A generalizable data-driven model of atrophy heterogeneity and progression in memory clinic settings.

Memory clinic patients are a heterogeneous population representing various aetiologies of pathological ageing. It is not known whether divergent spatiotemporal progression patterns of brain atrophy, as previously described in Alzheimer's disease patients, are prevalent and clinically meaningful in this group of older adults. To uncover distinct atrophy subtypes, we applied the Subtype and Stage Inference (SuStaIn) algorithm to baseline structural MRI data from 813 participants enrolled in the DELCODE cohort (mean ± standard deviation, age = 70.67 ± 6.07 years, 52% females). Participants were cognitively unimpaired (n = 285) or fulfilled diagnostic criteria for subjective cognitive decline (n = 342), mild cognitive impairment (n = 118) or dementia of the Alzheimer's type (n = 68). Atrophy subtypes were compared in baseline demographics, fluid Alzheimer's disease biomarker levels, the Preclinical Alzheimer Cognitive Composite (PACC-5) as well as episodic memory and executive functioning. PACC-5 trajectories over up to 240 weeks were examined. To test whether baseline atrophy subtype and stage predicted clinical trajectories before manifest cognitive impairment, we analysed PACC-5 trajectories and mild cognitive impairment conversion rates of cognitively unimpaired participants and those with subjective cognitive decline. Limbic-predominant and hippocampal-sparing atrophy subtypes were identified. Limbic-predominant atrophy initially affected the medial temporal lobes, followed by further temporal regions and, finally, the remaining cortical regions. At baseline, this subtype was related to older age, more pathological Alzheimer's disease biomarker levels, APOE ε4 carriership and an amnestic cognitive impairment. Hippocampal-sparing atrophy initially occurred outside the temporal lobe, with the medial temporal lobe spared up to advanced atrophy stages. This atrophy pattern also affected individuals with positive Alzheimer's disease biomarkers and was associated with more generalized cognitive impairment. Limbic-predominant atrophy, in all participants and in only unimpaired participants, was linked to more negative longitudinal PACC-5 slopes than observed in participants without or with hippocampal-sparing atrophy and increased the risk of mild cognitive impairment conversion. SuStaIn modelling was repeated in a sample from the Swedish BioFINDER-2 cohort. Highly similar atrophy progression patterns and associated cognitive profiles were identified. Cross-cohort model generalizability, at both the subject and the group level, was excellent, indicating reliable performance in previously unseen data. The proposed model is a promising tool for capturing heterogeneity among older adults at early at-risk states for Alzheimer's disease in applied settings. The implementation of atrophy subtype- and stage-specific end points might increase the statistical power of pharmacological trials targeting early Alzheimer's disease.

Letter to the Editor: on the stability and internal consistency of component-wise sparse mixture regression-based clustering.

Understanding the relationship between molecular markers and a phenotype of interest is often obfuscated by patient-level heterogeneity. To address this challenge, Chang et al. recently published a novel method called Component-wise Sparse Mixture Regression (CSMR), a regression-based clustering method that promises to detect heterogeneous relationships between molecular markers and a phenotype of interest under high-dimensional settings. In this Letter to the Editor, we raise awareness to several issues concerning the assessment of CSMR in Chang et al., particularly its assessment in settings where the number of features, P, exceeds the study sample size, N, and advocate for additional metrics/approaches when assessing the performance of regression-based clustering methodologies.

Improving the accuracy and internal consistency of regression-based clustering of high-dimensional datasets.

Component-wise Sparse Mixture Regression (CSMR) is a recently proposed regression-based clustering method that shows promise in detecting heterogeneous relationships between molecular markers and a continuous phenotype of interest. However, CSMR can yield inconsistent results when applied to high-dimensional molecular data, which we hypothesize is in part due to inherent limitations associated with the feature selection method used in the CSMR algorithm. To assess this hypothesis, we explored whether substituting different regularized regression methods (i.e. Lasso, Elastic Net, Smoothly Clipped Absolute Deviation (SCAD), Minmax Convex Penalty (MCP), and Adaptive-Lasso) within the CSMR framework can improve the clustering accuracy and internal consistency (IC) of CSMR in high-dimensional settings. We calculated the true positive rate (TPR), true negative rate (TNR), IC and clustering accuracy of our proposed modifications, benchmarked against the existing CSMR algorithm, using an extensive set of simulation studies and real biological datasets. Our results demonstrated that substituting Adaptive-Lasso within the existing feature selection method used in CSMR led to significantly improved IC and clustering accuracy, with strong performance even in high-dimensional scenarios. In conclusion, our modifications of the CSMR method resulted in improved clustering performance and may thus serve as viable alternatives for the regression-based clustering of high-dimensional datasets.

