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Supplementary Table from A Blood-Based Metabolite Panel for Distinguishing Ovarian Cancer from Benign Pelvic Masses

Purpose:To assess the contributions of circulating metabolites for improving upon the performance of the risk of ovarian malignancy algorithm (ROMA) for risk prediction of ovarian cancer among women with ovarian cysts.Experimental Design:Metabolomic profiling was performed on an initial set of sera from 101 serous and nonserous ovarian cancer cases and 134 individuals with benign pelvic masses (BPM). Using a deep learning model, a panel consisting of seven cancer-related metabolites [diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminate, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid] was developed for distinguishing early-stage ovarian cancer from BPM. The performance of the metabolite panel was evaluated in an independent set of sera from 118 ovarian cancer cases and 56 subjects with BPM. The contributions of the panel for improving upon the performance of ROMA were further assessed.Results:A 7-marker metabolite panel (7MetP) developed in the training set yielded an AUC of 0.86 [95% confidence interval (CI): 0.76–0.95] for early-stage ovarian cancer in the independent test set. The 7MetP+ROMA model had an AUC of 0.93 (95% CI: 0.84–0.98) for early-stage ovarian cancer in the test set, which was improved compared with ROMA alone [0.91 (95% CI: 0.84–0.98); likelihood ratio test P: 0.03]. In the entire specimen set, the combined 7MetP+ROMA model yielded a higher positive predictive value (0.68 vs. 0.52; one-sided P < 0.001) with improved specificity (0.89 vs. 0.78; one-sided P < 0.001) for early-stage ovarian cancer compared with ROMA alone.Conclusions:A blood-based metabolite panel was developed that demonstrates independent predictive ability and complements ROMA for distinguishing early-stage ovarian cancer from benign disease to better inform clinical decision making.

Supplementary Data from A Blood-Based Metabolite Panel for Distinguishing Ovarian Cancer from Benign Pelvic Masses

Supplementary Figure from A Blood-Based Metabolite Panel for Distinguishing Ovarian Cancer from Benign Pelvic Masses

Purpose: To assess the contributions of circulating metabolites for improving upon the performance of the risk of ovarian malignancy algorithm (ROMA) for risk prediction of ovarian cancer among women with ovarian cysts. Experimental Design: Metabolomic profiling was performed on an initial set of sera from 101 serous and nonserous ovarian cancer cases and 134 individuals with benign pelvic masses (BPM). Using a deep learning model, a panel consisting of seven cancer-related metabolites [diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminate, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid] was developed for distinguishing early-stage ovarian cancer from BPM. The performance of the metabolite panel was evaluated in an independent set of sera from 118 ovarian cancer cases and 56 subjects with BPM. The contributions of the panel for improving upon the performance of ROMA were further assessed. Results: A 7-marker metabolite panel (7MetP) developed in the training set yielded an AUC of 0.86 [95% confidence interval (CI): 0.76–0.95] for early-stage ovarian cancer in the independent test set. The 7MetP+ROMA model had an AUC of 0.93 (95% CI: 0.84–0.98) for early-stage ovarian cancer in the test set, which was improved compared with ROMA alone [0.91 (95% CI: 0.84–0.98); likelihood ratio test P: 0.03]. In the entire specimen set, the combined 7MetP+ROMA model yielded a higher positive predictive value (0.68 vs. 0.52; one-sided P < 0.001) with improved specificity (0.89 vs. 0.78; one-sided P < 0.001) for early-stage ovarian cancer compared with ROMA alone. Conclusions: A blood-based metabolite panel was developed that demonstrates independent predictive ability and complements ROMA for distinguishing early-stage ovarian cancer from benign disease to better inform clinical decision making.

