Checkpoint therapeutic target database (CKTTD): the first comprehensive database for checkpoint targets and their modulators in cancer immunotherapy

Cat-E: A comprehensive web tool for exploring cancer targeting strategies

Identifying potential cancer-associated genes and drug targets from omics data is challenging due to its diverse sources and analyses, requiring advanced skills and large amounts of time. To facilitate such analysis, we developed Cat-E (Cancer Target Explorer), a novel R/Shiny web tool designed for comprehensive analysis with evaluation according to cancer-related omics data. Cat-E is accessible at https://cat-e.bioinfo-wuerz.eu/. Cat-E compiles information on oncolytic viruses, cell lines, gene markers, and clinical studies by integrating molecular datasets from key databases such as OvirusTB, TCGA, DrugBANK, and PubChem. Users can use all datasets and upload their data to perform multiple analyses, such as differential gene expression analysis, metabolic pathway exploration, metabolic flux analysis, GO and KEGG enrichment analysis, survival analysis, immune signature analysis, single nucleotide variation analysis, dynamic analysis of gene expression changes and gene regulatory network changes, and protein structure prediction. Cancer target evaluation by Cat-E is demonstrated here on lung adenocarcinoma (LUAD) datasets. By offering a user-friendly interface and detailed user manual, Cat-E eliminates the need for advanced computational expertise, making it accessible to experimental biologists, undergraduate and graduate students, and oncology clinicians. It serves as a valuable tool for investigating genetic variations across diverse cancer types, facilitating the identification of novel diagnostic markers and potential therapeutic targets.

CTR-DB 2.0: an updated cancer clinical transcriptome resource, expanding primary drug resistance and newly adding acquired resistance datasets and enhancing the discovery and validation of predictive biomarkers

Drug resistance is a principal limiting factor in cancer treatment. CTR-DB, the Cancer Treatment Response gene signature DataBase, is the first data resource for clinical transcriptomes with cancer treatment response, and meanwhile supports various data analysis functions, providing insights into the molecular determinants of drug resistance. Here we proposed an upgraded version, CTR-DB 2.0 (http://ctrdb.ncpsb.org.cn). Around 190 up-to-date source datasets with primary resistance information (129% increase compared to version 1.0) and 13 acquired-resistant datasets (a new dataset type), covering 10 856 patient samples (111% increase), 39 cancer types (39% increase) and 346 therapeutic regimens (26% increase), have been collected. In terms of function, for the single dataset analysis and multiple-dataset comparison modules, CTR-DB 2.0 added new gene set enrichment, tumor microenvironment (TME) and signature connectivity analysis functions to help elucidate drug resistance mechanisms and their homogeneity/heterogeneity and discover candidate combinational therapies. Furthermore, biomarker-related functions were greatly extended. CTR-DB 2.0 newly supported the validation of cell types in the TME as predictive biomarkers of treatment response, especially the validation of a combinational biomarker panel and even the direct discovery of the optimal biomarker panel using user-customized CTR-DB patient samples. In addition, the analysis of users' own datasets, application programming interface and data crowdfunding were also added.

Cancer pharmacoinformatics: Databases and analytical tools

Cancer is a subject of extensive investigation, and the utilization of omics technology has resulted in the generation of substantial volumes of big data in cancer research. Numerous databases are being developed to manage and organize this data effectively. These databases encompass various domains such as genomics, transcriptomics, proteomics, metabolomics, immunology, and drug discovery. The application of computational tools into various core components of pharmaceutical sciences constitutes "Pharmacoinformatics", an emerging paradigm in rational drug discovery. The three major features of pharmacoinformatics include (i) Structure modelling of putative drugs and targets, (ii) Compilation of databases and analysis using statistical approaches, and (iii) Employing artificial intelligence/machine learning algorithms for the discovery of novel therapeutic molecules. The development, updating, and analysis of databases using statistical approaches play a pivotal role in pharmacoinformatics. Multiple software tools are associated with oncoinformatics research. This review catalogs the databases and computational tools related to cancer drug discovery and highlights their potential implications in the pharmacoinformatics of cancer.