Synovial Tissue Insights into Heterogeneity of Rheumatoid Arthritis.

PURPOSE OF REVIEW: Rheumatoid arthritis is one of the most common rheumatic and autoimmune diseases. While it can affect many different organ systems, RA primarily involves inflammation in the synovium, the tissue that lines joints. Patients with RA exhibit significant clinical heterogeneity in terms of presence or absence of autoantibodies, degree of permanent deformities, and most importantly, treatment response. These clinical characteristics point to heterogeneity in the cellular and molecular pathogenesis of RA, an area that several recent studies have begun to address. RECENT FINDINGS: Single-cell RNA-sequencing initiatives and deeper focused studies have revealed several RA-associated cell populations in synovial tissues, including peripheral helper T cells, autoimmunity-associated B cells (ABCs), and NOTCH3+ sublining fibroblasts. Recent large transcriptional studies and translational clinical trials present frameworks to capture cellular and molecular heterogeneity in RA synovium. Technological developments, such as spatial transcriptomics and machine learning, promise to further elucidate the different types of RA synovitis and the biological mechanisms that characterize them, key elements of precision medicine to optimize patient care and outcomes in RA. This review recaps the findings of those recent studies and puts our current knowledge and future challenges into scientific and clinical perspective.

Supervised clustering of high-dimensional data using regularized mixture modeling.

Identifying relationships between genetic variations and their clinical presentations has been challenged by the heterogeneous causes of a disease. It is imperative to unveil the relationship between the high-dimensional genetic manifestations and the clinical presentations, while taking into account the possible heterogeneity of the study subjects.We proposed a novel supervised clustering algorithm using penalized mixture regression model, called component-wise sparse mixture regression (CSMR), to deal with the challenges in studying the heterogeneous relationships between high-dimensional genetic features and a phenotype. The algorithm was adapted from the classification expectation maximization algorithm, which offers a novel supervised solution to the clustering problem, with substantial improvement on both the computational efficiency and biological interpretability. Experimental evaluation on simulated benchmark datasets demonstrated that the CSMR can accurately identify the subspaces on which subset of features are explanatory to the response variables, and it outperformed the baseline methods. Application of CSMR on a drug sensitivity dataset again demonstrated the superior performance of CSMR over the others, where CSMR is powerful in recapitulating the distinct subgroups hidden in the pool of cell lines with regards to their coping mechanisms to different drugs. CSMR represents a big data analysis tool with the potential to resolve the complexity of translating the clinical representations of the disease to the real causes underpinning it. We believe that it will bring new understanding to the molecular basis of a disease and could be of special relevance in the growing field of personalized medicine.

Towards precision medicine in non-alcoholic fatty liver disease.

Non-Alcoholic Fatty Liver Disease (NAFLD) refers to the accumulation of lipid laden vacuoles in hepatocytes, occurring in the context of visceral adiposity, insulin resistance and other features of the metabolic syndrome. Its more severe form (NASH, Non-Alcoholic Steatohepatitis) is becoming the leading aetiology of end-stage liver disease and hepatocellular carcinoma, and also contributes to cardiovascular disease, diabetes and extrahepatic malignancy. Management is currently limited to lifestyle modification and optimisation of the metabolic co-morbidities, with some of the drugs used for the latter also having shown some benefit for the liver. Licensed treatment modalities are currently lacking. A particular difficulty is the notorious heterogeneity of the patient population, which is poorly understood. A spectrum of disease severity associates in a non-linear way with a spectrum of severity of underlying metabolic factors. Heterogeneity of the liver in terms of mechanisms to cope with the metabolic and inflammatory stress and in terms of repair mechanisms, and a lack of knowledge hereof, further complicate the understanding of inter-individual variability. Genetic factors act as disease modifiers and potentially allow for some risk stratification, but also only explain a minor fraction of disease heterogeneity. Response to treatment shows a large variation in treatment response, again with little understanding of what is driving the absence of response in individual patients. Management can be tailored to patient's preferences in terms of diet modification, but tailoring treatment to knowledge on disease driving mechanisms in an individual patient is still in its infancy. Recent progress in analysing liver tissue as well as non-invasive tests hold, however, promise to rapidly improve our understanding of disease heterogeneity in NAFLD and provide individualised management.

