Immunotherapy Response Research Articles

Increased inflammatory signature in myeloid cells of non-small cell lung cancer patients with high clonal hematopoiesis burden.

Clonal hematopoiesis of indeterminate potential (CHIP) allows estimation of clonal dynamics and documentation of somatic mutations in the hematopoietic system. Recent studies utilizing large cohorts of the general population and patients have revealed significant associations of CHIP burden with age and disease status, including in cancer and chronic diseases. An increasing number of cancer patients are treated with immune checkpoint inhibitors (ICIs), but the association of ICI response in non-small cell lung cancer (NSCLC) patients with CHIP burden remains to be determined. We collected blood samples from 100 metastatic NSCLC patients before and after ICI for high-depth sequencing of the CHIP panel and 63 samples for blood single-cell RNA sequencing. Whole exome sequencing was performed in an independent replication cohort of 180 patients. The impact of CHIP status on the immunotherapy response was not significant. However, metastatic lung cancer patients showed higher CHIP prevalence (44/100 for patients vs. 5/42 for controls; p = 0.01). In addition, lung squamous cell carcinoma (LUSC) patients showed increased burden of larger clones compared to lung adenocarcinoma (LUAD) patients (8/43 for LUSC vs. 2/50 for LUAD; p = 0.04). Furthermore, single-cell RNA-seq analysis of the matched patients showed significant enrichment of inflammatory pathways mediated by NF-κB in myeloid clusters of the severe CHIP group. Our findings suggest minimal involvement of CHIP mutation and clonal dynamics during immunotherapy but a possible role of CHIP as an indicator of immunologic response in NSCLC patients.

Microbiota and Cytokine Modulation: Innovations in Enhancing Anticancer Immunity and Personalized Cancer Therapies

The gut microbiota plays a crucial role in modulating anticancer immunity, significantly impacting the effectiveness of various cancer therapies, including immunotherapy, chemotherapy, and radiotherapy. Its impact on the development of cancer is complex; certain bacteria, like Fusobacterium nucleatum and Bacteroides fragilis, can stimulate the growth of tumors by causing immunological evasion and inflammation, while advantageous strains, like Faecalibaculum rodentium, have the ability to suppress tumors by modifying immune responses. Cytokine activity and immune system regulation are intimately related. Cytokines including TGF-β, IL-6, and IL-10 promote tumor development by inhibiting efficient immune surveillance. The gut microbiome exhibits a delicate balance between pro- and anti-tumorigenic factors, as evidenced by the enhancement of anti-tumor immunity by cytokines such as IL-12 and IFN-γ. Improved immunotherapy responses are linked to a diverse microbiota, which is correlated with higher tumor infiltration and cytotoxic T-cell activation. Because microbial metabolites, especially short-chain fatty acids, affect cytokine expression and immune cell activation inside the tumor microenvironment, this link highlights the need to maintain microbial balance for optimal treatment effects. Additionally, through stimulating T-cell activation, bacteria like Lactobacillus rhamnosus and Bifidobacterium bifidum increase cytokine production and improve the efficacy of immune checkpoint inhibitors (ICIs). An option for overcoming ICI resistance is fecal microbiota transplantation (FMT), since research suggests that it improves melanoma outcomes by increasing CD8+ T-cell activation. This complex interaction provides an opportunity for novel cancer therapies by highlighting the possibility of microbiome modification as a therapeutic approach in personalized oncology approaches.

Tissue-matched analysis of MRI evaluating the tumor infiltrating lymphocytes in hepatocellular carcinoma.

