Waste, Leaks, and Failures in the Biomarker Pipeline

Opportunities and barriers in omics-based biomarker discovery for steatotic liver diseases

The rising prevalence of liver diseases related to obesity and excessive use of alcohol is fuelling an increasing demand for accurate biomarkers aimed at community screening, diagnosis of steatohepatitis and significant fibrosis, monitoring, prognostication and prediction of treatment efficacy. Breakthroughs in omics methodologies and the power of bioinformatics have created an excellent opportunity to apply technological advances to clinical needs, for instance in the development of precision biomarkers for personalised medicine. Via omics technologies, biological processes from the genes to circulating protein, as well as the microbiome - including bacteria, viruses and fungi, can be investigated on an axis. However, there are important barriers to omics-based biomarker discovery and validation, including the use of semi-quantitative measurements from untargeted platforms, which may exhibit high analytical, inter- and intra-individual variance. Standardising methods and the need to validate them across diverse populations presents a challenge, partly due to disease complexity and the dynamic nature of biomarker expression at different disease stages. Lack of validity causes lost opportunities when studies fail to provide the knowledge needed for regulatory approvals, all of which contributes to a delayed translation of these discoveries into clinical practice. While no omics-based biomarkers have matured to clinical implementation, the extent of data generated has enabled the hypothesis-free discovery of a plethora of candidate biomarkers that warrant further validation. To explore the many opportunities of omics technologies, hepatologists need detailed knowledge of commonalities and differences between the various omics layers, and both the barriers to and advantages of these approaches.

Blood-based biomarkers and novel technologies for the diagnosis of colorectal cancer and adenomas: a narrative review

Aim: Blood-based biomarkers have shown promise for diagnosing colorectal cancer (CRC) and adenomas (CRA). This review summarizes recent studies in this area. Methods: A literature search was undertaken for 01/01/2017-01/03/2023. Criteria included CRC, CRA, liquid-biopsy, blood-based tests and diagnosis. Results: 12,378 studies were reduced to 178 for data extraction. Sixty focused on proteomics, 53 on RNA species, 30 on cfDNA methylation, seven on antigens and autoantibodies and 28 on novel techniques. 169 case control and nine cohort studies. Number of participants ranged 100-54,297, mean age 58.26. CRC sensitivity and specificity ranged 9.10-100% and 20.40-100%, respectively. CRA sensitivity and specificity ranged 8.00-95.70% and 4.00-97.00%, respectively. Conclusion: Sensitive and specific blood-based tests exist for CRC and CRA. However, studies demonstrate heterogenous techniques and reporting quality. Further work should concentrate on validation and meta-analyzes.

Cohort profile of BIGPROMISE: a perioperative biobank of a high-risk surgical population

PURPOSE

Postoperative complications increase mortality, disability and costs. Advanced understanding of the risk factors for postoperative complications is needed to improve surgical outcomes. This paper discusses the rationale and profile of the BIGPROMISE (biomarkers to guide perioperative management and improve outcome in high-risk surgery) cohort, that aims to investigate risk factors, pathophysiology and outcomes related to postoperative complications.

PARTICIPANTS

Adult patients undergoing major surgery in two tertiary teaching hospitals. Clinical data and blood samples are collected before surgery, at the end of surgery and on the first, second and third postoperative day. At each time point a panel of cardiovascular, inflammatory, renal, haematological and metabolic biomarkers is assessed. Aliquots of plasma, serum and whole blood of each time point are frozen and stored. Data on severe complications are prospectively collected during 30 days after surgery. Functional status is assessed before surgery and after 120 days using the WHO Disability Assessment Schedule (WHODAS) 2.0. Mortality is followed up until 2 years after surgery.

FINDINGS TO DATE

The first patient was enrolled on 8 October 2021. Currently (1 January 2024) 3086 patients were screened for eligibility, of whom 1750 (57%) provided informed consent for study participation. Median age was 66 years (60; 73), 28% were female, and 68% of all patients were American Society of Anaesthesiologists (ASA) physical status class 3. Most common types of major surgery were cardiac (49%) and gastro-intestinal procedures (26%). The overall incidence of 30-day severe postoperative complications was 16%.

FUTURE PLANS

By the end of the recruitment phase, expected in 2026, approximately 3000 patients with major surgery will have been enrolled. This cohort allows us to investigate the role of pathophysiological perioperative processes in the cause of postoperative complications, and to discover and develop new biomarkers to improve risk stratification for adverse postoperative outcomes.

TRIAL REGISTRATION NUMBER

NCT05199025.

Design, cohort profile and comparison of the KTD-Innov study: a prospective multidimensional biomarker validation study in kidney allograft rejection

There is an unmet need for robust and clinically validated biomarkers of kidney allograft rejection. Here we present the KTD-Innov study (ClinicalTrials.gov, NCT03582436), an unselected deeply phenotyped cohort of kidney transplant recipients with a holistic approach to validate the clinical utility of precision diagnostic biomarkers. In 2018-2019, we prospectively enrolled consecutive adult patients who received a kidney allograft at seven French centers and followed them for a year. We performed multimodal phenotyping at follow-up visits, by collecting clinical, biological, immunological, and histological parameters, and analyzing a panel of 147 blood, urinary and kidney tissue biomarkers. The primary outcome was allograft rejection, assessed at each visit according to the international Banff 2019 classification. We evaluated the representativeness of participants by comparing them with patients from French, European, and American transplant programs transplanted during the same period. A total of 733 kidney transplant recipients (64.1% male and 35.9% female) were included during the study. The median follow-up after transplantation was 12.3 months (interquartile range, 11.9-13.1 months). The cumulative incidence of rejection was 9.7% at one year post-transplant. We developed a distributed and secured data repository in compliance with the general data protection regulation. We established a multimodal biomarker biobank of 16,736 samples, including 9331 blood, 4425 urinary and 2980 kidney tissue samples, managed and secured in a collaborative network involving 7 clinical centers, 4 analytical platforms and 2 industrial partners. Patients' characteristics, immune profiles and treatments closely resembled those of 41,238 French, European and American kidney transplant recipients. The KTD-Innov study is a unique holistic and multidimensional biomarker validation cohort of kidney transplant recipients representative of the real-world transplant population. Future findings from this cohort are likely to be robust and generalizable.

Target Product Profile for a Machine Learning-Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study

BACKGROUND

Diabetic eye screening (DES) represents a significant opportunity for the application of machine learning (ML) technologies, which may improve clinical and service outcomes. However, successful integration of ML into DES requires careful product development, evaluation, and implementation. Target product profiles (TPPs) summarize the requirements necessary for successful implementation so these can guide product development and evaluation.

OBJECTIVE

This study aims to produce a TPP for an ML-automated retinal imaging analysis software (ML-ARIAS) system for use in DES in England.

METHODS

This work will consist of 3 phases. Phase 1 will establish the characteristics to be addressed in the TPP. A list of candidate characteristics will be generated from the following sources: an overview of systematic reviews of diagnostic test TPPs; a systematic review of digital health TPPs; and the National Institute for Health and Care Excellence's Evidence Standards Framework for Digital Health Technologies. The list of characteristics will be refined and validated by a study advisory group (SAG) made up of representatives from key stakeholders in DES. This includes people with diabetes; health care professionals; health care managers and leaders; and regulators and policy makers. In phase 2, specifications for these characteristics will be drafted following a series of semistructured interviews with participants from these stakeholder groups. Data collected from these interviews will be analyzed using the shortlist of characteristics as a framework, after which specifications will be drafted to create a draft TPP. Following approval by the SAG, in phase 3, the draft will enter an internet-based Delphi consensus study with participants sought from the groups previously identified, as well as ML-ARIAS developers, to ensure feasibility. Participants will be invited to score characteristic and specification pairs on a scale from "definitely exclude" to "definitely include," and suggest edits. The document will be iterated between rounds based on participants' feedback. Feedback on the draft document will be sought from a group of ML-ARIAS developers before its final contents are agreed upon in an in-person consensus meeting. At this meeting, representatives from the stakeholder groups previously identified (minus ML-ARIAS developers, to avoid bias) will be presented with the Delphi results and feedback of the user group and asked to agree on the final contents by vote.

RESULTS

Phase 1 was completed in November 2023. Phase 2 is underway and expected to finish in March 2024. Phase 3 is expected to be complete in July 2024.

