Single-Cell Transcriptomic Analysis Reveals a Tumor-Reactive T Cell Signature Associated With Clinical Outcome and Immunotherapy Response In Melanoma

Deciphering the Immunomodulatory Function of GSN+ Inflammatory Cancer-Associated Fibroblasts in Renal Cell Carcinoma Immunotherapy: Insights From Pan-Cancer Single-Cell Landscape and Spatial Transcriptomics Analysis

The heterogeneity of cancer-associated fibroblasts (CAFs) could affect the response to immune checkpoint inhibitor (ICI) therapy. However, limited studies have investigated the role of inflammatory CAFs (iCAFs) in ICI therapy using pan-cancer single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics sequencing (ST-seq) analysis. We performed pan-cancer scRNA-seq and ST-seq analyses to identify the subtype of GSN+ iCAFs, exploring its spatial distribution characteristics in the context of ICI therapy. The pan-cancer scRNA-seq and bulk RNA-seq data are incorporated to develop the Caf.Sig model, which predicts ICI response based on CAF gene signatures and machine learning approaches. Comprehensive scRNA-seq analysis, along with in vivo and in vitro experiments, investigates the mechanisms by which GSN+ iCAFs influence ICI efficacy. The Caf.Sig model demonstrates well performances in predicting ICI therapy response in pan-cancer patients. A higher proportion of GSN+ iCAFs is observed in ICI non-responders compared to responders in the pan-cancer landscape and clear cell renal cell carcinoma (ccRCC). Using real-world immunotherapy data, the Caf.Sig model accurately predicts ICI response in pan-cancer, potentially linked to interactions between GSN+ iCAFs and CD8+ Tex cells. ST-seq analysis confirms that interactions and cellular distances between GSN+ iCAFs and CD8+ exhausted T (Tex) cells impact ICI efficacy. In a co-culture system of primary CAFs, primary tumour cells and CD8+ T cells, downregulation of GSN on CAFs drives CD8+ T cells towards a dysfunctional state in ccRCC. In a subcutaneously tumour-grafted mouse model, combining GSN overexpression with ICI treatment achieves optimal efficacy in ccRCC. Our study provides the Caf.Sig model as an outperforming approach for patient selection of ICI therapy, and advances our understanding of CAF biology and suggests potential therapeutic strategies for upregulating GSN in CAFs in cancer immunotherapy.

SPP1 + macrophages cause exhaustion of tumor-specific T cells in liver metastases

Functional tumor-specific CD8+ T cells are essential for effective anti-tumor immune response and immune checkpoint inhibitor therapy. Here we show that, compared to other organ sites, primary, metastatic liver tumors in murine models contain a higher number of tumor-specific CD8+ T cells which are also dysfunctional. High-dimensional, multi-omic analysis of patient samples reveals a higher frequency of exhausted tumor-reactive CD8+ T cells and enriched interactions between these cells and SPP1+ macrophages in profibrotic, alpha-SMA rich regions specifically in the liver. Differential pseudotime trajectory inference analysis reveals that extrahepatic signaling promotes an intermediate cell (IC) population in the liver, characterized by co-expression of VISG4, CSF1R, CD163, TGF-βR, IL-6R, and SPP1. Analysis of premetastatic adenocarcinoma patient samples reveals enrichment of this population may predict liver metastasis. These findings suggest a mechanism by which extrahepatic tumors drive liver metastasis by promoting an IC population that inhibits tumor-reactive CD8+ T cell function.

Deciphering ERR family genes as prognostic and immunological biomarkers through pan-cancer analysis with validation in gallbladder cancer

Background

The estrogen-related receptor family genes (ERRs), including ESRRA, ESRRB, and ESRRG, have been implicated in a few tumors, exhibiting distinct roles through diverse mechanisms. The purpose of our research is to explore the commonalities and underlying mechanism of ERRs in malignancies from a pan-cancer perspective and to validate the role and mechanisms of ESRRG in gallbladder cancer (GBC).

Methods

We leveraged public databases such as TCGA and GTEx to systematically investigate the potential functions of ERRs in malignancies. ESRRG expression was analyzed through immunohistochemical staining in gallbladder cancer and cholecystitis tissues. For functional validation, ESRRG was knocked down in GBC cell lines, followed by CCK-8, colony formation, scratch wound healing, Transwell migration, and invasion assays. Western blot, qPCR, and immunofluorescence were performed to evaluate the relationship between ESRRG, PD-L1, and CD8+ T cells.

