Multi-platform Affinity Proteomics Identify Proteins Linked to Metastasis and Immune Suppression in Ovarian Cancer Plasma

Effects of In Vitro Hemolysis and Repeated Freeze-Thaw Cycles in Protein Abundance Quantification Using the SomaScan and Olink Assays

SomaScan and Olink are affinity-based platforms that aim to estimate the relative abundance of thousands of human proteins with a broad range of endogenous concentrations. In this study, we investigated the effects of in vitro hemolysis and repeated freeze-thaw cycles in protein abundance quantification across 10,776 (11 K SomaScan) and 1472 (Olink Explore 1536) analytes, respectively. Using SomaScan, we found two distinct groups, each one consisting of 4% of all aptamers, affected by either hemolysis or freeze-thaw cycles. Using Olink, we found 6% of analytes affected by freeze-thaw cycles and nearly half of all measured probes significantly impacted by hemolysis. Moreover, we observed that Olink probes affected by hemolysis target proteins with a larger number of annotated protein-protein interactions. We found that Olink probes affected by hemolysis were significantly associated with the erythrocyte proteome, whereas SomaScan probes were not. Given the extent of the observed nuisance effects, we propose that unbiased, quantitative methods of evaluating hemolysis, such as the hemolysis index successfully implemented in many clinical laboratories, should be adopted in proteomics studies. We provide detailed results for each SomaScan and Olink probe in the form of extensive Supporting Information files to be used as resources for the growing user communities of both platforms.

Effects of in vitro hemolysis and repeated freeze-thaw cycles in protein abundance quantification using the SomaScan and Olink assays

SomaScan and Olink are affinity-based platforms that aim to estimate the relative abundance of thousands of human proteins with a broad range of endogenous concentrations. In this study, we investigated the effects of in vitro hemolysis and repeated freeze-thaw cycles in protein abundance quantification across 10,776 (11K SomaScan) and 1472 (Olink Explore 1536) analytes, respectively. Using SomaScan, we found two distinct groups, each one consisting of 4% of all aptamers, affected by either hemolysis or freeze-thaw cycles. Using Olink, we found 6% of analytes affected by freeze-thaw cycles and nearly half of all measured probes significantly impacted by hemolysis. Moreover, we observed that Olink probes affected by hemolysis target proteins with a larger number of annotated protein-protein interactions. We found that Olink probes affected by hemolysis were significantly associated with the erythrocyte proteome, whereas SomaScan probes were not. Given the extent of the observed nuisance effects, we propose that unbiased, quantitative methods of evaluating hemolysis, such as the hemolysis index successfully implemented in many clinical laboratories, should be adopted in proteomics studies. We provide detailed results for each SomaScan and Olink probe in the form of extensive Supplementary Data files to be used as resources for the growing user communities of both platforms.

Head-to-Head Comparison of Aptamer- and Antibody-Based Proteomic Platforms in Human Cerebrospinal Fluid Samples from a Real-World Memory Clinic Cohort

High-throughput proteomic platforms are crucial to identify novel Alzheimer's disease (AD) biomarkers and pathways. In this study, we evaluated the reproducibility and reliability of aptamer-based (SomaScan® 7k) and antibody-based (Olink® Explore 3k) proteomic platforms in cerebrospinal fluid (CSF) samples from the Ace Alzheimer Center Barcelona real-world cohort. Intra- and inter-platform reproducibility were evaluated through correlations between two independent SomaScan® assays analyzing the same samples, and between SomaScan® and Olink® results. Association analyses were performed between proteomic measures, CSF biological traits, sample demographics, and AD endophenotypes. Our 12-category metric of reproducibility combining correlation analyses identified 2428 highly reproducible SomaScan CSF measures, with over 600 proteins well reproduced on another proteomic platform. The association analyses among AD clinical phenotypes revealed that the significant associations mainly involved reproducible proteins. The validation of reproducibility in these novel proteomics platforms, measured using this scarce biomaterial, is essential for accurate analysis and proper interpretation of innovative results. This classification metric could enhance confidence in multiplexed proteomic platforms and improve the design of future panels.

Crossing the Halfway Point: Aptamer-Based, Highly Multiplexed Assay for the Assessment of the Proteome

Measuring responses in the proteome to various perturbations improves our understanding of biological systems. The value of information gained from such studies is directly proportional to the number of proteins measured. To overcome technical challenges associated with highly multiplexed measurements, we developed an affinity reagent-based method that uses aptamers with protein-like side chains along with an assay that takes advantage of their unique properties. As hybrid affinity reagents, modified aptamers are fully comparable to antibodies in terms of binding characteristics toward proteins, including epitope size, shape complementarity, affinity and specificity. Our assay combines these intrinsic binding properties with serial kinetic proofreading steps to allow highly effective partitioning of stable specific complexes from unstable nonspecific complexes. The use of these orthogonal methods to enhance specificity effectively overcomes the severe limitation to multiplexing inherent to the use of sandwich-based methods. Our assay currently measures half of the unique proteins encoded in the human genome with femtomolar sensitivity, broad dynamic range and exceptionally high reproducibility. Using machine learning to identify patterns of change, we have developed tests based on measurement of multiple proteins predictive of current health states and future disease risk to guide a holistic approach to precision medicine.

Protein biomarker signatures of preeclampsia - a longitudinal 5000-multiplex proteomics study

We aimed to explore novel biomarker candidates and biomarker signatures of late-onset preeclampsia (LOPE) by profiling samples collected in a longitudinal discovery cohort with a high-throughput proteomics platform. Using the Somalogic 5000-plex platform, we analyzed proteins in plasma samples collected at three visits (gestational weeks (GW) 12-19, 20-26 and 28-34 in 35 women with LOPE (birth ≥ 34 GW) and 70 healthy pregnant women). To identify biomarker signatures, we combined Elastic Net with Stability Selection for stable variable selection and validated their predictive performance in a validation cohort. The biomarker signature with the highest predictive performance (AUC 0.88 (95% CI 0.85-0.97)) was identified in the last trimester of pregnancy (GW 28-34) and included the Fatty acid amid hydrolase 2 (FAAH2), HtrA serine peptidase 1 (HTRA1) and Interleukin-17 receptor C (IL17RC) together with sFLT1 and maternal age, BMI and nulliparity. Our biomarker signature showed increased or similar predictive performance to the sFLT1/PGF-ratio within our data set, and we were able to validate the biomarker signature in a validation cohort (AUC ≥ 0.90). Further validation of these candidates should be performed using another protein quantification platform in an independent cohort where the negative and positive predictive values can be validly calculated.

Association of plasma proteomics with mortality in individuals with and without type 2 diabetes: Results from two population-based KORA cohort studies

BACKGROUND

Protein biomarkers may contribute to the identification of vulnerable subgroups for premature mortality. This study aimed to investigate the association of plasma proteins with all-cause and cause-specific mortality among individuals with and without baseline type 2 diabetes (T2D) and evaluate their impact on the prediction of all-cause mortality in two prospective Cooperative Health Research in the Region of Augsburg (KORA) studies.

METHODS

The discovery cohort comprised 1545 participants (median follow-up 15.6 years; 244 with T2D: 116 total, 62 cardiovascular, 31 cancer-related and 23 other-cause deaths; 1301 without T2D: 321 total, 114 cardiovascular, 120 cancer-related and 87 other-cause deaths). The validation cohort comprised 1031 participants (median follow-up 6.9 years; 203 with T2D: 76 total, 45 cardiovascular, 19 cancer-related and 12 other-cause deaths; 828 without T2D: 169 total, 74 cardiovascular, 39 cancer-related and 56 other-cause deaths). We used Cox regression to examine associations of 233 plasma proteins with all-cause and cause-specific mortality and Lasso regression to construct prediction models for all-cause mortality stratifying by baseline T2D. C-index, category-free net reclassification index (cfNRI), and integrated discrimination improvement (IDI) were conducted to evaluate the predictive performance of built prediction models.

