Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer

Recent advances in mass spectrometry based clinical proteomics: applications to cancer research

Cancer biomarkers have transformed current practices in the oncology clinic. Continued discovery and validation are crucial for improving early diagnosis, risk stratification, and monitoring patient response to treatment. Profiling of the tumour genome and transcriptome are now established tools for the discovery of novel biomarkers, but alterations in proteome expression are more likely to reflect changes in tumour pathophysiology. In the past, clinical diagnostics have strongly relied on antibody-based detection strategies, but these methods carry certain limitations. Mass spectrometry (MS) is a powerful method that enables increasingly comprehensive insights into changes of the proteome to advance personalized medicine. In this review, recent improvements in MS-based clinical proteomics are highlighted with a focus on oncology. We will provide a detailed overview of clinically relevant samples types, as well as, consideration for sample preparation methods, protein quantitation strategies, MS configurations, and data analysis pipelines currently available to researchers. Critical consideration of each step is necessary to address the pressing clinical questions that advance cancer patient diagnosis and prognosis. While the majority of studies focus on the discovery of clinically-relevant biomarkers, there is a growing demand for rigorous biomarker validation. These studies focus on high-throughput targeted MS assays and multi-centre studies with standardized protocols. Additionally, improvements in MS sensitivity are opening the door to new classes of tumour-specific proteoforms including post-translational modifications and variants originating from genomic aberrations. Overlaying proteomic data to complement genomic and transcriptomic datasets forges the growing field of proteogenomics, which shows great potential to improve our understanding of cancer biology. Overall, these advancements not only solidify MS-based clinical proteomics' integral position in cancer research, but also accelerate the shift towards becoming a regular component of routine analysis and clinical practice.

Proteomic Tissue-Based Classifier for Early Prediction of Prostate Cancer Progression

Although ~40% of screen-detected prostate cancers (PCa) are indolent, advanced-stage PCa is a lethal disease with 5-year survival rates around 29%. Identification of biomarkers for early detection of aggressive disease is a key challenge. Starting with 52 candidate biomarkers, selected from existing PCa genomics datasets and known PCa driver genes, we used targeted mass spectrometry to quantify proteins that significantly differed in primary tumors from PCa patients treated with radical prostatectomy (RP) across three study outcomes: (i) metastasis ≥1-year post-RP, (ii) biochemical recurrence ≥1-year post-RP, and (iii) no progression after ≥10 years post-RP. Sixteen proteins that differed significantly in an initial set of 105 samples were evaluated in the entire cohort (n = 338). A five-protein classifier which combined FOLH1, KLK3, TGFB1, SPARC, and CAMKK2 with existing clinical and pathological standard of care variables demonstrated significant improvement in predicting distant metastasis, achieving an area under the receiver-operating characteristic curve of 0.92 (0.86, 0.99, p = 0.001) and a negative predictive value of 92% in the training/testing analysis. This classifier has the potential to stratify patients based on risk of aggressive, metastatic PCa that will require early intervention compared to low risk patients who could be managed through active surveillance.

The proteome of IVF-induced aberrant embryo-maternal crosstalk by implantation stage in ewes

Background

Implantation failure limits the success of in vitro fertilization and embryo transfer (IVF-ET). Well-organized embryo-maternal crosstalk is essential for successful implantation. Previous studies mainly focused on the aberrant development of in vitro fertilized (IVF) embryos. In contrast, the mechanism of IVF-induced aberrant embryo-maternal crosstalk is not well defined.

Results

In the present study, using ewes as the model, we profiled the proteome that features aberrant IVF embryo-maternal crosstalk following IVF-ET. By comparing in vivo (IVO) and IVF conceptuses, as well as matched endometrial caruncular (C) and intercaruncular (IC) areas, we filtered out 207, 295, and 403 differentially expressed proteins (DEPs) in each comparison. Proteome functional analysis showed that the IVF conceptuses were characterized by the increased abundance of energy metabolism and proliferation-related proteins, and the decreased abundance of methyl metabolism-related proteins. In addition, IVF endometrial C areas showed the decreased abundance of endometrial remodeling and redox homeostasis-related proteins; while IC areas displayed the aberrant abundance of protein homeostasis and extracellular matrix (ECM) interaction-related proteins. Based on these observations, we propose a model depicting the disrupted embryo-maternal crosstalk following IVF-ET: Aberrant energy metabolism and redox homeostasis of IVF embryos, might lead to an aberrant endometrial response to conceptus-derived pregnancy signals, thus impairing maternal receptivity. In turn, the suboptimal uterine environment might stimulate a compensation effect of the IVF conceptuses, which was revealed as enhanced energy metabolism and over-proliferation.