CONCLUSIONS

The multistakeholder development of a TPP for an ML-ARIAS for use in DES in England will help developers produce tools that serve the needs of patients, health care providers, and their staff. The TPP development process will also provide methods and a template to produce similar documents in other disease areas.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/50568.

Prognostic Biomarkers in Kidney Transplantation: A Systematic Review and Critical Appraisal

SIGNIFICANCE STATEMENT

Why are there so few biomarkers accepted by health authorities and implemented in clinical practice, despite the high and growing number of biomaker studies in medical research ? In this meta-epidemiological study, including 804 studies that were critically appraised by expert reviewers, the authors have identified all prognostic kidney transplant biomarkers and showed overall suboptimal study designs, methods, results, interpretation, reproducible research standards, and transparency. The authors also demonstrated for the first time that the limited number of studies challenged the added value of their candidate biomarkers against standard-of-care routine patient monitoring parameters. Most biomarker studies tended to be single-center, retrospective studies with a small number of patients and clinical events. Less than 5% of the studies performed an external validation. The authors also showed the poor transparency reporting and identified a data beautification phenomenon. These findings suggest that there is much wasted research effort in transplant biomarker medical research and highlight the need to produce more rigorous studies so that more biomarkers may be validated and successfully implemented in clinical practice.

BACKGROUND

Despite the increasing number of biomarker studies published in the transplant literature over the past 20 years, demonstrations of their clinical benefit and their implementation in routine clinical practice are lacking. We hypothesized that suboptimal design, data, methodology, and reporting might contribute to this phenomenon.

METHODS

We formed a consortium of experts in systematic reviews, nephrologists, methodologists, and epidemiologists. A systematic literature search was performed in PubMed, Embase, Scopus, Web of Science, and Cochrane Library between January 1, 2005, and November 12, 2022 (PROSPERO ID: CRD42020154747). All English language, original studies investigating the association between a biomarker and kidney allograft outcome were included. The final set of publications was assessed by expert reviewers. After data collection, two independent reviewers randomly evaluated the inconsistencies for 30% of the references for each reviewer. If more than 5% of inconsistencies were observed for one given reviewer, a re-evaluation was conducted for all the references of the reviewer. The biomarkers were categorized according to their type and the biological milieu from which they were measured. The study characteristics related to the design, methods, results, and their interpretation were assessed, as well as reproducible research practices and transparency indicators.

RESULTS

A total of 7372 publications were screened and 804 studies met the inclusion criteria. A total of 1143 biomarkers were assessed among the included studies from blood ( n =821, 71.8%), intragraft ( n =169, 14.8%), or urine ( n =81, 7.1%) compartments. The number of studies significantly increased, with a median, yearly number of 31.5 studies (interquartile range [IQR], 23.8-35.5) between 2005 and 2012 and 57.5 (IQR, 53.3-59.8) between 2013 and 2022 ( P < 0.001). A total of 655 studies (81.5%) were retrospective, while 595 (74.0%) used data from a single center. The median number of patients included was 232 (IQR, 96-629) with a median follow-up post-transplant of 4.8 years (IQR, 3.0-6.2). Only 4.7% of studies were externally validated. A total of 346 studies (43.0%) did not adjust their biomarker for key prognostic factors, while only 3.1% of studies adjusted the biomarker for standard-of-care patient monitoring factors. Data sharing, code sharing, and registration occurred in 8.8%, 1.1%, and 4.6% of studies, respectively. A total of 158 studies (20.0%) emphasized the clinical relevance of the biomarker, despite the reported nonsignificant association of the biomarker with the outcome measure. A total of 288 studies assessed rejection as an outcome. We showed that these rejection studies shared the same characteristics as other studies.

CONCLUSIONS

Biomarker studies in kidney transplantation lack validation, rigorous design and methodology, accurate interpretation, and transparency. Higher standards are needed in biomarker research to prove the clinical utility and support clinical use.

Role of diagnostic tests for sepsis in children: a review

Paediatric sepsis has a significant global impact and highly heterogeneous clinical presentation. The clinical pathway encompasses recognition, escalation and de-escalation. In each aspect, diagnostics have a fundamental influence over outcomes in children. Biomarkers can aid in creating a larger low-risk group of children from those in the clinical grey area who would otherwise receive antibiotics 'just in case'. Current biomarkers include C reactive protein and procalcitonin, which are limited in their clinical use to guide appropriate and rapid treatment. Biomarker discovery has focused on single biomarkers, which, so far, have not outperformed current biomarkers, as they fail to recognise the complexity of sepsis. The identification of multiple host biomarkers that may form a panel in a clinical test has the potential to recognise the complexity of sepsis and provide improved diagnostic performance. In this review, we discuss novel biomarkers and novel ways of using existing biomarkers in the assessment and management of sepsis along with the significant challenges in biomarker discovery at present. Validation of biomarkers is made less meaningful due to methodological heterogeneity, including variations in sepsis diagnosis, biomarker cut-off values and patient populations. Therefore, the utilisation of platform studies is necessary to improve the efficiency of biomarkers in clinical practice.

Extracellular Vesicles as Liquid Biopsy Biomarkers across the Cancer Journey: From Early Detection to Recurrence

BACKGROUND

Cancer is a dynamic process and thus requires highly informative and reliable biomarkers to help guide patient care. Liquid-based biopsies have emerged as a clinical tool for tracking cancer dynamics. Extracellular vesicles (EVs), lipid bilayer delimited particles secreted by cells, are a new class of liquid-based biomarkers. EVs are rich in selectively sorted biomolecule cargos, which provide a spatiotemporal fingerprint of the cell of origin, including cancer cells.

CONTENT

This review summarizes the performance characteristics of EV-based biomarkers at different stages of cancer progression, from early malignancy to recurrence, while emphasizing their potential as diagnostic, prognostic, and screening biomarkers. We discuss the characteristics of effective biomarkers, consider challenges associated with the EV biomarker field, and report guidelines based on the biomarker discovery pipeline.

SUMMARY

Basic science and clinical trial studies have shown the potential of EVs as precision-based biomarkers for tracking cancer status, with promising applications for diagnosing disease, predicting response to therapy, and tracking disease burden. The multi-analyte cargos of EVs enhance the performance characteristics of biomarkers. Recent technological advances in ultrasensitive detection of EVs have shown promise with high specificity and sensitivity to differentiate early-cancer cases vs healthy individuals, potentially outperforming current gold-standard imaging-based cancer diagnosis. Ultimately, clinical translation will be dictated by how these new EV biomarker-based platforms perform in larger sample cohorts. Applying ultrasensitive, scalable, and reproducible EV detection platforms with better design considerations based upon the biomarker discovery pipeline should guide the field towards clinically useful liquid biopsy biomarkers.

Towards the Clinical Implementation of Extracellular Vesicle-Based Biomarker Assays for Cancer

BACKGROUND

Substantial research has been devoted to elucidating the role of extracellular vesicles (EVs) in the different hallmarks of cancer. Consequently, EVs are increasingly explored as a source of cancer biomarkers in body fluids. However, the heterogeneity in EVs, the complexity of body fluids, and the diversity in methods available for EV analysis, challenge the development and translation of EV-based biomarker assays.

CONTENT

Essential steps in EV-associated biomarker development are emphasized covering biobanking, biomarker discovery, verification and validation, and clinical implementation. A meticulous study design is essential and ideally results from close interactions between clinicians and EV researchers. A plethora of different EV preparation protocols exists which warrants quality control and transparency to ensure reproducibility and thus enable verification of EV-associated biomarker candidates identified in the discovery phase in subsequent independent cohorts. The development of an EV-associated biomarker assay requires thorough analytical and clinical validation. Finally, regulatory affairs must be considered for clinical implementation of EV-based biomarker assays.

SUMMARY

In this review, the current challenges that prevent us from exploiting the full potential of EV-based biomarker assays are identified. Guidelines and tools to overcome these hurdles are highlighted and are crucial to advance EV-based biomarker assays into clinical use.

