Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer

When less is more: Counterintuitive stoichiometries and cellular abundances are essential for ABC transporters' function

Prokaryotes acquire essential nutrients primarily through adenosine triphosphate-binding cassette (ABC) importers, consisting of an adenosine triphosphatase, a permease, and a substrate-binding protein. These importers are highly underrepresented in proteomic databases, limiting our knowledge about their cellular copy numbers, component stoichiometry, and the mechanistic implications of these parameters. We developed a tailored proteomic approach to compile the most comprehensive dataset to date of the Escherichia coli "ABC importome." Functional assays and analyses of deletion strains revealed mechanistic features linking molecular mechanisms to cellular abundances, colocalization, and component stoichiometries. We observed four to five orders of magnitude variation in import system abundances, with copy numbers tuned to nutrient hierarchies essential for growth. Abundances of substrate-binding proteins are unrelated to their substrate binding affinities but are tightly yet inversely correlated with their interaction affinity with permeases. Counterintuitive component stoichiometries are crucial for function, offering insights into the design principles of multicomponent protein systems, potentially extending beyond ABC importers.

Advances in multiparametric magnetic resonance imaging combined with biomarkers for the diagnosis of high-grade prostate cancer

Clinical decisions based on the test results for prostate-specific antigen often result in overdiagnosis and overtreatment. Multiparametric magnetic resonance imaging (mpMRI) can be used to identify high-grade prostate cancer (HGPCa; Gleason score ≥3 + 4); however, certain limitations remain such as inter-reader variability and false negatives. The combination of mpMRI and prostate cancer (PCa) biomarkers (prostate-specific antigen density, Proclarix, TMPRSS2:ERG gene fusion, Michigan prostate score, ExoDX prostate intelliscore, four kallikrein score, select molecular diagnosis, prostate health index, and prostate health index density) demonstrates high accuracy in the diagnosis of HGPCa, ensuring that patients avoid unnecessary prostate biopsies with a low leakage rate. This manuscript describes the characteristics and diagnostic performance of each biomarker alone and in combination with mpMRI, with the intension to provide a basis for decision-making in the diagnosis and treatment of HGPCa. Additionally, we explored the applicability of the combination protocol to the Asian population.

Understanding seminal plasma in male infertility: emerging markers and their implications

Infertility affects a significant proportion of the reproductive-aged population, with male-associated factors contributing to over half of the cases. However, current diagnostic tools have limitations, leading to an underestimation of the true prevalence of male infertility. While traditional semen parameters provide some insights, they fail to determine the true fertility potential in a substantial number of instances. Therefore, it is crucial to investigate additional molecular targets responsible for male infertility to improve understanding and identification of such cases. Seminal plasma, the main carrier of molecules derived from male reproductive glands, plays a crucial role in reproduction. Amongst its multifarious functions, it regulates processes such as sperm capacitation, sperm protection and maturation, and even interaction with the egg's zona pellucida. Seminal plasma offers a non-invasive sample for urogenital diagnostics and has shown promise in identifying biomarkers associated with male reproductive disorders. This review aims to provide an updated and comprehensive overview of seminal plasma in the diagnosis of male infertility, exploring its composition, function, methods used for analysis, and the application of emerging markers. Apart from the application, the potential challenges of seminal plasma analysis such as standardisation, marker interpretation and confounding factors have also been addressed. Moreover, we have also explored future avenues for enhancing its utility and its role in improving diagnostic strategies. Through comprehensive exploration of seminal plasma's diagnostic potential, the present analysis seeks to advance the understanding of male infertility and its effective management.

Proteomics of prostate cancer serum and plasma using low and high throughput approaches

Despite progress, MS-based proteomics in biofluids, especially blood, faces challenges such as dynamic range and throughput limitations in biomarker and disease studies. In this work, we used cutting-edge proteomics technologies to construct label-based and label-free workflows, capable of quantifying approximately 2,000 proteins in biofluids. With 70µL of blood and a single depletion strategy, we conducted an analysis of a homogenous cohort (n = 32), comparing medium-grade prostate cancer patients (Gleason score: 7(3 + 4); TNM stage: T2cN0M0, stage IIB) to healthy donors. The results revealed dozens of differentially expressed proteins in both plasma and serum. We identified the upregulation of Prostate Specific Antigen (PSA), a well-known biomarker for prostate cancer, in the serum of cancer cohort. Further bioinformatics analysis highlighted noteworthy proteins which appear to be differentially secreted into the bloodstream, making them good candidates for further exploration.

The Potential of Extracellular Matrix- and Integrin Adhesion Complex-Related Molecules for Prostate Cancer Biomarker Discovery

Prostate cancer is among the top five cancer types according to incidence and mortality. One of the main obstacles in prostate cancer management is the inability to foresee its course, which ranges from slow growth throughout years that requires minimum or no intervention to highly aggressive disease that spreads quickly and resists treatment. Therefore, it is not surprising that numerous studies have attempted to find biomarkers of prostate cancer occurrence, risk stratification, therapy response, and patient outcome. However, only a few prostate cancer biomarkers are used in clinics, which shows how difficult it is to find a novel biomarker. Cell adhesion to the extracellular matrix (ECM) through integrins is among the essential processes that govern its fate. Upon activation and ligation, integrins form multi-protein intracellular structures called integrin adhesion complexes (IACs). In this review article, the focus is put on the biomarker potential of the ECM- and IAC-related molecules stemming from both body fluids and prostate cancer tissue. The processes that they are involved in, such as tumor stiffening, bone turnover, and communication via exosomes, and their biomarker potential are also reviewed.

Development of a predictive model to distinguish prostate cancer from benign prostatic hyperplasia by integrating serum glycoproteomics and clinical variables

BACKGROUND

Prostate Cancer (PCa) represents the second leading cause of cancer-related death in men. Prostate-specific antigen (PSA) serum testing, currently used for PCa screening, lacks the necessary sensitivity and specificity. New non-invasive diagnostic tools able to discriminate tumoral from benign conditions and aggressive (AG-PCa) from indolent forms of PCa (NAG-PCa) are required to avoid unnecessary biopsies.

METHODS

In this work, 32 formerly N-glycosylated peptides were quantified by PRM (parallel reaction monitoring) in 163 serum samples (79 from PCa patients and 84 from individuals affected by benign prostatic hyperplasia (BPH)) in two technical replicates. These potential biomarker candidates were prioritized through a multi-stage biomarker discovery pipeline articulated in: discovery, LC-PRM assay development and verification phases. Because of the well-established involvement of glycoproteins in cancer development and progression, the proteomic analysis was focused on glycoproteins enriched by TiO2 (titanium dioxide) strategy.

RESULTS

Machine learning algorithms have been applied to the combined matrix comprising proteomic and clinical variables, resulting in a predictive model based on six proteomic variables (RNASE1, LAMP2, LUM, MASP1, NCAM1, GPLD1) and five clinical variables (prostate dimension, proPSA, free-PSA, total-PSA, free/total-PSA) able to distinguish PCa from BPH with an area under the Receiver Operating Characteristic (ROC) curve of 0.93. This model outperformed PSA alone which, on the same sample set, was able to discriminate PCa from BPH with an AUC of 0.79. To improve the clinical managing of PCa patients, an explorative small-scale analysis (79 samples) aimed at distinguishing AG-PCa from NAG-PCa was conducted. A predictor of PCa aggressiveness based on the combination of 7 proteomic variables (FCN3, LGALS3BP, AZU1, C6, LAMB1, CHL1, POSTN) and proPSA was developed (AUC of 0.69).

CONCLUSIONS

To address the impelling need of more sensitive and specific serum diagnostic tests, a predictive model combining proteomic and clinical variables was developed. A preliminary evaluation to build a new tool able to discriminate aggressive presentations of PCa from tumors with benign behavior was exploited. This predictor displayed moderate performances, but no conclusions can be drawn due to the limited number of the sample cohort. Data are available via ProteomeXchange with identifier PXD035935.