Results

Compared to adjacent normal tissues, ESRRA is overexpressed in most tumors, ESRRB is generally underexpressed, and ESRRG exhibits significant expression alterations across various tumors. All three ERRs demonstrate significant prognostic value across different cancers. Notably, the strong associations of ERRs with key immunological features-stromal scores, immune cell infiltration, microsatellite instability (MSI), and tumor mutational burden (TMB)-suggest their involvement in immune evasion and their potential utility in guiding immunotherapy strategies. All three ERRs display a positive correlation with advanced tumor stages in cholangiocarcinoma (CHOL). Specifically, in CHOL, ESRRG expression is closely associated with lymphatic metastasis, poorer overall survival, reduced immune infiltration, elevated PD-L1 expression, epithelial-mesenchymal transition (EMT), and DNA damage response. In GBC tissues, we subsequently confirmed that ESRRG expression positively correlates with pathological staging and PD-L1 expression, while negatively correlating with prognosis and CD8+ T cell infiltration. Knockdown of ESRRG in gallbladder cancer cells results in decreased proliferation, migration, and invasion. Moreover, the expression of PD-L1, MSH2, BRCA1, MMP2, and VIMENTIN decreased with ESRRG knockdown.

Conclusion

Our pan-cancer analysis reveals ERRs as critical regulators of tumor immunity and progression, with ESRRG emerging as a key oncogenic driver in GBC. The mechanistic link between ESRRG and PD-L1/EMT suggests its potential as a therapeutic target to enhance immunotherapy efficacy. These findings underscore the need for tissue-specific targeting strategies for ERR family members in precision oncology.

Comprehensive pan-cancer single-cell analysis reveals glycolysis-related signatures as predictive biomarkers for immunotherapy response and their role in bladder cancer

Glycolysis is a vital metabolic biological process in tumor progression and immune modulation. This study comprehensively investigated the roles of glycolysis in pan-cancer, especially in bladder cancer. Exploration of 34 single-cell RNA sequencing (scRNA-seq) cohorts, eight ICI-treated bulk RNA-seq cohorts, and TCGA bulk pan-cancer RNA-seq cohorts uncovered a Glycolysis.Sig which strongly correlated with immunotherapy response and demonstrated excellent predictive performance in prognosis and immune response. Hub-Glycolysis.Sig exhibited varying interactions with the immune microenvironment based on cancer type. In bladder cancer, higher glycolysis risk scores correlated with poorer prognosis, with distinct immune infiltration characteristics between subtypes. scRNA-seq revealed high glycolysis levels in bladder epithelial cells. COPB2 was highly expressed in bladder cancer, promoting cell proliferation, migration, and glycolytic activity in vitro and in vivo. Our large-scale data analysis confirmed the negative correlation between glycolysis and immunotherapy outcomes, identifying Glycolysis.Sig as a novel predictive biomarker. Hub-Glycolysis.Sig provides clinical insights for bladder cancer therapy strategies, while COPB2 and other potential therapeutic targets facilitate personalized cancer treatment.

Unravelling Cancer Immunity: Coagulation.Sig and BIRC2 as Predictive Immunotherapeutic Architects

Immune checkpoint inhibitors (ICIs) represent a groundbreaking advancement in cancer therapy, substantially improving patient survival rates. Our comprehensive research reveals a significant positive correlation between coagulation scores and immune-related gene expression across 30 diverse cancer types. Notably, tumours exhibiting high coagulation scores demonstrated enhanced infiltration of cytotoxic immune cells, including CD8+ T cells, natural killer (NK) cells, and macrophages. Leveraging the TCGA pan-cancer database, we developed the Coagulation.Sig model, a sophisticated predictive framework utilising a coagulation-related genes (CRGs) to forecast immunotherapy outcomes. Through rigorous analysis of ten ICI-treated cohorts, we identified and validated seven critical CRGs: BIRC2, HMGB1, STAT2, IFNAR1, BID, SPATA2, IL33 and IFNG, which form the foundation of our predictive model. Functional analyses revealed that low-risk tumours characterised by higher immune cell populations, particularly CD8+ T cells, demonstrated superior ICI responses. These tumours also exhibited increased mutation rates, elevated neoantigen loads, and greater TCR/BCR diversity. Conversely, high-risk tumours displayed pronounced intratumor heterogeneity (ITH) and elevated NRF2 pathway activity, mechanisms strongly associated with immune evasion. Experimental validation highlighted BIRC2 as a promising therapeutic target. Targeted BIRC2 knockdown, when combined with anti-PD-1 therapy, significantly suppressed tumour growth, enhanced CD8+ T cell infiltration, and amplified IFN-γ and TNF-α secretion in tumour models. Our findings position the Coagulation.Sig model as a novel, comprehensive approach to personalised cancer treatment, with BIRC2 emerging as both a predictive biomarker and a potential therapeutic intervention point.

