Search

In the enduring challenge against disease, advancements in medical technology have empowered clinicians with novel diagnostic platforms. Whilst in some cases, a single test may provide a confident diagnosis, often additional tests are required. However, to strike a balance between diagnostic accuracy and cost-effectiveness, one must rigorously construct the clinical pathways. Here, we developed a framework to build multi-platform precision pathways in an automated, unbiased way, recommending the key steps a clinician would take to reach a diagnosis. We achieve this by developing a confidence score, used to simulate a clinical scenario, where at each stage, either a confident diagnosis is made, or another test is performed. Our framework provides a range of tools to interpret, visualize and compare the pathways, improving communication and enabling their evaluation on accuracy and cost, specific to different contexts. This framework will guide the development of novel diagnostic pathways for different diseases, accelerating the implementation of precision medicine into clinical practice.

Potential benefits of precision medicine in cardiovascular disease (CVD) include more accurate phenotyping of individual patients with the same condition or presentation, using multiple clinical, imaging, molecular and other variables to guide diagnosis and treatment. An approach to realising this potential is the digital twin concept, whereby a virtual representation of a patient is constructed and receives real-time updates of a range of data variables in order to predict disease and optimise treatment selection for the real-life patient. We explored the term digital twin, its defining concepts, the challenges as an emerging field, and potentially important applications in CVD. A mapping review was undertaken using a systematic search of peer-reviewed literature. Industry-based participants and patent applications were identified through web-based sources. Searches of Compendex, EMBASE, Medline, ProQuest and Scopus databases yielded 88 papers related to cardiovascular conditions (28%, n = 25), non-cardiovascular conditions (41%, n = 36), and general aspects of the health digital twin (31%, n = 27). Fifteen companies with a commercial interest in health digital twin or simulation modelling had products focused on CVD. The patent search identified 18 applications from 11 applicants, of which 73% were companies and 27% were universities. Three applicants had cardiac-related inventions. For CVD, digital twin research within industry and academia is recent, interdisciplinary, and established globally. Overall, the applications were numerical simulation models, although precursor models exist for the real-time cyber-physical system characteristic of a true digital twin. Implementation challenges include ethical constraints and clinical barriers to the adoption of decision tools derived from artificial intelligence systems.

Microbiome feedbacks are proposed to influence Parkinson’s disease (PD) pathophysiology. A number of studies have evaluated the impact of oral medication on the gut microbiome (GM) in PD. However, the influence of PD device-assisted therapies (DATs) on the GM remains to be investigated. To profile acute gut microbial community alterations in response to PD DAT initiation. Clinical data and stool samples were collected from 21 PD patients initiating either deep brain stimulation (DBS) or levodopa–carbidopa intestinal gel (LCIG) and ten spousal healthy control (HC) subjects. 16S amplicon sequencing of stool DNA enabled comparison of temporal GM stability between groups and with clinical measures, including disease alterations relative to therapy initiation. We assessed GM response to therapy in the PD group by comparing pre-therapy (− 2 and 0 weeks) with post-therapy initiation timepoints (+ 2 and + 4 weeks) and HCs at baseline (0 weeks). Altered GM compositions were noted between the PD and HC groups at various taxonomic levels, including specific differences for DBS (overrepresentation of Clostridium_XlVa, Bilophila, Parabacteroides, Pseudoflavonifractor and underrepresentation of Dorea) and LCIG therapy (overrepresentation of Pseudoflavonifractor, Escherichia/Shigella, and underrepresentation of Gemmiger). Beta diversity changes were also found over the 4 week post-treatment initiation period. We report on initial short-term GM changes in response to the initiation of PD DATs. Prior to the introduction of the DAT, a PD-associated GM was observed. Following initiation of DAT, several DAT-specific changes in GM composition were identified, suggesting DATs can influence the GM in PD.

Introduction People with chronic kidney disease (CKD) experience reduced quality of life (QoL) because of the high symptom and treatment burden. Limited data exist on the factors associated with overall and domain-specific QoL across all CKD stages. Methods Using data from a prospective, multinational study (Australia, New Zealand, Canada, and Spain) in 1696 participants with CKD, we measured overall and domain-specific QoL (pain, self-care, activity, mobility, anxiety/depression) using the EuroQoL, 5 dimension, 3 level. Multivariable linear regression and logistic modeling were used to determine factors associated with overall and domain-specific QoL. Results QoL for patients with CKD stages 3 to 5 (n = 787; mean, 0.81; SD, 0.20) was higher than in patients on dialysis (n = 415; mean, 0.76; SD, 0.24) but lower than in kidney transplant recipients (n = 494; mean, 0.84; SD, 0.21). Factors associated with reduced overall QoL (β [95% confidence intervals]) included being on dialysis (compared with CKD stages 3–5: –0.06 [–0.08 to –0.03]), female sex (–0.03 [–0.05 to –0.006]), lower educational attainment (– 0.04 [–0.06 to –0.02), lacking a partner (–0.04 [–0.06 to –0.02]), having diabetes (–0.05 [–0.07 to –0.02]), history of stroke (–0.09 [–0.13 to –0.05]), cardiovascular disease (–0.06 [–0.08 to –0.03]), and cancer (–0.03 [–0.06 to –0.009]). Pain (43%) and anxiety/depression (30%) were the most commonly affected domains, with dialysis patients reporting decrements in all 5 domains. Predictors for domain-specific QoL included being on dialysis, presence of comorbidities, lower education, female sex, and lack of a partner. Conclusions Being on dialysis, women with CKD, those with multiple comorbidities, lack of a partner, and lower educational attainment were associated with lower QoL across all stages of CKD., Graphical abstract

Background Non-invasive biomarkers may predict adverse events such as acute rejection after kidney transplantation and may be preferable to existing methods because of superior accuracy and convenience. It is uncertain how these biomarkers, often derived from a single study, perform across different cohorts of recipients. Methods Using a cross-validation framework that evaluates the performance of biomarkers, the aim of this study was to devise an integrated gene signature set that predicts acute rejection in kidney transplant recipients. Inclusion criteria were publicly available datasets of gene signatures that reported acute rejection episodes after kidney transplantation. We tested the predictive probability for acute rejection using gene signatures within individual datasets and validated the set using other datasets. Eight eligible studies of 1454 participants, with a total of 512 acute rejections episodes were included. Results All sets of gene signatures had good positive and negative predictive values (79-96%) for acute rejection within their own cohorts, but the predictability reduced to less than 50% when tested in other independent datasets. By integrating signatures sets with high specificity scores across all studies, a set of 150 genes (included CXCL6, CXCL11, OLFM4 and PSG9) which are known to be associated with immune responses, had reasonable predictive values (varied between 69-90%). Conclusions A set of gene signatures for acute rejection derived from a specific cohort of kidney transplant recipients do not appear to provide adequate prediction in an independent cohort of transplant recipients. However, integration of gene signatures sets with high specificity scores may improve the prediction performance of these markers.