In Silico Models to Validate Novel Blood-Based Biomarkers

Biological validation of preliminary findings is a key prerequisite in biomarker discovery. In recent years, the development of advanced large-scale sequencing technologies combined with high-throughput computational analysis methods led to the extraction of considerable amount of data from healthy and diseased tissues. Stored in large open-access repositories, these data can be accessed and interrogated by researchers aiming at understanding the biological rationale behind their results. These so called in silico analyses, in opposite to in vitro analyses, have gained increasing importance in recent years, becoming a major component of research projects and publications. However, making sense of the large amount of data available can be challenging and may lead to a misinterpretation of the data. To reduce the dimensionality of this data, recent years have seen the development of statistical m\ethods and advanced graph analytics which help researchers summarize the available data and draw appropriate conclusions. In this chapter we will describe three in silico methods to investigate the biological underpinnings of a panel of seven blood-based biomarkers of Alzheimer's disease.

The Biomarker Toolkit - an evidence-based guideline to predict cancer biomarker success and guide development

BACKGROUND

An increased number of resources are allocated on cancer biomarker discovery, but very few of these biomarkers are clinically adopted. To bridge the gap between Biomarker discovery and clinical use, we aim to generate the Biomarker Toolkit, a tool designed to identify clinically promising biomarkers and promote successful biomarker translation.

METHODS

All features associated with a clinically useful biomarker were identified using mixed-methodology, including systematic literature search, semi-structured interviews, and an online two-stage Delphi-Survey. Validation of the checklist was achieved by independent systematic literature searches using keywords/subheadings related to clinically and non-clinically utilised breast and colorectal cancer biomarkers. Composite aggregated scores were generated for each selected publication based on the presence/absence of an attribute listed in the Biomarker Toolkit checklist.

RESULTS

Systematic literature search identified 129 attributes associated with a clinically useful biomarker. These were grouped in four main categories including: rationale, clinical utility, analytical validity, and clinical validity. This checklist was subsequently developed using semi-structured interviews with biomarker experts (n=34); and 88.23% agreement was achieved regarding the identified attributes, via the Delphi survey (consensus level:75%, n=51). Quantitative validation was completed using clinically and non-clinically implemented breast and colorectal cancer biomarkers. Cox-regression analysis suggested that total score is a significant driver of biomarker success in both cancer types (BC: p>0.0001, 95.0% CI: 0.869-0.935, CRC: p>0.0001, 95.0% CI: 0.918-0.954).

CONCLUSIONS

This novel study generated a validated checklist with literature-reported attributes linked with successful biomarker implementation. Ultimately, the application of this toolkit can be used to detect biomarkers with the highest clinical potential and shape how biomarker studies are designed/performed.

Striving for a pragmatic contribution of biomarkers results to lifelong health care

BACKGROUND

The increased role of preventive medicine in healthcare and the rapid technological advancements, have deeply changed the landscape of laboratory medicine. In particular, increased investments in newborn screening tests and policies have been observed. Aim of this paper is to characterize how laboratory professionals engaged in clinical chemistry or newborn screening, in collaboration with experts in econometric, bioinformatics, and biostatistics may address a pragmatic use of laboratory results in the decision-making process oriented toward improvement of health care outcomes.

CONTENT

The effectiveness of biomarkers on healthcare depends on several factors such as analytical performance, prevalence of the disease, integration of the test within the diagnostic algorithm, associated costs, and social/economic impact of false positive and false negative results. Cost-effectiveness analysis needs to be performed and reliability achieved, by overcoming analytical pitfalls and by improving interpretative criteria. These are challenging issues common to clinical chemistry and newborn screening tests. Following the experience in clinical chemistry, one of the main issues to be approached in newborn screening tests, is the lack of harmonization of results obtained by different methods and the limited healthcare effectiveness.

SUMMARY

The focus on prevention is a crucial opportunity for laboratory medicine to change how to approach the effectiveness of biomarkers on healthcare. The consolidation within clinical laboratories of professionals with different technical and methodological expertise coupled with the need to produce and manage large sets of data, require the cooperation of professionals from other disciplines to characterize the impact of the tests on epidemiological outcomes for health care policy making process.

Methods and biomarkers for early detection, prediction, and diagnosis of colorectal cancer

Colorectal cancer (CRC) is one of the most common digestive diseases worldwide. It has steadily ascended to the top three cancers in terms of incidence and mortality. The primary cause is the inability to diagnose it at an early stage. Therefore, early detection and diagnosis are essential for colorectal cancer prevention. Although there are now various methods for CRC early detection, in addition to recent developments in surgical and multimodal therapy, the poor prognosis and late detection of CRC still remain significant. Thus, it is important to investigate novel technologies and biomarkers to improve the sensitization and specification of CRC diagnosis. Here, we present some common methods and biomarkers for early detection and diagnosis of CRC, we hope this review will encourage the adoption of screening programs and the clinical use of these potential molecules as biomarkers for CRC early detection and prognosis.

Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects

Mass spectrometry focusing on small endogenous molecules has become an integral part of biomarker discovery in the pursuit of an in-depth understanding of the pathophysiology of various diseases, ultimately enabling the application of personalized medicine. While LC-MS methods allow researchers to gather vast amounts of data from hundreds or thousands of samples, the successful execution of a study as part of clinical research also requires knowledge transfer with clinicians, involvement of data scientists, and interactions with various stakeholders. The initial planning phase of a clinical research project involves specifying the scope and design, and engaging relevant experts from different fields. Enrolling subjects and designing trials rely largely on the overall objective of the study and epidemiological considerations, while proper pre-analytical sample handling has immediate implications on the quality of analytical data. Subsequent LC-MS measurements may be conducted in a targeted, semi-targeted, or non-targeted manner, resulting in datasets of varying size and accuracy. Data processing further enhances the quality of data and is a prerequisite for in-silico analysis. Nowadays, the evaluation of such complex datasets relies on a mix of classical statistics and machine learning applications, in combination with other tools, such as pathway analysis and gene set enrichment. Finally, results must be validated before biomarkers can be used as prognostic or diagnostic decision-making tools. Throughout the study, quality control measures should be employed to enhance the reliability of data and increase confidence in the results. The aim of this graphical review is to provide an overview of the steps to be taken when conducting an LC-MS-based clinical research project to search for small molecule biomarkers.

Immunoaffinity Mass Spectrometry Diagnostic Tests for Multi-Biomarker Assays

Immunoaffinity mass spectrometry as an approach for diagnostic biomarker assays combines the advantages of antibody selectivity with the multiplexing and analytical performance of mass spectrometry. A method has been developed to detect and quantify three protein biomarkers for a diabetic kidney disease prognostic assay, PromarkerD. The methodology reflects an immunoaffinity approach compatible with higher throughput and robust clinical application. After preparation and purification of antibody-bead conjugates for the three target proteins, an immunoaffinity capture step provides a solution for reduction, alkylation, and digestion on-bead. Targeted mass spectrometry provides a quantitative measure of each biomarker in a rapid 8 min run using a microflow LCMS workflow.

An Overview: Genetic Tumor Markers for Early Detection and Current Gene Therapy Strategies

Genomic instability is considered a fundamental factor involved in any neoplastic disease. Consequently, the genetically unstable cells contribute to intratumoral genetic heterogeneity and phenotypic diversity of cancer. These genetic alterations can be detected by several diagnostic techniques of molecular biology and the detection of alteration in genomic integrity may serve as reliable genetic molecular markers for the early detection of cancer or cancer-related abnormal changes in the body cells. These genetic molecular markers can detect cancer earlier than any other method of cancer diagnosis, once a tumor is diagnosed, then replacement or therapeutic manipulation of these cancer-related abnormal genetic changes can be possible, which leads toward effective and target-specific cancer treatment and in many cases, personalized treatment of cancer could be performed without the adverse effects of chemotherapy and radiotherapy. In this review, we describe how these genetic molecular markers can be detected and the possible ways for the application of this gene diagnosis for gene therapy that can attack cancerous cells, directly or indirectly, which lead to overall improved management and quality of life for a cancer patient.

Ethical considerations about the collection of biological samples for genetic analysis in clinical trials

Progress in precision medicine is being achieved through the design of clinical trials that use genetic biomarkers to guide stratification of patients and assignation to treatment or control groups. Genetic analysis of biomarkers is, therefore, essential to complete their objectives, and this involves the study of biological samples from donor patients that have been recruited according to criteria previously established in the design of the clinical trial. Nevertheless, it is becoming very common that, in the solicitation of biological samples, purposes that are beyond the objectives of the stated therapeutic trial research are introduced, like the development of ill-explained exploratory studies or the use in unspecified future research. In the digital era, the sequencing of patients’ DNA needs to be considered as a serious security matter, not only for the patients, but also for their relatives. Genetic information may be easily stored, even forever, in digital files. This engenders a permanent risk of being stolen or misused in many ways. Furthermore, re-identification of sample donors is technically feasible through their genetic data. For these reasons, genetic analysis of samples collected in clinical trials should be restricted to the accomplishment of their main objectives or well justified goals.