RESULTS

Thirty-five and 62 proteins, with 29 overlapping, were positively associated with all-cause mortality in the group with and without T2D, respectively. Out of these, in the group with T2D, 35, eight, and 26 were positively associated with cardiovascular, cancer-related, and other-cause mortality, while in the group without T2D, 55, 41, and 47 were positively associated with respective cause-specific outcomes in the pooled analysis of both cohorts. Regulation of insulin-like growth factor (IGF) transport and uptake by IGF-binding proteins emerged as a unique pathway enriched for all-cause and cardiovascular mortality in individuals with T2D. The combined model containing the selected proteins (five and 12 proteins, with four overlapping, in the group with and without T2D, respectively) and clinical risk factors improved the prediction of all-cause mortality by C-index, cfNRI, and IDI.

CONCLUSIONS

This study uncovered shared and unique mortality-related proteins in persons with and without T2D and emphasized the role of proteins in improving the prediction of mortality in different T2D subgroups.

Methods and clinical biomarker discovery for targeted proteomics using Olink technology

PURPOSE

This paper is to offer insights for designing research utilizing Olink technology to identify biomarkers and potential therapeutic targets for disease treatment.

EXPERIMENTAL DESIGN

We discusses the application of Olink technology in oncology, cardiovascular, respiratory and immune-related diseases, and Outlines the advantages and limitations of Olink technology.

RESULTS

Olink technology simplifies the search for therapeutic targets, advances proteomics research, reveals the pathogenesis of diseases, and ultimately helps patients develop precision treatments.

CONCLUSIONS

Although proteomics technology has been rapidly developed in recent years, each method has its own disadvantages, so in the future research, more methods should be selected for combined application to verify each other.

Secretome Analysis Using Affinity Proteomics and Immunoassays: A Focus on Tumor Biology

The study of the cellular secretome using proteomic techniques continues to capture the attention of the research community across a broad range of topics in biomedical research. Due to their untargeted nature, independence from the model system used, historically superior depth of analysis, as well as comparative affordability, mass spectrometry-based approaches traditionally dominate such analyses. More recently, however, affinity-based proteomic assays have massively gained in analytical depth, which together with their high sensitivity, dynamic range coverage as well as high throughput capabilities render them exquisitely suited to secretome analysis. In this review, we revisit the analytical challenges implied by secretomics and provide an overview of affinity-based proteomic platforms currently available for such analyses, using the study of the tumor secretome as an example for basic and translational research.

Phenotyping Tumor Heterogeneity through Proteogenomics: Study Models and Challenges

Tumor heterogeneity refers to the diversity observed among tumor cells: both between different tumors (inter-tumor heterogeneity) and within a single tumor (intra-tumor heterogeneity). These cells can display distinct morphological and phenotypic characteristics, including variations in cellular morphology, metastatic potential and variability treatment responses among patients. Therefore, a comprehensive understanding of such heterogeneity is necessary for deciphering tumor-specific mechanisms that may be diagnostically and therapeutically valuable. Innovative and multidisciplinary approaches are needed to understand this complex feature. In this context, proteogenomics has been emerging as a significant resource for integrating omics fields such as genomics and proteomics. By combining data obtained from both Next-Generation Sequencing (NGS) technologies and mass spectrometry (MS) analyses, proteogenomics aims to provide a comprehensive view of tumor heterogeneity. This approach reveals molecular alterations and phenotypic features related to tumor subtypes, potentially identifying therapeutic biomarkers. Many achievements have been made; however, despite continuous advances in proteogenomics-based methodologies, several challenges remain: in particular the limitations in sensitivity and specificity and the lack of optimal study models. This review highlights the impact of proteogenomics on characterizing tumor phenotypes, focusing on the critical challenges and current limitations of its use in different clinical and preclinical models for tumor phenotypic characterization.

An overview of CCN4 (WISP1) role in human diseases

CCN4 (cellular communication network factor 4), a highly conserved, secreted cysteine-rich matricellular protein is emerging as a key player in the development and progression of numerous disease pathologies, including cancer, fibrosis, metabolic and inflammatory disorders. Over the past two decades, extensive research on CCN4 and its family members uncovered their diverse cellular mechanisms and biological functions, including but not limited to cell proliferation, migration, invasion, angiogenesis, wound healing, repair, and apoptosis. Recent studies have demonstrated that aberrant CCN4 expression and/or associated downstream signaling is key to a vast array of pathophysiological etiology, suggesting that CCN4 could be utilized not only as a non-invasive diagnostic or prognostic marker, but also as a promising therapeutic target. The cognate receptor of CCN4 remains elusive till date, which limits understanding of the mechanistic insights on CCN4 driven disease pathologies. However, as therapeutic agents directed against CCN4 begin to make their way into the clinic, that may start to change. Also, the pathophysiological significance of CCN4 remains underexplored, hence further research is needed to shed more light on its disease and/or tissue specific functions to better understand its clinical translational benefit. This review highlights the compelling evidence of overlapping and/or diverse functional and mechanisms regulated by CCN4, in addition to addressing the challenges, study limitations and knowledge gaps on CCN4 biology and its therapeutic potential.

Machine Learning-Enhanced Extraction of Biomarkers for High-Grade Serous Ovarian Cancer from Proteomics Data

Comprehensive biomedical proteomic datasets are accumulating exponentially, warranting robust analytics to deconvolute them for identifying novel biological insights. Here, we report a strategic machine learning (ML)-based feature extraction workflow that was applied to unveil high-performing protein markers for high-grade serous ovarian carcinoma (HGSOC) from publicly available ovarian cancer tissue and serum proteomics datasets. Diagnosis of HGSOC, an aggressive form of ovarian cancer, currently relies on diagnostic methods based on tissue biopsy and/or non-specific biomarkers such as the cancer antigen 125 (CA125) and human epididymis protein 4 (HE4). Our newly developed ML-based approach enabled the identification of new serum proteomic biomarkers for HGSOC. The performance verification of these marker combinations using two independent cohorts affirmed their outperformance against known biomarkers for ovarian cancer including clinically used serum markers with >97% AUC. Our analysis also added novel biological insights such as enriched cancer-related processes associated with HGSOC.

Sample-Treatment with the Virucidal β-Propiolactone Does Not Preclude Analysis by Large Panel Affinity Proteomics, Including the Discovery of Biomarker Candidates

Virus inactivation is a prerequisite for safe handling of high-risk infectious samples. β-Propiolactone (BPL) is an established reagent with proven virucidal efficacy. BPL primarily reacts with DNA, RNA, and amino acids. The latter may modify antigenic protein epitopes interfering with binding properties of affinity reagents such as antibodies and aptamers used in affinity proteomic screens. We investigated (i) the impact of BPL treatment on the analysis of protein levels in plasma samples using the aptamer-based affinity proteomic platform SomaScan and (ii) effects on protein detection in conditioned medium samples using the proximity extension assay-based Olink Target platform. In the former setup, BPL-treated and native plasma samples from patients with ovarian cancer (n = 12) and benign diseases (n = 12) were analyzed using the SomaScan platform. In the latter, conditioned media samples collected from cultured T cells with (n = 3) or without (n = 3) anti-CD3 antibody stimulation were analyzed using the Olink Target platform. BPL-related changes in protein detection were evaluated comparing native and BPL-treated states, simulating virus inactivation, and impact on measurable group differences was assessed. While approximately one-third of SomaScan measurements were significantly changed by the BPL treatment, a majority of antigen/aptamer interactions remained unaffected. Interaction effects of BPL treatment and disease state, potentially altering detectability of group differences, were observable for less than one percent of targets (0.6%). BPL effects on protein detection with Olink Target were also limited, affecting 3.6% of detected proteins with no observable interaction effects. Thus, effects of BPL treatment only moderately interfere with affinity proteomic detectability of differential protein expression between different experimental groups. Overall, the results prove high-throughput affinity proteomics well suited for the analysis of high-risk samples inactivated using BPL.