Conclusion

Systematic proteomic profiling provides insights to understand the mechanisms that underlie the aberrant IVF embryo-maternal crosstalk. This might be helpful to develop practical strategies to prevent implantation failure following IVF-ET.

Assessing the performance of genome-wide association studies for predicting disease risk

To date more than 3700 genome-wide association studies (GWAS) have been published that look at the genetic contributions of single nucleotide polymorphisms (SNPs) to human conditions or human phenotypes. Through these studies many highly significant SNPs have been identified for hundreds of diseases or medical conditions. However, the extent to which GWAS-identified SNPs or combinations of SNP biomarkers can predict disease risk is not well known. One of the most commonly used approaches to assess the performance of predictive biomarkers is to determine the area under the receiver-operator characteristic curve (AUROC). We have developed an R package called G-WIZ to generate ROC curves and calculate the AUROC using summary-level GWAS data. We first tested the performance of G-WIZ by using AUROC values derived from patient-level SNP data, as well as literature-reported AUROC values. We found that G-WIZ predicts the AUROC with <3% error. Next, we used the summary level GWAS data from GWAS Central to determine the ROC curves and AUROC values for 569 different GWA studies spanning 219 different conditions. Using these data we found a small number of GWA studies with SNP-derived risk predictors that have very high AUROCs (>0.75). On the other hand, the average GWA study produces a multi-SNP risk predictor with an AUROC of 0.55. Detailed AUROC comparisons indicate that most SNP-derived risk predictions are not as good as clinically based disease risk predictors. All our calculations (ROC curves, AUROCs, explained heritability) are in a publicly accessible database called GWAS-ROCS (http://gwasrocs.ca). The G-WIZ code is freely available for download at https://github.com/jonaspatronjp/GWIZ-Rscript/.

Proteomic profiling of extracellular vesicles allows for human breast cancer subtyping

Extracellular vesicles (EVs) are a potential source of disease-associated biomarkers for diagnosis. In breast cancer, comprehensive analyses of EVs could yield robust and reliable subtype-specific biomarkers that are still critically needed to improve diagnostic routines and clinical outcome. Here, we show that proteome profiles of EVs secreted by different breast cancer cell lines are highly indicative of their respective molecular subtypes, even more so than the proteome changes within the cancer cells. Moreover, we detected molecular evidence for subtype-specific biological processes and molecular pathways, hyperphosphorylated receptors and kinases in connection with the disease, and compiled a set of protein signatures that closely reflect the associated clinical pathophysiology. These unique features revealed in our work, replicated in clinical material, collectively demonstrate the potential of secreted EVs to differentiate between breast cancer subtypes and show the prospect of their use as non-invasive liquid biopsies for diagnosis and management of breast cancer patients.

Multi-omics Biomarker Pipeline Reveals Elevated Levels of Protein-glutamine Gamma-glutamyltransferase 4 in Seminal Plasma of Prostate Cancer Patients

Seminal plasma, because of its proximity to prostate, is a promising fluid for biomarker discovery and noninvasive diagnostics. In this study, we investigated if seminal plasma proteins could increase diagnostic specificity of detecting primary prostate cancer and discriminate between high- and low-grade cancers. To select 147 most promising biomarker candidates, we combined proteins identified through five independent experimental or data mining approaches: tissue transcriptomics, seminal plasma proteomics, cell line secretomics, tissue specificity, and androgen regulation. A rigorous biomarker development pipeline based on selected reaction monitoring assays was designed to evaluate the most promising candidates. As a result, we qualified 76, and verified 19 proteins in seminal plasma of 67 negative biopsy and 152 prostate cancer patients. Verification revealed a prostate-specific, secreted and androgen-regulated protein-glutamine gamma-glutamyltransferase 4 (TGM4), which predicted prostate cancer on biopsy and outperformed age and serum Prostate-Specific Antigen (PSA). A machine-learning approach for data analysis provided improved multi-marker combinations for diagnosis and prognosis. In the independent verification set measured by an in-house immunoassay, TGM4 protein was upregulated 3.7-fold (p = 0.006) and revealed AUC = 0.66 for detecting prostate cancer on biopsy for patients with serum PSA ≥4 ng/ml and age ≥50. Very low levels of TGM4 (120 pg/ml) were detected in blood serum. Collectively, our study demonstrated rigorous evaluation of one of the remaining and not well-explored prostate-specific proteins within the medium-abundance proteome of seminal plasma. Performance of TGM4 warrants its further investigation within the distinct genomic subtypes and evaluation for the inclusion into emerging multi-biomarker panels.