Resolving the Temporal Splenic Proteome during Fungal Infection for Discovery of Putative Dual Perspective Biomarker Signatures

Fungal pathogens are emerging threats to global health with the rise of incidence associated with climate change and increased geographical distribution; factors also influencing host susceptibility to infection. Accurate detection and diagnosis of fungal infections is paramount to offer rapid and effective therapeutic options. For improved diagnostics, the discovery and development of protein biomarkers presents a promising avenue; however, this approach requires a priori knowledge of infection hallmarks. To uncover putative novel biomarkers of disease, profiling of the host immune response and pathogen virulence factor production is indispensable. In this study, we use mass-spectrometry-based proteomics to resolve the temporal proteome of Cryptococcus neoformans infection of the spleen following a murine model of infection. Dual perspective proteome profiling defines global remodeling of the host over a time course of infection, confirming activation of immune associated proteins in response to fungal invasion. Conversely, pathogen proteomes detect well-characterized C. neoformans virulence determinants, along with novel mapped patterns of pathogenesis during the progression of disease. Together, our innovative systematic approach confirms immune protection against fungal pathogens and explores the discovery of putative biomarker signatures from complementary biological systems to monitor the presence and progression of cryptococcal disease.

Accurate diagnosis of prostate cancer by combining Proclarix with magnetic resonance imaging

OBJECTIVES

To assess of the clinical performance of Proclarix® (a novel Conformité Européenne [CE]-marked biomarker test aiding in the identification of clinically significant prostate cancer [csPCa]) alone or in combination with multiparametric magnetic resonance imaging (mpMRI) to predict csPCa (International Society of Urological Pathology Grade Group ≥2).

PATIENTS AND METHODS

The study included blood samples from 721 men undergoing mpMRI followed by biopsy at University College London, London, and Vall d'Hebron University Hospital, Barcelona. Samples were tested blindly. The Proclarix-MRI model combining prostate volume, Proclarix and mpMRI results was trained using the UCL cohort (n = 159) and validated in the Vall d'Hebron cohort (n = 562). Its diagnostic performance was established in correlation to biopsy outcome and compared to available clinical parameters and risk calculators.

RESULTS

Clinical performance of the Proclarix-MRI model in the validation cohort did not significantly differ from the training cohort and resulted in a sensitivity for csPCa of 90%, 90% negative predictive value and 66% positive predictive value. The Proclarix-MRI score's specificity (68%) was significantly (P < 0.001) better than the MRI-European Randomized study of Screening for Prostate Cancer risk score (51%), Proclarix (27%) or mpMRI (28%) alone. In addition, Proclarix by itself was found to be useful in the MRI Prostate Imaging-Reporting and Data System (PI-RADS) score 3 subgroup by outperforming prostate-specific antigen density in terms of specificity (25% vs 13%, P = 0.004) at 100% sensitivity.

CONCLUSION

When combined with mpMRI and prostate volume, Proclarix reliably predicted csPCa and ruled out men with no or indolent cancer. A large reduction of two thirds of unneeded biopsies was achieved. Proclarix can further be used with high confidence to reliably detect csPCa in men with an indeterminate PI-RADS score 3 mpMRI. Despite these encouraging results, further validation is needed.

Intact glycopeptides identified by LC-MS/MS as biomarkers for response to chemotherapy of locally advanced cervical cancer

Objective

For locally advanced cervical cancer (LACC), patients who respond to chemotherapy have a potential survival advantage compared to nonresponsive patients. Thus, it is necessary to explore specific biological markers for the efficacy of chemotherapy, which is beneficial to personalized treatment.

Methods

In the present study, we performed a comprehensive screening of site-specific N-glycopeptides in serum glycoproteins to identify glycopeptide markers for predicting the efficacy of chemotherapy, which is beneficial to personalized treatment. In total, 20 serum samples before and after neoadjuvant chemotherapy (NACT) from 10 LACC patients (NACT response, n=6) and NACT nonresponse, n=4) cases) were analyzed using LC-MS/MS, and 20 sets of mass spectrometry (MS) data were collected using liquid chromatography coupled with high-energy collisional dissociation tandem MS (LC-HCD-MS/MS) for quantitative analysis on the novel software platform, Byos. We also identified differential glycopeptides before and after chemotherapy in chemo-sensitive and chemo-resistant patients.

Results

In the present study, a total of 148 glycoproteins, 496 glycosylation sites and 2279 complete glycopeptides were identified in serum samples of LACC patients. Before and after chemotherapy, there were 13 differentially expressed glycoproteins, 654 differentially expressed glycopeptides and 93 differentially expressed glycosites in the NACT responsive group, whereas there were 18 differentially expressed glycoproteins, 569 differentially expressed glycopeptides and 99 differentially expressed glycosites in the NACT nonresponsive group. After quantitative analysis, 6 of 570 glycopeptides were identified as biomarkers for predicting the sensitivity of neoadjuvant chemotherapy in LACC. The corresponding glycopeptides included MASP1, LUM, ATRN, CO8A, CO8B and CO6. The relative abundances of the six glycopeptides, including MASP1, LUM, ATRN, CO8A, CO8B and CO6, were significantly higher in the NACT-responsive group and were significantly decreased after chemotherapy. High levels of these six glycopeptides may indicate that chemotherapy is effective. Thus, these glycopeptides are expected to serve as biomarkers for predicting the efficacy of neoadjuvant chemotherapy in locally advanced cervical cancer.

Conclusion

The present study revealed that the N-glycopeptide of MASP1, LUM, ATRN, CO8A, CO8B and CO6 may be potential biomarkers for predicting the efficacy of chemotherapy for cervical cancer.

Relationship between Proclarix and the Aggressiveness of Prostate Cancer

INTRODUCTION

Proclarix is a CE-marked test that provides the risk of clinically significant prostate cancer (csPCa), ranging from 0% to 100%, based on the serum measurement of Thrombospondin-1, cathepsin D, prostate-specific antigen (PSA), and percentage of free PSA in addition to age. We hypothesize that Proclarix could be correlated with PCa aggressiveness. We analyzed the association of this new biomarker with four surrogates of aggressiveness: grade group (GG) in the biopsy, clinical stage, risk of biochemical recurrence after primary treatment of localized PCa, and pathology in the surgical specimen.

MATERIAL AND METHODS

This is a retrospective study from 606 men with suspicion of PCa [PSA of ≥ 3.0 ng/mL and/or abnormal digital rectal examination (DRE)], in whom Proclarix was assessed (0-100%). The GG was defined by the International Society of Urological Pathology categories. The TNM was used for clinical staging (cT based on DRE, whereas cN and cM were established with computed tomography and 99-technetium bone scintigraphy). The risk of biochemical recurrence of localized PCa after primary treatment was defined by combining PSA, GG, and cT. Finally, an unfavorable pathology in a surgical specimen was defined as GG > 2 or pT ≥ 3.

RESULTS

The median age of the cohort was 67 years old, with a median PSA of 7 ng/mL and a rate of abnormal DRE of 23.3%. CsPCa was detected in 254 men (41.9%), with a median Proclarix of 60.1% compared with 37.3% obtained in patients with insignificant PCa and 20.7% in men without PCa. Among patients with GG > 3, Proclarix was significantly higher (58.2%) than in those with GG of 3 or lower (33.1%, p < 0.001). Men with localized tumors exhibited a Proclarix median of 37.3% compared with those with advanced disease (60.1%, p < 0.001). Proclarix levels among 197 patients with low and intermediate risk of biochemical recurrence were 24.9% and 35.0%, respectively, significantly lower compared with patients with high-risk disease (58.7%, p < 0.001). Unfavorable pathology was observed in 35 patients out of the 79 who underwent radical prostatectomy, with a Proclarix median of 35.7% compared with 23.7% obtained in patients with favorable pathology (p = 0.013). Proclarix and magnetic resonance imaging were independent predictors of the four surrogates of aggressiveness analyzed.

CONCLUSION

There is a correlation between Proclarix and the aggressiveness of PCa.

Data-Independent Acquisition Mass Spectrometry of EPS-Urine Coupled to Machine Learning: A Predictive Model for Prostate Cancer

Prostate cancer (PCa) is annually the most frequently diagnosed cancer in the male population. To date, the diagnostic path for PCa detection includes the dosage of serum prostate-specific antigen (PSA) and the digital rectal exam (DRE). However, PSA-based screening has insufficient specificity and sensitivity; besides, it cannot discriminate between the aggressive and indolent types of PCa. For this reason, the improvement of new clinical approaches and the discovery of new biomarkers are necessary. In this work, expressed prostatic secretion (EPS)-urine samples from PCa patients and benign prostatic hyperplasia (BPH) patients were analyzed with the aim of detecting differentially expressed proteins between the two analyzed groups. To map the urinary proteome, EPS-urine samples were analyzed by data-independent acquisition (DIA), a high-sensitivity method particularly suitable for detecting proteins at low abundance. Overall, in our analysis, 2615 proteins were identified in 133 EPS-urine specimens obtaining the highest proteomic coverage for this type of sample; of these 2615 proteins, 1670 were consistently identified across the entire data set. The matrix containing the quantified proteins in each patient was integrated with clinical parameters such as the PSA level and gland size, and the complete matrix was analyzed by machine learning algorithms (by exploiting 90% of samples for training/testing using a 10-fold cross-validation approach, and 10% of samples for validation). The best predictive model was based on the following components: semaphorin-7A (sema7A), secreted protein acidic and rich in cysteine (SPARC), FT ratio, and prostate gland size. The classifier could predict disease conditions (BPH, PCa) correctly in 83% of samples in the validation set. Data are available via ProteomeXchange with the identifier PXD035942.

