Is ChatGPT 'ready' to be a learning tool for medical undergraduates and will it perform equally in different subjects? Comparative study of ChatGPT performance in tutorial and case-based learning questions in physiology and biochemistry.

PURPOSE: Generative AI will become an integral part of education in future. The potential of this technology in different disciplines should be identified to promote effective adoption. This study evaluated the performance of ChatGPT in tutorial and case-based learning questions in physiology and biochemistry for medical undergraduates. Our study mainly focused on the performance of GPT-3.5 version while a subgroup was comparatively assessed on GPT-3.5 and GPT-4 performances. MATERIALS AND METHODS: Answers were generated in GPT-3.5 for 44 modified essay questions (MEQs) in physiology and 43 MEQs in biochemistry. Each answer was graded by two independent examiners. Subsequently, a subset of 15 questions from each subject were selected to represent different score categories of the GPT-3.5 answers; responses were generated in GPT-4, and graded. RESULTS: The mean score for physiology answers was 74.7 (SD 25.96). GPT-3.5 demonstrated a statistically significant (p = .009) superior performance in lower-order questions of Bloom's taxonomy in comparison to higher-order questions. Deficiencies in the application of physiological principles in clinical context were noted as a drawback. Scores in biochemistry were relatively lower with a mean score of 59.3 (SD 26.9) for GPT-3.5. There was no statistically significant difference in the scores for higher and lower-order questions of Bloom's taxonomy. The deficiencies highlighted were lack of in-depth explanations and precision. The subset of questions where the GPT-4 and GPT-3.5 were compared demonstrated a better overall performance in GPT-4 responses in both subjects. This difference between the GPT-3.5 and GPT-4 performance was statistically significant in biochemistry but not in physiology. CONCLUSIONS: The differences in performance across the two versions, GPT-3.5 and GPT-4 across the disciplines are noteworthy. Educators and students should understand the strengths and limitations of this technology in different fields to effectively integrate this technology into teaching and learning.

'Insight' in medical training: what, why, and how?

The term 'insight' is generically defined in English language as the ability to perceive deeper truths about people and situations. In clinical practice, patient insight is known to have important implications in treatment compliance and clinical outcomes, and can be assessed clinically by looking for the presence of illness awareness, correct attribution of symptoms to underlying condition, and acceptance of treatment. In this article, we suggest that cultivating insight is actually a highly important, yet often overlooked, component of medical training, which may explain why some consistently learn well, communicate effectively, and quickly attain clinical competency, while others struggle throughout their clinical training and may even be difficult to remediate. We herein define 'insight' in the context of medical training as having an astute perception of personal cognitive processes, motivations, emotions, and ability (strengths, weaknesses, and limitations) that should drive self-improvement and effective behavioural regulation. We then describe the utility of cultivating 'insight' in medical training through three lenses of (i) promoting self-regulated, lifelong clinical learning, (ii) improving clinical competencies and person-centred care, and (iii) enhancing physician mental health and well-being. In addition, we review educational pedagogies that are helpful to create a medical eco-system that promotes the cultivation of insight among its trainees and practitioners. Finally, we highlight several tell-tale signs of poor insight and discuss psychological and non-psychological interventions that may help those severely lacking in insight to become more amenable to change and remediation.

The Singapore National Precision Medicine Strategy.

Precision medicine promises to transform healthcare for groups and individuals through early disease detection, refining diagnoses and tailoring treatments. Analysis of large-scale genomic-phenotypic databases is a critical enabler of precision medicine. Although Asia is home to 60% of the world's population, many Asian ancestries are under-represented in existing databases, leading to missed opportunities for new discoveries, particularly for diseases most relevant for these populations. The Singapore National Precision Medicine initiative is a whole-of-government 10-year initiative aiming to generate precision medicine data of up to one million individuals, integrating genomic, lifestyle, health, social and environmental data. Beyond technologies, routine adoption of precision medicine in clinical practice requires social, ethical, legal and regulatory barriers to be addressed. Identifying driver use cases in which precision medicine results in standardized changes to clinical workflows or improvements in population health, coupled with health economic analysis to demonstrate value-based healthcare, is a vital prerequisite for responsible health system adoption.

