Cancer staging and treatment presumes a division into localized or metastatic disease. We proposed an intermediate state defined by ≤ 5 cumulative metastasis(es), termed oligometastases. In contrast ...to widespread polymetastases, oligometastatic patients may benefit from metastasis-directed local treatments. However, many patients who initially present with oligometastases progress to polymetastases. Predictors of progression could improve patient selection for metastasis-directed therapy.
Here, we identified patterns of microRNA expression of tumor samples from oligometastatic patients treated with high-dose radiotherapy.
Patients who failed to develop polymetastases are characterized by unique prioritized features of a microRNA classifier that includes the microRNA-200 family. We created an oligometastatic-polymetastatic xenograft model in which the patient-derived microRNAs discriminated between the two metastatic outcomes. MicroRNA-200c enhancement in an oligometastatic cell line resulted in polymetastatic progression.
These results demonstrate a biological basis for oligometastases and a potential for using microRNA expression to identify patients most likely to remain oligometastatic after metastasis-directed treatment.
Strategies to stage and treat cancer rely on a presumption of either localized or widespread metastatic disease. An intermediate state of metastasis termed oligometastasis(es) characterized by ...limited progression has been proposed. Oligometastases are amenable to treatment by surgical resection or radiotherapy.
We analyzed microRNA expression patterns from lung metastasis samples of patients with ≤ 5 initial metastases resected with curative intent.
Patients were stratified into subgroups based on their rate of metastatic progression. We prioritized microRNAs between patients with the highest and lowest rates of recurrence. We designated these as high rate of progression (HRP) and low rate of progression (LRP); the latter group included patients with no recurrences. The prioritized microRNAs distinguished HRP from LRP and were associated with rate of metastatic progression and survival in an independent validation dataset.
Oligo- and poly- metastasis are distinct entities at the clinical and molecular level.
Due to the large number of putative microRNA gene targets predicted by sequence-alignment databases and the relative low accuracy of such predictions which are conducted independently of biological ...context by design, systematic experimental identification and validation of every functional microRNA target is currently challenging. Consequently, biological studies have yet to identify, on a genome scale, key regulatory networks perturbed by altered microRNA functions in the context of cancer. In this report, we demonstrate for the first time how phenotypic knowledge of inheritable cancer traits and of risk factor loci can be utilized jointly with gene expression analysis to efficiently prioritize deregulated microRNAs for biological characterization. Using this approach we characterize miR-204 as a tumor suppressor microRNA and uncover previously unknown connections between microRNA regulation, network topology, and expression dynamics. Specifically, we validate 18 gene targets of miR-204 that show elevated mRNA expression and are enriched in biological processes associated with tumor progression in squamous cell carcinoma of the head and neck (HNSCC). We further demonstrate the enrichment of bottleneckness, a key molecular network topology, among miR-204 gene targets. Restoration of miR-204 function in HNSCC cell lines inhibits the expression of its functionally related gene targets, leads to the reduced adhesion, migration and invasion in vitro and attenuates experimental lung metastasis in vivo. As importantly, our investigation also provides experimental evidence linking the function of microRNAs that are located in the cancer-associated genomic regions (CAGRs) to the observed predisposition to human cancers. Specifically, we show miR-204 may serve as a tumor suppressor gene at the 9q21.1-22.3 CAGR locus, a well established risk factor locus in head and neck cancers for which tumor suppressor genes have not been identified. This new strategy that integrates expression profiling, genetics and novel computational biology approaches provides for improved efficiency in characterization and modeling of microRNA functions in cancer as compared to the state of art and is applicable to the investigation of microRNA functions in other biological processes and diseases.
In a pair of papers in this issue of Science Translational Medicine, Butte et al. provide a concrete example of how reinterpreting and comparing genome-wide metrics allows us to effectively ...hypothesize which drugs from one disease-indication can be repurposed for another disease. The shift toward integrative genome-wide computational approaches has precedence in insightful scalar theories of biological information. Here, we discuss how this recent work in drug repurposing adheres to and takes advantage of the concepts surrounding this information paradigm.
Variation in gene expression has been observed in natural populations and associated with complex traits or phenotypes such as disease susceptibility and drug response. Gene expression itself is ...controlled by various genetic and non-genetic factors. The binding of a class of small RNA molecules, microRNAs (miRNAs), to mRNA transcript targets has recently been demonstrated to be an important mechanism of gene regulation. Because individual miRNAs may regulate the expression of multiple gene targets, a comprehensive and reliable catalogue of miRNA-regulated targets is critical to understanding gene regulatory networks. Though experimental approaches have been used to identify many miRNA targets, due to cost and efficiency, current miRNA target identification still relies largely on computational algorithms that aim to take advantage of different biochemical/thermodynamic properties of the sequences of miRNAs and their gene targets. A novel approach, ExprTarget, therefore, is proposed here to integrate some of the most frequently invoked methods (miRanda, PicTar, TargetScan) as well as the genome-wide HapMap miRNA and mRNA expression datasets generated in our laboratory. To our knowledge, this dataset constitutes the first miRNA expression profiling in the HapMap lymphoblastoid cell lines. We conducted diagnostic tests of the existing computational solutions using the experimentally supported targets in TarBase as gold standard. To gain insight into the biases that arise from such an analysis, we investigated the effect of the choice of gold standard on the evaluation of the various computational tools. We analyzed the performance of ExprTarget using both ROC curve analysis and cross-validation. We show that ExprTarget greatly improves miRNA target prediction relative to the individual prediction algorithms in terms of sensitivity and specificity. We also developed an online database, ExprTargetDB, of human miRNA targets predicted by our approach that integrates gene expression profiling into a broader framework involving important features of miRNA target site predictions.