Construction and validation of classification models for predicting the response to concurrent chemo-radiotherapy of patients with esophageal squamous cell carcinoma based on multi-omics data

BACKGROUND

Concurrent chemo-radiotherapy (CCRT) is the preferred non-surgical treatment for patients with locally advanced esophageal squamous cell carcinoma (ESCC). Unfortunately, some patients respond poorly, which leads to inappropriate or excessive treatment and affects patient survival. To accurately predict the response of ESCC patients to CCRT, we developed classification models based on the clinical, serum proteomic and radiomic data.

METHODS

A total of 138 ESCC patients receiving CCRT were enrolled in this study and randomly split into a training cohort (n = 92) and a test cohort (n = 46). All patients were classified into either complete response (CR) or incomplete response (non-CR) groups according to RECIST1.1. Radiomic features were extracted by 3Dslicer. Serum proteomic data was obtained by Olink proximity extension assay. The logistic regression model with elastic-net penalty and the R-package "rms" v6.2-0 were applied to construct classification and nomogram models, respectively. The area under the receiver operating characteristic curves (AUC) was used to evaluate the predictive performance of the models.

RESULTS

Seven classification models based on multi-omics data were constructed, of which Model-COR, which integrates five clinical, five serum proteomic, and seven radiomic features, achieved the best predictive performance on the test cohort (AUC = 0.8357, 95 % CI: 0.7158-0.9556). Meanwhile, patients predicted to be CR by Model-COR showed significantly longer overall survival than those predicted to be non-CR in both cohorts (Log-rank P = 0.0014 and 0.027, respectively). Furthermore, two nomogram models based on multi-omics data also performed well in predicting response to CCRT (AUC = 0.8398 and 0.8483, respectively).

CONCLUSION

We developed and validated a multi-omics based classification model and two nomogram models for predicting the response of ESCC patients to CCRT, which achieved the best prediction performance by integrating clinical, serum Olink proteomic, and radiomic data. These models could be useful for personalized treatment decisions and more precise clinical radiotherapy and chemotherapy for ESCC patients.

Reciprocal crosstalk between Th17 and mesothelial cells promotes metastasis-associated adhesion of ovarian cancer cells

BACKGROUND

IL-17A and TNF synergistically promote inflammation and tumorigenesis. Their interplay and impact on ovarian carcinoma (OC) progression are, however, poorly understood. We addressed this question focusing on mesothelial cells, whose interaction with tumor cells is known to play a pivotal role in transcoelomic metastasis formation.

METHODS

Flow-cytometry and immunohistochemistry experiments were employed to identify cellular sources of IL-17A and TNF. Changes in transcriptomes and secretomes were determined by bulk and single cell RNA sequencing as well as affinity proteomics. Functional consequences were investigated by microscopic analyses and tumor cell adhesion assays. Potential clinical implications were assessed by immunohistochemistry and survival analyses.

RESULTS

We identified Th17 cells as the main population of IL-17A- and TNF producers in ascites and detected their accumulation in early omental metastases. Both IL-17A and its receptor subunit IL-17RC were associated with short survival of OC patients, pointing to a role in clinical progression. IL-17A and TNF synergistically induced the reprogramming of mesothelial cells towards a pro-inflammatory mesenchymal phenotype, concomitantly with a loss of tight junctions and an impairment of mesothelial monolayer integrity, thereby promoting cancer cell adhesion. IL-17A and TNF synergistically induced the Th17-promoting cytokines IL-6 and IL-1β as well as the Th17-attracting chemokine CCL20 in mesothelial cells, indicating a reciprocal crosstalk that potentiates the tumor-promoting role of Th17 cells in OC.

CONCLUSIONS

Our findings reveal a novel function for Th17 cells in the OC microenvironment, which entails the IL-17A/TNF-mediated induction of mesothelial-mesenchymal transition, disruption of mesothelial layer integrity and consequently promotion of OC cell adhesion. These effects are potentiated by a positive feedback loop between mesothelial and Th17 cells. Together with the observed clinical associations and accumulation of Th17 cells in omental micrometastases, our observations point to a potential role in early metastases formation and thus to new therapeutic options.

Cancer biomarkers: Emerging trends and clinical implications for personalized treatment

The integration of cancer biomarkers into oncology has revolutionized cancer treatment, yielding remarkable advancements in cancer therapeutics and the prognosis of cancer patients. The development of personalized medicine represents a turning point and a new paradigm in cancer management, as biomarkers enable oncologists to tailor treatments based on the unique molecular profile of each patient's tumor. In this review, we discuss the scientific milestones of cancer biomarkers and explore future possibilities to improve the management of patients with solid tumors. This progress is primarily attributed to the biological characterization of cancers, advancements in testing methodologies, elucidation of the immune microenvironment, and the ability to profile circulating tumor fractions. Integrating these insights promises to continually advance the precision oncology field, fostering better patient outcomes.

Raludotatug Deruxtecan, a CDH6-Targeting Antibody-Drug Conjugate with a DNA Topoisomerase I Inhibitor DXd, Is Efficacious in Human Ovarian and Kidney Cancer Models

Cadherin-6 (CDH6) is expressed in several cancer types, but no CDH6-targeted therapy is currently clinically available. Here, we generated raludotatug deruxtecan (R-DXd; DS-6000), a novel CDH6-targeting antibody-drug conjugate with a potent DNA topoisomerase I inhibitor, and evaluated its properties, pharmacologic activities, and safety profile. In vitro pharmacologic activities and the mechanisms of action of R-DXd were assessed in serous-type ovarian cancer and renal cell carcinoma cell lines. In vivo pharmacologic activities were evaluated with several human cancer cell lines and patient-derived xenograft mouse models. The safety profile in cynomolgus monkeys was also assessed. R-DXd exhibited CDH6 expression-dependent cell growth-inhibitory activity and induced tumor regression in xenograft models. In this process, R-DXd specifically bound to CDH6, was internalized into cancer cells, and then translocated to the lysosome. The DXd released from R-DXd induced the phosphorylation of Chk1, a DNA damage marker, and cleaved caspase-3, an apoptosis marker, in cancer cells. It was also confirmed that the DXd payload had a bystander effect, passing through the cell membrane and impacting surrounding cells. The safety profile of R-DXd was favorable and the highest non-severely toxic dose was 30 mg/kg in cynomolgus monkeys. R-DXd demonstrated potent antitumor activity against CDH6-expressing tumors in mice and an acceptable safety profile in monkeys. These findings indicate the potential of R-DXd as a new treatment option for patients with CDH6-expressing serous-type ovarian cancer and renal cell carcinoma in a clinical setting.