Quantitative proteomic analysis of a genetically induced prostate inflammation mouse model via custom 4-plex DiLeu isobaric labeling

Inflammation is involved in many prostate pathologies including infection, benign prostatic hyperplasia, and prostate cancer. Preclinical models are critical to our understanding of disease mechanisms, yet few models are genetically tractable. Here, we present a comparative quantitative proteomic analysis of urine from mice with and without prostate-specific inflammation induced by conditional prostate epithelial IL-1β expression. Relative quantification and sample multiplexing was achieved using custom 4-plex N,N-dimethyl leucine (DiLeu) isobaric tags and nanoflow ultrahigh-performance liquid chromatography coupled to high-resolution tandem mass spectrometry. Each set of 4-plex DiLeu reagents allows four urine samples to be analyzed simultaneously, providing high-throughput and accurate quantification of urinary proteins. Proteins involved in the acute phase response, including haptoglobin, inter-α-trypsin inhibitor, and α1-antitrypsin 1-1, were differentially represented in the urine of mice with prostate inflammation. Mass spectrometry-based quantitative urinary proteomics represents a promising bioanalytical strategy for biomarker discovery and the elucidation of molecular mechanisms in urological research.

N-Glycoproteomics of Patient-Derived Xenografts: A Strategy to Discover Tumor-Associated Proteins in High-Grade Serous Ovarian Cancer

High-grade serous ovarian carcinoma (HGSC) is the most common and lethal subtype of gynecologic malignancy in women. The current standard of treatment combines cytoreductive surgery and chemotherapy. Despite the efficacy of initial treatment, most patients develop cancer recurrence, and 70% of patients die within 5 years of initial diagnosis. CA125 is the current FDA-approved biomarker used in the clinic to monitor response to treatment and recurrence, but its impact on patient survival is limited. New strategies for the discovery of HGSC biomarkers are urgently needed. Here, we describe a proteomics strategy to detect tumor-associated proteins in serum of HGSC patient-derived xenograft models. We demonstrate proof-of-concept applicability using two independent, longitudinal serum cohorts from HGSC patients.

Clinical potential of mass spectrometry-based proteogenomics

Cancer genomics research aims to advance personalized oncology by finding and targeting specific genetic alterations associated with cancers. In genome-driven oncology, treatments are selected for individual patients on the basis of the findings of tumour genome sequencing. This personalized approach has prolonged the survival of subsets of patients with cancer. However, many patients do not respond to the predicted therapies based on the genomic profiles of their tumours. Furthermore, studies pairing genomic and proteomic analyses of samples from the same tumours have shown that the proteome contains novel information that cannot be discerned through genomic analysis alone. This observation has led to the concept of proteogenomics, in which both types of data are leveraged for a more complete view of tumour biology that might enable patients to be more successfully matched to effective treatments than they would using genomics alone. In this Perspective, we discuss the added value of proteogenomics over the current genome-driven approach to the clinical characterization of cancers and summarize current efforts to incorporate targeted proteomic measurements based on selected/multiple reaction monitoring (SRM/MRM) mass spectrometry into the clinical laboratory to facilitate clinical proteogenomics.

