Cancer Immunotherapy Response Research Articles

Classification of SLC family-related genes involved in ferroptosis predicts lung cancer prognosis and immunotherapy response

Lung adenocarcinoma, the most frequent type of lung cancer, is the leading cause of cancer-related deaths worldwide. Ferroptosis, controlled cell death that involves a high degree of iron-dependent lipid peroxidation, has been linked to tumor therapy sensitivity, patient prognosis, and cancer development. The solute carrier superfamily has over 400 members and comprises the largest class of transporters in the human genome. Solute carrier proteins can facilitate the movement of different substrates across biological membranes, which is crucial for physiological activities, including ferroptosis. Here, we developed a new model to further explore the role of the solute carrier family in ferroptosis in the lung adenocarcinoma immunological milieu. We used consensus clustering to classify patients with lung cancer into two subgroups (cluster1 and cluster2). Patients in the cluster1 subtype had a better prognosis and higher immune cell infiltration ratios than those in the cluster2 subtype. Furthermore, to evaluate the prognosis, the immune cell infiltration ratio, and the medication sensitivity of patients with lung adenocarcinoma, we developed gene scores related to the solute carrier family. In conclusion, we successfully developed a model incorporating the solute carrier family and ferroptosis to predict survival and the impact of immunotherapy on patients with lung cancer.

Immune activity score to assess the prognosis, immunotherapy and chemotherapy response in gastric cancer and experimental validation.

Gastric cancer (GC) is an extremely heterogeneous malignancy with a complex tumor microenvironment (TME) that contributes to unsatisfactory prognosis. The overall activity score for assessing the immune activity of GC patients was developed based on cancer immune cycle activity index in the Tracking Tumor Immunophenotype (TIP). Genes potentially affected by the overall activity score were screened using weighted gene co-expression network analysis (WGCNA). Based on the expression profile data of GC in The Cancer Genome Atlas (TCGA) database, COX analysis was applied to create an immune activity score (IAS). Differences in TME activity in the IAS groups were analyzed. We also evaluated the value of IAS in estimating immunotherapy and chemotherapy response based on immunotherapy cohort. Gene expression in IAS model and cell viability were determined by real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) and Cell Counting Kit-8 (CCK-8) assay, respectively. WGCAN analysis screened 629 overall activity score-related genes, which were mainly associated with T cell response and B cell response. COX analysis identified AKAP5, CTLA4, LRRC8C, AOAH-IT1, NPC2, RGS1 and SLC2A3 as critical genes affecting the prognosis of GC, based on which the IAS was developed. Further RT-qPCR analysis data showed that the expression of AKAP5 and CTLA4 was downregulated, while that of LRRC8C, AOAH-IT1, NPC2, RGS1 and SLC2A3 was significantly elevated in GC cell lines. Inhibition of AKAP5 increased cell viability but siAOAH-IT1 promoted viability of GC cells. IAS demonstrated excellent robustness in predicting immunotherapy outcome and GC prognosis, with low-IAS patients having better prognosis and immunotherapy. In addition, resistance to Erlotinib, Rapamycin, MG-132, Cyclopamine, AZ628, and Sorafenib was reduced in patients with low IAS. IAS was a reliable prognostic indicator. For GC patients, IAS showed excellent robustness in predicting GC prognosis, immune activity status, immunotherapy response, and chemotherapeutic drug resistance. Our study provided novel insights into the prognostic assessment in GC.