Analytical validation of the PROphet test for treatment decision-making guidance in metastatic non-small cell lung cancer

The blood proteome, consisting of thousands of proteins engaged in various biological processes, acts as a valuable source of potential biomarkers for various medical applications. PROphet is a plasma proteomics-based test that serves as a decision-support tool for non-small cell lung cancer (NSCLC) patients, combining proteomic profiling using SomaScan technology and subsequent computational algorithm. PROphet was implemented as a laboratory developed test (LDT). Under the Clinical Laboratory Improvement Amendments (CLIA) and Commission on Office Laboratory Accreditation (COLA) regulations, prior to releasing patient test results, a clinical laboratory located in the United States employing an LDT must examine its performance characteristics with regard to analytical validity. This study describes the experimental and computational analytical validity of the PROphet test, as required by CLIA/COLA regulations. Experimental precision analysis displayed a median coefficient of variation (CV) of 3.9 % and 4.7 % for intra-plate and inter-plate examination, respectively, and the median accuracy rate between sites was 88 %. Computational precision exhibited a high accuracy rate, with 93 % of samples displaying complete concordance in results. A cross-platform comparison between SomaScan and other proteomics platforms yielded a median Spearman's rank correlation coefficient of 0.51, affirming the consistency and reliability of the SomaScan platform as used under the PROphet test. Our study presents a robust framework for evaluating the analytical validity of a platform that combines an experimental assay with subsequent computational algorithms. When applied to the PROphet test, strong analytical performance of the test was demonstrated.

Differential Proteins Expression Distinguished Between Patients With Infectious and Noninfectious Uveitis

PURPOSE

We investigated the aqueous humor proteome and associated plasma proteome in patients with infectious or noninfectious uveitis.

METHODS

AH and plasma were obtained from 28 patients with infectious uveitis (IU), 29 patients with noninfectious uveitis (NIU) and 35 healthy controls undergoing cataract surgery. The proteins profile was analyzed by SomaScan technology.

RESULTS

We found 1844 and 2484 proteins up-regulated and 124 and 161 proteins down-regulated in the AH from IU and NIU groups, respectively. In the plasma, three proteins were up-regulated in NIU patients, and one and five proteins were down-regulated in the IU and NIU patients, respectively. The results of pathway enrichment analysis for both IU and NIU groups were related mostly to inflammatory and regulatory processes.

CONCLUSION

SomaScan was able to detect novel AH and plasma protein biomarkers in IU and NIU patients. Also, the unique proteins found in both AH and plasma suggest a protein signature that could distinguish between infectious and noninfectious uveitis.

The application of Aptamer in biomarker discovery

Biomarkers are detectable molecules that can reflect specific physiological states of cells, organs, and organisms and therefore be regarded as indicators for specific diseases. And the discovery of biomarkers plays an essential role in cancer management from the initial diagnosis to the final treatment regime. Practically, reliable clinical biomarkers are still limited, restricted by the suboptimal methods in biomarker discovery. Nucleic acid aptamers nowadays could be used as a powerful tool in the discovery of protein biomarkers. Nucleic acid aptamers are single-strand oligonucleotides that can specifically bind to various targets with high affinity. As artificial ssDNA or RNA, aptamers possess unique advantages compared to conventional antibodies. They can be flexible in design, low immunogenicity, relative chemical/thermos stability, as well as modifying convenience. Several SELEX (Systematic Evolution of Ligands by Exponential Enrichment) based methods have been generated recently to construct aptamers for discovering new biomarkers in different cell locations. Secretome SELEX-based aptamers selection can facilitate the identification of secreted protein biomarkers. The aptamers developed by cell-SELEX can be used to unveil those biomarkers presented on the cell surface. The aptamers from tissue-SELEX could target intracellular biomarkers. And as a multiplexed protein biomarker detection technology, aptamer-based SOMAScan can analyze thousands of proteins in a single run. In this review, we will introduce the principle and workflow of variations of SELEX-based methods, including secretome SELEX, ADAPT, Cell-SELEX and tissue SELEX. Another powerful proteome analyzing tool, SOMAScan, will also be covered. In the second half of this review, how these methods accelerate biomarker discovery in various diseases, including cardiovascular diseases, cancer and neurodegenerative diseases, will be discussed.

Novel proteomic technologies to address gaps in pre-clinical ovarian cancer biomarker discovery efforts

INTRODUCTION

An estimated 20,000 women in the United States will receive a diagnosis of ovarian cancer in 2023. Late-stage diagnosis is associated with poor prognosis. There is a need for novel diagnostic biomarkers for ovarian cancer to improve early-stage detection and novel prognostic biomarkers to improve patient treatment.

AREAS COVERED

This review provides an overview of the clinicopathological features of ovarian cancer and the currently available biomarkers and treatment options. Two affinity-based platforms using proximity extension assays (Olink) and DNA aptamers (SomaLogic) are described in the context of highly reproducible and sensitive multiplexed assays for biomarker discovery. Recent developments in ion mobility spectrometry are presented as novel techniques to apply to the biomarker discovery pipeline. Examples are provided of how these aforementioned methods are being applied to biomarker discovery efforts in various diseases, including ovarian cancer.

EXPERT OPINION

Translating novel ovarian cancer biomarkers from candidates in the discovery phase to bona fide biomarkers with regulatory approval will have significant benefits for patients. Multiplexed affinity-based assay platforms and novel mass spectrometry methods are capable of quantifying low abundance proteins to aid biomarker discovery efforts by enabling the robust analytical interrogation of the ovarian cancer proteome.

Mass Spectrometry-based Proteomics of Epithelial Ovarian Cancers: a Clinical Perspective

Increasing proteomic studies focused on epithelial ovarian cancer (EOC) have attempted to identify early disease biomarkers, establish molecular stratification, and discover novel druggable targets. Here we review these recent studies from a clinical perspective. Multiple blood proteins have been used clinically as diagnostic markers. The ROMA test integrates CA125 and HE4, while the OVA1 and OVA2 tests analyze multiple proteins identified by proteomics. Targeted proteomics has been widely used to identify and validate potential diagnostic biomarkers in EOCs, but none has yet been approved for clinical adoption. Discovery proteomic characterization of bulk EOC tissue specimens has uncovered a large number of dysregulated proteins, proposed new stratification schemes, and revealed novel targets of therapeutic potential. A major hurdle facing clinical translation of these stratification schemes based on bulk proteomic profiling is intra-tumor heterogeneity, namely that single tumor specimens may harbor molecular features of multiple subtypes. We reviewed over 2500 interventional clinical trials of ovarian cancers since 1990, and cataloged 22 types of interventions adopted in these trials. Among 1418 clinical trials which have been completed or are not recruiting new patients, about 50% investigated chemotherapies. Thirty-seven clinical trials are at phase 3 or 4, of which 12 focus on PARP, 10 on VEGFR, 9 on conventional anti-cancer agents, and the remaining on sex hormones, MEK1/2, PD-L1, ERBB, and FRα. Although none of the foregoing therapeutic targets were discovered by proteomics, newer targets discovered by proteomics, including HSP90 and cancer/testis antigens, are being tested also in clinical trials. To accelerate the translation of proteomic findings to clinical practice, future studies need to be designed and executed to the stringent standards of practice-changing clinical trials. We anticipate that the rapidly evolving technology of spatial and single-cell proteomics will deconvolute the intra-tumor heterogeneity of EOCs, further facilitating their precise stratification and superior treatment outcomes.

The 2022 Report on the Human Proteome from the HUPO Human Proteome Project

The 2022 Metrics of the Human Proteome from the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 407 (93.2%) of the 19 750 predicted proteins coded in the human genome, a net gain of 50 since 2021 from data sets generated around the world and reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 78 from 1421 to 1343. This represents continuing experimental progress on the human proteome parts list across all the chromosomes, as well as significant reclassifications. Meanwhile, applying proteomics in a vast array of biological and clinical studies continues to yield significant findings and growing integration with other omics platforms. We present highlights from the Chromosome-Centric HPP, Biology and Disease-driven HPP, and HPP Resource Pillars, compare features of mass spectrometry and Olink and Somalogic platforms, note the emergence of translation products from ribosome profiling of small open reading frames, and discuss the launch of the initial HPP Grand Challenge Project, "A Function for Each Protein".