Rapidly Assessing the Quality of Targeted Proteomics Experiments through Monitoring Stable-Isotope Labeled Standards

Targeted proteomics experiments based on selected reaction monitoring (SRM) have gained wide adoption in the use of clinical biomarkers, cellular modeling, and numerous other biological experiments due to their highly accurate and reproducible quantification. The quantitative accuracy in targeted proteomics experiments is reliant on the stable-isotope, heavy-labeled peptide standards that are spiked into a sample and used as a reference when calculating the abundance of endogenous peptides. Therefore, the quality of measurement for these standards is a critical factor in determining whether data acquisition was successful. With improved mass spectrometry (MS) instrumentation that enables the monitoring of hundreds of peptides in hundreds to thousands of samples, quality assessment is increasingly important and cannot be performed manually. We present Q4SRM, a software tool that rapidly checks the signal from all heavy-labeled peptides and flags those that fail quality-control metrics. Using four metrics, the tool detects problems with both individual SRM transitions and the collective group of transitions that monitor a single peptide. The program's speed and simplicity enable its use at the point of data acquisition and can be ideally run immediately upon the completion of a liquid chromatography-SRM-MS analysis.

Combining discovery and targeted proteomics reveals a prognostic signature in oral cancer

Different regions of oral squamous cell carcinoma (OSCC) have particular histopathological and molecular characteristics limiting the standard tumor−node−metastasis prognosis classification. Therefore, defining biological signatures that allow assessing the prognostic outcomes for OSCC patients would be of great clinical significance. Using histopathology-guided discovery proteomics, we analyze neoplastic islands and stroma from the invasive tumor front (ITF) and inner tumor to identify differentially expressed proteins. Potential signature proteins are prioritized and further investigated by immunohistochemistry (IHC) and targeted proteomics. IHC indicates low expression of cystatin-B in neoplastic islands from the ITF as an independent marker for local recurrence. Targeted proteomics analysis of the prioritized proteins in saliva, combined with machine-learning methods, highlights a peptide-based signature as the most powerful predictor to distinguish patients with and without lymph node metastasis. In summary, we identify a robust signature, which may enhance prognostic decisions in OSCC and better guide treatment to reduce tumor recurrence or lymph node metastasis.

Oral cancer has region-specific histopathological and molecular characteristics, complicating its classification by the standard tumor-node-metastasis system. Here, the authors combine discovery and targeted proteomics with IHC to identify region-specific and saliva biomarkers for oral cancer prognosis.

Distinct urinary glycoprotein signatures in prostate cancer patients

Novel biomarkers are needed to complement prostate specific antigen (PSA) in prostate cancer (PCa) diagnostic screening programs. Glycoproteins represent a hitherto largely untapped resource with a great potential as specific and sensitive tumor biomarkers due to their abundance in bodily fluids and their dynamic and cancer-associated glycosylation. However, quantitative glycoproteomics strategies to detect potential glycoprotein cancer markers from complex biospecimen are only just emerging. Here, we describe a glycoproteomics strategy for deep quantitative mapping of N- and O-glycoproteins in urine with a view to investigate the diagnostic value of the glycoproteome to discriminate PCa from benign prostatic hyperplasia (BPH), two conditions that remain difficult to clinically stratify. Total protein extracts were obtained, concentrated and digested from urine of six PCa patients (Gleason score 7) and six BPH patients. The resulting peptide mixtures were TMT-labeled and mixed prior to a multi-faceted sample processing including hydrophilic interaction liquid chromatography (HILIC) and titanium dioxide SPE based enrichment, endo-/exoglycosidase treatment and HILIC-HPLC pre-fractionation. The isolated N- and O-glycopeptides were detected and quantified using high resolution mass spectrometry. We accurately quantified 729 N-glycoproteins spanning 1,310 unique N-glycosylation sites and observed 954 and 965 unique intact N- and O-glycopeptides, respectively, across the two disease conditions. Importantly, a panel of 56 intact N-glycopeptides perfectly discriminated PCa and BPH (ROC: AUC = 1). This study has generated a panel of intact glycopeptides that has a potential for PCa detection.

