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Highlights
• A panel of 20 biomarkers was identified capable of differentiating BD patients from controls.
• Excellent discrimination between established BD patients and controls.
• Good to excellent discrimination between misdiagnosed BD patients and first onset MDD patients.
• Fair to good discrimination between pre-diagnostic BD patients and controls.
• Study demonstrates the potential utility of a protein biomarker panel as a diagnostic test for BD.
Abstract
Background: Bipolar disorder (BD) is a costly, devastating and life shortening mental disorder that is often misdiagnosed, especially on initial presentation. Misdiagnosis frequently results in ineffective treatment. We investigated the utility of a biomarker panel as a diagnostic test for BD.
Methods and findings: We performed a meta-analysis of eight case-control studies to define a diagnostic biomarker panel for BD. After validating the panel on established BD patients, we applied it to undiagnosed BD patients. We analysed 249 BD, 122 pre-diagnostic BD, 75 pre-diagnostic schizophrenia and 90 first onset major depression disorder (MDD) patients and 371 controls. The biomarker panel was identified using ten-fold cross-validation with lasso regression applied to the 87 analytes available across the meta-analysis studies.
We identified 20 protein analytes with excellent predictive performance [area under the curve (AUC) ⩾ 0.90]. Importantly, the panel had a good predictive performance (AUC 0.84) to differentiate 12 misdiagnosed BD patients from 90 first onset MDD patients, and a fair to good predictive performance (AUC 0.79) to differentiate between 110 pre-diagnostic BD patients and 184 controls. We also demonstrated the disease specificity of the panel.
Conclusions: An early and accurate diagnosis has the potential to delay or even prevent the onset of BD. This study demonstrates the potential utility of a biomarker panel as a diagnostic test for BD.
Purpose: The WSG-PRIMe Study prospectively evaluated the impact of the 70-gene signature MammaPrint® (MP) and the 80-gene molecular subtyping assay BluePrint® on clinical therapy decisions in luminal early breast cancer.
Methods: 452 hormone receptor (HR)-positive and HER2-negative patients were recruited (N0, N1). Physicians provided initial therapy recommendations based on clinicopathological factors. After prospective risk classification by MammaPrint/BluePrint was revealed, post-test treatment recommendations and actual treatment were recorded. Decisional Conflict and anxiety were measured by questionnaires.
Results: Post-test switch (in chemotherapy (CT) recommendation) occurred in 29.1% of cases. Overall, physician adherence to MP risk assessment was 92.3% for low-risk and 94.3% for high-risk MP scores. Adherence was remarkably high in “discordant” groups: 74.7% of physicians initially recommending CT switched to CT omission following low-risk MP scores; conversely, 88.9% of physicians initially recommending CT omission switched to CT recommendations following high-risk MP scores. Most patients (99.2%) recommended to forgo CT post-test and 21.3% of patients with post-test CT recommendations did not undergo CT; among MP low-risk patients with pre-test and post-test CT recommendations, 40% did not actually undergo CT. Luminal subtype assessment by BluePrint was discordant with IHC assessment in 34% of patients. Patients’ State Anxiety scores improved significantly overall, particularly in MP low-risk patients. Trait Anxiety scores increased slightly in MP high risk and decreased slightly in MP low-risk patients.
Conclusions: MammaPrint and BluePrint test results strongly impacted physicians’ therapy decisions in luminal EBC with up to three involved lymph nodes. The high adherence to genetically determined risk assessment represents a key prerequisite for achieving a personalized cost-effective approach to disease management of early breast cancer.