Immune pathway upregulation and lower genomic instability distinguish EBV-positive nodal T/NK-cell lymphoma from ENKTL and PTCL-NOS.

Primary Epstein-Barr virus (EBV)-positive nodal T/NK-cell lymphoma (PTCL-EBV) is a poorly understood disease which shows features resembling extranodal NK/T-cell lymphoma (ENKTL) and is currently not recognized as a distinct entity but categorized as a variant of primary T-cell lymphoma not otherwise specified (PTCL-NOS). Herein, we analyzed copynumber aberrations (n=77) with a focus on global measures of genomic instability and homologous recombination deficiency and performed gene expression (n=84) and EBV miRNA expression (n=24) profiling as well as targeted mutational analysis (n=16) to further characterize PTCL-EBV in relation to ENKTL and PTCL-NOS. Multivariate analysis revealed that patients with PTCL-EBV had a significantly worse outcome compared to patients with PTCL-NOS (P=0.002) but not to those with ENKTL. Remarkably, PTCL-EBV exhibited significantly lower genomic instability and homologous recombination deficiency scores compared to ENKTL and PTCL-NOS. Gene set enrichment analysis revealed that many immune-related pathways, interferon α/γ response, and IL6_JAK_STAT3 signaling were significantly upregulated in PTCLEBV and correlated with lower genomic instability scores. We also identified that NFκB-associated genes, BIRC3, NFKB1 (P50) and CD27, and their proteins are upregulated in PTCL-EBV. Most PTCL-EBV demonstrated a type 2 EBV latency pattern and, strikingly, exhibited downregulated expression of most EBV miRNA compared to ENKTL and their target genes were also enriched in immune-related pathways. PTCL-EBV also showed frequent mutations of TET2, PIK3CD and STAT3, and are characterized by microsatellite stability. Overall, poor outcome, low genomic instability, upregulation of immune pathways and downregulation of EBV miRNA are distinctive features of PTCL-EBV. Our data support the concept that PTCL-EBV could be considered as a distinct entity, provide novel insights into the pathogenesis of the disease and offer potential new therapeutic targets for this tumor.

An Alignment-Independent Approach for the Study of Viral Sequence Diversity at Any Given Rank of Taxonomy Lineage.

The study of viral diversity is imperative in understanding sequence change and its implications for intervention strategies. The widely used alignment-dependent approaches to study viral diversity are limited in their utility as sequence dissimilarity increases, particularly when expanded to the genus or higher ranks of viral species lineage. Herein, we present an alignment-independent algorithm, implemented as a tool, UNIQmin, to determine the effective viral sequence diversity at any rank of the viral taxonomy lineage. This is done by performing an exhaustive search to generate the minimal set of sequences for a given viral non-redundant sequence dataset. The minimal set is comprised of the smallest possible number of unique sequences required to capture the diversity inherent in the complete set of overlapping k-mers encoded by all the unique sequences in the given dataset. Such dataset compression is possible through the removal of unique sequences, whose entire repertoire of overlapping k-mers can be represented by other sequences, thus rendering them redundant to the collective pool of sequence diversity. A significant reduction, namely ~44%, ~45%, and ~53%, was observed for all reported unique sequences of species Dengue virus, genus Flavivirus, and family Flaviviridae, respectively, while still capturing the entire repertoire of nonamer (9-mer) viral peptidome diversity present in the initial input dataset. The algorithm is scalable for big data as it was applied to ~2.2 million non-redundant sequences of all reported viruses. UNIQmin is open source and publicly available on GitHub. The concept of a minimal set is generic and, thus, potentially applicable to other pathogenic microorganisms of non-viral origin, such as bacteria.

Transposon mutagenesis identifies cooperating genetic drivers during keratinocyte transformation and cutaneous squamous cell carcinoma progression.