Integrating biomarkers across omic platforms: an approach to improve stratification of patients with indolent and aggressive prostate cancer

Classifying indolent prostate cancer represents a significant clinical challenge. We investigated whether integrating data from different omic platforms could identify a biomarker panel with improved performance compared to individual platforms alone. DNA methylation, transcripts, protein and glycosylation biomarkers were assessed in a single cohort of patients treated by radical prostatectomy. Novel multiblock statistical data integration approaches were used to deal with missing data and modelled via stepwise multinomial logistic regression, or LASSO. After applying leave‐one‐out cross‐validation to each model, the probabilistic predictions of disease type for each individual panel were aggregated to improve prediction accuracy using all available information for a given patient. Through assessment of three performance parameters of area under the curve (AUC) values, calibration and decision curve analysis, the study identified an integrated biomarker panel which predicts disease type with a high level of accuracy, with Multi AUC value of 0.91 (0.89, 0.94) and Ordinal C‐Index (ORC) value of 0.94 (0.91, 0.96), which was significantly improved compared to the values for the clinical panel alone of 0.67 (0.62, 0.72) Multi AUC and 0.72 (0.67, 0.78) ORC. Biomarker integration across different omic platforms significantly improves prediction accuracy. We provide a novel multiplatform approach for the analysis, determination and performance assessment of novel panels which can be applied to other diseases. With further refinement and validation, this panel could form a tool to help inform appropriate treatment strategies impacting on patient outcome in early stage prostate cancer.

Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment

Precision medicine is medicine optimized to the genotypic and phenotypic characteristics of an individual and, when present, his or her disease. It has a host of targets, including genes and their transcripts, proteins, and metabolites. Studying precision medicine involves a systems biology approach that integrates mathematical modeling and biology genomics, transcriptomics, proteomics, and metabolomics. Moreover, precision medicine must consider not only the relatively static genetic codes of individuals, but also the dynamic and heterogeneous genetic codes of cancers. Thus, precision medicine relies not only on discovering identifiable targets for treatment and surveillance modification, but also on reliable, noninvasive methods of identifying changes in these targets over time. Imaging via radiomics and radiogenomics is poised for a central role. Radiomics, which extracts large volumes of quantitative data from digital images and amalgamates these together with clinical and patient data into searchable shared databases, potentiates radiogenomics, which is the combination of genetic and radiomic data. Radiogenomics may provide voxel-by-voxel genetic information for a complete, heterogeneous tumor or, in the setting of metastatic disease, set of tumors and thereby guide tailored therapy. Radiogenomics may also quantify lesion characteristics, to better differentiate between benign and malignant entities, and patient characteristics, to better stratify patients according to risk for disease, thereby allowing for more precise imaging and screening. This report provides an overview of precision medicine and discusses radiogenomics specifically in breast cancer. ^© RSNA, 2018.

Machine learning to detect signatures of disease in liquid biopsies - a user's guide

New technologies that measure sparse molecular biomarkers from easily accessible bodily fluids (e.g. blood, urine, and saliva) are revolutionizing disease diagnostics and precision medicine. Microchip devices can measure more disease biomarkers with better sensitivity and specificity each year, but clinical interpretation of these biomarkers remains a challenge. Single biomarkers in 'liquid biopsy' often cannot accurately predict the state of a disease due to heterogeneity in phenotype and disease expression across individuals. To address this challenge, investigators are combining multiplexed measurements of different biomarkers that together define robust signatures for specific disease states. Machine learning is a useful tool to automatically discover and detect these signatures, especially as new technologies output increasing quantities of molecular data. In this paper, we review the state of the field of machine learning applied to molecular diagnostics and provide practical guidance to use this tool effectively and to avoid common pitfalls.

Prostate cancer proteomics: clinically useful protein biomarkers and future perspectives

INTRODUCTION

Although prostate cancer constitutes one of the most important, death-related diseases in the male population, there is still a need for identification of sensitive biomarkers that could precociously detect the disease and differentiate aggressive from indolent cancers, in order to decrease overtreatment. Proteomics research has improved understanding on mechanisms underlying tumorigenesis, cancer cells migration and invasion potential, and castration resistance. This review has focused on proteomic studies of prostate cancer published in the recent years, with a special emphasis on determination of biomarkers for cancer progression and diagnosis. Areas covered: Shotgun and targeted-proteomic studies of prostate cancer in different matrices are reviewed, i.e., prostate tissue, prostate cell lines, blood (serum and plasma), urine, seminal plasma, and exosomes. The most important biomarkers for cancer diagnosis and aggressiveness characterization are highlighted. Expert commentary: In general, results demonstrate alteration in cell cycle control, DNA repair, proteasomal degradation, and metabolic activity. However, these studies suffer from low reproducibility due to heterogeneity of the cancer itself, as well as to techniques utilized for protein identification/quantification. Downstream confirmatory studies in separate cohorts are warranted in order to demonstrate accuracy of these results.

