Cancer Proteomics Research Articles

Alterations of the Platelet Proteome in Lung Cancer: Accelerated F13A1 and ER Processing as New Actors in Hypercoagulability.

In order to comprehensively expose cancer-related biochemical changes, we compared the platelet proteome of two types of cancer with a high risk of thrombosis (22 patients with brain cancer, 19 with lung cancer) to 41 matched healthy controls using unbiased two-dimensional differential in-gel electrophoresis. The examined platelet proteome was unchanged in patients with brain cancer, but considerably affected in lung cancer with 15 significantly altered proteins. Amongst these, the endoplasmic reticulum (ER) proteins calreticulin (CALR), endoplasmic reticulum chaperone BiP (HSPA5) and protein disulfide-isomerase (P4HB) were significantly elevated. Accelerated conversion of the fibrin stabilising factor XIII was detected in platelets of patients with lung cancer by elevated levels of a coagulation factor XIII (F13A1) 55 kDa fragment. A significant correlation of this F13A1 cleavage product with plasma levels of the plasmin-α-2-antiplasmin complex and D-dimer suggests its enhanced degradation by the fibrinolytic system. Protein association network analysis showed that lung cancer-related proteins were involved in platelet degranulation and upregulated ER protein processing. As a possible outcome, plasma FVIII, an immediate end product for ER-mediated glycosylation, correlated significantly with the ER-executing chaperones CALR and HSPA5. These new data on the differential behaviour of platelets in various cancers revealed F13A1 and ER chaperones as potential novel diagnostic and therapeutic targets in lung cancer patients.

Increased expression of peptides from non-coding genes in cancer proteomics datasets suggests potential tumor neoantigens

Neoantigen-based immunotherapy has yielded promising results in clinical trials. However, it is limited to tumor-specific mutations, and is often tailored to individual patients. Identifying suitable tumor-specific antigens is still a major challenge. Previous proteogenomics studies have identified peptides encoded by predicted non-coding sequences in human genome. To investigate whether tumors express specific peptides encoded by non-coding genes, we analyzed published proteomics data from five cancer types including 933 tumor samples and 275 matched normal samples and compared these to data from 31 different healthy human tissues. Our results reveal that many predicted non-coding genes such as DGCR9 and RHOXF1P3 encode peptides that are overexpressed in tumors compared to normal controls. Furthermore, from the non-coding genes-encoded peptides specifically detected in cancers, we predict a large number of “dark antigens” (neoantigens from non-coding genomic regions), which may provide an alternative source of neoantigens beyond standard tumor specific mutations.

Metabolic flexibility determines human NK cell functional fate in the tumor microenvironment

NK cells are central to anti-tumor immunity and recently showed efficacy for treating hematologic malignancies. However, their dysfunction in the hostile tumor microenvironment remains a pivotal barrier for cancer immunotherapies against solid tumors. Using cancer patient samples and proteomics, we found that human NK cell dysfunction in the tumor microenvironment is due to suppression of glucose metabolism via lipid peroxidation-associated oxidative stress. Activation of the Nrf2 antioxidant pathway restored NK cell metabolism and function and resulted in greater anti-tumor activity invivo. Strikingly, expanded NK cells reprogrammed with complete metabolic substrate flexibility not only sustained metabolic fitness but paradoxically augmented their tumor killing in the tumor microenvironment and in response to nutrient deprivation. Our results uncover that metabolic flexibility enables a cytotoxic immune cell to exploit the metabolic hostility of tumors for their advantage, addressing a critical hurdle for cancer immunotherapy.

Tubulin Beta 2C Chain (TBB2C), a Potential Marker of Ovarian Cancer, an Insight from Ovarian Cancer Proteome Profile.

