Biomarker discovery for heterogeneous diseases

Nonparametric estimation of linear personalized diagnostics rules via efficient grid algorithm

Many diseases are heterogeneous, comprised of multiple disease subgroups. It is of great interest but highly unlikely to find a single biomarker that can accurately detect such heterogeneous diseases across different subgroups. In this article, we propose to estimate a personalized diagnostic rule (PDR) to tailor more effective biomarkers to each individual according to a linear combination of his or her profiles. A standard grid search algorithm can be used to estimate the optimal linear PDR that maximizes the area under the receiver operating characteristics curve (AUC) among all the linear PDRs, but it is time-consuming especially when the number of variables is large. Alternatively, we developed an efficient grid rotation algorithm that provides a nearly suboptimal solution and studied its variation to find the optimal solution. We implemented the cross-validated forward variable selection method to find a subset of useful variables while avoid overfitting. Extensive simulations show that our proposed method reduces bias and variance. Analysis of a gastric cancer biomarker study and censored survival outcome data illustrates the practical utility of our proposed method. The proposed method is implemented in the open-source R package persDx.

Insights into Melanoma Clinical Practice: A Perspective for Future Research

BACKGROUND

Early diagnosis is the key to improving outcomes for patients with melanoma, and this requires a standardized histological assessment approach. The objective of this survey was to understand the challenges faced by clinicians when assessing melanoma cases, and to provide a perspective for future studies.

METHODS

Between April 2022 and February 2023, national and international dermatologists, pathologists, general practitioners, and laboratory managers were invited to participate in a six-question online survey. The data from the survey were assessed using descriptive statistics and qualitative responses.

RESULTS

A total of 54 responses were received, with a 51.4% (n = 28) full completion rate. Of the respondents, 96.4% reported ambiguity in their monthly melanoma diagnosis, and 82.1% routinely requested immunohistochemistry (IHC) testing to confirm diagnosis. SOX10 was the most frequently requested marker, and most respondents preferred multiple markers over a single marker. Diagnostic and prognostic tests, as well as therapeutic options and patient management, were all identified as important areas for future research.

CONCLUSIONS

The respondents indicated that the use of multiple IHC markers is essential to facilitate diagnostic accuracy in melanoma assessment. Survey responses indicate there is an urgent need to develop new biomarkers for clinical decision making at multiple critical intervention points.

Comparative Analysis of Antimicrobial Antibodies between Mild and Severe COVID-19

Patients with 2019 coronavirus disease (COVID-19) exhibit a broad spectrum of clinical presentations. A person's antimicrobial antibody profile, as partially shaped by past infection or vaccination, can reflect the immune system health that is critical to control and resolve the infection. We performed an explorative immunoproteomics study using microbial protein arrays displaying 318 full-length antigens from 77 viruses and 3 bacteria. We compared antimicrobial antibody profiles between 135 patients with mild COVID-19 disease and 215 patients with severe disease in 3 independent cohorts from Mexico and Italy. Severe disease patients were older with higher prevalence of comorbidities. We confirmed that severe disease patients elicited a stronger anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) response. We showed that antibodies against HCoV-229E and HcoV-NL63 but not against HcoV-HKU1 and HcoV-OC43 were also higher in those who had severe disease. We revealed that for a set of IgG and IgA antibodies targeting coronaviruses, herpesviruses, and other respiratory viruses, a subgroup of patients with the highest reactivity levels had a greater incidence of severe disease compared to those with mild disease across all three cohorts. On the contrary, fewer antibodies showed consistent greater prevalence in mild disease in all 3 cohorts. IMPORTANCE The clinical presentations of COVID-19 range from asymptomatic to critical illness that may lead to intensive care or even death. The health of the immune system, as partially shaped by past infections or vaccinations, is critical to control and resolve the infection. Using an innovative protein array platform, we surveyed antibodies against hundreds of full-length microbial antigens from 80 different viruses and bacteria in COVID-19 patients from different geographic regions with mild or severe disease. We not only confirmed the association of severe COVID-19 disease with higher reactivity of antibody responses to SARS-CoV-2 but also uncovered known and novel associations with antibody responses against herpesviruses and other respiratory viruses. Our study represents a significant step forward in understanding the factors contributing to COVID-19 disease severity. We also demonstrate the power of comprehensive antimicrobial antibody profiling in deciphering risk factors for severe COVID-19. We anticipate that our approach will have broad applications in infectious diseases.

Unraveling patient heterogeneity in complex diseases through individualized co-expression networks: a perspective

This perspective highlights the potential of individualized networks as a novel strategy for studying complex diseases through patient stratification, enabling advancements in precision medicine. We emphasize the impact of interpatient heterogeneity resulting from genetic and environmental factors and discuss how individualized networks improve our ability to develop treatments and enhance diagnostics. Integrating system biology, combining multimodal information such as genomic and clinical data has reached a tipping point, allowing the inference of biological networks at a single-individual resolution. This approach generates a specific biological network per sample, representing the individual from which the sample originated. The availability of individualized networks enables applications in personalized medicine, such as identifying malfunctions and selecting tailored treatments. In essence, reliable, individualized networks can expedite research progress in understanding drug response variability by modeling heterogeneity among individuals and enabling the personalized selection of pharmacological targets for treatment. Therefore, developing diverse and cost-effective approaches for generating these networks is crucial for widespread application in clinical services.

Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology

There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.

Decentralised clinical trials in multiple sclerosis research

Randomised controlled trials (RCTs) play an important role in multiple sclerosis (MS) research, ensuring that new interventions are safe and efficacious before their introduction into clinical practice. Trials have been evolving to improve the robustness of their designs and the efficiency of their conduct. Advances in digital and mobile technologies in recent years have facilitated this process and the first RCTs with decentralised elements became possible. Decentralised clinical trials (DCTs) are conducted remotely, enabling participation of a more heterogeneous population who can participate in research activities from different locations and at their convenience. DCTs also rely on digital and mobile technologies which allows for more flexible and frequent assessments. While hospitals quickly adapted to e-health and telehealth assessments during the COVID-19 pandemic, the conduct of conventional RCTs was profoundly disrupted. In this paper, we review the existing evidence and gaps in knowledge in the design and conduct of DCTs in MS.

Plasma autoantibodies IgG and IgM to PD1/PDL1 as potential biomarkers and risk factors of lung cancer

Antibodies targeting programmed cell death-1 (PD1) and its ligand (PDL1) have transformed current cancer therapy while little is known about the expression of anti-PD1/PDL1 autoantibodies between lung cancer (LC) patients and normal controls (NC). The expression level of anti-PD1/PDL1 IgG and IgM was detected in plasma of 325 LC and 324 NC by indirect enzyme-linked immune sorbent assay (ELISA). Western blot and indirect immunofluorescence (IIF) were used to verify the ELISA results. The association analysis was used to evaluate the odds ratio (OR) of LC. The expression of anti-PD1/PDL1 IgG in LC samples was significantly higher than NC (P < 0.001 and P < 0.05, respectively). The positive rate of anti-PD1/PDL1 IgG in LC was significantly higher than NC and significant difference was also shown in LC samples of different clinical characteristics, such as clinical stage, nodules diameter, lymph node metastasis and distant metastasis (P < 0.001). Moreover, PD1/PDL1 expression in tissues showed no significant relation with that in plasma (P > 0.05). Anti-PD1/PDL1 IgG were the risk factors related to LC (OR (95% CI): 22.433 (5.426-92.745) and 5.051 (1.316-19.386)), while anti-PD1/PDL1 IgM were the risk factors for LC with ≤ 60 years (OR (95% CI): 6.122 (1.365-27.455) and 7.664 (1.715-34.251)) and anti-PD1 IgM was also the risk factor for male LC cases(OR (95% CI): 6.948 (1.076-44.868)). Plasma anti-PD1/PDL1 IgG and IgM might serve as potential biomarkers and risk predictors for LC.

Is there a maternal blood biomarker that can predict spontaneous preterm birth prior to labour onset? A systematic review

INTRODUCTION

The ability to predict spontaneous preterm birth (sPTB) prior to labour onset is a challenge, and it is currently unclear which biomarker(s), may be potentially predictive of sPTB, and whether their predictive power has any utility. A systematic review was conducted to identify maternal blood biomarkers of sPTB.

METHODS

This study was conducted according to PRISMA protocol for systematic reviews. Four databases (MEDLINE, EMBASE, CINAHL, Scopus) were searched up to September 2021 using search terms: "preterm labor", "biomarker" and "blood OR serum OR plasma". Studies assessing blood biomarkers prior to labour onset against the outcome sPTB were eligible for inclusion. Risk of bias was assessed based on the Newcastle Ottawa scale. Increased odds of sPTB associated with maternal blood biomarkers, as reported by odds ratios (OR), or predictive scores were synthesized. This review was not prospectively registered.

RESULTS

Seventy-seven primary research articles met the inclusion criteria, reporting 278 unique markers significantly associated with and/or predictive of sPTB in at least one study. The most frequently investigated biomarkers were those measured during maternal serum screen tests for aneuploidy, or inflammatory cytokines, though no single biomarker was clearly predictive of sPTB based on the synthesized evidence. Immune and signaling pathways were enriched within the set of biomarkers and both at the level of protein and gene expression.

CONCLUSION

There is currently no known predictive biomarker for sPTB. Inflammatory and immune biomarkers show promise, but positive reporting bias limits the utility of results. The biomarkers identified may be more predictive in multi-marker models instead of as single predictors. Omics-style studies provide promising avenues for the identification of novel (and multiple) biomarkers. This will require larger studies with adequate power, with consideration of gestational age and the heterogeneity of sPTB to identify a set of biomarkers predictive of sPTB.

Patient-Specific Precision Injury Signatures to Optimize Orthopaedic Interventions in Multiply Injured Patients (PRECISE STUDY)

SUMMARY

Optimal timing and procedure selection that define staged treatment strategies can affect outcomes dramatically and remain an area of major debate in the treatment of multiply injured orthopaedic trauma patients. Decisions regarding timing and choice of orthopaedic procedure(s) are currently based on the physiologic condition of the patient, resource availability, and the expected magnitude of the intervention. Surgical decision-making algorithms rarely rely on precision-type data that account for demographics, magnitude of injury, and the physiologic/immunologic response to injury on a patient-specific basis. This study is a multicenter prospective investigation that will work toward developing a precision medicine approach to managing multiply injured patients by incorporating patient-specific indices that quantify (1) mechanical tissue damage volume; (2) cumulative hypoperfusion; (3) immunologic response; and (4) demographics. These indices will formulate a precision injury signature, unique to each patient, which will be explored for correspondence to outcomes and response to surgical interventions. The impact of the timing and magnitude of initial and staged surgical interventions on patient-specific physiologic and immunologic responses will be evaluated and described. The primary goal of the study will be the development of data-driven models that will inform clinical decision-making tools that can be used to predict outcomes and guide intervention decisions.