A Novel Blood Proteomic Signature for Prostate Cancer

Prostate cancer is the most common malignant tumour in men. Improved testing for diagnosis, risk prediction, and response to treatment would improve care. Here, we identified a proteomic signature of prostate cancer in peripheral blood using data-independent acquisition mass spectrometry combined with machine learning. A highly predictive signature was derived, which was associated with relevant pathways, including the coagulation, complement, and clotting cascades, as well as plasma lipoprotein particle remodeling. We further validated the identified biomarkers against a second cohort, identifying a panel of five key markers (GP5, SERPINA5, ECM1, IGHG1, and THBS1) which retained most of the diagnostic power of the overall dataset, achieving an AUC of 0.91. Taken together, this study provides a proteomic signature complementary to PSA for the diagnosis of patients with localised prostate cancer, with the further potential for assessing risk of future development of prostate cancer. Data are available via ProteomeXchange with identifier PXD025484.

Incubating Green Synthesized Iron Oxide Nanorods for Proteomics-Derived Motif Exploration: A Fusion to Deep Learning Oncogenesis

The nanotechnological arena has revolutionized the diagnostic efficacies by investigating the protein corona. This displays provoking proficiencies in determining biomarkers and diagnostic fingerprints for early detection and advanced therapeutics. The green synthesized iron oxide nanoparticles were prepared via Withania coagulans and were well characterized using UV-visible spectroscopy, X-ray diffraction analysis, Fourier transform infrared spectroscopy, and nano-LC mass spectrophotometry. Iron oxides were rod-shaped with an average size of 17.32 nm and have crystalline properties. The as-synthesized nanotool mediated firm nano biointeraction with the proteins in treatment with nine different cancers. The resultant of the proteome series was filtered oddly that highlighted the variant proteins within the differentially expressed proteins on behalf of nano-bioinformatics. Further magnification focused on S13_N, RS15, RAB, and 14_3_3 domains and few abundant motifs that aid scanning biomarkers. The entire set of variant proteins contracting to common proteins elucidates the underlining mechanical proteins that are marginally assessed using the robotic nanotechnology. Additionally, the iron rods indirectly possess a prognostic effect in manipulating expression of proteins through a smarter route. Thereby, such biologically designed nanotools provide a dual approach for medical studies.

Serum protein profiling of lung, pancreatic, and colorectal cancers reveals alcohol consumption-mediated disruptions in early-stage cancer detection

While the link between serum proteins and cancer has been studied in an effort to enable early-stage cancer detection, factors that might perturb this link has been poorly understood. To ask this question, we performed serum protein profiling on a prospective cohort of 601 individuals with or without lung, pancreatic, or colorectal cancers and identified ten distinct serum protein signatures with distinct link to the patient metadata. Importantly, we discovered that a positive history of alcohol consumption is a major factor that diminishes the sensitivity of serum protein-mediated liquid biopsy in early-stage malignancies, resulting in a 44% decline in the sensitivity of detecting American Joint Committee on Cancer (AJCC) stage I malignancies. Our data provide evidence that patient lifestyle can affect the sensitivity of liquid biopsy and suggest the potential need for abstinence from alcohol before measurement during serum protein-based cancer screening.

New strategy for the identification of prostate cancer: The combination of Proclarix and the prostate health index

OBJECTIVES

Prostate health index (PHI) and, more recently, Proclarix have been proposed as serum biomarkers for prostate cancer (PCa). In this study, we aimed to evaluate Proclarix and PHI for predicting clinically significant prostate cancer (csPCa).

PATIENTS AND METHODS

Proclarix and PHI were measured using samples of 344 men from two different centers. All patients underwent prostate biopsy, and among those, 188 men with PCa on biopsy had an additional radical prostatectomy (RP). All men had a prostate-specific antigen (PSA) between 2 and 10 ng/ml. Evaluation of area under the curve (AUC) and performance at predefined cut-offs of Proclarix and PHI risk scores as well as the linear combination thereof was performed to predict csPCa. PSA density was used as an independent comparator.

RESULTS

The cohort median age and PSA were 65 (interquartile range [IQR]: 60-71) and 5.6 (IQR: 4.3-7.2) ng/ml, respectively. CsPCa was diagnosed in 161 (47%) men based on the RP specimen. ROC analysis showed that Proclarix and PHI accurately predicted csPCa with no significant difference (AUC of 0.79 and 0.76, p = 0.378) but significantly better when compared to PSA density (AUC of 0.66, p < 0.001). When using specific cut-offs, Proclarix (cut-off 10) revealed higher specificity and positive predictive value than PHI (cut-off 27) at similar sensitivities. The combination of Proclarix and PHI provided a significant increase in the AUC (p ≤ 0.007) compared to the individual tests alone and the highest clinical benefit was achieved.

CONCLUSION

Results of this study show that both Proclarix and PHI accurately detect the presence of csPCa. The model combining Proclarix and PHI revealed the synergistic effect and improved the diagnostic performance of the individual tests.

Proclarix, A New Biomarker for the Diagnosis of Clinically Significant Prostate Cancer: A Systematic Review

INTRODUCTION

Multiparametric magnetic resonance imaging (mpMRI) has improved the early detection of clinically significant prostate cancer (csPCa). However, an appropriate selection of men for mpMRI or prostate biopsy is still challenging, which is why new biomarkers or predictive models are recommended to determine those patients who will benefit from prostate biopsy. Proclarix is a new test that provides the risk of csPCa based on thrombospondin-1 (THBS1), cathepsin D (CTSD), prostate-specific antigen (PSA), and percentage of free PSA (%fPSA), as well as age. This systematic review analyzes the current clinical status of Proclarix and future development.

EVIDENCE ACQUISITION

A systematic review of the literature was carried out by two independent reviewers. The Medical Subject Heading (MeSH) terms 'prostate', 'thrombospondin-1', 'cathepsin-D' and 'Proclarix' were used. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Population, Intervention, Comparison and Outcomes (PICO) selection criteria were followed. Finally, four articles analyzed the clinical usefulness of Proclarix.

EVIDENCE SYNTHESIS

Proclarix has been developed in men with PSA levels between 2 and 10 ng/mL, normal digital rectal examination (DRE), and prostate volume (PV)​ ≥ 35 cm³. Proclarix is associated with the PCa grade group and is more effective than %fPSA in detecting csPCa. Two studies analyzed the efficacy of Proclarix in men undergoing guided and systematic biopsies, obtaining similar results to PSA density.

CONCLUSION

Initial studies have shown the potential benefit of Proclarix in patients with specific characteristics. Future studies are needed to verify the clinical usefulness of Proclarix in men with suspected PCa before and after mpMRI.

Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer

Background

Non-invasive liquid biopsies could complement current pathological nomograms for risk stratification of prostate cancer patients. Development and testing of potential liquid biopsy markers is time, resource, and cost-intensive. For most protein targets, no antibodies or ELISAs for efficient clinical cohort pre-evaluation are currently available. We reasoned that mass spectrometry-based prescreening would enable the cost-effective and rational preselection of candidates for subsequent clinical-grade ELISA development.

Methods

Using Mass Spectrometry-GUided Immunoassay DEvelopment (MS-GUIDE), we screened 48 literature-derived biomarker candidates for their potential utility in risk stratification scoring of prostate cancer patients. Parallel reaction monitoring was used to evaluate these 48 potential protein markers in a highly multiplexed fashion in a medium-sized patient cohort of 78 patients with ground-truth prostatectomy and clinical follow-up information. Clinical-grade ELISAs were then developed for two of these candidate proteins and used for significance testing in a larger, independent patient cohort of 263 patients.

