Combined Mapping of Multiple clUsteriNg ALgorithms (COMMUNAL): A Robust Method for Selection of Cluster Number, K

CpG Island Methylator Phenotype-A Hope for the Future or a Road to Nowhere?

The CpG island methylator phenotype (CIMP) can be regarded as the most notable emanation of epigenetic instability in cancer. Since its discovery in the late 1990s, CIMP has been extensively studied, mainly in colorectal cancers (CRC) and gliomas. Consequently, knowledge on molecular and pathological characteristics of CIMP in CRC and other tumour types has rapidly expanded. Concordant and widespread hypermethylation of multiple CpG islands observed in CIMP in multiple cancers raised hopes for future epigenetically based diagnostics and treatments of solid tumours. However, studies on CIMP in solid tumours were hampered by a lack of generalisability and reproducibility of epigenetic markers. Moreover, CIMP was not a satisfactory marker in predicting clinical outcomes. The idea of targeting epigenetic abnormalities such as CIMP for cancer therapy has not been implemented for solid tumours, either. Twenty-one years after its discovery, we aim to cover both the fundamental and new aspects of CIMP and its future application as a diagnostic marker and target in anticancer therapies.

Major Histocompatibility Complex Genes as Therapeutic Opportunity for Immune Cold Molecular Cancer Subtypes

Current immunotherapies are effective only in a subset of patients, likely due to several factors including defects in tumor cell antigen presentation, decreased response to immune effectors, and molecular heterogeneity of cancers. Recent molecular classifications enable the categorization of many tumor types. However, deregulation of major histocompatibility complex (MHC) gene expression is poorly characterized in the context of molecular cancer subtypes. To suppress the confounding effect of immune infiltrates on expression patterns of immunoregulators, we identified and removed genes with strong correlation to estimated immune compartment levels in each tumor type. Next, we reanalyzed a total of 13 TCGA cancer types encompassing 5651 tumors and 485 normal adjacent tissues by performing unsupervised clustering of 14 MHC genes. Subsequently, resultant clusters were statistically compared in terms of expression of other immune-related genes. Three MHC expression clusters were discovered by unsupervised clustering. We identified concordantly decreased expression of MHC genes (MHC-low) in 26 out of 55 molecular subtypes. Consequently, our study underlines the urgent need for designing strategies to enhance tumor MHC expression that could improve immune cold tumor rejection by cytotoxic T lymphocytes.

Transcriptomic Profiling for the Autophagy Pathway in Colorectal Cancer

The role of autophagy in colorectal cancer (CRC) pathogenesis appears to be crucial. Autophagy acts both as a tumor suppressor, by removing redundant cellular material, and a tumor-promoting factor, by providing access to components necessary for growth, metabolism, and proliferation. To date, little is known about the expression of genes that play a basal role in the autophagy in CRC. In this study, we aimed to compare the expression levels of 46 genes involved in the autophagy pathway between tumor-adjacent and tumor tissue, employing large RNA sequencing (RNA-seq) and microarray datasets. Additionally, we verified our results using data on 38 CRC cell lines. Gene set enrichment analysis revealed a significant deregulation of autophagy-related gene sets in CRC. The unsupervised clustering of tumors using the mRNA levels of autophagy-related genes revealed the existence of two major clusters: microsatellite instability (MSI)-enriched and -depleted. In cluster 1 (MSI-depleted), ATG9B and LAMP1 genes were the most prominently expressed, whereas cluster 2 (MSI-enriched) was characterized by DRAM1 upregulation. CRC cell lines were also clustered according to MSI-enriched/-depleted subgroups. The moderate deregulation of autophagy-related genes in cancer tissue, as compared to adjacent tissue, suggests a prominent field cancerization or early disruption of autophagy. Genes differentiating these clusters are promising candidates for CRC targeting therapy worthy of further investigation.