Multibiomarker approach to assess the magnitude of occupational exposure and effects induced by a mixture of metals

Metals and metalloids including lead (Pb), arsenic (As) and manganese (Mn) can occur as mixtures in occupational contexts, such as mines. These chemicals are all known to be neurotoxic and provoke changes in heme metabolism also known to induce neurotoxicity. The objective of this work was to propose a multi-biomarker (BM) methodology to screen subjects exposed to the mixture of Pb, As and Mn and assess the severity of their exposure/effects, in an individual basis. The urinary levels of the metals, dela-aminolevulinic acid (ALA) and porphyrins were determined in Portuguese miners and in a control group. The combination of Pb and As urinary levels had the highest capability to identify subjects occupationally exposed to this mixture in mines, as evaluated through Receiver Operating Characteristic (ROC) (A = 98.2%; p < 0.05), allowing that 94.2% of 86 studied subjects were properly identified and the generation of an equation indicating the odd of a subject be considered as exposed to the metal mixture. The combination of urinary ALA and porphyrins revealed to be best one to be applied in the assessment of subjects with high, intermediate, and low magnitudes of exposure/effects, with 95.7% of 46 miners classified correctly according to their severity sub-group and allowing to generate equations, which can be applied in new subjects. The proposed methodology showed a satisfactory performance, evaluating in an integrated manner the magnitude of exposure/effects of the exposed workers, may contributing to improve the control of their health.

Lithographic Processes for the Scalable Fabrication of Micro- and Nanostructures for Biochips and Biosensors

Since the early 2000s, extensive research has been performed to address numerous challenges in biochip and biosensor fabrication in order to use them for various biomedical applications. These biochips and biosensor devices either integrate biological elements (e.g., DNA, proteins or cells) in the fabrication processes or experience post fabrication of biofunctionalization for different downstream applications, including sensing, diagnostics, drug screening, and therapy. Scalable lithographic techniques that are well established in the semiconductor industry are now being harnessed for large-scale production of such devices, with additional development to meet the demand of precise deposition of various biological elements on device substrates with retained biological activities and precisely specified topography. In this review, the lithographic methods that are capable of large-scale and mass fabrication of biochips and biosensors will be discussed. In particular, those allowing patterning of large areas from 10 cm² to m², maintaining cost effectiveness, high throughput (>100 cm² h^-1), high resolution (from micrometer down to nanometer scale), accuracy, and reproducibility. This review will compare various fabrication technologies and comment on their resolution limit and throughput, and how they can be related to the device performance, including sensitivity, detection limit, reproducibility, and robustness.

Emerging Technologies and Platforms for the Immunodetection of Multiple Biochemical Markers in Osteoarthritis Research and Therapy

Biomarkers, especially biochemical markers, are important in osteoarthritis (OA) research, clinical trials, and drug development and have potential for more extensive use in therapeutic monitoring. However, they have not yet had any significant impact on disease diagnosis and follow-up in a clinical context. Nevertheless, the development of immunoassays for the detection and measurement of biochemical markers in OA research and therapy is an active area of research and development. The evaluation of biochemical markers representing low-grade inflammation or extracellular matrix turnover may permit OA prognosis and expedite the development of personalized treatment tailored to fit particular disease severities. However, currently detection methods have failed to overcome specific hurdles such as low biochemical marker concentrations, patient-specific variation, and limited utility of single biochemical markers for definitive characterization of disease status. These challenges require new and innovative approaches for development of detection and quantification systems that incorporate clinically relevant biochemical marker panels. Emerging platforms and technologies that are already on the way to implementation in routine diagnostics and monitoring of other diseases could potentially serve as good technological and strategic examples for better assessment of OA. State-of-the-art technologies such as advanced multiplex assays, enhanced immunoassays, and biosensors ensure simultaneous screening of a range of biochemical marker targets, the expansion of detection limits, low costs, and rapid analysis. This paper explores the implementation of such technologies in OA research and therapy. Application of novel immunoassay-based technologies may shed light on poorly understood mechanisms in disease pathogenesis and lead to the development of clinically relevant biochemical marker panels. More sensitive and specific biochemical marker immunodetection will complement imaging biomarkers and ensure evidence-based comparisons of intervention efficacy. We discuss the challenges hindering the development, testing, and implementation of new OA biochemical marker assays utilizing emerging multiplexing technologies and biosensors.

Impact of tumor heterogeneity and tissue sampling for genetic mutation testing: a systematic review and post hoc analysis

OBJECTIVE

The objective of the study was to identify guidelines to assist systematic reviewers or clinical researchers in identifying sampling bias due to tumor heterogeneity (TH) in solid cancers assayed for somatic mutations. We also assessed current reporting standards to determine the impact of TH on sample bias.

STUDY DESIGN AND SETTING

We conducted a systematic review searching 13 databases (to January 2019) to identify guidelines. A post hoc analysis was performed using 12 prostate tumor somatic mutation data sets from a previous systematic review to assess reporting on TH.