Immune Cell Interactions and Immune Checkpoints in the Tumor Microenvironment of Gastric Cancer

Gastric cancer (GC) ranks as the fifth most prevalent malignant neoplasm globally, with an increased death rate despite recent advancements in research and therapeutic options. Different molecular subtypes of GC have distinct interactions with the immune system, impacting the tumor microenvironment (TME), prognosis, and reaction to immunotherapy. Tumor-infiltrating lymphocytes (TILs) in the TME are crucial for preventing tumor growth and metastasis, as evidenced by research showing that patients with GC who have a significant density of TILs have better survival rates. But cancer cells have evolved a variety of mechanisms to evade immune surveillance, both sialic acid-binding immunoglobulin-like lectin 15 (Siglec-15) and Programmed Death-Ligand 1 (PD-L1) playing a pivotal role in the development of an immunosuppressive TME. They prevent T cell activation and proliferation resulting in a decrease in the immune system's capacity to recognize and eliminate malignant cells. These immune checkpoint molecules function via different but complementary mechanisms, the expression of Siglec-15 being mutually exclusive with PD-L1 and, therefore, providing a different therapeutic approach. The review explores how TILs affect tumor growth and patient outcomes in GC, with particular emphasis on their interactions within the TME and potential targeting of the PD-L1 and Siglec-15 pathways for immunotherapy.

Navigating the immune landscape with plasma cells: A pan-cancer signature for precision immunotherapy

Immunotherapy has revolutionized cancer treatment; however, predicting patient response remains a significant challenge. Our study identified a novel plasma cell signature, Plasma cell.Sig, through a pan-cancer single-cell RNA sequencing analysis, which predicts patient outcomes to immunotherapy with remarkable accuracy. The signature was developed using rigorous machine learning algorithms and validated across multiple cohorts, demonstrating superior predictive power with an area under the curve (AUC) exceeding 0.7. Notably, the low-risk group, as classified by Plasma cell.Sig, exhibited enriched immune cell infiltration and heightened tumor immunogenicity, indicating an enhanced responsiveness to immunotherapy. Conversely, the high-risk group showed reduced immune activity and potential mechanisms of immune evasion. These findings not only enhance understanding of the intrinsic and extrinsic immune landscapes within the tumor microenvironment but also pave the way for more precise, biomarker-guided immunotherapy approaches in oncology.

Mime: A flexible machine-learning framework to construct and visualize models for clinical characteristics prediction and feature selection

The widespread use of high-throughput sequencing technologies has revolutionized the understanding of biology and cancer heterogeneity. Recently, several machine-learning models based on transcriptional data have been developed to accurately predict patients' outcome and clinical response. However, an open-source R package covering state-of-the-art machine-learning algorithms for user-friendly access has yet to be developed. Thus, we proposed a flexible computational framework to construct a machine learning-based integration model with elegant performance (Mime). Mime streamlines the process of developing predictive models with high accuracy, leveraging complex datasets to identify critical genes associated with prognosis. An in silico combined model based on de novo PIEZO1-associated signatures constructed by Mime demonstrated high accuracy in predicting the outcomes of patients compared with other published models. Furthermore, the PIEZO1-associated signatures could also precisely infer immunotherapy response by applying different algorithms in Mime. Finally, SDC1 selected from the PIEZO1-associated signatures demonstrated high potential as a glioma target. Taken together, our package provides a user-friendly solution for constructing machine learning-based integration models and will be greatly expanded to provide valuable insights into current fields. The Mime package is available on GitHub (https://github.com/l-magnificence/Mime).

A Paradoxical Tumor Antigen Specific Response in the Liver

Functional tumor-specific CD8+ T cells are essential for an effective anti-tumor immune response and the efficacy of immune checkpoint inhibitor therapy. In comparison to other organ sites, we found higher numbers of tumor-specific CD8+ T cells in primary, metastatic liver tumors in murine tumor models. Despite their abundance, CD8+ T cells in the liver displayed an exhausted phenotype. Depletion of CD8+ T cells showed that liver tumor-reactive CD8+ T failed to control liver tumors but was effective against subcutaneous tumors. Similarly, analysis of single-cell RNA sequencing data from patients showed a higher frequency of exhausted tumor-reactive CD8+ T cells in liver metastasis compared to paired primary colon cancer. High-dimensional, multi-omic analysis combining proteomic CODEX and scRNA-seq data revealed enriched interaction of SPP1+ macrophages and CD8+ tumor-reactive T cells in profibrotic, alpha-SMA rich regions in the liver. Liver tumors grew less in Spp1-/- mice and the tumor-specific CD8+ T cells were less exhausted. Differential pseudotime trajectory inference analysis revealed extrahepatic signaling promoting an intermediate cell (IC) population in the liver, characterized by co-expression of VISG4, CSF1R, CD163, TGF-βR, IL-6R, SPP1. scRNA-seq of a third data set of premetastatic adenocarcinoma showed that enrichment of this population may predict liver metastasis. Our data suggests a mechanism by which extrahepatic tumors facilitate the formation of liver metastasis by promoting an IC population inhibiting tumor-reactive CD8+ T cell function.