The systematic identification of genetic events driving cellular transformation and tumor progression in the absence of a highly recurrent oncogenic driver mutation is a challenge in cutaneous oncology. In cutaneous squamous cell carcinoma (cuSCC), the high UV-induced mutational burden poses a hurdle to achieve a complete molecular landscape of this disease. Here, we utilized the Sleeping Beauty transposon mutagenesis system to statistically define drivers of keratinocyte transformation and cuSCC progression in vivo in the absence of UV-IR, and identified both known tumor suppressor genes and novel oncogenic drivers of cuSCC. Functional analysis confirms an oncogenic role for the ZMIZ genes, and tumor suppressive roles for KMT2C, CREBBP and NCOA2, in the initiation or progression of human cuSCC. Taken together, our in vivo screen demonstrates an extremely heterogeneous genetic landscape of cuSCC initiation and progression, which can be harnessed to better understand skin oncogenic etiology and prioritize therapeutic candidates.

A multi-task CNN learning model for taxonomic assignment of human viruses.

BACKGROUND: Taxonomic assignment is a key step in the identification of human viral pathogens. Current tools for taxonomic assignment from sequencing reads based on alignment or alignment-free k-mer approaches may not perform optimally in cases where the sequences diverge significantly from the reference sequences. Furthermore, many tools may not incorporate the genomic coverage of assigned reads as part of overall likelihood of a correct taxonomic assignment for a sample. RESULTS: In this paper, we describe the development of a pipeline that incorporates a multi-task learning model based on convolutional neural network (MT-CNN) and a Bayesian ranking approach to identify and rank the most likely human virus from sequence reads. For taxonomic assignment of reads, the MT-CNN model outperformed Kraken 2, Centrifuge, and Bowtie 2 on reads generated from simulated divergent HIV-1 genomes and was more sensitive in identifying SARS as the closest relation in four RNA sequencing datasets for SARS-CoV-2 virus. For genomic region assignment of assigned reads, the MT-CNN model performed competitively compared with Bowtie 2 and the region assignments were used for estimation of genomic coverage that was incorporated into a naïve Bayesian network together with the proportion of taxonomic assignments to rank the likelihood of candidate human viruses from sequence data. CONCLUSIONS: We have developed a pipeline that combines a novel MT-CNN model that is able to identify viruses with divergent sequences together with assignment of the genomic region, with a Bayesian approach to ranking of taxonomic assignments by taking into account both the number of assigned reads and genomic coverage. The pipeline is available at GitHub via https://github.com/MaHaoran627/CNN_Virus .

An epidemiological surveillance of hand foot and mouth disease in paediatric patients and in community: A Singapore retrospective cohort study, 2013-2018.

BACKGROUND: While hand, foot and mouth disease (HFMD) is primarily self-resolving-soaring incidence rate of symptomatic HFMD effectuates economic burden in the Asia-Pacific region. Singapore has seen a conspicuous rise in the number of HFMD cases from 2010s. Here, we aims to identify the serology and genotypes responsible for such outbreaks in hospitals and childcare facilities. METHODS: We studied symptomatic paediatric HFMD cases from 2013 to 2018 in Singapore. Surveillance for subclinical enterovirus infections was also performed in childcares at the same time period. RESULTS: Genotyping 101 symptomatic HFMD samples revealed CV-A6 as the major etiological agent for recent outbreaks. We detected infections with CV-A6 (41.0%), EV-A71 (7%), CV-A16 (3.0%), coxsackievirus A2, CV-A2 (1.0%) and coxsackievirus A10, CV-A10 (1.0%). Phylogenetic analysis of local CV-A6 strains revealed a high level of heterogeneity compared against others worldwide, dissimilar to other HFMD causative enteroviruses for which the dominant strains and genotypes are highly region specific. We detected sub-clinical enterovirus infections in childcare centres; 17.1% (n = 245) tested positive for enterovirus in saliva, without HFMD indicative symptoms at the point of sample collection. CONCLUSIONS: CV-A6 remained as the dominant HFMD causative strain in Singapore. Silent subclinical enteroviral infections were detected and warrant further investigations.

T-Cell Lymphoma Clonality by Copy Number Variation Analysis of T-Cell Receptor Genes.