RESULTS

Searches identified 2,085 records. No formal guidelines were identified. Forty publications contained incidental recommendations across five major themes: using multiple tumor samples (n = 29), sample purity thresholds (n = 14), using specific sequencing methods (n = 8), using liquid biopsies (n = 4), and microdissection (n = 4). In post hoc analyses, 50% (6 of 12) clearly reported pathology methods. Forty-two percent (5 of 12) did not report pathology results. Forty-two percent (5 of 12) confirmed the pathology of the sample by direct diagnosis rather than inference. Forty-two percent (5 of 12) used multiple samples per patient. Fifty-eight percent (7 of 12) reported on tumor purity (reported ranges 10% to 100%).

CONCLUSIONS

As precision medicine progresses to the clinic, guidelines are required to help evidence-based decision makers understand how TH may impact sample bias. Authors need to clearly report pathology methods and results and tumor purity methods and results.

Autoantibody profiling identifies predictive biomarkers of response to anti-PD1 therapy in cancer patients

Background: Programmed cell death protein 1 (PD1) inhibitors have revolutionized cancer therapy, yet many patients fail to respond. Thus, the identification of accurate predictive biomarkers of therapy response will improve the clinical benefit of anti-PD1 therapy.

Method: We assessed the baseline serological autoantibody (AAb) profile against ~2300 proteins in 10 samples and ~4600 proteins in 35 samples with alveolar soft part sarcoma (ASPS), non-small-cell lung cancer (NSCLC) and lymphoma using Nucleic Acid Programmable Protein Arrays (NAPPA). 23 selected potential AAb biomarkers were verified using simple, affordable and rapid enzyme linked immune sorbent assay (ELISA) technology with baseline plasma samples from 12 ASPS, 16 NSCLC and 46 lymphoma patients. SIX2 and EIF4E2 AAbs were further validated in independent cohorts of 17 NSCLC and 43 lymphoma patients, respectively, using ELISA. The IgG subtypes in response to therapy were also investigated.

Results: Distinct AAb profiles between ASPS, NSCLC and lymphoma were observed. In ASPS, the production of P53 and PD1 AAbs were significantly increased in non-responders (p=0.037). In NSCLC, the SIX2 AAb was predictive of response with area under the curve (AUC) of 0.87, 0.85 and 0.90 at 3 months, 4.5 months, 6 months evaluation time points, respectively. In the validation cohort, the SIX2 AAb was consistently up-regulated in non-responders (p=0.024). For lymphoma, the EIF4E2 AAb correlated with a favorable response with AUCs of 0.68, 0.70, and 0.70 at 3 months, 4.5 months, and 6 months, respectively. In the validation cohort, the AUCs were 0.74, 0.75 and 0.66 at 3 months, 4.5 months, and 6 months, respectively. The PD1 and PD-L1 IgG2 AAbs were highly produced in ~20% of lymphoma responders. Furthermore, bioinformatics analysis revealed antigen functions of these AAb biomarkers.

Conclusion: This study provides the first evidence that AAb biomarkers selected using high-throughput protein microarrays can predict anti-PD1 therapeutic response and guide anti-PD1 therapy.

Tumor-associated autoantibodies in combination with alpha-fetoprotein for detection of early stage hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) continues to be a leading challenge in modern oncology. Early detection via blood-based screening tests has the potential to cause a stage-shift at diagnosis and improve clinical outcomes. Tumor associated autoantibodies (TA-AAbs) have previously shown the ability to distinguish HCC from patients with high-risk liver disease. This research aimed to further show the utility of TA-AAbs as biomarkers of HCC and assess their use in combination with Alpha-fetoprotein (AFP) for detection of HCC across multiple tumor stages.

METHODS

Levels of circulating G class antibodies to 44 recombinant tumor associated antigens and circulating AFP were measured in the serum of patients with HCC, non-cancerous chronic liver disease (NCCLD) and healthy controls via enzyme-linked immunosorbent assay (ELISA). TA-AAb cut-offs were set at the highest Youden's J statistic at a specificity ≥95.00%. Panels of TA-AAbs were formed using net reclassification improvement. AFP was assessed at a cut-off of 200 ng/ml.

RESULTS

Sensitivities ranged from 1.01% to 12.24% at specificities of 95.96% to 100.00% for single TA-AAbs. An ELISA test measuring a panel of 10 of these TA-AAbs achieved a combined sensitivity of 36.73% at a specificity of 89.89% when distinguishing HCC from NCCLD controls. At a cut-off of 200 ng/ml, AFP achieved a sensitivity of 31.63% at a specificity of 100.00% in the same cohort. Combination of the TA-AAb panel with AFP significantly increased the sensitivity for stage one (40.00%) and two (55.00%) HCC over the TA-AAb panel or AFP alone.

CONCLUSIONS

A panel of TA-AAbs in combination with AFP could be clinically relevant as a replacement for measuring levels of AFP alone in surveillance and diagnosis strategies. The increased early stage sensitivity could lead to a stage shift with positive prognostic outcomes.