Sex and Gender Differences in Neurodegenerative Diseases: Challenges for Therapeutic Opportunities.

The term "neurodegenerative diseases" (NDs) identifies a group of heterogeneous diseases characterized by progressive loss of selectively vulnerable populations of neurons, which progressively deteriorates over time, leading to neuronal dysfunction. Protein aggregation and neuronal loss have been considered the most characteristic hallmarks of NDs, but growing evidence confirms that significant dysregulation of innate immune pathways plays a crucial role as well. NDs vary from multiple sclerosis, in which the autoimmune inflammatory component is predominant, to more "classical" NDs, such as Parkinson's disease, Alzheimer's disease, amyotrophic lateral sclerosis, and spinal muscular atrophy. Of interest, many of the clinical differences reported in NDs seem to be closely linked to sex, which may be justified by the significant changes in immune mechanisms between affected females and males. In this review, we examined some of the most studied NDs by looking at their pathogenic and phenotypical features to highlight sex-related discrepancies, if any, with particular interest in the individuals' responses to treatment. We believe that pointing out these differences in clinical practice may help achieve more successful precision and personalized care.

No Association of Multiple Sclerosis with C9orf72 Hexanucleotide Repeat Size in an Austrian Cohort.

Multiple Sclerosis (MS) is a common immune-mediated disorder of the central nervous system that affects young adults and is characterized by demyelination and neurodegeneration. Recent studies have associated C9orf72 intermediate repeat expansions with MS. The objective of this study was to investigate whether C9orf72 repeat length is associated with MS or with a specific disease course in a monocentric Austrian MS cohort. Genotyping of 382 MS patients and 643 non-neurological controls for C9orf72 repeat expansions was performed. The study did not find a difference in the distribution of repeat numbers between controls and MS cases (median repeat units = 2; p = 0.39). Additionally, sub-analysis did not establish a link between intermediate repeats and MS (p = 0.23) and none of the patients with progressive disease course carried an intermediate allele (20-30 repeat units). Exploratory analysis for different cut-offs (of ≥7, ≥17, and ≥24) did not reveal any significant differences in allele frequencies between MS and controls. However, the study did identify a progressive MS patient with a pathogenic C9orf72 expansion and probable co-existing behavioral variant frontotemporal dementia (bvFTD) in a retrospective chart review. In conclusion, this study did not find evidence supporting an association between C9orf72 repeat length and MS or a specific disease course in the Austrian MS cohort. However, the identification of a progressive MS patient with a pathogenic C9orf72 expansion and probable co-existing with FTD highlights the complexity and challenges involved in recognizing distinct neurodegenerative diseases that may co-occur in MS patients.

Breast cancer risk factors by mode of detection among screened women in the Cancer Prevention Study-II.

BACKGROUND: Identifying risk factors for women at high risk of symptom-detected breast cancers that were missed by screening would enable targeting of enhanced screening regimens. To this end, we examined associations of breast cancer risk factors by mode of detection in screened women from the Cancer Prevention Study (CPS)-II Nutrition Cohort. METHODS: Among 77,206 women followed for a median of 14.8 years, 2711 screen-detected and 1281 symptom-detected breast cancer cases were diagnosed. Multivariable-adjusted associations were estimated using joint Cox proportional hazards regression models with person-time calculated contingent on screening. RESULTS: Factors associated with higher risks of symptom-detected and screen-detected breast cancer included current combined hormone therapy (HT) use (HR 2.07, 95% CI 1.72-2.48 and 1.45, 1.27-1.65, respectively) and history of benign breast disease (1.85, 1.64-2.08 and 1.43, 1.31-1.55, respectively). Current estrogen-only HT use was associated with symptom-detected (1.40, 1.15-1.71) but not screen-detected (0.95, 0.83-1.09) breast cancer. Higher risk of screen-detected but not symptom-detected breast cancer was observed for obese vs. normal body mass index (1.22, 1.01-1.48 and 0.76, 0.56-1.01, respectively), per 3 h/day sitting time (1.10, 1.04-1.16 and 0.97, 0.89-1.06, respectively), and ≥ 2 drinks per day vs. nondrinker (1.40, 1.16-1.69 and 1.27, 0.97-1.66, respectively). CONCLUSIONS: Differences in risk factors for symptom-detected vs. screen-detected breast cancer were observed and most notably, use of combined and estrogen-only HT and a history of benign breast disease were associated with increased risk of symptomatic detected breast cancer. IMPACT: If confirmed, these data suggest that such women may benefit from more intensive screening to facilitate early detection.

