Cluster analysis and visualisation of electronic health records data to identify undiagnosed patients with rare genetic diseases.

Rare genetic diseases affect 5-8% of the population but are often undiagnosed or misdiagnosed. Electronic health records (EHR) contain large amounts of data, which provide opportunities for analysing and mining. Data mining, in the form of cluster analysis and visualisation, was performed on a database containing deidentified health records of 1.28 million patients across 3 major hospitals in Singapore, in a bid to improve the diagnostic process for patients who are living with an undiagnosed rare disease, specifically focusing on Fabry Disease and Familial Hypercholesterolaemia (FH). On a baseline of 4 patients, we identified 2 additional patients with potential diagnosis of Fabry disease, suggesting a potential 50% increase in diagnosis. Similarly, we identified > 12,000 individuals who fulfil the clinical and laboratory criteria for FH but had not been diagnosed previously. This proof-of-concept study showed that it is possible to perform mining on EHR data albeit with some challenges and limitations.

Family history assessment significantly enhances delivery of precision medicine in the genomics era.

BACKGROUND: Family history has traditionally been an essential part of clinical care to assess health risks. However, declining sequencing costs have precipitated a shift towards genomics-first approaches in population screening programs rendering the value of family history unknown. We evaluated the utility of incorporating family history information for genomic sequencing selection. METHODS: To ascertain the relationship between family histories on such population-level initiatives, we analysed whole genome sequences of 1750 research participants with no known pre-existing conditions, of which half received comprehensive family history assessment of up to four generations, focusing on 95 cancer genes. RESULTS: Amongst the 1750 participants, 866 (49.5%) had high-quality standardised family history available. Within this group, 73 (8.4%) participants had an increased family history risk of cancer (increased FH risk cohort) and 1 in 7 participants (n = 10/73) carried a clinically actionable variant inferring a sixfold increase compared with 1 in 47 participants (n = 17/793) assessed at average family history cancer risk (average FH risk cohort) (p = 0.00001) and a sevenfold increase compared to 1 in 52 participants (n = 17/884) where family history was not available (FH not available cohort) (p = 0.00001). The enrichment was further pronounced (up to 18-fold) when assessing only the 25 cancer genes in the American College of Medical Genetics (ACMG) Secondary Findings (SF) genes. Furthermore, 63 (7.3%) participants had an increased family history cancer risk in the absence of an apparent clinically actionable variant. CONCLUSIONS: These findings demonstrate that the collection and analysis of comprehensive family history and genomic data are complementary and in combination can prioritise individuals for genomic analysis. Thus, family history remains a critical component of health risk assessment, providing important actionable data when implementing genomics screening programs. TRIAL REGISTRATION: ClinicalTrials.gov NCT02791152 . Retrospectively registered on May 31, 2016.

Evaluation of family health history collection methods impact on data and risk assessment outcomes.

Information technology applications for patient-collection of family health history (FHH) increase identification of elevated-risk individuals compared to usual care. It is unknown if the method of collection impacts data collected or if simply going directly to the patient is what makes the difference. The objective of this study was to examine differences in data detail and risk identification rates between FHH collection directly from individuals using paper-based forms and an interactive web-based platform. This is a non-randomized epidemiologic study in Singaporean population from 2016 to 2018. Intervention was paper-based versus web-based interactive platform for FHH collection. Participant demographics, FHH detail, and risk assessment results were analyzed. 882 participants enrolled in the study, 481 in the paper-based group and 401 in the web-based group with mean (SD) age of 45.4 (12.98) years and 47.5% male. Web-based FHH collection participants had an increased number of conditions per relative (p-value <0.001), greater frequency of reporting age of onset (p-value <0.001), and greater odds of receiving ≥1 risk recommendation both overall (OR: 3.99 (2.41, 6.59)) and within subcategories of genetic counselling for hereditary cancer syndromes (p-value = 0.041) and screening and prevention for breast (p-value = 0.002) and colon cancer (p-value = 0.005). This has significant implications for clinical care and research efforts where FHH is being assessed. Using interactive information technology platforms to collect FHH can improve the completeness of the data collected and result in increased rates of risk identification. Methods of data collection to maximize benefit should be taken into account in future studies and clinical care.

Ethical frameworks for obtaining informed consent in tumour profiling: an evidence-based case for Singapore.

BACKGROUND: Genomic profiling of malignant tumours has assisted clinicians in providing targeted therapies for many serious cancer-related illnesses. Although the characterisation of somatic mutations is the primary aim of tumour profiling for treatment, germline mutations may also be detected given the heterogenous origin of mutations observed in tumours. Guidance documents address the return of germline findings that have health implications for patients and their genetic relations. However, the implications of discovering a potential but unconfirmed germline finding from tumour profiling are yet to be fully explored. Moreover, as tumour profiling is increasingly applied in oncology, robust ethical frameworks are required to encourage large-scale data sharing and data aggregation linking molecular data to clinical outcomes, to further understand the role of genetics in oncogenesis and to develop improved cancer therapies. RESULTS: This paper reports on the results of empirical research that is broadly aimed at developing an ethical framework for obtaining informed consent to return results from tumour profiling tests and to share the biomolecular data sourced from tumour tissues of cancer patients. Specifically, qualitative data were gathered from 36 semi-structured interviews with cancer patients and oncology clinicians at a cancer treatment centre in Singapore. The interview data indicated that patients had a limited comprehension of cancer genetics and implications of tumour testing. Furthermore, oncology clinicians stated that they lacked the time to provide in depth explanations of the tumour profile tests. However, it was accepted from both patients and oncologist that the return potential germline variants and the sharing of de-identified tumour profiling data nationally and internationally should be discussed and provided as an option during the consent process. CONCLUSIONS: Findings provide support for the return of tumour profiling results provided that they are accompanied with an adequate explanation from qualified personnel. They also support the use of broad consent regiments within an ethical framework that promotes trust and benefit sharing with stakeholders and provides accountability and transparency in the storage and sharing of biomolecular data for research.

Technical Validation of a Next-Generation Sequencing Assay for Detecting Actionable Mutations in Patients with Gastrointestinal Cancer.

Targeted next-generation sequencing is becoming increasingly common as a clinical diagnostic and prognostic test for patient- and tumor-specific genetic profiles as well as to optimally select targeted therapies. Here, we describe a custom-developed, next-generation sequencing test for detecting single-nucleotide variants (SNVs) and short insertions and deletions (indels) in 93 genes related to gastrointestinal cancer from routine formalin-fixed, paraffin-embedded clinical specimens. We implemented a validation strategy, based on the College of American Pathologists requirements, using reference DNA mixtures from cell lines with known genetic variants, which model a broad range of allele frequencies. Test sensitivity achieved >99% for both SNVs and indels, with allele frequencies >10%, with high specificity (97.4% for SNVs and 93.6% for indels). We further confirmed test accuracies using primary formalin-fixed, paraffin-embedded colorectal cancer specimens characterized by alternative and conventional clinical diagnostic technologies. Robust performance was observed on the formalin-fixed, paraffin-embedded specimens: sensitivity was 97.2% and specificity was 99.2%. We also observed high intrarun and inter-run reproducibility, as well as a low cross-contamination rate. Overall assessment using cell line samples and formalin-fixed, paraffin-embedded samples showed that our custom next-generation sequencing assay has consistent detection sensitivity down to 10% variant frequency.