Ovarian cancer (OC) is the most lethal among female reproductive system malignancies. Depending upon the stage at presentation, the five year survival ratio varies from ∼92 to ∼30%. The role of biomarkers in early cancer diagnosis, including OC, is well understood. In our previous study, through an initial screening, we have analyzed eleven proteins that exhibited differential expression in OC using two-dimensional gel electrophoresis (2D-GE) and matrix-assisted laser desorption/ionization-time of flight mass spectrometric (MALDI-TOF MS) analysis. In continuation of our previous study, the present work describes analysis of twenty more proteins that showed aberrant expression in OC. Among these, six showed consistent significant deregulation in the OC false discovery rate [FDR ≤ 0.05]. Upon MS analysis, they were identified as vimentin, tubulin beta 2C chain, tubulin alpha 1C chain, actin cytoplasmic 2, apolipoprotein A-I, and collagen alpha 2(VI) chain [peptide mass fingerprint (PMF) score ≥ 79]. One of the differentially regulated proteins, tubulin beta 2C chain, was found to be significantly (fold change, 2.5) enhanced in OC. Verification by western blot and enzyme-linked immunosorbent assay (ELISA) demonstrated that the tubulin beta 2C chain may serve as a valuable marker for OC (ANOVA p < 0.0001). The assessment of the likely association of TBB2C with OC in a larger population will not only help in developing clinically useful biomarkers in the future but also improve our understanding of the progression of OC disease.

Quantitative Proteomic Approach Reveals Altered Metabolic Pathways in Response to the Inhibition of Lysine Deacetylases in A549 Cells under Normoxia and Hypoxia.

Growing evidence is showing that acetylation plays an essential role in cancer, but studies on the impact of KDAC inhibition (KDACi) on the metabolic profile are still in their infancy. Here, we analyzed, by using an iTRAQ-based quantitative proteomics approach, the changes in the proteome of KRAS-mutated non-small cell lung cancer (NSCLC) A549 cells in response to trichostatin-A (TSA) and nicotinamide (NAM) under normoxia and hypoxia. Part of this response was further validated by molecular and biochemical analyses and correlated with the proliferation rates, apoptotic cell death, and activation of ROS scavenging mechanisms in opposition to the ROS production. Despite the differences among the KDAC inhibitors, up-regulation of glycolysis, TCA cycle, oxidative phosphorylation and fatty acid synthesis emerged as a common metabolic response underlying KDACi. We also observed that some of the KDACi effects at metabolic levels are enhanced under hypoxia. Furthermore, we used a drug repositioning machine learning approach to list candidate metabolic therapeutic agents for KRAS mutated NSCLC. Together, these results allow us to better understand the metabolic regulations underlying KDACi in NSCLC, taking into account the microenvironment of tumors related to hypoxia, and bring new insights for the future rational design of new therapies.

Identification of a Novel Protein-Based Signature to Improve Prognosis Prediction in Renal Clear Cell Carcinoma.

BackgroundClear cell renal cell carcinoma (ccRCC) is one of the most common types of malignant adult kidney cancer, and its incidence and mortality are not optimistic. It is well known that tumor-related protein markers play an important role in cancer detection, prognosis prediction, or treatment selection, such as carcinoembryonic antigen (CEA), programmed cell death 1 (PD-1), programmed cell death 1 ligand 1 (PD-L1), and cytotoxic T lymphocyte antigen 4 (CTLA-4), so a comprehensive analysis was performed in this study to explore the prognostic value of protein expression in patients with ccRCC.Materials and MethodsProtein expression data were obtained from The Cancer Proteome Atlas (TCPA), and clinical information were downloaded from The Cancer Genome Atlas (TCGA). We selected 445 patients with complete information and then separated them into a training set and testing set. We performed univariate, least absolute shrinkage and selection operator (LASSO) Cox analyses to find prognosis-related proteins (PRPs) and constructed a protein signature. Then, we used stratified analysis to fully verify the prognostic significance of the prognostic-related protein signature score (PRPscore). Besides, we also explored the differences in immunotherapy response and immune cell infiltration level in high and low score groups. The consensus clustering analysis was also performed to identify potential cancer subgroups.ResultsFrom the training set, a total of 233 PRPs were selected, and a seven-protein signature was constructed, including ACC1, AR, MAPK, PDK1, PEA15, SYK, and BRAF. Based on the PRPscore, patients could be divided into two groups with significantly different overall survival rates. Univariate and multivariate Cox regression analyses proved that this signature was an independent prognostic factor for patients (P < 0.001). Moreover, the signature showed a high ability to distinguish prognostic outcomes among subgroups, and the low score group had a better prognosis (P < 0.001) and better immunotherapy response (P = 0.003) than the high score group.ConclusionWe constructed a novel protein signature with robust predictive power and high clinical value. This will help to guide the disease management and individualized treatment of ccRCC patients.

