Browsing by Subject "prognostic marker"

Now showing 1 - 3 of 3
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    Alkaline phosphatase in metastatic castration-resistant prostate cancer: reassessment of an older biomarker
    (Future Medicine, 2018-06-21) Heinrich, Daniel; Bruland, Øyvind; Guise, Theresa A; Suzuki, Hiroyoshi; Sartor, Oliver; Medicine, School of Medicine
    Since most patients with metastatic castration-resistant prostate cancer (mCRPC) have bone metastases, it is important to understand the potential impact of therapies on prognostic biomarkers, such as ALP. Clinical studies involving mCRPC life-prolonging agents (i.e., sipuleucel-T, abiraterone, enzalutamide, docetaxel, cabazitaxel, and radium-223) have shown that baseline ALP level is prognostic for overall survival, and may be a better prognostic marker for overall survival than prostate-specific antigen in patients with bone-dominant mCRPC. Mechanism of action differences between therapies may partly explain ALP dynamics during treatment. ALP changes can be interpreted within the context of other parameters while monitoring disease activity to better understand the underlying pathology. This review evaluates the current role of ALP in mCRPC.
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    Integrative Analysis of Histopathological Images and Genomic Data Predicts Clear Cell Renal Cell Carcinoma Prognosis
    (AACR, 2017-11) Cheng, Jun; Zhang, Jie; Han, Yatong; Wang, Xusheng; Ye, Xiufen; Meng, Yuebo; Parwani, Anil; Han, Zhi; Feng, Qianjin; Huang, Kun; Medicine, School of Medicine
    In cancer, both histopathologic images and genomic signatures are used for diagnosis, prognosis, and subtyping. However, combining histopathologic images with genomic data for predicting prognosis, as well as the relationships between them, has rarely been explored. In this study, we present an integrative genomics framework for constructing a prognostic model for clear cell renal cell carcinoma. We used patient data from The Cancer Genome Atlas (n = 410), extracting hundreds of cellular morphologic features from digitized whole-slide images and eigengenes from functional genomics data to predict patient outcome. The risk index generated by our model correlated strongly with survival, outperforming predictions based on considering morphologic features or eigengenes separately. The predicted risk index also effectively stratified patients in early-stage (stage I and stage II) tumors, whereas no significant survival difference was observed using staging alone. The prognostic value of our model was independent of other known clinical and molecular prognostic factors for patients with clear cell renal cell carcinoma. Overall, this workflow and the shared software code provide building blocks for applying similar approaches in other cancers.
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    Soluble Urokinase-Type Plasminogen Activator Receptor as a Prognostic Marker of Ugandan Children at Risk of Severe and Fatal Malaria
    (Oxford, 2023-02-01) Stefanova, Veselina; Ngai, Michelle; Weckman, Andrea M.; Wright, Julie K.; Zhong, Kathleen; Richard-Greenblatt, Melissa; McDonald, Chloe R.; Conroy, Andrea L.; Namasopo, Sophie; Opoka, Robert O.; Hawkes, Michael; Kain, Kevin C.; Pediatrics, School of Medicine
    Background Current malaria diagnostic tests do not reliably identify children at risk of severe and fatal infection. Host immune and endothelial activation contribute to malaria pathogenesis. Soluble urokinase-type plasminogen activator receptor (suPAR) is a marker of these pathways. We hypothesized that measuring suPAR at presentation could risk-stratify children with malaria. Methods Plasma suPAR levels were determined in consecutive febrile children with malaria at presentation to hospital in Jinja, Uganda. We evaluated the accuracy of suPAR in predicting in-hospital mortality, and whether suPAR could improve a validated clinical scoring system (Lambaréné Organ Dysfunction Score [LODS]). Results Of the 1226 children with malaria, 39 (3.2%) died. suPAR concentrations at presentation were significantly higher in children who went on to die than in those who survived (P < .0001). suPAR levels were associated with disease severity (LODS: 0 vs 1, P = .001; 1 vs 2, P < .001; 2 vs 3, 0 vs 2, 1 vs 3, and 0 vs 3, P < .0001). suPAR concentrations were excellent predictors of in-hospital mortality (area under the receiver operating characteristic curve [AUROC], 0.92 [95% confidence interval {CI}, .91–.94]). The prognostic accuracy of LODS (AUROC, 0.93 [95% CI, .91–.94]) was improved when suPAR was added (AUROC, 0.97 [95% CI, .96–.98]; P < .0001). Conclusions Measuring suPAR at presentation can identify children at risk of severe and fatal malaria. Adding suPAR to clinical scores could improve the recognition and triage of children at risk of death. suPAR can be detected with a point-of-care test and can now be evaluated in prospective trials.