Exploring a specialized programmed-cell death patterns to predict the prognosis and sensitivity of immunotherapy in cutaneous melanoma via machine learning

The mortality and therapeutic failure in cutaneous melanoma (CM) are mainly caused by wide metastasis and chemotherapy resistance. Meanwhile, immunotherapy is considered a crucial therapy strategy for CM patients. However, the efficiency of currently available methods and biomarkers in predicting the response of immunotherapy and prognosis of CM is limited. Programmed cell death (PCD) plays a significant role in the occurrence, development, and therapy of various malignant tumors. In this research, we integrated fourteen types of PCD, multi-omics data from TCGA-SKCM and other cohorts in GEO, and clinical CM patients to develop our analysis. Based on significant PCD patterns, two PCD-related CM clusters with different prognosis, tumor microenvironment (TME), and response to immunotherapy were identified. Subsequently, seven PCD-related features, especially CD28, CYP1B1, JAK3, LAMP3, SFN, STAT4, and TRAF1, were utilized to establish the prognostic signature, namely cell death index (CDI). CDI accurately predicted the response to immunotherapy in both CM and other cancers. A nomogram with potential superior predictive ability was constructed, and potential drugs targeting CM patients with specific CDI have also been identified. Given all the above, a novel CDI gene signature was indicated to predict the prognosis and exploit precision therapeutic strategies of CM patients, providing unique opportunities for clinical intelligence and new management methods for the therapy of CM.

Single-cell spatial multiomics reveals tumor microenvironment vulnerabilities in cancer resistance to immunotherapy

Heterogeneous resistance to immunotherapy remains a major challenge in cancer treatment, often leading to disease progression and death. Using CITE-seq and matched 40-plex PhenoCycler tissue imaging, we performed longitudinal multimodal single-cell analysis of tumors from metastatic melanoma patients with innate resistance, acquired resistance, or response to immunotherapy. We established the multimodal integration toolkit to align transcriptomic features, cellular epitopes, and spatial information to provide deeper insights into the tumors. With longitudinal analysis, we identified an "immune-striving" tumor microenvironment marked by peri-tumor lymphoid aggregates and low infiltration of T cells in the tumor and the emergence of MITF+SPARCL1+ and CENPF+ melanoma subclones after therapy. The enrichment of B cell-associated signatures in the molecular composition of lymphoid aggregates was associated with better survival. These findings provide further insights into the establishment of microenvironmental cell interactions and molecular composition of spatial structures that could inform therapeutic intervention.

Pan-Cancer Single-Cell and Spatial-Resolved Profiling Reveals the Immunosuppressive Role of APOE+ Macrophages in Immune Checkpoint Inhibitor Therapy

The heterogeneity of macrophages influences the response to immune checkpoint inhibitor (ICI) therapy. However, few studies explore the impact of APOE+ macrophages on ICI therapy using single-cell RNA sequencing (scRNA-seq) and machine learning methods. The scRNA-seq and bulk RNA-seq data are Integrated to construct an M.Sig model for predicting ICI response based on the distinct molecular signatures of macrophage and machine learning algorithms. Comprehensive single-cell analysis as well as in vivo and in vitro experiments are applied to explore the potential mechanisms of the APOE+ macrophage in affecting ICI response. The M.Sig model shows clear advantages in predicting the efficacy and prognosis of ICI therapy in pan-cancer patients. The proportion of APOE+ macrophages is higher in ICI non-responders of triple-negative breast cancer compared with responders, and the interaction and longer distance between APOE+ macrophages and CD8+ exhausted T (Tex) cells affecting ICI response is confirmed by multiplex immunohistochemistry. In a 4T1 tumor-bearing mice model, the APOE inhibitor combined with ICI treatment shows the best efficacy. The M.Sig model using real-world immunotherapy data accurately predicts the ICI response of pan-cancer, which may be associated with the interaction between APOE+ macrophages and CD8+ Tex cells.

Prediction of immunotherapy responsiveness in melanoma through single-cell sequencing-based characterization of the tumor immune microenvironment

Immune checkpoint inhibitors (ICB) therapy have emerged as effective treatments for melanomas. However, the response of melanoma patients to ICB has been highly heterogenous. Here, by analyzing integrated scRNA-seq datasets from melanoma patients, we revealed significant differences in the TiME composition between ICB-resistant and responsive tissues, with resistant or responsive tissues characterized by an abundance of myeloid cells and CD8+ T cells or CD4+ T cell predominance, respectively. Among CD4+ T cells, CD4+ CXCL13+ Tfh-like cells were associated with an immunosuppressive phenotype linked to immune escape-related genes and negative regulation of T cell activation. We also develop an immunotherapy response prediction model based on the composition of the immune compartment. Our predictive model was validated using CIBERSORTx on bulk RNA-seq datasets from melanoma patients pre- and post-ICB treatment and showed a better performance than other existing models. Our study presents an effective immunotherapy response prediction model with potential for further translation, as well as underscores the critical role of the TiME in influencing the response of melanomas to immunotherapy.