Coffee intake may lower prostate cancer risk and progression, but postdiagnosis outcomes by caffeine metabolism genotype are not well characterized.To evaluate associations between coffee intake, caffeine metabolism genotype, and survival in a large, multicenter study of men with prostate cancer.Data from The PRACTICAL Consortium database for 5727 men with prostate cancer from seven US, Australian, and European studies were included. The cases included had data available for the CYP1A2 -163CA rs762551 single-nucleotide variant associated with caffeine metabolism, coffee intake, and6 mo of follow-up.Multivariable-adjusted Cox proportional hazards models across pooled patient-level data were used to compare the effect of coffee intake (categorized as low [reference], high, or none/very low) in relation to overall survival (OS) and prostate cancer-specific survival (PCSS), with stratified analyses conducted by clinical disease risk and genotype.High coffee intake appeared to be associated with longer PCSS (hazard ratio [HR] 0.85, 95% confidence interval [CI] 0.68-1.08; p = 0.18) and OS (HR 0.90, 95% CI 0.77-1.07; p = 0.24), although results were not statistically significant. In the group with clinically localized disease, high coffee intake was associated with longer PCSS (HR 0.66, 95% CI 0.44-0.98; p = 0.040), with comparable results for the group with advanced disease (HR 0.92, 95% CI 0.69-1.23; p = 0.6). High coffee intake was associated with longer PCSS among men with the CYP1A2 AA (HR 0.67, 95% CI 0.49-0.93; p = 0.017) but not the AC/CC genotype (p = 0.8); an interaction was detected (p = 0.042). No associations with OS were observed in subgroup analyses (p 0.05). Limitations include the nominal statistical significance and residual confounding.Coffee intake was associated with longer PCSS among men with a CYP1A2 -163AA (*1F/*1F) genotype, a finding that will require further replication.It is likely that coffee intake is associated with longer prostate cancer-specific survival in certain groups, but more research is needed to fully understand which men may benefit and why.

Purpose: To investigate the utility of integrating a panel of circulating protein biomarkers in combination with a risk model based on subject characteristics for identifying individuals at high risk of harboring a lethal lung cancer. Methods: Data for a 4-marker protein panel (4MP) (consisting of the precursor form of surfactant protein B, cancer antigen 125, carcinoembryonic antigen, and cytokeratin-19 fragment) previously assayed in pre-diagnostic sera from 552 lung cancer cases and 2,193 non-cases from the Prostate Lung Colorectal and Ovarian (PLCO) cohort as well as a scores from a logistic regression model that combined the 4MP with the PLCOm2012 risk model were used for this study. Of the 552 lung cancer cases, 387 (70%) died from lung cancer. Cumulative incidence of lung cancer death as well as sub-distributional and cause-specific hazard ratios were calculated based on 4MP+PLCOm2012 risk scores at a pre-defined 1.0%/6-year risk threshold, which corresponds to current US Preventive Services Task Force screening criteria. For hazard modeling, stage at the time of clinical diagnosis and treatment modality were considered as co-variables. Results: When considering cases diagnosed within 1 year of blood draw and all non-cases, the performance estimate of the 4MP+PLCOm2012 model for risk prediction of lung cancer death was 0.88 (95% CI: 0.86-0.90). Among cases only, at the 1.0%/6-year risk threshold the cumulative incidence of lung cancer death was statistically significantly higher in ‘test positive’ cases compared to ‘test negative’ cases (Chi-square test: 5.42, P: 0.02). The adjusted sub distributional hazard ratio for ‘test positive’ cases was 1.67 (95% CI: 1.02-2.74). The adjusted lung-cancer death-specific hazard ratio for ‘test positive’ cases was 2.02 (95% CI: 1.28-3.17). Conclusions and Relevance: The blood-based biomarker panel in combination with PLCOm2012 identifies individuals at high risk of a lethal lung cancer. Citation Format: Ehsan Irajizad, Johannes F. Fahrmann, Jody V. Vykoukal, Jennifer B. Dennison, James P. Long, Kim-Anh Do, Edwin J. Ostrin, Sam Hanash. Impact of blood-based biomarkers for predicting lung cancer mortality in the PLCO cohort. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P013.

Anti-CD19 chimeric antigen receptor (CAR) T cell therapy for relapsed or refractory (r/r) large B cell lymphoma (LBCL) results in durable response in only a subset of patients. MYC overexpression in LBCL tumors is associated with poor response to treatment. We tested whether an MYC-driven polyamine signature, as a liquid biopsy, is predictive of response to anti-CD19 CAR-T therapy in patients with r/r LBCL. Elevated plasma acetylated polyamines were associated with non-durable response. Concordantly, increased expression of spermidine synthase, a key enzyme that regulates levels of acetylated spermidine, was prognostic for survival in r/r LBCL. A broad metabolite screen identified additional markers that resulted in a 6-marker panel (6MetP) consisting of acetylspermidine, diacetylspermidine, and lysophospholipids, which was validated in an independent set from another institution as predictive of non-durable response to CAR-T therapy. A polyamine centric metabolomics liquid biopsy panel has predictive value for response to CAR-T therapy in r/r LBCL.