High-Multiplex Aptamer-Based Serum Proteomics to Identify Candidate Serum Biomarkers of Oral Squamous Cell Carcinoma

Improved serological biomarkers are needed for the early detection, risk stratification and treatment surveillance of patients with oral squamous cell carcinoma (OSCC). We performed an exploratory study using advanced, highly specific, DNA-aptamer-based serum proteomics (SOMAscan, 1305-plex) to identify distinct proteomic changes in patients with OSCC pre- vs. post-resection and compared to healthy controls. A total of 63 significantly differentially expressed serum proteins (each p < 0.05) were found that could discriminate between OSCC and healthy controls with 100% accuracy. Furthermore, 121 proteins were detected that were significantly altered between pre- and post-resection sera, and 12 OSCC-associated proteins reversed to levels equivalent to healthy controls after resection. Of these, 6 were increased and 6 were decreased relative to healthy controls, highlighting the potential relevance of these proteins as OSCC tumor markers. Pathway analyses revealed potential pathophysiological mechanisms associated with OSCC. Hence, quantitative proteome analysis using SOMAscan technology is promising and may aid in the development of defined serum marker assays to predict tumor occurrence, progression and recurrence in OSCC, and to guide personalized therapies.

Using Artificial Intelligence to Better Predict and Develop Biomarkers

Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.

Comparison of Proteomic Measurements Across Platforms in the Atherosclerosis Risk in Communities (ARIC) Study

BACKGROUND

The plasma proteome can be quantified using different types of highly multiplexed technologies, including aptamer-based and proximity-extension immunoassay methods. There has been limited characterization of how these protein measurements correlate across platforms and with absolute measures from targeted immunoassays.

METHODS

We assessed the comparability of (a) highly multiplexed aptamer-based (SomaScan v4; Somalogic) and proximity-extension immunoassay (OLINK Proseek® v5003; Olink) methods in 427 Atherosclerosis Risk in Communities (ARIC) Study participants (Visit 5, 2011-2013), and (b) 18 of the SomaScan protein measurements against targeted immunoassays in 110 participants (55 cardiovascular disease cases, 55 controls). We calculated Spearman correlations (r) between the different measurements and compared associations with case-control status.

RESULTS

There were 417 protein comparisons (366 unique proteins) between the SomaScan and Olink platforms. The average correlation was r = 0.46 (range: -0.21 to 0.97; 79 [19%] with r ≥ 0.8). For the comparison of SomaScan and targeted immunoassays, 6 of 18 assays (growth differentiation factor 15 [GDF15], interleukin-1 receptor-like 1 [ST2], interstitial collagenase [MMP1], adiponectin, leptin, and resistin) had good correlations (r ≥ 0.8), 2 had modest correlations (0.5 ≤ r < 0.8; osteopontin and interleukin-6 [IL6]), and 10 were poorly correlated (r < 0.5; metalloproteinase inhibitor 1 [TIMP1], stromelysin-1 [MMP3], matrilysin [MMP7], C-C motif chemokine 2 [MCP1], interleukin-10 [IL10], vascular cell adhesion protein 1 [VCAM1], intercellular adhesion molecule 1 [ICAM1], interleukin-18 [IL18], tumor necrosis factor [TNFα], and visfatin) overall. Correlations for SomaScan and targeted immunoassays were similar according to case status.

CONCLUSIONS

There is variation in the quantitative measurements for many proteins across aptamer-based and proximity-extension immunoassays (approximately 1/2 showing good or modest correlation and approximately 1/2 poor correlation) and also for correlations of these highly multiplexed technologies with targeted immunoassays. Design and interpretation of protein quantification studies should be informed by the variation across measurement techniques for each protein.

Multi-platform proteomic analysis of Alzheimer's disease cerebrospinal fluid and plasma reveals network biomarkers associated with proteostasis and the matrisome

Robust and accessible biomarkers that can capture the heterogeneity of Alzheimer's disease and its diverse pathological processes are urgently needed. Here, we undertook an investigation of Alzheimer's disease cerebrospinal fluid (CSF) and plasma from the same subjects (n=18 control, n=18 AD) using three different proteomic platforms-SomaLogic SomaScan, Olink proximity extension assay, and tandem mass tag-based mass spectrometry-to assess which protein markers in these two biofluids may serve as reliable biomarkers of AD pathophysiology observed from unbiased brain proteomics studies. Median correlation of overlapping protein measurements across platforms in CSF (r~0.7) and plasma (r~0.6) was good, with more variability in plasma. The SomaScan technology provided the most measurements in plasma. Surprisingly, many proteins altered in AD CSF were found to be altered in the opposite direction in plasma, including important members of AD brain co-expression modules. An exception was SMOC1, a key member of the brain matrisome module associated with amyloid-β deposition in AD, which was found to be elevated in both CSF and plasma. Protein co-expression analysis on greater than 7000 protein measurements in CSF and 9500 protein measurements in plasma across all proteomic platforms revealed strong changes in modules related to autophagy, ubiquitination, and sugar metabolism in CSF, and endocytosis and the matrisome in plasma. Cross-platform and cross-biofluid proteomics represents a promising approach for AD biomarker development.

Long-term changes in plasma proteomic profiles in premenopausal and postmenopausal Black and White women: the Atherosclerosis Risk in Communities study

OBJECTIVE

The activity, localization, and turnover of proteins within cells and plasma may contribute to physiologic changes during menopause and may influence disease occurrence. We examined cross-sectional differences and long-term changes in plasma proteins between premenopausal and naturally postmenopausal women.

METHODS

We used data from 4,508 (19% Black) women enrolled in the Atherosclerosis Risk in Communities study. SOMAscan multiplexed aptamer technology was used to measure 4,697 plasma proteins. Linear regression models were used to compare differences in proteins at baseline (1993-1995) and 18-year change in proteins from baseline to 2011-2013.

RESULTS

At baseline, 472 women reported being premenopausal and 4,036 women reported being postmenopausal, with average ages of 52.3 and 61.4 years, respectively. A greater proportion of postmenopausal women had diabetes (15 vs 9%), used hypertension (38 vs 27%) and lipid-lowering medications (10 vs 3%), and had elevated total cholesterol and waist girth. In multivariable adjusted models, 38 proteins differed significantly between premenopausal and postmenopausal women at baseline, with 29 of the proteins also showing significantly different changes between groups over the 18-year follow-up as the premenopausal women also reached menopause. These proteins were associated with various molecular/cellular functions (cellular development, growth, proliferation and maintenance), physiological system development (skeletal and muscular system development, and cardiovascular system development and function), and diseases/disorders (hematological and metabolic diseases and developmental disorders).

CONCLUSIONS

We observed significantly different changes between premenopausal and postmenopausal women in several plasma proteins that reflect many biological processes. These processes may help to understand disease development during the postmenopausal period.

Apoptosis and tissue thinning contribute to symmetric cell division in the developing mouse epidermis in a nonautonomous way

Mitotic spindle orientation (SO) is a conserved mechanism that governs cell fate and tissue morphogenesis. In the developing epidermis, a balance between self-renewing symmetric divisions and differentiative asymmetric divisions is necessary for normal development. While the cellular machinery that executes SO is well characterized, the extrinsic cues that guide it are poorly understood. Here, we identified the basal cell adhesion molecule (BCAM), a β1 integrin coreceptor, as a novel regulator of epidermal morphogenesis. In utero RNAi-mediated depletion of Bcam in the mouse embryo did not hinder β1 integrin distribution or cell adhesion and polarity. However, Bcam depletion promoted apoptosis, thinning of the epidermis, and symmetric cell division, and the defects were reversed by concomitant overexpression of the apoptosis inhibitor Xiap. Moreover, in mosaic epidermis, depletion of Bcam or Xiap induced symmetric divisions in neighboring wild-type cells. These results identify apoptosis and epidermal architecture as extrinsic cues that guide SO in the developing epidermis.