Background Nutrigenomics aims at understanding the interaction between nutrition and gene information. Due to the complex interactions of nutrients and genes, their relationship exhibits non-linearity. One of the most effective and efficient methods to explore their relationship is the nutritional geometry framework which fits a response surface for the gene expression over two prespecified nutrition variables. However, when the number of nutrients involved is large, it is challenging to find combinations of informative nutrients with respect to a certain gene and to test whether the relationship is stronger than chance. Methods for identifying informative combinations are essential to understanding the relationship between nutrients and genes. Results We introduce Local Consistency Nutrition to Graphics (LC-N2G), a novel approach for ranking and identifying combinations of nutrients with gene expression. In LC-N2G, we first propose a model-free quantity called Local Consistency statistic to measure whether there is non-random relationship between combinations of nutrients and gene expression measurements based on (1) the similarity between samples in the nutrient space and (2) their difference in gene expression. Then combinations with small LC are selected and a permutation test is performed to evaluate their significance. Finally, the response surfaces are generated for the subset of significant relationships. Evaluation on simulated data and real data shows the LC-N2G can accurately find combinations that are correlated with gene expression. Conclusion The LC-N2G is practically powerful for identifying the informative nutrition variables correlated with gene expression. Therefore, LC-N2G is important in the area of nutrigenomics for understanding the relationship between nutrition and gene expression information.

The remarkable flexibility and adaptability of ensemble methods and deep learning models have led to the proliferation of their application in bioinformatics research. Traditionally, these two machine learning techniques have largely been treated as independent methodologies in bioinformatics applications. However, the recent emergence of ensemble deep learning—wherein the two machine learning techniques are combined to achieve synergistic improvements in model accuracy, stability and reproducibility—has prompted a new wave of research and application. Here, we share recent key developments in ensemble deep learning and look at how their contribution has benefited a wide range of bioinformatics research from basic sequence analysis to systems biology. While the application of ensemble deep learning in bioinformatics is diverse and multifaceted, we identify and discuss the common challenges and opportunities in the context of bioinformatics research. We hope this Review Article will bring together the broader community of machine learning researchers, bioinformaticians and biologists to foster future research and development in ensemble deep learning, and inspire novel bioinformatics applications that are unattainable by traditional methods. Recent developments in machine learning have seen the merging of ensemble and deep learning techniques. The authors review advances in ensemble deep learning methods and their applications in bioinformatics, and discuss the challenges and opportunities going forward.

Background As a rare subtype of prostate carcinoma, basal cell carcinoma (BCC) has not been studied extensively and thus lacks systematic molecular characterization. Methods Here, we applied single-cell genomic amplification and RNA-Seq to a specimen of human prostate BCC (CK34βE12+ /P63+ /PAP- /PSA- ). The mutational landscape was obtained via whole exome sequencing of the amplification mixture of 49 single cells, and the transcriptomes of 69 single cells were also obtained. Results The five putative driver genes mutated in BCC are CASC5, NUTM1, PTPRC, KMT2C, and TBX3, and the top three nucleotide substitutions are C>T, T>C, and C>A, similar to common prostate cancer. The distribution of the variant allele frequency values indicated that these single cells are from the same tumor clone. The 69 single cells were clustered into tumor, stromal, and immune cells based on their global transcriptomic profiles. The tumor cells specifically express basal cell markers like KRT5, KRT14, and KRT23 and epithelial markers EPCAM, CDH1, and CD24. The transcription factor covariance network analysis showed that the BCC tumor cells have distinct regulatory networks. By comparison with current prostate cancer datasets, we found that some of the bulk samples exhibit basal cell signatures. Interestingly, at single-cell resolution the gene expression patterns of prostate BCC tumor cells show uniqueness compared with that of common prostate cancer-derived circulating tumor cells. Conclusions This study, for the first time, discloses the comprehensive mutational and transcriptomic landscapes of prostate BCC, which lays a foundation for the understanding of its tumorigenesis mechanism and provides new insights into prostate cancers in general.

Cell reprogramming offers a potential treatment to many diseases, by regenerating specialized somatic cells. Despite decades of research, discovering the transcription factors that promote cell reprogramming has largely been accomplished through trial and error, a time-consuming and costly method. A computational model for cell reprogramming, however, could guide the hypothesis formulation and experimental validation, to efficiently utilize time and resources. Current methods often cannot account for the heterogeneity observed in cell reprogramming, or they only make short-term predictions, without modelling the entire reprogramming process. Here, we present scREMOTE, a novel computational model for cell reprogramming that leverages single cell multiomics data, enabling a more holistic view of the regulatory mechanisms at cellular resolution. This is achieved by first identifying the regulatory potential of each transcription factor and gene to uncover regulatory relationships, then a regression model is built to estimate the effect of transcription factor perturbations. We show that scREMOTE successfully predicts the long-term effect of overexpressing two key transcription factors in hair follicle development by capturing higher-order gene regulations. Together, this demonstrates that integrating the multimodal processes governing gene regulation creates a more accurate model for cell reprogramming with significant potential to accelerate research in regenerative medicine.

The microbiome plays a fundamental role in human health and diet is one of the strongest modulators of the gut microbiome. However, interactions between microbiota and host health are complex and diverse. Understanding the interplay between diet, the microbiome and health state could enable the design of personalized intervention strategies and improve the health and wellbeing of affected individuals. A common approach to this is to divide the study population into smaller cohorts based on dietary preferences in the hope of identifying specific microbial signatures. However, classification of patients based solely on diet is unlikely to reflect the microbiome-host health relationship or the taxonomic microbiome makeup. To this end, we present a novel approach, the Nutrition-Ecotype Mixture of Experts (NEMoE) model, for establishing associations between gut microbiota and health state that accounts for diet-specific cohort variability using a regularized mixture of experts model framework with an integrated parameter sharing strategy to ensure data driven diet-cohort identification consistency across taxonomic levels. The success of our approach was demonstrated through a series of simulation studies, in which NEMoE showed robustness with regard to parameter selection and varying degrees of data heterogeneity. Further application to real-world microbiome data from a Parkinson’s disease cohort revealed that NEMoE is capable of not only improving predictive performance for Parkinson’s Disease but also for identifying diet-specific microbiome markers of disease. Our results indicate that NEMoE can be used to uncover diet-specific relationships between nutritional-ecotype and patient health and to contextualize precision nutrition for different diseases.

Liquid chromatography-mass spectrometry-based metabolomics studies are increasingly applied to large population cohorts, which run for several weeks or even years in data acquisition. This inevitably introduces unwanted intra- and inter-batch variations over time that can overshadow true biological signals and thus hinder potential biological discoveries. To date, normalisation approaches have struggled to mitigate the variability introduced by technical factors whilst preserving biological variance, especially for protracted acquisitions. Here, we propose a study design framework with an arrangement for embedding biological sample replicates to quantify variance within and between batches and a workflow that uses these replicates to remove unwanted variation in a hierarchical manner (hRUV). We use this design to produce a dataset of more than 1000 human plasma samples run over an extended period of time. We demonstrate significant improvement of hRUV over existing methods in preserving biological signals whilst removing unwanted variation for large scale metabolomics studies. Our tools not only provide a strategy for large scale data normalisation, but also provides guidance on the design strategy for large omics studies., Mass spectrometry-based metabolomics is a powerful method for profiling large clinical cohorts but batch variations can obscure biologically meaningful differences. Here, the authors develop a computational workflow that removes unwanted data variation while preserving biologically relevant information.