Results

Machine learning-based analysis of the parallel reaction monitoring data of the liquid biopsies prequalified fibronectin and vitronectin as candidate biomarkers. We evaluated their predictive value for prostate cancer biochemical recurrence scoring in an independent validation cohort of 263 prostate cancer patients using clinical-grade ELISAs. The results of our prostate cancer risk stratification test were statistically significantly 10% better than results of the current gold standards PSA alone, PSA plus prostatectomy biopsy Gleason score, or the National Comprehensive Cancer Network score in prediction of recurrence.

Conclusion

Using MS-GUIDE we identified fibronectin and vitronectin as candidate biomarkers for prostate cancer risk stratification.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-022-09349-x.

The Efficacy of Proclarix to Select Appropriate Candidates for Magnetic Resonance Imaging and Derived Prostate Biopsies in Men with Suspected Prostate Cancer

PURPOSE

To analyze how Proclarix is valuable to appropriately select candidates for multiparametric magnetic resonance imaging (mpMRI) and derived biopsies, among men with suspected prostate cancer (PCa). Proclarix is a new marker computing the clinically significant PCa (csPCa) risk, based on serum thosmbospondin-1, cathepsin D, prostate-specific antigen (PSA) and percent free PSA, in addition to age, that has been developed in men with serum PSA 2 to 10 ng/mL, prostate volume ≥35 mL, and normal digital rectal examination (DRE).

MATERIALS AND METHODS

Proclarix score (0%-100%) is analyzed in a prospective frozen serum collection of 517 correlative men scheduled for guided and/or systematic biopsies after mpMRI. Outcome variables were csPCa detection (grade group ≥2), insignificant PCa (iPCa) overdetection and avoided mpMRIs.

RESULTS

The area under the curve of Proclarix was 0.701 (95% CI 0.637-0.765) among 281 men with serum PSA 2 to 10 ng/mL, prostate volume ≥35 mL, and -normal DRE, and 0.754 (95% CI 0.701-0.807) in the others, p=0.038. Net benefit of Proclarix existed in all men. After selecting 10% threshold, Proclarix was integrated in an algorithm which also used the serum PSA level and DRE. A reduction of 25.4% of mpMRIs request was observed and 17.7% of prostate biopsies. Overdetection of iPCa was reduced in 18.2% and 2.6% of csPCa were misdiagnosed.

CONCLUSIONS

Proclarix is valuable in all men with suspected PCa. An algorithm integrating Proclarix score, serum PSA, and DRE can avoid mpMRI requests, unnecessary prostate biopsies and iPCa overdetection, with minimal loss of csPCa detection.

Development and validation of a multiparametric MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer

OBJECTIVE

To develop and validate a non-invasive MRI-based radiomics signature for distinguishing between indolent and aggressive prostate cancer (PCa) prior to therapy.

METHODS

In all, 139 qualified and pathology-confirmed PCa patients were divided into a training set (n = 93) and a validation set (n = 46). A total of 1576 radiomics features were extracted from the T₂WI (n = 788) and diffusion-weighted imaging (n = 788) for each patient. The Select K Best and the least absolute shrinkage and selection operator regression algorithm were used to construct a radiomics signature in the training set. The predictive performance of the radiomics signature was assessed in the training set and then validated in the validation set by receiver operating characteristic curve analysis. We computed the calibration curve and the decision curve to evaluate the calibration and clinical usefulness of the signature.

RESULTS

Nine radiomics features were identified to form the radiomics signature. The radiomics score (Rad-score) was significantly different between indolent and aggressive PCa (p < 0.001). The radiomics signature exhibited favorable discrimination between the indolent and aggressive PCa groups in the training set (AUC: 0.853, 95% CI: 0.766 to 0.941) and validation set (AUC: 0.901, 95% CI: 0.793 to 1.000). The decision curve analysis showed that a greater net benefit would be obtained when the threshold probability ranged from 20 to 90%.

CONCLUSION

The multiparametric MRI-based radiomics signature can potentially serve as a non-invasive tool for distinguishing between indolent and aggressive PCa prior to therapy.

ADVANCES IN KNOWLEDGE

The multiparametric MRI-based radiomics signature has the potential to non-invasively distinguish between the indolent and aggressive PCa, which might aid clinicians in making personalized therapeutic decisions.

Who with suspected prostate cancer can benefit from Proclarix after multiparametric magnetic resonance imaging?

Proclarix is a new blood-based test to assess the likelihood of clinically significant prostate cancer (csPCa) defined as >2 grade group. In this study, we analyzed whether Proclarix and PSA density (PSAD) could improve the selection of candidates for prostate biopsy after multiparametric magnetic resonance imaging (mpMRI). Proclarix and PSAD were assessed in 567 consecutive men with suspected PCa in whom pre-biopsy 3 Tesla mpMRI, scoring with Prostate Imaging-Report and Data System (PI-RADS) v.2, and guided and/or systematic biopsies were performed. Proclarix and PSAD thresholds having csPCa sensitivity over 90% were found at 10% and 0.07 ng/(mL*cm³), respectively. Among 100 men with negative mpMRI (PI-RADS <3), csPCa was detected in 6 cases, which would have been undetected if systematic biopsies were avoided. However, Proclarix suggested performing a biopsy on 70% of men with negative mpMRI. In contrast, PSAD only detected 50% of csPCa and required 71% of prostate biopsies. In 169 men with PI-RADS 3, Proclarix avoided 21.3% of prostate biopsies and detected all 25 cases of csPCa, while PSAD avoided 26.3% of biopsies, but missed 16% of csPCa. In 190 men with PI-RADS 4 and 108 with PI-RADS 5, Proclarix avoided 12.1% and 5.6% of prostate biopsies, but missed 4.8% and 1% of csPCa, respectively. PSAD avoided 18.4% and 9.3% of biopsies, but missed 11.4% and 4.2% csPCa, respectively. We conclude that Proclarix outperformed PSAD in the selection of candidates for prostate biopsy, especially in men with PI-RADS <3.

Untargeted urine metabolite profiling by mass spectrometry aided by multivariate statistical analysis to predict prostate cancer treatment outcome

One of the key barriers to the prostate cancer is monitor treatment response. Here we described a conceptually new ‘MS-statistical analysis-metabolome’ strategy for discovery of metabolic features.

Proteomic discovery of non-invasive biomarkers of localized prostate cancer using mass spectrometry

Prostate cancer is the second most frequently diagnosed non-skin cancer in men worldwide. Patient outcomes are remarkably heterogeneous and the best existing clinical prognostic tools such as International Society of Urological Pathology Grade Group, pretreatment serum PSA concentration and T-category, do not accurately predict disease outcome for individual patients. Thus, patients newly diagnosed with prostate cancer are often overtreated or undertreated, reducing quality of life and increasing disease-specific mortality. Biomarkers that can improve the risk stratification of these patients are, therefore, urgently needed. The ideal biomarker in this setting will be non-invasive and affordable, enabling longitudinal evaluation of disease status. Prostatic secretions, urine and blood can be sources of biomarker discovery, validation and clinical implementation, and mass spectrometry can be used to detect and quantify proteins in these fluids. Protein biomarkers currently in use for diagnosis, prognosis and relapse-monitoring of localized prostate cancer in fluids remain centred around PSA and its variants, and opportunities exist for clinically validating novel and complimentary candidate protein biomarkers and deploying them into the clinic.

A novel serum biomarker quintet reveals added prognostic value when combined with standard clinical parameters in prostate cancer patients by predicting biochemical recurrence and adverse pathology

The objective was to determine the prognostic utility of a new biomarker combination in prostate cancer (PCa) patients undergoing Radical Prostatectomy (RP). Serum samples and clinical data of 557 men who underwent RP for PCa with pathological stage (pT) <3 at Martini Clinic (Hamburg, Germany) were used for analysis. Clinical Grade Group and clinical stage was determined using biopsy samples while tumor marker concentrations were measured in serum using immunoassays. The prognostic utility of the proposed marker combination was assessed using Cox proportional hazard regression and Kaplan-Meier analysis. The performance was compared to the Cancer of the Prostate Risk Assessment (CAPRA) score in the overall cohort and in a low-risk patient subset. A multivariable model comprising fibronectin 1, galectin-3-binding protein, lumican, matrix metalloprotease 9, thrombospondin-1 and PSA together with clinical Grade Group (GG) and clinical stage (cT) was created. The proposed model was a significant predictor of biochemical recurrence (BCR) (HR 1.29 per 5 units score, 95%CI 1.20–1.38, p<0.001). The Kaplan-Meier analysis showed that the proposed model had a better prediction for low-risk disease after RP compared to CAPRA (respectively 5.0% vs. 9.1% chance of BCR). In a pre-defined low risk population subset, the risk of BCR using the proposed model was below 5.2% and thus lower when compared to CAPRA = 0–2 (9%), GG<2 (7%) and NCCN = low-risk (6%) subsets. Additionally, the proposed model could significantly (p<0.001) discriminate patients with adverse pathology (AP) events at RP from those without. In conclusion, the proposed model is superior to CAPRA for the prediction of BCR after RP in the overall cohort as well as a in a pre-defined low risk patient population subset. It is also significantly associated with AP at RP.