Current State and Future Perspectives on Personalized Metabolomics

Metabolomics is one of the most promising 'omics' sciences for the implementation in medicine by developing new diagnostic tests and optimizing drug therapy. Since in metabolomics, the end products of the biochemical processes in an organism are studied, which are under the influence of both genetic and environmental factors, the metabolomics analysis can detect any changes associated with both lifestyle and pathological processes. Almost every case-controlled metabolomics study shows a high diagnostic accuracy. Taking into account that metabolomics processes are already described for most nosologies, there are prerequisites that a high-speed and comprehensive metabolite analysis will replace, in near future, the narrow range of chemical analyses used today, by the medical community. However, despite the promising perspectives of personalized metabolomics, there are currently no FDA-approved metabolomics tests. The well-known problem of complexity of personalized metabolomics data analysis and their interpretation for the end-users, in addition to a traditional need for analytical methods to address the quality control, standardization, and data treatment are reported in the review. Possible ways to solve the problems and change the situation with the introduction of metabolomics tests into clinical practice, are also discussed.

Progress with Metabolomic Blood Tests for Gastrointestinal Cancer Diagnosis-An Assessment of Biomarker Translation

There is an urgent need for cost-effective, non-invasive tools to detect early stages of gastrointestinal cancer (colorectal, gastric, and esophageal cancers). Despite many publications suggesting circulating metabolites acting as accurate cancer biomarkers, few have reached the clinic. In upper gastrointestinal cancer this is critically important, as there is no test to complement gold-standard endoscopic evaluation in patients with mild symptoms that do not meet referral criteria. Therefore, this study aimed to describe and solve this translational gap. Studies reporting diagnostic accuracy of metabolomic blood-based gastrointestinal cancer biomarkers from 2007 to 2020 were systematically reviewed and progress of each biomarker along the discovery-validation-adoption pathway was mapped. Successful biomarker translation was defined as a composite endpoint, including patent protection/FDA approval/recommendation in national guidelines. The review found 77 biomarker panels of gastrointestinal cancer, including 25 with an AUROC >0.9. All but one was stalled at the discovery phase, 9.09% were patented and none were clinically approved, confirming the extent of biomarker translational gap. In addition, there were numerous "re-discoveries," including histidine, discovered in 7 colorectal studies. Finally, this study quantitatively supports the presence of a translational gap between discovery and clinical adoption, despite clear evidence of highly performing biomarkers with significant potential clinical value.

Recommendations to improve use and reporting of statistics in animal experiments

Flaws in experimental statistics are a major contributor to the poor reproducibility of animal experiments. Informed decisions about whether conclusions are justified requires clear reporting of experimental data and the statistical methods used to analyse them. When data are misinterpreted, manipulated or concealed to generate publications, it creates an illusion that chance observations are robust data which confirm the hypotheses presented. Attempts to reproduce and advance such observations can propagate large areas of irreproducible science. This hinders scientific progress, erodes public support for research, damages reputations and wastes resources. This review analyses and explains recommendations to improve use and reporting of statistics in animal experiments.

Correlation between apelin and VEGF levels in retinopathy of prematurity: a matched case-control study

Background

Although several clinical studies have analysed the relationship between the levels of vascular endothelial growth factor (VEGF) and apelin-13 in venous blood and retinopathy of prematurity (ROP), no definitive conclusions have been reached. This study aimed to investigate the relationship between apelin-13 levels and VEGF levels and ROP.

Methods

Differences in plasma apelin-13 and VEGF levels were analysed in two groups of infants born with birth weight < 1500 g and gestational age < 32 weeks at Peking University People’ s Hospital. One group comprised infants diagnosed with ROP and the other group was a control group comprising infants without ROP.

Results

Apelin-13 levels were significantly lower in the ROP group than in the control group, while VEGF levels showed the opposite result (both P < 0.001). Infants with severe ROP had lower apelin-13 levels and higher VEGF levels than with mild ROP (both P < 0.05).The receiver operating characteristic curve for apelin-13 level as the indicator of ROP showed that a cut-off value of 119.6 pg/mL yielded a sensitivity of 84.8% and a specificity of 63.6%, while for VEGF level, the cut-off value of 84.3 pg/mL exhibited a sensitivity of 84.8% and a specificity of 66.7%.

Conclusions

Plasma apelin-13 and VEGF levels at 4–6 weeks of age may play a role in assisting the diagnosis of ROP.

Lessons From a Systematic Literature Search on Diagnostic DNA Methylation Biomarkers for Colorectal Cancer: How to Increase Research Value and Decrease Research Waste?

OBJECTIVES

To improve colorectal cancer (CRC) survival and lower incidence rates, colonoscopy and/or fecal immunochemical test screening are widely implemented. Although candidate DNA methylation biomarkers have been published to improve or complement the fecal immunochemical test, clinical translation is limited. We describe technical and methodological problems encountered after a systematic literature search and provide recommendations to increase (clinical) value and decrease research waste in biomarker research. In addition, we present current evidence for diagnostic CRC DNA methylation biomarkers.

METHODS

A systematic literature search identified 331 diagnostic DNA methylation marker studies published before November 2020 in PubMed, EMBASE, Cochrane Library, and Google Scholar. For 136 bodily fluid studies, extended data extraction was performed. STARD criteria and level of evidence were registered to assess reporting quality and strength for clinical translation.

RESULTS

Our systematic literature search revealed multiple issues that hamper the development of DNA methylation biomarkers for CRC diagnosis, including methodological and technical heterogeneity and lack of validation or clinical translation. For example, clinical translation and independent validation were limited, with 100 of 434 markers (23%) studied in bodily fluids, 3 of 434 markers (0.7%) translated into clinical tests, and independent validation for 92 of 411 tissue markers (22%) and 59 of 100 bodily fluids markers (59%).

DISCUSSION

This systematic literature search revealed that major requirements to develop clinically relevant diagnostic CRC DNA methylation markers are often lacking. To avoid the resulting research waste, clinical needs, intended biomarker use, and independent validation should be better considered before study design. In addition, improved reporting quality would facilitate meta-analysis, thereby increasing the level of evidence and enabling clinical translation.

Proteomic Profiling and Biomarker Discovery in Colorectal Liver Metastases

Colorectal liver metastases (CRLM) are the leading cause of death among patients with metastatic colorectal cancer (CRC). As part of multimodal therapy, liver resection is the mainstay of curative-intent treatment for select patients with CRLM. However, effective treatment of CRLM remains challenging as recurrence occurs in most patients after liver resection. Proposed clinicopathologic factors for predicting recurrence are inconsistent and lose prognostic significance over time. The rapid development of next-generation sequencing technologies and decreasing DNA sequencing costs have accelerated the genomic profiling of various cancers. The characterisation of genomic alterations in CRC has significantly improved our understanding of its carcinogenesis. However, the functional context at the protein level has not been established for most of this genomic information. Furthermore, genomic alterations do not always result in predicted changes in the corresponding proteins and cancer phenotype, while post-transcriptional and post-translational regulation may alter synthesised protein levels, affecting phenotypes. More recent advancements in mass spectrometry-based technology enable accurate protein quantitation and comprehensive proteomic profiling of cancers. Several studies have explored proteomic biomarkers for predicting CRLM after oncologic resection of primary CRC and recurrence after curative-intent resection of CRLM. The current review aims to rationalise the proteomic complexity of CRC and explore the potential applications of proteomic biomarkers in CRLM.