A Targeted Proteomics Approach for Screening Serum Biomarkers Observed in the Early Stage of Type I Endometrial Cancer

Endometrial cancer (EC) is the most common gynecologic malignancy, and it arises in the inner part of the uterus. Identification of serum biomarkers is essential for diagnosing the disease at an early stage. In this study, we selected 44 healthy controls and 44 type I EC at tumor stage 1, and we used the Immuno-oncology panel and the Target 96 Oncology III panel to simultaneously detect the levels of 92 cancer-related proteins in serum, using a proximity extension assay. By applying this methodology, we identified 20 proteins, associated with the outcome at binary logistic regression, with a p-value below 0.01 for the first panel and 24 proteins with a p-value below 0.02 for the second one. The final multivariate logistic regression model, combining proteins from the two panels, generated a model with a sensitivity of 97.67% and a specificity of 83.72%. These results support the use of the proposed algorithm after a validation phase.

Prospective evaluation of 92 serum protein biomarkers for early detection of ovarian cancer

BACKGROUND

CA125 is the best available yet insufficiently sensitive biomarker for early detection of ovarian cancer. There is a need to identify novel biomarkers, which individually or in combination with CA125 can achieve adequate sensitivity and specificity for the detection of earlier-stage ovarian cancer.

METHODS

In the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we measured serum levels of 92 preselected proteins for 91 women who had blood sampled ≤18 months prior to ovarian cancer diagnosis, and 182 matched controls. We evaluated the discriminatory performance of the proteins as potential early diagnostic biomarkers of ovarian cancer.

RESULTS

Nine of the 92 markers; CA125, HE4, FOLR1, KLK11, WISP1, MDK, CXCL13, MSLN and ADAM8 showed an area under the ROC curve (AUC) of ≥0.70 for discriminating between women diagnosed with ovarian cancer and women who remained cancer-free. All, except ADAM8, had shown at least equal discrimination in previous case-control comparisons. The discrimination of the biomarkers, however, was low for the lag-time of >9-18 months and paired combinations of CA125 with any of the 8 markers did not improve discrimination compared to CA125 alone.

CONCLUSION

Using pre-diagnostic serum samples, this study identified markers with good discrimination for the lag-time of 0-9 months. However, the discrimination was low in blood samples collected more than 9 months prior to diagnosis, and none of the markers showed major improvement in discrimination when added to CA125.

Using Artificial Intelligence to Better Predict and Develop Biomarkers

Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.

Identification and verification of the molecular mechanisms and prognostic values of the cadherin gene family in gastric cancer

While cadherin (CDH) genes are aberrantly expressed in cancers, the functions of CDH genes in gastric cancer (GC) remain poorly understood. The clinical significance and molecular mechanisms of CDH genes in GC were assessed in this study. Data from a total of 1226 GC patients included in The Cancer Genome Atlas (TCGA) and Kaplan–Meier plotter database were used to independently explore the value of CDH genes in clinical application. The TCGA RNA sequencing dataset was used to explore the molecular mechanisms of CDH genes in GC. Using enrichment analysis tools, CDH genes were found to be related to cell adhesion and calcium ion binding in function. In TCGA cohort, 12 genes were found to be differentially expressed between GC para-carcinoma and tumor tissue. By analyzing GC patients in two independent cohorts, we identified and verified that CDH2, CDH6, CDH7 and CDH10 were significantly associated with a poor GC prognosis. In addition, CDH2 and CDH6 were used to construct a GC risk score signature that can significantly improve the accuracy of predicting the 5-year survival of GC patients. The GSEA approach was used to explore the functional mechanisms of the four prognostic CDH genes and their associated risk scores. It was found that these genes may be involved in multiple classic cancer-related signaling pathways, such as the Wnt and phosphoinositide 3-kinase signaling pathways in GC. In the subsequent CMap analysis, three small molecule compounds (anisomycin, nystatin and bumetanide) that may be the target molecules that determine the risk score in GC, were initially screened. In conclusion, our current study suggests that four CDH genes can be used as potential biomarkers for GC prognosis. In addition, a prognostic signature based on the CDH2 and CDH6 genes was constructed, and their potential functional mechanisms and drug interactions explored.

Uncovering the Depths of the Human Proteome: Antibody-based Technologies for Ultrasensitive Multiplexed Protein Detection and Quantification

Probing the human proteome in tissues and biofluids such as plasma is attractive for biomarker and drug target discovery. Recent breakthroughs in multiplex, antibody-based, proteomics technologies now enable the simultaneous quantification of thousands of proteins at as low as sub fg/ml concentrations with remarkable dynamic ranges of up to 10-log. We herein provide a comprehensive guide to the methodologies, performance, technical comparisons, advantages, and disadvantages of established and emerging technologies for the multiplexed ultrasensitive measurement of proteins. Gaining holistic knowledge on these innovations is crucial for choosing the right multiplexed proteomics tool for applications at hand to critically complement traditional proteomics methods. This can bring researchers closer than ever before to elucidating the intricate inner workings and cross talk that spans multitude of proteins in disease mechanisms.

Slow Off-Rate Modified Aptamer (SOMAmer) Proteomic Analysis of Patient-Derived Malignant Glioma Identifies Distinct Cellular Proteomes

Malignant gliomas derive from brain glial cells and represent >75% of primary brain tumors. This includes anaplastic astrocytoma (grade III; AS), the most common and fatal glioblastoma multiforme (grade IV; GBM), and oligodendroglioma (ODG). We have generated patient-derived AS, GBM, and ODG cell models to study disease mechanisms and test patient-centered therapeutic strategies. We have used an aptamer-based high-throughput SOMAscan^® 1.3K assay to determine the proteomic profiles of 1307 different analytes. SOMAscan^® proteomes of AS and GBM self-organized into closely adjacent proteomes which were clearly distinct from ODG proteomes. GBM self-organized into four proteomic clusters of which SOMAscan^® cluster 4 proteome predicted a highly inter-connected proteomic network. Several up- and down-regulated proteins relevant to glioma were successfully validated in GBM cell isolates across different SOMAscan^® clusters and in corresponding GBM tissues. Slow off-rate modified aptamer proteomics is an attractive analytical tool for rapid proteomic stratification of different malignant gliomas and identified cluster-specific SOMAscan^® signatures and functionalities in patient GBM cells.

Comparison of two multiplexed technologies for profiling >1,000 serum proteins that may associate with tumor burden

Background: In this pilot study, we perform a preliminary comparison of two targeted multiplex proteomics technologies for discerning serum protein concentration changes that may correlate to tumor burden in ovarian cancer (OC) patients. Methods: Using the proximity extension assay (PEA) and Quantibody® Kiloplex Array (QKA), we measured >1,000 proteins in the pre-surgical and post-surgical serum from nine OC patients (N=18 samples). We expect that proteins that have decreased significantly in the post-surgical serum concentration may correlate to tumor burden in each patient. Duplicate sera from two healthy individuals were used as controls (N=4 samples). We employed in-house ELISAs to measure five proteins with large serum concentration changes in pre- and post-surgical sera, from four of the original nine patients and the two original controls. Results: Both platforms showed a weak correlation with clinical cancer antigen 125 (CA125) data. The two multiplexed platforms showed a significant correlation with each other for >400 overlapping proteins. PEA uncovered 15 proteins, while QKA revealed 11 proteins, with more than a two-fold post-surgical decrease in at least six of the nine patients. Validation using single enzyme-linked immunosorbent assays (ELISAs) showed at least a two-fold post-surgical decrease in serum concentration of the same patients, as indicated by the two multiplex assays. Conclusion: Both methods identified proteins that had significantly decreased in post-surgical serum concentration, as well as recognizing proteins that had been implicated in OC patients. Our findings from a limited sample size suggest that novel targeted proteomics platforms are promising tools for identifying candidate serological tumor-related proteins.  However further studies are essential for the improvement of accuracy and avoidance of false results.

