Bioinformatics Analysis of the Human Surfaceome Reveals New Targets for a Variety of Tumor Types

Surfaceome Profiling Suggests Potential of Anti-MUC1×EGFR Bispecific Antibody for Breast Cancer Targeted Therapy

Breast cancer (BC) treatment has traditionally been challenging due to tumor heterogeneity. Bispecific antibodies (bsAbs) offer a promising approach for overcoming these challenges by targeting multiple specific epitopes. In the current study, we designed a new bsAb against the most common BC cell surface proteins (SPs). To achieve this, we analyzed RNA-sequencing data to identify differentially expressed genes, which were further evaluated using Gene Ontology enrichment, Hidden Markov Models, clinical trial data, and survival analysis to identify druggable gene-encoding cell SPs. Based on these analyses, we constructed and expressed a bsAb targeting the mucin 1 (MUC1) and epidermal growth factor receptor (EGFR) proteins, which are the dominant druggable gene-encoding cell SPs in BC. The recombinant anti-MUC1×EGFR bsAb demonstrated efficient production and high specificity for MUC1 and EGFR + cell lines and BC tissue. Furthermore, the bsAb significantly reduced the proliferation and migration of BC cells. Our results suggested that simultaneous targeting with bsAbs could be a promising targeted therapy for improving the overall efficacy of BC treatment.

An Assessment of the Penile Squamous Cell Carcinoma Surfaceome for Biomarker and Therapeutic Target Discovery

Penile squamous cell carcinoma (PSCC) is a rare malignancy in most parts of the world and the underlying mechanisms of this disease have not been fully investigated. About 30-50% of cases are associated with high-risk human papillomavirus (HPV) infection, which may have prognostic value. When PSCC becomes resistant to upfront therapies there are limited options, thus further research is needed in this venue. The extracellular domain-facing protein profile on the cell surface (i.e., the surfaceome) is a key area for biomarker and drug target discovery. This research employs computational methods combined with cell line translatomic (n = 5) and RNA-seq transcriptomic data from patient-derived tumors (n = 18) to characterize the PSCC surfaceome, evaluate the composition dependency on HPV infection, and explore the prognostic impact of identified surfaceome candidates. Immunohistochemistry (IHC) was used to validate the localization of select surfaceome markers. This analysis characterized a diverse surfaceome within patient tumors with 25% and 18% of the surfaceome represented by the functional classes of receptors and transporters, respectively. Significant differences in protein classes were noted by HPV status, with the most change being seen in transporter proteins (25%). IHC confirmed the robust surface expression of select surfaceome targets in the top 85% of expression and a superfamily immunoglobulin protein called BSG/CD147 was prognostic of survival. This study provides the first description of the PSCC surfaceome and its relation to HPV infection and sets a foundation for novel biomarker and drug target discovery in this rare cancer.

Data-Driven Identification of Targets for Fluorescence-Guided Surgery in Non-Small Cell Lung Cancer

PURPOSE

Intraoperative identification of lung tumors can be challenging. Tumor-targeted fluorescence-guided surgery can provide surgeons with a tool for real-time intraoperative tumor detection. This study evaluated cell surface biomarkers, partially selected via data-driven selection software, as potential targets for fluorescence-guided surgery in non-small cell lung cancers: adenocarcinomas (ADC), adenocarcinomas in situ (AIS), and squamous cell carcinomas (SCC).

PROCEDURES

Formalin-fixed paraffin-embedded tissue slides of resection specimens from 15 patients with ADC and 15 patients with SCC were used and compared to healthy tissue. Molecular targets were selected based on two strategies: (1) a data-driven selection using > 275 multi-omics databases, literature, and experimental evidence; and (2) the availability of a fluorescent targeting ligand in advanced stages of clinical development. The selected targets were carbonic anhydrase 9 (CAIX), collagen type XVII alpha 1 chain (collagen XVII), glucose transporter 1 (GLUT1), G protein-coupled receptor 87 (GPR87), transmembrane protease serine 4 (TMPRSS4), carcinoembryonic antigen (CEA), epithelial cell adhesion molecule (EpCAM), folate receptor alpha (FRα), integrin αvβ6 (αvβ6), and urokinase-type plasminogen activator receptor (uPAR). Tumor expression of these targets was assessed by immunohistochemical staining. A total immunostaining score (TIS, range 0-12), combining the percentage and intensity of stained cells, was calculated. The most promising targets in ADC were explored in six AIS tissue slides to explore its potential in non-palpable lesions.