Tumor-infiltrating lymphocytes (TILs) play critical roles in the tumor microenvironment and immunotherapy response. This study aims to explore the feasibility of multi-parametric magnetic resonance imaging (MRI) in evaluating TILs and to develop an evaluation model that considers spatial heterogeneity. Multi-parametric MRI was performed on hepatocellular carcinoma (HCC) mice (N = 28). Three-dimensional (3D) printing was employed for tissue sampling, to match the multi-parametric MRI data with tumor tissues, followed by flow cytometry analysis and next-generation RNA-sequencing. Pearson's correlation, multivariate logistic regression, and receiver operating characteristic (ROC) curve analyses were utilized to model TIL-related MRI parameters. MRI quantitative parameters, including T1 relaxation times and perfusion, were correlated with the infiltration of leukocytes, T-cells, CD4+ T-cells, CD8+ T-cells, PD1 + CD8+ T-cells, B-cells, macrophages, and regulatory T-cells (correlation coefficients ranged from -0.656 to 0.482, p <.05) in tumor tissues. TILs were clustered into inflamed and non-inflamed subclasses, with the proportion of T-cells, CD8+ T-cells, and PD1 + CD8+ T-cells significantly higher in the inflamed group compared to the non-inflamed group (43.37% vs. 25.45%, 50.83% vs. 34.90%, 40.45% vs. 29.47%, respectively; p <.001). The TIL evaluation model, based on the Z-score combining Kep and T1post, was able to distinguish between these subgroups, yielding an area under the curve of 0.816 (95% confidence interval 0.721-0.910) and a cut-off value of -0.03 (sensitivity 68.4%, specificity 91.3%). Additionally, the Z-score was related to the gene expression of T-cell activation, chemokine production, and cell adhesion. The tissue-matched analysis of multi-parametric MRI offers a feasible method of regional evaluation and can distinguish between TIL subclasses.

Integrated explainable machine learning and multi-omics analysis for survival prediction in cancer with immunotherapy response.

To demonstrate the efficacy of machine learning models in predicting mortality in melanoma cancer, we developed an interpretability model for better understanding the survival prediction of cancer. To this end, the optimal features were identified, ten different machine learning models were utilized to predict mortality across various datasets. Then we have utilized the important features identified by those machines learning methods to construct a new model named NKECLR to forecast mortality of patient with cancer. To explicitly clarify the model's decision-making process and uncover novel findings, an interpretable technique incorporating machine learning and SHapley Additive exPlanations (SHAP), as well as LIME, has been employed, and four genes EPGN, PHF11, RBM34, and ZFP36 were identified from those machine learning(ML). The experimental analysis conducted on training and validation datasets demonstrated that the proposed model has a good performance com- pared to existing methods with AUC value 81.8%, and 79.3%, respectively. Moreover, when combined our NKECLR with PD-L1, PD-1, and CTLA-4 the AUC value was 83%0. Finally, these findings have been applied to comprehend the response of drugs and immunotherapy. Our research introduced an innovative predictive NKECLR model utilizing natural killer(NK) cell marker genes for cohorts with melanoma cancer. The NKECLR model can effectively predict the survival of melanoma cancer cohorts and treatment results, revealing distinct immune cell infiltration in the high-risk group.

Colorectal cancer patients-derived immunity-organoid platform unveils cancer-specific tissue markers associated with immunotherapy resistance

Colorectal cancer (CRC) is a devastating disease, ranking as the second leading cause of cancer-related deaths worldwide. Immune checkpoint inhibitors (ICIs) have emerged as promising treatments; however, their efficacy is largely restricted to a subgroup of microsatellite instable (MSI) CRCs. In contrast, microsatellite stable (MSS) CRCs, which account for the majority of cases, exhibit variable and generally weaker response to ICIs, with only a subset demonstrating exceptional responsiveness. Identifying novel cancer-specific tissue (CST) markers predictive of immunotherapy response is crucial for refining patient selection and overcoming treatment resistance. In this study, we developed clinically relevant CRC organoids and autologous immune system interaction platforms to model ICI response. We conducted a comprehensive molecular characterization of both responder and non-responder models, identifying CST markers that predict ICI response. Validation of these findings was performed using an independent cohort of patient specimens through multiplex immunofluorescence. Furthermore, we demonstrated that knocking out a key gene from the identified predictive signature in resistant organoids restored immune sensitivity and induced T-cell-mediated apoptosis. Overall, our results provide novel insights into the mechanisms underlying immunotherapy resistance and suggest new markers for enhancing patient selection. These findings may pave the way for new therapeutic options in MSS patients, potentially broadening the cohort of individuals eligible for immunotherapy.