Survival analysis is a branch of statistics that deals with both, the tracking of time and of the survival status simultaneously as the dependent response. Current comparisons of survival model performance mostly center on clinical data with classic statistical survival models, with prediction accuracy often serving as the sole metric of model performance. Moreover, survival analysis approaches for censored omics data have not been thoroughly investigated. The common approach is to binarise the survival time and perform a classification analysis.Here, we develop a benchmarking framework, SurvBenchmark, that evaluates a diverse collection of survival models for both clinical and omics datasets. SurvBenchmark not only focuses on classical approaches such as the Cox model, but it also evaluates state-of-art machine learning survival models. All approaches were assessed using multiple performance metrics, these include model predictability, stability, flexibility and computational issues. Our systematic comparison framework with over 320 comparisons (20 methods over 16 datasets) shows that the performances of survival models vary in practice over real-world datasets and over the choice of the evaluation metric. In particular, we highlight that using multiple performance metrics is critical in providing a balanced assessment of various models. The results in our study will provide practical guidelines for translational scientists and clinicians, as well as define possible areas of investigation in both survival technique and benchmarking strategies.Contactjean.yang@sydney.edu.au

High-throughput single cell technologies hold the promise of discovering novel cellular relationships with disease and necessitate the use of effective analytical workflows. When manual gating is used to define cell types, the gating hierarchy can be used to identify cell types whose abundances change relative to a parent population. This strategy allows subtle changes to be observed that could be missed if small subsets were compared to all measured cells. However, typical analyses that employ unsupervised clustering overlook the valuable hierarchical structure present in cell type definitions by exclusively quantifying the proportions of cell type clusters relative to all cells. We present treekoR, a framework that facilitates multiple quantifications and comparisons of cell type proportions. Our results from twelve case studies reinforce the importance of quantifying proportions relative to parent populations in the analyses of cytometry data -- as failing to do so can lead to missing important biological insights.

We introduce a new method of performing high-dimensional discriminant analysis (DA), which we call multiDA. Starting from multiclass diagonal DA classifiers which avoid the problem of high-dimensio...

© 2018 IEEE. Class labels are required for supervised learning but may be corrupted or missing in various applications. In binary classification, for example, when only a subset of positive instances is labeled whereas the remaining are unlabeled, positive-unlabeled (PU) learning is required to model from both positive and unlabeled data. Similarly, when class labels are corrupted by mislabeled instances, methods are needed for learning in the presence of class label noise (LN). Here we propose adaptive sampling (AdaSampling), a framework for both PU learning and learning with class LN. By iteratively estimating the class mislabeling probability with an adaptive sampling procedure, the proposed method progressively reduces the risk of selecting mislabeled instances for model training and subsequently constructs highly generalizable models even when a large proportion of mislabeled instances is present in the data. We demonstrate the utilities of proposed methods using simulation and benchmark data, and compare them to alternative approaches that are commonly used for PU learning and/or learning with LN. We then introduce two novel bioinformatics applications where AdaSampling is used to: 1) identify kinase-substrates from mass spectrometry-based phosphoproteomics data and 2) predict transcription factor target genes by integrating various next-generation sequencing data.

Purpose: Combination PD-1 and CTLA-4 inhibitor therapy has dramatically improved the survival of patients with advanced melanoma but is also associated with significant immune-related toxicities. This study sought to identify circulating cytokine biomarkers of treatment response and immune-related toxicity. Experimental Design: The expression of 65 cytokines was profiled longitudinally in 98 patients with melanoma treated with PD-1 inhibitors, alone or in combination with anti-CTLA-4, and in an independent validation cohort of 49 patients treated with combination anti-PD-1 and anti-CTLA-4. Cytokine expression was correlated with RECIST response and immune-related toxicity, defined as toxicity that warranted permanent discontinuation of treatment and administration of high-dose steroids. Results: Eleven cytokines were significantly upregulated in patients with severe immune-related toxicities at baseline (PRE) and early during treatment (EDT). The expression of these 11 cytokines was integrated into a single toxicity score, the CYTOX (cytokine toxicity) score, and the predictive utility of this score was confirmed in the discovery and validation cohorts. The AUC for the CYTOX score in the validation cohort was 0.68 at PRE [95% confidence interval (CI), 0.51–0.84; P = 0.037] and 0.70 at EDT (95% CI, 0.55–0.85; P = 0.017) using ROC analysis. Conclusions: The CYTOX score is predictive of severe immune-related toxicity in patients with melanoma treated with combination anti-CTLA-4 and anti-PD-1 immunotherapy. This score, which includes proinflammatory cytokines such as IL1a, IL2, and IFNα2, may help in the early management of severe, potentially life-threatening immune-related toxicity. See related commentary by Johnson and Balko, p. 1452

Purpose: BRAF V600E and V600K melanomas have distinct clinicopathologic features, and V600K appear to be less responsive to BRAFi±MEKi. We investigated mechanisms for this and explored whether genotype affects response to immunotherapy. Experimental Design: Pretreatment formalin-fixed paraffin-embedded tumors from patients treated with BRAFi±MEKi underwent gene expression profiling and DNA sequencing. Molecular results were validated using The Cancer Genome Atlas (TCGA) data. An independent cohort of V600E/K patients treated with anti–PD-1 immunotherapy was examined. Results: Baseline tissue and clinical outcome with BRAFi±MEKi were studied in 93 patients (78 V600E, 15 V600K). V600K patients had numerically less tumor regression (median, −31% vs. −52%, P = 0.154) and shorter progression-free survival (PFS; median, 5.7 vs. 7.1 months, P = 0.15) compared with V600E. V600K melanomas had lower expression of the ERK pathway feedback regulator dual-specificity phosphatase 6, confirmed with TCGA data (116 V600E, 17 V600K). Pathway analysis showed V600K had lower expression of ERK and higher expression of PI3K-AKT genes than V600E. Higher mutational load was observed in V600K, with a higher proportion of mutations in PIK3R1 and tumor-suppressor genes. In patients treated with anti–PD-1, V600K (n = 19) had superior outcomes than V600E (n = 84), including response rate (53% vs. 29%, P = 0.059), PFS (median, 19 vs. 2.7 months, P = 0.049), and overall survival (20.4 vs. 11.7 months, P = 0.081). Conclusions: BRAF V600K melanomas appear to benefit less from BRAFi±MEKi than V600E, potentially due to less reliance on ERK pathway activation and greater use of alternative pathways. In contrast, these melanomas have higher mutational load and respond better to immunotherapy.

