Two-stage, in silico deconvolution of the lymphocyte compartment of the peripheral whole blood transcriptome in the context of acute kidney allograft rejection

The Future Role of Machine Learning in Clinical Transplantation

The use of artificial intelligence and machine learning (ML) has revolutionized our daily lives and will soon be instrumental in healthcare delivery. The rise of ML is due to multiple factors: increasing access to massive datasets, exponential increases in processing power, and key algorithmic developments that allow ML models to tackle increasingly challenging questions. Progressively more transplantation research is exploring the potential utility of ML models throughout the patient journey, although this has not yet widely transitioned into the clinical domain. In this review, we explore common approaches used in ML in solid organ clinical transplantation and consider opportunities for ML to help clinicians and patients. We discuss ways in which ML can aid leverage of large complex datasets, generate cutting-edge prediction models, perform clinical image analysis, discover novel markers in molecular data, and fuse datasets to generate novel insights in modern transplantation practice. We focus on key areas in transplantation in which ML is driving progress, explore the future potential roles of ML, and discuss the challenges and limitations of these powerful tools.

Progress and challenge for computational quantification of tissue immune cells

Tissue immune cells have long been recognized as important regulators for the maintenance of balance in the body system. Quantification of the abundance of different immune cells will provide enhanced understanding of the correlation between immune cells and normal or abnormal situations. Currently, computational methods to predict tissue immune cell compositions from bulk transcriptomes have been largely developed. Therefore, summarizing the advantages and disadvantages is appropriate. In addition, an examination of the challenges and possible solutions for these computational models will assist the development of this field. The common hypothesis of these models is that the expression of signature genes for immune cell types might represent the proportion of immune cells that contribute to the tissue transcriptome. In general, we grouped all reported tools into three groups, including reference-free, reference-based scoring and reference-based deconvolution methods. In this review, a summary of all the currently reported computational immune cell quantification tools and their applications, limitations, and perspectives are presented. Furthermore, some critical problems are found that have limited the performance and application of these models, including inadequate immune cell type, the collinearity problem, the impact of the tissue environment on the immune cell expression level, and the deficiency of standard datasets for model validation. To address these issues, tissue specific training datasets that include all known immune cells, a hierarchical computational framework, and benchmark datasets including both tissue expression profiles and the abundances of all the immune cells are proposed to further promote the development of this field.

Multi-gene technical assessment of qPCR and NanoString n-Counter analysis platforms in cynomolgus monkey cardiac allograft recipients

Quantitative gene expression profiling of cardiac allografts characterizes the phenotype of the alloimmune response, yields information regarding differential effects that may be associated with various anti-rejection drug regimens, and generates testable hypotheses regarding the pathogenesis of the chronic rejection lesions typically observed in non-human primate heart transplant models. The goal of this study was to assess interplatform performance and variability between the relatively novel NanoString nCounter Analysis System, ΔΔCT (relative) RT-qPCR, and standard curve (absolute) RT-qPCR utilizing cynomolgus monkey cardiac allografts. Methods for RNA isolation and preamplification were also systematically evaluated and effective methods are proposed. In this study, we demonstrate strong correlation between the two RT-qPCR methods, but variable and, at times, weak correlation between RT-qPCR and NanoString. NanoString fold change results demonstrate less sensitivity to small changes in gene expression than RT-qPCR. These findings appear to be driven by technical aspects of each platform that influence the conditions under which each technique is ideal. Collectively, our data contribute to the general effort to optimally utilize gene expression profiling techniques, not only for transplanted tissues, but for many other applications where accurate rank-order of gene expression versus precise quantification of absolute gene transcript number may be relatively valuable.

Deconvolution of transcriptomes and miRNomes by independent component analysis provides insights into biological processes and clinical outcomes of melanoma patients

BACKGROUND

The amount of publicly available cancer-related "omics" data is constantly growing and can potentially be used to gain insights into the tumour biology of new cancer patients, their diagnosis and suitable treatment options. However, the integration of different datasets is not straightforward and requires specialized approaches to deal with heterogeneity at technical and biological levels.

METHODS

Here we present a method that can overcome technical biases, predict clinically relevant outcomes and identify tumour-related biological processes in patients using previously collected large discovery datasets. The approach is based on independent component analysis (ICA) - an unsupervised method of signal deconvolution. We developed parallel consensus ICA that robustly decomposes transcriptomics datasets into expression profiles with minimal mutual dependency.