Subphenotypes in critical care: translation into clinical practice

Despite progress in the supportive care available for critically ill patients, few advances have been made in the search for effective disease-modifying therapeutic options. The fact that many trials in critical care medicine have not identified a treatment benefit is probably due, in part, to the underlying heterogeneity of critical care syndromes. Numerous approaches have been proposed to divide populations of critically ill patients into more meaningful subgroups (subphenotypes), some of which might be more useful than others. Subclassification systems driven by clinical features and biomarkers have been proposed for acute respiratory distress syndrome, sepsis, acute kidney injury, and pancreatitis. Identifying the systems that are most useful and biologically meaningful could lead to a better understanding of the pathophysiology of critical care syndromes and the discovery of new treatment targets, and allow recruitment in future therapeutic trials to focus on predicted responders. This Review discusses proposed subphenotypes of critical illness syndromes and highlights the issues that will need to be addressed to translate subphenotypes into clinical practice.

Unsupervised Analysis of Transcriptomics in Bacterial Sepsis Across Multiple Datasets Reveals Three Robust Clusters

OBJECTIVES

To find and validate generalizable sepsis subtypes using data-driven clustering.

DESIGN

We used advanced informatics techniques to pool data from 14 bacterial sepsis transcriptomic datasets from eight different countries (n = 700).

SETTING

Retrospective analysis.

SUBJECTS

Persons admitted to the hospital with bacterial sepsis.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A unified clustering analysis across 14 discovery datasets revealed three subtypes, which, based on functional analysis, we termed "Inflammopathic, Adaptive, and Coagulopathic." We then validated these subtypes in nine independent datasets from five different countries (n = 600). In both discovery and validation data, the Adaptive subtype is associated with a lower clinical severity and lower mortality rate, and the Coagulopathic subtype is associated with higher mortality and clinical coagulopathy. Further, these clusters are statistically associated with clusters derived by others in independent single sepsis cohorts.

CONCLUSIONS

The three sepsis subtypes may represent a unifying framework for understanding the molecular heterogeneity of the sepsis syndrome. Further study could potentially enable a precision medicine approach of matching novel immunomodulatory therapies with septic patients most likely to benefit.

The impact of DNA methylation on the cancer proteome

Aberrant DNA methylation disrupts normal gene expression in cancer and broadly contributes to oncogenesis. We previously developed MethylMix, a model-based algorithmic approach to identify epigenetically regulated driver genes. MethylMix identifies genes where methylation likely executes a functional role by using transcriptomic data to select only methylation events that can be linked to changes in gene expression. However, given that proteins more closely link genotype to phenotype recent high-throughput proteomic data provides an opportunity to more accurately identify functionally relevant abnormal methylation events. Here we present a MethylMix analysis that refines nominations for epigenetic driver genes by leveraging quantitative high-throughput proteomic data to select only genes where DNA methylation is predictive of protein abundance. Applying our algorithm across three cancer cohorts we find that using protein abundance data narrows candidate nominations, where the effect of DNA methylation is often buffered at the protein level. Next, we find that MethylMix genes predictive of protein abundance are enriched for biological processes involved in cancer including functions involved in epithelial and mesenchymal transition. Moreover, our results are also enriched for tumor markers which are predictive of clinical features like tumor stage and we find clustering using MethylMix genes predictive of protein abundance captures cancer subtypes.

To elucidate the molecular basis of cancer we examine the variation and dynamics characterizing the flow of information from epigenome to the transcriptome and proteome. Conducting the first genome wide analysis of epigenome-proteome associations, we present a MethylMix analysis that leverages protein abundance data taking advantage of recent high-throughput proteomic data generated using mass-spectrometry technology to elucidate the role of DNA methylation in cancer. By integrating across molecular data types, we confirm the benefit of using protein abundance data to provide additional insights into pathways and processes involved in oncogenesis and how they manifest as clinical phenotypes. Applying our method across three large cancer cohorts including breast cancer, ovarian cancer and colorectal cancer, MethylMix identifies key genes and describes molecular features and subtypes in these cancers.

Multilevel omic data clustering reveals variable contribution of methylator phenotype to integrative cancer subtypes

AIM

We aimed to assess to what extent CpG island methylator phenotype (CIMP) contributes to cancer subtypes obtained by multilevel omic data analysis.