HaloTag-based conjugation of proteins to barcoding-oligonucleotides

Highly sensitive protein quantification enables the detection of a small number of protein molecules that serve as markers/triggers for various biological phenomena, such as cancer. Here, we describe the development of a highly sensitive protein quantification system called HaloTag protein barcoding. The method involves covalent linking of a target protein to a unique molecule counting oligonucleotide at a 1:1 conjugation ratio based on an azido-cycloalkyne click reaction. The sensitivity of the HaloTag-based barcoding was remarkably higher than that of a conventional luciferase assay. The HaloTag system was successfully validated by analyzing a set of protein-protein interactions, with the identification rate of 44% protein interactions between positive reference pairs reported in the literature. Desmoglein 3, the target antigen of pemphigus vulgaris, an IgG-mediated autoimmune blistering disease, was used in a HaloTag protein barcode assay to detect the anti-DSG3 antibody. The dynamic range of the assay was over 104-times wider than that of a conventional enzyme-linked immunosorbent assay (ELISA). The technology was used to detect anti-DSG3 antibody in patient samples with much higher sensitivity compared to conventional ELISA. Our detection system, with its superior sensitivity, enables earlier detection of diseases possibly allowing the initiation of care/treatment at an early disease stage.

Measurement of Organ-Specific and Acute-Phase Blood Protein Levels in Early Lyme Disease

Lyme disease results from infection of humans with the spirochete Borrelia burgdorferi. The first and most common clinical manifestation is the circular, inflamed skin lesion referred to as erythema migrans; later manifestations result from infections of other body sites. Laboratory diagnosis of Lyme disease can be challenging in patients with erythema migrans because of the time delay in the development of specific diagnostic antibodies against Borrelia. Reliable blood biomarkers for the early diagnosis of Lyme disease in patients with erythema migrans are needed. Here, we performed selected reaction monitoring, a targeted mass spectrometry-based approach, to measure selected proteins that (1) are known to be predominantly expressed in one organ (i.e., organ-specific blood proteins) and whose blood concentrations may change as a result of Lyme disease, or (2) are involved in acute immune responses. In a longitudinal cohort of 40 Lyme disease patients and 20 healthy controls, we identified 10 proteins with significantly altered serum levels in patients at the time of diagnosis, and we also developed a 10-protein panel identified through multivariate analysis. In an independent cohort of patients with erythema migrans, six of these proteins, APOA4, C9, CRP, CST6, PGLYRP2, and S100A9, were confirmed to show significantly altered serum levels in patients at time of presentation. Nine of the 10 proteins from the multivariate panel were also verified in the second cohort. These proteins, primarily innate immune response proteins or proteins specific to liver, skin, or white blood cells, may serve as candidate blood biomarkers requiring further validation to aid in the laboratory diagnosis of early Lyme disease.

Integrating molecular nuclear imaging in clinical research to improve anticancer therapy

Effective patient selection before or early during treatment is important to increasing the therapeutic benefits of anticancer treatments. This selection process is often predicated on biomarkers, predominantly biospecimen biomarkers derived from blood or tumour tissue; however, such biomarkers provide limited information about the true extent of disease or about the characteristics of different, potentially heterogeneous tumours present in an individual patient. Molecular imaging can also produce quantitative outputs; such imaging biomarkers can help to fill these knowledge gaps by providing complementary information on tumour characteristics, including heterogeneity and the microenvironment, as well as on pharmacokinetic parameters, drug-target engagement and responses to treatment. This integrative approach could therefore streamline biomarker and drug development, although a range of issues need to be overcome in order to enable a broader use of molecular imaging in clinical trials. In this Perspective article, we outline the multistage process of developing novel molecular imaging biomarkers. We discuss the challenges that have restricted the use of molecular imaging in clinical oncology research to date and outline future opportunities in this area.

The Search for Clinically Useful Biomarkers of Complex Disease: A Data Analysis Perspective

Unmet clinical diagnostic needs exist for many complex diseases, which (it is hoped) will be solved by the discovery of metabolomics biomarkers. However, at present, no diagnostic tests based on metabolomics have yet been introduced to the clinic. This review is presented as a research perspective on how data analysis methods in metabolomics biomarker discovery may contribute to the failure of biomarker studies and suggests how such failures might be mitigated. The study design and data pretreatment steps are reviewed briefly in this context, and the actual data analysis step is examined more closely.

Screening for Preterm Birth: Potential for a Metabolomics Biomarker Panel

The aim of this preliminary study was to investigate the potential of maternal serum to provide metabolomic biomarker candidates for the prediction of spontaneous preterm birth (SPTB) in asymptomatic pregnant women at 15 and/or 20 weeks’ gestation. Metabolomics LC-MS datasets from serum samples at 15- and 20-weeks’ gestation from a cohort of approximately 50 cases (GA < 37 weeks) and 55 controls (GA > 41weeks) were analysed for candidate biomarkers predictive of SPTB. Lists of the top ranked candidate biomarkers from both multivariate and univariate analyses were produced. At the 20 weeks’ GA time-point these lists had high concordance with each other (85%). A subset of 4 of these features produce a biomarker panel that predicts SPTB with a partial Area Under the Curve (pAUC) of 12.2, a sensitivity of 87.8%, a specificity of 57.7% and a p-value of 0.0013 upon 10-fold cross validation using PanelomiX software. This biomarker panel contained mostly features from groups already associated in the literature with preterm birth and consisted of 4 features from the biological groups of “Bile Acids”, “Prostaglandins”, “Vitamin D and derivatives” and “Fatty Acids and Conjugates”.