Editorial: Investigating tumor immunotherapy responses in lung cancer using deep learning

Editorial: Investigating tumor immunotherapy responses in lung cancer using deep learning

Research on the influence of radiotherapy-related genes on immune infiltration, immunotherapy response and prognosis in melanoma based on multi-omics

BackgroundSkin cutaneous melanoma (SKCM) is a significant oncological challenge due to its aggressive nature and poor treatment outcomes. This study explores the comprehensive effects of radiotherapy (RT) in SKCM, focusing on cell signaling pathways, immune infiltration, immune gene correlations, immunotherapy response, and prognosis.MethodsUsing the Cancer Genome Atlas (TCGA) database, differentially expressed genes (DEGs) in SKCM patients undergoing RT were identified. A risk score model based on these DEGs was developed to assess the effects of RT-related genes on drug sensitivity, immune cell infiltration, immunotherapy response, and prognosis through multi-omics analysis. Human melanoma cells UACC62 and UACC257 were irradiated with 8 Gy gamma ray to establish an in vitro model, verifying the impact of radiotherapy on gene expression.ResultsThe risk score demonstrated significant prognostic value and emerged as an independent prognostic factor. miRNA-mRNA and transcription factor regulatory networks underscored its clinical significance. Four key genes were identified: DUSP1, CXCL13, SLAMF7, and EVI2B. Analysis of single-cell and immunotherapy datasets indicated that these genes enhance immune response and immunotherapy efficacy in melanoma patients. PCR results confirmed that gamma rays increased the expression of these genes in human melanoma cells UACC62 and UACC257.ConclusionUsing a multi-omics approach, we analyzed and validated the impact of RT on the immune landscape of melanoma patients. Our findings highlight the critical role of RT-related genes in predicting SKCM prognosis and guiding personalized therapy strategies, particularly in the context of immunotherapy. These contribute to understanding the role of radiotherapy combined with immunotherapy in melanoma.

Exosomes as carriers to stimulate an anti-cancer immune response in immunotherapy and as predictive markers.

During this era of rapid advancements in cancer immunotherapy, the application of cell-released small vesicles that activate the immune system is of considerable interest. Exosomes are cell-derived nanovesicles that show great promise for the immunological treatment of cancer because of their immunogenicity and molecular transfer capacity. Recent technological advancements have enabled the identification of functional functions that exosome cargoes perform in controlling immune responses. Exosomes are originated specifically from immune cells and tumor cells and they show unique composition patterns directly related to the immunotherapy against cancer. Exosomes can also deliver their cargo to particular cells, which can affect the phenotypic and immune-regulatory functions of those cells. Exosomes can influence the course of cancer and have therapeutic benefits by taking part in several cellular processes; as a result, they have the dual properties of activating and restraining cancer. Exosomes have tremendous potential for cancer immunotherapy; they may develop into the most powerful cancer vaccines and carriers of targeted antigens and drugs. Comprehending the potential applications of exosomes in immune therapy is significant for regulating cancer progression. This review offers an analysis of the function of exosomes in immunotherapy, specifically as carriers that function as diagnostic indicators for immunological activation and trigger an anti-cancer immune response. Moreover, it summarizes the fundamental mechanism and possible therapeutic applications of exosome-based immunotherapy for human cancer.

Small-Vessel Vasculitis or Perifolliculitis in Small-Fiber Neuropathy With TS-HDS, FGFR-3, or Plexin D1 Antibodies

Abstract Introduction: Small-fiber neuropathy (SFN) is highly prevalent but often idiopathic. TS-HDS, FGFR-3, and Plexin D1 autoantibodies (seropositive) may be present in more than 40% of idiopathic cases. Another autoimmune biomarker is a non–length-dependent (NLD) skin biopsy pattern. Our goal was to demonstrate that small-vessel vasculitis and perifolliculitis (inflammation) on skin biopsies are additional biomarkers. Methods: All pure SFN skin biopsy reports were reviewed for inflammation, and their charts were examined for other relevant history. Results: Seven of 80 patients with pure SFN had inflammation (8.8%); 5 patients were female (71%) and 2 were male (29%); average age was 45 (16–67). All 7 patients with inflammation were seropositive (100%, P = 0.0495), and 6 patients (86%) had either NLD inflammation or NLD pathology (P = 0.0003). Discussion: Inflammation is present only in a small portion of punch biopsies, but may be another autoimmune SFN biomarker. It is strongly associated with seropositivity and NLD-pathology. Further studies are likely indicated to assess inflammation pathophysiology and immunotherapy responsiveness.