Proteomic Landscape of Prostate Cancer: The View Provided by Quantitative Proteomics, Integrative Analyses, and Protein Interactomes

Prostate cancer is the second most frequent cancer of men worldwide. While the genetic landscapes and heterogeneity of prostate cancer are relatively well-known already, methodological developments now allow for studying basic and dynamic proteomes on a large scale and in a quantitative fashion. This aids in revealing the functional output of cancer genomes. It has become evident that not all aberrations at the genetic and transcriptional level are translated to the proteome. In addition, the proteomic level contains heterogeneity, which increases as the cancer progresses from primary prostate cancer (PCa) to metastatic and castration-resistant prostate cancer (CRPC). While multiple aspects of prostate adenocarcinoma proteomes have been studied, less is known about proteomes of neuroendocrine prostate cancer (NEPC). In this review, we summarize recent developments in prostate cancer proteomics, concentrating on the proteomic landscapes of clinical prostate cancer, cell line and mouse model proteomes interrogating prostate cancer-relevant signaling and alterations, and key prostate cancer regulator interactomes, such as those of the androgen receptor (AR). Compared to genomic and transcriptomic analyses, the view provided by proteomics brings forward changes in prostate cancer metabolism, post-transcriptional RNA regulation, and post-translational protein regulatory pathways, requiring the full attention of studies in the future.

Mass Spectrometry-Based Glycoproteomics and Prostate Cancer

Aberrant glycosylation has long been known to be associated with cancer, since it is involved in key mechanisms such as tumour onset, development and progression. This review will focus on protein glycosylation studies in cells, tissue, urine and serum in the context of prostate cancer. A dedicated section will cover the glycoforms of prostate specific antigen, the molecule that, despite some important limitations, is routinely tested for helping prostate cancer diagnosis. Our aim is to provide readers with an overview of mass spectrometry-based glycoproteomics of prostate cancer. From this perspective, the first part of this review will illustrate the main strategies for glycopeptide enrichment and mass spectrometric analysis. The molecular information obtained by glycoproteomic analysis performed by mass spectrometry has led to new insights into the mechanism linking aberrant glycosylation to cancer cell proliferation, migration and immunoescape.

Artificial intelligence driven next-generation renal histomorphometry

PURPOSE OF REVIEW

Successful integration of artificial intelligence into extant clinical workflows is contingent upon a number of factors including clinician comprehension and interpretation of computer vision. This article discusses how image analysis and machine learning have enabled comprehensive characterization of kidney morphology for development of automated diagnostic and prognostic renal pathology applications.

RECENT FINDINGS

The primordial digital pathology informatics work employed classical image analysis and machine learning to prognosticate renal disease. Although this classical approach demonstrated tremendous potential, subsequent advancements in hardware technology rendered artificial neural networks '(ANNs) the method of choice for machine vision in computational pathology'. Offering rapid and reproducible detection, characterization and classification of kidney morphology, ANNs have facilitated the development of diagnostic and prognostic applications. In addition, modern machine learning with ANNs has revealed novel biomarkers in kidney disease, demonstrating the potential for machine vision to elucidate novel pathologic mechanisms beyond extant clinical knowledge.

SUMMARY

Despite the revolutionary developments potentiated by modern machine learning, several challenges remain, including data quality control and curation, image annotation and ontology, integration of multimodal data and interpretation of machine vision or 'opening the black box'. Resolution of these challenges will not only revolutionize diagnostic pathology but also pave the way for precision medicine and integration of artificial intelligence in the process of care.

PROPOSe: A Real-life Prospective Study of Proclarix, a Novel Blood-based Test to Support Challenging Biopsy Decision-making in Prostate Cancer

BACKGROUND

Prostate-specific antigen (PSA)-based detection of prostate cancer (PCa) often leads to negative biopsy results or detection of clinically insignificant PCa, more frequently in the PSA range of 2-10 ng/ml, in men with increased prostate volume and normal digital rectal examination (DRE).

OBJECTIVE

This study evaluated the accuracy of Proclarix, a novel blood-based diagnostic test, to help in biopsy decision-making in this challenging patient population.

DESIGN, SETTING, AND PARTICIPANTS

Ten clinical sites prospectively enrolled 457 men presenting for prostate biopsy with PSA between 2 and 10 ng/ml, normal DRE, and prostate volume ≥35 cm³. Transrectal ultrasound-guided and multiparametric magnetic resonance imaging (mpMRI)-guided biopsy techniques were allowed.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Serum samples were tested blindly at the end of the study. Diagnostic performance of Proclarix risk score was established in correlation to systematic biopsy outcome and its performance compared with %free PSA (%fPSA) and the European Randomised Study of Screening for Prostate Cancer (ERSPC) risk calculator (RC) as well as Proclarix density compared with PSA density in men undergoing mpMRI.

RESULTS AND LIMITATIONS

The sensitivity of Proclarix risk score for clinically significant PCa (csPCa) defined as grade group (GG) ≥2 was 91% (n = 362), with higher specificity than both %fPSA (22% vs 14%; difference = 8% [95% confidence interval {CI}, 2.6-14%], p = 0.005) and RC (22% vs 15%; difference = 7% [95% CI, 0.7-12%], p = 0.028). In the subset of men undergoing mpMRI-fusion biopsy (n = 121), the specificity of Proclarix risk score was significantly higher than PSA density (26% vs 8%; difference = 18% [95% CI, 7-28%], p < 0.001), and at equal sensitivity of 97%, Proclarix density had an even higher specificity of 33% [95% CI, 23-43%].

CONCLUSIONS

In a routine use setting, Proclarix accurately discriminated csPCa from no or insignificant PCa in the most challenging patients. Proclarix represents a valuable rule-out test in the diagnostic algorithm for PCa, alone or in combination with mpMRI.

PATIENT SUMMARY

Proclarix is a novel blood-based test with the potential to accurately rule out clinically significant prostate cancer, and therefore to reduce the number of unneeded biopsies.

The Complexity and Dynamics of the Tissue Glycoproteome Associated With Prostate Cancer Progression

The complexity and dynamics of the immensely heterogeneous glycoproteome of the prostate cancer (PCa) tumor microenvironment remain incompletely mapped, a knowledge gap that impedes our molecular-level understanding of the disease. To this end, we have used sensitive glycomics and glycoproteomics to map the protein-, cell-, and tumor grade-specific N- and O-glycosylation in surgically removed PCa tissues spanning five histological grades (n = 10/grade) and tissues from patients with benign prostatic hyperplasia (n = 5). Quantitative glycomics revealed PCa grade-specific alterations of the oligomannosidic-, paucimannosidic-, and branched sialylated complex-type N-glycans, and dynamic remodeling of the sialylated core 1- and core 2-type O-glycome. Deep quantitative glycoproteomics identified ∼7400 unique N-glycopeptides from 500 N-glycoproteins and ∼500 unique O-glycopeptides from nearly 200 O-glycoproteins. With reference to a recent Tissue and Blood Atlas, our data indicate that paucimannosidic glycans of the PCa tissues arise mainly from immune cell-derived glycoproteins. Furthermore, the grade-specific PCa glycosylation arises primarily from dynamics in the cellular makeup of the PCa tumor microenvironment across grades involving increased oligomannosylation of prostate-derived glycoproteins and decreased bisecting GlcNAcylation of N-glycans carried by the extracellular matrix proteins. Furthermore, elevated expression of several oligosaccharyltransferase subunits and enhanced N-glycoprotein site occupancy were observed associated with PCa progression. Finally, correlations between the protein-specific glycosylation and PCa progression were observed including increased site-specific core 2-type O-glycosylation of collagen VI. In conclusion, integrated glycomics and glycoproteomics have enabled new insight into the complexity and dynamics of the tissue glycoproteome associated with PCa progression generating an important resource to explore the underpinning disease mechanisms.