In-depth serum proteomics reveals biomarkers of psoriasis severity and response to traditional Chinese medicine

Serum and plasma contain abundant biological information that reflect the body's physiological and pathological conditions and are therefore a valuable sample type for disease biomarkers. However, comprehensive profiling of the serological proteome is challenging due to the wide range of protein concentrations in serum. Methods: To address this challenge, we developed a novel in-depth serum proteomics platform capable of analyzing the serum proteome across ~10 orders or magnitude by combining data obtained from Data Independent Acquisition Mass Spectrometry (DIA-MS) and customizable antibody microarrays. Results: Using psoriasis as a proof-of-concept disease model, we screened 50 serum proteomes from healthy controls and psoriasis patients before and after treatment with traditional Chinese medicine (YinXieLing) on our in-depth serum proteomics platform. We identified 106 differentially-expressed proteins in psoriasis patients involved in psoriasis-relevant biological processes, such as blood coagulation, inflammation, apoptosis and angiogenesis signaling pathways. In addition, unbiased clustering and principle component analysis revealed 58 proteins discriminating healthy volunteers from psoriasis patients and 12 proteins distinguishing responders from non-responders to YinXieLing. To further demonstrate the clinical utility of our platform, we performed correlation analyses between serum proteomes and psoriasis activity and found a positive association between the psoriasis area and severity index (PASI) score with three serum proteins (PI3, CCL22, IL-12B). Conclusion: Taken together, these results demonstrate the clinical utility of our in-depth serum proteomics platform to identify specific diagnostic and predictive biomarkers of psoriasis and other immune-mediated diseases.

Proteomics and Metabolomics for AKI Diagnosis

Acute kidney injury (AKI) is a severe and frequent condition in hospitalized patients. Currently, no efficient therapy of AKI is available. Therefore, efforts focus on early prevention and potentially early initiation of renal replacement therapy to improve the outcome in AKI. The detection of AKI in hospitalized patients implies the need for early, accurate, robust, and easily accessible biomarkers of AKI evolution and outcome prediction because only a narrow window exists to implement the earlier-described measures. Even more challenging is the multifactorial origin of AKI and the fact that the changes of molecular expression induced by AKI are difficult to distinguish from those of the diseases associated or causing AKI as shock or sepsis. During the past decade, a considerable number of protein biomarkers for AKI have been described and we expect from recent advances in the field of omics technologies that this number will increase further in the future and be extended to other sorts of biomolecules, such as RNAs, lipids, and metabolites. However, most of these biomarkers are poorly defined by their AKI-associated molecular context. In this review, we describe the state-of-the-art tissue and biofluid proteomic and metabolomic technologies and new bioinformatics approaches for proteomic and metabolomic pathway and molecular interaction analysis. In the second part of the review, we focus on AKI-associated proteomic and metabolomic biomarkers and briefly outline their pathophysiological context in AKI.

A systematic review of metabolite biomarkers of schizophrenia

Current diagnosis of schizophrenia relies exclusively on the potentially subjective interpretation of clinical symptoms and social functioning as more objective biological measurement and medical diagnostic tests are not presently available. The use of metabolomics in the discovery of disease biomarkers has grown in recent years. Metabolomic methods could aid in the discovery of diagnostic biomarkers of schizophrenia. This systematic review focuses on biofluid metabolites associated with schizophrenia. A systematic search of Web of Science and Ovid Medline databases was conducted and 63 studies investigating metabolite biomarkers of schizophrenia were included. A review of these studies revealed several potential metabolite signatures of schizophrenia including reduced levels of essential polyunsaturated fatty acids (EPUFAs), vitamin E and creatinine; and elevated levels of lipid peroxidation metabolites and glutamate. Further research is needed to validate these biomarkers and would benefit from large cohort studies and more homogeneous and well-defined subject groups.

Innovative methods for biomarker discovery in the evaluation and development of cancer precision therapies

The discovery of biomarkers able to detect cancer at an early stage, to evaluate its aggressiveness, and to predict the response to therapy remains a major challenge in clinical oncology and precision medicine. In this review, we summarize recent achievements in the discovery and development of cancer biomarkers. We also highlight emerging innovative methods in biomarker discovery and provide insights into the challenges faced in their evaluation and validation.

Advances in cell-free protein array methods

INTRODUCTION

Cell-free protein microarrays represent a special form of protein microarray which display proteins made fresh at the time of the experiment, avoiding storage and denaturation. They have been used increasingly in basic and translational research over the past decade to study protein-protein interactions, the pathogen-host relationship, post-translational modifications, and antibody biomarkers of different human diseases. Their role in the first blood-based diagnostic test for early stage breast cancer highlights their value in managing human health. Cell-free protein microarrays will continue to evolve to become widespread tools for research and clinical management. Areas covered: We review the advantages and disadvantages of different cell-free protein arrays, with an emphasis on the methods that have been studied in the last five years. We also discuss the applications of each microarray method. Expert commentary: Given the growing roles and impact of cell-free protein microarrays in research and medicine, we discuss: 1) the current technical and practical limitations of cell-free protein microarrays; 2) the biomarker discovery and verification pipeline using protein microarrays; and 3) how cell-free protein microarrays will advance over the next five years, both in their technology and applications.