Clinicians' Perspectives on Barriers and Facilitators for the Adoption of Non-Invasive Liver Tests for NAFLD: A Mixed-Method Study

(1) Background: Given the high prevalence of non-alcoholic fatty liver disease (NAFLD) and the limitations of liver biopsies, multiple non-invasive tests (NITs) have been developed to identify non-alcoholic fatty liver disease (NAFLD) patients at-risk of progression. The availability of these new NITs varies from country to country, and little is known about their implementation and adoption in routine clinical practice. This study aims to explore barriers and facilitators that influence the adoption of NAFLD NITs, from healthcare professionals’ perspectives. (2) Methods: A cross-sectional study was performed using an exploratory mixed-methods approach. Twenty-seven clinicians from eight different countries with different specialties filled in our questionnaire. Of those, 16 participated in semi-structured interviews. Qualitative and quantitative data were collected and summarized using the recently published Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework for new medical technologies in healthcare organizations. (3) Results: Several factors were reported as influencing the uptake of NITs for NAFLD in clinical practice. Among those: insufficient awareness of tests; lack of practical guidelines and evidence for the performance of tests in appropriate patient populations and care settings; and absence of sufficient reimbursement systems were reported as the most important barriers. Other factors, most notably ‘local champions’, proper functional payment systems, and sufficient resources in academic hospitals, were indicated as important facilitating factors. (4) Conclusions: Clinicians see the adoption of NITs for NAFLD as a complex process that is modulated by several factors, such as robust evidence, practical guidelines, a proper payment system, and local champions. Future research could explore perspectives from other stakeholders on the adoption of NITs.

Diagnostic circulating biomarkers to detect vision-threatening diabetic retinopathy: Potential screening tool of the future?

With the increasing prevalence of diabetes in developing and developed countries, the socio-economic burden of diabetic retinopathy (DR), the leading complication of diabetes, is growing. Diabetic retinopathy (DR) is currently one of the leading causes of blindness in working-age adults worldwide. Robust methodologies exist to detect and monitor DR; however, these rely on specialist imaging techniques and qualified practitioners. This makes detecting and monitoring DR expensive and time-consuming, which is particularly problematic in developing countries where many patients will be remote and have little contact with specialist medical centres. Diabetic retinopathy (DR) is largely asymptomatic until late in the pathology. Therefore, early identification and stratification of vision-threatening DR (VTDR) is highly desirable and will ameliorate the global impact of this disease. A simple, reliable and more cost-effective test would greatly assist in decreasing the burden of DR around the world. Here, we evaluate and review data on circulating protein biomarkers, which have been verified in the context of DR. We also discuss the challenges and developments necessary to translate these promising data into clinically useful assays, to detect VTDR, and their potential integration into simple point-of-care testing devices.

Technical considerations in PCR-based assay design for diagnostic DNA methylation cancer biomarkers

Background

DNA methylation biomarkers for early detection, risk stratification and treatment response in cancer have been of great interest over the past decades. Nevertheless, clinical implementation of these biomarkers is limited, as only < 1% of the identified biomarkers is translated into a clinical or commercial setting. Technical factors such as a suboptimal genomic location of the assay and inefficient primer or probe design have been emphasized as important pitfalls in biomarker research. Here, we use eleven diagnostic DNA methylation biomarkers for colorectal cancer (ALX4, APC, CDKN2A, MGMT, MLH1, NDRG4, SDC2, SFRP1, SFRP2, TFPI1 and VIM), previously described in a systematic literature search, to evaluate these pitfalls.

Results

To assess the genomic assay location, the optimal genomic locations according to TCGA data were extracted and compared to the genomic locations used in the published assays for all eleven biomarkers. In addition, all primers and probes were technically evaluated according to several criteria, based on literature and expert opinion. Both assay location and assay design quality varied widely among studies.

Conclusions

Large variation in both assay location and design hinders the development of future DNA methylation biomarkers as well as inter-study comparability.

Health Technology Assessment to assess value of biomarkers in the decision-making process

Clinical practice guidelines (CPGs) on screening, surveillance, and treatment of several diseases recommend the selective use of biomarkers with central role in clinical decision-making and move towards including patients in this process. To this aim we will clarify the multidisciplinary interactions required to properly measure the cost-effectiveness of biomarkers with regard to the risk-benefit of the patients and how Health Technology Assessment (HTA) approach may assess value of biomarkers integrated within the decision-making process. HTA through the interaction of different skills provides high-quality research information on the effectiveness, costs, and impact of health technologies, including biomarkers. The biostatistical methodology is relevant to HTA but only meta-analysis is covered in depth, whereas proper approaches are needed to estimate the benefit-risk balance ratio. Several biomarkers underwent HTA evaluation and the final reports have pragmatically addressed: 1) a redesign of the screening based on biomarker; 2) a de-implementation/replacement of the test in clinical practice; 3) a selection of biomarkers with potential predictive ability and prognostic value; and 4) a stronger monitoring of the appropriateness of test request. The COVID-19 pandemic has disclosed the need to create a robust and sustainable system to urgently deal with global health concerns and the HTA methodology enables rapid cost-effective implementation of diagnostic tests allowing healthcare providers to make critical patient-management decisions.

Methods for Stratification and Validation Cohorts: A Scoping Review

Personalized medicine requires large cohorts for patient stratification and validation of patient clustering. However, standards and harmonized practices on the methods and tools to be used for the design and management of cohorts in personalized medicine remain to be defined. This study aims to describe the current state-of-the-art in this area. A scoping review was conducted searching in PubMed, EMBASE, Web of Science, Psycinfo and Cochrane Library for reviews about tools and methods related to cohorts used in personalized medicine. The search focused on cancer, stroke and Alzheimer’s disease and was limited to reports in English, French, German, Italian and Spanish published from 2005 to April 2020. The screening process was reported through a PRISMA flowchart. Fifty reviews were included, mostly including information about how data were generated (25/50) and about tools used for data management and analysis (24/50). No direct information was found about the quality of data and the requirements to monitor associated clinical data. A scarcity of information and standards was found in specific areas such as sample size calculation. With this information, comprehensive guidelines could be developed in the future to improve the reproducibility and robustness in the design and management of cohorts in personalized medicine studies.

Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests

Metabolomics refers to the large-scale detection, quantification, and analysis of small molecules (metabolites) in biological media. Although metabolomics, alone or combined with other omics data, has already demonstrated its relevance for patient stratification in the frame of research projects and clinical studies, much remains to be done to move this approach to the clinical practice. This is especially true in the perspective of being applied to personalized/precision medicine, which aims at stratifying patients according to their risk of developing diseases, and tailoring medical treatments of patients according to individual characteristics in order to improve their efficacy and limit their toxicity. In this review article, we discuss the main challenges linked to analytical chemistry that need to be addressed to foster the implementation of metabolomics in the clinics and the use of the data produced by this approach in personalized medicine. First of all, there are already well-known issues related to untargeted metabolomics workflows at the levels of data production (lack of standardization), metabolite identification (small proportion of annotated features and identified metabolites), and data processing (from automatic detection of features to multi-omic data integration) that hamper the inter-operability and reusability of metabolomics data. Furthermore, the outputs of metabolomics workflows are complex molecular signatures of few tens of metabolites, often with small abundance variations, and obtained with expensive laboratory equipment. It is thus necessary to simplify these molecular signatures so that they can be produced and used in the field. This last point, which is still poorly addressed by the metabolomics community, may be crucial in a near future with the increased availability of molecular signatures of medical relevance and the increased societal demand for participatory medicine.

Liquid Chromatography-Mass Spectrometry for Clinical Metabolomics: An Overview

Metabolomics is a discipline that offers a comprehensive analysis of metabolites in biological samples. In the last decades, the notable evolution in liquid chromatography and mass spectrometry technologies has driven an exponential progress in LC-MS-based metabolomics. Targeted and untargeted metabolomics strategies are important tools in health and medical science, especially in the study of disease-related biomarkers, drug discovery and development, toxicology, diet, physical exercise, and precision medicine. Clinical and biological problems can now be understood in terms of metabolic phenotyping. This overview highlights the current approaches to LC-MS-based metabolomics analysis and its applications in the clinical research.

Lipid Profiling in Cancer Diagnosis with Hand-Held Ambient Mass Spectrometry Probes: Addressing the Late-Stage Performance Concerns

Untargeted lipid fingerprinting with hand-held ambient mass spectrometry (MS) probes without chromatographic separation has shown promise in the rapid characterization of cancers. As human cancers present significant molecular heterogeneities, careful molecular modeling and data validation strategies are required to minimize late-stage performance variations of these models across a large population. This review utilizes parallels from the pitfalls of conventional protein biomarkers in reaching bedside utility and provides recommendations for robust modeling as well as validation strategies that could enable the next logical steps in large scale assessment of the utility of ambient MS profiling for cancer diagnosis. Six recommendations are provided that range from careful initial determination of clinical added value to moving beyond just statistical associations to validate lipid involvements in disease processes mechanistically. Further guidelines for careful selection of suitable samples to capture expected and unexpected intragroup variance are provided and discussed in the context of demographic heterogeneities in the lipidome, further influenced by lifestyle factors, diet, and potential intersect with cancer lipid pathways probed in ambient mass spectrometry profiling studies.