Immunotherapy response in microsatellite stable metastatic colorectal cancer is influenced by site of metastases

BackgroundPrior studies indicate that colorectal cancer patients with liver metastases did not benefit from regorafenib, nivolumab (REGONIVO) or regorafenib, ipilimumab, nivolumab (RIN) treatments, while those without liver metastases showed significant response. This study explores the impact of metastatic sites on treatment outcomes. MethodsChemotherapy-refractory colorectal cancer patients treated with REGONIVO or RIN were evaluated, focusing on 2-month organ-specific response, ORR, PFS and OS based on metastatic sites. ResultsOf the 96 patients analyzed (58 REGONIVO, 38 RIN), liver or peritoneal metastases led to poor outcomes, with 0 % ORR, and median PFS of 2.0 and 1.5 months respectively. In contrast, lung-only metastases had an ORR of 56.3 % and a PFS of 14 months. The presence of concurrent LN or other extrahepatic metastatic disease in patients with lung metastatic disease diminished but did not prohibit responses. The 2-month response assessment revealed activity in the lungs, soft tissues, and distant lymph nodes. ConclusionsREGONIVO and RIN were most active in lung-only metastases. Liver and peritoneal metastases were resistant. Future checkpoint inhibitor trials in MSS colorectal cancer should stratify patients based on metastatic locations.

Non-invasive CT imaging biomarker to predict immunotherapy response in gastric cancer: a multicenter study

BackgroundDespite remarkable benefits have been provided by immune checkpoint inhibitors in gastric cancer (GC), predictions of treatment response and prognosis remain unsatisfactory, making identifying biomarkers desirable. The aim of this...

61. A cell-free DNA 5-hydroxymethylcytosine marker predicts immunotherapy response in lung cancer

61. A cell-free DNA 5-hydroxymethylcytosine marker predicts immunotherapy response in lung cancer

Identification of angiogenesis-related subtypes, the development of a prognosis model, and features of tumor microenvironment in colon cancer.

Angiogenesis is associated with tumor progression, prognosis, and treatment effect. However, the angiogenesis' underlying mechanisms in the tumor microenvironment (TME) still remain unclear. Understanding the dynamic interactions between angiogenesis and TME in colon adenocarcinoma (COAD) is necessary. We downloaded the transcriptome data and corresponding clinical data of colon cancer patients from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, respectively. We identified two distinct angiogenesis-related molecular subtypes (subtype A and subtype B) and assessed the clinical features, prognosis, and infiltrating immune cells of patients in the two subtypes. According to the prognostic differential genes, we defined two different gene clusters to further explore the correlation between angiogenesis and tumor heterogeneity. Then, we construct the prognostic risk scoring model angiogenesis-related gene (ARG-score) including seven genes (ARMCX2, latent transforming growth factor β binding protein 1, ADAM8, FABP4, CCL11, CXCL11, ITLN1) using Lasso-multivariate cox method. We analyzed the correlation between ARG-score and prognosis, clinicopathological features, TME, molecular feature, cancer stem cells (CSCs), and microsatellite instability (MSI) status. To assess the application value of ARG-score in clinical treatment, immunophenotype score was used to predict patients' immunotherapy response in colon cancer. We found the mutations of ARGs in TCGA-COAD dataset from genetic levels and discussed their expression patterns based on TCGA and GEO datasets. We observed important differences in clinicopathological features, prognosis, immune feature, molecular feature between the two molecular subgroups. Then, we established an ARG-score for predicting OS and validated its predictive capability. A high ARG-score characterized by higher transcription level of ARGs, suggested lower MSI-high (MSI-H), lower immune score, and worse clinical stage and survival outcome. Additionally, the ARG-score was remarkably related to the CSCs index and immunotherapy sensitivity. We found two new molecular subtypes and two gene clusters based on ARGs and established an ARG-score. Multilayered analysis revealed that ARGs were remarkably correlated to the heterogeneity of colon cancer patients and explained the process of tumorigenesis and progression better. The ARG-score can help us better assess patients' survival outcomes and provide guidance for individualized treatment.

Machine Learning Developed a Programmed Cell Death Signature for Predicting Prognosis, Ecosystem, and Drug Sensitivity in Ovarian Cancer.