Single-cell RNA-seq (scRNA-seq) data simulation is critical for evaluating computational methods for analysing scRNA-seq data especially when ground truth is experimentally unattainable. The reliability of evaluation depends on the ability of simulation methods to capture properties of experimental data. However, while many scRNA-seq data simulation methods have been proposed, a systematic evaluation of these methods is lacking. We develop a comprehensive evaluation framework, SimBench, including a kernel density estimation measure to benchmark 12 simulation methods through 35 scRNA-seq experimental datasets. We evaluate the simulation methods on a panel of data properties, ability to maintain biological signals, scalability and applicability. Our benchmark uncovers performance differences among the methods and highlights the varying difficulties in simulating data characteristics. Furthermore, we identify several limitations including maintaining heterogeneity of distribution. These results, together with the framework and datasets made publicly available as R packages, will guide simulation methods selection and their future development., Simulation is useful for developing and evaluating computational methods. Here, the authors develop a comprehensive evaluation framework, SimBench, to benchmark Single-cell RNA-seq simulation methods through a diverse collection of experimental datasets.

Publisher Copyright: © The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America. Genetic and environmental factors play a major role in metabolic health. However, they do not act in isolation, as a change in an environmental factor such as diet may exert different effects based on an individual's genotype. Here, we sought to understand how such gene-diet interactions influenced nutrient storage and utilization, a major determinant of metabolic disease. We subjected 178 inbred strains from the Drosophila genetic reference panel (DGRP) to diets varying in sugar, fat, and protein. We assessed starvation resistance, a holistic phenotype of nutrient storage and utilization that can be robustly measured. Diet influenced the starvation resistance of most strains, but the effect varied markedly between strains such that some displayed better survival on a high carbohydrate diet (HCD) compared to a high-fat diet while others had opposing responses, illustrating a considerable gene x diet interaction. This demonstrates that genetics plays a major role in diet responses. Furthermore, heritability analysis revealed that the greatest genetic variability arose from diets either high in sugar or high in protein. To uncover the genetic variants that contribute to the heterogeneity in starvation resistance, we mapped 566 diet-responsive SNPs in 293 genes, 174 of which have human orthologs. Using whole-body knockdown, we identified two genes that were required for glucose tolerance, storage, and utilization. Strikingly, flies in which the expression of one of these genes, CG4607 a putative homolog of a mammalian glucose transporter, was reduced at the whole-body level, displayed lethality on a HCD. This study provides evidence that there is a strong interplay between diet and genetics in governing survival in response to starvation, a surrogate measure of nutrient storage efficiency and obesity. It is likely that a similar principle applies to higher organisms thus supporting the case for nutrigenomics as an important health strategy.

Genetic heterogeneity of tumor is closely related to its clonal evolution, phenotypic diversity and treatment resistance, and such heterogeneity has only been characterized at single-cell sub-chromosomal scale in liver cancer. Here we reconstructed the single-variant resolution clonal evolution in human liver cancer based on single-cell mutational profiles. The results indicated that key genetic events occurred early during tumorigenesis, and an early metastasis followed by independent evolution was observed in primary liver tumor and intrahepatic metastatic portal vein tumor thrombus. By parallel single-cell RNA-Seq, the transcriptomic phenotype of HCC was found to be related with genetic heterogeneity. For the first time we reconstructed the single-cell and single-variant clonal evolution in human liver cancer, and dissection of both genetic and phenotypic heterogeneity will facilitate better understanding of their relationship.

Single-cell multi-omics data continues to grow at an unprecedented pace, and effectively integrating different modalities holds the promise for better characterization of cell identities. Although a number of methods have demonstrated promising results in integrating multiple modalities from the same tissue, the complexity and scale of data compositions typically present in cell atlases still pose a significant challenge for existing methods. Here we present scJoint, a transfer learning method to integrate atlas-scale, heterogeneous collections of scRNA-seq and scATAC-seq data. scJoint leverages information from annotated scRNA-seq data in a semi-supervised framework and uses a neural network to simultaneously train labeled and unlabeled data, enabling label transfer and joint visualization in an integrative framework. Using multiple atlas data and a biologically varying multi-modal data, we demonstrate scJoint is computationally efficient and consistently achieves significantly higher cell type label accuracy than existing methods while providing meaningful joint visualizations. This suggests scJoint is effective in overcoming the heterogeneity in different modalities towards a more comprehensive understanding of cellular phenotypes.

Genetic heterogeneity of tumor is closely related to clonal evolution, phenotypic diversity and treatment resistance. Such heterogeneity has been characterized in liver cancer at single-cell sub-chromosomal scale, and a more precise single-variant resolution analysis is lacking. Here we employed a strategy to analyze both the single-cell genomic mutations and transcriptomic changes in 5 patients with liver cancer. Target sequencing was done for a total of 480 single cells in a patient-specific manner. DNA copy number status of point mutations was obtained from single-cell mutational profiling. The clonal structures of liver cancers were then uncovered at single-variant resolution, and mutation combinations in single cells enabled reconstruction of their evolutionary history. A common origin but independent evolutionary fate was revealed for primary liver tumor and intrahepatic metastatic portal vein tumor thrombus. The mutational signature suggested early evolutionary process may be related to specific etiology like aristolochic acids. By parallel single-cell RNA-Seq, the transcriptomic phenotype was found to be related with genetic heterogeneity in liver cancer. We reconstructed the single-cell and single-variant resolution clonal evolutionary history of liver cancer, and dissection of both genetic and phenotypic heterogeneity provides knowledge for mechanistic understanding of liver cancer initiation and progression.

COVID-19 patients display a wide range of disease severity, ranging from asymptomatic to critical symptoms with high mortality risk. Our ability to understand the interaction of SARS-CoV-2 infected cells within the lung, and of protective or dysfunctional immune responses to the virus, is critical to effectively treat these patients. Currently, our understanding of cell-cell interactions across different disease states, and how such interactions may drive pathogenic outcomes, is incomplete. Here, we developed a generalizable workflow for identifying cells that are differentially interacting across COVID-19 patients with distinct disease outcomes and use it to examine five public single-cell RNA-seq datasets with a total of 85 individual samples. By characterizing the cell-cell interaction patterns across epithelial and immune cells in lung tissues for patients with varying disease severity, we illustrate diverse communication patterns across individuals, and discover heterogeneous communication patterns among moderate and severe patients. We further illustrate patterns derived from cell-cell interactions are potential signatures for discriminating between moderate and severe patients.

Liquid chromatography-mass spectrometry based metabolomics studies are increasingly applied to large population cohorts, running for several weeks to months, even extending to years of data acquisition. This inevitably introduces unwanted intra- and inter-batch variations over time that can overshadow true biological signals and thus hinder potential biological discoveries. To date, normalization approaches have struggled to mitigate the variability introduced by technical factors whilst preserving biological variance, especially for protracted acquisitions. Here, we designed an experiment with an arrangement to embed biological sample replicates to measure the variance within and between batches for over 1,000 human plasma samples run over 44 days. We integrate these replicates in a novel workflow to remove unwanted variation in a hierarchical structure (hRUV) by progressively merging the adjustments in neighbouring batches. We demonstrate significant improvement of hRUV over existing methods in maintaining biological signals whilst removing unwanted variation for large scale metabolomics studies.