Prognostic Research in Traumatic Brain Injury: Markers, Modeling, and Methodological Principles

Prognostic assessment in traumatic brain injury (TBI) is embedded deeply in clinical care. Considering the limitations of current prognostic indicators, there is increasing interest in understanding the role of new biomarkers, and in finding other prognostic indicators of long-term outcomes following TBI. New prognostic indicators may result in the development of more accurate prediction models that could be useful for both risk stratification and clinical decision making. We aimed to review methodological issues and provide tentative guidelines for prognostic research in TBI. Prognostic factor research focuses on the role of a specific patient or disease-related characteristic in relation to outcome. Typically, univariable relations of the prognostic factor are studied, followed by analyses adjusting for other variables related to the outcome. Following existing guidelines, we emphasize the importance of transparent reporting of patient and specimen characteristics, study design, clinical end-points, and statistical analysis. Prognostic model research considers combinations of predictors, with challenges for model specification, estimation, evaluation, validation, and presentation. We highlight modern approaches and opportunities related to missing values, exploration of non-linear effects, and assessing between-study heterogeneity. Prognostic research in TBI can be improved if key methodological principles are adhered to and when research is performed in collaboration among multiple centers to ensure generalizability.

Elevated tumor expression of Astroprincin (FAM171A1) is an independent marker of poor prognosis in colon cancer

BACKGROUND

Colon cancer (CC) is one of the most commonly diagnosed malignancies worldwide. Several biomarkers have been suggested for improved prognostic evaluation, but few have been implemented in clinical practice. There is a need for biomarkers that predict the tumor behavior in CC and allow stratification of patients that would benefit from adjuvant therapy. We recently identified and functionally characterized a previously unknown protein that we called ASTROPRINCIN (APCN) due to its abundance in astrocytes. APCN, also annotated as FAM171A1, is found in trophoblasts of early placenta. We demonstrated that high expression levels of APCN in cancer cells induced motility and ability of invasive growth in semisolid medium.

METHODS

We screened by immunohistochemistry a tissue microarray material from the tumors of 429 CC patients with clinical follow-up in a test series and 255 CC patients in a validation series.

RESULTS

We showed that low or absent APCN expression correlates with a favorable prognosis while high APCN expression was a sign of an adverse outcome. Cox uni- and multivariable analysis revealed that elevated tumor expression of APCN constitutes a robust marker of poor prognosis independent of stage, grade, patient's age, or gender.

CONCLUSION

Our findings demonstrate that APCN is a novel independent prognostic marker in CC and could potentially select patients for more intense postoperative adjuvant treatment and follow-up.

A Novel Framework for Phenotyping Children With Suspected or Confirmed Infection for Future Biomarker Studies

Background: The limited diagnostic accuracy of biomarkers in children at risk of a serious bacterial infection (SBI) might be due to the imperfect reference standard of SBI. We aimed to evaluate the diagnostic performance of a new classification algorithm for biomarker discovery in children at risk of SBI. Methods: We used data from five previously published, prospective observational biomarker discovery studies, which included patients aged 0- <16 years: the Alder Hey emergency department (n = 1,120), Alder Hey pediatric intensive care unit (n = 355), Erasmus emergency department (n = 1,993), Maasstad emergency department (n = 714) and St. Mary's hospital (n = 200) cohorts. Biomarkers including procalcitonin (PCT) (4 cohorts), neutrophil gelatinase-associated lipocalin-2 (NGAL) (3 cohorts) and resistin (2 cohorts) were compared for their ability to classify patients according to current standards (dichotomous classification of SBI vs. non-SBI), vs. a proposed PERFORM classification algorithm that assign patients to one of eleven categories. These categories were based on clinical phenotype, test outcomes and C-reactive protein level and accounted for the uncertainty of final diagnosis in many febrile children. The success of the biomarkers was measured by the Area under the receiver operating Curves (AUCs) when they were used individually or in combination. Results: Using the new PERFORM classification system, patients with clinically confident bacterial diagnosis ("definite bacterial" category) had significantly higher levels of PCT, NGAL and resistin compared with those with a clinically confident viral diagnosis ("definite viral" category). Patients with diagnostic uncertainty had biomarker concentrations that varied across the spectrum. AUCs were higher for classification of "definite bacterial" vs. "definite viral" following the PERFORM algorithm than using the "SBI" vs. "non-SBI" classification; summary AUC for PCT was 0.77 (95% CI 0.72-0.82) vs. 0.70 (95% CI 0.65-0.75); for NGAL this was 0.80 (95% CI 0.69-0.91) vs. 0.70 (95% CI 0.58-0.81); for resistin this was 0.68 (95% CI 0.61-0.75) vs. 0.64 (0.58-0.69) The three biomarkers combined had summary AUC of 0.83 (0.77-0.89) for "definite bacterial" vs. "definite viral" infections and 0.71 (0.67-0.74) for "SBI" vs. "non-SBI." Conclusion: Biomarkers of bacterial infection were strongly associated with the diagnostic categories using the PERFORM classification system in five independent cohorts. Our proposed algorithm provides a novel framework for phenotyping children with suspected or confirmed infection for future biomarker studies.

A panel of differentially methylated regions enable prognosis prediction for colorectal cancer

We aim to identify a panel of differentially methylated regions (DMRs) for predicting survival outcomes for patients with CRC from the TCGA (n = 393). Four DMRs (MUC12, TBX20, CHN2, and B3GNT7) were selected as candidate prognostic markers for CRC. The prediction potential of selected DMRs was validated by the targeted bisulfite sequencing method in an independent cohort with 251 Chinese CRC patients. DMR methylation scores (DMSs) were constructed to evaluate the prognosis of CRC. Results of the validation cohort confirmed that higher DMSs were associated with poor overall survival (OS) of CRC, with hazard ratio (HR) value ranged from 1.445 to 2.698 in multivariable Cox models. Patients in the high prognostic index (high-PI) group showed a markedly unfavorable prognosis compared to the low-PI group in both TCGA discovery cohort (HR = 3.508, 95%CI: 2.196-5.604, P < 0.001) and independent validation cohort (HR = 1.912, 95%CI: 1.258-2.907, P = 0.002).

Dried Blood Spot (DBS) Methodology Study for Biomarker Discovery in Lysosomal Storage Disease (LSD)

Lysosomal storage diseases (LSDs) are a heterogeneous group of inherited metabolic diseases caused by mutations in genes encoding for proteins involved in the lysosomal degradation of macromolecules. They occur in approximately 1 in 5000 live births and pose a lifelong risk. Therefore, to achieve the maximum benefit from LSDs therapies, a fast and early diagnosis of the disease is required. In this framework, biomarker discovery is a significant factor in disease diagnosis and in predicting its outcomes. On the other hand, the dried blood spot (DBS) based metabolomics platform can open up new pathways for studying non-directional hypothesis approaches to biomarker discovery. This study aims to increase the efficiency of the developed methods for biomarker development in the context of rare diseases, with an improved impact on the reliability of the detected compounds. Thereby, we conducted two independent experiments and integrated them into the screening of the human blood metabolome: (1) comparison of EDTA blood and filter cards in terms of their suitability for metabolomics studies; (2) optimization of the extraction method: a side-by-side comparison of a series of buffers to the best utility to the disease of interest. The findings were compared to previous studies across parameters such as metabolite coverage, sample type suitability, and stability. The results indicate that measurements of metabolites are susceptible to differences in pre-analytical conditions and extraction solvents. This proposed approach can increase the positive rate of the future development of biomarkers. Altogether, the procedure can be easily adapted and applied to other studies, where the limited number of samples is a common barrier.

Identifying Novel Biomarkers Ready for Evaluation in Low-Prevalence Populations for the Early Detection of Lower Gastrointestinal Cancers: A Systematic Review and Meta-Analysis

INTRODUCTION

Lower gastrointestinal (GI) cancers are a major cause of cancer deaths worldwide. Prognosis improves with earlier diagnosis, and non-invasive biomarkers have the potential to aid with early detection. Substantial investment has been made into the development of biomarkers; however, studies are often carried out in specialist settings and few have been evaluated for low-prevalence populations.