Ovarian cancer (OC) is the leading cause of gynecological cancer death and the fifth most common cause of cancer-related death in women in America. Programmed cell death played a vital role in tumor progression and immunotherapy response in cancer. The prognostic cell death signature (CDS) was constructed with an integrative machine learning procedure, including 10 methods, using TCGA, GSE14764, GSE26193, GSE26712, GSE63885, and GSE140082 datasets. Several methods and single-cell analysis were used to explore the correlation between CDS and the ecosystem and therapy response of OC patients. The prognostic CDS constructed by the combination of StepCox (n = both) + Enet (alpha = 0.2) acted as an independent risk factor for the overall survival (OS) of OC patients and showed stable and powerful performance in predicting the OS rate of OC patients. Compared with tumor grade, clinical stage, and many developed signatures, the CDS had a higher C-index. OC patients with low CDS score had a higher level of CD8+ cytotoxic T, B cell, and M1-like macrophage, representing a related immunoactivated ecosystem. A low CDS score indicated a higher PD1 and CTLA4 immunophenoscore, higher tumor mutation burden score, lower tumor immune dysfunction and exclusion score, and lower tumor escape score in OC, demonstrating a better immunotherapy response. OC patients with high CDS score had a higher gene set score of cancer-related hallmarks, including angiogenesis, epithelial-mesenchymal transition, hypoxia, glycolysis, and notch signaling. The current study constructed a novel CDS for OC, which could serve as an indicator for predicting the prognosis, ecosystem, and immunotherapy benefits of OC patients.

A SARS-CoV-2 related signature that explores the tumor microenvironment and predicts immunotherapy response in esophageal squamous cell cancer.

The existing therapeutic approaches for combating tumors are insufficient in completely eradicating malignancy, as cancer facilitates tumor relapse and develops resistance to treatment interventions. The potential mechanistic connection between SARS-CoV-2 and ESCC has received limited attention. Therefore, our objective was to investigate the characteristics of SARS-CoV-2-related-genes (SCRGs) in esophageal squamous cancer (ESCC). Raw data were obtained from the TCGA and GEO databases. Clustering of SCRGs from the scRNA-seq data was conducted using the Seurat R package. A risk signature was then generated using Lasso regression, incorporating prognostic genes related to SCRGs. Subsequently, a nomogram model was developed based on the clinicopathological characteristics and the risk signature. Eight clusters of SCRGs were identified in ESCC utilizing scRNA-seq data, of which three exhibited prognostic implications. A risk signature was then made up with bulk RNA-seq, which displayed substantial correlations with immune infiltration. The novel signature was verified to have excellent prognostic efficacy. The utilization of risk signatures based on SCRGs can efficiently forecast the prognosis of ESCC. A thorough characterization of the SCRGs signature in ESCC could facilitate the interpretation of ESCC's response to immunotherapy and offer innovative approaches to cancer therapy.

Regorafenib alone or in combination with high/low-dose radiotherapy plus toripalimab as third-line treatment in patients with metastatic colorectal cancer: protocol for a prospective, randomized, controlled phase II clinical trial (SLOT).

https://clinicaltrials.gov/study/NCT05963490?cond=NCT05963490&rank=1, identifier NCT05963490.

Transmembrane serine protease 2, a SARS-CoV-2 internalization protease, correlates with clinical outcome, molecular features, and immunotherapy response in colorectal cancer.

Transmembrane serine protease 2 (TMPRSS2) mediates the entry of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into host cells. The relevant research indicates the intestine to be a target of SARS-CoV-2 infection, and thus we aimed to investigate the correlation between TMPRSS2 expression and the prognosis, molecular features, and immunotherapy response in patients with colorectal cancer (CRC). The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used in this study and a total of 1,385 patients were identified. The CIBERSORT algorithms were used to evaluate the relative infiltration levels of immune cell types in the tumor microenvironment (TME). The correlation between TMPRSS2 expression and immunotherapy response rate was assessed in another 2 independent cohorts. TMPRSS2 expression was significantly downregulated in cancer tissue compared to the adjacent normal tissue, and patients with CRC with lower TMPRSS2 expression showed notably poorer prognosis. Functional enrichment analysis found that low TMPRSS2 expression was significantly associated with cancer metastasis-related pathways. Further analysis based on the miRWalk tool and JASPAR database identified a list of microRNAs (miRNAs) and transcriptional factors targeting TMPRSS2. Distinct differences in immune cell infiltration and tumor purity reflected by estimate and mutant-allele tumor heterogeneity score were observed between patients with low and high TMPRSS2 expression levels. Interestingly, patients with a low TMPRSS2 expression level showed a higher response rate to immunotherapy. CRC cells may be more resistant to SARS-CoV-2 infection due to the decreased expression of TMPRSS2, which could be a newly identified biomarker for prognosis and immunotherapy response prediction in patients with CRC.

