Specificity of Genetic Biomarker Studies in Cancer Research: A Systematic Review
Garrett GreenRuben CarmonaKaveh ZakeriChih-Han LeeSaif M. BorganZaid MarhoonAndrew B. SharabiLoren K. Mell
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As genetic information becomes more readily available, there is increasing demand from both patients and providers to develop personalized approaches to cancer care. Investigators are increasingly reporting numbers of studies correlating genomic signatures and other biomarkers to survival endpoints. The extent to which cancer-specific and non-specific effects are reported in contemporary studies is unknown. In this review of 85 high-impact studies associating genetic biomarkers with cancer outcomes, 95% reported significant associations with event-free survival outcomes, yet less than half reported effects on a cancer-specific endpoint. This methodology leaves open the possibility that observed associations are unrelated to cancer.Keywords:
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Alzheimer's disease (AD) is considered to be a fatal neurodegenerative disease affecting many people over the age of 65. As life expectancies increase around the world, the incidence of AD also grows with it. Current research reports no effective treatment or definitive antemortem diagnostic test for AD. In recent years, a number of biomarkers have been identified for early and accurate diagnosis of the disease but most biomarker diagnostic tests are expensive and involve invasive procedures, resulting in very few AD patients having gone through more than one biomarker test. In this study, a mathematical formula based on single biomarker data has been developed to improve the accuracy of single biomarker prediction of susceptibility to AD. Biomarker datasets used in this work were wholly obtained from other published works.
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A pathophysiologic model of Alzheimer's disease (AD) has been recently proposed in which beta amyloid accumulation occurs earlier (indexed by abnormal CSF Abeta42), followed by tau-mediated neuronal injury and dysfunction (abnormal CSF tau or FDG-PET) and lastly atrophic changes (abnormal hippocampal volume, HV). The aim of this study is to validate this model by comparing clinical features and conversion to AD and other dementias among groups of patients with mild cognitive impairment (MCI) with different abnormal biomarker profiles. The patients of this study were 58 with MCI in whom AD biomarkers (CSF Abeta42 and tau, temporoparietal hypometabolism on 18F-FDG PET, and hippocampal volume) were collected. Patients were divided into 3 groups of no abnormal biomarker, AD biomarker pattern (including 3 subgroups of early = only abnormal Abeta42, intermediate = abnormal Abeta42 and FDG-PET or tau, and late = abnormal Abeta42, FDG-PET or tau, and HV), and any other biomarker combination. MCI patients with AD biomarker pattern had lower behavioral disturbances than patients with any other biomarker combination (P <.0005) and lower performance on verbal and nonverbal memory than the other two groups (P = .07 and P = .004, respectively). Within the 3 subgroups with AD biomarker pattern there was a significant trend to higher rate of conversion to dementia (p for trend = .006). Moreover, AD was the type of incident dementia in 100% of patients with an AD biomarker pattern, but 0% and 27% in converters with no abnormal biomarker and any other biomarker combination, respectively (P = .002). Clinical cases representative of the three groups were also described. The results of this study provide evidence in favor of the dynamic biomarker model and support the use of biomarkers for the diagnosis of MCI due to AD according to the new recently published research criteria.
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miRNA-21 is among the most abundant and highly conserved microRNAs (miRNAs) recognized. It is expressed in essentially all cells where it performs vital regulatory roles in health and disease. It is also frequently claimed to be a biomarker of diseases such as cancer and heart disease in bodily-fluid based miRNA studies. Here we dissociate its contributions to cellular physiology and pathology from its potential as a biomarker. We show how it has been claimed as a specific predictive or prognostic biomarker by at least 29 diseases. Thus, it has no specificity to any one disease. As a result, it should not be considered a viable candidate to be a biomarker, despite its continued evaluation as such. This theme of multiple assignments of a miRNA as a biomarker is shared with other common, ubiquitous miRNAs and should be concerning for them as well.
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Abstract The goal of personalized medicine is to find the right medicine for the right person at the right time. Although this concept has been widely heralded as the future of medicine, only now are we beginning to see the first examples of personalized medicine at work. In this article, we discuss examples of personalized medicine that use in vitro diagnostic (IVD) tests. IVD tests have always been useful in guiding therapy; the difference is that in personalized medicine, many more aspects of disease management can be addressed through in vitro testing. In time, personalized medicine will affect all aspects of medicine from risk assessment to response monitoring.
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Different CSF biomarker combinations can provide conflicting diagnostic information in Alzheimer′s disease (AD). This is often attributed to differences in sensitivity and specificity, at the cohort level, between CSF markers (Aβ42, t-Tau, p-Tau181, t-Tau/Aβ42, and p-Tau181/Aβ42). When these biomarkers are analyzed against the same gold standard independently, conflicting biomarker information can also result from biomarker substructures not obvious to investigators. Previous studies have not examined conflicting biomarker information at the individual level (e.g., a profile showing normal Aβ42 levels but abnormal t-Tau/Aβ42 ratio may be interprted as AD-like even though the normal Aβ42 level argues against amyloid pathology). The prevalence of these conflicts and ways to resolve them are unknown. We measured CSF AD biomarker levels in one consecutive series (n=431) from Emory University using the multiplex AlzBio3 assay and surveyed the concordance rates between CSF biomarkers at the individual level. We also compared these results with those from clinical testing through a comparable ELISA. To resolve the issue of differential sensitivity and biomarker substructure, we then analyzed CSF AD biomarker levels through two-step clustering to identify naturally existing subgroups of biomarker profiles. Finally, to determine if the cluster membership or the combination of independent biomarker information confers greater information on prognosis, we analyzed if either predicted longitudinal cognitive changes in the Alzheimer’s Disease Neuro-Imaging Initiative (ADNI, n=409). Conflicting CSF biomarker information was very common: 59% of the Emory subjects and 37% of ADNI subjects had at least one biomarker providing diagnostic information distinct from the other biomarkers. Clustering analysis revealed three groupings: one characterized by p-Tau181/Aβ42>0.131 and longitudinal cognitive decline in MCI, and two others (including one characterized by Aβ42>258.5pg/mL) associated with cognitive stability. Within each cluster, concordant or discordant biomarker findings did not further distinguish rates of longitudinal cognitive decline. Conflicting information from different CSF AD biomarkers was common. A data-driven strategy accounting for all biomarker combinations identified naturally existing groupings each characterized by similar biochemical and prognostic profiles.
Concordance
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