METHODS

We aimed to identify novel biomarkers for the detection of lower GI cancers that have the potential to be evaluated for use in primary care. MEDLINE, Embase, Emcare and Web of Science were systematically searched for studies published in English from January 2000 to October 2019. Reference lists of included studies were also assessed. Studies had to report on measures of diagnostic performance for biomarkers (single or in panels) used to detect colorectal or anal cancers. We included all designs and excluded studies with fewer than 50 cases/controls. Data were extracted from published studies on types of biomarkers, populations and outcomes. Narrative synthesis was used, and measures of specificity and sensitivity were meta-analysed where possible.

RESULTS

We identified 142 studies reporting on biomarkers for lower GI cancers, for 24,844 cases and 45,374 controls. A total of 378 unique biomarkers were identified. Heterogeneity of study design, population type and sample source precluded meta-analysis for all markers except methylated septin 9 (mSEPT9) and pyruvate kinase type tumour M2 (TuM2-PK). The estimated sensitivity and specificity of mSEPT9 was 80.6% (95% CI 76.6-84.0%) and 88.0% (95% CI 79.1-93.4%) respectively; TuM2-PK had an estimated sensitivity of 81.6% (95% CI 75.2-86.6%) and specificity of 80.1% (95% CI 76.7-83.0%).

CONCLUSION

Two novel biomarkers (mSEPT9 and TuM2-PK) were identified from the literature with potential for use in lower-prevalence populations. Further research is needed to validate these biomarkers in primary care for screening and assessment of symptomatic patients.

Spectral Resting-State EEG (rsEEG) in Chronic Aphasia Is Reliable, Sensitive, and Correlates With Functional Behavior

We investigated spectral resting-state EEG in persons with chronic stroke-induced aphasia to determine its reliability, sensitivity, and relationship to functional behaviors. Resting-state EEG has not yet been characterized in this population and was selected given the demonstrated potential of resting-state investigations using other neuroimaging techniques to guide clinical decision-making. Controls and persons with chronic stroke-induced aphasia completed two EEG recording sessions, separated by approximately 1 month, as well as behavioral assessments of language, sensorimotor, and cognitive domains. Power in the classic frequency bands (delta, theta, alpha, and beta) was examined via spectral analysis of resting-state EEG data. Results suggest that power in the theta, alpha, and beta bands is reliable for use as a repeated measure. Significantly greater theta and lower beta power was observed in persons with aphasia (PWAs) than controls. Finally, in PWAs theta power negatively correlated with performance on a discourse informativeness measure, while alpha and beta power positively correlated with performance on the same measure. This indicates that spectral rsEEG slowing observed in PWAs in the chronic stage is pathological and suggests a possible avenue for directly altering brain activation to improve behavioral function. Taken together, these results suggest that spectral resting-state EEG holds promise for sensitive measurement of functioning and change in persons with chronic aphasia. Future studies investigating the utility of these measures as biomarkers of frank or latent aphasic deficits and treatment response in chronic stroke-induced aphasia are warranted.

Mass spectrometry-based metabolomics diagnostics - myth or reality?

ABSTACTIntroduction: Metabolomics, one of the most high-promising technologies, is the most recently developed post-genomics discipline for developing new diagnostic tests for future implementation in medicine. More than 2,000 scientific papers, using mass spectrometry-based (MS-based) metabolomics analysis for human disease diagnostics, have been published during the past two decades, and almost every metabolomics study shows high diagnostic accuracy. However, despite the great results and promising perspectives, there are currently no diagnostic tests based on metabolomics that have been approved and introduced into clinics.Areas covered: In this report, the advantages and challenges of MS-based metabolomics are discussed with a focus on its developing role in diagnostics, and the current trends in implementing metabolomics diagnostics in the clinic.Expert opinion: In the development of new clinical diagnostics tests, MS-based metabolomics has potential as both a preliminary discovery base for routine testing and a multi-test prototype, which is hoped to be introduced into clinical practice in the near future. A laboratory-developed test (LDT) is one possible way that multi-testing could be developed.

Identifying Novel Biomarkers Ready for Evaluation in Low-Prevalence Populations for the Early Detection of Upper Gastrointestinal Cancers: A Systematic Review

Introduction

Detecting upper gastrointestinal (GI) cancers in primary care is challenging, as cancer symptoms are common, often non-specific, and most patients presenting with these symptoms will not have cancer. Substantial investment has been made to develop biomarkers for cancer detection, but few have reached routine clinical practice. We aimed to identify novel biomarkers for upper GI cancers which have been sufficiently validated to be ready for evaluation in low-prevalence populations.

Methods

We systematically searched MEDLINE, Embase, Emcare, and Web of Science for studies published in English from January 2000 to October 2019 (PROSPERO registration CRD42020165005). Reference lists of included studies were assessed. Studies had to report on second measures of diagnostic performance (beyond discovery phase) for biomarkers (single or in panels) used to detect pancreatic, oesophageal, gastric, and biliary tract cancers. We included all designs and excluded studies with less than 50 cases/controls. Data were extracted on types of biomarkers, populations and outcomes. Heterogeneity prevented pooling of outcomes.

Results

We identified 149 eligible studies, involving 22,264 cancer cases and 49,474 controls. A total of 431 biomarkers were identified (183 microRNAs and other RNAs, 79 autoantibodies and other immunological markers, 119 other proteins, 36 metabolic markers, 6 circulating tumour DNA and 8 other). Over half (n = 231) were reported in pancreatic cancer studies. Only 35 biomarkers had been investigated in at least two studies, with reported outcomes for that individual marker for the same tumour type. Apolipoproteins (apoAII-AT and apoAII-ATQ), and pepsinogens (PGI and PGII) were the most promising biomarkers for pancreatic and gastric cancer, respectively.

Conclusion

Most novel biomarkers for the early detection of upper GI cancers are still at an early stage of matureness. Further evidence is needed on biomarker performance in low-prevalence populations, in addition to implementation and health economic studies, before extensive adoption into clinical practice can be recommended.

Electronic Supplementary Material

The online version of this article (10.1007/s12325-020-01571-z) contains supplementary material, which is available to authorized users.

DNA methylation markers detected in blood, stool, urine, and tissue in colorectal cancer: a systematic review of paired samples

PURPOSE

Methylated cell-free DNA in liquid biopsies are promising non-invasive biomarkers for colorectal cancer (CRC). Optimal markers would have high sensitivity and specificity for early detection of CRC and could be detected in more than one type of material from the patient. We systematically reviewed the literature on DNA methylation markers of colorectal cancer, detected in more than one type of material, regarding their potential as contributors to a panel for screening and follow-up of CRC.

METHODS

The databases MEDLINE, Web of Science, and Embase were systematically searched. Data extraction and review was performed by two authors independently. Agreement between methylation status in tissue and other materials (blood/stool/urine) was analyzed using the McNemar test and Cohen's kappa.

RESULTS

From the 51 included studies, we identified seven single markers with sensitivity ≥ 75% and specificity ≥ 90% for CRC. We also identified one promising plasma panel and two stool panels. The correspondence of methylation status was evaluated as very good for four markers, but only marginal for most of the other markers investigated (12 of 21).

CONCLUSION

The included studies reported only some of the variables and markers of interest and included few patients. Hence, a meta-analysis was not possible at this point. Larger, prospective studies must be designed to study the discordant detection of markers in tissue and liquid biopsies. When reporting their findings, such studies should use a standardized format.

The role of impulsivity in neurorehabilitation: A prospective cohort study of a potential cognitive biomarker for fall risk?

BACKGROUND

Executive dysregulation and impulsivity can both predispose individuals to risk-prone actions. Although the risk of falls is well established in people with poor executive function, its association to impulsivity is less clear.

PURPOSE

To describe and assess the prognostic capabilities of the relationship between impulsivity, executive function, functional capability, and falls in the in-patient neurorehabilitation population.