Therapeutic prognosis of prostate cancer using surface-enhanced Raman scattering of patient urine and multivariate statistical analysis

Surface-enhanced Raman scattering (SERS) is highly sensitive and label-free analytical technique based on Raman spectroscopy aided by field-multiplying plasmonic nanostructures. We report the use of SERS measurements of patient urine in conjunction with biostatistical algorithms to assess the treatment response of prostate cancer (PCa) in 12 recurrent (Re) and 63 nonrecurrent (NRe) patient cohorts. Multiple Raman spectra are collected from each urine sample using monodisperse silver nanoparticles (AgNPs) for Raman signal enhancement. Genetic algorithms-partial least squares-linear discriminant analysis (GA-PLS-LDA) was employed to analyze the Raman spectra. Comprehensive GA-PLS-LDA analyses of these Raman spectral features (p = 3.50 × 10^-16 ) yield an accuracy of 86.6%, sensitivity of 86.0%, and specificity 87.1% in differentiating the Re and NRe cohorts. Our study suggests that SERS combined with multivariate GA-PLS-LDA algorithm can potentially be used to detect and monitor the risk of PCa relapse and to aid with decision-making for optimal intermediate secondary therapy to recurred patients.

Identification and Validation of Leucine-rich α-2-glycoprotein 1 as a Noninvasive Biomarker for Improved Precision in Prostate Cancer Risk Stratification

Background

More accurate risk assessments are needed to improve prostate cancer management.

Objective

To identify blood-based protein biomarkers that provided prognostic information for risk stratification.

Design, setting, and participants

Mass spectrometry was used to identify biomarker candidates from blood, and validation studies were performed in four independent cohorts retrospectively collected between 1988 and 2015.

Outcome measurements and statistical analysis

The primary outcome objectives were progression-free survival, prostate cancer–specific survival (PCSS), and overall survival. Statistical analyses to assess survival and model performance were performed.

Results and limitation

Serum leucine-rich α-2-glycoprotein 1 (LRG1) was found to be elevated in fatal prostate cancer. LRG1 provided prognostic information independent of metastasis and increased the accuracy in predicting PCSS, particularly in the first 3 yr. A high LRG1 level is associated with an average of two-fold higher risk of disease-progression and mortality in both high-risk and metastatic patients. However, our study design, with a retrospective analysis of samples spanning several decades back, limits the assessment of the clinical utility of LRG1 in today’s clinical practice. Thus, independent prospective studies are needed to establish LRG1 as a clinically useful biomarker for patient management.

Conclusions

High blood levels of LRG1 are unfavourable in newly diagnosed high-risk and metastatic prostate cancer, and LRG1 increased the accuracy of risk stratification of prostate cancer patients.

Patient summary

High blood levels of leucine-rich α-2-glycoprotein 1 are unfavourable in newly diagnosed high-risk and metastatic prostate cancer.

Scalable Signature-Based Molecular Diagnostics Through On-chip Biomarker Profiling Coupled with Machine Learning

Molecular diagnostics have traditionally relied on discrete biological substances as diagnostic markers. In recent years however, advances in on-chip biomarker screening technologies and data analytics have enabled signature-based diagnostics. Such diagnostics aim to utilize unique combinations of multiple biomarkers or diagnostic 'fingerprints' rather than discrete analyte measurements. This approach has shown to improve both diagnostic accuracy and diagnostic specificity. In this review, signature-based diagnostics enabled by microfluidic and micro-/nano- technologies will be reviewed with a focus on device design and data analysis pipelines and methodologies. With increasing amounts of data available from microfluidic biomarker screening, isolation, and detection platforms, advanced data handling and analytics approaches can be employed. Thus, current data analysis approaches including machine learning and recent advances with image processing, along with potential future directions will be explored. Lastly, the needs and gaps in current literature will be elucidated to inform future efforts towards development of molecular diagnostics and biomarker screening technologies.

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.

Analytical performance of thrombospondin-1 and cathepsin D immunoassays part of a novel CE-IVD marked test as an aid in the diagnosis of prostate cancer

The Prostate Specific Antigen (PSA) test suffers from low specificity for the diagnosis of Prostate Cancer (PCa). We originally discovered two cancer-related proteins thrombospondin-1 (THBS1) and cathepsin D (CTSD) using a mass-spectrometry-based proteomics approach. The two serum proteins were shown to improve the diagnosis of high-grade PCa. Thus, we developed quantitative ELISAs for the determination of their concentration in human serum. Here we report their analytical performance in terms of limit of detection, specificity, precision, linearity and interferences, which were determined based on CLSI guidelines. Further, we investigated the influence of pre-analytical factors on concentration measurements. For this, blood from 4-6 donors was collected in different tubes and stored at room temperature for different times prior to centrifugation at different centrifugal forces and temperatures. Stability of THBS1 and CTSD under different storage temperatures was also evaluated. Our results show that the assays are specific, linear and sensitive enough to allow measurement of clinical samples. Precision in terms of repeatability and total within-laboratory coefficient of variation (CV) are 5.5% and 8.1% for THBS1 and 4.3% and 7.2% for CTSD, respectively. Relative laboratory-to-laboratory differences were -6.3% for THBS1 and -3% for CTSD. Both THBS1 and CTSD were stable in serum samples, with 80-120% recoveries of concentrations across donors, sample preparation and storage. In conclusion, the ELISAs as part of the novel commercial in vitro diagnostic test Proclarix are suitable for the use in clinical practice. THBS1 and CTSD can be accurately measured for their intended use independent of the lot and laboratory when conditions consistent with routine practice for PSA sampling and storage are used.

Proteomic and transcriptomic profiling of Pten gene-knockout mouse model of prostate cancer

BACKGROUND

The prostate-specific phosphatase and tensin homolog deleted on chromosome 10 (Pten) gene-conditional knockout (KO) mouse carcinogenesis model is highly desirable for studies of prostate cancer biology and chemoprevention due to its close resemblance of primary molecular defect and many histopathological features of human prostate cancer including androgen response and disease progression from prostatic intraepithelial neoplasia to invasive adenocarcinoma. Here, we profiled the proteome and transcriptome of the Pten-KO mouse prostate tumors for global macromolecular expression alterations for signaling changes and biomarker signatures.

METHODS

For proteomics, four pairs of whole prostates from tissue-specific conditional knockout Pten-KO mice (12-15 weeks of age) and their respective wild-type littermates housed in the same cages were analyzed by 8-plex isobaric tags for relative and absolute quantitation iTRAQ. For microarray transcriptomic analysis, three additional matched pairs of prostate/tumor specimens from respective mice at 20 to 22 weeks of age were used. Real-time quantitative reverse transcription-polymerase chain reaction was used to verify the trends of protein and RNA expression changes. Gene Set Enrichment Analysis and Ingenuity Pathway Analysis were carried out for bioinformatic characterizations of pathways and networks.

RESULTS

At the macromolecular level, proteomic and transcriptomic analyses complement and cross-validate to reveal overexpression signatures including inflammation and immune alterations, in particular, neutrophil/myeloid lineage suppressor cell features, chromatin/histones, ion and nutrient transporters, and select glutathione peroxidases and transferases in Pten-KO prostate tumors. Suppressed expression patterns in the Pten-KO prostate tumors included glandular differentiation such as secretory proteins and androgen receptor targets, smooth muscle features, and endoplasmic reticulum stress proteins. Bioinformatic analyses identified immune and inflammation responses as the most profound macromolecular landscape changes, and the predicted key nodal activities through Akt, nuclear factor-kappaB, and P53 in the Pten-KO prostate tumor. Comparison with other genetically modified mouse prostate carcinogenesis models revealed notable molecular distinctions, especially the dominance of immune and inflammation features in the Pten-KO prostate tumors.

CONCLUSIONS

Our work identified prominent macromolecular signatures and key nodal molecules that help to illuminate the patho- and immunobiology of Pten-loss driven prostate cancer and can facilitate the choice of biomarkers for chemoprevention and interception studies in this clinically relevant mouse prostate cancer model.