There is no consensus methodology that can account for the variation in omics signatures when they are acquired across different platforms and times. This poses a significant barrier to the implementation of valuable biomarkers into clinical practice. We present a novel procedure (Cross-Platform Omics Prediction) that accounts for these variations and demonstrate its utility in three risk models for different diseases that is suitable for prospective and multi-centre clinical implementation.

The molecular mechanisms underlying development of the pentameral body of adult echinoderms are poorly understood but are important to solve with respect to evolution of a unique body plan that contrasts with the bilateral body plan of other deuterostomes. As Nodal and BMP2/4 signalling is involved in axis formation in larvae and development of the echinoderm body plan, we used the developmental transcriptome generated for the asterinid seastar Parvulastra exigua to investigate the temporal expression patterns of Nodal and BMP2/4 genes from the embryo and across metamorphosis to the juvenile. For echinoderms, the Asteroidea represents the basal-type body architecture with a distinct (separated) ray structure. Parvulastra exigua has lecithotrophic development forming the juvenile soon after gastrulation providing ready access to the developing adult stage. We identified 39 genes associated with the Nodal and BMP2/4 network in the P. exigua developmental transcriptome. Clustering analysis of these genes resulted in 6 clusters with similar temporal expression patterns across development. A co-expression analysis revealed genes that have similar expression profiles as Nodal and BMP2/4. These results indicated genes that may have a regulatory relationship in patterning morphogenesis of the juvenile seastar. Developmental RNA-seq analyses of Parvulastra exigua show changes in Nodal and BMP2/4 signalling genes across the metamorphic transition. We provide the foundation for detailed analyses of this cascade in the evolution of the unusual pentameral echinoderm body and its deuterostome affinities.

Background There is increasing awareness that perinatal psychosocial adversity experienced by mothers, children, and their families, may influence health and well-being across the life course. To maximise the impact of population-based interventions for optimising perinatal wellbeing, health services can utilise empirical methods to identify subgroups at highest risk of poor outcomes relative to the overall population. Methods This study sought to identify sub-groups using latent class analysis within a population of mothers in Sydney, Australia, based on their differing experience of self-reported indicators of psychosocial adversity. This study sought to identify sub-groups using latent class analysis within a population of mothers in Sydney, Australia, based on their differing experience of self-reported indicators of psychosocial adversity. Subgroup differences in antenatal and postnatal depressive symptoms were assessed using the Edinburgh Postnatal Depression Scale. Results Latent class analysis identified four distinct subgroups within the cohort, who were distinguished empirically on the basis of their native language, current smoking status, previous involvement with Family-and-Community Services (FaCS), history of child abuse, presence of a supportive partner, and a history of intimate partner psychological violence. One group consisted of socially supported ‘local’ women who speak English as their primary language (Group L), another of socially supported ‘migrant’ women who speak a language other than English as their primary language (Group M), another of socially stressed ‘local’ women who speak English as their primary language (Group Ls), and socially stressed ‘migrant’ women who speak a language other than English as their primary language (Group Ms.). Compared to local and not socially stressed residents (L group), the odds of antenatal depression were nearly three times higher for the socially stressed groups (Ls OR: 2.87 95%CI 2.10–3.94) and nearly nine times more in the Ms. group (Ms OR: 8.78, 95%CI 5.13–15.03). Antenatal symptoms of depression were also higher in the not socially stressed migrant group (M OR: 1.70 95%CI 1.47–1.97) compared to non-migrants. In the postnatal period, Group M was 1.5 times more likely, while the Ms. group was over five times more likely to experience suboptimal mental health compared to Group L (OR 1.50, 95%CI 1.22–1.84; and OR 5.28, 95%CI 2.63–10.63, for M and Ms. respectively). Conclusions The application of empirical subgrouping analysis permits an informed approach to targeted interventions and resource allocation for optimising perinatal maternal wellbeing.

Automated cell type identification is a key computational challenge in single‐cell RNA‐sequencing (scRNA‐seq) data. To capitalise on the large collection of well‐annotated scRNA‐seq datasets, we developed scClassify, a multiscale classification framework based on ensemble learning and cell type hierarchies constructed from single or multiple annotated datasets as references. scClassify enables the estimation of sample size required for accurate classification of cell types in a cell type hierarchy and allows joint classification of cells when multiple references are available. We show that scClassify consistently performs better than other supervised cell type classification methods across 114 pairs of reference and testing data, representing a diverse combination of sizes, technologies and levels of complexity, and further demonstrate the unique components of scClassify through simulations and compendia of experimental datasets. Finally, we demonstrate the scalability of scClassify on large single‐cell atlases and highlight a novel application of identifying subpopulations of cells from the Tabula Muris data that were unidentified in the original publication. Together, scClassify represents state‐of‐the‐art methodology in automated cell type identification from scRNA‐seq data., scClassify is a multiscale classification framework based on ensemble learning and cell type hierarchies, enabling sample size estimation required for accurate cell type classification and joint classification of cells using multiple references.

The investigation of brain networks has yielded many insights that have helped to characterise many neurological and psychiatric disorders. In particular, network classification of functional magnetic resonance imaging (fMRI) data is an important tool for the identification of prognostic and diagnostic biomarkers of brain connectivity disorders such as schizophrenia and depression. However, existing generic network classification methods provide no direct information on the underlying molecular mechanisms of the selected functional connectivity features when applied to fMRI data. To address this, we propose a novel fMRI network classification method that incor-porates brain transcriptional data using a user-specified gene set collection (GSC) to construct feature groups for use in classification of brain connectivity data. The use of GSCs are an opportunity to incorporate knowledge of potential molecular mechanisms which may be associated with a disease. The inclusion of transcriptional data yields improved prediction accuracy on publicly available schizophrenia fMRI data for several of the GSCs we consider. We also introduce a post-hoc interpretation framework to provide transcriptional-data-guided biological interpretations for discriminative functional connectivity features identified by existing fMRI network classification methods.