Clinical and genetic correlates of islet-autoimmune signatures in juvenile-onset type 1 diabetes.

AIMS/HYPOTHESIS: Heterogeneity in individuals with type 1 diabetes has become more generally appreciated, but has not yet been extensively and systematically characterised. Here, we aimed to characterise type 1 diabetes heterogeneity by creating immunological, genetic and clinical profiles for individuals with juvenile-onset type 1 diabetes in a cross-sectional study. METHODS: Participants were HLA-genotyped to determine HLA-DR-DQ risk, and SNP-genotyped to generate a non-HLA genetic risk score (GRS) based on 93 type 1 diabetes-associated SNP variants outside the MHC region. Islet autoimmunity was assessed as T cell proliferation upon stimulation with the beta cell antigens GAD65, islet antigen-2 (IA-2), preproinsulin (PPI) and defective ribosomal product of the insulin gene (INS-DRIP). Clinical parameters were collected retrospectively. RESULTS: Of 80 individuals, 67 had proliferation responses to one or more islet antigens, with vast differences in the extent of proliferation. Based on the multitude and amplitude of the proliferation responses, individuals were clustered into non-, intermediate and high responders. High responders could not be characterised entirely by enrichment for the highest risk HLA-DR3-DQ2/DR4-DQ8 genotype. However, high responders did have a significantly higher non-HLA GRS. Clinically, high T cell responses to beta cell antigens did not reflect in worsened glycaemic control, increased complications, development of associated autoimmunity or younger age at disease onset. The number of beta cell antigens that an individual responded to increased with disease duration, pointing to chronic islet autoimmunity and epitope spreading. CONCLUSIONS/INTERPRETATION: Collectively, these data provide new insights into type 1 diabetes disease heterogeneity and highlight the importance of stratifying patients on the basis of their genetic and autoimmune signatures for immunotherapy and personalised disease management.

Addressing heterogeneity in amyotrophic lateral sclerosis CLINICAL TRIALS.

Amyotrophic lateral sclerosis (ALS) is a debilitating neurodegenerative disorder with complex biology and significant clinical heterogeneity. Many preclinical and early phase ALS clinical trials have yielded promising results that could not be replicated in larger phase 3 confirmatory trials. One reason for the lack of reproducibility may be ALS biological and clinical heterogeneity. Therefore, in this review, we explore sources of ALS heterogeneity that may reduce statistical power to evaluate efficacy in ALS trials. We also review efforts to manage clinical heterogeneity, including use of validated disease outcome measures, predictive biomarkers of disease progression, and individual clinical risk stratification. We propose that personalized prognostic models with use of predictive biomarkers may identify patients with ALS for whom a specific therapeutic strategy may be expected to be more successful. Finally, the rapid application of emerging clinical and biomarker strategies may reduce heterogeneity, increase trial efficiency, and, in turn, accelerate ALS drug development.

Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer's disease.

We used a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxelwise structural MRIs of late-onset Alzheimer's disease (AD) dementia patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus, and amygdala), a subcortical atrophy factor (striatum, thalamus, and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal, and lateral occipital cortices). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloid-positive (Aß+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in Aß+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, whereas the cortical factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared with temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Factor compositions of participants and code used in this article are publicly available for future research.

Biological exacerbation clusters demonstrate asthma and chronic obstructive pulmonary disease overlap with distinct mediator and microbiome profiles.