CDCP1 overexpression drives prostate cancer progression and can be targeted in vivo

The mechanisms by which prostate cancer shifts from an indolent castration-sensitive phenotype to lethal castration-resistant prostate cancer (CRPC) are poorly understood. Identification of clinically relevant genetic alterations leading to CRPC may reveal potential vulnerabilities for cancer therapy. Here we find that CUB domain-containing protein 1 (CDCP1), a transmembrane protein that acts as a substrate for SRC family kinases (SFKs), is overexpressed in a subset of CRPC. Notably, CDCP1 cooperates with the loss of the tumor suppressor gene PTEN to promote the emergence of metastatic prostate cancer. Mechanistically, we find that androgens suppress CDCP1 expression and that androgen deprivation in combination with loss of PTEN promotes the upregulation of CDCP1 and the subsequent activation of the SRC/MAPK pathway. Moreover, we demonstrate that anti-CDCP1 immunoliposomes (anti-CDCP1 ILs) loaded with chemotherapy suppress prostate cancer growth when administered in combination with enzalutamide. Thus, our study identifies CDCP1 as a powerful driver of prostate cancer progression and uncovers different potential therapeutic strategies for the treatment of metastatic prostate tumors.

Highly efficient enrichment method for human plasma glycoproteome analyses using tandem hydrophilic interaction liquid chromatography workflow

Selective enrichment of glycopeptides from complex sample with hydrophilic interaction liquid chromatography (HILIC) method, followed by cleavage of N-glycans by PNGase F to expose an easily detectable mark on the former glycosylation sites is used extensively as a sample preparation for comprehensive glycoproteome analysis. However, the coenrichment of hydrophilic nonglycosylated peptides and the released N-glycans seriously affect the identification of deglycopeptides with nano-LC-MS/MS. Here, we developed a new method for highly efficient and specific enrichment of human plasma N-glycopeptides using HILIC-PNGaseF-HILIC workflow (HPH). The first HILIC enriches the N-glycopeptides from the complex peptide mixtures. After the enriched N-glycopeptides are deglycosylated with PNGase F, the second HILIC captures the coenrichment of hydrophilic nonglycosylated peptides and the N-glycans, and then further enriches the deglycosylated peptides. The glycopeptide enrichment efficiency can be notably improved by employing HPH, evaluated by the highly recovery (more than 93.6%) and specific capturing glycopeptides from tryptic digest of IgG and BSA up to the molar ratios of 1:200. Meanwhile, we found that the alkylated proteins with IAA can affect the enrichment efficiency for N-glycopeptides with HILIC method. Moreover, after optimism the protein digestion, this novel HPH strategy allowed for the identified 722 N-glycopeptides within 202 unique glycoproteins from 1 µL human plasma digest using PNGase F in H₂¹⁶O. Meanwhile, this new HPH strategy identified an average 501 N-glycopeptides within averagely 134 unique glycoproteins from 1 µL human plasma digest using PNGase F in H₂¹⁸O. The enhanced glycopeptide detection was promoted by a substantial depletion of nonglycosylated peptides in the second HILIC. It was found that 52.2% more N-glycosylation peptides were identified by the HPH strategy compared with the using one HILIC enrichment alone.

Development of a novel nanoflow liquid chromatography-parallel reaction monitoring mass spectrometry-based method for quantification of angiotensin peptides in HUVEC cultures

BACKGROUND

This study aimed to develop an analytical method using liquid chromatography tandem mass spectrometry (LC-MS/MS) for the determination of angiotensin (Ang) I, Ang (1-9), Ang II, Ang (1-7), Ang (1-5), Ang III, Ang IV in human umbilical vein endothelial cell (HUVEC) culture supernatant.

METHODS

HUVEC culture supernatant was added with gradient concentrations (0.05-1,000 ng/ml) of standard solutions of the Ang peptides. These samples underwent C18 solid-phase extraction and separation using a preconcentration nano-liquid chromatography mass spectrometry system. The target peptides were detected by a Q Exactive quadrupole orbitrap high-resolution mass spectrometer in the parallel reaction monitoring mode. Ang converting enzyme (ACE) in HUVECs was silenced to examine Ang I metabolism.

RESULTS

The limit of detection was 0.1 pg for Ang II and Ang III, and 0.5 pg for Ang (1-9), Ang (1-7), and Ang (1-5). The linear detection range was 0.1-2,000 pg (0.05-1,000 ng/ml) for Ang II and Ang III, and 0.5-2,000 pg (0.25-1,000 ng/ml) for Ang (1-9) and Ang (1-5). Intra-day and inter-day precisions (relative standard deviation) were <10%. Ang II, Ang III, Ang IV, and Ang (1-5) were positively correlated with ACE expression by HUVECs, while Ang I, Ang (1-7), and Ang (1-9) were negatively correlated.

CONCLUSION

The nanoflow liquid chromatography-parallel reaction monitoring mass spectrometry-based methodology established in this study can evaluate the Ang peptides simultaneously in HUVEC culture supernatant.

Similarities and Differences of Blood N-Glycoproteins in Five Solid Carcinomas at Localized Clinical Stage Analyzed by SWATH-MS

Cancer is mostly incurable when diagnosed at a metastatic stage, making its early detection via blood proteins of immense clinical interest. Proteomic changes in tumor tissue may lead to changes detectable in the protein composition of circulating blood plasma. Using a proteomic workflow combining N-glycosite enrichment and SWATH mass spectrometry, we generate a data resource of 284 blood samples derived from patients with different types of localized-stage carcinomas and from matched controls. We observe whether the changes in the patient's plasma are specific to a particular carcinoma or represent a generic signature of proteins modified uniformly in a common, systemic response to many cancers. A quantitative comparison of the resulting N-glycosite profiles discovers that proteins related to blood platelets are common to several cancers (e.g., THBS1), whereas others are highly cancer-type specific. Available proteomics data, including a SWATH library to study N-glycoproteins, will facilitate follow-up biomarker research into early cancer detection.

Bottlenecks in Proteomics: An Update

Mass spectrometry (MS) is the core for advanced methods in proteomic experiments. When effectively used, proteomics may provide extensive information about proteins and their post-translational modifications, as well as their interaction partners. However, there are also many problems that one can encounter during a proteomic experiment, including, but not limited to sample preparation, sample fractionation, sample analysis, data analysis & interpretation and biological significance. Here we discuss some of the problems that researchers should be aware of when performing a proteomic experiment.

A Targeted Mass Spectrometry Strategy for Developing Proteomic Biomarkers: A Case Study of Epithelial Ovarian Cancer

Protein biomarkers for epithelial ovarian cancer are critical for the early detection of the cancer to improve patient prognosis and for the clinical management of the disease to monitor treatment response and to detect recurrences. Unfortunately, the discovery of protein biomarkers is hampered by the limited availability of reliable and sensitive assays needed for the reproducible quantification of proteins in complex biological matrices such as blood plasma. In recent years, targeted mass spectrometry, exemplified by selected reaction monitoring (SRM) has emerged as a method, capable of overcoming this limitation. Here, we present a comprehensive SRM-based strategy for developing plasma-based protein biomarkers for epithelial ovarian cancer and illustrate how the SRM platform, when combined with rigorous experimental design and statistical analysis, can result in detection of predictive analytes.Our biomarker development strategy first involved a discovery-driven proteomic effort to derive potential N-glycoprotein biomarker candidates for plasma-based detection of human ovarian cancer from a genetically engineered mouse model of endometrioid ovarian cancer, which accurately recapitulates the human disease. Next, 65 candidate markers selected from proteins of different abundance in the discovery dataset were reproducibly quantified with SRM assays across a large cohort of over 200 plasma samples from ovarian cancer patients and healthy controls. Finally, these measurements were used to derive a 5-protein signature for distinguishing individuals with epithelial ovarian cancer from healthy controls. The sensitivity of the candidate biomarker signature in combination with CA125 ELISA-based measurements currently used in clinic, exceeded that of CA125 ELISA-based measurements alone. The SRM-based strategy in this study is broadly applicable. It can be used in any study that requires accurate and reproducible quantification of selected proteins in a high-throughput and multiplexed fashion.