RESULTS

Statistically significant differences in TIS between healthy lung and tumor tissue for ADC samples were found for CEA, EpCAM, FRα, αvβ6, CAIX, collagen XVII, GLUT-1, and TMPRSS4, and of these, CEA, CAIX, and collagen XVII were also found in AIS. For SCC, EpCAM, uPAR, CAIX, collagen XVII, and GLUT-1 were found to be overexpressed.

CONCLUSIONS

EpCAM, CAIX, and Collagen XVII were identified using concomitant use of data-driven selection software and clinical evidence as promising targets for intraoperative fluorescence imaging for both major subtypes of non-small cell lung carcinomas.

Mast cell surfaceome characterization reveals CD98 heavy chain is critical for optimal cell function

BACKGROUND

Mast cells are involved in many distinct pathologic conditions, suggesting that they recognize and respond to various stimuli and thus require a rich repertoire of cell surface proteins. However, mast cell surface proteomes have not been comprehensively characterized.

OBJECTIVE

We aimed to further characterize the mast cell surface proteome to obtain a better understanding of how mast cells function in health and disease.

METHODS

We enriched for glycosylated surface proteins expressed in mouse bone marrow-derived cultured mast cells (BMCMCs) and identified them using mass spectrometry analysis. The presence of novel surface proteins in mast cells was validated by real-time quantitative PCR and flow cytometry analysis in BMCMCs and peritoneal mast cells (PMCs). We developed a clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) gene editing approach to disrupt genes of interest in BMCMCs.

RESULTS

The glycoprotein enrichment approach resulted in the identification of 1270 proteins in BMCMCs, 378 of which were localized to the plasma membrane. The most common protein classes among plasma membrane proteins were small GTPases, receptors, and transporters. One such cell surface protein was CD98 heavy chain (CD98hc), encoded by the Slc3a2 gene. Slc3a2 gene disruption resulted in a significant reduction in CD98hc expression, adhesion, and proliferation.

CONCLUSIONS

Glycoprotein enrichment coupled with mass spectrometry can be used to identify novel surface molecules in mast cells. Moreover, CD98hc plays an important role in mast cell function.

A Human IgSF Cell-Surface Interactome Reveals a Complex Network of Protein-Protein Interactions

Cell-surface protein-protein interactions (PPIs) mediate cell-cell communication, recognition, and responses. We executed an interactome screen of 564 human cell-surface and secreted proteins, most of which are immunoglobulin superfamily (IgSF) proteins, using a high-throughput, automated ELISA-based screening platform employing a pooled-protein strategy to test all 318,096 PPI combinations. Screen results, augmented by phylogenetic homology analysis, revealed ∼380 previously unreported PPIs. We validated a subset using surface plasmon resonance and cell binding assays. Observed PPIs reveal a large and complex network of interactions both within and across biological systems. We identified new PPIs for receptors with well-characterized ligands and binding partners for “orphan” receptors. New PPIs include proteins expressed on multiple cell types and involved in diverse processes including immune and nervous system development and function, differentiation/proliferation, metabolism, vascularization, and reproduction. These PPIs provide a resource for further biological investigation into their functional relevance and may offer new therapeutic drug targets.

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Human IgSF interactome reveals complex network of cell-surface protein interactions

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Phylogenetic homology analysis predicts protein-protein interactions

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∼380 previously unknown protein-protein interactions identified

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Deorphanization of receptors and new binding partners for well-studied receptors

Cellinker: a platform of ligand-receptor interactions for intercellular communication analysis

MOTIVATION

Ligand-receptor (L-R) interactions mediate cell adhesion, recognition and communication and play essential roles in physiological and pathological signaling. With the rapid development of single-cell RNA sequencing (scRNA-seq) technologies, systematically decoding the intercellular communication network involving L-R interactions has become a focus of research. Therefore, construction of a comprehensive, high-confidence and well-organized resource to retrieve L-R interactions in order to study the functional effects of cell-cell communications would be of great value.

RESULTS

In this study, we developed Cellinker, a manually curated resource of literature-supported L-R interactions that play roles in cell-cell communication. We aimed to provide a useful platform for studies on cell-cell communication mediated by L-R interactions. The current version of Cellinker documents over 3,700 human and 3,200 mouse L-R protein-protein interactions (PPIs) and embeds a practical and convenient webserver with which researchers can decode intercellular communications based on scRNA-seq data. And over 400 endogenous small molecule (sMOL) related L-R interactions were collected as well. Moreover, to help with research on coronavirus (CoV) infection, Cellinker collects information on 16 L-R PPIs involved in CoV-human interactions (including 12 L-R PPIs involved in SARS-CoV-2 infection). In summary, Cellinker provides a user-friendly interface for querying, browsing and visualizing L-R interactions as well as a practical and convenient web tool for inferring intercellular communications based on scRNA-seq data. We believe this platform could promote intercellular communication research and accelerate the development of related algorithms for scRNA-seq studies.

