How difficult is the validation of clinical biomarkers?

Advances in screening and diagnostic lab-on-chip tools for gynaecological cancers - a review

Gynaecological cancers are a major global health concern due to the lack of effective screening programmes for ovarian and endometrial cancer, for example, and variable access to vaccination and screening tests for cervical cancer in many countries. Recent research on portable and cost-effective lab-on-a-chip (LoC) technologies show promise for mass screening and diagnostic procedures for gynaecological cancers. However, most LoCs for gynaecological cancer are still in development, with a need to establish and clinically validate factors such as the type of biomarker, sample and method of detection, before patient use. Multiplex approaches, detecting a panel of gynaecological biomarkers in a single LoC, offer potential for more reliable diagnosis. This review highlights the current research on LoCs for gynaecological cancer screening and diagnosis, emphasizing the need for further research and validation prior to their widespread adoption in clinical practice.

Multiplexed 3D atlas of state transitions and immune interaction in colorectal cancer

Advanced solid cancers are complex assemblies of tumor, immune, and stromal cells characterized by high intratumoral variation. We use highly multiplexed tissue imaging, 3D reconstruction, spatial statistics, and machine learning to identify cell types and states underlying morphological features of known diagnostic and prognostic significance in colorectal cancer. Quantitation of these features in high-plex marker space reveals recurrent transitions from one tumor morphology to the next, some of which are coincident with long-range gradients in the expression of oncogenes and epigenetic regulators. At the tumor invasive margin, where tumor, normal, and immune cells compete, T cell suppression involves multiple cell types and 3D imaging shows that seemingly localized 2D features such as tertiary lymphoid structures are commonly interconnected and have graded molecular properties. Thus, while cancer genetics emphasizes the importance of discrete changes in tumor state, whole-specimen imaging reveals large-scale morphological and molecular gradients analogous to those in developing tissues.

Roadmap for translating results from the micronucleus assay into clinical practice: From observational studies to randomized controlled trials

According to the definition delivred by the WHO, a biomarker, independently from its role that may be indicative of exposure, response or effect, is inevitably linked to a clinical outcome or to a disease. The presence of a continuum from early biological events to therapy, and prognosis is the unifying mechanism that justifies this conclusion. Traditionally, the technical and inter-individual variability of the assays, together with the long duration between early pathogenetic events and the disease, prevented clinical applications to these biomarkers. These limitations became less important with the emerging of personalized preventive medicine because of the focus on disease prediction and prevention, and the recommended use of all data concerning measurable patient's features. Several papers have been published on the best validation procedures for translating biomarkers to real life. The history of cholesterol concentration is extensively discussed as a reliable example of a biomarker that - after a long and controversial validation process - is currently used in clinical practice. The frequency of micronucleated cells is a reliable biomarker for the pathogenesis of cancer and other non-communicable diseases, and the link with clinical outcomes is substantiated by epidemiological evidence and strong mechanistic basis. Available literature concerning the use of the micronucleus assay in clinical studies is discussed, and a suitable three-levels road-map driving this biomarker towards clinical practice is presented. Under the perspective of personalized medicine, the use of the micronucleus assays can play a decisive role in addressing preventive and therapeutic strategies of chronic diseases. In many cases the MN assay is either currently used in clinical practice or classified as adequate to consider translation into practice. The roadmap to clinical validation of the micronucleus assay finds inspiration from the history of biomarkers such as cholesterol, which clearly showed that the evidence from prospective studies or RCTs is critical to achieve the required level of trust from the healthcare profession. (307 words).

Research Techniques Made Simple: Feature Selection for Biomarker Discovery

Molecular biomarkers can be powerful tools for aiding in the efficiency and precision of clinical decision-making. Feature selection methods, machine-learning, and biostatistics have been applied to discover subsets of molecular markers that identify target classes of clinical cases. For example, in the field of dermatology, these approaches have been used to develop predictive models that identify skin diseases, ranging from melanoma to psoriasis, based upon a variety of biomarkers. However, a continuous increase in the variety and size of datasets from which candidate biomarkers can be derived, and limitations in the computational tools used to analyze them, have hindered the interpretability of biomarker discovery studies. In this article, the various methods of feature selection are described along with the important steps needed to properly validate the performance of the selected methods. Limitations and suggestions toward uses of these methods are discussed.

The Use of Antibody Arrays in the Discovery of New Plasma Biomarkers for Endometriosis

A noninvasive diagnostic test for endometriosis is needed to shorten the current diagnostic delay of 8-11 years. The goal of this study was to discover new biomarkers for endometriosis using an antibody array approach. A total of 103 plasma samples from patients with laparoscopically confirmed presence (n = 68) or absence (n = 35) of endometriosis were selected. Samples were pooled according to disease status, cycle phase, disease stage, and phenotype. Pooled samples were screened for possible biomarkers using the L-series 1000 and Quantibody 660 arrays from RayBiotech. Technical verification of ten markers was done using a custom-made multiplex immunoassay identifying ten proteins (10-plex) and later by single ELISA. Due to the limited reproducibility of the L-series 1000 immunoassay, the biomarker screening was performed using the Quantibody 660, a sandwich-based multiplex immunoassay, which showed that 280 proteins were upregulated, and 29 proteins downregulated in the endometriosis pool versus the control pool. In order to assess the reproducibility of these results, ten preselected proteins were analyzed using a custom 10-plex. Four proteins (CD48, DNAM-1, IL-31, and XIAP) were confirmed to be differentially expressed when comparing the endometriosis and control pool. However, only IL-31 showed a univariate statistical difference between endometriosis and control groups in individual samples that were part of the initial pools. In conclusion, discovery and verification of potential markers proved challenging using multiplex immunoassay methods, mainly due to issues with reproducibility. Only IL-31 showed potential as possible biomarker for endometriosis.

