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Background: The increasing number of available cancer therapies render medical decision-making (MDM) less straightforward. Patients want to know about the outcomes of similarly treated patients. Objective: The goal of this study was to design a breast cancer dashboard (BCD) tool that presents survival information to support MDM activities. Methods: Clinical variables during the clinic visit were determined via provider meetings and evaluated for accessibility from medical databases. Women with breast cancer (BC) were interviewed about their health care experiences after cancer diagnosis. We created a cohort of BC adult women treated at our institution from 1995 to 2012, from which clinical scenarios were defined and used to test survival outcomes. For the BCD, a simple, graphical user interface was built to present point-of-care clinical and survival data. Results: It is feasible to build the BCD using our institution’s databases and generate survival plots to facilitate MDM activities. Patients with early-stage BC had the highest survival rate (82.3%) and the longest mean life years of 7.0 (SD 4.5) years. In late-stage BC, poor prognosis outweighs the influence of number of comorbidities on mortality. The BCD tool promotes more predictive, personalized, and collaborative health care.

BACKGROUND:First-line treatment for metastatic renal cell carcinoma (mRCC) is rapidly changing. It currently includes VEGF targeted therapies (TT), multi-target tyrosine kinase inhibitors (TKIs), mTOR inhibitors, and immunotherapy. To optimize outcomes for individual patients, genomic markers of response to therapy are needed. Here, we aim to identify tumor-based genomic markers of response to VEGF TT to optimize treatment selection. METHODS:From an institutional database, primary tumor tissue was obtained from 79 patients with clear cell mRCC, and targeted sequencing was performed. Clinical outcomes were obtained retrospectively. Progression-free survival (PFS) on first-line VEGF TT was correlated to genomic alterations (GAs) using Kaplan-Meier methodology and Cox proportional hazard models. A composite model of significant GAs predicting PFS in the first-line setting was developed. RESULTS:Absence of VHL mutation was associated with inferior PFS on first-line VEGF TT. A trend for inferior PFS was observed with GAs in TP53 and FLT1 C/C variant. A composite model of these 3 GAs was associated with inferior PFS in a dose-dependent manner. CONCLUSION:In mRCC, a composite model of TP53 mutation, wild type VHL, and FLT1 C/C variant strongly predicted PFS on first-line VEGF TT in a dose-dependent manner. These findings require external validation.