Predictive biomarkers are becoming increasingly important tools in drug development and clinical research. The importance of using both guidelines for specimen acquisition and analytical methods for biomarker measurements that are standardized has become recognized widely as an important issue, which must be addressed in order to provide high-quality, validated assays. Herein, we review the major challenges in biomarker validation processes, including pre-analytical (sample-related), analytical, and post-analytical (data-related) aspects of assay development. Recommendations for improving biomarker assay development and method validation are proposed to facilitate the use of predictive biomarkers in clinical trials and the practice of oncology.
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http://dx.doi.org/10.1038/nrclinonc.2014.202 | DOI Listing |
Discov Oncol
January 2025
Department of Otolaryngology-Head and Neck Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
The zinc finger protein 32 (ZNF32) has been associated with high expression in various cancers, underscoring its significant function in both cancer biology and immune response. To further elucidate the biological role of ZNF32 and identify potential immunotherapy targets in cancer, we conducted an in-depth analysis of ZNF32. We comprehensively investigated the expression of ZNF32 across tumors using diverse databases, including TCGA, CCLE, TIMER2.
View Article and Find Full Text PDFEMBO Mol Med
January 2025
Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA.
The exposome is the measure of all the exposures of an individual in a lifetime and how those exposures relate to health. Exposomics is the emerging field of research to measure and study the totality of the exposome. Exposomics can assist with molecular medicine by furthering our understanding of how the exposome influences cellular and molecular processes such as gene expression, epigenetic modifications, metabolic pathways, and immune responses.
View Article and Find Full Text PDFNat Med
January 2025
Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
Sleep tests commonly diagnose sleep disorders, but the diverse sleep-related biomarkers recorded by such tests can also provide broader health insights. In this study, we leveraged the uniquely comprehensive data from the Human Phenotype Project cohort, which includes 448 sleep characteristics collected from 16,812 nights of home sleep apnea test monitoring in 6,366 adults (3,043 male and 3,323 female participants), to study associations between sleep traits and body characteristics across 16 body systems. In this analysis, which identified thousands of significant associations, visceral adipose tissue (VAT) was the body characteristic that was most strongly correlated with the peripheral apnea-hypopnea index, as adjusted by sex, age and body mass index (BMI).
View Article and Find Full Text PDFSci Rep
January 2025
BioResource Research Center, RIKEN, 3-1-1, Koyadai, Tsukuba, 305-0074, Ibaraki, Japan.
Omics data provide a plethora of quantifiable information that can potentially be used to identify biomarkers targeting the physiological processes and ecological phenomena of organisms. However, omics data have not been fully utilized because current prediction methods in biomarker construction are susceptible to data multidimensionality and noise. We developed OmicSense, a quantitative prediction method that uses a mixture of Gaussian distributions as the probability distribution, yielding the most likely objective variable predicted for each biomarker.
View Article and Find Full Text PDFAcad Radiol
January 2025
Medical Image Processing Group, 602 Goddard building, 3710 Hamilton Walk, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104 (M.L., M.A., J.K.U., Y.T., C.W., N.P., S.M., D.A.T.). Electronic address:
Rationale And Objectives: Cardiovascular toxicity is a well-known complication of thoracic radiation therapy (RT), leading to increased morbidity and mortality, but existing techniques to predict cardiovascular toxicity have limitations. Predictive biomarkers of cardiovascular toxicity may help to maximize patient outcomes.
Methods: The machine learning optimal biomarker (OBM) method was employed to predict development of cardiotoxicity (based on serial echocardiographic measurements of left ventricular ejection fraction and longitudinal strain) from computed tomography (CT) images in patients with thoracic malignancy undergoing RT.
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