Unveiling Immunotherapy Evasion in Lung Cancer: The Role of Fanconi Anemia and Stemness Genes in Shaping an Immunosuppressive Microenvironment.

The study aimed to investigate the fanconi anemia (FA)-related and stemness-related genes in lung cancer (LC) patients. Firstly, we identified stemness-related genes through weighted gene co-expression network analysis combined with TCGA database. Further combined stemness-related genes with FA-related genes to screen for prognostic-related genes. Risk score was constructed from the screened genes and comprehensive bioinformatics analyses were performed. Finally, single-cell data and in vitro experiment were used to validate our results. We screened a total of eight genes to construct a risk score. The risk score was an independent prognostic factor for LC. The validation results of multiple GEO databases were consistent with our results. Functional and pathway enrichment analysis showed that risk score was associated with cell cycle, DNA replication, DNA damage repair, and immune-related pathways. The results showed to be related to the stem cell self-renewal and proliferation. Besides, we also found that patients with higher risk scores had lower immune activity and function, and the effectiveness of immunotherapy might be poorer, with a higher rate of immune escape. Finally, our results revealed that SLC2A1 had the highest correlation with B cells in single-cell data analysis, and we validated its correlation with B cells and its expression with FA-related genes, tumor invasiveness, stemness, and drug sensitivity. Our research constructed a risk score based on FA-related and tumor stemness-related specific genes. In addition to accurately predicting the prognosis of patients with LC, the risk score may also serve as an innovative and viable predictor of immunotherapy response.

SLC12A9 is an immunological and prognostic biomarker for glioma

BackgroundGlioma is one of the most common malignant brain tumors. It has a high rate of progression and a poor prognosis, and effective biomarkers still need to be identified. The solute carrier family 12 (SLC12) family has been reported to be involved in various physiological and pathological processes, but their functional roles in glioma remain unclear. MethodsUsing public datasets, we studied the mutation and expression level of SLC12 family genes in glioma and identified the significantly differentially expressed member solute carrier family 12 member 9 (SLC12A9). We further predicted the prognostic role of SLC12A9 in glioma by using the Kaplan–Meier method and Cox regression analysis. Then, we performed biological functional enrichment analysis. We focused on the relationships between SLC12A9 expression and immune infiltration in glioma. Meanwhile, we conducted in vitro experiments to evaluate the effect of SLC12A9 expression on glioma cells. ResultsAmong the members of the SLC12 family, SLC12A9 had the highest mutation rate in glioma, with gene amplification as the major mutation type, and its expression was significantly upregulated in glioma. Higher SLC12A9 expression was significantly associated with older age, higher grade, wild-type isocitrate dehydrogenase (IDH), and a worse prognosis. The functional enrichment analysis indicated that SLC12A9 is mainly related to ion channel annotation. Gene set enrichment analysis (GSEA) revealed that SLC12A9 was mainly related to the DNA replication pathway. Furthermore, we found that SLC12A9 correlated with tumor-infiltrating immune cells and immune checkpoints. Thus, SLC12A9 may be involved in regulating the immune response of glioma. Finally, our in vitro experiments revealed that silencing SLC12A9 dramatically inhibited glioma cell growth and migration. ConclusionsWe showed that SLC12A9 may be a new predictive biomarker for glioma diagnosis, prognosis, and immunotherapy response, offering helpful guidelines to advance glioma treatment.