T-cell lymphomas arise from a single neoplastic clone and exhibit identical patterns of deletions in T-cell receptor (TCR) genes. Whole genome sequencing (WGS) data represent a treasure trove of information for the development of novel clinical applications. However, the use of WGS to identify clonal T-cell proliferations has not been systematically studied. In this study, based on WGS data, we identified monoclonal rearrangements (MRs) of T-cell receptors (TCR) genes using a novel segmentation algorithm and copy number computation. We evaluated the feasibility of this technique as a marker of T-cell clonality using T-cell lymphomas (TCL, n = 44) and extranodal NK/T-cell lymphomas (ENKTLs, n = 20), and identified 98% of TCLs with one or more TCR gene MRs, against 91% detected using PCR. TCR MRs were absent in all ENKTLs and NK cell lines. Sensitivity-wise, this platform is sufficiently competent, with MRs detected in the majority of samples with tumor content under 25% and it can also distinguish monoallelic from biallelic MRs. Understanding the copy number landscape of TCR using WGS data may engender new diagnostic applications in hematolymphoid pathology, which can be readily adapted to the analysis of B-cell receptor loci for B-cell clonality determination.

STAT3 Regulates Mouse Neural Progenitor Proliferation and Differentiation by Promoting Mitochondrial Metabolism.

The proliferation and differentiation of neural progenitor lay the foundation for brain development. In neural progenitors, activation of Signal Transducer and Activator of Transcription 3 (STAT3) has been found to promote proliferation and astrocytogenesis while suppressing neurogenesis. However, our study found that Stat3 conditional knockout in neural progenitors (Stat3 cKO) also results in increased proliferation and suppressed neurogenesis. To investigate how STAT3 regulates these processes, we attempted to identify potential STAT3 target genes by RNA-seq profiling of the control (CTL) and Stat3 cKO neural progenitors. We found that STAT3 promotes the expression of genes involved in the mitochondrial oxidative phosphorylation (OXPHOS), and thereby promotes mitochondrial respiration and negatively regulates reactive oxygen species (ROS) production. In addition, we demonstrated that Stat3 loss-of-function promotes proliferation via regulation of mitochondrial metabolism and downstream signaling pathways. Our study provides novel insights into the relation between STAT3, mitochondrial metabolism and the process of embryonic neurogenesis.

Architecture of population-differentiated polymorphisms in the human genome.

Population variation in disease and other phenotype are partly attributed to single nucleotide polymorphisms (SNPs) in the human genome. Due to selection pressure, two individuals from the same ancestral population have more genetic similarity compared to individuals from further geographic regions. Here, we elucidated the genomic population differentiation pattern, by interrogating >22,000,000 SNPs. Majority of population-differentiated (pd) SNPs (~95%), including the potentially functional (pf) (~84%) subset reside in non-genic regions, compared to the proportion of all SNPs (58%) found in non-genic regions. This suggests that differences between populations are more likely due to differences in gene regulation rather than protein function. Actin Cytoskeleton, Axonal Guidance and Protein Kinase A signaling pathways are enriched with genes carrying at least three pdSNPs (enriched pdGenes), while Antigen Presentation, Hepatic Fibrosis and Huntington Disease Signalling pathways are over-represented by enriched pf-pdGenes. An inverse correlation between chromosome size and the proportion of pd-/pf-pdSNPs was observed. Smaller chromosomes have relatively more of such SNPs including genes carrying these SNPs. Genes associated with common diseases and enriched with these pd-/pfpdSNPs are localized to 11 different chromosomes, with immune-related disease pd/pf-pdGenes mainly residing in chromosome 6 while neurological disease pd/pf-pdGenes residing in smaller chromosomes including chromosome 21/22. The associated diseases were reported to show population differences in incidence, severity and/or etiology. In summary, this study highlights the non-sporadic nature of population differentiation footprint in the human genome, which can potentially lead to the identification of genomic regions that play roles in the manifestation of phenotypic differences, including in disease predisposition and drug response.

Identification of novel fusion transcripts in multiple myeloma.