Molecular classification and tumor microenvironment characteristics in pheochromocytomas

Pheochromocytomas (PCCs) are rare neuroendocrine tumors that originate from chromaffin cells in the adrenal gland. However, the cellular molecular characteristics and immune microenvironment of PCCs are incompletely understood. Here, we performed single-cell RNA sequencing (scRNA-seq) on 16 tissues from 4 sporadic unclassified PCC patients and 1 hereditary PCC patient with Von Hippel-Lindau (VHL) syndrome. We found that intra-tumoral heterogeneity was less extensive than the inter-individual heterogeneity of PCCs. Further, the unclassified PCC patients were divided into two types, metabolism-type (marked by NDUFA4L2 and COX4I2) and kinase-type (marked by RET and PNMT), validated by immunohistochemical staining. Trajectory analysis of tumor evolution revealed that metabolism-type PCC cells display phenotype of consistently active metabolism and increased metastasis potential, while kinase-type PCC cells showed decreased epinephrine synthesis and neuron-like phenotypes. Cell-cell communication analysis showed activation of the annexin pathway and a strong inflammation reaction in metabolism-type PCCs and activation of FGF signaling in the kinase-type PCC. Although multispectral immunofluorescence staining showed a lack of CD8+ T cell infiltration in both metabolism-type and kinase-type PCCs, only the kinase-type PCC exhibited downregulation of HLA-I molecules that possibly regulated by RET, suggesting the potential of combined therapy with kinase inhibitors and immunotherapy for kinase-type PCCs; in contrast, the application of immunotherapy to metabolism-type PCCs (with antigen presentation ability) is likely unsuitable. Our study presents a single-cell transcriptomics-based molecular classification and microenvironment characterization of PCCs, providing clues for potential therapeutic strategies to treat PCCs.

S100A8 is a prognostic signature and associated with immune response in diffuse large B-cell lymphoma

Background

S100A8, a calcium-binding protein belonging to the S100 family, is involved in immune responses and multiple tumor pathogens. Diffuse large B-cell lymphoma (DLBCL) is one of the most common types of B-cell lymphoma and remains incurable in 40% of patients. However, the role of S100A8 and its regulation of the immune response in DLBCL remain unclear.

Methods

The differential expression of S100A8 was identified via the GEO and TCGA databases. The prognostic role of S100A8 in DLBCL was calculated using the Kaplan-Meier curve. The function enrichment of differentially expressed genes (DEGs) was explored through GO, KEGG, GSEA, and PPI analysis. In our cohort, the expression of S100A8 was verified. Meanwhile, the biological function of S100A8 was applied after the inhibition of S100A8 in an in vitro experiment. The association between S100A8 and immune cell infiltration and treatment response in DLBCL was analyzed.

Results

S100A8 was significantly overexpressed and related to a poor prognosis in DLBCL patients. Function enrichment analysis revealed that DEGs were mainly enriched in the IL-17 signaling pathway. Our cohort also verified this point. In vitro experiments suggested that inhibition of S100A8 should promote cell apoptosis and suppress tumor growth. Single-cell RNA sequence analysis indicated that S100A8 might be associated with features of the tumor microenvironment (TME), and immune infiltration analyses discovered that S100A8 expression was involved in TME. In terms of drug screening, we predicted that many drugs were associated with preferable sensitivity.

Conclusion

Elevated S100A8 expression is associated with a poor prognosis and immune infiltration in DLBCL. Inhibition of S100A8 could promote cell apoptosis and suppress tumor growth. Meanwhile, S100A8 has the potential to be a promising immunotherapeutic target for patients with DLBCL.