BACKGROUND: Exacerbations of asthma and chronic obstructive pulmonary disease (COPD) are heterogeneous. OBJECTIVE: We sought to investigate the sputum cellular, mediator, and microbiome profiles of both asthma and COPD exacerbations. METHODS: Patients with severe asthma or moderate-to-severe COPD were recruited prospectively to a single center. Sputum mediators were available in 32 asthmatic patients and 73 patients with COPD assessed at exacerbation. Biologic clusters were determined by using factor and cluster analyses on a panel of sputum mediators. Patterns of clinical parameters, sputum mediators, and microbiome communities were assessed across the identified clusters. RESULTS: The asthmatic patients and patients with COPD had different clinical characteristics and inflammatory profiles but similar microbial ecology. Three exacerbation biologic clusters were identified. Cluster 1 was COPD predominant, with 27 patients with COPD and 7 asthmatic patients exhibiting increased blood and sputum neutrophil counts, proinflammatory mediators (IL-1ß, IL-6, IL-6 receptor, TNF-α, TNF receptors 1 and 2, and vascular endothelial growth factor), and proportions of the bacterial phylum Proteobacteria. Cluster 2 had 10 asthmatic patients and 17 patients with COPD with increased blood and sputum eosinophil counts, type 2 mediators (IL-5, IL-13, CCL13, CCL17, and CCL26), and proportions of the bacterial phylum Bacteroidetes. Cluster 3 had 15 asthmatic patients and 29 patients with COPD with increased type 1 mediators (CXCL10, CXCL11, and IFN-γ) and proportions of the phyla Actinobacteria and Firmicutes. CONCLUSIONS: A biologic clustering approach revealed 3 subgroups of asthma and COPD exacerbations, each with different percentages of patients with overlapping asthma and COPD. The sputum mediator and microbiome profiles were distinct between clusters.

Innate immune activity as a predictor of persistent insulin secretion and association with responsiveness to CTLA4-Ig treatment in recent-onset type 1 diabetes.

AIMS/HYPOTHESIS: The study aimed to determine whether discrete subtypes of type 1 diabetes exist, based on immunoregulatory profiles at clinical onset, as this has significant implications for disease treatment and prevention as well as the design and analysis of clinical trials. METHODS: Using a plasma-based transcriptional bioassay and a gene-ontology-based scoring algorithm, we examined local participants from the Children's Hospital of Wisconsin and conducted an ancillary analysis of TrialNet CTLA4-Ig trial (TN-09) participants. RESULTS: The inflammatory/regulatory balance measured during the post-onset period was highly variable. Notably, a significant inverse relationship was identified between baseline innate inflammatory activity and stimulated C-peptide AUC measured at 3, 6, 12, 18 and 24 months post onset among placebo-treated individuals (p ≤ 0.015). Further, duration of persistent insulin secretion was negatively related to baseline inflammation (p ≤ 0.012) and positively associated with baseline abundance of circulating activated regulatory T cells (CD4+/CD45RA-/FOXP3high; p = 0.016). Based on these findings, data from participants treated with CTLA4-Ig were stratified by inflammatory activity at onset; in this way, we identified pathways and transcripts consistent with inhibition of T cell activation and enhanced immunoregulation. Variance among baseline plasma-induced signatures of TN-09 participants was further examined with weighted gene co-expression network analysis and related to clinical metrics. Four age-independent subgroups were identified that differed in terms of baseline innate inflammatory/regulatory bias, rate of C-peptide decline and response to CTLA4-Ig treatment. CONCLUSIONS/INTERPRETATION: These data support the existence of multiple type 1 diabetes subtypes characterised by varying levels of baseline innate inflammation that are associated with the rate of C-peptide decline. DATA AVAILABILITY: Gene expression data files are publicly available through the National Center for Biotechnology Information Gene Expression Omnibus (accession number GSE102234).

Pancreatic adenocarcinoma: insights into patterns of recurrence and disease behavior.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive cancers with high metastatic potential. Clinical observations suggest that there is disease heterogeneity among patients with different sites of distant metastases, yielding distinct clinical outcomes. Herein, we investigate the impact of clinical and pathological parameters on recurrence patterns and compare survival outcomes for patients with a first site of recurrence in the liver versus lung from PDAC following original curative surgical resection. METHODS: Using the Memorial Sloan Kettering Cancer Center ICD billing codes and tumor registry database over a 10 years period (January 2004-December 2014), we identified PDAC patients who underwent resection and subsequently presented with either liver or lung recurrence. Time from relapse to death (TRD) was calculated from date of recurrence to date of death. Using the Kaplan-Meier method, TRD was estimated and compared by recurrence site using log-rank test. RESULTS: The median overall follow-up was 37.3 months among survivors in the entire cohort. Median TRD in this cohort was 10.7 months (95%CI: 8.9-14.6 months). Patients with first site of lung recurrence had a more favorable outcome compared to patients who recurred with liver metastasis as the first site of recurrence (median TRD of 15 versus 9 months respectively, P = 0.02). Moderate to poorly or poor differentiation was associated more often with liver than lung recurrence (40% vs 21% respectively, P = 0.047). A trend to increased lymph node metastasis in the lung recurrence cohort was observed. CONCLUSION: PDAC patients who recur with a first site of lung metastasis have an improved clinical outcome compared to patients with first site of liver recurrence. Our data suggests there may be epidemiologic and pathologic determinants related to patterns of recurrence in PDAC.