To determine the incremental gains in graft and patient survival under a risk-based, deceased donor kidney allocation compared with the current Australian algorithm.Risk-based matching algorithms were applied to first graft, kidney only recipients (n = 7513) transplanted in Australia between 1994 and 2013. Probabilistic models were used to compare the waiting time, life, and QALYs and graft years between the 8 risk-based allocation strategies against current practice.Compared with current practice, Kidney Donor Risk Index-Estimated Posttransplant Survival matching of the lowest 20% of scores reduced median waiting time by 0.64 years (95% confidence interval [CI], 0.52-0.73) for recipients aged 30 years or younger, but increased waiting time by 0.94 years (95% CI, 0.79-1.09) for recipients older than 60 years. Among all age groups, the greatest gains occurred if Kidney Donor Risk Index-Estimated Posttransplant Survival matching of the lowest 30% of scores was used, incurring a median overall gain of 0.63 (95% CI, 0.03-1.25) life years and 0.78 (95% CI, 0.30-1.26) graft years compared with the current practice. A median gain in survival of 1.91 years for younger recipients (aged 30-45 years) was offset by a median reduction in survival (by 0.95 life years) among the older recipients. Prioritization of lower-quality donor kidneys for older candidates reduced the waiting time for recipients older than 45 years, but no changes in graft and patient survivals were observed.Risk-based matching engendered a moderate, overall increase in graft and patient survivals, accrued through benefits for recipients 45 years or younger but disadvantage to recipients older than 60 years.

Validating newly discovered biomarkers in large, publicly available data sets is often difficult and requires specialized computer programming skills. Melanoma Explorer is a web application that enables easy interrogation of melanoma omics data sets that are freely available in online data repositories with a point-and-click interface. Two use cases are demonstrated. First, the relationship of lysozyme mRNA expression is shown to be prognostic in two independent gene expression microarray data sets. Second, a figure from a journal article showing the relationship of tumour thickness and miR-382 abundance is reproduced. Melanoma Explorer is demonstrated to be a useful tool for reproducing results of published studies and providing additional evidence for biomarkers in independent data sets.

Purpose: To examine the relationship between immune activity, PD-L1 expression, and tumor cell signaling, in metastatic melanomas prior to and during treatment with targeted MAPK inhibitors. Experimental Design: Thirty-eight tumors from 17 patients treated with BRAF inhibitor (n = 12) or combination BRAF/MEK inhibitors (n = 5) with known PD-L1 expression were analyzed. RNA expression arrays were performed on all pretreatment (PRE, n = 17), early during treatment (EDT, n = 8), and progression (PROG, n = 13) biopsies. HLA-A/HLA-DPB1 expression was assessed by IHC. Results: Gene set enrichment analysis (GSEA) of PRE, EDT, and PROG melanomas revealed that transcriptome signatures indicative of immune cell activation were strongly positively correlated with PD-L1 staining. In contrast, MAPK signaling and canonical Wnt/-β-catenin activity was negatively associated with PD-L1 melanoma expression. The expression of PD-L1 and immune activation signatures did not simply reflect the degree or type of immune cell infiltration, and was not sufficient for tumor response to MAPK inhibition. Conclusions: PD-L1 expression correlates with immune cells and immune activity signatures in melanoma, but is not sufficient for tumor response to MAPK inhibition, as many PRE and PROG melanomas displayed both PD-L1 positivity and immune activation signatures. This confirms that immune escape is common in MAPK inhibitor–treated tumors. This has important implications for the selection of second-line immunotherapy because analysis of mechanisms of immune escape will likely be required to identify patients likely to respond to such therapies. Clin Cancer Res; 23(20); 6054–61. ©2017 AACR.

Motivation DNA binding proteins such as chromatin remodellers, transcription factors (TFs), histone modifiers and co-factors often bind cooperatively to activate or repress their target genes in a cell type-specific manner. Nonetheless, the precise role of cooperative binding in defining cell-type identity is still largely uncharacterized. Results Here, we collected and analyzed 214 public datasets representing chromatin immunoprecipitation followed by sequencing (ChIP-Seq) of 104 DNA binding proteins in embryonic stem cell (ESC) lines. We classified their binding sites into those proximal to gene promoters and those in distal regions, and developed a web resource called Proximal And Distal (PAD) clustering to identify their co-localization at these respective regions. Using this extensive dataset, we discovered an extensive co-localization of BRG1 and CHD7 at distal but not proximal regions. The comparison of co-localization sites to those bound by either BRG1 or CHD7 alone showed an enrichment of ESC master TFs binding and active chromatin architecture at co-localization sites. Most notably, our analysis reveals the co-dependency of BRG1 and CHD7 at distal regions on regulating expression of their common target genes in ESC. This work sheds light on cooperative binding of TF binding proteins in regulating gene expression in ESC, and demonstrates the utility of integrative analysis of a manually curated compendium of genome-wide protein binding profiles in our online resource PAD. Availability and Implementation PAD is freely available at http://pad.victorchang.edu.au/ and its source code is available via an open source GPL 3.0 license at https://github.com/VCCRI/PAD/ Supplementary information Supplementary data are available at Bioinformatics online.

Kidney transplant recipients and transplant physicians face important clinical questions where machine learning methods may help improve the decision-making process. This mini-review explores potential applications of machine learning methods to key stages of a kidney transplant recipient's journey, from initial waitlisting and donor selection, to personalization of immunosuppression and prediction of post-transplantation events. Both unsupervised and supervised machine learning methods are presented, including k-means clustering, principal components analysis, k-nearest neighbors, and random forests. The various challenges of these approaches are also discussed.

BackgroundSingle-cell RNA-sequencing (scRNA-seq) is a transformative technology, allowing global transcriptomes of individual cells to be profiled with high accuracy. An essential task in scRNA-seq data analysis is the identification of cell types from complex samples or tissues profiled in an experiment. To this end, clustering has become a key computational technique for grouping cells based on their transcriptome profiles, enabling subsequent cell type identification from each cluster of cells. Due to the high feature-dimensionality of the transcriptome (i.e. the large number of measured genes in each cell) and because only a small fraction of genes are cell type-specific and therefore informative for generating cell type-specific clusters, clustering directly on the original feature/gene dimension may lead to uninformative clusters and hinder correct cell type identification.ResultsHere, we propose an autoencoder-based cluster ensemble framework in which we first take random subspace projections from the data, then compress each random projection to a low-dimensional space using an autoencoder artificial neural network, and finally apply ensemble clustering across all encoded datasets for generating clusters of cells. We employ four evaluation metrics to benchmark clustering performance and our experiments demonstrate that the proposed autoencoder-based cluster ensemble can lead to substantially improved cell type-specific clusters when applied with both the standard k-means clustering algorithm and a state-of-the-art kernel-based clustering algorithm (SIMLR) designed specifically for scRNA-seq data. Compared to directly using these clustering algorithms on the original datasets, the performance improvement in some cases is up to 100%, depending on the evaluation metrics used.ConclusionsOur results suggest that the proposed framework can facilitate more accurate cell type identification as well as other downstream analyses. The code for creating the proposed autoencoder-based cluster ensemble framework is freely available from https://github.com/gedcom/autoencoder_cluster_ensemble