RESULTS

By applying the method to a small cohort of primary melanoma and control samples combined with a large discovery melanoma dataset, we demonstrate that our method distinguishes cell-type specific signals from technical biases and allows to predict clinically relevant patient characteristics. We showed the potential of the method to predict cancer subtypes and estimate the activity of key tumour-related processes such as immune response, angiogenesis and cell proliferation. ICA-based risk score was proposed and its connection to patient survival was validated with an independent cohort of patients. Additionally, through integration of components identified for mRNA and miRNA data, the proposed method helped deducing biological functions of miRNAs, which would otherwise not be possible.

CONCLUSIONS

We present a method that can be used to map new transcriptomic data from cancer patient samples onto large discovery datasets. The method corrects technical biases, helps characterizing activity of biological processes or cell types in the new samples and provides the prognosis of patient survival.

Computational deconvolution of transcriptomics data from mixed cell populations

Summary

Gene expression analyses of bulk tissues often ignore cell type composition as an important confounding factor, resulting in a loss of signal from lowly abundant cell types. In this review, we highlight the importance and value of computational deconvolution methods to infer the abundance of different cell types and/or cell type-specific expression profiles in heterogeneous samples without performing physical cell sorting. We also explain the various deconvolution scenarios, the mathematical approaches used to solve them and the effect of data processing and different confounding factors on the accuracy of the deconvolution results.

Contact

katleen.depreter@ugent.be.

Supplementary information

Supplementary data are available at Bioinformatics online.

Dynamic molecular changes during the first week of human life follow a robust developmental trajectory

Systems biology can unravel complex biology but has not been extensively applied to human newborns, a group highly vulnerable to a wide range of diseases. We optimized methods to extract transcriptomic, proteomic, metabolomic, cytokine/chemokine, and single cell immune phenotyping data from <1 ml of blood, a volume readily obtained from newborns. Indexing to baseline and applying innovative integrative computational methods reveals dramatic changes along a remarkably stable developmental trajectory over the first week of life. This is most evident in changes of interferon and complement pathways, as well as neutrophil-associated signaling. Validated across two independent cohorts of newborns from West Africa and Australasia, a robust and common trajectory emerges, suggesting a purposeful rather than random developmental path. Systems biology and innovative data integration can provide fresh insights into the molecular ontogeny of the first week of life, a dynamic developmental phase that is key for health and disease.

The first week of life impacts health for all of life, but the mechanisms are little-understood. Here the authors extract multi-omic data from small volumes of blood to study the dynamic molecular changes during the first week of life, revealing a robust developmental trajectory common to different populations.

Comparison of gene expression and flow cytometry for immune profiling in chronic lymphocytic leukaemia

Understanding how cancer and cancer therapies affect the immune system is integral to the rational application of immunotherapies. Flow cytometry is the gold standard method of peripheral blood immune cell profiling. However, the requirement for viable cells can limit its applicability, especially in studies of retrospective clinical cohorts. We aimed to determine if gene expression, analysed using the NanoString platform, could be used to quantify the immune populations present in cryopreserved peripheral blood mononuclear cell (PBMC) samples from patients with chronic lymphocytic leukaemia. Cell abundance scores derived from gene expression analysis were significantly correlated with the population frequency quantified by flow cytometry for all subsets analysed, including T cells, NK cells and Monocytes. This study demonstrates that gene expression analysis can be applied to cryopreserved PBMC and provides a concordant and complementary understanding of the immune profile to flow cytometry.

A Bloody Primer: Analysis of RNA-Seq from Tissue Admixtures

RNA sequencing is a powerful technology that allows for unbiased profiling of the entire transcriptome. The analysis of transcriptome profiles from heterogeneous tissues, cell admixtures with relative proportions that can vary several fold across samples, poses a significant challenge. Blood is perhaps the most egregious example. Here, we describe in detail a computational pipeline for RNA-Seq data preparation and statistical analysis, with development of a means of estimating the cell type composition of blood samples from their bulk RNA-Seq profiles. We also illustrate the importance of adjusting for the potential confounding effect of cellular heterogeneity in the context of statistical inference in a whole blood RNA-Seq dataset.

The Use of Genomics and Pathway Analysis in Our Understanding and Prediction of Clinical Renal Transplant Injury

The development and application of high-throughput molecular profiling have transformed the study of human diseases. The problem of handling large, complex data sets has been facilitated by advances in complex computational analysis. In this review, the recent literature regarding the application of transcriptional genomic information to renal transplantation, with specific reference to acute rejection, acute kidney injury in allografts, chronic allograft injury, and tolerance is discussed, as is the current published data regarding other "omics" strategies-proteomics, metabolomics, and the microRNA transcriptome. These data have shed new light on our understanding of the pathogenesis of specific disease conditions after renal transplantation, but their utility as a biomarker of disease has been hampered by study design and sample size. This review aims to highlight the opportunities and obstacles that exist with genomics and other related technologies to better understand and predict renal allograft outcome.