Primary islet autoantibody at initial seroconversion and autoantibodies at diagnosis of type 1 diabetes as markers of disease heterogeneity.

OBJECTIVE: The relationship between patterns of islet autoantibodies at diagnosis and specificity of the first islet autoantibody at the initiation of autoimmunity was analyzed with the aim of identifying patterns informative of the primary autoantibodies. METHODS: Information about a single first autoantibody at seroconversion and autoantibody data at diagnosis were available for 128 children participating in the follow-up cohort of the Finnish Type 1 Diabetes Prediction and Prevention (DIPP) study. Autoantibody data at diagnosis and genotyping results were also obtained from children in the Finnish Pediatric Diabetes Register (FPDR). RESULTS: Insulin autoantibodies (IAA) were the most common primary antibodies (N = 68), followed by those for glutamic acid decarboxylase (GADA; N = 38), IA-2 antigen (IA-2A; N = 13), and zinc transporter 8 (ZnT8A; N = 9), whereas at diagnosis, IA-2A were most frequent (N = 103), followed by IAA (N = 78), ZnT8A (N = 73), and GADA (N = 71). Accordingly, the presence of many specific autoantibodies at diagnosis was due to the secondary antibodies appearing after primary antibodies, and in some cases, the primary autoantibody, most often IAA, had already disappeared at the time of diagnosis. Many of the autoantibody combinations present at diagnosis could be assembled into groups associated with either IAA or GADA as first autoantibodies. These combinations, in children diagnosed below the age of 10 years in the FPDR, were found to be strongly associated with risk genotypes in either INS (IAA first) or IKZF4-ERBB3 (GADA first) genes. CONCLUSIONS: Autoantibody patterns at diagnosis may be informative on primary autoantibodies initiating autoimmunity in young children developing type 1 diabetes.

Moving toward endotypes in atopic dermatitis: Identification of patient clusters based on serum biomarker analysis.

BACKGROUND: Atopic dermatitis (AD) is a complex, chronic, inflammatory skin disease with a diverse clinical presentation. However, it is unclear whether this diversity exists at a biological level. OBJECTIVE: We sought to test the hypothesis that AD is heterogeneous at the biological level of individual inflammatory mediators. METHODS: Sera from 193 adult patients with moderate-to-severe AD (six area, six sign atopic dermatitis [SASSAD] score: geometric mean, 22.3 [95% CI, 21.3-23.3] and 39.1 [95% CI, 37.5-40.9], respectively) and 30 healthy control subjects without AD were analyzed for 147 serum mediators, total IgE levels, and 130 allergen-specific IgE levels. Population heterogeneity was assessed by using principal component analysis, followed by unsupervised k-means cluster analysis of the principal components. RESULTS: Patients with AD showed pronounced evidence of inflammation compared with healthy control subjects. Principal component analysis of data on sera from patients with AD revealed the presence of 4 potential clusters. Fifty-seven principal components described approximately 90% of the variance. Unsupervised k-means cluster analysis of the 57 largest principal components delivered 4 distinct clusters of patients with AD. Cluster 1 had high SASSAD scores and body surface areas with the highest levels of pulmonary and activation-regulated chemokine, tissue inhibitor of metalloproteinases 1, and soluble CD14. Cluster 2 had low SASSAD scores with the lowest levels of IFN-α, tissue inhibitor of metalloproteinases 1, and vascular endothelial growth factor. Cluster 3 had high SASSAD scores with the lowest levels of IFN-ß, IL-1, and epithelial cytokines. Cluster 4 had low SASSAD scores but the highest levels of the inflammatory markers IL-1, IL-4, IL-13, and thymic stromal lymphopoietin. CONCLUSION: AD is a heterogeneous disease both clinically and biologically. Four distinct clusters of patients with AD have been identified that could represent endotypes with unique biological mechanisms. Elucidation of these endotypes warrants further investigation and will require future intervention trials with specific agents, such as biologics.