Fatty Acid Synthase Confers Tamoxifen Resistance to ER+/HER2+ Breast Cancer.

Simple SummaryOveractivation of the human epidermal growth factor receptor 2 (HER2) is one of the main drivers of tamoxifen resistance in estrogen receptor (ER)-positive breast cancer patients. Combined targeting of HER2 and ER, however, has yielded disappointing results in the clinical setting. Therefore, other potential mechanisms for tamoxifen resistance would not be overcome by solely blocking the cross-talk between ER and HER2 at the receptor(s) level. Using cell lines, animal models, and clinical data, we provide evidence to support a critical role of fatty acid synthase (FASN)—the major site for endogenous fat synthesis—in HER2-driven tamoxifen resistance. Importantly, treatment with a FASN inhibitor impeded the estrogen-like tumor-promoting effects of tamoxifen and fully restored the anti-estrogenic activity of tamoxifen in ER+/HER2-overexpressing breast cancer xenografts. We postulate FASN as a biological determinant of HER2-driven tamoxifen resistance and FASN inhibition as a novel therapeutic approach to restore tamoxifen sensitivity in endocrine-resistant breast cancer.The identification of clinically important molecular mechanisms driving endocrine resistance is a priority in estrogen receptor-positive (ER+) breast cancer. Although both genomic and non-genomic cross-talk between the ER and growth factor receptors such as human epidermal growth factor receptor 2 (HER2) has frequently been associated with both experimental and clinical endocrine therapy resistance, combined targeting of ER and HER2 has failed to improve overall survival in endocrine non-responsive disease. Herein, we questioned the role of fatty acid synthase (FASN), a lipogenic enzyme linked to HER2-driven breast cancer aggressiveness, in the development and maintenance of hormone-independent growth and resistance to anti-estrogens in ER/HER2-positive (ER+/HER2+) breast cancer. The stimulatory effects of estradiol on FASN gene promoter activity and protein expression were blunted by anti-estrogens in endocrine-responsive breast cancer cells. Conversely, an AKT/MAPK-related constitutive hyperactivation of FASN gene promoter activity was unaltered in response to estradiol in non-endocrine responsive ER+/HER2+ breast cancer cells, and could be further enhanced by tamoxifen. Pharmacological blockade with structurally and mechanistically unrelated FASN inhibitors fully impeded the strong stimulatory activity of tamoxifen on the soft-agar colony forming capacity—an in vitro metric of tumorigenicity—of ER+/HER2+ breast cancer cells. In vivo treatment with a FASN inhibitor completely prevented the agonistic tumor-promoting activity of tamoxifen and fully restored its estrogen antagonist properties against ER/HER2-positive xenograft tumors in mice. Functional cancer proteomic data from The Cancer Proteome Atlas (TCPA) revealed that the ER+/HER2+ subtype was the highest FASN protein expressor compared to basal-like, HER2-enriched, and ER+/HER2-negative breast cancer groups. FASN is a biological determinant of HER2-driven endocrine resistance in ER+ breast cancer. Next-generation, clinical-grade FASN inhibitors may be therapeutically relevant to countering resistance to tamoxifen in FASN-overexpressing ER+/HER2+ breast carcinomas.

Construction of a robust prognostic model for adult adrenocortical carcinoma: Results from bioinformatics and real-world data.