Discovery of CASP8 as a potential biomarker for high-risk prostate cancer through a high-multiplex immunoassay

Prostate cancer remains the most common non-cutaneous malignancy among men in the United States. To discover potential serum-based biomarkers for high-risk prostate cancer, we performed a high-multiplex immunoassay utilizing patient-matched pre-operative and post-operative serum samples from ten men with high-grade and high-volume prostate cancer. Our study identified six (CASP8, MSLN, FGFBP1, ICOSLG, TIE2 and S100A4) out of 174 proteins that were significantly decreased after radical prostatectomy. High levels of CASP8 were detected in pre-operative serum samples when compared to post-operative serum samples and serum samples from patients with benign prostate hyperplasia (BPH). By immunohistochemistry, CASP8 protein was expressed at higher levels in prostate cancer tissues compared to non-cancerous and BPH tissues. Likewise, CASP8 mRNA expression was significantly upregulated in prostate cancer when compared to benign prostate tissues in four independent clinical datasets. In addition, mRNA levels of CASP8 were higher in patients with recurrent prostate cancer when compared to patients with non-recurrent prostate cancer and high expression of CASP8 was associated with worse disease-free survival and overall survival in renal cancer. Together, our results suggest that CASP8 may potentially serve as a biomarker for high-risk prostate cancer and possibly renal cancer.

The Kunitz-type serine protease inhibitor Spint2 is required for cellular cohesion, coordinated cell migration and cell survival during zebrafish hatching gland development

We have previously shown that the Kunitz-type serine protease inhibitor Spint1a, also named Hai1a, is required in the zebrafish embryonic epidermis to restrict the activity of the type II transmembrane serine protease (TTSP) Matriptase1a/St14a, thereby ensuring epidermal homeostasis. A closely related Kunitz-type inhibitor is Spint2/Hai2, which in mammals plays multiple developmental roles that are either redundant or non-redundant with those of Spint1. However, the molecular bases for these non-redundancies are not fully understood. Here, we study spint2 during zebrafish development. It is co-expressed with spint1a in multiple embryonic epithelia, including the outer/peridermal layer of the epidermis. However, unlike spint1a, spint2 expression is absent from the basal epidermal layer but present in hatching gland cells. Hatching gland cells derive from the mesendodermal prechordal plate, from where they undergo a thus far undescribed transit into, and coordinated sheet migration within, the interspace between the outer and basal layer of the epidermis to reach their final destination on the yolk sac. Hatching gland cells usually survive their degranulation that drives embryo hatching but die several days later. In spint2 mutants, cohesion among hatching gland cells and their collective intra-epidermal migration are disturbed, leading to a discontinuous organization of the gland. In addition, cells undergo precocious cell death before degranulation, so that embryos fail to hatch. Chimera analyses show that Spint2 is required in hatching gland cells, but not in the overlying periderm, their potential migration and adhesion substrate. Spint2 acts independently of all tested Matriptases, Prostasins and other described Spint1 and Spint2 mediators. However, it displays a tight genetic interaction with and acts at least partly via the cell-cell adhesion protein E-cadherin, promoting both hatching gland cell cohesiveness and survival, in line with formerly reported effects of E-cadherin during morphogenesis and cell death suppression. In contrast, no such genetic interaction was observed between Spint2 and the cell-cell adhesion molecule EpCAM, which instead interacts with Spint1a. Our data shed new light onto the mechanisms of hatching gland morphogenesis and hatching gland cell survival. In addition, they reveal developmental roles of Spint2 that are strikingly different from those of Spint1, most likely due to differences in the expression patterns and relevant target proteins.

CCN Family Proteins in Cancer: Insight Into Their Structures and Coordination Role in Tumor Microenvironment

The crosstalk between tumor cells and the tumor microenvironment (TME), triggers a variety of critical signaling pathways and promotes the malignant progression of cancer. The success rate of cancer therapy through targeting single molecule of this crosstalk may be extremely low, whereas co-targeting multiple components could be complicated design and likely to have more side effects. The six members of cellular communication network (CCN) family proteins are scaffolding proteins that may govern the TME, and several studies have shown targeted therapy of CCN family proteins may be effective for the treatment of cancer. CCN protein family shares similar structures, and they mutually reinforce and neutralize each other to serve various roles that are tightly regulated in a spatiotemporal manner by the TME. Here, we review the current knowledge on the structures and roles of CCN proteins in different types of cancer. We also analyze CCN mRNA expression, and reasons for its diverse relationship to prognosis in different cancers. In this review, we conclude that the discrepant functions of CCN proteins in different types of cancer are attributed to diverse TME and CCN truncated isoforms, and speculate that targeting CCN proteins to rebalance the TME could be a potent anti-cancer strategy.

Advances in Aptamer-Based Biomarker Discovery

The discovery and identification of biomarkers promote the rational and fast development of medical diagnosis and therapeutics. Clinically, the application of ideal biomarkers still is limited due to the suboptimal technology in biomarker discovery. Aptamers are single-stranded deoxyribonucleic acid or ribonucleic acid molecules and can selectively bind to varied targets with high affinity and specificity. Compared with antibody, aptamers have desirable advantages, such as flexible design, easy synthesis and convenient modification with different functional groups. Currently, different aptamer-based technologies have been developed to facilitate biomarker discovery, especially CELL-SELEX and SOMAScan technology. CELL-SELEX technology is mainly used to identify cell membrane surface biomarkers of various cells. SOMAScan technology is an unbiased biomarker detection method that can analyze numerous and even thousands of proteins in complex biological samples at the same time. It has now become a large-scale multi-protein biomarker discovery platform. In this review, we introduce the aptamer-based biomarker discovery technologies, and summarize and highlight the discovered emerging biomarkers recently in several diseases.

Plasma protein expression profiles, cardiovascular disease, and religious struggles among South Asians in the MASALA study

Blood protein concentrations are clinically useful, predictive biomarkers of cardiovascular disease (CVD). Despite a higher burden of CVD among U.S. South Asians, no CVD-related proteomics study has been conducted in this sub-population. The aim of this study is to investigate the associations between plasma protein levels and CVD incidence, and to assess the potential influence of religiosity/spirituality (R/S) on significant protein-CVD associations, in South Asians from the MASALA Study. We used a nested case–control design of 50 participants with incident CVD and 50 sex- and age-matched controls. Plasma samples were analyzed by SOMAscan for expression of 1305 proteins. Multivariable logistic regression models and model selection using Akaike Information Criteria were performed on the proteins and clinical covariates, with further effect modification analyses conducted to assess the influence of R/S measures on significant associations between proteins and incident CVD events. We identified 36 proteins that were significantly expressed differentially among CVD cases compared to matched controls. These proteins are involved in immune cell recruitment, atherosclerosis, endothelial cell differentiation, and vascularization. A final multivariable model found three proteins (Contactin-5 [CNTN5], Low affinity immunoglobulin gamma Fc region receptor II-a [FCGR2A], and Complement factor B [CFB]) associated with incident CVD after adjustment for diabetes (AUC = 0.82). Religious struggles that exacerbate the adverse impact of stressful life events, significantly modified the effect of Contactin-5 and Complement factor B on risk of CVD. Our research is this first assessment of the relationship between protein concentrations and risk of CVD in a South Asian sample. Further research is needed to understand patterns of proteomic profiles across diverse ethnic communities, and the influence of resources for resiliency on proteomic signatures and ultimately, risk of CVD.