Although harnessing the immune system for cancer therapy has shown success, response to immunotherapy has been limited. The immunopeptidome of cancer cells presents an opportunity to discover novel antigens for immunotherapy applications. These neoantigens bind to MHC class I and class II molecules. Remarkably, the immunopeptidome encompasses protein post-translation modifications (PTMs) that may not be evident from genome or transcriptome profiling. A case in point is citrullination, which has been demonstrated to induce a strong immune response. In this review, we cover how the immunopeptidome, with a special focus on PTMs, can be utilized to identify cancer-specific antigens for immunotherapeutic applications.

BackgroundCitrulline post-translational modification of proteins is mediated by protein arginine deiminase (PADI) family members and has been associated with autoimmune diseases. The role of PADI-citrullinome in immune response in cancer has not been evaluated. We hypothesized that PADI-mediated citrullinome is a source of neoantigens in cancer that induces immune response.MethodsProtein expression of PADI family members was evaluated in 196 cancer cell lines by means of indepth proteomic profiling. Gene expression was assessed using messenger RNA data sets from The Cancer Genome Atlas. Immunohistochemical analysis of PADI2 and peptidyl-citrulline was performed using breast cancer tissue sections. Citrullinated 12–34-mer peptides in the putative Major Histocompatibility Complex-II (MHC-II) binding range were profiled in breast cancer cell lines to investigate the relationship between protein citrullination and antigen presentation. We further evaluated immunoglobulin-bound citrullinome by mass spectrometry using 156 patients with breast cancer and 113 cancer-free controls.ResultsProteomic and gene expression analyses revealed PADI2 to be highly expressed in several cancer types including breast cancer. Immunohistochemical analysis of 422 breast tumor tissues revealed increased expression of PADI2 in ER− tumors (pConclusionsAn immune response associated with citrullinome is a rich source of neoantigens in breast cancer with a potential for diagnostic and therapeutic applications.

The entire lung epithelium arises from SOX9 (SRY box 9)-expressing progenitors that form the respiratory tree and differentiate into airway and alveolar cells. Despite progress in understanding their initial specification within the embryonic foregut, how these progenitors are subsequently maintained is less clear. Using inducible, progenitor-specific genetic mosaic mouse models, we show that beta-Catenin (CTNNB1) maintains the lung progenitors by promoting a hierarchical lung progenitor gene signature, suppressing gastrointestinal (GI) genes, and regulating NKX2.1 (NK2 homeobox 1) and SOX2 (SRY box 2) in a developmental stage dependent manner. At the early, but not later, stage post lung specification, CTNNB1 cell-autonomously maintains the normal NKX2.1 expression level and suppresses ectopic SOX2 expression. Genetic epistasis analyses reveal that CTNNB1 is required for Fgf (fibroblast growth factor)/Kras (Kirsten rat sarcoma viral oncogene homolog)-mediated promotion of the progenitors. In silico screening of Eurexpress and TRAP (translating ribosome affinity purification)-RNAseq identify a progenitor gene signature, a subset of which depends on CTNNB1. Wnt signaling also maintains NKX2.1 expression and suppresses GI genes in cultured human lung progenitors that are derived from embryonic stem cells.

In-depth quantitative profiling of the proteome and sub-proteomes of tumor cells has relevance to tumor classification, the development of novel therapeutics, and of prognostic and predictive markers and to disease monitoring. In particular the tumor cell surface represents a highly relevant compartment for the development of targeted therapeutics and immunotherapy. We have developed a proteomic platform to profile tumor cells that encompasses enrichment of surface membrane proteins, intact protein fractionation and label-free mass spectrometry based absolute quantification. Here we describe the methodology for capture, identification and quantification of cell surface proteins using biotinylation for labeling of the cell surface, avidin for capture of biotinylated proteins and ion mobility mass spectrometry for protein identification and quantification.

T-cell-based immunotherapies are promising treatments for cancer patients. Although durable responses can be achieved in some patients, many patients fail to respond to these therapies, underscoring the need for improvement with combination therapies. From a screen of 850 bioactive compounds, we identify HSP90 inhibitors as candidates for combination with immunotherapy. We show that inhibition of HSP90 with ganetespib enhances T-cell-mediated killing of patient-derived human melanoma cells by their autologous T cells in vitro and potentiates responses to anti-CTLA4 and anti-PD1 therapy in vivo. Mechanistic studies reveal that HSP90 inhibition results in upregulation of interferon response genes, which are essential for the enhanced killing of ganetespib treated melanoma cells by T cells. Taken together, these findings provide evidence that HSP90 inhibition can potentiate T-cell-mediated anti-tumor immune responses, and rationale to explore the combination of immunotherapy and HSP90 inhibitors., Many patients fail to respond to T cell based immunotherapies. Here, the authors, through a high-throughput screening, identify HSP90 inhibitors as a class of preferred drugs for treatment combination with immunotherapy.