Serum exocrine pancreas enzymes are biomarkers of immunotherapy response in new-onset type 1 diabetes

Serum exocrine pancreas enzymes are biomarkers of immunotherapy response in new-onset type 1 diabetes

Lactylation-related gene signature accurately predicts prognosis and immunotherapy response in gastric cancer

Lactylation-related gene signature accurately predicts prognosis and immunotherapy response in gastric cancer

Comprehensive Analysis Reveals That ISCA1 Is Correlated with Ferroptosis-Related Genes Across Cancers and Is a Biomarker in Thyroid Carcinoma

Background: ISCA1 (Iron–Sulfur Cluster Assembly 1) is involved in the assembly of iron–sulfur (Fe–S) clusters, which are vital for electron transport and enzyme activity. Some studies suggest the potential involvement of ISCA1 in tumor progression through interactions with ferroptosis-related genes (FRGs) and the tumor immune microenvironment (TME). However, there has been no systematic analysis of its role in FRGs and the TME or its predictive value for prognosis and immunotherapy response across different cancer types. Methods: In this study, we analyzed the expression and prognosis of ISCA1 RNA, CNV, methylation, and protein in multiple tumor tissues via data from the TCGA and CPTAC databases and clinical information. We conducted a comprehensive analysis of the correlations between ISCA1 and FRGs, immune-related genes (including immune regulatory genes and immune checkpoint genes), immune cell infiltration, immune infiltration scores, tumor stemness, and genomic heterogeneity. Results: We performed drug prediction and validation through molecular docking and molecular dynamics analysis to identify candidate drugs that could promote or inhibit ISCA1 RNA expression. Our findings revealed that ISCA1 could serve as a biomarker in thyroid carcinoma, play a role with different FRGs in various cell types, and mediate different ligand–receptor pathways for cell–cell communication. Conclusions: Overall, our study highlights the potential of ISCA1 as a novel biomarker for predicting prognosis and immunotherapeutic efficacy in thyroid carcinoma and suggests its potential for developing novel antitumor drugs or improving immunotherapy.

Qualitative Transcriptional Signature for Predicting the Pathological Response of Colorectal Cancer to FOLFIRI Therapy

FOLFIRI (5-FU, leucovorin, irinotecan) is the first-line chemotherapy for metastatic colorectal cancer (mCRC), but response rates are under 50%. This study aimed to develop a predictive signature for FOLFIRI response in mCRC patients. Firstly, Spearman’s rank correlation and Wilcoxon rank-sum test were used to select chemotherapy response genes and gene pairs, respectively. Then, an optimization procedure was used to determine the final signature. A predictive signature consisting of three gene pairs (3-GPS) was identified. In the training set, 3-GPS achieved an accuracy of 0.94. In a validation set of 60 samples, predicted responders had significantly better progression-free survival than the predicted non-responders (HR = 0.47, p = 0.01). A comparable result was observed in an additional validation set of 27 samples (HR = 0.06, p = 0.02). The co-expressed genes of the signature were enriched in pathways associated with the immunotherapy response, and they interacted extensively with FOLFIRI-related genes. Notably, the expression of signature genes significantly correlated with various immune cell types, including plasma cells and memory-resting CD4+ T cells. In conclusion, the REO-based signature effectively identifies mCRC patients likely to benefit from FOLFIRI. Furthermore, these signature genes may play a crucial role in the chemotherapy.

Role of CALCR expression in liver cancer: Implications for the immunotherapy response.