CDS-DB, an omnibus for patient-derived gene expression signatures induced by cancer treatment

Patient-derived gene expression signatures induced by cancer treatment, obtained from paired pre- and post-treatment clinical transcriptomes, can help reveal drug mechanisms of action (MOAs) in cancer patients and understand the molecular response mechanism of tumor sensitivity or resistance. Their integration and reuse may bring new insights. Paired pre- and post-treatment clinical transcriptomic data are rapidly accumulating. However, a lack of systematic collection makes data access, integration, and reuse challenging. We therefore present the Cancer Drug-induced gene expression Signature DataBase (CDS-DB). CDS-DB has collected 78 patient-derived, paired pre- and post-treatment transcriptomic source datasets with uniformly reprocessed expression profiles and manually curated metadata such as drug administration dosage, sampling time and location, and intrinsic drug response status. From these source datasets, 2012 patient-level gene perturbation signatures were obtained, covering 85 therapeutic regimens, 39 cancer subtypes and 3628 patient samples. Besides data browsing, download and search, CDS-DB also supports single signature analysis (including differential gene expression, functional enrichment, tumor microenvironment and correlation analyses), signature comparative analysis and signature connectivity analysis. This provides insights into drug MOA and its heterogeneity in patients, drug resistance mechanisms, drug repositioning and drug (combination) discovery, etc. CDS-DB is available at http://cdsdb.ncpsb.org.cn/.

A global database for modeling tumor-immune cell communication

Communications between tumor cells and surrounding immune cells help shape the tumor immunity continuum. Recent breakthroughs in high-throughput technologies as well as computational algorithms had reported many important tumor-immune cell (TIC) communications, which were scattered in thousands of published studies and impeded systematical characterization of the TIC communications across cancer. Here, a comprehensive database, TICCom, was developed to model TIC communications, containing 739 experimentally-validated or manually-curated interactions collected from more than 3,000 literatures as well as 4,537,709 predicted interactions inferred via six computational algorithms by reanalyzing 32 scRNA-seq datasets and bulk RNA-seq data across 25 cancer types. The communications between tumor cells and 14 types of immune cells were characterized, and the involved ligand-receptor interactions were further integrated. 14190 human and 3650 mouse integrated ligand-receptor interactions with supplemented corresponding function information were also stored in the TICCom database. Our database would serve as a valuable resource for investigating TIC communications.

ICI efficacy information portal: a knowledgebase for responder prediction to immune checkpoint inhibitors

Immune checkpoint inhibitors (ICIs) have led to durable responses in cancer patients, yet their efficacy varies significantly across cancer types and patients. To stratify patients based on their potential clinical benefits, there have been substantial research efforts in identifying biomarkers and computational models that can predict the efficacy of ICIs, and it has become difficult to keep track of all of them. It is also difficult to compare findings of different studies since they involve different cancer types, ICIs, and various other details. To make it easy to access the latest information about ICI efficacy, we have developed a knowledgebase and a corresponding web-based portal (https://iciefficacy.org/). Our knowledgebase systematically records information about latest publications related to ICI efficacy, predictors proposed, and datasets used to test them. All information recorded is checked carefully by a manual curation process. The web-based portal provides functions to browse, search, filter, and sort the information. Digests of method details are provided based on the original descriptions in the publications. Evaluation results of the effectiveness of the predictors reported in the publications are summarized for quick overviews. Overall, our resource provides centralized access to the burst of information produced by the vibrant research on ICI efficacy.

Immune Checkpoint Inhibitor (ICI) Genes and Aging in Clear Cell Renal Cell Carcinoma (ccRCC): Clinical and Genomic Study