Objectives To evaluate the role of caveolin-1 as a predictor of disease reclassification in men with early prostate cancer undergoing active surveillance. Patients and Methods We analyzed archived plasma samples prospectively collected from men with early prostate cancer in a single-institution active surveillance study. Of 825 patients enrolled, 542 had 1 or more years of follow-up. Baseline and longitudinal plasma caveolin-1 levels were measured using an enzyme-linked immunosorbent assay. Tumor volume or Gleason grade increases were criteria for disease reclassification. Logistic regression analyses assessed associations between clinicopathologic characteristics and reclassification risk. Results In 542 patients, 480 (88.6%) had stage cT1c disease, 542 (100.0%) had a median prostate-specific antigen level of 4.1 ng/mL, and 531 (98.0%) had a median Cancer of the Prostate Risk Assessment score of 1. In all, 473 (87.3%) had a Gleason score of 3+3. After a median of 3.1 years’ follow-up, disease was reclassified in 163 (30.1%). Baseline caveolin-1 levels were 2.2±8.5 ng/mL (mean) and 0.2 ng/mL (range, 0–85.5 ng/mL) (median). In univariate analysis, baseline caveolin-1 was a significant predictor for risk of disease reclassification (OR, 1.82, 95% CI 1.24–2.65, p=0.002); in multivariate analysis, with adjustments for age, tumor length, group risk stratification, and number of positive cores, reclassification risk associated with caveolin-1 remained significant (OR 1.91, 95% CI 1.28–2.84, p=0.001). Conclusion Baseline plasma caveolin-1 level was an independent predictor of disease classification. New methods for refining active surveillance and intervention may result. This article is protected by copyright. All rights reserved.

We have developed a proteome database (DB), BiomarkerDigger (http://biomarkerdigger.org) that automates data analysis, searching, and metadata-gathering function. The metadata-gathering function searches proteome DBs for protein-protein interaction, Gene Ontology, protein domain, Online Mendelian Inheritance in Man, and tissue expression profile information and integrates it into protein data sets that are accessed through a search function in BiomarkerDigger. This DB also facilitates cross-proteome comparisons by classifying proteins based on their annotation. BiomarkerDigger highlights relationships between a given protein in a proteomic data set and any known biomarkers or biomarker candidates. The newly developed BiomarkerDigger system is useful for multi-level synthesis, comparison, and analyses of data sets obtained from currently available web sources. We demonstrate the application of this resource to the identification of a serological biomarker for hepatocellular carcinoma by comparison of plasma and tissue proteomic data sets from healthy volunteers and cancer patients.

Background The use of autoantibodies for the early detection of breast cancer has generated much interest as antibodies can be readily assayed in serum when antigen levels are low. Ideally, diagnostic autoantibodies would be identified in individuals who harbored pre-invasive disease/high risk lesions leading to malignancy. Prospectively collected human serum samples from these individuals are rare and not often available for biomarker discovery. We questioned whether transgenic animals could be used to identify cancer-associated autoantibodies present at the earliest stages of the malignant transformation of breast cancer. Methods We collected sera from transgenic mice (TgMMTV-neu) from the time of birth to death by spontaneous mammary tumors. Using sera from a time point prior to the development of tumor, i.e. “pre-diagnostic”, we probed cDNA libraries derived from syngeneic tumors to identify proteins recognized by IgG antibodies. Once antigens were identified, selected proteins were evaluated via protein arrays, for autoantibody responses using plasma from women obtained prior to the development of breast cancer and matched controls. The ability of the antigens to discriminate cases from controls was assessed using receiver-operating-characteristic curve analyses and estimates of the area under the curve. Results We identified 6 autoantibodies that were present in mice prior to the development of mammary cancer: Pdhx, Otud6b, Stk39, Zpf238, Lgals8, and Vps35. In rodent validation cohorts, detecting both IgM and IgG antibody responses against a subset of the identified proteins could discriminate pre-diagnostic sera from non-transgenic control sera with an AUC of 0.924. IgG and IgM autoantibodies, specific for a subset of the identified antigens, could discriminate the samples of women who eventually developed breast cancer from case-matched controls who did not develop disease. The discriminatory potential of the pre-diagnostic autoantibodies was enhanced if plasma samples were collected greater than 5 months prior to a breast cancer diagnosis (AUC 0.68; CI 0.565-0.787, p = 0.0025). Conclusion Genetically engineered mouse models of cancer may provide a facile discovery tool for identifying autoantibodies useful for human cancer diagnostics.