Spatio-Temporal Genomic Heterogeneity, Phylogeny, and Metastatic Evolution in Salivary Adenoid Cystic Carcinoma

Background

Adenoid cystic carcinoma (ACC), an uncommon and indolent salivary gland malignancy, is characterized by varied morphologic and clinical manifestations. Molecular genetic studies of ACC identified certain structural and mutational alterations that may play a driver role in tumor development. The evolution and regional consistency of these events in ACC development progression are uncertain.

Methods

To investigate the spatial and temporal clonal landscape of ACC, whole-genome sequencing and variant analyses were performed on 34 regionally sampled primary tumors and their concurrent and metachronous metastatic deposits from eight patients.

Results

The average mutation rate per case (primary and metastasis) was 0.32 per million base pairs, and the average incidence of shared mutations in primary and metastatic specimens in each case was 21.9% (range = 0%-44.4%). The analyses revealed considerable spatial clonal differences within and between primary tumors and metastatic disease. Phylogeny formation displayed branching evolution with a main trunk and two distinct mono-splits in all cases. One of the main branches represented intratumor subclonal diversity, and the other delineated metastatic departure and progression. All metastatic tumors shared clonal linkage to their matching primary in concordance with parallel dissemination of metastasis. Synchronous metastases were genomically more similar to their primary than metachronous metastatic disease. Truncal genetic alterations included somatic mutations in the NOTCH pathway genes (NOTCH1 and SPEN) and t(6;9) associated gene fusions.

Conclusions

Our study delineated clonal and subclonal phylogeny for primary and metastatic ACC, defined early genetic drivers, and provides a conceptual framework for a rational strategy to integrate heterogeneity in clinical assessment.

Antigen arrays for profiling autoantibody repertoires

Autoantibodies are a key component for the diagnosis, prognosis and monitoring of various diseases. In order to discover novel autoantibody targets, highly multiplexed assays based on antigen arrays hold a great potential and provide possibilities to analyze hundreds of body fluid samples for their reactivity pattern against thousands of antigens in parallel. Here, we provide an overview of the available technologies for producing antigen arrays, highlight some of the technical and methodological considerations and discuss their applications as discovery tools. Together with recent studies utilizing antigen arrays, we give an overview on how the different types of antigen arrays have and will continue to deliver novel insights into autoimmune diseases among several others.

Crucial considerations for pipelines to validate circulating biomarkers for breast cancer

Despite decades of progress in breast imaging, breast cancer remains the second most common cause of cancer mortality in women. The rapidly proliferative breast cancers that are associated with high relapse rates and mortality frequently present in younger women, in unscreened individuals, or in the intervals between screening mammography. Biomarkers exist for monitoring metastatic disease, such as CEA, CA27.29 and CA15-3, but there are no circulating biomarkers clinically available for early detection, prognosis, or monitoring for clinical relapse. There has been significant progress in the discovery of potential circulating biomarkers, including proteins, autoantibodies, nucleic acids, exosomes, and circulating tumor cells, but the vast majority of these biomarkers have not progressed beyond initial research discovery, and none have yet been approved for clinical use in early stage disease. Here, the authors review the crucial considerations of developing pipelines for the rapid evaluation of circulating biomarkers for breast cancer.

Plasma Autoantibodies Associated with Basal-like Breast Cancers

BACKGROUND

Basal-like breast cancer (BLBC) is a rare aggressive subtype that is less likely to be detected through mammographic screening. Identification of circulating markers associated with BLBC could have promise in detecting and managing this deadly disease.

METHODS

Using samples from the Polish Breast Cancer study, a high-quality population-based case-control study of breast cancer, we screened 10,000 antigens on protein arrays using 45 BLBC patients and 45 controls, and identified 748 promising plasma autoantibodies (AAbs) associated with BLBC. ELISA assays of promising markers were performed on a total of 145 BLBC cases and 145 age-matched controls. Sensitivities at 98% specificity were calculated and a BLBC classifier was constructed.

RESULTS

We identified 13 AAbs (CTAG1B, CTAG2, TP53, RNF216, PPHLN1, PIP4K2C, ZBTB16, TAS2R8, WBP2NL, DOK2, PSRC1, MN1, TRIM21) that distinguished BLBC from controls with 33% sensitivity and 98% specificity. We also discovered a strong association of TP53 AAb with its protein expression (P = 0.009) in BLBC patients. In addition, MN1 and TP53 AAbs were associated with worse survival [MN1 AAb marker HR = 2.25, 95% confidence interval (CI), 1.03-4.91; P = 0.04; TP53, HR = 2.02, 95% CI, 1.06-3.85; P = 0.03]. We found limited evidence that AAb levels differed by demographic characteristics.

CONCLUSIONS

These AAbs warrant further investigation in clinical studies to determine their value for further understanding the biology of BLBC and possible detection.

IMPACT

Our study identifies 13 AAb markers associated specifically with BLBC and may improve detection or management of this deadly disease.

Translating clinical proteomics: the importance of study design

Mass spectrometry-based clinical proteomics approaches were introduced into the biomedical field more than two decades ago. Despite recent developments both in the field of mass spectrometry and bioinformatics, the gap between proteomics results and their translation into clinical practice still needs to be closed, as implementation of proteomics results in the clinic appears to be scarce. An extra focus on the importance of the experimental design is therefore of crucial importance.

Improving the quality of biomarker discovery research: the right samples and enough of them

BACKGROUND

Biomarker discovery research has yielded few biomarkers that validate for clinical use. A contributing factor may be poor study designs.

