanded at C2D1 while total PD1+/CD8+ cells decreased. Decrease of PD1+CD8+ cells and other subtypes in blood at C2D1 coincided with TME infiltration by CD8+ cells. Serum effector cytokines including IL12, IFN-gamma and IL2, showed no biologically meaningful changes following treatment or differences between responders and non-responders. Conclusions: At baseline, most patients exhibited low CD8+ TIL, PD-L1 and TIS, with enhanced immune activation following treatment in the TME and blood including increased immune-related gene expression, CD8+ TIL, peritumoral CD8+ T cells and TIS. Four of 5 pts with negative predictive baseline biomarkers [CD8+TIL/PD-L1/TIS]low experienced pCRs supporting activity of IL12/anti-PD1 based regimens in this setting. Citation Format: Ahmad A. Tarhini, Zeynep Eroglu, Jonathan S. Zager, Ricardo J. Gonzalez, Amod A. Sarnaik, C Wayne Cruse, Deanryan B. De Aquino, Edith Abraham, Diana M. Acevedo, Allison Richards, Michael J. Schell, Denise Kalos, Pei-Ling Chen, Jane L. Messina, David A. Canton, Vernon K. Sondak. Tumoral and systemic immune modulation with neoadjuvant (NeoAd) intratumoral (IT) TAVO-EP (plasmid IL-12 electro gene transfer) and nivolumab (NIVO) in patients (pts) with operable locoregionally advanced melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3734.

Identification of prognostic models for glycosylation-related subtypes and tumor microenvironment infiltration characteristics in clear cell renal cell cancer

One of the most fatal forms of cancer of the urinary system, renal cell carcinoma (RCC), significantly negatively impacts human health. Recent research reveals that abnormal glycosylation contributes to the growth and spread of tumors. However, there is no information on the function of genes related to glycosylation in RCC. In this study, we created a technique that can be used to guide the choice of immunotherapy and chemotherapy regimens for RCC patients while predicting their survival prognosis. The Cancer Genome Atlas (TCGA) provided us with patient information, while the GeneCards database allowed us to collect genes involved in glycosylation. GSE29609 was used as external validation to assess the accuracy of prognostic models. The "ConsensusClusterPlus" program created molecular subtypes based on genes relevant to glycosylation discovered using differential expression analysis and univariate Cox analysis. We examined immune cell infiltration as measured by estimate, CIBERSORT, TIMER, and ssGSEA algorithms, Tumor Immune Dysfunction and Exclusion (TIDE) and exclusion of tumour stemness indices (TSIs) based on glycosylation-related molecular subtypes and risk profiles. Stratification, somatic mutation, nomogram creation, and chemotherapy response prediction were carried out based on risk factors. We built and verified 16 gene signatures associated with the prognosis of ccRCC patients, which are independent prognostic variables, and identified glycosylation-related genes by bioinformatics research. Cluster 2 is associated with lower human leukocyte antigen expression, worse overall survival, higher immunological checkpoints, and higher immune escape scores. In addition, cluster 2 had significantly better angiogenic activity, mesenchymal EMT, and stem ability scores. Higher immune checkpoint genes and human leukocyte antigens are associated with lower overall survival and a higher risk score. Higher estimated and immune scores, lesser tumor purity, lower mesenchymal EMT, and higher stem scores were all characteristics of the high-risk group. High amounts of tumor-infiltrating lymphocytes, a high mutation load, and a high copy number alteration frequency were present in the high-risk group.Discussion.According to our research, the 16-gene prognostic signature may be helpful in predicting prognosis and developing individualized treatments for patients with renal clear cell carcinoma, which may result in new personalized management options for these patients.

Identification of ferroptosis-related subtypes, characteristics of TME infiltration and development of prognostic models in gastric cancer