MATERIALS AND METHODS

A prospective cohort study in a 26-bed neurorehabilitation unit in London, recruiting 121 patients, of whom 94 were deemed eligible for inclusion. Cognitive-behavioural assessment was undertaken using the short (16-item) version of the Urgency-Premeditation-Perseverance-Sensation Seeking-Positive Urgency (UPPS) impulsive behaviour scale, and the Trail Making Test (TMT). Patients also underwent a functional assessment at admission and discharge using the UK Functional Independence and Assessment Measure tool (FIM + FAM). The main outcome of interest was falling during an in-patient episode, which are routinely recorded in a computerized registry of adverse incidents.

RESULTS

Measurements of impulsivity (based on the UPPS-Short form) and executive function (based on the Trail Making Test) were not found to be significantly associated with functional improvement, or risk of falling. Predictive modelling experiments demonstrated that neither of the aforementioned results were capable of identifying individuals at risk of falling more accurately than an informed guess.

CONCLUSION

Where impulsivity is present, measurement using structured tools such as the UPPS may be informative to guide individualized rehabilitation programmes; however, its usefulness as the basis of risk prediction models for falls is less likely given the results of this study.

Liquid Biopsy Strategies to Distinguish Progression from Pseudoprogression and Radiation Necrosis in Glioblastomas

Liquid biopsy for the detection and monitoring of central nervous system tumors is of significant clinical interest. At initial diagnosis, the majority of patients with central nervous system tumors undergo magnetic resonance imaging (MRI), followed by invasive brain biopsy to determine the molecular diagnosis of the WHO 2016 classification paradigm. Despite the importance of MRI for long-term treatment monitoring, in the majority of patients who receive chemoradiation therapy for glioblastoma, it can be challenging to distinguish between radiation treatment effects including pseudoprogression, radiation necrosis, and recurrent/progressive disease based on imaging alone. Tissue biopsy-based monitoring is high risk and not always feasible. However, distinguishing these entities is of critical importance for the management of patients and can significantly affect survival. Liquid biopsy strategies including circulating tumor cells, circulating free DNA, and extracellular vesicles have the potential to afford significant useful molecular information at both the stage of diagnosis and monitoring for these tumors. Here, current liquid biopsy-based approaches in the context of tumor monitoring to differentiate progressive disease from pseudoprogression and radiation necrosis are reviewed.

A semi-supervised approach for rapidly creating clinical biomarker phenotypes in the UK Biobank using different primary care EHR and clinical terminology systems

OBJECTIVES

The UK Biobank (UKB) is making primary care electronic health records (EHRs) for 500 000 participants available for COVID-19-related research. Data are extracted from four sources, recorded using five clinical terminologies and stored in different schemas. The aims of our research were to: (a) develop a semi-supervised approach for bootstrapping EHR phenotyping algorithms in UKB EHR, and (b) to evaluate our approach by implementing and evaluating phenotypes for 31 common biomarkers.

MATERIALS AND METHODS

We describe an algorithmic approach to phenotyping biomarkers in primary care EHR involving (a) bootstrapping definitions using existing phenotypes, (b) excluding generic, rare, or semantically distant terms, (c) forward-mapping terminology terms, (d) expert review, and (e) data extraction. We evaluated the phenotypes by assessing the ability to reproduce known epidemiological associations with all-cause mortality using Cox proportional hazards models.

RESULTS

We created and evaluated phenotyping algorithms for 31 biomarkers many of which are directly related to COVID-19 complications, for example diabetes, cardiovascular disease, respiratory disease. Our algorithm identified 1651 Read v2 and Clinical Terms Version 3 terms and automatically excluded 1228 terms. Clinical review excluded 103 terms and included 44 terms, resulting in 364 terms for data extraction (sensitivity 0.89, specificity 0.92). We extracted 38 190 682 events and identified 220 978 participants with at least one biomarker measured.

DISCUSSION AND CONCLUSION

Bootstrapping phenotyping algorithms from similar EHR can potentially address pre-existing methodological concerns that undermine the outputs of biomarker discovery pipelines and provide research-quality phenotyping algorithms.

Feature set optimization in biomarker discovery from genome-scale data

MOTIVATION

Omics technologies have the potential to facilitate the discovery of new biomarkers. However, only few omics-derived biomarkers have been successfully translated into clinical applications to date. Feature selection is a crucial step in this process that identifies small sets of features with high predictive power. Models consisting of a limited number of features are not only more robust in analytical terms, but also ensure cost effectiveness and clinical translatability of new biomarker panels. Here we introduce GARBO, a novel multi-island adaptive genetic algorithm to simultaneously optimize accuracy and set size in omics-driven biomarker discovery problems.

RESULTS

Compared to existing methods, GARBO enables the identification of biomarker sets that best optimize the trade-off between classification accuracy and number of biomarkers. We tested GARBO and six alternative selection methods with two high relevant topics in precision medicine: cancer patient stratification and drug sensitivity prediction. We found multivariate biomarker models from different omics data types such as mRNA, miRNA, copy number variation, mutation and DNA methylation. The top performing models were evaluated by using two different strategies: the Pareto-based selection, and the weighted sum between accuracy and set size (w = 0.5). Pareto-based preferences show the ability of the proposed algorithm to search minimal subsets of relevant features that can be used to model accurate random forest-based classification systems. Moreover, GARBO systematically identified, on larger omics data types, such as gene expression and DNA methylation, biomarker panels exhibiting higher classification accuracy or employing a number of features much lower than those discovered with other methods. These results were confirmed on independent datasets.

AVAILABILITY AND IMPLEMENTATION

github.com/Greco-Lab/GARBO.

CONTACT

dario.greco@tuni.fi.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Biobanking for Cancer Biomarker Research: Issues and Solutions

Biomarkers are critical tools that underpin precision medicine. However there has been slow progress and frequent failure of biomarker development. The root causes are multifactorial. Here, we focus on the need for fast, efficient, and reliable access to quality biospecimens as a critical area that impacts biomarker development. We discuss the past history of biobanking and the evolution of biobanking processes relevant to the specific area of cancer biomarker development as an example, and describe some solutions that can improve this area, thus potentially accelerating biomarker research.

Improving Translation by Identifying Evidence for More Human-Relevant Preclinical Strategies

Preclinical animal studies are performed to analyse the safety and efficacy of new treatments, with the aim to protect humans. However, there are questions and concerns about the quality and usefulness of preclinical animal research. Translational success rates vary between 0 and 100%, and no clear relationship has been found with possible predictive factors such as animal species or field of research. Therefore, it is not yet possible to indicate what factors predict successful translation. Translational strategies were therefore discussed at an international conference held in the Netherlands in November 2019, aiming to develop practical guidelines for more robust animal-to-human translation. The conference was organised during the course of a research project funded by the Dutch Research Council (313-99-310), addressing possible solutions for the low translational values that had been published for a multitude of animal studies in human health care. This article provides an overview of the project and the conference discussions. Based on the conference results and the findings from the research project, we define four points of attention that are crucial in the search for improved translational success rates: (a) optimising the methods and design of studies; (b) incorporation of the complexity of the human patient in research; (c) start with the patient rather than existing animal models as the gold standard; and (d) more and better collaboration within the chain from funding to pharmacy. We conclude that this requires improved organization and use of procedures, as well as a change of attitude and culture in research, including a consideration of the translational value of animal-free innovations and human-relevant science.

Proteogenomic biomarkers in colorectal cancers: clinical applications

INTRODUCTION

Colorectal cancer (CRC) is one of the leading cancers in terms of incidence and mortality, rate requiring a multidisciplinary approach. The discovery of specific CRC biomarkers has caused a paradigm shift in its clinical management.

AREAS COVERED

The aim is to illustrate the possible clinical applications of CRC biomarkers through an updated literature review (from 2015 to 2020) based on the PubMed database. A relationship between cancer localization and genetic profile has been identified. Nowadays, the tumor markers are largely used to select patients that could really benefit from a specific type of adjuvant therapy, in order to optimize treatment programs, especially in metastatic patients. This review highlights both CRC biomarkers' advantages and critical issues.

EXPERT OPINION

New biomarker discoveries allow to set noninvasive tests that could increase patient's compliance with therapy. They also permit a cost-effective early diagnosis, as well as patient-tailored treatments, improving the overall survival. The CRC biomarkers could also have a prognostic value, and usually, they are included in follow-up programs. However, despite the continuous progression of new technologies, their clinical validation is still debated. In this context, additional clinical studies are still necessary to identify, among potential markers, the most effective ones.