Enumerateblood - an R package to estimate the cellular composition of whole blood from Affymetrix Gene ST gene expression profiles

Background

Measuring genome-wide changes in transcript abundance in circulating peripheral whole blood is a useful way to study disease pathobiology and may help elucidate the molecular mechanisms of disease, or discovery of useful disease biomarkers. The sensitivity and interpretability of analyses carried out in this complex tissue, however, are significantly affected by its dynamic cellular heterogeneity. It is therefore desirable to quantify this heterogeneity, either to account for it or to better model interactions that may be present between the abundance of certain transcripts, specific cell types and the indication under study. Accurate enumeration of the many component cell types that make up peripheral whole blood can further complicate the sample collection process, however, and result in additional costs. Many approaches have been developed to infer the composition of a sample from high-dimensional transcriptomic and, more recently, epigenetic data. These approaches rely on the availability of isolated expression profiles for the cell types to be enumerated. These profiles are platform-specific, suitable datasets are rare, and generating them is expensive. No such dataset exists on the Affymetrix Gene ST platform.

Results

We present ‘Enumerateblood’, a freely-available and open source R package that exposes a multi-response Gaussian model capable of accurately predicting the composition of peripheral whole blood samples from Affymetrix Gene ST expression profiles, outperforming other current methods when applied to Gene ST data.

Conclusions

‘Enumerateblood’ significantly improves our ability to study disease pathobiology from whole blood gene expression assayed on the popular Affymetrix Gene ST platform by allowing a more complete study of the various components of this complex tissue without the need for additional data collection. Future use of the model may allow for novel insights to be generated from the ~400 Affymetrix Gene ST blood gene expression datasets currently available on the Gene Expression Omnibus (GEO) website.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3460-1) contains supplementary material, which is available to authorized users.

Impact of Statins on Gene Expression in Human Lung Tissues

Statins are 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors that alter the synthesis of cholesterol. Some studies have shown a significant association of statins with improved respiratory health outcomes of patients with asthma, chronic obstructive pulmonary disease and lung cancer. Here we hypothesize that statins impact gene expression in human lungs and may reveal the pleiotropic effects of statins that are taking place directly in lung tissues. Human lung tissues were obtained from patients who underwent lung resection or transplantation. Gene expression was measured on a custom Affymetrix array in a discovery cohort (n = 408) and two replication sets (n = 341 and 282). Gene expression was evaluated by linear regression between statin users and non-users, adjusting for age, gender, smoking status, and other covariables. The results of each cohort were combined in a meta-analysis and biological pathways were studied using Gene Set Enrichment Analysis. The discovery set included 141 statin users. The lung mRNA expression levels of eighteen and three genes were up-regulated and down-regulated in statin users (FDR < 0.05), respectively. Twelve of the up-regulated genes were replicated in the first replication set, but none in the second (p-value < 0.05). Combining the discovery and replication sets into a meta-analysis improved the significance of the 12 up-regulated genes, which includes genes encoding enzymes and membrane proteins involved in cholesterol biosynthesis. Canonical biological pathways altered by statins in the lung include cholesterol, steroid, and terpenoid backbone biosynthesis. No genes encoding inflammatory, proteases, pro-fibrotic or growth factors were altered by statins, suggesting that the direct effect of statin in the lung do not go beyond its antilipidemic action. Although more studies are needed with specific lung cell types and different classes and doses of statins, the improved health outcomes and survival observed in statin users with chronic lung diseases do not seem to be mediated through direct regulation of gene expression in the lung.

New approaches to understanding the immune response to vaccination and infection

The immune system is a network of specialized cell types and tissues that communicates via cytokines and direct contact, to orchestrate specific types of defensive responses. Until recently, we could only study immune responses in a piecemeal, highly focused fashion, on major components like antibodies to the pathogen. But recent advances in technology and in our understanding of the many components of the system, innate and adaptive, have made possible a broader approach, where both the multiple responding cells and cytokines in the blood are measured. This systems immunology approach to a vaccine response or an infection gives us a more holistic picture of the different parts of the immune system that are mobilized and should allow us a much better understanding of the pathways and mechanisms of such responses, as well as to predict vaccine efficacy in different populations well in advance of efficacy studies. Here we summarize the different technologies and methods and discuss how they can inform us about the differences between diseases and vaccines, and how they can greatly accelerate vaccine development.