Real-world Data for Clinical Evidence Generation in Oncology

Conventional cancer clinical trials can be slow and costly, often produce results with limited external validity, and are difficult for patients to participate in. Recent technological advances and a dynamic policy landscape in the United States have created a fertile ground for the use of real-world data (RWD) to improve current methods of clinical evidence generation. Sources of RWD include electronic health records, insurance claims, patient registries, and digital health solutions outside of conventional clinical trials. A definition focused on the original intent of data collected at the point of care can distinguish RWD from conventional clinical trial data. When the intent of data collection at the point of care is research, RWD can be generated using experimental designs similar to those employed in conventional clinical trials, but with several advantages that include gains in efficient execution of studies with an appropriate balance between internal and external validity. RWD can support active pharmacovigilance, insights into the natural history of disease, and the development of external control arms. Prospective collection of RWD can enable evidence generation based on pragmatic clinical trials (PCTs) that support randomized study designs and expand clinical research to the point of care. PCTs may help address the growing demands for access to experimental therapies while increasing patient participation in cancer clinical trials. Conducting valid real-world studies requires data quality assurance through auditable data abstraction methods and new incentives to drive electronic capture of clinically relevant data at the point of care.

Targeted proteomics reveals promising biomarkers of disease activity and organ involvement in antineutrophil cytoplasmic antibody-associated vasculitis

BACKGROUND

Targeted proteomics, which involves quantitative analysis of targeted proteins using selected reaction monitoring (SRM) mass spectrometry, has emerged as a new methodology for discovery of clinical biomarkers. In this study, we used targeted serum proteomics to identify circulating biomarkers for prediction of disease activity and organ involvement in antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV).

METHODS

A large-scale SRM assay targeting 135 biomarker candidates was established using a triple-quadrupole mass spectrometer coupled with nanoflow liquid chromatography. Target proteins in serum samples from patients in the active and remission (6 months after treatment) stages were quantified using the established assays. Identified marker candidates were further validated by enzyme-linked immunosorbent assay using serum samples (n = 169) collected in a large-cohort Japanese study (the RemIT-JAV-RPGN study).

RESULTS

Our proteomic analysis identified the following proteins as biomarkers for discriminating patients with highly active AAV from those in remission or healthy control subjects: tenascin C (TNC), C-reactive protein (CRP), tissue inhibitor of metalloproteinase 1 (TIMP1), leucine-rich alpha-2-glycoprotein 1, S100A8/A9, CD93, matrix metalloproteinase 9, and transketolase (TKT). Of these, TIMP1 was the best-performing marker of disease activity, allowing distinction between mildly active AAV and remission. Moreover, in contrast to CRP, serum levels of TIMP1 in patients with active AAV were significantly higher than those in patients with infectious diseases. The serum levels of TKT and CD93 were higher in patients with renal involvement than in those without, and they predicted kidney outcome. The level of circulating TNC was elevated significantly in patients with lung infiltration. AAV severity was associated with markers reflecting organ involvement (TKT, CD93, and TNC) rather than inflammation. The eight markers and myeloperoxidase (MPO)-ANCA were clustered into three groups: MPO-ANCA, renal involvement (TKT and CD93), and inflammation (the other six markers).

CONCLUSIONS

We have identified promising biomarkers of disease activity, disease severity, and organ involvement in AAV with a targeted proteomics approach using serum samples obtained from a large-cohort Japanese study. Especially, our analysis demonstrated the effectiveness of TIMP1 as a marker of AAV activity. In addition, we identified TKT and CD93 as novel markers for evaluation of renal involvement and kidney outcome in AAV.