Global and site-specific analysis of protein glycosylation in complex biological systems with Mass Spectrometry

Protein glycosylation is ubiquitous in biological systems and plays essential roles in many cellular events. Global and site-specific analysis of glycoproteins in complex biological samples can advance our understanding of glycoprotein functions and cellular activities. However, it is extraordinarily challenging because of the low abundance of many glycoproteins and the heterogeneity of glycan structures. The emergence of mass spectrometry (MS)-based proteomics has provided us an excellent opportunity to comprehensively study proteins and their modifications, including glycosylation. In this review, we first summarize major methods for glycopeptide/glycoprotein enrichment, followed by the chemical and enzymatic methods to generate a mass tag for glycosylation site identification. We next discuss the systematic and quantitative analysis of glycoprotein dynamics. Reversible protein glycosylation is dynamic, and systematic study of glycoprotein dynamics helps us gain insight into glycoprotein functions. The last part of this review focuses on the applications of MS-based proteomics to study glycoproteins in different biological systems, including yeasts, plants, mice, human cells, and clinical samples. Intact glycopeptide analysis is also included in this section. Because of the importance of glycoproteins in complex biological systems, the field of glycoproteomics will continue to grow in the next decade. Innovative and effective MS-based methods will exponentially advance glycoscience, and enable us to identify glycoproteins as effective biomarkers for disease detection and drug targets for disease treatment. © 2019 Wiley Periodicals, Inc. Mass Spec Rev 9999: XX-XX, 2019.

The Relevance of Mass Spectrometry Analysis for Personalized Medicine through Its Successful Application in Cancer "Omics"

Mass spectrometry (MS) is an essential analytical technology on which the emerging omics domains; such as genomics; transcriptomics; proteomics and metabolomics; are based. This quantifiable technique allows for the identification of thousands of proteins from cell culture; bodily fluids or tissue using either global or targeted strategies; or detection of biologically active metabolites in ultra amounts. The routine performance of MS technology in the oncological field provides a better understanding of human diseases in terms of pathophysiology; prevention; diagnosis and treatment; as well as development of new biomarkers; drugs targets and therapies. In this review; we argue that the recent; successful advances in MS technologies towards cancer omics studies provides a strong rationale for its implementation in biomedicine as a whole.

Large-Scale Automatic Feature Selection for Biomarker Discovery in High-Dimensional OMICs Data

The identification of biomarker signatures in omics molecular profiling is usually performed to predict outcomes in a precision medicine context, such as patient disease susceptibility, diagnosis, prognosis, and treatment response. To identify these signatures, we have developed a biomarker discovery tool, called BioDiscML. From a collection of samples and their associated characteristics, i.e., the biomarkers (e.g., gene expression, protein levels, clinico-pathological data), BioDiscML exploits various feature selection procedures to produce signatures associated to machine learning models that will predict efficiently a specified outcome. To this purpose, BioDiscML uses a large variety of machine learning algorithms to select the best combination of biomarkers for predicting categorical or continuous outcomes from highly unbalanced datasets. The software has been implemented to automate all machine learning steps, including data pre-processing, feature selection, model selection, and performance evaluation. BioDiscML is delivered as a stand-alone program and is available for download at https://github.com/mickaelleclercq/BioDiscML.

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.

Development of biomarkers of genitourinary cancer using mass spectrometry-based clinical proteomics

Prostate, bladder and kidney cancer are the three most common types of genitourinary cancer in the world. Of these, prostate and bladder cancers are within the top 10 most common cancers in men. Notably, kidney cancer causes no obvious symptoms in the early stages. To satisfy clinical-management requirements, researchers have developed numerous biomarkers by applying proteomic approaches using clinical serum, urine and tissue specimens, as well as cell and animal models. Through application of biomarker pipeline protocols, including discovery, verification and validation phases, and mass-spectrometric based proteomic platforms coupled with multiplexed quantification assays, these studies have led to recent rapid progress in this area. With improvements in mass-spectrometric based proteomic techniques, numerous promising biomarker candidates and marker panels for various clinical purposes have been proposed. Verification of novel protein biomarker candidates is very resource demanding (e.g. on the clinical and laboratory sides). With the support of national consortia, it is now possible to investigate the future clinical use of such biomarker strategies and assess their cost-effectiveness in personalized medicine.

High-throughput detection of low abundance sialylated glycoproteins in human serum by TiO2 enrichment and targeted LC-MS/MS analysis: application to a prostate cancer sample set

Glycopeptide enrichment can be a strategy to allow the detection of peptides belonging to low abundance proteins in complex matrixes such as blood serum or plasma. Though several glycopeptide enrichment protocols have shown excellent sensitivities in this respect, few reports have demonstrated the applicability of these methods to relatively large sample cohorts. In this work, a fast protocol based on TiO₂ enrichment and highly sensitive mass spectrometric analysis by Selected Reaction Monitoring (SRM) has been applied to a cohort of serum samples from prostate cancer and benign prostatic hyperplasia patients in order to detect low abundance proteins in a single LC-MS/MS analysis in nanoscale format, without immunodepletion or peptide fractionation. A peptide library of over 700 formerly N-glycosylated peptides was created by data dependent analysis. Then, 16 medium to low abundance proteins were selected for detection by single injection LC-MS/MS based on selected-reaction monitoring. Results demonstrated the consistent detection of the low-level proteins under investigation. Following label-free quantification, four proteins (Adipocyte plasma membrane-associated protein, Periostin, Cathepsin D and Lysosome-associated membrane glycoprotein 2) were found significantly increased in prostate cancer sera compared to the control group. Graphical abstract ᅟ.

Thrombospondin 1 and cathepsin D improve prostate cancer diagnosis by avoiding potentially unnecessary prostate biopsies

Objectives

To investigate and further validate if two novel cancer‐related glycoproteins, discovered by a genetic‐guided proteomics approach, can distinguish benign disease from prostate cancer (PCa) in men with enlarged prostates.

Patients and Methods

A retrospective study was performed that included men with a total prostate‐specific antigen (PSA) concentration of 2.0–10 ng/mL, negative digital rectal examination and enlarged prostate (volume ≥35 mL). Serum samples were collected between 2011 and 2016 at a single centre from 474 men before they underwent prostate biopsy. Serum concentrations of thrombospondin 1 (THBS1) and cathepsin D (CTSD) glycoproteins were combined with the percentage of free PSA to total PSA ratio (%fPSA) to predict any or significant cancer at biopsy.

Results

The multivariable logistic regression model including THBS1, CTSD and %fPSA discriminated among biopsy‐positive and biopsy‐negative patients in the validation set with an area under the curve (AUC) of 0.86 (P < 0.001, 95% confidence interval (CI) 0.82–0.91), while %fPSA alone showed an AUC of 0.64 (P < 0.001, 95% CI 0.57–0.71). At 90% sensitivity for PCa, the specificity of the model was 62%, while %fPSA had a specificity of 23%. For high grade (Gleason score ≥ 7 in prostatectomy specimen) PCa, the specificity was 48% at 90% sensitivity, with an AUC of 0.83, (P < 0.001, 95% CI 0.77 to 0.88). Limitations of the study include the retrospective set‐up and single‐centre cohort.

Conclusions

A model combining two cancer‐related glycoproteins (THBS1 and CTSD) and %fPSA can improve PCa diagnosis and may reduce the number of unnecessary prostate biopsies because of its improved specificity for PCa when compared to %fPSA alone.

Prostate cancer detection from multi-institution multiparametric MRIs using deep convolutional neural networks

Multiparametric magnetic resonance imaging (mpMRI) of the prostate aids in early diagnosis of prostate cancer, but is difficult to interpret and subject to interreader variability. Our objective is to generate probability maps, overlaid on original mpMRI images to help radiologists identify where a cancer is suspected as a computer-aided diagnostic (CAD). We optimized the holistically nested edge detection (HED) deep convolutional neural network. Our dataset contains T2, apparent diffusion coefficient, and high b -value images from 186 patients across six institutions worldwide: 92 with an endorectal coil (ERC) and 94 without. Ground-truth was based on tumor segmentations manually drawn by expert radiologists based on histologic evidence of cancer. The training set consisted of 120 patients and the validation set and test set included 19 and 47, respectively. Slice-level probability maps are evaluated at the lesion level of analysis. The best model: HED using 5 × 5 convolutional kernels, batch normalization, and optimized using Adam. This CAD performed significantly better ( p < 0.001 ) in the peripheral zone ( AUC = 0.94 ± 0.01 ) than the transition zone. It outperforms a previous CAD from our group in a head-to-head comparison on the same ERC-only test cases ( AUC = 0.97 ± 0.01 ; p < 0.001 ). Our CAD establishes a state-of-the-art performance for predicting prostate cancer lesions on mpMRIs.

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.