Liver hepatocellular carcinoma (LIHC) is a prevalent and lethal malignancy with a complex molecular landscape. Fibrosis and ferroptosis are implicated in LIHC progression, yet their roles remain to be elucidated. The present study investigated the expression and prognostic significance of calcitonin receptor (CALCR), a gene that intersects the pathways of fibrosis and ferroptosis, across LIHC and other types of cancer. Data were obtained from The Cancer Genome Atlas and the Molecular Signatures Database. LIHC patients were classified into two clusters based on fibrosis‑related gene expression using ConsensusClusterPlus. Single‑sample gene set enrichment analysis was employed to quantify fibrosis and ferroptosis levels. Correlation, survival and nomogram analyses were performed to assess the prognostic value of CALCR. Additionally, single‑cell RNA sequencing data from the Tumor Immune Single Cell Hub 2 (TISCH2) and pan‑cancer analyses of genomic heterogeneity features were incorporated. The present study also identified a putative regulatory role for CALCR in LIHC cell migration, proliferation and apoptosis. CALCR was identified as a significant prognostic marker for LIHC. Patients with high CALCR expression exhibited shortened overall survival (OS) and disease‑specific survival (DSS). Specifically, the hazard ratios (HRs) for OS and DSS were 1.76 [95% confidence interval (CI): 1.23=2.49) and 1.77 (95% CI: 1.13=2.78], respectively, with corresponding P‑values of 0.002 for OS and 0.013 for DSS. Analyses of immune cell infiltration revealed a more complex immune environment in patients with low CALCR expression, suggesting differential responses to immunotherapy. Furthermore, in HepG‑2 and HuH‑7 cells, small interfering (si)‑CALCR increased apoptosis while reducing proliferation and migration compared with si‑negative control. CALCR serves as a significant prognostic biomarker for LIHC, influencing both molecular pathways and the immune landscape. Its expression is associated with improved survival outcomes and distinct genomic features, positioning it as a potential therapeutic target and predictor of immunotherapy efficacy.

Genomic profiling of NSCLC tumors with the TruSight oncology 500 assay provides broad coverage of clinically actionable genomic alterations and detection of known and novel associations between genomic alterations, TMB, and PD-L1

IntroductionMatching patients to an effective targeted therapy or immunotherapy is a challenge for advanced and metastatic non-small cell lung cancer (NSCLC), especially when relying on assays that test one marker at a time. Unlike traditional single marker tests, comprehensive genomic profiling (CGP) can simultaneously assess NSCLC tumors for hundreds of genomic biomarkers and markers for immunotherapy response, leading to quicker and more precise matches to therapeutics.MethodsIn this study, we performed CGP on 7,606 patients with advanced or metastatic NSCLC using the Illumina TruSight Oncology 500 (TSO 500) CGP assay to show its coverage and utility in detecting known and novel features of NSCLC.ResultsTesting revealed distinct genomic profiles of lung adenocarcinoma and squamous cell carcinomas and detected variants with a current targeted therapy or clinical trial in &amp;gt;72% of patient tumors. Known associations between genomic alterations and immunotherapy markers were observed including significantly lower TMB levels in tumors with therapy-associated alterations and significantly higher PD-L1 levels in tumors with ALK, MET, BRAF, or ROS1 driver mutations. Co-occurrence analysis followed by network analysis with gene module detection revealed known and novel co-occurrences between genomic alterations. Further, certain modules of genes with co-occurring genomic alterations had dose-dependent relationships with histology and increasing or decreasing levels of PD-L1 and TMB, suggesting a complex relationship between PD-L1, TMB, and genomic alterations in these gene modules.DiscussionThis study is the largest clinical study to date utilizing the TSO 500. It provides an opportunity to further characterize the landscape of NSCLC using this newer technology and show its clinical utility in detecting known and novel facets of NSCLC to inform treatment decision-making.

The intratumoral landscape of T cell receptor repertoire in esophageal squamous cell carcinoma

BackgroundEsophageal squamous cell carcinoma (ESCC) is a malignant neoplasm with detrimental implications for human health. The landscape of ESCC therapy has been revolutionized by the introduction of immunotherapy, specifically involving immune checkpoint inhibitors (ICIs). A number of studies have documented the prognostic significance of T-cell receptor (TCR) repertoire and its association with many tumors. Nevertheless, the TCR repertoire landscape and its significance in ESCC still need to be explored.MethodsIn this study, we conducted RNA-Seq analysis to investigate the characteristics of the TCR repertoire in 90 patients. Moreover, high-throughput TCR sequencing was performed on tumor tissues from 41 patients who received immunotherapy. Additionally, a comprehensive analysis of the T-cell receptor repertoire landscape within ESCC tumors was carried out through immunohistochemical staining on all patient samples.ResultsWe noticed a diminished diversity of TCR repertoire within the tumor compared to its adjacent normal tissue. In terms of immunotherapy responses, non-responsive patients exhibited higher TCR repertoire diversity indices and an increased frequency of common V and J genes. Additionally, elevated TCR repertoire diversity correlated with improved overall survival rates. Lastly, immunohistochemical staining results indicated a correlation between TCR repertoire diversity and the tumor immune microenvironment (TIME).ConclusionsOur study primarily describes the landscape of TCR repertoires in ESCC through three aspects: differences in tumor tissues, immune response to immunotherapy, and survival prognosis of patients. These results emphasize the importance of TCR repertoire characteristics as unique and relevant biomarkers for ESCC immunotherapy.