A novel pathway-based distance score enhances assessment of disease heterogeneity in gene expression.

BACKGROUND: Distance based unsupervised clustering of gene expression data is commonly used to identify heterogeneity in biologic samples. However, high noise levels in gene expression data and relatively high correlation between genes are often encountered, so traditional distances such as Euclidean distance may not be effective at discriminating the biological differences between samples. An alternative method to examine disease phenotypes is to use pre-defined biological pathways. These pathways have been shown to be perturbed in different ways in different subjects who have similar clinical features. We hypothesize that differences in the expressions of genes in a given pathway are more predictive of differences in biological differences compared to standard approaches and if integrated into clustering analysis will enhance the robustness and accuracy of the clustering method. To examine this hypothesis, we developed a novel computational method to assess the biological differences between samples using gene expression data by assuming that ontologically defined biological pathways in biologically similar samples have similar behavior. RESULTS: Pre-defined biological pathways were downloaded and genes in each pathway were used to cluster samples using the Gaussian mixture model. The clustering results across different pathways were then summarized to calculate the pathway-based distance score between samples. This method was applied to both simulated and real data sets and compared to the traditional Euclidean distance and another pathway-based clustering method, Pathifier. The results show that the pathway-based distance score performs significantly better than the Euclidean distance, especially when the heterogeneity is low and genes in the same pathways are correlated. Compared to Pathifier, we demonstrated that our approach achieves higher accuracy and robustness for small pathways. When the pathway size is large, by downsampling the pathways into smaller pathways, our approach was able to achieve comparable performance. CONCLUSIONS: We have developed a novel distance score that represents the biological differences between samples using gene expression data and pre-defined biological pathway information. Application of this distance score results in more accurate, robust, and biologically meaningful clustering results in both simulated data and real data when compared to traditional methods. It also has comparable or better performance compared to Pathifier.

The challenges of stratifying patients for trials in inflammatory bowel disease.

Immunotherapy with biological agents or small molecules is revolutionising the treatment of chronic inflammatory disease in humans; however, a significant proportion of patients fail to respond or lose responsiveness. This is particularly evident in inflammatory bowel disease (IBD), a group of chronic, immune-mediated disorders of the gastrointestinal tract. Different responsiveness to treatment in IBD can be explained by substantial disease heterogeneity, which is being increasingly recognised by genetic and immunological studies. The current enthusiasm for stratified medicine suggests that it may become possible to identify clinical, immunological, biochemical or genetic biomarkers to target immunotherapy to patients more likely to respond. Here, we identify and highlight the opportunities and the challenges of this strategy in the context of IBD.

Challenges and prospects for biomarker research: a current perspective from the developing world.

Majority of deaths due to communicable and non-communicable diseases occur in the low and middle-income nations (LMNs), mainly due to the lack of early diagnoses and timely treatments. In such a scenario, biomarkers serve as an indispensible resource that can be used as indicators of biological processes, specific disease conditions or response to therapeutic interventions. Evaluation, diagnosis and management of diseases in developing world by following/extrapolating the findings obtained on the basis of the research work involving only the populations from the developed countries, could often be highly misleading due to existence of diverse patterns of diseases in developing countries compared to the developed world. Biomarker candidates identified from high-throughput integrated omics technologies have promising potential; however, their actual clinical applications are found to be limited, primarily due to the challenges of disease heterogeneity and pre-analytical variability associated with the biomarker discovery pipeline. Additionally, in the developing world, economic crunches, lack of awareness and education, paucity of biorepositories, enormous diversities in socio-epidemiological background, ethnicity, lifestyle, diet, exposure to various environmental risk factors and infectious agents, and ethical and social issues also cumulatively hinder biomarker discovery ventures. Establishment of standard operating procedures, comprehensive data repositories and exchange of scientific findings are crucial for reducing the variability and fragmentation of data. This review highlights the challenges associated with the discovery, validation and translational phases of biomarker research in LMNs with some of their amenable solutions and future prospects. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.