METHODS

The goal in discovery research is to identify a subset of potentially useful markers from a large set of candidates assayed on case and control samples. We recommend the PRoBE design for selecting samples. We propose sample size calculations that require specifying: (i) a definition for biomarker performance; (ii) the proportion of useful markers the study should identify (Discovery Power); and (iii) the tolerable number of useless markers amongst those identified (False Leads Expected, FLE).

RESULTS

We apply the methodology to a study of 9,000 candidate biomarkers for risk of colon cancer recurrence where a useful biomarker has positive predictive value ≥ 30%. We find that 40 patients with recurrence and 160 without recurrence suffice to filter out 98% of useless markers (2% FLE) while identifying 95% of useful biomarkers (95% Discovery Power). Alternative methods for sample size calculation required more assumptions.

CONCLUSIONS

Biomarker discovery research should utilize quality biospecimen repositories and include sample sizes that enable markers meeting prespecified performance characteristics for well-defined clinical applications to be identified.

IMPACT

The scientific rigor of discovery research should be improved.

Novel droplet platforms for the detection of disease biomarkers

Personalized medicine - healthcare based on individual genetic variation - has the potential to transform the way healthcare is delivered to patients. The promise of personalized medicine has been predicated on the predictive and diagnostic power of genomic and proteomic biomarkers. Biomarker screening may help improve health outcomes, for example, by identifying individuals' susceptibility to diseases and predicting how patients will respond to drugs. Microfluidic droplet technology offers an exciting opportunity to revolutionize the accessibility of personalized medicine. A framework for the role of droplet microfluidics in biomarker detection can be based on two main themes. Emulsion-based microdroplet platforms can provide new ways to measure and detect biomolecules. In addition, microdroplet platforms facilitate high-throughput screening of biomarkers. Meanwhile, surface-based droplet platforms provide an opportunity to develop miniaturized diagnostic systems. These platforms may function as portable benchtop environments that dramatically shorten the transition of a benchtop assay into a point-of-care format.

Identification of B-cell lymphoma subsets by plasma protein profiling using recombinant antibody microarrays

B-cell lymphoma (BCL) heterogeneity represents a key issue, often making the classification and clinical management of these patients challenging. In this pilot study, we outlined the first resolved view of BCL disease heterogeneity on the protein level by deciphering disease-associated plasma biomarkers, specific for chronic lymphocytic leukemia, diffuse large B-cell lymphoma, follicular lymphoma, and mantle cell lymphoma, using recombinant antibody microarrays targeting mainly immunoregulatory proteins. The results showed the BCLs to be heterogeneous, and revealed potential novel subgroups of each BCL. In the case of diffuse large B-cell lymphoma, we also indicated a link between the novel subgroups and survival.

Challenges and prospects for biomarker research: a current perspective from the developing world

Majority of deaths due to communicable and non-communicable diseases occur in the low and middle-income nations (LMNs), mainly due to the lack of early diagnoses and timely treatments. In such a scenario, biomarkers serve as an indispensible resource that can be used as indicators of biological processes, specific disease conditions or response to therapeutic interventions. Evaluation, diagnosis and management of diseases in developing world by following/extrapolating the findings obtained on the basis of the research work involving only the populations from the developed countries, could often be highly misleading due to existence of diverse patterns of diseases in developing countries compared to the developed world. Biomarker candidates identified from high-throughput integrated omics technologies have promising potential; however, their actual clinical applications are found to be limited, primarily due to the challenges of disease heterogeneity and pre-analytical variability associated with the biomarker discovery pipeline. Additionally, in the developing world, economic crunches, lack of awareness and education, paucity of biorepositories, enormous diversities in socio-epidemiological background, ethnicity, lifestyle, diet, exposure to various environmental risk factors and infectious agents, and ethical and social issues also cumulatively hinder biomarker discovery ventures. Establishment of standard operating procedures, comprehensive data repositories and exchange of scientific findings are crucial for reducing the variability and fragmentation of data. This review highlights the challenges associated with the discovery, validation and translational phases of biomarker research in LMNs with some of their amenable solutions and future prospects. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Statistical design for biospecimen cohort size in proteomics-based biomarker discovery and verification studies

Protein biomarkers are needed to deepen our understanding of cancer biology and to improve our ability to diagnose, monitor, and treat cancers. Important analytical and clinical hurdles must be overcome to allow the most promising protein biomarker candidates to advance into clinical validation studies. Although contemporary proteomics technologies support the measurement of large numbers of proteins in individual clinical specimens, sample throughput remains comparatively low. This problem is amplified in typical clinical proteomics research studies, which routinely suffer from a lack of proper experimental design, resulting in analysis of too few biospecimens to achieve adequate statistical power at each stage of a biomarker pipeline. To address this critical shortcoming, a joint workshop was held by the National Cancer Institute (NCI), National Heart, Lung, and Blood Institute (NHLBI), and American Association for Clinical Chemistry (AACC) with participation from the U.S. Food and Drug Administration (FDA). An important output from the workshop was a statistical framework for the design of biomarker discovery and verification studies. Herein, we describe the use of quantitative clinical judgments to set statistical criteria for clinical relevance and the development of an approach to calculate biospecimen sample size for proteomic studies in discovery and verification stages prior to clinical validation stage. This represents a first step toward building a consensus on quantitative criteria for statistical design of proteomics biomarker discovery and verification research.