Metabolomic-Based Methods in Diagnosis and Monitoring Infection Progression

A robust biomarker screening and validation is crucial for overcoming the current limits in the clinical management of infectious diseases. In this chapter, a general workflow for metabolomics is summarized. Subsequently, an overview of the major contributions of this omics science to the field of biomarkers of infectious diseases is discussed. Different approaches using a variety of analytical platforms can be distinguished to unveil the key metabolites for the diagnosis, prognosis, response to treatment and susceptibility for infectious diseases. To allow the implementation of such biomarkers into the clinics, the performance of large-scale studies employing solid validation criteria becomes essential. Focusing on the etiological agents and after an extensive review of the field, we present a comprehensive revision of the main metabolic biomarkers of viral, bacterial, fungal, and parasitic diseases. Finally, we discussed several articles which show the strongest validation criteria. Following these research avenues, precious clinical resources will be revealed, allowing for reduced misdiagnosis, more efficient therapies, and affordable costs, ultimately leading to a better patient management.

Challenges and Opportunities for Biomarkers of Clinical Response to AHSCT in Autoimmunity

Autoimmunity represents a broad category of diseases that involve a variety of organ targets and distinct autoantigens. For patients with autoimmune diseases who fail to respond to approved disease-modifying treatments, autologous hematopoietic stem cell transplantation (AHSCT) after high-dose immunosuppressive therapy provides an alternative strategy. Although more than 100 studies have been published on AHSCT efficacy in autoimmunity, the mechanisms that confer long-term disease remission as opposed to continued deterioration or disease reactivation remain to be determined. In a phase II clinical trial, high-dose immunosuppressive therapy combined with autologous CD34⁺ hematopoietic stem cell transplant in treatment-resistant, relapsing-remitting multiple sclerosis (RRMS) resulted in 69.2% of participants achieving long-term remission through 60 months follow-up. Flow cytometry data from the 24 transplanted participants in the high-dose immunosuppression and autologous stem cell transplantation for poor prognosis multiple sclerosis (HALT-MS) trial are presented to illustrate immune reconstitution out to 36 months in patients with aggressive RRMS treated with AHSCT and to highlight experimental challenges inherent in identifying biomarkers for relapse and long-term remission through 60 months follow-up. AHSCT induced changes in numbers of CD4 T cells and in the composition of CD4 and CD8 T cells that persisted through 36 months in participants who maintained disease remission through 60 months. However, changes in T cell phenotypes studied were unable to clearly discriminate durable remission from disease reactivation after AHSCT, possibly due to the small sample size, limited phenotypes evaluated in this real-time assay, and other limitations of the HALT-MS study population. Strategies and future opportunities for identifying biomarkers of clinical outcome to AHSCT in autoimmunity are also discussed.

Genomics, microRNA, epigenetics, and proteomics for future diagnosis, treatment and monitoring response in upper GI cancers

One major objective for our evolving understanding in the treatment of cancers will be to address how a combination of diagnosis and treatment strategies can be used to integrate patient and tumor variables with an outcome-oriented approach. Such an approach, in a multimodal therapy setting, could identify those patients (1) who should undergo a defined treatment (personalized therapy) (2) in whom modifications of the multimodal therapy due to observed responses might lead to an improvement of the response and/or prognosis (individualized therapy), (3) who might not benefit from a particular toxic treatment regimen, and (4) who could be identified early on and thereby be spared the morbidity associated with such treatments. These strategies could lead in the direction of precision medicine and there is hope of integrating translational molecular data to improve cancer classifications. In order to achieve these goals, it is necessary to understand the key issues in different aspects of biotechnology to anticipate future directions of personalized and individualized diagnosis and multimodal treatment strategies. Providing an overview of translational data in cancers proved to be a challenge as different methods and techniques used to obtain molecular data are used and studies are based on different tumor entities with different tumor biology and prognoses as well as vastly different therapeutic approaches. The pros and cons of the available methodologies and the potential response data in genomics, microRNA, epigenetics and proteomics with a focus on upper gastrointestinal cancers are considered herein to allow for an understanding of where these technologies stand with respect to cancer diagnosis, prognosis and treatment.

Biomarkers and their dependence on well-reported antibodies

Jan Voskuil is the Chief Scientific Officer at antibody manufacturer Everest Biotech in Oxfordshire, UK. After specializing in prokaryotic cell biology through his PhD program in Amsterdam, The Netherlands and a postdoctorate position at Stanford (CA, USA), he switched to the science of neurodegenerative diseases at Oxford, UK through postdoctorate positions at Dunn School of Pathology and at MRC and through a leading position at the Alzheimer drug discovery company Synaptica. He subsequently gained experience in a Good Laboratory Practice-regulatory environment in contract research organization companies both in Oxfordshire and Cambridgeshire, validating assays in Flow Cytometry and ELISA platforms and writing standard operating procedures. His extensive experience with generating and characterizing monoclonal and polyclonal antibodies in combination with accrued knowledge on most immunoassays in academic and commercial environments made him the ideal candidate to take charge in putting Everest Biotech on the global map by ever raising the quality and size of its catalog and by delivery of adequate technical support. As a result, Everest antibodies are currently part of most globally well-known catalogs, and its products are increasingly recognized as useful alternatives to unfit monoclonal antibodies.