Background: It is anticipated that there will be a large rise in the number of tumor diagnoses and mortality in those aged 65 and older over the course of upcoming decades. Immune checkpoint inhibitors, often known as ICIs, boost immune system activity by selectively targeting ICI genes. On the other hand, old age may be connected with unfavorable results. Methods: The Cancer Genome Atlas (TCGA) provided gene expression data from ccRCC tissue and key clinical variables. ICI gene databases were applied and verified using the GEO database. Results: We identified 14 ICI genes as risk gene signatures among 528 ccRCC patients using univariate and multivariable cox hazard models, and the elderly group was linked with poor survival. Then, by utilizing a new nomogram method, the TNFSF15 gene and age predicting values were estimated at one, three, and five years (85%, 81%, and 81%), respectively, and our age-related risk score was significant even after multivariable analysis (HR = 1.518, p = 0.009, CI = 1.1102.076). TNFSF15 gene expression was lower in elderly ccRCC patients (p = 0.0001). A negative connection between age and the TNFSF15 gene expression was discovered by correlation analysis (p = 0.0001). The verification of the gene by utilizing GEO (GSE167093) with 604 patients was obtained as external validation that showed significant differences in the TNFSF15 gene between young and elderly patients (p = 0.007). Additionally, the protein-protein interactions of the TNFSF15 gene with other ICI genes and aging-related genes was determined. In addition, the TNFSF15 expression was significantly correlated with pathological stages (p = 0.018). Furthermore, it was discovered that the biological processes of senescence, cellular senescence, the immune system, and many immune cell infiltration and immune function types are all closely tied. Conclusions: Along with the risk score evaluation, the ICI gene TNFSF15 was identified as a tumor suppressor gene related to inequalities in age survival and is associated with pathological stages and different immunity statuses. The aging responses of ccRCC patients and related gene expression need further investigation in order to identify potential therapeutic targets.

Immune checkpoint inhibitor (ICI) genes and aging in malignant melanoma patients: a clinicogenomic TCGA study

Background

Cancer diagnoses and deaths among the elderly (65 +) are expected to increase significantly over the next decade. Immune checkpoint inhibitors specifically target ICI genes and enhance immune system function. However, poor outcomes may be associated with aging.

Methods

We downloaded the Genomic Data Commons from the Cancer Genome Atlas (TCGA) and collected gene expression data from malignant melanoma (MM) tissues, the third level as the primary site. The CKTTD ICI genes database were applied and validated using the GEO database and lab experiments.

Results

In 414 patients, 13 ICI genes were obtained as risk gene signature by univariate and multivariate Cox hazard models and were associated with poor survival in the older group. At 1, 3, and 5 years (79%, 76%, and 76%, respectively), we investigate TNFRFS4 gene and age prediction using novel nomogram-associated aging (HR = 1.79, P 0.001, CI = 1.32–2.45) with higher sensitivity testing.TNFRSF4 gene expression was significantly high in younger (15 years interval) MM patients (P < 0.001). By correlation analysis, a significant negative association was determined (P < 0.001). The validation of gene correlation from GEO (GSE59455) and (GSE22153) was obtained as external validation. We tested the TNFRSF4 protein levels by IHC in 14 melanoma tissue samples. TNFRSF4 expression was observed to be lower expressed in the older of melanoma tissues, and higher in the younger age group (P = 0.02). Besides the connectivity of ICI gene proteins, the biological processes of cell aging, aging, and the immune system were found to be highly related.

Conclusions

Along with the risk score evaluation, the ICI gene (TNFRSF4) was identified as a tumor suppressor gene related to inequalities in age survival and associated with immune cell infiltrations. The aging responses of melanoma patients and related gene expression need further investigation in order to identify potential therapeutic targets.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09860-2.

Immune cell gene expression signatures in diffuse glioma are associated with IDH mutation status, patient outcome and malignant cell state, and highlight the importance of specific cell subsets in glioma biology

The tumor micro-environment (TME) plays an important role in various cancers, including gliomas. We estimated immune cell type-specific gene expression profiles in 3 large clinically annotated glioma datasets using CIBERSORTx and LM22/LM10 blood-based immune signatures and found that the proportions and estimated gene expression patterns of specific immune cells significantly varied according to IDH mutation status. When IDH-WT and IDH-MUT tumors were considered separately, cluster-of-cluster analyses of immune cell gene expression identified groups with distinct survival outcomes. We confirmed and extended these findings by applying a signature matrix derived from single-cell RNA-sequencing data derived from 19 glioma tumor samples to the bulk profiling data, validating findings from the LM22/LM10 results. To link immune cell signatures with outcomes in checkpoint therapy, we then showed a significant association of monocytic lineage cell gene expression clusters with patient survival and with mesenchymal gene expression scores. Integrating immune cell-based gene expression with previously described malignant cell states in glioma demonstrated that macrophage M0 abundance significantly correlated with mesenchymal state in IDH-WT gliomas, with evidence of a previously implicated role of the Oncostatin-M receptor and macrophages in the mesenchymal state. Among IDH-WT tumors that were enriched for the mesenchymal cell state, the estimated M0 macrophage expression signature coordinately also trended to a mesenchymal signature. We also examined IDH-MUT tumors stratified by 1p/19q status, showing that a mesenchymal gene expression signature the M0 macrophage fraction was enriched in IDH-MUT, non-codeleted tumors. Overall, these results highlight the biological and clinical significance of the immune cell environment related to IDH mutation status, patient prognosis and the mesenchymal state in diffuse gliomas.