Pan-cancer analysis implicates novel insights of lactate metabolism into immunotherapy response prediction and survival prognostication

BACKGROUND

Immunotherapy has emerged as a potent clinical approach for cancer treatment, but only subsets of cancer patients can benefit from it. Targeting lactate metabolism (LM) in tumor cells as a method to potentiate anti-tumor immune responses represents a promising therapeutic strategy.

METHODS

Public single-cell RNA-Seq (scRNA-seq) cohorts collected from patients who received immunotherapy were systematically gathered and scrutinized to delineate the association between LM and the immunotherapy response. A novel LM-related signature (LM.SIG) was formulated through an extensive examination of 40 pan-cancer scRNA-seq cohorts. Then, multiple machine learning (ML) algorithms were employed to validate the capacity of LM.SIG for immunotherapy response prediction and survival prognostication based on 8 immunotherapy transcriptomic cohorts and 30 The Cancer Genome Atlas (TCGA) pan-cancer datasets. Moreover, potential targets for immunotherapy were identified based on 17 CRISPR datasets and validated via in vivo and in vitro experiments.

RESULTS

The assessment of LM was confirmed to possess a substantial relationship with immunotherapy resistance in 2 immunotherapy scRNA-seq cohorts. Based on large-scale pan-cancer data, there exists a notably adverse correlation between LM.SIG and anti-tumor immunity as well as imbalance infiltration of immune cells, whereas a positive association was observed between LM.SIG and pro-tumorigenic signaling. Utilizing this signature, the ML model predicted immunotherapy response and prognosis with an AUC of 0.73/0.80 in validation sets and 0.70/0.87 in testing sets respectively. Notably, LM.SIG exhibited superior predictive performance across various cancers compared to published signatures. Subsequently, CRISPR screening identified LDHA as a pan-cancer biomarker for estimating immunotherapy response and survival probability which was further validated using immunohistochemistry (IHC) and spatial transcriptomics (ST) datasets. Furthermore, experiments demonstrated that LDHA deficiency in pancreatic cancer elevated the CD8+ T cell antitumor immunity and improved macrophage antitumoral polarization, which in turn enhanced the efficacy of immunotherapy.

CONCLUSIONS

We unveiled the tight correlation between LM and resistance to immunotherapy and further established the pan-cancer LM.SIG, holds the potential to emerge as a competitive instrument for the selection of patients suitable for immunotherapy.

Discovery of a novel ROS-based signature for predicting prognosis and immunosuppressive tumor microenvironment in lung adenocarcinoma

The role of reactive oxygen species (ROS) is critical in the emergence and progression of lung adenocarcinoma (LUAD), affecting cell survival, proliferation, angiogenesis, and metastasis. Further investigations are needed to elucidate these effects' precise pathways and devise therapeutic approaches targeting ROS. Moreover, the expression pattern and clinical significance of the ROS-related genes in LUAD remain elusive. Through comprehensive analysis incorporating 1494 LUAD cases from The Cancer Genome Atlas, six Gene Expression Omnibus series, and a Chinese LUAD cohort, we identified a ROS-related signature with substantial predictive value in various LUAD patient cohorts. The ROS-related signature has demonstrated a significant negative relationship with antitumor immunity and dendritic cell maturation and activation. Moreover, The ROS-related signature showed predictive value on immunotherapy outcomes across multiple types of solid tumors, including LUAD. These findings reinforce the ROS-related signature as a predictive tool for LUAD and provide new insights into its link with antitumor immunity and immunotherapy efficacy. These results have implications for refining clinical assessments and tailoring immunotherapeutic strategies for patients with LUAD.

Malignant cell receptor-ligand subtypes guide the prediction of prognosis and personalized immunotherapy of liver cancer

OBJECTIVE

Liver cancer is a prevalent disease with a dismal prognosis. The aim of the research is to identify subgroups based on malignant cell receptor ligand gene from single-cell RNA, which might lead to customized immunotherapy for patients with liver cancer.

METHODS

Based on scRNA-seq data, we identified the receptor-ligand genes associated with prognosis and classify patients into molecular subtypes by univariate Cox regression and consensus clustering. LASSO regression was performed to construct a prognostic model, which was validated in TCGA and ICGC datasets. Immune infiltration and prediction of immunotherapy response were analyzed using ssGSEA, ESTIMATE, TIDE, and TRS score calculation. Finally, qPCR and Western blot validation of key genes and protein levels in cell lines.

RESULTS

A risk model using 16-gene expression levels predicted liver cancer patients' prognosis. The RiskScore associated significantly with tumor clinical characteristics and immunity, integrated with clinicopathological features for survival prediction. Differential expression of SRXN1 was verified in hepatocellular carcinoma and normal liver cells.

CONCLUSION

Our study utilizes single-cell analysis to investigate the communication between malignant cells and other cell types, identifying molecular subtypes based on malignant cell receptor ligand genes, offering new insights for the development of personalized immunotherapy and prognostic prediction models.

