Development of a Web-Based Interactive Tool for Visualizing Breast Cancer Clinical Trial Tolerability Data.

PURPOSE: Longitudinal patient tolerability data collected as part of randomized controlled trials are often summarized in a way that loses information and does not capture the treatment experience. To address this, we developed an interactive web application to empower clinicians and researchers to explore and visualize patient tolerability data. METHODS: We used adverse event (AE) data (Common Terminology Criteria for Adverse Events) and patient-reported outcomes (PROs) from the NSABP-B35 phase III clinical trial, which compared anastrozole with tamoxifen for breast cancer-free survival, to demonstrate the tools. An interactive web application was developed using R and the Shiny web application framework that generates Sankey diagrams to visualize AEs and PROs using four tools: AE Explorer, PRO Explorer, Cohort Explorer, and Custom Explorer. RESULTS: To illustrate how users can use the interactive tool, examples for each of the four applications are presented using data from the NSABP-B35 phase III trial and the NSABP-B30 trial for the Custom Explorer. In the AE and PRO explorers, users can select AEs or PROs to visualize within specified time periods and compare across treatments. In the cohort explorer, users can select a subset of patients with a specific symptom, severity, and treatment received to visualize the trajectory over time within a specified time interval. With the custom explorer, users can upload and visualize structured longitudinal toxicity and tolerability data. CONCLUSION: We have created an interactive web application and tool for clinicians and researchers to explore and visualize clinical trial tolerability data. This adaptable tool can be extended for other clinical trial data visualization and incorporated into future patient-clinician interactions regarding treatment decisions.

Integrated Analysis of RNA and DNA from the Phase III Trial CALGB 40601 Identifies Predictors of Response to Trastuzumab-Based Neoadjuvant Chemotherapy in HER2-Positive Breast Cancer.

Purpose: Response to a complex trastuzumab-based regimen is affected by multiple features of the tumor and its microenvironment. Developing a predictive algorithm is key to optimizing HER2-targeting therapy.Experimental Design: We analyzed 137 pretreatment tumors with mRNA-seq and DNA exome sequencing from CALGB 40601, a neoadjuvant phase III trial of paclitaxel plus trastuzumab with or without lapatinib in stage II to III HER2-positive breast cancer. We adopted an Elastic Net regularized regression approach that controls for covarying features within high-dimensional data. First, we applied 517 known gene expression signatures to develop an Elastic Net model to predict pCR, which we validated on 143 samples from four independent trials. Next, we performed integrative analyses incorporating clinicopathologic information with somatic mutation status, DNA copy number alterations (CNA), and gene signatures.Results: The Elastic Net model using only gene signatures predicted pCR in the validation sets (AUC = 0.76). Integrative analyses showed that models containing gene signatures, clinical features, and DNA information were better pCR predictors than models containing a single data type. Frequently selected variables from the multiplatform models included amplifications of chromosome 6p, TP53 mutation, HER2-enriched subtype, and immune signatures. Variables predicting resistance included Luminal/ER+ features.Conclusions: Models using RNA only, as well as integrated RNA and DNA models, can predict pCR with improved accuracy over clinical variables. Somatic DNA alterations (mutation, CNAs), tumor molecular subtype (HER2E, Luminal), and the microenvironment (immune cells) were independent predictors of response to trastuzumab and paclitaxel-based regimens. This highlights the complexity of predicting response in HER2-positive breast cancer. Clin Cancer Res; 24(21); 5292-304. ©2018 AACR.

Promoting quality and evidence-based care in early-stage breast cancer follow-up.

Evidence-based guidelines for long-term follow-up of early-stage breast cancer patients developed by oncology societies in the United States and Europe recommend that breast cancer survivors undergo regular evaluation with history and physical examination, as well as annual mammography. Routine blood tests, circulating tumor markers, and/or surveillance imaging studies beyond mammography are not recommended in the absence of concerning symptoms or physical examination findings because of lack of supportive clinical evidence. Despite these guidelines, studies have shown that 20% to 40% of oncologists assess serum tumor markers as part of routine monitoring of early-stage breast cancer patients. As part of efforts to both address the financial challenges confronting the health-care system and optimize patient outcomes, the American Society of Clinical Oncology's Cost of Care Task Force identified adherence to breast cancer surveillance guidelines as an opportunity to improve care and reduce cost. However, these recommendations are based on trials done in an era of outdated technology and limited therapeutic options. It is possible that recent improvements in diagnostics and treatments could make earlier detection of recurrent disease important for improving both survival and quality of life outcomes. Research is necessary to further inform optimal breast cancer follow-up strategies, which could impact these recommendations. At this time, outside of well-conducted clinical trials, there is no role for ordering routine serial blood or imaging tests in monitoring for recurrence in early-stage breast cancer patients.