Tumor-derived, circulating proteins are potentially useful as biomarkers for detection of cancer, for monitoring of disease progression, regression and recurrence, and for assessment of therapeutic response. Here we interrogated how a protein's stability, cellular localization, and abundance affect its observability in blood by mass-spectrometry-based proteomics techniques. We performed proteomic profiling on tumors and plasma from two different xenograft mouse models. A statistical analysis of this data revealed protein properties indicative of the detection level in plasma. Though 20% of the proteins identified in plasma were tumor-derived, only 5% of the proteins observed in the tumor tissue were found in plasma. Both intracellular and extracellular tumor proteins were observed in plasma; however, after normalizing for tumor abundance, extracellular proteins were seven times more likely to be detected. Although proteins that were more abundant in the tumor were also more likely to be observed in plasma, the relationship was nonlinear: Doubling the spectral count increased detection rate by only 50%. Many secreted proteins, even those with relatively low spectral count, were observed in plasma, but few low abundance intracellular proteins were observed. Proteins predicted to be stable by dipeptide composition were significantly more likely to be identified in plasma than less stable proteins. The number of tryptic peptides in a protein was not significantly related to the chance of a protein being observed in plasma. Quantitative comparison of large versus small tumors revealed that the abundance of proteins in plasma as measured by spectral count was associated with the tumor size, but the relationship was not one-to-one; a 3-fold decrease in tumor size resulted in a 16-fold decrease in protein abundance in plasma. This study provides quantitative support for a tumor-derived marker prioritization strategy that favors secreted and stable proteins over all but the most abundant intracellular proteins.

The current state of proteomics technologies has sufficiently advanced to allow in-depth quantitative analysis of the plasma proteome and development of a related knowledge base. Here we review approaches that have been applied to increase depth of analysis by mass spectrometry given the substantial complexity of plasma and the vast dynamic range of protein abundance. Fractionation strategies resulting in reduced complexity of individual fractions followed by mass spectrometry analysis of digests from individual fractions has allowed well in excess of 1000 proteins to be identified and quantified with high confidence that span more than seven logs of protein abundance. Such depth of analysis has contributed to elucidation of plasma proteome variation in health and of protein changes associated with disease states.

The proteomics field has experienced rapid growth with technologies achieving ever increasing accuracy, sensitivity, and throughput, and with availability of computational tools to address particular applications. Given that the proteome represents the most functional component encoded for in the genome, a systems approach to disease investigations and biomarker identification benefits substantially from integration of proteome level studies. Here we present proteomic approaches that have allowed systematic searches for potential cancer markers by integrating cancer cell profiling with additional sources of data, as illustrated with recent studies of ovarian cancer.

Publisher Summary Biological fluids and disease related effusions that are proximal to the disease tissue might represent useful sources for discovery of circulating biomarkers. Proximal fluids potentially contain higher concentrations of proteins released from tumor tissue into extracellular fluids through secretion or cell and tissue breakdown. The use of isolated cell populations and cell lines to identify candidate markers requires that the cell material utilized adequately reflects the tumor type for which biomarkers are being sought. The study of posttranslational modifications (PTMs) as a source of biomarkers in cancer or other disease is still at a relatively early stage. Research going back several decades has yielded evidence that cancer cells exhibit altered glycans relative to normal cells. Some effort within the field of proteomics has focused on glycoproteins because of their biological significance and because of their importance as sources of biomarkers. Microarrays, using various formats that contain proteins, lysates, or affinity capture agents, are increasingly relied upon for investigations of signaling processes in cells and tissues and for identification of biomarkers. Microarrays containing the repertoire of natural proteins expressed in tumor cells have the potential to substantially accelerate the pace of discovery of tumor antigens and could provide a molecular signature for immune responses in different types of cancer. Further progress toward biomarker development requires validation studies to be conducted.