Characterizing ligand-receptor interactions and unveiling the pro-tumorigenic role of CCL16-CCR1 axis in the microenvironment of hepatocellular carcinoma

Background

The heterogeneity of the tumor microenvironment significantly influences the prognosis of hepatocellular carcinoma (HCC) patients, with cell communication through ligand-receptor complexes playing a central role.

Methods

We conducted single-cell transcriptomic analysis on ten HCC tissues to identify ligand-receptor genes involved in malignant HCC cell communication using CellChat. Leveraging RNA-Seq data from the TCGA Liver Cancer (TCGA-LIHC) and Liver Cancer - RIKEN, JP (LIRI-JP) cohorts, we employed Cox regression analysis to screen for prognosis-related genes. Prognostic risk models were constructed through unsupervised clustering and differential gene expression analysis. Subsequently, a co-culture system involving tumor cells and macrophages was established. A series of experiments, including Transwell assays, immunofluorescence staining, immunoprecipitation, flow cytometry, and immunohistochemistry, were conducted to elucidate the mechanism through which HCC cells recruit macrophages via the CCL16-CCR1 axis.

Results

Single-cell analysis unveiled significant interactions between malignant HCC cells and macrophages, identifying 76 related ligand-receptor genes. Patients were classified into three subtypes based on the expression patterns of eight prognosis-related ligand-receptor genes. The subtype with the worst prognosis exhibited reduced infiltration of T cell-related immune cells, downregulation of immune checkpoint genes, and increased M2-like tumor-associated macrophage scores. In vitro experiments confirmed the pivotal role of the CCL16-CCR1 axis in the recruitment and M2 polarization of tumor-associated macrophages. Clinical samples demonstrated a significant association between CCL16 protein expression levels and advanced stage, lymph node metastasis, and distant metastasis. Immunohistochemistry and immunofluorescence staining further confirmed the correlation between CCL16 and CCR1, CD68, and CD206, as well as CD68+CCR1+ macrophage infiltration.

Conclusions

Our study identified molecular subtypes, a prognostic model, and immune microenvironment features based on ligand-receptor interactions in malignant HCC cell communication. Moreover, we revealed the pro-tumorigenic role of HCC cells in recruiting M2-like tumor-associated macrophages through the CCL16-CCR1 axis.

Comprehensive genomic profiling on metastatic Melanoma: results from a network screening from 7 Italian Cancer Centres

BACKGROUND

The current therapeutic algorithm for Advanced Stage Melanoma comprises of alternating lines of Targeted and Immuno-therapy, mostly via Immune-Checkpoint blockade. While Comprehensive Genomic Profiling of solid tumours has been approved as a companion diagnostic, still no approved predictive biomarkers are available for Melanoma aside from BRAF mutations and the controversial Tumor Mutational Burden. This study presents the results of a Multi-Centre Observational Clinical Trial of Comprehensive Genomic Profiling on Target and Immuno-therapy treated advanced Melanoma.

METHODS

82 samples, collected from 7 Italian Cancer Centres of FFPE-archived Metastatic Melanoma and matched blood were sequenced via a custom-made 184-gene amplicon-based NGS panel. Sequencing and bioinformatics analysis was performed at a central hub. Primary analysis was carried out via the Ion Reporter framework. Secondary analysis and Machine Learning modelling comprising of uni and multivariate, COX/Lasso combination, and Random Forest, was implemented via custom R/Python scripting.

RESULTS

The genomics landscape of the ACC-mela cohort is comparable at the somatic level for Single Nucleotide Variants and INDELs aside a few gene targets. All the clinically relevant targets such as BRAF and NRAS have a comparable distribution thus suggesting the value of larger scale sequencing in melanoma. No comparability is reached at the CNV level due to biotechnological biases and cohort numerosity. Tumour Mutational Burden is slightly higher in median for Complete Responders but fails to achieve statistical significance in Kaplan-Meier survival analysis via several thresholding strategies. Mutations on PDGFRB, NOTCH3 and RET were shown to have a positive effect on Immune-checkpoint treatment Overall and Disease-Free Survival, while variants in NOTCH4 were found to be detrimental for both endpoints.

CONCLUSIONS

The results presented in this study show the value and the challenge of a genomics-driven network trial. The data can be also a valuable resource as a validation cohort for Immunotherapy and Target therapy genomic biomarker research.