In this era of data science-driven bioinformatics, machine learning research has focused on feature selection as users want more interpretation and post-hoc analyses for biomarker detection. However, ...when there are more features (i.e., transcripts) than samples (i.e., mice or human samples) in a study, it poses major statistical challenges in biomarker detection tasks as traditional statistical techniques are underpowered in high dimension. Second and third order interactions of these features pose a substantial combinatoric dimensional challenge. In computational biology, random forest (RF) classifiers are widely used due to their flexibility, powerful performance, their ability to rank features, and their robustness to the "P > > N" high-dimensional limitation that many matrix regression algorithms face. We propose binomialRF, a feature selection technique in RFs that provides an alternative interpretation for features using a correlated binomial distribution and scales efficiently to analyze multiway interactions.
In both simulations and validation studies using datasets from the TCGA and UCI repositories, binomialRF showed computational gains (up to 5 to 300 times faster) while maintaining competitive variable precision and recall in identifying biomarkers' main effects and interactions. In two clinical studies, the binomialRF algorithm prioritizes previously-published relevant pathological molecular mechanisms (features) with high classification precision and recall using features alone, as well as with their statistical interactions alone.
binomialRF extends upon previous methods for identifying interpretable features in RFs and brings them together under a correlated binomial distribution to create an efficient hypothesis testing algorithm that identifies biomarkers' main effects and interactions. Preliminary results in simulations demonstrate computational gains while retaining competitive model selection and classification accuracies. Future work will extend this framework to incorporate ontologies that provide pathway-level feature selection from gene expression input data.
DNA variants that affect alternative splicing and the relative quantities of different gene transcripts have been shown to be risk alleles for some Mendelian diseases. However, for complex traits ...characterized by a low odds ratio for any single contributing variant, very few studies have investigated the contribution of splicing variants. The overarching goal of this study is to discover and characterize the role that variants affecting alternative splicing may play in the genetic etiology of complex traits, which include a significant number of the common human diseases. Specifically, we hypothesize that single nucleotide polymorphisms (SNPs) in splicing regulatory elements can be characterized in silico to identify variants affecting splicing, and that these variants may contribute to the etiology of complex diseases as well as the inter-individual variability in the ratios of alternative transcripts. We leverage high-throughput expression profiling to 1) experimentally validate our in silico predictions of skipped exons and 2) characterize the molecular role of intronic genetic variations in alternative splicing events in the context of complex human traits and diseases. We propose that intronic SNPs play a role as genetic regulators within splicing regulatory elements and show that their associated exon skipping events can affect protein domains and structure. We find that SNPs we would predict to affect exon skipping are enriched among the set of SNPs reported to be associated with complex human traits.
Differences in the lung microbial community influence idiopathic pulmonary fibrosis (IPF) progression. Whether the lung microbiome influences IPF host defense remains unknown.
To explore the host ...immune response and microbial interaction in IPF as they relate to progression-free survival (PFS), fibroblast function, and leukocyte phenotypes.
Paired microarray gene expression data derived from peripheral blood mononuclear cells as well as 16S ribosomal RNA sequencing data from bronchoalveolar lavage obtained as part of the COMET-IPF (Correlating Outcomes with Biochemical Markers to Estimate Time-Progression in Idiopathic Pulmonary Fibrosis) study were used to conduct association pathway analyses. The responsiveness of paired lung fibroblasts to Toll-like receptor 9 (TLR9) stimulation by CpG-oligodeoxynucleotide (CpG-ODN) was integrated into microbiome-gene expression association analyses for a subset of individuals. The relationship between associated pathways and circulating leukocyte phenotypes was explored by flow cytometry.
Down-regulation of immune response pathways, including nucleotide-binding oligomerization domain (NOD)-, Toll-, and RIG1-like receptor pathways, was associated with worse PFS. Ten of the 11 PFS-associated pathways correlated with microbial diversity and individual genus, with species accumulation curve richness as a hub. Higher species accumulation curve richness was significantly associated with inhibition of NODs and TLRs, whereas increased abundance of Streptococcus correlated with increased NOD-like receptor signaling. In a network analysis, expression of up-regulated signaling pathways was strongly associated with decreased abundance of operational taxonomic unit 1341 (OTU1341; Prevotella) among individuals with fibroblasts responsive to CpG-ODN stimulation. The expression of TLR signaling pathways was also linked to CpG-ODN responsive fibroblasts, OTU1341 (Prevotella), and Shannon index of microbial diversity in a network analysis. Lymphocytes expressing C-X-C chemokine receptor 3 CD8 significantly correlated with OTU1348 (Staphylococcus).
These findings suggest that host-microbiome interactions influence PFS and fibroblast responsiveness.
Chang et al discuss their study which tested their hypothesis that rhinovirus infections could increase expression of ACE2 and subsequently activate cytokine pathways associated with severe COVID-19 ...infections. The study involved the development of air-liquid interface (ALI) cultures from nasal tissues biopsied from 30 adults with physician-diagnosed asthma. The results suggest that RV infections are potential mechanisms of ACE2 overexpression in patients with asthma and ACE2 activation regulates multiple cytokine antiviral responses. These results suggest that viral infections associated with asthma exacerbations exhibit synergistic biomolecular interactions with SARS-CoV-2 infection.
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