AIMS: Multiple myeloma (MM) is a heterogeneous disease characterised by genetically complex abnormalities. The classical mutational spectrum includes recurrent chromosomal aberrations and gene-level mutations. Recurrent translocations involving the IGH gene such as t(11;14), t(4;14) and t(14;16) are well known. However, the presence of complex genetic abnormalities raises the possibility that fusions other than the recurrent IGH translocations exist. We therefore employed a targeted RNA-sequencing panel to identify novel putative fusions in a local cohort of MM. METHODS: Targeted RNA-sequencing was performed on 21 patient samples using the Illumina TruSight RNA Pan-Cancer Panel (comprising 1385 genes). Fusion calls were generated from the Illumina RNA-Sequencing Alignment software (V.1.0.0). These samples had conventional cytogenetic and fluorescence in situ hybridisation data for the common recurrent chromosomal abnormalities (t(11;14), t(4;14), t(14;16) and 17p13 deletion). The MMRF CoMMpass dataset was analysed using the TopHat-fusion pipeline. RESULTS: A total of 10 novel fusions were identified by the TruSight RNA Pan-Cancer Panel. Two of these fusions, HGF/CACNA2D1 and SMC3/MXI1, were validated by reverse transcription PCR and Sanger sequencing as they involve genes that may have biological relevance in MM genesis. Four of these (MAP2K4/MAP2K4P1) are likely to be spurious secondary to misalignment of reads to a pseudogene. One record of the HGF/CACNA2D1 fusion was identified from the MMRF CoMMpass dataset. CONCLUSIONS: The identification of novel fusions offers insights into the biology of MM and might have clinical relevance. Further functional studies are required to determine the biological and clinical relevance of these novel fusions.

Linking FOXO3, NCOA3, and TCF7L2 to Ras pathway phenotypes through a genome-wide forward genetic screen in human colorectal cancer cells.

BACKGROUND: The Ras pathway genes KRAS, BRAF, or ERBBs have somatic mutations in ~ 60% of human colorectal carcinomas. At present, it is unknown whether the remaining cases lack mutations activating the Ras pathway or whether they have acquired mutations in genes hitherto unknown to belong to the pathway. METHODS: To address the second possibility and extend the compendium of Ras pathway genes, we used genome-wide transposon mutagenesis of two human colorectal cancer cell systems deprived of their activating KRAS or BRAF allele to identify genes enabling growth in low glucose, a Ras pathway phenotype, when targeted. RESULTS: Of the 163 recurrently targeted genes in the two different genetic backgrounds, one-third were known cancer genes and one-fifth had links to the EGFR/Ras/MAPK pathway. When compared to cancer genome sequencing datasets, nine genes also mutated in human colorectal cancers were identified. Among these, stable knockdown of FOXO3, NCOA3, and TCF7L2 restored growth in low glucose but reduced MEK/MAPK phosphorylation, reduced anchorage-independent growth, and modulated expressions of GLUT1 and Ras pathway related proteins. Knockdown of NCOA3 and FOXO3 significantly decreased the sensitivity to cetuximab of KRAS mutant but not wild-type cells. CONCLUSIONS: This work establishes a proof-of-concept that human cell-based genome-wide forward genetic screens can assign genes to pathways with clinical importance in human colorectal cancer.

CEBPA mutational analysis in acute myeloid leukaemia by a laboratory-developed next-generation sequencing assay.

AIM: The presence of biallelic CEBPA mutations is a favourable prognostic feature in acute myeloid leukaemia (AML). CEBPA mutations are currently identified through conventional capillary sequencing (CCS). With the increasing adoption of next-generation sequencing (NGS) platforms, challenges with regard to amplification efficiency of CEBPA due to the high GC content may be encountered, potentially resulting in suboptimal coverage. Here, the performance of an amplicon-based NGS method using a laboratory-developed CEBPA-specific Nextera XT (CEBNX) was evaluated. METHODS: Mutational analyses of the CEBPA gene of 137 AML bone marrow or peripheral blood retrospective specimens were performed by the amplification of the CEBPA gene using the Expand Long Range dNTPack and the amplicons processed by CCS and NGS. CEBPA-specific libraries were then constructed using the Nextera XT V.2 kit. All FASTQ files were then processed with the MiSeq Reporter V.2.6.2.3 using the PCR Amplicon workflow via the customised CEBPA-specific manifest file. The variant calling format files were analysed using the Illumina Variant Studio V.2.2. RESULTS: A coverage per base of 3631X to 28184X was achieved. 22 samples (16.1%) were found to contain CEBPA mutations, with variant allele frequencies (VAF) ranging from 3.8% to 58.2%. Taking CCS as the 'gold standard', sensitivity and specificity of 97% and 97% was achieved. For the transactivation domain 2 polymorphism (c.584_589dupACCCGC/p.His195_Pro196dup), the CEBNX achieved 100% sensitivity and 100% specificity relative to CCS. CONCLUSIONS: Our laboratory-developed CEBNX workflow shows high coverage and thus overcomes the challenges associated with amplification efficiency and low coverage of CEBPA. Therefore, our assay is suitable for deployment in the clinical laboratory.