Mass spectrometry-assisted gel-based proteomics in cancer biomarker discovery: approaches and application

There is a critical need for the discovery of novel biomarkers for early detection and targeted therapy of cancer, a major cause of deaths worldwide. In this respect, proteomic technologies, such as mass spectrometry (MS), enable the identification of pathologically significant proteins in various types of samples. MS is capable of high-throughput profiling of complex biological samples including blood, tissues, urine, milk, and cells. MS-assisted proteomics has contributed to the development of cancer biomarkers that may form the foundation for new clinical tests. It can also aid in elucidating the molecular mechanisms underlying cancer. In this review, we discuss MS principles and instrumentation as well as approaches in MS-based proteomics, which have been employed in the development of potential biomarkers. Furthermore, the challenges in validation of MS biomarkers for their use in clinical practice are also reviewed.

A multivariate predictive modeling approach reveals a novel CSF peptide signature for both Alzheimer's Disease state classification and for predicting future disease progression

To determine if a multi-analyte cerebrospinal fluid (CSF) peptide signature can be used to differentiate Alzheimer’s Disease (AD) and normal aged controls (NL), and to determine if this signature can also predict progression from mild cognitive impairment (MCI) to AD, analysis of CSF samples was done on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The profiles of 320 peptides from baseline CSF samples of 287 subjects over a 3–6 year period were analyzed. As expected, the peptide most able to differentiate between AD vs. NL was found to be Apolipoprotein E. Other peptides, some of which are not classically associated with AD, such as heart fatty acid binding protein, and the neuronal pentraxin receptor, also differentiated disease states. A sixteen-analyte signature was identified which differentiated AD vs. NL with an area under the receiver operating characteristic curve of 0.89, which was better than any combination of amyloid beta (1–42), tau, and phospho-181 tau. This same signature, when applied to a new and independent data set, also strongly predicted both probability and rate of future progression of MCI subjects to AD, better than traditional markers. These data suggest that multivariate peptide signatures from CSF predict MCI to AD progression, and point to potentially new roles for certain proteins not typically associated with AD.

Prostate cancer proteomics: Current trends and future perspectives for biomarker discovery

The clinical and fundamental research in prostate cancer - the most common urological cancer in men - is currently entering the proteomic and genomic era. The focus has switched from one single marker (PSA) to panels of biomarkers (including proteins involved in ribosomal function and heat shock proteins). Novel genetic markers (such as Transmembrane protease serine 2 (TMPRSS2)-ERG fusion gene mRNA) or prostate cancer gene 3 (PCA3) had already entered the clinical practice, raising the question whether subsequent protein changes impact the evolution of the disease and the response to treatment. Proteomic technologies such as MALDI-MS, SELDI-MS, i-TRAQ allow a qualitative/quantitative analysis of the proteome variations, in both serum and tumor tissue. A new trend in prostate cancer research is proteomic analysis of prostasomes (prostate-specific exosomes), for the discovery of new biomarkers. This paper provides an update of novel clinical tests used in research and clinical diagnostic, as well as of potential tissue or fluid biomarkers provided by extensive proteomic research data.

Pathology-Driven Comprehensive Proteomic Profiling of the Prostate Cancer Tumor Microenvironment

Prostate cancer is the second most common cancer in men worldwide. Gleason grading is an important predictor of prostate cancer outcomes and is influential in determining patient treatment options. Clinical decisions based on a Gleason score of 7 are difficult as the prognosis for individuals diagnosed with Gleason 4+3 cancer is much worse than for those diagnosed with Gleason 3+4 cancer. Laser capture microdissection (LCM) is a highly precise method to isolate specific cell populations or discrete microregions from tissues. This report undertook a detailed molecular characterization of the tumor microenvironment in prostate cancer to define the proteome in the epithelial and stromal regions from tumor foci of Gleason grades 3 and 4. Tissue regions of interest were isolated from several Gleason 3+3 and Gleason 4+4 tumors using telepathology to leverage specialized pathology expertise to support LCM. Over 2,000 proteins were identified following liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis of all regions of interest. Statistical analysis revealed significant differences in protein expression (>100 proteins) between Gleason 3 and Gleason 4 regions-in both stromal and epithelial compartments. A subset of these proteins has had prior strong association with prostate cancer, thereby providing evidence for the authenticity of the approach. Finally, validation of these proteins by immunohistochemistry has been obtained using an independent cohort of prostate cancer tumor specimens.Implications: This unbiased strategy provides a strong foundation for the development of biomarker protein panels with significant diagnostic and prognostic potential. Mol Cancer Res; 15(3); 281-93. ©2017 AACR.