MicroRNA expression signature as a biomarker in the diagnosis of nodal T-cell lymphomas.

BACKGROUND: The diagnosis of T-cell lymphomas is typically established through a multiparameter approach that combines clinical, morphologic, immunophenotypic, and genetic features, utilizing a variety of histopathologic and molecular techniques. However, accurate diagnosis of such lymphomas and distinguishing them from reactive lymph nodes remains challenging due to their low prevalence and heterogeneous features, hence limiting the confidence of pathologists. We investigated the use of microRNA (miRNA) expression signatures as an adjunctive tool in the diagnosis and classification of T-cell lymphomas that involve lymph nodes. This study seeks to distinguish reactive lymph nodes (RLN) from two types of frequently occurring nodal T-cell lymphomas: nodal T-follicular helper (TFH) cell lymphomas (nTFHL) and peripheral T-cell lymphomas, not otherwise specified (nPTCL). METHODS: From the formalin-fixed paraffin-embedded (FFPE) samples from a cohort of 88 subjects, 246 miRNAs were quantified and analyzed by differential expression. Two-class logistic regression and random forest plot models were built to distinguish RLN from the nodal T-cell lymphomas. Gene set enrichment analysis was performed on the target genes of the miRNA to identify pathways and transcription factors that may be regulated by the differentially expressed miRNAs in each subtype. RESULTS: Using logistic regression analysis, we identified miRNA signatures that can distinguish RLN from nodal T-cell lymphomas (AUC of 0.92 ± 0.05), from nTFHL (AUC of 0.94 ± 0.05) and from nPTCL (AUC of 0.94 ± 0.08). Random forest plot modelling was also capable of distinguishing between RLN and nodal T-cell lymphomas, but performed worse than logistic regression. However, the miRNA signatures are not able to discriminate between nTFHL and nPTCL, owing to large similarity in miRNA expression patterns. Bioinformatic analysis of the gene targets of unique miRNA expression revealed the enrichment of both known and potentially understudied signalling pathways and genes in such lymphomas. CONCLUSION: This study suggests that miRNA biomarkers may serve as a promising, cost-effective tool to aid the diagnosis of nodal T-cell lymphomas, which can be challenging. Bioinformatic analysis of differentially expressed miRNAs revealed both relevant or understudied signalling pathways that may contribute to the progression and development of each T-cell lymphoma entity. This may help us gain further insight into the biology of T-cell lymphomagenesis.

A genomic enhancer signature associates with hepatocellular carcinoma prognosis.

Background & Aims: Lifestyle and environmental-related exposures are important risk factors for hepatocellular carcinoma (HCC), suggesting that epigenetic dysregulation significantly underpins HCC. We profiled 30 surgically resected tumours and the matched adjacent normal tissues to understand the aberrant epigenetic events associated with HCC. Methods: We identified tumour differential enhancers and the associated genes by analysing H3K27 acetylation (H3K27ac) chromatin immunoprecipitation sequencing (ChIP-seq) and Hi-C/HiChIP data from the resected tumour samples of 30 patients with early-stage HCC. This epigenome dataset was analysed with previously reported genome and transcriptome data of the overlapping group of patients from the same cohort. We performed patient-specific differential expression testing using multiregion sequencing data to identify genes that undergo both enhancer and gene expression changes. Based on the genes selected, we identified two patient groups and performed a recurrence-free survival analysis. Results: We observed large-scale changes in the enhancer distribution between HCC tumours and the adjacent normal samples. Many of the gain-in-tumour enhancers showed corresponding upregulation of the associated genes and vice versa, but much of the enhancer and gene expression changes were patient-specific. A subset of the upregulated genes was activated in a subgroup of patients' tumours. Recurrence-free survival analysis revealed that the patients with a more robust upregulation of those genes showed a worse prognosis. Conclusions: We report the genomic enhancer signature associated with differential prognosis in HCC. Findings that cohere with oncofoetal reprogramming in HCC were underpinned by genome-wide enhancer rewiring. Our results present the epigenetic changes in HCC that offer the rational selection of epigenetic-driven gene targets for therapeutic intervention or disease prognostication in HCC. Impact and Implications: Lifestyle and environmental-related exposures are the important risk factors of hepatocellular carcinoma (HCC), suggesting that tumour-associated epigenetic dysregulations may significantly underpin HCC. We profiled tumour tissues and their matched normal from 30 patients with early-stage HCC to study the dysregulated epigenetic changes associated with HCC. By also analysing the patients' RNA-seq and clinical data, we found the signature genes - with epigenetic and transcriptomic dysregulation - associated with worse prognosis. Our findings suggest that systemic approaches are needed to consider the surrounding cellular environmental and epigenetic changes in HCC tumours.