Impact of Helicobacter pylori infection status on outcomes among patients with advanced gastric cancer treated with immune checkpoint inhibitors

BackgroundGut microbiota composition can influence cancer immunotherapy response. Recent evidence suggests Helicobacter pylori infection may reduce immune checkpoint inhibitor (ICI) efficacy in lung cancer and melanoma, but thorough characterization of...

Multicellular Spatial Organization Determines Immunotherapy Response.

Cellular composition and spatial organization can predict immunotherapy response in triple-negative breast cancer.

Integrating single-cell and bulk RNA sequencing to predict prognosis and immunotherapy response in prostate cancer

Prostate cancer (PCa) stands as a prominent contributor to morbidity and mortality among males on a global scale. Cancer-associated fibroblasts (CAFs) are considered to be closely connected to tumour growth, invasion, and metastasis. We explored the role and characteristics of CAFs in PCa through bioinformatics analysis and built a CAFs-based risk model to predict prognostic treatment and treatment response in PCa patients. First, we downloaded the scRNA-seq data for PCa from the GEO. We extracted bulk RNA-seq data for PCa from the TCGA and GEO and adopted “ComBat” to remove batch effects. Then, we created a Seurat object for the scRNA-seq data using the package “Seurat” in R and identified CAF clusters based on the CAF-related genes (CAFRGs). Based on CAFRGs, a prognostic model was constructed by univariate Cox, LASSO, and multivariate Cox analyses. And the model was validated internally and externally by Kaplan–Meier analysis, respectively. We further performed GO and KEGG analyses of DEGs between risk groups. Besides, we investigated differences in somatic mutations between different risk groups. We explored differences in the immune microenvironment landscape and ICG expression levels in the different groups. Finally, we predicted the response to immunotherapy and the sensitivity of antitumour drugs between the different groups. We screened 4 CAF clusters and identified 463 CAFRGs in PCa scRNA-seq. We constructed a model containing 10 prognostic CAFRGs by univariate Cox, LASSO, and multivariate Cox analysis. Somatic mutation analysis revealed that TTN and TP53 were significantly more mutated in the high-risk group. Finally, we screened 31 chemotherapeutic drugs and targeted therapeutic drugs for PCa. In conclusion, we identified four clusters based on CAFs and constructed a new CAFs-based prognostic signature that could predict PCa patient prognosis and response to immunotherapy and might suggest meaningful clinical options for the treatment of PCa.

Ubiquilin-4 induces immune escape in gastric cancer by activating the notch signaling pathway.