This study aims to construct a robust prognostic model for adult adrenocortical carcinoma (ACC) by large‐scale multiomics analysis and real‐world data. The RPPA data, gene expression profiles and clinical information of adult ACC patients were obtained from The Cancer Proteome Atlas (TCPA), Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Integrated prognosis‐related proteins (IPRPs) model was constructed. Immunohistochemistry was used to validate the prognostic value of the IPRPs model in Fudan University Shanghai Cancer Center (FUSCC) cohort. 76 ACC cases from TCGA and 22 ACC cases from GSE10927 in NCBI’s GEO database with full data for clinical information and gene expression were utilized to validate the effectiveness of the IPRPs model. Higher FASN (P = .039), FIBRONECTIN (P < .001), TFRC (P < .001), TSC1 (P < .001) expression indicated significantly worse overall survival for adult ACC patients. Risk assessment suggested significantly a strong predictive capacity of IPRPs model for poor overall survival (P < .05). IPRPs model showed a little stronger ability for predicting prognosis than Ki‐67 protein in FUSCC cohort (P = .003, HR = 3.947; P = .005, HR = 3.787). In external validation of IPRPs model using gene expression data, IPRPs model showed strong ability for predicting prognosis in TCGA cohort (P = .005, HR = 3.061) and it exhibited best ability for predicting prognosis in GSE10927 cohort (P = .0898, HR = 2.318). This research constructed IPRPs model for predicting adult ACC patients’ prognosis using proteomic data, gene expression data and real‐world data and this prognostic model showed stronger predictive value than other biomarkers (Ki‐67, Beta‐catenin, etc) in multi‐cohorts.

Application of Advanced Mass Spectrometry-Based Proteomics to Study Hypoxia Driven Cancer Progression.

Cancer is one of the largest contributors to the burden of chronic disease in the world and is the second leading cause of death globally. It is associated with episodes of low-oxygen stress (hypoxia or ischemia/reperfusion) that promotes cancer progression and therapeutic resistance. Efforts have been made in the past using traditional proteomic approaches to decipher oxygen deprivation stress-related mechanisms of the disease initiation and progression and to identify key proteins as a therapeutic target for the treatment and prevention. Despite the potential benefits of proteomic in translational research for the discovery of new drugs, the therapeutic outcome with this approach has not met expectations in clinical trials. This is mainly due to the disease complexity which possess a multifaceted molecular pathology. Therefore, novel strategies to identify and characterize clinically important sets of modulators and molecular events for multi-target drug discovery are needed. Here, we review important past and current studies on proteomics in cancer with an emphasis on recent pioneered labeling approaches in mass spectrometry (MS)-based systematic quantitative analysis to improve clinical success. We also discuss the results of the selected innovative publications that integrate advanced proteomic technologies (e.g. MALDI-MSI, pSILAC/SILAC/iTRAQ/TMT-LC-MS/MS, MRM-MS) for comprehensive analysis of proteome dynamics in different biosystems, including cell type, cell species, and subcellular proteome (i.e. secretome and chromatome). Finally, we discuss the future direction and challenges in the application of these technological advancements in mass spectrometry within the context of cancer and hypoxia.

Abstract PS11-13: Multidimensional molecular profiling of repeated metastatic TNBC biopsies in the intensive trial of omics &amp;lt;ITOMIC&amp;gt; safely guides treatment decisions