Molecular nephropathology: ready for prime time?

In the current era of precision medicine, the existing nephropathology paradigm of light microscopy, immunofluorescence, and electron microscopy will become increasingly insufficient. There will be an expectation to supplement these traditional diagnostic tools with patient-specific information related to a growing understanding of molecular pathophysiology. Next generation sequencing technologies are expected to play a key role in the future of nephropathology, but transcriptomics is poised to represent the first major foray into routine molecular testing. The introduction of molecular techniques into clinical nephropathology has been hindered in part by the reliance of existing platforms on fresh tissue samples. The NanoString gene expression system works with formalin-fixed paraffin-embedded tissue and thus represents a promising solution to this technical barrier that may finally allow for the translation of recent transcriptomics discoveries into the enhancement of patient care. Widespread adoption of this new diagnostic dimension will require ongoing multidisciplinary cooperation between pathologists and clinicians, including molecular testing consensus generation and rigorous multicenter validation.

Assessment of immune status using blood transcriptomics and potential implications for global health

The immune system plays a key role in health maintenance and pathogenesis of a wide range of diseases. Leukocytes that are present in the blood convey valuable information about the status of the immune system. Blood transcriptomics, which consists in profiling blood transcript abundance on genome-wide scales, has gained in popularity over the past several years. Indeed, practicality and simplicity largely makes up for what this approach may lack in terms of cell population-level resolution. An extensive survey of the literature reveals increasingly widespread use across virtually all fields of medicine as well as across a number of different animal species, including model organisms but also animals of economical importance. Dissemination across such a wide range of disciplines holds the promise of adding a new perspective, breadth or context, to the considerable depth afforded by whole genome profiling of blood transcript abundance. Indeed, it is only through such contextualization that a truly global perspective will be gained from the use of systems approaches. Also discussed are opportunities that may arise for the fields of immunology and medicine from using blood transcriptomics as a common denominator for developing interactions and cooperation across fields of research that have traditionally been and largely remain compartmentalized. Finally, an argument is made for building immunology research capacity using blood transcriptomics platforms in low-resource and high-disease burden settings.

Gene-expression molecular subtyping of triple-negative breast cancer tumours: importance of immune response

Introduction

Triple-negative breast cancers need to be refined in order to identify therapeutic subgroups of patients.

Methods

We conducted an unsupervised analysis of microarray gene-expression profiles of 107 triple-negative breast cancer patients and undertook robust functional annotation of the molecular entities found by means of numerous approaches including immunohistochemistry and gene-expression signatures. A triple-negative external cohort (n = 87) was used for validation.

Results

Fuzzy clustering separated triple-negative tumours into three clusters: C1 (22.4%), C2 (44.9%) and C3 (32.7%). C1 patients were older (mean = 64.6 years) than C2 (mean = 56.8 years; P = 0.03) and C3 patients (mean = 51.9 years; P = 0.0004). Histological grade and Nottingham prognostic index were higher in C2 and C3 than in C1 (P < 0.0001 for both comparisons). Significant event-free survival (P = 0.03) was found according to cluster membership: patients belonging to C3 had a better outcome than patients in C1 (P = 0.01) and C2 (P = 0.02). Event-free survival analysis results were confirmed when our cohort was pooled with the external cohort (n = 194; P = 0.01). Functional annotation showed that 22% of triple-negative patients were not basal-like (C1). C1 was enriched in luminal subtypes and positive androgen receptor (luminal androgen receptor). C2 could be considered as an almost pure basal-like cluster. C3, enriched in basal-like subtypes but to a lesser extent, included 26% of claudin-low subtypes. Dissection of immune response showed that high immune response and low M2-like macrophages were a hallmark of C3, and that these patients had a better event-free survival than C2 patients, characterized by low immune response and high M2-like macrophages: P = 0.02 for our cohort, and P = 0.03 for pooled cohorts.

Conclusions

We identified three subtypes of triple-negative patients: luminal androgen receptor (22%), basal-like with low immune response and high M2-like macrophages (45%), and basal-enriched with high immune response and low M2-like macrophages (33%). We noted out that macrophages and other immune effectors offer a variety of therapeutic targets in breast cancer, and particularly in triple-negative basal-like tumours. Furthermore, we showed that CK5 antibody was better suited than CK5/6 antibody to subtype triple-negative patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-015-0550-y) contains supplementary material, which is available to authorized users.