We aimed to investigate the role of ubiquilin-4 in predicting the immunotherapy response in gastric cancer. Retrospective RNA-sequencing and immunohistochemical analysis were performed for patients with gastric cancer who received programmed death-1 blockade therapy after recurrence. Multiplex immunohistochemistry identified immune cell types in gastric cancer tissues. We used immunocompetent 615 mice and immunodeficient nude mice to perform tumorigenic experiments. Ubiquilin-4 expression was significantly higher in responders (p < 0.05, false discovery rate > 2.5) and showed slight superiority over programmed death ligand 1 in predicting programmed death-1 inhibitor therapy response (area under the curve: 87.08 vs. 72.50). Ubiquilin-4-high patients exhibited increased CD4+ and CD8+ T cells, T follicular helper cells, monocytes, and macrophages. Ubiquilin-4-overexpressed mouse forestomach carcinoma cells showed significantly enhanced growth in immunocompetent mice but not in immunodeficient mice. Upregulation or downregulation of ubiquilin-4 synergistically affected programmed death ligand 1 at the protein and messenger RNA levels. Functional enrichment analysis revealed significant enrichment of the Notch, JAK-STAT, and WNT signaling pathways in ubiquilin-4-high gastric cancers. Ubiquilin-4 promoted Numb degaration, activating the Notch signaling pathway and upregulating programmed death ligand 1. Ubiquilin-4 may contribute to immune escape in gastric cancer by upregulating programmed death ligand 1 expression in tumor cells through Notch signaling activation. Thus, ubiquilin-4 could serve as a predictive marker for programmed death ligand 1 inhibitor therapy response in gastric cancer.

Regulatory T cells are associated with the tumor immune microenvironment and immunotherapy response in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer with a high risk of distant metastasis, an extremely poor prognosis, and a high risk of death. Regulatory T cells (Tregs) contribute to the formation of a tumor immunosuppressive microenvironment, which plays an important role in the progression and treatment resistance of TNBC. A public single-cell sequencing dataset demonstrated increased infiltration of Tregs in TNBC tissues relative to normal breast tissue. Weighted gene co-expression network analysis was used to identify Treg infiltration-related modules for METABRIC TNBC samples. Subsequently, we obtained two Treg infiltration-associated clusters of TNBC by applying consensus clustering and further constructed a prognostic model based on this Treg infiltration-associated gene module. The ability of the selected gene in the prognostic model, thymidine kinase-1 (TK1), to promote the progression of TNBC was evaluated in vitro. We concluded that two Treg infiltration-associated clusters had different prognoses and sensitivities to drugs commonly used in breast cancer treatment, and multi-omics analysis revealed that the two clusters had different copy number variations of key tumor progression genes. The 7-gene risk score based on TNBC Treg infiltration was a reliable prognostic indicator both in the training and validation cohorts. Moreover, patients with TNBC with high Treg infiltration-related scores lacked the activation of immune activation pathways and exhibited resistance to anti-PD1 immunotherapy. Knocking down TK1 led to impaired proliferation, migration, and invasion of TNBC cells in vitro. In addition, specimens from patients with TNBC with high TK1 expression showed significantly higher Treg infiltration in tumors. Results of spatial transcriptome analysis showed that TK1 positive cells mainly localize in tumor area, and Treg cell infiltration in TNBC tissues was associated with high expression of TK1. Pan-cancer analysis also demonstrated that TK1 is associated with poor prognosis and activation of proliferation pathways in multiple cancers. We established a prognostic model related to Treg infiltration and this model can be used to establish a clinically relevant classification of TNBC progression. Additionally, our work revealed the underestimable potential of TK1 as a tumor biomarker and immunotherapeutic target.

Spatial predictors of immunotherapy response in triple-negative breast cancer

Immune checkpoint blockade (ICB) benefits some patients with triple-negative breast cancer, but what distinguishes responders from non-responders is unclear1. Because ICB targets cell–cell interactions2, we investigated the impact of multicellular spatial organization on response, and explored how ICB remodels the tumour microenvironment. We show that cell phenotype, activation state and spatial location are intimately linked, influence ICB effect and differ in sensitive versus resistant tumours early on-treatment. We used imaging mass cytometry3 to profile the in situ expression of 43 proteins in tumours from patients in a randomized trial of neoadjuvant ICB, sampled at three timepoints (baseline, n = 243; early on-treatment, n = 207; post-treatment, n = 210). Multivariate modelling showed that the fractions of proliferating CD8+TCF1+T cells and MHCII+ cancer cells were dominant predictors of response, followed by cancer–immune interactions with B cells and granzyme B+ T cells. On-treatment, responsive tumours contained abundant granzyme B+ T cells, whereas resistant tumours were characterized by CD15+ cancer cells. Response was best predicted by combining tissue features before and on-treatment, pointing to a role for early biopsies in guiding adaptive therapy. Our findings show that multicellular spatial organization is a major determinant of ICB effect and suggest that its systematic enumeration in situ could help realize precision immuno-oncology.