Pan-immune-inflammation value predicts immunotherapy response and reflects local antitumor immune response in rectal cancer.

The pan-immune-inflammation value reflects the systemic inflammatory response, and tumor-infiltrating lymphocytes indicate a local immune response in rectal cancer. However, the association between systemic inflammatory response, as indicated by the pan-immune-inflammation value, and local immune responses in rectal cancer remains unclear. This study analyzed 915 treatment-naïve rectal cancer patients from the Peking Union Medical College Hospital and PLA General Hospital (PLAGH) cohorts who underwent radical surgery to investigate the relationship between the pan-immune-inflammation value and immune responses. Lower pan-immune-inflammation value was significantly associated with improved disease-free survival and cancer-specific survival. Multivariate Cox regression models identified the pan-immune-inflammation value as an independent prognostic factor. In the PLAGH cohort, patients with low pan-immune-inflammation values had higher immune cell levels, activated immune pathways, and increased expression of immune checkpoint genes according to RNA sequencing. Hematoxylin and eosin staining and immunohistochemical analysis revealed that lower pan-immune-inflammation value was associated with higher tumor-infiltrating lymphocyte density, more mature tertiary lymphoid structures, increased CD8+ T cells, and elevated human lymphocyte antigen class I expression. Conversely, patients with high pan-immune-inflammation values exhibited pathways linked to tumor progression, such as angiogenesis, epithelial-mesenchymal transition, hypoxia, KRAS signaling, and TGF-ß signaling. Among patients receiving anti-PD-1 therapy, responders had low pre- and post-treatment pan-immune-inflammation values. The pan-immune-inflammation value is a reliable marker associated with distinct immune microenvironment characteristics and can effectively predict disease-free survival, cancer-specific survival, and response to immunotherapy.

Unveiling the role of TGF-β signaling pathway in breast cancer prognosis and immunotherapy

IntroductionThe TGF-β signaling pathway (TSP) is pivotal in tumor progression. Nonetheless, the connection between genes associated with the TSP and the clinical outcomes of breast cancer, as well as their impact on the tumor microenvironment and immunotherapeutic responses, remains elusive.MethodsBreast cancer transcriptomic and single-cell sequencing data were obtained from the The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. We identified 54 genes associated with the TSP from the Molecular Signatures Database (MSigDB) and analyzed both data types to evaluate TSP activity. Using weighted gene co-expression network analysis (WGCNA), we identified modules linked to TSP activity. To assess patient risk, we used 101 machine learning algorithms to develop an optimal TGF-β pathway-related prognostic signature (TSPRS). We then examined immune activity and response to immune checkpoint inhibitors and chemotherapy in these groups. Finally, we validated ZMAT3 expression levels clinically and confirmed its relevance in breast cancer using CCK-8 and migration assays.ResultsAt the single-cell level, TSP activity was most notable in endothelial cells, with higher activity in normal tissues compared to tumors. TSPRS was developed. This signature's accuracy was confirmed through internal and external validations. A nomogram incorporating the TSPRS was created to improve prediction accuracy. Further studies showed that breast cancer patients categorized as low-risk by the TSPRS had higher immune phenotype scores and more immune cell infiltration, leading to better prognosis and enhanced immunotherapy response. Additionally, a strong link was found between the TSPRS risk score and the effectiveness of anti-tumor agents. Silencing the ZMAT3 gene in the TSPRS significantly reduced the proliferation and invasiveness of breast cancer cells.DiscussionOur study developed a TSPRS, which emerges as a potent predictive instrument for the prognosis of breast cancer, offering novel perspectives on the immunotherapeutic approach to the disease.