Background Single-cell RNA-sequencing (scRNA-seq) is a fast emerging technology allowing global transcriptome profiling on the single cell level. Cell type identification from scRNA-seq data is a critical task in a variety of research such as developmental biology, cell reprogramming, and cancers. Typically, cell type identification relies on human inspection using a combination of prior biological knowledge (e.g. marker genes and morphology) and computational techniques (e.g. PCA and clustering). Due to the incompleteness of our current knowledge and the subjectivity involved in this process, a small amount of cells may be subject to mislabelling. Results Here, we propose a semi-supervised learning framework, named scReClassify, for ‘post hoc’ cell type identification from scRNA-seq datasets. Starting from an initial cell type annotation with potentially mislabelled cells, scReClassify first performs dimension reduction using PCA and next applies a semi-supervised learning method to learn and subsequently reclassify cells that are likely mislabelled initially to the most probable cell types. By using both simulated and real-world experimental datasets that profiled various tissues and biological systems, we demonstrate that scReClassify is able to accurately identify and reclassify misclassified cells to their correct cell types. Conclusions scReClassify can be used for scRNA-seq data as a post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure. It is implemented as an R package and is freely available from https://github.com/SydneyBioX/scReClassify

Background Differences in cell-type composition across subjects and conditions often carry biological significance. Recent advancements in single cell sequencing technologies enable cell-types to be identified at the single cell level, and as a result, cell-type composition of tissues can now be studied in exquisite detail. However, a number of challenges remain with cell-type composition analysis – none of the existing methods can identify cell-type perfectly and variability related to cell sampling exists in any single cell experiment. This necessitates the development of method for estimating uncertainty in cell-type composition. Results We developed a novel single cell differential composition (scDC) analysis method that performs differential cell-type composition analysis via bootstrap resampling. scDC captures the uncertainty associated with cell-type proportions of each subject via bias-corrected and accelerated bootstrap confidence intervals. We assessed the performance of our method using a number of simulated datasets and synthetic datasets curated from publicly available single cell datasets. In simulated datasets, scDC correctly recovered the true cell-type proportions. In synthetic datasets, the cell-type compositions returned by scDC were highly concordant with reference cell-type compositions from the original data. Since the majority of datasets tested in this study have only 2 to 5 subjects per condition, the addition of confidence intervals enabled better comparisons of compositional differences between subjects and across conditions. Conclusions scDC is a novel statistical method for performing differential cell-type composition analysis for scRNA-seq data. It uses bootstrap resampling to estimate the standard errors associated with cell-type proportion estimates and performs significance testing through GLM and GLMM models. We have made this method available to the scientific community as part of the scdney package (Single Cell Data Integrative Analysis) R package, available from https://github.com/SydneyBioX/scdney.

The use of circulating tumor DNA (ctDNA) to monitor cancer progression and response to therapy has significant potential but there is only limited data on whether this technique can detect the presence of low frequency subclones that may ultimately confer therapy resistance. In this study, we sought to evaluate whether whole-exome sequencing (WES) of ctDNA could accurately profile the mutation landscape of metastatic melanoma. We used WES to identify variants in matched, tumor-derived genomic DNA (gDNA) and plasma-derived ctDNA isolated from a cohort of 10 metastatic cutaneous melanoma patients. WES parameters such as sequencing coverage and total sequencing reads were comparable between gDNA and ctDNA. The mutant allele frequency of common single nucleotide variants was lower in ctDNA, reflecting the lower read depth and minor fraction of ctDNA within the total circulating free DNA pool. There was also variable concordance between gDNA and ctDNA based on the total number and identity of detected variants and this was independent of the tumor biopsy site. Nevertheless, established melanoma driver mutations and several other melanoma-associated mutations were concordant between matched gDNA and ctDNA. This study highlights that WES of ctDNA could capture clinically relevant mutations present in melanoma metastases and that enhanced sequencing sensitivity will be required to identify low frequency mutations.

Motivation Multi-modal profiling of single cells represents one of the latest technological advancements in molecular biology. Among various single-cell multi-modal strategies, cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) allows simultaneous quantification of two distinct species: RNA and cell-surface proteins. Here, we introduce CiteFuse, a streamlined package consisting of a suite of tools for doublet detection, modality integration, clustering, differential RNA and protein expression analysis, antibody-derived tag evaluation, ligand–receptor interaction analysis and interactive web-based visualization of CITE-seq data. Results We demonstrate the capacity of CiteFuse to integrate the two data modalities and its relative advantage against data generated from single-modality profiling using both simulations and real-world CITE-seq data. Furthermore, we illustrate a novel doublet detection method based on a combined index of cell hashing and transcriptome data. Finally, we demonstrate CiteFuse for predicting ligand–receptor interactions by using multi-modal CITE-seq data. Collectively, we demonstrate the utility and effectiveness of CiteFuse for the integrative analysis of transcriptome and epitope profiles from CITE-seq data. Availability and implementation CiteFuse is freely available at http://shiny.maths.usyd.edu.au/CiteFuse/ as an online web service and at https://github.com/SydneyBioX/CiteFuse/ as an R package. Contact pengyi.yang@sydney.edu.au Supplementary information Supplementary data are available at Bioinformatics online.

Single-cell RNA-sequencing has transformed our ability to examine cell fate choice. For example, in the context of development and differentiation, computational ordering of cells along ‘pseudotime’ enables the expression profiles of individual genes, including key transcription factors, to be examined at fine scale temporal resolution. However, while cell fate decisions are typically marked by profound changes in expression, many such changes are observed in genes downstream of the initial cell fate decision. By contrast, the genes directly involved in the cell fate decision process are likely to interact in subtle ways, potentially resulting in observed changes in patterns of correlation and variation rather than mean expression prior to cell fate commitment. Herein, we describe a novel approach, scHOT – single cell Higher Order Testing - which provides a flexible and statistically robust framework for identifying changes in higher order interactions among genes. scHOT is general and modular in nature, can be run in multiple data contexts such as along a continuous trajectory, between discrete groups, and over spatial orientations; as well as accommodate any higher order measurement such as variability or correlation. We demonstrate the utility of scHOT by studying embryonic development of the liver, where we find coordinated changes in higher order interactions of programs related to differentiation and liver function. We also demonstrate its ability to find subtle changes in gene-gene correlation patterns across space using spatially-resolved expression data from the mouse olfactory bulb. scHOT meaningfully adds to first order effect testing, such as differential expression, and provides a framework for interrogating higher order interactions from single cell data.

The Echinodermata is characterized by a secondarily evolved pentameral body plan. While the evolutionary origin of this body plan has been the subject of debate, the molecular mechanisms underlying its development are poorly understood. We assembled a de novo developmental transcriptome from the embryo through metamorphosis in the sea star Parvulastra exigua. We use the asteroid model as it represents the basal-type echinoderm body architecture. Global variation in gene expression distinguished the gastrula profile and showed that metamorphic and juvenile stages were more similar to each other than to the pre-metamorphic stages, pointing to the marked changes that occur during metamorphosis. Differential expression and gene ontology (GO) analyses revealed dynamic changes in gene expression throughout development and the transition to pentamery. Many GO terms enriched during late metamorphosis were related to neurogenesis and signalling. Neural transcription factor genes exhibited clusters with distinct expression patterns. A suite of these genes was up-regulated during metamorphosis (e.g. Pax6, Eya, Hey, NeuroD, FoxD, Mbx, and Otp). In situ hybridization showed expression of neural genes in the CNS and sensory structures. Our results provide a foundation to understand the metamorphic transition in echinoderms and the genes involved in development and evolution of pentamery.