Publisher Summary This chapter addresses in depth the quantitative proteomics approaches currently in use that are applicable to cell populations and to biological fluids. The field of proteomics is experiencing rapid growth, with technologies achieving ever increasing accuracy, sensitivity and throughput, and computational tools to address particular applications. A system approach to biology and disease benefits substantially from integration of proteome- and genome-level studies. Much progress has been made in developing strategies for mining complex proteomes using technologies that are suitable for analyzing particular aspects of proteins. A near exhaustive analysis of the protein content of cells is currently possible, and analysis of biological fluids spanning seven logs of protein abundance is feasible with the use of fractionation technologies. A multitude of approaches for cell protein analysis have yielded quantitative information with respect to cellular protein composition and responses to environmental changes and to gene manipulations. Proteomics is particularly promising for the analysis of biological fluids and biomarker identification. Biomarkers are indicators of specific physiologic and pathologic states. Protein microarrays provide a high throughput and low-volume sample consumption platform for various assays, including determining the interactions between proteins and other molecules of interest, such as other proteins, antibodies, drugs, and other ligands. Discovery technologies that allow comprehensive unbiased proteomics investigations have also been considered, in contrast to assays that target a relatively small set of proteins. Microarrays with expanded content reaching the entire genome complement in recombinant proteins and a large repertoire of affinity-capture agents are likely to emerge, thus further empowering a systemwide approach to mining proteomes in health and in disease.

We integrated five sets of proteomics data profiling the constituents of cerebrospinal fluid (CSF) derived from Huntington disease (HD)-affected and -unaffected individuals with genomics data profiling various human and mouse tissues, including the human HD brain. Based on an integrated analysis, we found that brain-specific proteins are 1.8 times more likely to be observed in CSF than in plasma, that brain-specific proteins tend to decrease in HD CSF compared with unaffected CSF, and that 81% of brain-specific proteins have quantitative changes concordant with transcriptional changes identified in different regions of HD brain. The proteins found to increase in HD CSF tend to be liver-associated. These protein changes are consistent with neurodegeneration, microgliosis, and astrocytosis known to occur in HD. We also discuss concordance between laboratories and find that ratios of individual proteins can vary greatly, but the overall trends with respect to brain or liver specificity were consistent. Concordance is highest between the two laboratories observing the largest numbers of proteins.

The open-source Computational Proteomics Analysis System (CPAS) contains an entire data analysis and management pipeline for Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) proteomics, including experiment annotation, protein database searching and sequence management, and mining LC-MS/MS peptide and protein identifications. CPAS architecture and features, such as a general experiment annotation component, installation software, and data security management, make it useful for collaborative projects across geographical locations and for proteomics laboratories without substantial computational support.

The emerging technology of protein microarrays has a far-reaching potential to meet the needs for measuring not just the abundance of protein constituents but also their functional states and their interactions with other biomolecules, with the requisite high throughput and high sensitivity. While protein microarrays still present some challenging problems, recent progress makes it likely that it will be possible soon to interrogate proteins at a proteome scale, using protein microarrays, analogous to DNA microarrays, thus making a substantial impact on biomedical research. Keywords: protein microarray; protein profiling; antibody microarray; proteome; disease proteomics; biomarker; protein–protein interaction

The sequencing of the human genome and that of numerous pathogens has opened the door for proteomics by providing a sequence-based framework for mining proteomes. As a result, there is intense interest in applying proteomics to foster a better understanding of disease processes, develop new biomarkers for diagnosis and early detection of disease, and accelerate drug development. This interest creates numerous opportunities as well as challenges to meet the needs for high sensitivity and high throughput required for disease-related investigations.

Although gain of oncogene functions and loss of tumor suppressor functions are driving forces in tumor development, the tumor microenvironment, comprising the extracellular matrix, surrounding stroma, signaling molecules and infiltrating immune and other cell populations, is now also recognized as crucial to tumor development and metastasis. Many interactions at the tumor cell-environment interface occur at the protein level. Proteomic approaches are contributing to the definition of the protein constituents of the microenvironment and their sources, modifications, interactions and turnover, as well as providing information on how these features relate to tumor development and progression. Recently, proteomic studies have revealed how cancer cells modulate the microenvironment through their secreted proteins and how they can alter their protein constituents to adapt to the microenvironment. Moreover, the release of proteins from the microenvironment into the circulatory system has relevance for the development of blood-based cancer diagnostics. Here, we review how proteomic approaches are being applied to studies of the tumor microenvironment to decipher tumor-stroma interactions and to elucidate the role of host cells in the tumor microenvironment.