AVAILABILITY

Cellinker is available at http://www.rna-society.org/cellinker/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Deregulation of cell adhesion molecules is associated with progression and poor outcomes in endometrial cancer: Analysis of The Cancer Genome Atlas data

Cell adhesion molecules (CAMs) determine the behavior of cancer cells during metastasis. Although some CAMs are dysregulated in certain types of cancer and are associated with cancer progression, to the best of our knowledge, a comprehensive study of CAMs has not been undertaken, particularly in endometrial cancer (EC). In the present study the expression of 225 CAMs in EC patients with various clinicopathological phenotypes were evaluated by statistical analysis using publicly available data from The Cancer Genome Atlas database. The Kaplan-Meier method, and univariate and multivariate Cox proportional hazards regression models were used for survival analyses. Among the differentially expressed CAMs that were associated with aggressive clinicopathological phenotypes, 10 CAM genes were independent prognostic factors compared with other clinicopathological prognostic factors, including stage, grade, age, lymph node status, peritoneal cytology and histological subtype. A total of six genes (L1 cell adhesion molecule, mucin 15, cell surface associated, cell adhesion associated, oncogene regulated, immunoglobulin superfamily member 9B, protocadherin 9 and protocadherin β1) were selected for integrative analysis. The six-gene signature was demonstrated to be an independent prognostic factor and could effectively stratify patients with different risks. Patients with more high-expression CAMs had a higher risk of poor overall survival (OS) rate. The mortality risk for patients with elevation of >4 CAMs was 11 times of that in those without elevation of these 6 CAMs. Similar results were obtained when relapse-free survival (RFS) time was used during the analysis. Prognostic reliability of the six-gene model was validated using data of an independent cohort from the International Cancer Genome Consortium. In conclusion, a combination of CAM alterations contributed to progression and aggressiveness of EC. The six-gene signature was effective for predicting worse OS and RFS in patients with EC and could be complementary to the present clinical prognostic criteria.

Identification of Key Genes and Prognostic Value Analysis in Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

Emerging evidence indicates that various functional genes with altered expression are involved in the tumor progression of human cancers. This study is aimed at identifying novel key genes that may be used for hepatocellular carcinoma (HCC) diagnosis, prognosis, and targeted therapy. This study included 3 expression profiles (GSE45267, GSE74656, and GSE84402), which were obtained from the Gene Expression Omnibus (GEO). GEO2R was used to analyze the differentially expressed genes (DEGs) between HCC and normal samples. The functional and pathway enrichment analysis was performed by the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network of the identified DEGs was constructed using the Search Tool for the Retrieval of Interacting Gene, and hub genes were identified. ONCOMINE and CCLE databases were used to verify the expression of the hub genes in HCC tissues and cells. Kaplan-Meier plotter was used to assess the effects of the hub genes on the overall survival of HCC patients. A total of 99 DEGs were identified from the 3 expression profiles. These DEGs were enriched with functional processes and pathways related to HCC pathogenesis. From the PPI network, 5 hub genes were identified. The expression of the 5 hub genes was all upregulated in HCC tissues and cells compared with the control tissues and cells. Kaplan-Meier survival curves indicated that high expression of cyclin-dependent kinase (CDK1), cyclin B1 (CCNB1), cyclin B2 (CCNB2), MAD2 mitotic arrest deficient-like 1 (MAD2L1), and topoisomerase IIα (TOP2A) predicted poor overall survival in HCC patients (all log-rank P < 0.01). These results revealed that the DEGs may serve as candidate key genes during HCC pathogenesis. The 5 hub genes, including CDK1, CCNB1, CCNB2, MAD2L1, and TOP2A, may serve as promising prognostic biomarkers in HCC.

