The prospective, multicenter TESTBREAST study was initiated with the aim of identifying a novel panel of blood-based protein biomarkers to enable early breast cancer detection for moderate-to-high... Show moreThe prospective, multicenter TESTBREAST study was initiated with the aim of identifying a novel panel of blood-based protein biomarkers to enable early breast cancer detection for moderate-to-high-risk women. Serum samples were collected every (half) year up until diagnosis. Protein levels were longitudinally measured to determine intrapatient and interpatient variabilities. To this end, protein cluster patterns were evaluated to form a conceptual basis for further clinical analyses. Using a mass spectrometry-based bottom-up proteomics strategy, the protein abundance of 30 samples was analyzed: five sequential serum samples from six high-risk women; three who developed a breast malignancy (cases) and three who did not (controls). Serum samples were chromatographically fractionated and an in-depth serum proteome was acquired. Cluster analyses were applied to indicate differences between and within protein levels in serum samples of individuals. Statistical analyses were performed using ANOVA to select proteins with a high level of clustering. Cluster analyses on 30 serum samples revealed unique patterns of protein clustering for each patient, indicating a greater interpatient than intrapatient variability in protein levels of the longitudinally acquired samples. Moreover, the most distinctive proteins in the cluster analysis were identified. Strong clustering patterns within longitudinal intrapatient samples have demonstrated the importance of identifying small changes in protein levels for individuals over time. This underlines the significance of longitudinal serum measurements, that patients can serve as their own controls, and the relevance of the current study set-up for early detection. The TESTBREAST study will continue its pursuit toward establishing a protein panel for early breast cancer detection. Show less
The prospective, multicenter TESTBREAST study was initiated with the aim of identifying a novel panel of blood-based protein biomarkers to enable early breast cancer detection for moderate-to-high... Show moreThe prospective, multicenter TESTBREAST study was initiated with the aim of identifying a novel panel of blood-based protein biomarkers to enable early breast cancer detection for moderate-to-high-risk women. Serum samples were collected every (half) year up until diagnosis. Protein levels were longitudinally measured to determine intrapatient and interpatient variabilities. To this end, protein cluster patterns were evaluated to form a conceptual basis for further clinical analyses. Using a mass spectrometry-based bottom-up proteomics strategy, the protein abundance of 30 samples was analyzed: five sequential serum samples from six high-risk women; three who developed a breast malignancy (cases) and three who did not (controls). Serum samples were chromatographically fractionated and an in-depth serum proteome was acquired. Cluster analyses were applied to indicate differences between and within protein levels in serum samples of individuals. Statistical analyses were performed using ANOVA to select proteins with a high level of clustering. Cluster analyses on 30 serum samples revealed unique patterns of protein clustering for each patient, indicating a greater interpatient than intrapatient variability in protein levels of the longitudinally acquired samples. Moreover, the most distinctive proteins in the cluster analysis were identified. Strong clustering patterns within longitudinal intrapatient samples have demonstrated the importance of identifying small changes in protein levels for individuals over time. This underlines the significance of longitudinal serum measurements, that patients can serve as their own controls, and the relevance of the current study set-up for early detection. The TESTBREAST study will continue its pursuit toward establishing a protein panel for early breast cancer detection. Show less
Objective: Unmet health care needs require additional care resources to achieve optimal patient well-being. In this nationwide study we examined associations between a number of risk factors and... Show moreObjective: Unmet health care needs require additional care resources to achieve optimal patient well-being. In this nationwide study we examined associations between a number of risk factors and unmet needs after treatment among women with breast cancer, while taking into account their health care practices. We expected that more care use would be associated with lower levels of unmet needs. Methods: A multicenter, prospective, observational design was employed. Women with primary breast cancer completed questionnaires 6 and 15 months post-diagnosis. Medical data were retrieved from medical records. Direct and indirect associations between sociodemographic and clinical risk factors, distress, care use, and unmet needs were investigated with structural equation modeling. Results: Seven hundred forty-six participants completed both questionnaires (response rate 73.7%). The care services received were not negatively associated with the reported levels of unmet needs after treatment. Comorbidity was associated with higher physical and daily living needs. Higher age was associated with higher health system-related and informational needs. Having had chemotherapy and a mastectomy were associated with higher sexuality needs and breast cancer-specific issues, respectively. A higher level of distress was associated with higher levels of unmet need in all domains. Conclusions: Clinicians may use these results to timely identify which women are at risk of developing specific unmet needs after treatment. Evidence-based, cost-effective (online) interventions that target distress, the most influential risk factor, should be further implemented and disseminated among patients and clinicians. Show less
