The therapy is making me sick: how online portal communications between breast cancer patients and physicians indicate medication discontinuation.

Objective: Online platforms have created a variety of opportunities for breast patients to discuss their hormonal therapy, a long-term adjuvant treatment to reduce the chance of breast cancer occurrence and mortality. The goal of this investigation is to ascertain the extent to which the messages breast cancer patients communicated through an online portal can indicate their potential for discontinuing hormonal therapy. Materials and Methods: We studied the de-identified electronic medical records of 1106 breast cancer patients who were prescribed hormonal therapy at Vanderbilt University Medical Center over a 12-year period. We designed a data-driven approach to investigate patients' patterns of messaging with healthcare providers, the topics they communicated, and the extent to which these messaging behaviors associate with the likelihood that a patient will discontinue a prescribed 5-year regimen of therapy. Results: The results indicates that messaging rate over time [hazard ratio (HR) = 1.373, P = 0.002], mentions of side effects (HR = 1.214, P = 0.006), and surgery-related topics (HR = 1.170, P = 0.034) were associated with increased risk of early medication discontinuation. In contrast, seeking professional suggestions (HR = 0.766, P = 0.002), expressing gratitude to healthcare providers (HR = 0.872, P = 0.044), and mentions of drugs used to treat side effects (HR = 0.807, P = 0.013) were associated with decreased risk of medication discontinuation. Discussion and Conclusion: This investigation suggests that patient-generated content can inform the study of health-related behaviors. Given that approximately 50% of breast cancer patients do not complete a course of hormonal therapy as described, the identification of factors associated with medication discontinuation can facilitate real-time interventions to prevent early discontinuation.

Analysis of Adjuvant Endocrine Therapy in Practice From Electronic Health Record Data of Patients With Breast Cancer.

PURPOSE: Adjuvant endocrine therapy is a long-term drug therapy prescribed to prevent recurrence of hormone receptor-positive breast cancer. Data on adjuvant endocrine therapy are reported though clinical trials, which may differ from treatment practice and outcomes in the general population of patients with breast cancer. With secondary use of electronic health record (EHR) data, we summarize adjuvant endocrine treatment practice and outcomes in real-world settings. METHODS: We analyzed treatment data derived from EHR data on 1,587 patients with stage I to III breast cancer at a National Cancer Institute-designated comprehensive cancer center to learn the frequencies of real-world adjuvant endocrine drug switches and discontinuation and to explore the potential cause for drug switches and discontinuation from medical records. We measured rates of drug use, drug switches, early drug discontinuation, adverse events, recurrence, and death. We also measured adverse events and change in menopause status as potential causes for drug switch and discontinuation. RESULTS: Within the study population, approximately 49% of patients were lost to follow-up or did not complete adjuvant treatment through 5 years. Fifty-two percent of patients switched to a different endocrine therapy drug during their treatment. We found that age is correlated with drug switches and that adverse events are correlated with drug switches and discontinuation. We also found that patients who switched to an alternative endocrine therapy during treatment were more likely to complete 5 years of treatment. CONCLUSION: This study describes long-term adjuvant endocrine treatment in real-world settings and demonstrates the ability to leverage longitudinal EHR data to characterize oral medication treatment patterns in patients with cancer.

Synthetic lectin arrays for the detection and discrimination of cancer associated glycans and cell lines.

Aberrant glycosylation is a hallmark of various disease states, including cancer, and effective detection and discrimination between healthy and diseased cells is an important challenge for the diagnosis and treatment of many diseases. Here, we describe the use of boronic acid functionalized synthetic lectins (SLs) in an array format for the differentiation of structurally similar cancer associated glycans and cancer cell lines; discrimination is based on subtle variations in glycosylation patterns. We further demonstrate the utility of our SLs in recognizing glycoproteins with up to 50-fold selectivity, even in 95% human serum. Given their robust and selective nature, these SLs were able to effectively distinguish (a) five structurally similar glycans with 94% accuracy; (b) seven normal, cancerous and metastatic colon cancer cell lines, including three isogenic cell lines, with 92% accuracy; and (c) these same seven cell lines using a guided statistical analysis to improve our analysis to 97% accuracy. In total, these data suggest that an SL-based array will be useful for the diagnosis of cancer.