Laser capture microdissection provides a novel molecular profile of human primary cutaneous melanoma

Melanoma accounts for the majority of skin cancer-related mortality, highlighting the need to better understand melanoma initiation and progression. In-depth molecular analysis of neoplastic melanocytes in whole tissue biopsies may be diluted by inflammatory infiltration, which may obscure gene signatures specific to neoplastic cells. Thus, a method is needed to precisely uncover molecular changes specific to tumor cells from a limited sample of primary melanomas. Here, we performed laser capture microdissection (LCM) and gene expression profiling of patient-derived frozen sections of pigmented lesions and primary cutaneous melanoma. Compared to bulk tissue analysis, analysis of LCM-derived samples identified 9528 additional differentially expressed genes (DEGs) including melanocyte-specific genes like PMEL and TYR, with enriched of pathways related to cell proliferation. LCM methodology also identified potentially targetable kinases specific to melanoma cells that were not detected by bulk tissue analysis. Taken together, our data demonstrate that there are marked differences in gene expression profiles depending on the method of sample isolation. We found that LCM captured higher expression of melanoma-related genes while whole tissue biopsy identified a wider range of inflammatory markers. Taken together, our data demonstrate that LCM is a valid approach to identify melanoma-specific changes using a relatively small amount of primary patient-derived melanoma sample.

FAM72 family proteins as poor prognostic markers in clear cell renal carcinoma

Purpose

This study aimed to investigate the prognostic significance of the Family with Sequence Similarity 72 member (FAM72) gene family in clear cell renal carcinoma (ccRCC) using a bioinformatic approach.

Patients and methods

To investigate the association between FAM72 and ccRCC, we utilized various databases and analysis tools, including TCGA, GEPIA, Metscape, cBioPortal, and MethSurv. We conducted an analysis of FAM72 expression levels in ccRCC tissues compared to normal kidney tissues and performed univariate and multivariate Cox analysis to determine the relationship between FAM72 expression and patient prognosis. Furthermore, we carried out Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) to identify enriched biological processes associated with FAM72 expression. Additionally, we analyzed immune cell infiltration and the level of methylation in ccRCC patients. Our bioinformatic analysis revealed that FAM72 expression levels were significantly higher in ccRCC tissues than in normal kidney tissues. High expression of FAM72 was associated with poor prognosis in ccRCC patients and was found to be an independent prognostic factor for ccRCC. GO and GSEA analyses indicated that FAM72 was enriched in biological processes related to mitosis, cell cycle, and DNA metabolism. Moreover, we found a significant correlation between FAM72 and immune cell infiltration and the level of methylation in ccRCC patients.

Conclusion

Our findings suggest that FAM72 could serve as an unfavorable prognostic molecular marker for ccRCC. A comprehensive understanding of FAM72 could provide crucial insights into tumor progression and prognosis.

Dissecting the molecular profiling and tumor immune microenvironment of three subtypes of esophageal cancer

BACKGROUND

Great improvements have been made in the prognosis of esophageal cancer (ESCA) with the application of chemotherapy and immunotherapy. However, the majority of cases remain resistant to these regimens. Hence there is an urgent need to characterize the subtypes of ESCA with favorable survival outcome and drug responsiveness.

METHODS

We characterized the malignant cells of ESCA and explored their communication with immune cells using the Cellchat algorithm. The ligand-receptor interaction pairs were then used as inputting information to identify the subtypes of ESCA by unsupervised clustering analysis. Further investigation aimed to dissect the different patterns of tumor immune microenvironment (TIME), tumor mutation burden, immunotherapy responsiveness and drug sensitivity among the various subtypes of ESCA. A nomogram was also constructed to predict the survival rate of ESCA patients by conducting Cox regression and decision curve analysis.

RESULTS

Three subtypes were identified based on the ligand-receptor interaction pairs. Patients in cluster 2 showed a longer survival time and less likelihood of response to immunotherapy compared with cluster 1 or 3. Eight hub genes were screened to construct a prognostic signature, which can stratify patients well into high- and low-risk groups with distinct survival outcomes and drug sensitivities. The nomogram showed quite good performance in predicting patient survival rates of 1 and 3 years.

CONCLUSION

This study characterized the molecular profiling and TIME patterns of three subtypes of ESCA. The relative findings will provide emergent insights for the treatment of ESCA.

Immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with skin cutaneous melanoma

Skin cutaneous melanoma (SKCM) is the skin cancer that causes the highest number of deaths worldwide. There is growing evidence that the tumour immune microenvironment is associated with cancer prognosis, however, there is little research on the role of immune status in melanoma prognosis. In this study, data on patients with Skin cutaneous melanoma were downloaded from the GEO, TCGA, and GTEx databases. Genes associated with the immune pathway were screened from published papers and lncRNAs associated with them were identified. We performed immune microenvironment and functional enrichment analyses. The analysis was followed by applying univariate/multivariate Cox regression algorithms to finally identify three lncRNAs associated with the immune pathway for the construction of prognostic prediction models (CXCL10, RXRG, and SCG2). This stepwise downscaling method, which finally screens out prognostic factors and key genes and then uses them to build a risk model, has excellent predictive power. According to analyses of the model's reliability, it was able to differentiate the prognostic value and continued existence of Skin cutaneous melanoma patient populations more effectively. This study is an analysis of the immune pathway that leads lncRNAs in Skin cutaneous melanoma in an effort to open up new treatment avenues for Skin cutaneous melanoma.