The in silico human surfaceome

Cell-surface proteins are of great biomedical importance, as demonstrated by the fact that 66% of approved human drugs listed in the DrugBank database target a cell-surface protein. Despite this biomedical relevance, there has been no comprehensive assessment of the human surfaceome, and only a fraction of the predicted 5,000 human transmembrane proteins have been shown to be located at the plasma membrane. To enable analysis of the human surfaceome, we developed the surfaceome predictor SURFY, based on machine learning. As a training set, we used experimentally verified high-confidence cell-surface proteins from the Cell Surface Protein Atlas (CSPA) and trained a random forest classifier on 131 features per protein and, specifically, per topological domain. SURFY was used to predict a human surfaceome of 2,886 proteins with an accuracy of 93.5%, which shows excellent overlap with known cell-surface protein classes (i.e., receptors). In deposited mRNA data, we found that between 543 and 1,100 surfaceome genes were expressed in cancer cell lines and maximally 1,700 surfaceome genes were expressed in embryonic stem cells and derivative lines. Thus, the surfaceome diversity depends on cell type and appears to be more dynamic than the nonsurface proteome. To make the predicted surfaceome readily accessible to the research community, we provide visualization tools for intuitive interrogation (wlab.ethz.ch/surfaceome). The in silico surfaceome enables the filtering of data generated by multiomics screens and supports the elucidation of the surfaceome nanoscale organization.

A 44-gene set constructed for predicting the prognosis of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most frequent type of renal cell carcinoma (RCC). The present study aimed to examine prognostic markers and construct a prognostic prediction system for ccRCC. The mRNA sequencing data of ccRCC was downloaded from The Cancer Genome Atlas (TCGA) database, and the GSE40435 dataset was obtained from the Gene Expression Omnibus database. Using the Limma package, the differentially expressed genes (DEGs) in the TCGA dataset and GSE40435 dataset were obtained, respectively, and the overlapped DEGs were selected. Subsequently, Cox regression analysis was applied for screening prognosis-associated genes. Following visualization of the co-expression network using Cytoscape software, the network modules were examined using the GraphWeb tool. Functional annotation for genes in the network was performed using the clusterProfiler package. Finally, a prognostic prediction system was constructed through Bayes discriminant analysis and confirmed with the GSE29609 validation dataset. The results revealed a total of 263 overlapped DEGs and 161 prognosis-associated genes. Following construction of the co-expression network, 16 functional terms and three pathways were obtained for genes in the network. In addition, red, yellow (Involving chemokine ligand 10 (CXCL10), CD27 molecule (CD27) and runt-related transcription factor 3 (RUNX3)], green (Involving angiopoietin-like 4 (ANGPTL4), stannio-calcin 2 (STC2), and sperm associated antigen 4 (SPAG4)], and cyan modules were extracted from the co-expression network. Additionally, the prognostic prediction system involving 44 signature genes, including ANGPTL4, STC2, CXCL10, SPAG4, CD27, matrix metalloproteinase (MMP9) and RUNX3, was identified and confirmed. In conclusion, the 44-gene prognostic prediction system involving ANGPTL4, STC2, CXCL10, SPAG4, CD27, MMP9 and RUNX3 may be utilized for predicting the prognosis of patients with ccRCC.

Prospects for combined use of oncolytic viruses and CAR T-cells

With the approval of talimogene laherparepvec (T-VEC) for inoperable locally advanced or metastatic malignant melanoma in the USA and Europe, oncolytic virotherapy is now emerging as a viable therapeutic option for cancer patients. In parallel, following the favourable results of several clinical trials, adoptive cell transfer using chimeric antigen receptor (CAR)-redirected T-cells is anticipated to enter routine clinical practice for the management of chemotherapy-refractory B-cell malignancies. However, CAR T-cell therapy for patients with advanced solid tumours has proved far less successful. This Review draws upon recent advances in the design of novel oncolytic viruses and CAR T-cells and provides a comprehensive overview of the synergistic potential of combination oncolytic virotherapy with CAR T-cell adoptive cell transfer for the management of solid tumours, drawing particular attention to the methods by which recombinant oncolytic viruses may augment CAR T-cell trafficking into the tumour microenvironment, mitigate or reverse local immunosuppression and enhance CAR T-cell effector function and persistence.

Genome-wide identification of cancer/testis genes and their association with prognosis in a pan-cancer analysis

Cancer/testis (CT) genes are excellent candidates for cancer immunotherapies because of their restrict expression in normal tissues and the capacity to elicit an immune response when expressed in tumor cells. In this study, we provide a genome-wide screen for CT genes with the identification of 745 putative CT genes. Comparison with a set of known CT genes shows that 201 new CT genes were identified. Integration of gene expression and clinical data led us to identify dozens of CT genes associated with either good or poor prognosis. For the CT genes related to good prognosis, we show that there is a direct relationship between CT gene expression and a signal for CD8+ cells infiltration for some tumor types, especially melanoma.