Multi-region sampling with paired sample sequencing analyses reveals sub-groups of patients with novel patient-specific dysregulation in Hepatocellular Carcinoma.

BACKGROUND: Conventional differential expression (DE) testing compares the grouped mean value of tumour samples to the grouped mean value of the normal samples, and may miss out dysregulated genes in small subgroup of patients. This is especially so for highly heterogeneous cancer like Hepatocellular Carcinoma (HCC). METHODS: Using multi-region sampled RNA-seq data of 90 patients, we performed patient-specific differential expression testing, together with the patients' matched adjacent normal samples. RESULTS: Comparing the results from conventional DE analysis and patient-specific DE analyses, we show that the conventional DE analysis omits some genes due to high inter-individual variability present in both tumour and normal tissues. Dysregulated genes shared in small subgroup of patients were useful in stratifying patients, and presented differential prognosis. We also showed that the target genes of some of the current targeted agents used in HCC exhibited highly individualistic dysregulation pattern, which may explain the poor response rate. DISCUSSION/CONCLUSION: Our results highlight the importance of identifying patient-specific DE genes, with its potential to provide clinically valuable insights into patient subgroups for applications in precision medicine.

Developing critical thinking in STEM education through inquiry-based writing in the laboratory classroom.

Laboratory pedagogy is moving away from step-by-step instructions and toward inquiry-based learning, but only now developing methods for integrating inquiry-based writing (IBW) practices into the laboratory course. Based on an earlier proposal (Science 2011;332:919), we designed and implemented an IBW sequence in a university bioinformatics course. We automatically generated unique, double-blinded, biologically plausible DNA sequences for each student. After guided instruction, students investigated sequences independently and responded through IBW writing assignments. IBW assignments were structured as condensed versions of a scientific research article, and because the sequences were double blinded, they were also assessed as authentic science and evaluated on clarity and persuasiveness. We piloted the approach in a seven-day workshop (35 students) at Perdana University in Malaysia. We observed dramatically improved student engagement and indirect evidence of improved learning outcomes over a similar workshop without IBW. Based on student feedback, initial discomfort with the writing component abated in favor of an overall positive response and increasing comfort with the high demands of student writing. Similarly, encouraging results were found in a semester length undergraduate module at the National University of Singapore (155 students).

Bivalent genes that undergo transcriptional switching identify networks of key regulators of embryonic stem cell differentiation.

BACKGROUND: Bivalent promoters marked with both H3K27me3 and H3K4me3 histone modifications are characteristic of poised promoters in embryonic stem (ES) cells. The model of poised promoters postulates that bivalent chromatin in ES cells is resolved to monovalency upon differntiation. With the availability of single-cell RNA sequencing (scRNA-seq) data, subsequent switches in transcriptional state at bivalent promoters can be studied more closely. RESULTS: We develop an approach for capturing genes undergoing transcriptional switching by detecting 'bimodal' gene expression patterns from scRNA-seq data. We integrate the identification of bimodal genes in ES cell differentiation with analysis of chromatin state, and identify clear cell-state dependent patterns of bimodal, bivalent genes. We show that binarization of bimodal genes can be used to identify differentially expressed genes from fractional ON/OFF proportions. In time series data from differentiating cells, we build a pseudotime approximation and use a hidden Markov model to infer gene activity switching pseudotimes, which we use to infer a regulatory network. We identify pathways of switching during differentiation, novel details of those pathway, and transcription factor coordination with downstream targets. CONCLUSIONS: Genes with expression levels too low to be informative in conventional scRNA analysis can be used to infer transcriptional switching networks that connect transcriptional activity to chromatin state. Since chromatin bivalency is a hallmark of gene promoters poised for activity, this approach provides an alternative that complements conventional scRNA-seq analysis while focusing on genes near the ON/OFF boundary of activity. This offers a novel and productive means of inferring regulatory networks from scRNA-seq data.