Construction of five cuproptosis-related lncRNA signature for predicting prognosis and immune activity in skin cutaneous melanoma

Cuproptosis is a newly discovered new mechanism of programmed cell death, and its unique pathway to regulate cell death is thought to have a unique role in understanding cancer progression and guiding cancer therapy. However, this regulation has not been studied in SKCM at present. In this study, data on Skin Cutaneous Melanoma (SKCM) patients were downloaded from the TCGA database. We screened the genes related to cuproptosis from the published papers and confirmed the lncRNAs related to them. We applied Univariate/multivariate and LASSO Cox regression algorithms, and finally identified 5 cuproptosis-related lncRNAs for constructing prognosis prediction models (VIM-AS1, AC012443.2, MALINC1, AL354696.2, HSD11B1-AS1). The reliability and validity test of the model indicated that the model could well distinguish the prognosis and survival of SKCM patients. Next, immune microenvironment, immunotherapy analysis, and functional enrichment analysis were also performed. In conclusion, this study is the first analysis based on cuproptosis-related lncRNAs in SKCM and aims to open up new directions for SKCM therapy.

A B-cell or a key player? The different roles of B-cells and antibodies in melanoma

The B‐cell system plays an important role in the melanoma immune response; however, consensus has yet to be reached in many facets. Here, we comprehensively review human studies only, due to fundamental differences in the humoral response with animal models. Tumour‐infiltrating B‐cells are associated with contradictory prognostic values, reflecting a lack of agreement between studies on cell subset classification and differences in the markers used, particularly the common use of a single marker not differentiating multiple subsets. Tertiary lymphoid structures (TLS) organise T‐cells and B‐cells within tumours to generate a local anti‐tumour response and TLS presence associates with improved survival in response to immune checkpoint blockade, in late‐stage disease. Autoantibody production is increased in melanoma patients and has been proposed as biomarkers for diagnosis, prognosis and treatment/toxicity response; however, no consistent targets are yet identified. The function of antibodies in an anti‐tumour response is determined by its isotype and subclass; IgG₄ is immune‐suppressive and robustly correlate with poor patient survival in melanoma. We conclude that the current B‐cell literature needs careful interpretation based on the methods used and that we need a consensus of markers to define B‐cells and associated lymphoid organs. Furthermore, future studies need to not only examine antibody targets, but also isotypes when considering functional roles.

Integrated analysis of single-cell and bulk RNA sequencing data reveals a pan-cancer stemness signature predicting immunotherapy response

BACKGROUND

Although immune checkpoint inhibitor (ICI) is regarded as a breakthrough in cancer therapy, only a limited fraction of patients benefit from it. Cancer stemness can be the potential culprit in ICI resistance, but direct clinical evidence is lacking.

METHODS

Publicly available scRNA-Seq datasets derived from ICI-treated patients were collected and analyzed to elucidate the association between cancer stemness and ICI response. A novel stemness signature (Stem.Sig) was developed and validated using large-scale pan-cancer data, including 34 scRNA-Seq datasets, The Cancer Genome Atlas (TCGA) pan-cancer cohort, and 10 ICI transcriptomic cohorts. The therapeutic value of Stem.Sig genes was further explored using 17 CRISPR datasets that screened potential immunotherapy targets.

RESULTS

Cancer stemness, as evaluated by CytoTRACE, was found to be significantly associated with ICI resistance in melanoma and basal cell carcinoma (both P < 0.001). Significantly negative association was found between Stem.Sig and anti-tumor immunity, while positive correlations were detected between Stem.Sig and intra-tumoral heterogenicity (ITH) / total mutational burden (TMB). Based on this signature, machine learning model predicted ICI response with an AUC of 0.71 in both validation and testing set. Remarkably, compared with previous well-established signatures, Stem.Sig achieved better predictive performance across multiple cancers. Moreover, we generated a gene list ranked by the average effect of each gene to enhance tumor immune response after genetic knockout across different CRISPR datasets. Then we matched Stem.Sig to this gene list and found Stem.Sig significantly enriched 3% top-ranked genes from the list (P = 0.03), including EMC3, BECN1, VPS35, PCBP2, VPS29, PSMF1, GCLC, KXD1, SPRR1B, PTMA, YBX1, CYP27B1, NACA, PPP1CA, TCEB2, PIGC, NR0B2, PEX13, SERF2, and ZBTB43, which were potential therapeutic targets.

CONCLUSIONS

We revealed a robust link between cancer stemness and immunotherapy resistance and developed a promising signature, Stem.Sig, which showed increased performance in comparison to other signatures regarding ICI response prediction. This signature could serve as a competitive tool for patient selection of immunotherapy. Meanwhile, our study potentially paves the way for overcoming immune resistance by targeting stemness-associated genes.