Breast cancer (BC) in the Asia Pacific regions is enriched in younger patients and rapidly rising in incidence yet its molecular bases remain poorly characterized. Here we analyze the whole exomes and transcriptomes of 187 primary tumors from a Korean BC cohort (SMC) enriched in pre-menopausal patients and perform systematic comparison with a primarily Caucasian and post-menopausal BC cohort (TCGA). SMC harbors higher proportions of HER2+ and Luminal B subtypes, lower proportion of Luminal A with decreased ESR1 expression compared to TCGA. We also observe increased mutation prevalence affecting BRCA1, BRCA2, and TP53 in SMC with an enrichment of a mutation signature linked to homologous recombination repair deficiency in TNBC. Finally, virtual microdissection and multivariate analyses reveal that Korean BC status is independently associated with increased TIL and decreased TGF-β signaling expression signatures, suggesting that younger Asian BCs harbor more immune-active microenvironment than western BCs.

The increasing accumulation of healthcare data provides researchers with ample opportunities to build machine learning approaches for clinical decision support and to improve the quality of health care. Several studies have developed conventional machine learning approaches that rely heavily on manual feature engineering and result in task-specific models for health care. In contrast, healthcare researchers have begun to use deep learning, which has emerged as a revolutionary machine learning technique that obviates manual feature engineering but still achieves impressive results in research fields such as image classification. However, few of them have addressed the lack of the interpretability of deep learning models although interpretability is essential for the successful adoption of machine learning approaches by healthcare communities. In addition, the unique characteristics of healthcare data such as high dimensionality and temporal dependencies pose challenges for building models on healthcare data. To address these challenges, we develop a gated recurrent unit-based recurrent neural network with hierarchical attention for mortality prediction, and then, using the diagnostic codes from the Medical Information Mart for Intensive Care, we evaluate the model. We find that the prediction accuracy of the model outperforms baseline models and demonstrate the interpretability of the model in visualizations.

We compare methods for filtering RNA-seq lowexpression genes and investigate the effect of filtering on detection of differentially expressed genes (DEGs). Although RNA-seq technology has improved the dynamic range of gene expression quantification, low-expression genes may be indistinguishable from sampling noise. The presence of noisy, low-expression genes can decrease the sensitivity of detecting DEGs. Thus, identification and filtering of these low-expression genes may improve DEG detection sensitivity. Using the SEQC benchmark dataset, we investigate the effect of different filtering methods on DEG detection sensitivity. Moreover, we investigate the effect of RNA-seq pipelines on optimal filtering thresholds. Results indicate that the filtering threshold that maximizes the total number of DEGs closely corresponds to the threshold that maximizes DEG detection sensitivity. Transcriptome reference annotation, expression quantification method, and DEG detection method are statistically significant RNA-seq pipeline factors that affect the optimal filtering threshold.

Inflammation is a double-edged sword with both detrimental and beneficial consequences. Understanding of the mechanisms of crosstalk between the inflammatory milieu and human adult mesenchymal stem cells is an important basis for clinical efforts. Here, we investigate changes in the transcriptional response of human adipose-derived stem cells to physiologically relevant levels of IL-2 (IL-2 priming) upon replicative senescence. Our data suggest that replicative senescence might dramatically impede human mesenchymal stem cell (MSC) function via global transcriptional deregulation in response to IL-2. We uncovered a novel senescence-associated transcriptional signature in human adipose-derived MSCs hADSCs after exposure to pro-inflammatory environment: significant enhancement of the expression of the genes encoding potent growth factors and cytokines with anti-inflammatory and migration-promoting properties, as well as genes encoding angiogenic and anti-apoptotic promoting factors, all of which could participate in the establishment of a unique microenvironment. We observed transcriptional up-regulation of critical components of the nitric oxide synthase pathway (iNOS) in hADSCs upon replicative senescence suggesting, that senescent stem cells can acquire metastasis-promoting properties via stem cell-mediated immunosuppression. Our study highlights the importance of age as a factor when designing cell-based or pharmacological therapies for older patients and predicts measurable biomarkers characteristic of an environment that is conducive to cancer cells invasiveness and metastasis.

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Despite the myriad peer-reviewed papers demonstrating novel Artificial Intelligence (AI)-based solutions to COVID-19 challenges during the pandemic, few have made a significant clinical impact, especially in diagnosis and disease precision staging. One major cause for such low impact is the lack of model transparency, significantly limiting the AI adoption in real clinical practice. To solve this problem, AI models need to be explained to users. Thus, we have conducted a comprehensive study of Explainable Artificial Intelligence (XAI) using PRISMA technology. Our findings suggest that XAI can improve model performance, instill trust in the users, and assist users in decisionmaking. In this systematic review, we introduce common XAI techniques and their utility with specific examples of their application. We discuss the evaluation of XAI results because it is an important step for maximizing the value of AI-based clinical decision support systems. Additionally, we present the traditional, modern, and advanced XAI models to demonstrate the evolution of novel techniques. Finally, we provide a best practice guideline that developers can refer to during the model experimentation. We also offer potential solutions with specific examples for common challenges in AI model experimentation. This comprehensive review, hopefully, can promote AI adoption in biomedicine and healthcare.

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