Abstract Background Metastatic triple negative breast cancer (mTNBC) is an inherently diverse disease and while molecular classification of mTNBC has assisted in treatment decisions, if based on only an initial biopsy, it does not take into account the evolution of metastatic cancer. Characterization of emerging metastases is needed to reveal both new resistance or sensitivity to available therapeutics. The goal of “Intensive Trial of OMics in Cancer (ITOMIC) - Intensive Longitudinal Monitoring in Subjects With Triple-Negative Breast Cancer” (NCT01957514) - was to determine the feasibility of longitudinal collection of patient biopsies that would be subjected to molecular analysis to provide actionable, relevant and timely information to guide treatment decisions.Methods Multiple biopsies were collected longitudinally, including pre- and post-treatment, from 29 mTNBC patients enrolled in the ITOMIC study and subjected to multi-dimensional molecular profiling including WES, WGS, cancer gene panel sequencing, RNA-seq, and proteomics and/or IHC for tumor biomarkers. This information was used to guide iterative, patient- and tumor- individualized treatment recommendations made by a multi-institutional ITOMIC Tumor Board (ITB) and conveyed to each subject’s oncologist.Results Longitudinal biopsy collection was found to be safe. Molecular profiling revealed that 2 of an original 31 enrolled subjects likely had lung cancer rather than mTNBC, supporting the merit of repeated tissue analysis. While the other 29 subjects had all been given a diagnosis of mTNBC before entering the trial, estrogen receptor, progesterone receptor, and/or HER2 were found to be over-expressed in at least one sample for 12 subjects; appearance of receptor positivity suggests targeted therapy may be effective. Tumor evolution in response to the first on-study treatment for most subjects (cisplatin) was revealed by copy number alterations, changes in single nucleotide variants, and insertions/deletions in pre-/post-treatment biopsies. Over the course of the study, the ITB convened 54 times and 39 of 182 recommended treatments were evaluated and accessed through either an existing clinical trial, a single patient IND, approved off label or label indication. While not all ITB treatment recommendations were followed, 24 subjects did receive at least one ITB-recommended drug, frequently as part of a clinical trial. Currently, for 27 subjects (2 withdrew) median survival is ~31 months. There are 4 surviving patients in treatment with a remarkable median survival of &amp;gt;51 months.Conclusion Collection and molecular analysis of multiple biopsies during the course of patient’s disease, shown here to be safe and feasible, provides information vital to appropriate treatment choice and reveals new targets for and resistance to therapy in metastatic TNBC. Citation Format: Kimberly A Burton, Eric Q Konnick, Sibel Blau, Michael O Dorschner, Julie Gralow, Rahul Parulkar, Elisabeth Mahen, Patricia Spilman, Stephanie Parker, Francis M Senecal, Colin Pritchard, Christopher Szeto, Jing Zhu, Vijayakrishna K Gadi, Stephen C Benz, Shahrooz Rabizadeh, Patrick Soon-Shiong, Carl Anthony Blau. Multidimensional molecular profiling of repeated metastatic TNBC biopsies in the intensive trial of omics &amp;lt;ITOMIC&amp;gt; safely guides treatment decisions [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS11-13.

Prognostic Implications of Pan-Cancer CMTM6 Expression and Its Relationship with the Immune Microenvironment.

CKLF-like MARVEL transmembrane domain-containing 6 (CMTM6) reportedly stabilizes programmed death-ligand 1 (PD-L1) and enhances the efficacy of immunotherapy. However, correlations between CMTM6 expression and the immune microenvironment and its prognostic value remain unknown in a variety of tumors. CMTM6 expression data were obtained from The Cancer Genome Atlas (TCGA) for 33 cancer types classified into high and low expression subgroups according to the median CMTM6 expression value. Pan-cancer analysis of CMTM6 protein expression in 20 tumor types was performed using a cohort from the Human Protein Atlas (HPA). PD-L1 protein expression data were obtained from The Cancer Proteome Atlas (TCPA) for 32 cancer types. Frequencies of CMTM6 copy number alterations and mutations were analyzed using cBioPortal. MANTIS was employed to estimate microsatellite instability in the TCGA cohort. CIBERSORT and the ESTIMATE algorithm were applied to estimate the relative fractions of infiltrating immune cell types and immune scores, respectively. Kaplan–Meier survival curve analysis was performed to assess the pan-cancer prognostic value of CMTM6.CMTM6 is heterogeneously expressed in diverse cancers. Further, the results revealed low CMTM6 mutation frequencies in multiple cancers. Among them, CMTM6 mutation frequency was the highest in uterine cancer. Additionally, CMTM6 expression was related to PD-L1 protein expression in breast invasive carcinoma, cervical squamous cell carcinoma and endocervical adenocarcinoma, cholangiocarcinoma, glioblastoma multiforme (GBM), head and neck squamous cell carcinoma, kidney renal papillary cell carcinoma, sarcoma (SARC), stomach adenocarcinoma, and uterine carcinosarcoma. Increased CMTM6 expression may be associated with increased infiltration of neutrophils in some types of cancer. Finally, pan-cancer analysis indicated that CMTM6 expression was closely related to overall survival in adrenocortical carcinoma, GBM, acute myeloid leukemia, liver hepatocellular carcinoma, mesothelioma, SARC, thymoma, and uveal melanoma. Taken together, these findings highlight that CMTM6 plays an important role in the tumor immune microenvironment, and CMTM6 has been identified to have prognostic value in some types of cancers. Thus, CMTM6 is a potential target for cancer immunotherapy and effective prognostic biomarker.

