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Taking Prognostic Signatures to the Clinic
Published in Brian Leyland-Jones, Pharmacogenetics of Breast Cancer, 2020
Michail Ignatiadis, Christos Sotiriou
Besides the above studies aiming at providing a molecular taxonomy of breast tumors, several independent groups, including our own, have conducted gene expression profiling studies with the objective of improving upon traditional prognostic markers used in the clinic for prediction of outcome. Two conceptually different, supervised approaches for prognostic marker discovery on a genome-wide scale have been applied so far, the “top-down” approach and the “bottom-up” or “hypothesis-driven” approach. The former derives from a prognostic model simply by looking for gene expression patterns associated with the clinical outcome without any a priori biological scenario, whereas the latter approach first identifies gene expression profiles linked with a specific biological phenotype and subsequently correlates these findings to survival.
The essence of R in head and neck cancer
Published in Ruijiang Li, Lei Xing, Sandy Napel, Daniel L. Rubin, Radiomics and Radiogenomics, 2019
Hesham Elhalawani, Arvind Rao, Clifton D. Fuller
Radiomics has been intensively invested in development and validation of myriad of radiomics biomarkers of prognostic and predictive capacity in several types of cancer (Elhalawani et al. 2018a, Huang et al. 2016, Leijenaar et al. 2015, Liang et al. 2016, Vallieres et al. 2015). Biomarker can be defined as “A characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes, or pharmacological responses to a therapeutic intervention” (Hodgson et al. 2009). Prognostic marker is “a marker that predicts the prognosis of a patient (e.g., the likelihood of relapse, progression, and/or death) independent of future treatment effects,” whereas predictive biomarkers are “measurements associated with response to or lack of response to a particular therapy.” A factor can be both prognostic and predictive (Rosenthal et al. 2016). Multiple imaging modalities and techniques have been investigated for potential biomarker derivation.
Trial Design for Precision Medicine
Published in Mark Chang, John Balser, Jim Roach, Robin Bliss, Innovative Strategies, Statistical Solutions and Simulations for Modern Clinical Trials, 2019
Mark Chang, John Balser, Jim Roach, Robin Bliss
Baseline classifier biomarkers can be used for population segmentation or enrichment design and optimization of target patient population. Early measured treatment effect of a predictive biomarker can be used to design BIGSD and other biomarker-adaptive designs. A good predictive biomarker may be labeled and considered as a surrogate that can be used to replace the primary endpoint. However, identifying a surrogate marker is extremely difficult. A prognostic marker will predict the outcome irrespective to the treatment. A higher response rate of a prognostic marker in the test group than the placebo does not necessarily lead to a higher response in the primary endpoint as we have discussed in the non-transitivity of “friendships.” We also discussed the design methods for biomarker-stratified design, biomarker-adaptive winner design, and biomarker-informed GSD and provided simulations programs.
Hyperglycaemia, diabetes mellitus and COVID-19 in a tertiary hospital in KwaZulu-Natal
Published in Journal of Endocrinology, Metabolism and Diabetes of South Africa, 2022
We recommend that all patients hospitalised with COVID-19 have a random blood glucose performed on admission, as well as prompt active management of hyperglycaemia. The intended benefit is threefold. First, it may serve as a prognostic marker for severe disease and poorer outcome. Second, in those patients identified with hyperglycaemia, active glycaemic control measures can then be implemented to potentially avert the associated adverse consequences. Lastly, those patients with hyperglycaemia (and no history of DM) can be screened for DM – and more so if other risk factors are present. Further studies of such nature with larger cohorts need to be conducted to assess the strength of this association, as well as the effects of pre-hospital and in-hospital glycaemic control on COVID-19 outcomes in African populations. Additionally, although it has been suggested that COVID-19 may precipitate new-onset DM in the acute setting, there is a paucity of data regarding the long-term effects of this virus on glycaemic status.8 In this regard, further studies investigating the long-term effects will be forthcoming.
Investigation of copeptin levels in foetal congenital central nervous system anomalies
Published in Journal of Obstetrics and Gynaecology, 2021
Copeptin (CPP) is the c-terminal part of the precursor of arginine vasopressin (AVP). It is released from the neurohypophysis with AVP and its serum levels are more stable than AVP. AVP is a potent synergistic factor of the corticotropin-releasing hormone (CRH), as a hypothalamic stimulator of the hypothalamic-pituitary-adrenal (HPA) axis (Morgenthaler et al. 2006). Detection of retarded brain development in rats in the absence of AVP has revealed that AVP could play a role in brain development (Boer et al. 1982). Measurement of AVP is difficult during the pregnancy, adding to its measurement difficulties placental vasopressinase breaks down the AVP. CPP, a stable fragment of the AVP precursor, is produced in a 1:1 ratio to AVP and has no known physiological function. It has been proposed as a prognostic marker in different illnesses and disorders where it may help in early detection and diagnostic accuracy (Morgenthaler et al. 2008; Dobsa and Edozien 2013). Some studies have shown that increased levels of CPP were correlated with poor prognosis of cerebrovascular disease and increased mortality, indicating it can help to optimise the prognosis stratification assessment of cerebrovascular disease (Katan et al. 2009; Lewandowski and Brabant 2016).
LncRNA CAIF was downregulated in end-stage cardiomyopathy and is a promising diagnostic and prognostic marker for this disease
Published in Biomarkers, 2019
Di Wu, Yanqiu Zhou, Yudong Fan, Qingjun Zhang, Feifei Gu, Wen Mao, Miaomiao Zhang
Accurate diagnosis is critical for the treatment of severe human diseases including end-stage cardiomyopathy (Wexler et al. 2009). In our study, ROC curve analysis showed that CAIF expression in myocardial tissues and serum can be used to effectively distinguish end-stage cardiomyopathy patients from healthy controls. Poor prognosis after treatment is a major cause of the unacceptable higher mortality and modality rate of patients with end-stage cardiomyopathy (Calvert et al. 1997). Therefore, a sensitive prognostic marker is urgently needed to improve the survival of those patients. In this study, low expression level of CAIF in myocardial tissues and serum was proved to be significantly correlated with shorter survival times. Those data suggested that CAIF may serve as an effective prognostic and diagnostic biomarker for end-stage cardiomyopathy.