Editorial Comment to Identification of tumor immunophenotypes associated with immunotherapy response in bladder cancer.

Editorial Comment to Identification of tumor immunophenotypes associated with immunotherapy response in bladder cancer.

A tumor-infiltrating immune cells-related pseudogenes signature based on machine-learning predicts outcomes and immunotherapy responses in ovarian cancer

Previous researches have provided evidence for the significant involvement of pseudogenes in immune-related functions across different types of cancer. However, the mechanisms by which pseudogenes regulate immunity in ovarian cancer (OV) and their potential impact on clinical outcomes remain unclear. To address this gap in knowledge, our study utilized a novel computational framework to analyze a total of 491 samples from three public datasets. We employed a combination of 10 machine-learning algorithms to construct a signature known as the tumor-infiltrating immune cells-related pseudogenes signature (TIICPS). The TIICPS, consisting of 12 pseudogenes, demonstrated independent prognostic value for overall survival, surpassing conventional clinical traits, 62 published signatures, and TP53 and BRCA mutation status in three cohorts. Patients with low TIICPS exhibited heightened immune-related pathways, intricate genomic alterations, substantial immune infiltration, and greater potential for immunotherapy efficacy. Consequently, TIICPS holds promise as a predictive tool for prognosis and immunotherapy response in ovarian cancer.

A tumor-associated endothelial signature score model in immunotherapy and prognosis across pan-cancers.

Background: The tumor-associated endothelial cell (TAE) component plays a vital role in tumor immunity. However, systematic tumor-associated endothelial-related gene assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers have not been explored. Herein, we investigated a TAE gene risk model to predict CIT responses and patient survival in a pan-cancer analysis. Methods: We analyzed publicly available datasets of tumor samples with gene expression and clinical information, including gastric cancer, metastatic urothelial cancer, metastatic melanoma, non-small cell lung cancer, primary bladder cancer, and renal cell carcinoma. We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm. Results: The model demonstrated a high predictive accuracy in both training and validation cohorts. The response rate of the high score group to immunotherapy in the training cohort was significantly higher than that of the low score group, with CIT response rates of 51% and 27%, respectively. The survival analysis showed that the prognosis of the high score group was significantly better than that of the low score group (all p < 0·001). Tumor-associated endothelial gene signature scores positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may benefit patients in the high score group. The analysis of TAE scores across 33 human cancers revealed that the TAE model could reflect immune cell infiltration and predict the survival of cancer patients. Conclusion: The TAE signature model could represent a CIT response prediction model with a prognostic value in multiple cancer types.

A cancer-associated fibroblast subtypes-based signature enables the evaluation of immunotherapy response and prognosis in bladder cancer

Bladder cancer (BLCA) is one of the most prevalent and heterogeneous urinary malignant tumors. Previous researches have reported a significant association between cancer-associated fibroblasts (CAFs) and poor prognosis of tumor patients. However, uncertainty surrounds the role of CAFs in the BLCA tumor microenvironment, necessitating further investigation into the CAFs-related gene signatures in BLCA. In this study, we identified three CAF subtypes in BLCA according to single-cell RNA-seq data and constructed CAFs-related risk score (CRRS) by screening 102,714 signatures. The survival analysis, ROC curves, and nomogram suggested that CRRS was a valuable predictor in 2,042 patients from 9 available public datasets and Xiangya real-world cohort. We further revealed the significant correlation between CRRS and clinicopathological characteristics, genome alterations, and epithelial-mesenchymal transition (EMT). A high CRRS indicated a non-inflamed phenotype and a lower remission rate of immunotherapy in BLCA. In conclusion, the CRRS had the potential to predict the prognosis and immunotherapy response of BLCA patients.