Proteome of breast cancer derived microvesicles

The aim of the research. To examine the proteomic profi le of breast cancer exosomes. Material and methods. Cell lines used for this study were MDA-MB-231 female epithelial breast cancer cells (ATCC HTB-26) and MCF10A non-tumorigenic epithelial breast tissue cells. MVs were isolated using diff erential ultracentrifugation. Samples were lysed, reduced, alkylated, digested, and analyzed by an Orbitrap Fusion mass spectrometer. MS raw fi les were analyzed using MaxQuant version 1.6.12.0. Peptides were searched against the human UniProt FASTA database using the Andromeda search engine, integrated into MaxQuant. Results. MVs derived from MCF10A and MDA-MB-231 cell lines were analyzed, and 1427 and 547 proteins were identifi ed in the MDA-MB-231 and MCF10A-derived MVs, respectively. In total, 455 proteins were common to both MDA-MB-231 and MCF10A MVs. MVs derived from MCF10A and MDAMB-231 cell lines were analyzed, and 1427 and 547 proteins were identifi ed in the MDA-MB-231 and MCF10A-derived MVs, respectively. In total, 455 proteins were common to both MDA-MB-231 and MCF10A MVs. Th e unique MDA-MB-231 MV proteins were searched against the DisGeNET human diseases database. Out of 972 MDA-MB-231 MV proteins, 112 were cancer-related while 32 were specifi cally associated with BC. In the MDA-MB-231 MV proteome, 23 Wnt signaling pathway proteins were identifi ed based on their GO biological process. Proteomic analysis identifi ed enzymes OAT, TALDO1, and BLMH were only in MVs from metastatic MDA-MB-231 cell line. The specific activity of OAT and TALDO1 was higher in MV fractions of MDA-MB-231 in comparison to the non-cancerous MCF10A cell line-derived MVs. Th ese fi ndings might suggest that these enzymes might play a role in BC. In our present study, we found that some enzymes identifi ed from MV fractions were already proposed to play a role in cancer therapy as therapeutic targets (OAT, TALDO1) and resistance against chemotherapy agents (BLMH).

Sharing Data from an Academic Cancer Center Biospecimen and Proteomic Core Facilities through the Proteomics Data Commons

Data sharing is critical for open science and often required by funding organizations and journals. NCI has developed the Proteomics Data Commons (PDC) as part of the Cancer Research Data Commons, an infrastructure that allows users to share, analyze, and store results, utilizing the storage and compute resources of the cloud. To date most of the data available in the various Data Commons are submitted from large multi-institution research programs funded by NCI with teams of specialists from multiple scientific disciplines. Here we describe our experiences and summarize the recommended best practices for sharing a set of proteomics and related biospecimen data and analyses results from smaller scale proteomics studies conducted in an academic medical center core facility using patient samples of lung adenocarcinoma. Mapping and depositing data in the manner described here harmonizes user’s data to a common data model and community standards, making it possible to view the data alongside other high value cancer studies available in the PDC. Availability: Data, metadata, protocols with peptide and protein identifications are available at the PDC. (https:// pdc.cancer.gov/pdc/study/PDC000231).

Integrative Analysis of Incongruous Cancer Genomics and Proteomics Datasets.

Cancer is a complex disease characterized by molecular heterogeneity and the involvement of several cellular mechanisms throughout its evolution and pathogenesis. Despite the great efforts made to untangle these mechanisms, cancer pathophysiology remains far from clear. So far, panels of biomarkers have been reported from high-throughput data generated through different platforms. These biomarkers are primarily focused on one type of coding molecules such as transcripts or proteins, mainly due to the apparent heterogeneity of output data resulting from the use of various techniques specific to the molecular type. Hence, there is a major need to understand how these molecules interact and complement each other to be able to explain the deregulated processes involved. The breadth of large-scale data availability as well as the lack of in-depth analysis of publicly available data has raised concerns and enabled opportunities for new strategies to analyze "Big data" more comprehensively. Here, a new protocol to perform integrative analysis based on a systems biology approach is described. The foundation of the approach relies on groups of datasets from published studies compared within the original described groups and organized in a designated format to allow the integration and cross-comparison among different studies and different platforms. This approach follows an unbiased hypothesis-free methodology that will facilitate the identification of commonalities among different data-set sources, and ultimately map and characterize specific molecular pathways using significantly deregulated molecules. This in turn will generate novel insights about the mechanisms deregulated in complex diseases such as cancer.

