Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene ...analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets.
To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments.
GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.
Critical driver genomic events in colorectal cancer have been shown to affect the response to targeted agents that were initially developed under the 'one gene, one drug' paradigm of precision ...medicine. Our current knowledge of the complexity of the cancer genome, clonal evolution patterns under treatment pressure and pharmacodynamic effects of target inhibition support the transition from a one gene, one drug approach to a 'multi-gene, multi-drug' model when making therapeutic decisions. Better characterization of the transcriptomic subtypes of colorectal cancer, encompassing tumour, stromal and immune components, has revealed convergent pathway dependencies that mandate a 'multi-molecular' perspective for the development of therapies to treat this disease.
The ability to measure human aging from molecular profiles has practical implications in many fields, including disease prevention and treatment, forensics, and extension of life. Although ...chronological age has been linked to changes in DNA methylation, the methylome has not yet been used to measure and compare human aging rates. Here, we build a quantitative model of aging using measurements at more than 450,000 CpG markers from the whole blood of 656 human individuals, aged 19 to 101. This model measures the rate at which an individual's methylome ages, which we show is impacted by gender and genetic variants. We also show that differences in aging rates help explain epigenetic drift and are reflected in the transcriptome. Moreover, we show how our aging model is upheld in other human tissues and reveals an advanced aging rate in tumor tissue. Our model highlights specific components of the aging process and provides a quantitative readout for studying the role of methylation in age-related disease.
► Human aging rates can be quantified from the methylome ► Aging rates are affected by both gender and genetics ► Different aging rates account for part of epigenetic drift ► Methylome aging rates correspond with patterns of transcription
mutation is a common canonical mutation in colorectal cancer, found at differing frequencies in all consensus molecular subtypes (CMS). The independent immunobiological impacts of RAS mutation and ...CMS are unknown. Thus, we explored the immunobiological effects of
mutation across the CMS spectrum.
Expression analysis of immune genes/signatures was performed using The Cancer Genome Atlas (TCGA) RNA-seq and the KFSYSCC microarray datasets. Multivariate analysis included
status, CMS, tumor location, MSI status, and neoantigen load. Protein expression of STAT1, HLA-class II, and CXCL10 was analyzed by digital IHC.
The Th1-centric co-ordinate immune response cluster (CIRC) was significantly, albeit modestly, reduced in
-mutant colorectal cancer in both datasets. Cytotoxic T cells, neutrophils, and the IFNγ pathway were suppressed in
-mutant samples. The expressions of STAT1 and CXCL10 were reduced at the mRNA and protein levels. In multivariate analysis,
mutation, CMS2, and CMS3 were independently predictive of reduced CIRC expression. Immune response was heterogeneous across
-mutant colorectal cancer:
-mutant CMS2 samples have the lowest CIRC expression, reduced expression of the IFNγ pathway,
and
, and reduced infiltration of cytotoxic cells and neutrophils relative to CMS1 and CMS4 and to
wild-type CMS2 samples in the TCGA. These trends held in the KFSYSCC dataset.
mutation is associated with suppressed Th1/cytotoxic immunity in colorectal cancer, the extent of the effect being modulated by CMS subtype. These results add a novel immunobiological dimension to the biological heterogeneity of colorectal cancer.
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Comprehensive genomic profiling is expected to revolutionize cancer therapy. In this Prospective, we present the prevalence of mutations and copy-number alterations with predictive associations ...across solid tumors at different levels of stringency for gene-drug targetability. More than 90% of The Cancer Genome Atlas samples have potentially targetable alterations, the majority with multiple events, illustrating the challenges for treatment prioritization given the complexity of the genomic landscape. Nearly 80% of the variants in rarely mutated oncogenes are of uncertain functional significance, reflecting the gap in our understanding of the relevance of many alterations potentially linked to therapeutic actions. Access to targeted agents in early clinical trials could affect treatment decision in 75% of patients with cancer. Prospective implementation of large-scale molecular profiling and standardized reports of predictive biomarkers are fundamental steps for making precision cancer medicine a reality.
Synthetic health data have the potential to mitigate privacy concerns in supporting biomedical research and healthcare applications. Modern approaches for data generation continue to evolve and ...demonstrate remarkable potential. Yet there is a lack of a systematic assessment framework to benchmark methods as they emerge and determine which methods are most appropriate for which use cases. In this work, we introduce a systematic benchmarking framework to appraise key characteristics with respect to utility and privacy metrics. We apply the framework to evaluate synthetic data generation methods for electronic health records data from two large academic medical centers with respect to several use cases. The results illustrate that there is a utility-privacy tradeoff for sharing synthetic health data and further indicate that no method is unequivocally the best on all criteria in each use case, which makes it evident why synthetic data generation methods need to be assessed in context.
Identifying the cells of origin of lung cancer may lead to new therapeutic strategies. Previous work has focused upon the putative bronchoalveolar stem cell at the bronchioalveolar duct junction as a ...cancer cell of origin when a codon 12 K-Ras mutant is induced via adenoviral Cre inhalation. In the present study, we use two "knock-in" Cre-estrogen receptor alleles to inducibly express K-RasG12D in CC10+ epithelial cells and Sftpc+ type II alveolar cells of the adult mouse lung. Analysis of these mice identifies type II cells, Clara cells in the terminal bronchioles, and putative bronchoalveolar stem cells as cells of origin for K-Ras–induced lung hyperplasia. However, only type II cells appear to progress to adenocarcinoma.
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