MATERIALS & METHODS

16 The Cancer Genome Atlas datasets encompassing three data layers in 4688 tumor samples were analyzed. We identified cancer integrative subtypes (ISs) by the use of similarity network fusion and consensus clustering. CIMP high (CIMP-H) associated ISs were profiled by gene sets and transcriptional regulators enrichment analysis.

RESULTS & CONCLUSION

In nine out of 16 cancer datasets CIMP-H clusters significantly overlaped with unique ISs. The contribution of CIMP-H on integrative molecular profiling is variable; therefore, only in a subset of cancer types does CIMP-H contribute to homogenous integrative subtype. CIMP-H associated ISs are heterogenous groups with regard to deregulated pathways and transcriptional regulators.

Immunological landscape of consensus clusters in colorectal cancer

Recent, large-scale expression–based subtyping has advanced our understanding of the genomic landscape of colorectal cancer (CRC) and resulted in a consensus molecular classification that enables the categorization of most CRC tumors into one of four consensus molecular subtypes (CMS). Currently, major progress in characterization of immune landscape of tumor-associated microenvironment has been made especially with respect to microsatellite status of CRCs. While these studies profoundly improved the understanding of molecular and immunological profile of CRCs heterogeneity less is known about repertoire of the tumor infiltrating immune cells of each CMS.

In order to comprehensively characterize the immune landscape of CRC we re-analyzed a total of 15 CRC genome-wide expression data sets encompassing 1597 tumors and 125 normal adjacent colon tissues. After quality filtering, CRC clusters were discovered using a combination of multiple clustering algorithms and multiple validity metrics. CIBERSORT algorithm was used to compute relative proportions of 22 human leukocyte subpopulations across CRC clusters and normal colon tissue. Subsequently, differential expression specific to tumor epithelial cells was calculated to characterize mechanisms of tumor escape from immune surveillance occurring in particular CRC clusters.

Our results not only characterize the common and cluster-specific influx of immune cells into CRCs but also identify several deregulated gene targets that may contribute to improvement of immunotherapeutic strategies in CRC.

Pan-cancer analysis reveals presence of pronounced DNA methylation drift in CpG island methylator phenotype clusters

AIM

To provide characteristics of major genomic correlates in CpG island methylator phenotype-high (CIMP-H) subgroups in relation to corresponding non-CIMP-H subgroups by use of phenotypic, DNA methylation and RNAseq data.

MATERIALS & METHODS

Twenty-three datasets generated by The Cancer Genome Atlas project encompassing over 7200 unique samples were analyzed. We identified 23 CIMP-H clusters by use of unsupervised clustering.

RESULTS & CONCLUSION

More than 90% of CIMP-H clusters were significantly associated with accelerated epigenetic mitotic clock, demethylation of enhancer sites, backbone and repetitive sequences. Pronounced epigenetic drift observed in majority of CIMP-H subgroups may be related to increased cell division rate, which leads to expansion of DNA methylation errors. This may explain pan-cancer mechanism of establishing CIMP-H in majority of tissue types.

optCluster: An R Package for Determining the Optimal Clustering Algorithm

There exist numerous programs and packages that perform validation for a given clustering solution; however, clustering algorithms fare differently as judged by different validation measures. If more than one performance measure is used to evaluate multiple clustering partitions, an optimal result is often difficult to determine by visual inspection alone. This paper introduces optCluster, an R package that uses a single function to simultaneously compare numerous clustering partitions (created by different algorithms and/or numbers of clusters) and obtain a "best" option for a given dataset. The method of weighted rank aggregation is utilized by this package to objectively aggregate various performance measure scores, thereby taking away the guesswork that often follows a visual inspection of cluster results. The optCluster package contains biological validation measures as well as clustering algorithms developed specifically for RNA sequencing data, making it a useful tool for clustering genomic data.

AVAILABILITY

This package is available for free through the Comprehensive R Archive Network (CRAN) at http://cran.rproject.org/web/packages/optCluster/.