BackgroundFerroptosis is a distinct form of cell death characterized by unique morphology, biochemistry, and genetics, playing a crucial role in the initiation, progression, prognosis, and therapeutic strategies of tumors. However, the impact of ferroptosis-related genes (FRGs) on the tumor microenvironment (TME) remains unclear. This study may advance the existing knowledge of FRGs in gastric cancer, and push ahead with more effective prognostic assessment and the development of more effective immunotherapy approaches. MethodsFRGs were acquired from the FerrDb database and a consensus clustering technique was adopted to categorize patients with GC into groups in line with the expression profiles of 44 FRGs in order to further investigate the expression properties of these proteins. Assessment of the immune status, microsatellite instability (MSI) and cancer stem cell (CSC) index between the high- and low- risk groups to assess the proportion of TIICs in the TME, ssGSVA was adopted to detect the abundance of infiltrating immune cells from the low-risk and high-risk groups. Expression levels of eight ferroptosis-related genes of prognostic signature in GC tissues and adjacent normal tissues was detected by RT-PCR. ResultsIn the GC cohort, TP53 has the highest mutation frequency (44 %), and was shown to be highly linked with the expression levels of 11 FRGs. In accordance with the Kaplan-Meier curve, the overall survival time of patients with subtype A (Low FRG-score) discernibly exceeded that of patients with subtype B (High FRG-score).In addition, there is a significant difference in the infiltration of most immune cells between subtype A and subtype B, and some important immune checkpoints (CTLA4, PDCD1, CD274, LAG3, PDCD1LG2, and HAVCR2) have higher expression in cluster A. Finally, low FRG-scores were significantly associated with MSI-H status, while high FRG-scores were significantly associated with microsatellite stable status (MSS). FRG-score is negatively related to the cancer stem cell (CSC). ConclusionLow FRG-score, due to its high microsatellite instability (MSI-H), high mutational load and immune activation, indicates the possible advantage of OS. In addition, the FRG-score was closely related to the cancer stem cell (CSC) index and the sensitive degree of chemotherapeutic drug.

Identifying molecular subtypes and tumor microenvironment infiltration signatures in kidney renal clear cell carcinoma based on stemness-associated disulfidptosis genes by integrating machine learning, single-cell analyses and experimental validation

Clear cell renal cell carcinoma (ccRCC) is an aggressive malignant tumor. Disulfidptosis is a new programmed cell death mechanism, which is characterized by the abnormal accumulation of intracellular disulfides that are highly toxic to cells. However, the contribution of disulfidptosis to ccRCC progression has not been fully clarified. In this study, two different molecular subtypes related to disulfidptosis were identified in ccRCC patients by the non-negative matrix factorization (NMF) algorithm. The cluster 1 was characterized by a worse prognosis and higher mRNAsi levels. Then, difference analysis and weighted gene co-expression network analysis (WGCNA) were conducted to search modular genes that are highly associated with tumor stemness and tumor microenvironment. Subsequently, a SADG signature containing nine genes was constructed stepwise by WGCNA and least absolute shrinkage and selection operator (LASSO) Cox regression analysis. The high-risk score group had a worse outcome, and immune regulation and metabolic signatures might be responsible for cancer progression in the high-risk group. After that, a predictive nomogram was constructed, and the predicting power of the risk model was verified using inter and three independent external validation datasets. Nine SADGs were shown to significantly correlate with immune infiltration, tumor mutation burden (TMB), microsatellite instability (MSI) and immune checkpoint. In addition, based on the single-cell RNA sequencing dataset (GSE139555), the distribution and expression of nine hub genes in various types of immune cells were analyzed. Finally, the expression level of the nine genes was verified in clinical samples by qRT-PCR.

Molecular subtypes of disulfidptosis-regulated genes and prognosis models for predicting prognosis, tumor microenvironment infiltration, and therapeutic response in hepatocellular carcinoma

Disulfidptosis, a recently identified mode of cellular demise marked by excess SLC7A11-reliant cystine, has been proved to affect the development and resilience of tumor cells through the production of glutathione from cystine. Glutathione synthesis plays a crucial role in chemotherapy resistance and the survival of liver cancer cells. Thus, understanding the relationship between disulfidptosis and hepatocellular carcinoma (HCC) is imperative. A molecular typing approach was employed to classify patients with HCC into two distinct subtypes, namely disulfidptosis and disulfide-homeostasis, based on the expression of genes associated with disulfidptosis. Patients with disulfidptosis exhibited a longer survival time, improved immune status, and heightened sensitivity to conventional chemotherapeutic drugs and immunotherapy. Patients with disulfide-homeostasis demonstrated an immunosuppressive microenvironment, drug resistance, and unfavorable prognosis. A prognostic model was constructed utilizing the significant prognostic variables of the disulfidptosis-regulated genes. A real-world cohort was subjected to multiplex immunofluorescence to validate the clinical outcomes and immune context. Ultimately, our study delved into the prognostic relevance of disulfidptosis in HCC and provides insights into potential avenues for future research.

Gasdermin C sensitizes tumor cells to PARP inhibitor therapy in cancer models.

