Berry, Anna

PURPOSE The emergence of large real-world clinical databases and tools to mine electronic medical records has allowed for an unprecedented look at large data sets with clinical and epidemiologic correlates. In clinical cancer genetics, real-world databases allow for the investigation of prevalence and effectiveness of prevention strategies and targeted treatments and for the identification of barriers to better outcomes. However, real-world data sets have inherent biases and problems (eg, selection bias, incomplete data, measurement error) that may hamper adequate analysis and affect statistical power. METHODS Here, we leverage a real-world clinical data set from a large health network for patients with breast cancer tested for variants in BRCA1 and BRCA2 (N = 12,423). We conducted data cleaning and harmonization, cross-referenced with publicly available databases, performed variant reassessment and functional assays, and used functional data to inform a variant's clinical significance applying American College of Medical Geneticists and the Association of Molecular Pathology guidelines. RESULTS In the cohort, White and Black patients were over-represented, whereas non-White Hispanic and Asian patients were under-represented. Incorrect or missing variant designations were the most significant contributor to data loss. While manual curation corrected many incorrect designations, a sizable fraction of patient carriers remained with incorrect or missing variant designations. Despite the large number of patients with clinical significance not reported, original reported clinical significance assessments were accurate. Reassessment of variants in which clinical significance was not reported led to a marked improvement in data quality. CONCLUSION We identify the most common issues with BRCA1 and BRCA2 testing data entry and suggest approaches to minimize data loss and keep interpretation of clinical significance of variants up to date.

Biospecimens acquired during routine medical practice are the primary sources of molecular information about patients and their diseases that underlies precision medicine and translational research. In cancer care, molecular analysis of biospecimens is especially common because it often determines treatment choices and may be used to monitor therapy in real time. However, patient specimens are collected, handled, and processed according to routine clinical procedures during which they are subjected to factors that may alter their molecular quality and composition. Such artefactual alteration may skew data from molecular analyses, render analysis data uninterpretable, or even preclude analysis altogether if the integrity of a specimen is severely compromised. As a result, patient care and safety may be affected, and medical research dependent on patient samples may be compromised. Despite these issues, there is currently no requirement to control or record preanalytical variables in clinical practice with the single exception of breast cancer tissue handled according to the guideline jointly developed by the American Society of Clinical Oncology and College of American Pathologists (CAP) and enforced through the CAP Laboratory Accreditation Program. Recognizing the importance of molecular data derived from patient specimens, the CAP Personalized Healthcare Committee established the Preanalytics for Precision Medicine Project Team to develop a basic set of evidence-based recommendations for key preanalytics for tissue and blood specimens. If used for biospecimens from patients, these preanalytical recommendations would ensure the fitness of those specimens for molecular analysis and help to assure the quality and reliability of the analysis data.