In silico tools and databases for designing cancer immunotherapy

Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage. Recent preferments in high-throughput sequencing and computational immunology leads to the development of targeted immunotherapy for precision oncology. In the last few decades, several computational methods and resources have been developed for designing immunotherapy against cancer. In this review, we have summarized cancer-associated genomic, transcriptomic, and mutation profile repositories. We have also enlisted in silico methods for the prediction of vaccine candidates, HLA binders, cytokines inducing peptides, and potential neoepitopes. Of note, we have incorporated the most important bioinformatics pipelines and resources for the designing of cancer immunotherapy. Moreover, to facilitate the scientific community, we have developed a web portal entitled ImmCancer (https://webs.iiitd.edu.in/raghava/immcancer/), comprises cancer immunotherapy tools and repositories.

CanImmunother: a manually curated database for identification of cancer immunotherapies associating with biomarkers, targets, and clinical effects

As immunotherapy is evolving into an essential armamentarium against cancers, numerous translational studies associated with relevant biomarkers, targets, and clinical effects have been reported in recent years. However, a large amount of associated experimental data remains unexplored due to the difficulty in accessibility and utilization. Here, we established a comprehensive high-quality database for cancer immunotherapy called CanImmunother (http://www.biomedical-web.com/cancerit/) through manual curation on 4515 publications. CanImmunother contains 3267 experimentally validated associations between 218 cancer sub-types across 34 body parts and 484 immunotherapies with 642 biomarkers, 108 targets, and 121 control therapies. Each association was manually curated by professional curators, incorporated with valuable annotation and cross references, and assigned with an association score for prioritization. To help clinicians and researchers in identifying and discovering better cancer immunotherapy and their respective biomarkers and targets, CanImmunother offers user-friendly web applications including search, browse, excel table, association prioritization, and network visualization. CanImmunother presents a landscape of experimental cancer immunotherapy association data, serving as a useful resource to improve our insight and to facilitate further discovery of advanced immunotherapy options for cancer patients.

Overexpression of poliovirus receptor is associated with poor prognosis in head and neck squamous cell carcinoma patients

PURPOSE

We aimed to investigate the prognostic value of multiple immune cell markers including programmed death-ligand 1 (PD-L1) and poliovirus receptor (PVR) in head and neck squamous cell carcinoma (HNSCC) using archival tumor tissues METHODS: Patients diagnosed with HNSCC who have undergone surgical resection in 2005-2012 were included. Correlations between PVR and PD-L1 expression and patient characteristics were analyzed by analysis of variance. The Kaplan-Meier method and log-rank test were used to estimate survival. P values < 0.05 were considered statistically significant.

RESULTS

In total, 375 primary tumor tissues were analyzed using immunohistochemistry. High PVR expression was associated with a poor prognosis in terms of overall survival (OS) and recurrence-free survival (RFS), and tumors with high PVR expression were associated with a short OS. PD-L1 tumor expression did not have a prognostic impact on survival. Univariate analysis revealed that OS and RFS were affected by age and p16 and PVR expression; multivariate analysis revealed that age and p16 and PVR expression were the most important determinants of RFS.

CONCLUSION

PVR overexpression is a poor prognostic factor in patients with HNSCC and co-targeting PVR and PD-L1 may be a promising therapeutic option that needs further investigation.