Proteomic profiling reveals a signature for optimizing prognostic prediction in Colon Cancer.

Previous studies developed prognostic signatures largely depended on transcriptome profiles. The purpose of our present study was to develop a proteomic signature to optimize the evaluation of prognosis of colon cancer patients. The proteomic data of colon cancer patient cohorts were downloaded from The Cancer Proteome Atlas (TCPA). Patients were randomized 3:2 to train set and internal validation set. Univariate Cox regression and lasso Cox regression analysis were performed to identify the prognostic proteins. A four-protein signature was developed to divide patients into a high-risk group and low-risk group with significantly different survival outcomes in both train set and internal validation set. Time-dependent receiver-operating characteristic at 1 year demonstrated that the proteomic signature presented more prognostic accuracy [area under curve (AUC = 0.704)] than the American Joint Commission on Cancer tumor-node-metastasis (AJCC-TNM) staging system (AUC = 0.681) in entire set. In conclusion, we developed a proteomic signature which can improve prognostic accuracy of patients with colon cancer and optimize the therapeutic and follow-up strategies.

Multi-omics reveals novel prognostic implication of SRC protein expression in bladder cancer and its correlation with immunotherapy response

Purpose This study aims to identify potential prognostic biomarkers of bladder cancer (BCa) based on large-scale multi-omics data and investigate the role of SRC in improving predictive outcomes for BCa patients and those receiving immune checkpoint therapies (ICTs). Methods Large-scale multi-comic data were enrolled from the Cancer Proteome Atlas, the Cancer Genome Atlas and gene expression omnibus based on machining-learning methods. Immune infiltration, survival and other statistical analyses were implemented using R software in cancers (n = 12,452). The predictive value of SRC was performed in 81 BCa patients receiving ICT from aa validation cohort (n = 81). Results Landscape of novel candidate prognostic protein signatures of BCa patients was identified. Differential BECLIN, EGFR, PKCALPHA, ANNEXIN1, AXL and SRC expression significantly correlated with the outcomes for BCa patients from multiply cohorts (n = 906). Notably, risk score of the integrated prognosis-related proteins (IPRPs) model exhibited high diagnostic accuracy and consistent predictive ability (AUC = 0.714). Besides, we tested the clinical relevance of baseline SRC protein and mRNA expression in two independent confirmatory cohorts (n = 566) and the prognostic value in pan-cancers. Then, we found that elevated SRC expression contributed to immunosuppressive microenvironment mediated by immune checkpoint molecules of BCa and other cancers. Next, we validated SRC expression as a potential biomarker in predicting response to ICT in 81 BCa patient from FUSCC cohort, and found that expression of SRC in the baseline tumour tissues correlated with improved survival benefits, but predicts worse ICT response. Conclusion This study first performed the large-scale multi-omics analysis, distinguished the IPRPs (BECLIN, EGFR, PKCALPHA, SRC, ANNEXIN1 and AXL) and revealed novel prediction model, outperforming the currently traditional prognostic indicators for anticipating BCa progression and better clinical strategies. Additionally, this study provided insight into the importance of biomarker SRC for better prognosis, which may inversely improve predictive outcomes for patients receiving ICT and enable patient selection for future clinical treatment.

Construction of an Immune-Associated Gene-Based Signature in Muscle-Invasive Bladder Cancer.