Cell type identification is a key computational challenge in single-cell RNA-sequencing (scRNA-seq) data. To capitalize on the large collections of well-annotated scRNA-seq datasets, we present scClassify, a hierarchical classification framework based on ensemble learning. scClassify can identify cells from published scRNA-seq datasets more accurately and more finely than in the original publications. We also estimate the cell number needed for accurate classification anywhere in a cell type hierarchy.

Hepatocellular Carcinoma (HCC) is a type of malignant solid tumor, causing high morbidity and mortality around the world and the major portion of HCC patients is from China. Cancer immunotherapies have shown some clinical responses in treating some types of cancer but did not shown significant efficiency in HCC treatment. This in part due to the impact of immune cells in the tumor microenvironment. It is commonly believed that HCC is a heterogeneous solid tumor and the microenvironment of HCC plays an important role in tumorgenesis and development. Currently, the residents of the microenvironment of HCC is not well-defined and clarification, especially the immune cells, which we believe that paly pivotal roles in tumorgenesis and development. To depict the landscape of the composition, lineage and functional states of the immune cells in HCC, we performed single-cell RNA sequencing on Diethylnitrosamine (DEN)-induced mouse HCC model. We observed heterogeneity within the immune and hepatocytes both in the precancerous condition of tumorigenesis and cancerous condition of HCC. In this study we found that the disease-associated changes appeared early in pathological progression and were highly cell-type specific. Specific subsets of T and B cells preferentially enriched in HCC, and we identified signature genes for each subset. Additionally, we mapped this group of specific cells to the human TCGA database. We found a cluster of naïve B cells characterized by high expression of CD38 associated with better prognosis of human HCC. Our study demonstrates signaling interaction map based on receptor-ligand bonding on the single-cell level could broaden our comprehending of cellular networks in varies status. Our finding provides a new approach for patient stratification and will help further understand the functional states, dynamics and signaling interaction of B cells in hepatocellular carcinoma, and may provide a novel insight and therapeutics for the HCC.

Genetic and environmental factors play a major role in metabolic health. However, they do not act in isolation, as a change in an environmental factor such as diet may exert different effects based on an individual’s genotype. Here, we sought to understand how such gene-diet interactions influenced nutrient storage and utilisation, a major determinant of metabolic disease. We subjected the Drosophila Genetic Reference Panel (DGRP), comprising 200 genetically divergent inbred fly strains, to diets varying in sugar, fat and protein. We assessed starvation resistance, a holistic phenotype of nutrient storage and utilisation that can be robustly measured. Diet influenced the starvation resistance of each strain, but this effect varied markedly between strains. This demonstrates that genetics plays a major role in the response to diet. Furthermore, heritability analysis revealed that the greatest variability arose from diets either high in sugar or high in protein. To uncover the genetic underpinnings of this variation, we mapped 1,239 diet-responsive SNPs in 534 genes, 325 of which have human orthologues. Using whole-body knockdown, we confirmed that 30 candidate genes were required for glucose tolerance, storage and utilization. In particular, we characterised CG4607, a GLUT6/GLUT8 homolog, as a key protein involved in sugar tolerance. Overall, this provides strong evidence that genetics is a major contributor to how individuals respond to diets of varying nutrient composition. It is likely that a similar principle may be applied to metabolic disease in higher organisms thus supporting the case for nutrigenomics as an important health strategy.

Transcriptomic signatures designed to predict melanoma patient responses to PD-1 blockade have been reported but rarely validated. We now show that intra-patient heterogeneity of tumor responses to PD-1 inhibition limit the predictive performance of these signatures. We reasoned that resistance mechanisms will reflect the tumor microenvironment, and thus we examined PD-1 inhibitor resistance relative to T-cell activity in 94 melanoma tumors collected at baseline and at time of PD-1 inhibitor progression. Tumors were analyzed using RNA sequencing and flow cytometry, and validated functionally. These analyses confirm that major histocompatibility complex (MHC) class I downregulation is a hallmark of resistance to PD-1 inhibitors and is associated with the MITFlow/AXLhigh de-differentiated phenotype and cancer-associated fibroblast signatures. We demonstrate that TGFß drives the treatment resistant phenotype (MITFlow/AXLhigh) and contributes to MHC class I downregulation in melanoma. Combinations of anti-PD-1 with drugs that target the TGFß signaling pathway and/or which reverse melanoma de-differentiation may be effective future therapeutic strategies., A significant proportion of patients develop innate or acquired resistance to immune checkpoint inhibitors. Here, the authors show that resistance to anti-PD-1 blockade is associated with TGF-beta driven major histocompatibility complex I (MHCI) down-regulation and a de-differentiated phenotype in melanoma patients.

Background In patients with CKD, the risk of developing colorectal cancer is high and outcomes are poor. Screening using fecal immunochemical testing (FIT) is effective in reducing mortality from colorectal cancer, but performance characteristics of FIT in CKD are unknown. Methods To determine the detection rates and performance characteristics of FIT for advanced colorectal neoplasia (ACN) in patients with CKD, we used FIT to prospectively screen patients aged 35–74 years with CKD (stages 3–5 CKD, dialysis, and renal transplant) from 11 sites in Australia, New Zealand, Canada, and Spain. All participants received clinical follow-up at 2 years. We used a two-step reference standard approach to estimate disease status. Results Overall, 369 out of 1706 patients who completed FIT (21.6%) tested positive; 323 (87.5%) underwent colonoscopies. A total of 1553 (91.0%) completed follow-up; 82 (4.8%) had died and 71 (4.2%) were lost. The detection rate of ACN using FIT was 6.0% (5.6%, 7.4%, and 5.6% for stages 3–5 CKD, dialysis, and transplant). Sensitivity, specificity, and positive and negative predictive values of FIT for ACN were 0.90, 0.83, 0.30, and 0.99, respectively. Of participants who underwent colonoscopy, five (1.5%) experienced major colonoscopy-related complications, including bowel perforation and major bleeding. Conclusions FIT appears to be an accurate screening test for patients with CKD, such that a negative test may rule out the diagnosis of colorectal cancer within 2 years. However, the risk of major complications from work-up colonoscopy are at least ten-fold higher than in the general population.

Concerted examination of multiple collections of single-cell RNA sequencing (RNA-seq) data promises further biological insights that cannot be uncovered with individual datasets. Here we present scMerge, an algorithm that integrates multiple single-cell RNA-seq datasets using factor analysis of stably expressed genes and pseudoreplicates across datasets. Using a large collection of public datasets, we benchmark scMerge against published methods and demonstrate that it consistently provides improved cell type separation by removing unwanted factors; scMerge can also enhance biological discovery through robust data integration, which we show through the inference of development trajectory in a liver dataset collection.