Several poly (ADP-ribose) polymerase (PARP) inhibitors (PARPi) are approved by FDA to treat cancer with BRCA mutations. BRCA mutations are considered to fuel a PARPi killing effect by inducing apoptosis. However, resistance to PARPi is frequently observed in the clinic due to an incomplete understanding on the molecular basis of PARPi function and a lack of good markers, beyond BRCA mutations, to predict response. Here, we show that gasdermin C (GSDMC) sensitized tumor cells to PARPi in vitro and in immunocompetent mice and caused durable tumor regression in an immune-dependent manner. A high expression level of GSDMC predicted better response to PARPi treatment in patients with triple-negative breast cancer (TNBC). PARPi treatment triggered GSDMC/caspase-8-mediated cancer cell pyroptosis (CCP) that enhanced PARPi killing of tumor cells. GSDMC-mediated CCP increased memory CD8+ T cell population in lymph node (LN), spleen, and tumor and, thus, promoted cytotoxic CD8+ T cell infiltration in the tumor microenvironment. T cell-derived granzyme B (GZMB) activated caspase-6, which subsequently cleaved GSDMC to induce pyroptosis. Interestingly, IFN-γ induced GSDMC expression, which, in turn, enhanced the cytotoxicity of PARPi and T cells. Importantly, GSDMC promoted tumor clearance independent of BRCA deficiency in multiple cancer types with PARPi treatment. This study identifies a general marker and target for PARPi therapy and offers insights into the mechanism of PARPi function.

Identification of Necroptosis-related Subtypes and Characterization of Tumor Microenvironment Infiltration in Non-small Cell Lung Cancer.

Necroptosis is correlated with the development, prognosis, and treatment of tumors. However, the function of necroptosis-associated genes (NRGs) in the tumor microenvironment (TME) of non-small cell lung cancer (NSCLC) remains unclear. In this study, 1210 NSCLC samples were classified into different subtypes based on the expression of 66 NRGs by unsupervised clustering analysis, and further analyzed the TME characteristics of these subtypes. In addition, we identified common differentially expressed genes (co-DEGs) in NRG subtypes and constructed the NRG score using principal component analysis (PCA) to assess the NRG-mediated TME characteristics of patients with NSCLC. Using unsupervised cluster analysis, 1210 NSCLC samples were divided into NRGcluster A and B subtypes. The NRGcluster B survived significantly better than the NRGcluster A. TME characterization revealed that NRGcluster B was upregulated in immune and stromal signaling activation, whereas NRGcluster A was upregulated in oncogenic signaling. The NRG score constructed based on co-DEGs of the two NRG-related subtypes was positively correlated with immune cell infiltration and negatively correlated with the number of cancer stem cells (CSCs) and tumor mutational burden (TMB). In addition, survival was significantly worse in the low-NRG-score group compared to the high-NRG-score group. Finally, the assessment of immunotherapeutic efficacy showed that immunotherapeutic response was significantly worse in the low-NRG-score group compared to the high- NRG-score group. This research reveals that NRGs are associated with the complexity and diversity of TME in NSCLC. Adopting the NRG score to quantitatively assess NRG-mediated TME in individual patients with NSCLC may help in planning clinical treatment strategies.

M5 C regulator-mediated methylation modification patterns and tumor microenvironment infiltration characteristics in acute myeloid leukemia.

Recently, many studies have been conducted to examine immune response modification at epigenetic level, but the candidate effect of RNA 5-methylcytosine (m5 C) modification on tumor microenvironment (TME) of acute myeloid leukemia (AML) is still unknown at present. We assessed the patterns of m5 C modification among 417 AML cases by using nine m5 C regulators. Thereafter, we associated those identified modification patterns with TME cell infiltration features. Additionally, stepwise regression and LASSO Cox regression analyses were conducted for quantifying patterns of m5 C modification among AML cases to establish the m5 C-score. Meanwhile, we validated the expression of genes in the m5C-score model by qRT-PCR. Finally, the present work analyzed the association between m5 C-score and AML clinical characteristics and prognostic outcomes. In total, three different patterns of m5 C modification (m5 C-clusters) were identified, and highly differentiated TME cell infiltration features were also identified. On this basis, evaluating patterns of m5 C modification in single cancer samples was important for evaluating the immune/stromal activities in TME and for predicting prognosis. In addition, the m5 C-score was established, which showed a close relation with the overall survival (OS) of test and training set samples. Moreover, multivariate Cox analysis suggested that our constructed m5 C-score served as the independent predicting factor for the prognosis of AML (hazard ratio = 1.57, 95% confidence interval = 1.38-1.79, p < 1e-5 ). This study shows that m5 C modification may be one of the key roles in the formation of diversity and complexity of TME. Meanwhile, assessing the patterns of m5 C modification among individual cancer samples is of great importance, which provides insights into cell infiltration features within TME, thereby helping to develop relevant immunotherapy and predict patient prognostic outcomes.