Background In recent years, immune-associated genes (IAGs) have been documented as having critical roles in the occurrence and progression of muscle-invasive bladder cancer (MIBC). Novel immune-related biomarkers and a robust prognostic signature for MIBC patients are still limited. The study is aimed at developing an IAG-based signature to predict the prognosis of MIBC patients. Methods In the present study, we identified differentially expressed IAGs in MIBC by using transcriptomics data from The Cancer Genome Atlas (TCGA) database and proteomics data from our samples. We further constructed an IAG-based signature and evaluated its prognostic and predictive value by survival analysis and nomogram. Tumor Immune Estimation Resource (TIMER) was applied to explore the correlation between the IAG-based signature and immune cell infiltration in the microenvironment of MIBC. Results A total of 22 differentially expressed IAGs were identified, and 2 IAGs (NR2F6 and AHNAK) were used to establish a prognostic signature. Subsequently, survival analysis showed that high-risk scores were significantly correlated with poor overall survival (OS), progression-free survival (PFS), and disease-free survival (DFS) of MIBC patients. A prognostic nomogram was constructed by integrating clinical factors with the IAG-based signature risk score. In addition, the IAG-based signature risk score was positively associated with the infiltration of macrophages and dendritic cells in MIBC. Conclusions We constructed and verified a novel IAG-based signature, which could predict the prognosis of MIBC and might reflect the status of the immune microenvironment of MIBC. Further studies in more independent clinical cohorts and further experimental exploration of the prognostic IAG-based signature are still needed.

Cancer Omics in Africa: Present and Prospects.

During the last century, cancer biology has been arguably one of the most investigated research fields. To gain deeper insight into cancer mechanisms, scientists have been attempting to integrate multi omics data in cancer research. Cancer genomics, transcriptomics, metabolomics, proteomics, and metagenomics are the main multi omics strategies used currently in the diagnosis, prognosis, treatment, and biomarker discovery in cancer. In this review, we describe the use of different multi omics strategies in cancer research in the African continent and discuss the main challenges facing the implementation of these approaches in African countries such as the lack of training programs in bioinformatics in general and omics strategies in particular and suggest paths to address deficiencies. As a way forward, we advocate for the establishment of an “African Cancer Genomics Consortium” to promote intracontinental collaborative projects and enhance engagement in research activities that address indigenous aspects for cancer precision medicine.

Proteomics of Colorectal Cancer: Tumors, Organoids, and Cell Cultures-A Minireview.

Proteomics, the study of the complete protein composition of a sample, is an important field for cancer research. Changes in the proteome can serve as a biomarker of cancer or lead to the development of a targeted therapy. This minireview will focus on mass spectrometry-based proteomics studies applied specifically to colorectal cancer, particularly the variety of cancer model systems used, including tumor samples, two-dimensional (2D) and three-dimensional (3D) cell cultures such as spheroids and organoids. A thorough discussion of the application of these systems will accompany the review of the literature, as each provides distinct advantages and disadvantages for colorectal cancer research. Finally, we provide conclusions and future perspectives for the application of these model systems to cancer research as a whole.

Selective Labeling and Identification of the Tumor Cell Proteome of Pancreatic Cancer In Vivo.

Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest cancers. Dissecting the tumor cell proteome from that of the non-tumor cells in the PDAC tumor bulk is critical for tumorigenesis studies, biomarker discovery, and development of therapeutics. However, investigating the tumor cell proteome has proven evasive due to the tumor's extremely complex cellular composition. To circumvent this technical barrier, we have combined bioorthogonal noncanonical amino acid tagging (BONCAT) and data-independent acquisition mass spectrometry (DIA-MS) in an orthotopic PDAC model to specifically identify the tumor cell proteome in vivo. Utilizing the tumor cell-specific expression of a mutant tRNA synthetase transgene, this approach provides tumor cells with the exclusive ability to incorporate an azide-bearing methionine analogue into newly synthesized proteins. The azide-tagged tumor cell proteome is subsequently enriched and purified via a bioorthogonal reaction and then identified and quantified using DIA-MS. Applying this workflow to the orthotopic PDAC model, we have identified thousands of proteins expressed by the tumor cells. Furthermore, by comparing the tumor cell and tumor bulk proteomes, we showed that the approach can distinctly differentiate proteins produced by tumor cells from those of non-tumor cells within the tumor microenvironment. Our study, for the first time, reveals the tumor cell proteome of PDAC under physiological conditions, providing broad applications for tumorigenesis, therapeutics, and biomarker studies in various human cancers.