Identifying large indels in targeted next generation sequencing assays for myeloid neoplasms: a cautionary tale of the ZRSR1 pseudogene.

Targeted next generation sequencing platforms have been increasingly utilised for identification of novel mutations in myeloid neoplasms, such as acute myeloid leukaemia (AML), and hold great promise for use in routine clinical diagnostics. In this study, we evaluated the utility of an open source variant caller in detecting large indels in a targeted sequencing of AML samples. While we found that this bioinformatics pipeline has the potential to accurately capture large indels (>20 bp) in patient samples, we highlighted the pitfall of a confounding ZRSR1 pseudogene that led to an erroneous ZRSR2 variant call. We further discuss possible clinical implications of the ZRSR1 pseudogene in myeloid neoplasms based on its molecular features. Knowledge of the confounding ZRSR1 pseudogene in ZRSR2 sequencing assays could be particularly important in AML diagnostics because the detection of ZRSR2 in AML patients is highly specific for an s-AML diagnosis.

Single-cell genomic profiling of acute myeloid leukemia for clinical use: A pilot study.

Although bulk high-throughput genomic profiling studies have led to a significant increase in the understanding of cancer biology, there is increasing awareness that bulk profiling approaches do not completely elucidate tumor heterogeneity. Single-cell genomic profiling enables the distinction of tumor heterogeneity, and may improve clinical diagnosis through the identification and characterization of putative subclonal populations. In the present study, the challenges associated with a single-cell genomics profiling workflow for clinical diagnostics were investigated. Single-cell RNA-sequencing (RNA-seq) was performed on 20 cells from an acute myeloid leukemia bone marrow sample. Putative blasts were identified based on their gene expression profiles and principal component analysis was performed to identify outlier cells. Variant calling was performed on the single-cell RNA-seq data. The present pilot study demonstrates a proof of concept for clinical single-cell genomic profiling. The recognized limitations include significant stochastic RNA loss and the relatively low throughput of the current proposed platform. Although the results of the present study are promising, further technological advances and protocol optimization are necessary for single-cell genomic profiling to be clinically viable.

Transposon insertional mutagenesis in mice identifies human breast cancer susceptibility genes and signatures for stratification.

Robust prognostic gene signatures and therapeutic targets are difficult to derive from expression profiling because of the significant heterogeneity within breast cancer (BC) subtypes. Here, we performed forward genetic screening in mice using Sleeping Beauty transposon mutagenesis to identify candidate BC driver genes in an unbiased manner, using a stabilized N-terminal truncated ß-catenin gene as a sensitizer. We identified 134 mouse susceptibility genes from 129 common insertion sites within 34 mammary tumors. Of these, 126 genes were orthologous to protein-coding genes in the human genome (hereafter, human BC susceptibility genes, hBCSGs), 70% of which are previously reported cancer-associated genes, and ∼16% are known BC suppressor genes. Network analysis revealed a gene hub consisting of E1A binding protein P300 (EP300), CD44 molecule (CD44), neurofibromin (NF1) and phosphatase and tensin homolog (PTEN), which are linked to a significant number of mutated hBCSGs. From our survival prediction analysis of the expression of human BC genes in 2,333 BC cases, we isolated a six-gene-pair classifier that stratifies BC patients with high confidence into prognostically distinct low-, moderate-, and high-risk subgroups. Furthermore, we proposed prognostic classifiers identifying three basal and three claudin-low tumor subgroups. Intriguingly, our hBCSGs are mostly unrelated to cell cycle/mitosis genes and are distinct from the prognostic signatures currently used for stratifying BC patients. Our findings illustrate the strength and validity of integrating functional mutagenesis screens in mice with human cancer transcriptomic data to identify highly prognostic BC subtyping biomarkers.

