Big data in healthcare can enable unprecedented understanding of diseases and their treatment, particularly in oncology. These data may include electronic health records, medical imaging, genomic sequencing, payor records, and data from pharmaceutical research, wearables, and medical devices. The ability to combine datasets and use data across many analyses is critical to the successful use of big data and is a concern for those who generate and use the data. Interoperability and data quality continue to be major challenges when working with different healthcare datasets. Mapping terminology across datasets, missing and incorrect data, and varying data structures make combining data an onerous and largely manual undertaking. Data privacy is another concern addressed by the Health Insurance Portability and Accountability Act, the Common Rule, and the General Data Protection Regulation. The use of big data is now included in the planning and activities of the FDA and the European Medicines Agency. The willingness of organizations to share data in a precompetitive fashion, agreements on data quality standards, and institution of universal and practical tenets on data privacy will be crucial to fully realizing the potential for big data in medicine.

Malnutrition and sarcopenia commonly overlap and contribute to adverse health outcomes. Previously, chronic supplementation with two oral nutritional supplements (ONS), control (C_ONS) and experimental ONS enriched with protein, vitamin D and β-hydroxy β-methylbutyrate (HMB) (E_ONS), improved muscle strength and quality in malnourished sarcopenic older adults, with E_ONS demonstrating early strength benefits at 12 weeks. To understand the underlying biological mechanisms contributing to the observed early strength benefits of E_ONS, we examined serum biomarker changes in response to 12-week supplementation. Serum samples (E_ONS (n = 90) and C_ONS (n = 103)) collected at baseline and 12 weeks were analyzed. Biomarkers (n = 243) were measured using multiplexed immunoassay, commercial immunoassays and ELISAs. Sixty markers were excluded with levels below assay detection limits. Sixteen biomarkers significantly changed in response to both interventions including nutritional and metabolic markers. Thirteen biomarkers significantly changed in response to E_ONS but not C_ONS. Increases in immunoglobulins, myoglobin, total protein, vitamin E and magnesium were observed with E_ONS. Inflammation-related ferritin and osteopontin decreased, while soluble receptors for cytokines increased, suggesting decreased inflammation. Sex hormone-binding globulin associated with sarcopenia also decreased with E_ONS. Biomarkers reflective of multiple biological systems were impacted by nutritional intervention in sarcopenic older adults. Incremental biomarker changes were observed in response to E_ONS containing HMB that possibly link to improvements in skeletal muscle health.

The biomarkers alpha-fetoprotein (AFP) and protein induced by vitamin K absence/antagonist-II (PIVKA-II) may be useful for detecting early-stage hepatocellular carcinoma (HCC). We evaluated the performance of AFP and PIVKA-II levels, alone and in combination with clinical factors, for the early detection of HCC. In a case–control study, serum AFP and PIVKA-II were measured using the ARCHITECT immunoassay analyzer system in a cohort of 119 patients with HCC, 215 patients with non-malignant liver disease, and 34 healthy subjects. Five predictive models for detecting HCC were developed based on age, gender, AFP, and/or PIVKA-II levels; the best model was validated in an independent cohort of 416 patients with HCC and 412 control subjects with cirrhosis. In both cohorts, AFP and PIVKA-II concentrations were higher in patients with HCC compared to healthy controls and patients with non-malignant liver disease. The model that combined AFP and PIVKA-II, age, and gender had the highest AUC of 0.95 (0.95, 95% CI 0.93–0.98), with a sensitivity of 93% and a specificity of 84% in the development cohort, and an AUC of 0.87 (95% CI 0.85–0.90), sensitivity of 74%, and specificity of 85% in the validation cohort. When limiting the validation cohort to only early-stage HCC, the AUC was 0.85 (95% CI 0.81–0.88), sensitivity was 70%, and specificity was 86%. Compared to each biomarker alone, the combination of AFP and PIVKA-II with age and gender improved the accuracy of detecting HCC and differentiating HCC from non-malignant liver disease.

The analysis of big healthcare data has enormous potential as a tool for advancing oncology drug development and patient treatment, particularly in the context of precision medicine. However, there are challenges in organizing, sharing, integrating, and making these data readily accessible to the research community. This review presents five case studies illustrating various successful approaches to addressing such challenges. These efforts are CancerLinQ, the American Association for Cancer Research Project GENIE, Project Data Sphere, the National Cancer Institute Genomic Data Commons, and the Veterans Health Administration Clinical Data Initiative. Critical factors in the development of these systems include attention to the use of robust pipelines for data aggregation, common data models, data deidentification to enable multiple uses, integration of data collection into physician workflows, terminology standardization and attention to interoperability, extensive quality assurance and quality control activity, incorporation of multiple data types, and understanding how data resources can be best applied. By describing some of the emerging resources, we hope to inspire consideration of the secondary use of such data at the earliest possible step to ensure the proper sharing of data in order to generate insights that advance the understanding and the treatment of cancer.

Periostin is being investigated as a potential biomarker for T-helper-2 (Th2)-driven asthma or eosinophilic inflammation and may help to identify patients more likely to benefit from interleukin-13-targeted treatments. We report the development and analytic performance of the investigational use only ARCHITECT Periostin Immunoassay, a new automated assay developed to detect serum periostin concentrations. We assessed assay performance in terms of precision, sensitivity, linearity, interference from classical immunoassay interferents and representatives of common asthma medications, specimen handling, and isoform reactivity. The assay was also used to assess the biological variability of serum periostin concentrations in samples from healthy volunteers and from subjects with uncontrolled asthma (the intended use population). The percentage CVs for 5-day total precision, assessed using two instruments, was <6% across 2 controls and one serum-based panel. Limit of quantitation was 4ng/mL (dilution adjusted concentration), suiting the needs for this application. Dilution analysis yielded linear results and no endogenous sample or drug interferences were observed. All known periostin isoforms expressed in the mature human lung were detected by the assay. Our studies provide support that the ARCHITECT Periostin Immunoassay is a reliable and robust test for measuring serum periostin concentrations.