Proteome-wide Systems Genetics to Identify Functional Regulators of Complex Traits

Proteomic technologies now enable the rapid quantification of thousands of proteins across genetically diverse samples. Integration of these data with systems-genetics analyses is a powerful approach to identify new regulators of economically important or disease-relevant phenotypes in various populations. In this review, we summarize the latest proteomic technologies and discuss technical challenges for their use in population studies. We demonstrate how the analysis of correlation structure and loci mapping can be used to identify genetic factors regulating functional protein networks and complex traits. Finally, we provide an extensive summary of the use of proteome-wide systems genetics throughout fungi, plant, and animal kingdoms and discuss the power of this approach to identify candidate regulators and drug targets in large human consortium studies.

Phosphoproteomics identify arachidonic-acid-regulated signal transduction pathways modulating macrophage functions with implications for ovarian cancer

Arachidonic acid (AA) is a polyunsaturated fatty acid present at high concentrations in the ovarian cancer (OC) microenvironment and associated with a poor clinical outcome. In the present study, we have unraveled a potential link between AA and macrophage functions.

Methods: AA-triggered signal transduction was studied in primary monocyte-derived macrophages (MDMs) by phosphoproteomics, transcriptional profiling, measurement of intracellular Ca²⁺ accumulation and reactive oxygen species production in conjunction with bioinformatic analyses. Functional effects were investigated by actin filament staining, quantification of macropinocytosis and analysis of extracellular vesicle release.

Results: We identified the ASK1 - p38δ/α (MAPK13/14) axis as a central constituent of signal transduction pathways triggered by non-metabolized AA. This pathway was induced by the Ca²⁺-triggered activation of calmodulin kinase II, and to a minor extent by ROS generation in a subset of donors. Activated p38 in turn was linked to a transcriptional stress response associated with a poor relapse-free survival. Consistent with the phosphorylation of the p38 substrate HSP27 and the (de)phosphorylation of multiple regulators of Rho family GTPases, AA impaired actin filament organization and inhibited actin-driven macropinocytosis. AA also affected the phosphorylation of proteins regulating vesicle biogenesis, and consistently, AA enhanced the release of tetraspanin-containing exosome-like vesicles. Finally, we identified phospholipase A₂ group 2A (PLA2G2A) as the clinically most relevant enzyme producing extracellular AA, providing further potentially theranostic options.

Conclusion: Our results suggest that AA contributes to an unfavorable clinical outcome of OC by impacting the phenotype of tumor-associated macrophages. Besides critical AA-regulated signal transduction proteins identified in the present study, PLA2G2A might represent a potential prognostic tool and therapeutic target to interfere with OC progression.

The Prevalence, Associated Factors for Lung Metastases Development and Prognosis in Ovarian Serous Cancer Based on SEER Database

Ovarian carcinoma (OC) is one of the 3 most common gynecological malignancies, and the prognosis of patients with lung metastasis was the worst. SEER documented OC patients, diagnosed between 2010 and 2016, were included in the study. Univariable and multivariable logistic regression analyses were performed to identify associated factors for lung metastases (LM) development. Kaplan–Meier analysis was used to estimate the overall survival for OC patients with LM. A total of 10146 eligible serous ovarian cancer (SOC) patients were included, the prevalence of LM was 3.77% (N = 378). Patients with T4 stage (χ² = 128.515; P = 0.000), N1 stage (χ² = 49.536; P = 0.000), right laterality (χ² = 18.756; P = 0.000) (compared with left side), undifferentiated grade (χ² = 36.174; P = 0.000), bone metastasis (χ² = 183.529); P = 0.000), brain metastasis (χ² = 117.539; P = 0.000), liver metastasis (χ² = 442.472; P = 0.000) had a larger probability of LM than other groups. Results showed that T3/N1 stage, bone metastases, liver metastases, chemotherapy, surgery were positively correlated with LM. Multivariable cox analysis showed that age, bone metastasis, no chemotherapy, no surgery were independent risk factors in SOC-LM patients. This study provided new research insights on the prevalent LM in patients with SOC. The factors associated with LM development and prognosis can be potentially used for LM early screening and professional care.

Multiplatform Approach for Plasma Proteomics: Complementarity of Olink Proximity Extension Assay Technology to Mass Spectrometry-Based Protein Profiling

The plasma proteome is the ultimate target for biomarker discovery. It stores an endless amount of information on the pathophysiological status of a living organism, which is, however, still difficult to comprehensively access. The high complexity of the plasma proteome can be addressed by either a system-wide and unbiased tool such as mass spectrometry (LC-MS/MS) or a highly sensitive targeted immunoassay such as the proximity extension assay (PEA). To address relevant differences and important shared characteristics, we tested the performance of LC-MS/MS in the data-dependent and data-independent acquisition modes and Olink PEA to measure circulating plasma proteins in 173 human plasma samples from a Southern German population-based cohort. We demonstrated the measurement of more than 300 proteins with both LC-MS/MS approaches applied, mainly including high-abundance plasma proteins. By the use of the PEA technology, we measured 728 plasma proteins, covering a broad dynamic range with high sensitivity down to pg/mL concentrations. Then, we quantified 35 overlapping proteins with all three analytical platforms, verifying the reproducibility of data distributions, measurement correlation, and gender-based differential expression. Our work highlights the limitations and the advantages of both targeted and untargeted approaches and proves their complementary strengths. We demonstrated a significant gain in proteome coverage depth and subsequent biological insight by a combination of platforms-a promising approach for future biomarker and mechanistic studies.

Finding a Needle in a Haystack: Proteomics in Heart Failure

•

Proteomics has aided HF biomarker discovery, which allows for greater disease insights.

•

Experiment design can be tailored to HF research to discover novel biomarkers.

•

Primary methods include MS, protein microarray, aptamer, and PEA-based technologies.

•

Proteomics can detect unique low abundance proteins and detect protein modifications.

Circulating protein biomarkers provide information regarding pathways in heart failure (HF) and can add important value to clinicians. Advancements in proteomics allow researchers to measure a multitude of proteins simultaneously with excellent sensitivity and selectivity to detect low abundance proteins. This helps identify previously unrecognized pathways in HF and discover biomarkers and potential targets for HF therapies. Although several proteomic methods exist, including mass spectrometry, protein microarray, aptamer, and proximity extension assay−based techniques, each have their unique advantages. This paper provides an overview of the various proteomic methods, with examples of how each has contributed to understanding the pathways in HF.

Biomarkers, Master Regulators and Genomic Fabric Remodeling in a Case of Papillary Thyroid Carcinoma

Publicly available (own) transcriptomic data have been analyzed to quantify the alteration in functional pathways in thyroid cancer, establish the gene hierarchy, identify potential gene targets and predict the effects of their manipulation. The expression data have been generated by profiling one case of papillary thyroid carcinoma (PTC) and genetically manipulated BCPAP (papillary) and 8505C (anaplastic) human thyroid cancer cell lines. The study used the genomic fabric paradigm that considers the transcriptome as a multi-dimensional mathematical object based on the three independent characteristics that can be derived for each gene from the expression data. We found remarkable remodeling of the thyroid hormone synthesis, cell cycle, oxidative phosphorylation and apoptosis pathways. Serine peptidase inhibitor, Kunitz type, 2 (SPINT2) was identified as the Gene Master Regulator of the investigated PTC. The substantial increase in the expression synergism of SPINT2 with apoptosis genes in the cancer nodule with respect to the surrounding normal tissue (NOR) suggests that SPINT2 experimental overexpression may force the PTC cells into apoptosis with a negligible effect on the NOR cells. The predictive value of the expression coordination for the expression regulation was validated with data from 8505C and BCPAP cell lines before and after lentiviral transfection with DDX19B.