Development of a clinical decision support system for diabetes care: A pilot study.

Management of complex chronic diseases such as diabetes requires the assimilation and interpretation of multiple laboratory test results. Traditional electronic health records tend to display laboratory results in a piecemeal and segregated fashion. This makes the assembly and interpretation of results related to diabetes care challenging. We developed a diabetes-specific clinical decision support system (Diabetes Dashboard) interface for displaying glycemic, lipid and renal function results, in an integrated form with decision support capabilities, based on local clinical practice guidelines. The clinical decision support system included a dashboard feature that graphically summarized all relevant laboratory results and displayed them in a color-coded system that allowed quick interpretation of the metabolic control of the patients. An alert module informs the user of tests that are due for repeat testing. An interactive graph module was also developed for better visual appreciation of the trends of the laboratory results of the patient. In a pilot study involving case scenarios administered via an electronic questionnaire, the Diabetes Dashboard, compared to the existing laboratory reporting interface, significantly improved the identification of abnormal laboratory results, of the long-term trend of the laboratory tests and of tests due for repeat testing. However, the Diabetes Dashboard did not significantly improve the identification of patients requiring treatment adjustment or the amount of time spent on each case scenario. In conclusion, we have developed and shown that the use of the Diabetes Dashboard, which incorporates several decision support features, can improve the management of diabetes. It is anticipated that this dashboard will be most helpful when deployed in an outpatient setting, where physicians can quickly make clinical decisions based on summarized information and be alerted to pertinent areas of care that require additional attention.

Clinical implications of DNMT3A mutations in a Southeast Asian cohort of acute myeloid leukaemia patients.

AIMS: In recent years, genomic technologies have enabled the identification of mutations in acute myeloid leukaemia (AML). DNMT3A is a recurrently mutated epigenetic modifier gene in AML. To date, the prognostic significance of DNMT3A mutations has not been studied in a Southeast Asian AML population. We sought to investigate the clinical implications of DNMT3A mutations in a Southeast Asian cohort of AML patients. METHODS: DNMT3A mutations were identified using a targeted next-generation sequencing panel in 157 AML patients. We studied the molecular and clinical features of patients with DNMT3A mutations and assessed the prognostic impact of DNMT3A mutations. RESULTS: DNMT3A mutations were found in 33 of 157 (21.0%) AML patients. 114 patients were included for statistical analysis. Pretreatment data revealed that patients with DNMT3A mutations were older (≥60 years old), had a higher white blood cell count at diagnosis, had more adverse cytogenetic risk profiles and were more often associated with NPM1 mutations compared with patients with wild-type DNMT3A. Survival analysis showed that DNMT3A mutations were associated with poorer clinical outcomes. This was especially when associated with NPM1 and FLT3-ITD mutations (AML NPM1/FLT3/DNMT3A ), which are common. The AML NPM1/FLT3/DNMT3A subtype was an independent predictor for poorer overall survival (OS). Other independent risk factors for poorer OS include advanced age at diagnosis and adverse cytogenetic risk stratification. CONCLUSIONS: DNMT3A mutations are associated with an unfavourable clinical outcome in our Southeast Asian AML patient cohort. In particular, AML NPM1/FLT3/DNMT3A patients had the poorest prognosis.

Transposon mutagenesis identifies genes that cooperate with mutant Pten in breast cancer progression.

Triple-negative breast cancer (TNBC) has the worst prognosis of any breast cancer subtype. To better understand the genetic forces driving TNBC, we performed a transposon mutagenesis screen in a phosphatase and tensin homolog (Pten) mutant mice and identified 12 candidate trunk drivers and a much larger number of progression genes. Validation studies identified eight TNBC tumor suppressor genes, including the GATA-like transcriptional repressor TRPS1 Down-regulation of TRPS1 in TNBC cells promoted epithelial-to-mesenchymal transition (EMT) by deregulating multiple EMT pathway genes, in addition to increasing the expression of SERPINE1 and SERPINB2 and the subsequent migration, invasion, and metastasis of tumor cells. Transposon mutagenesis has thus provided a better understanding of the genetic forces driving TNBC and discovered genes with potential clinical importance in TNBC.