Sexually-dimorphic targeting of functionally-related genes in COPD

BACKGROUND

There is growing evidence that many diseases develop, progress, and respond to therapy differently in men and women. This variability may manifest as a result of sex-specific structures in gene regulatory networks that influence how those networks operate. However, there are few methods to identify and characterize differences in network structure, slowing progress in understanding mechanisms driving sexual dimorphism.

RESULTS

Here we apply an integrative network inference method, PANDA (Passing Attributes between Networks for Data Assimilation), to model sex-specific networks in blood and sputum samples from subjects with Chronic Obstructive Pulmonary Disease (COPD). We used a jack-knifing approach to build an ensemble of likely networks for each sex. By adapting statistical methods to compare these network ensembles, we were able to identify strong differential-targeting patterns associated with functionally-related sets of genes, including those involved in mitochondrial function and energy metabolism. Network analysis also identified several potential sex- and disease-specific transcriptional regulators of these pathways.

CONCLUSIONS

Network analysis yielded insight into potential mechanisms driving sexual dimorphism in COPD that were not evident from gene expression analysis alone. We believe our ensemble approach to network analysis provides a principled way to capture sex-specific regulatory relationships and could be applied to identify differences in gene regulatory patterns in a wide variety of diseases and contexts.

Genetic control of gene expression at novel and established chronic obstructive pulmonary disease loci

Genetic risk loci have been identified for a wide range of diseases through genome-wide association studies (GWAS), but the relevant functional mechanisms have been identified for only a small proportion of these GWAS-identified loci. By integrating results from the largest current GWAS of chronic obstructive disease (COPD) with expression quantitative trait locus (eQTL) analysis in whole blood and sputum from 121 subjects with COPD from the ECLIPSE Study, this analysis identifies loci that are simultaneously associated with COPD and the expression of nearby genes (COPD eQTLs). After integrative analysis, 19 COPD eQTLs were identified, including all four previously identified genome-wide significant loci near HHIP, FAM13A, and the 15q25 and 19q13 loci. For each COPD eQTL, fine mapping and colocalization analysis to identify causal shared eQTL and GWAS variants identified a subset of sites with moderate-to-strong evidence of harboring at least one shared variant responsible for both the eQTL and GWAS signals. Transcription factor binding site (TFBS) analysis confirms that multiple COPD eQTL lead SNPs disrupt TFBS, and enhancer enrichment analysis for loci with the strongest colocalization signals showed enrichment for blood-related cell types (CD3 and CD4+ T cells, lymphoblastoid cell lines). In summary, integrative eQTL and GWAS analysis confirms that genetic control of gene expression plays a key role in the genetic architecture of COPD and identifies specific blood-related cell types as likely participants in the functional pathway from GWAS-associated variant to disease phenotype.

Abstract 3269: QTLs in breast tumor and breast normal adjacent FFPE specimens from the Nurses’ Health Study

Rationale: Genome-wide association studies (GWAS) of breast cancer have identified 71 risk alleles, majority of which are located in intergenic or intronic regions. We used a network medicine approach to associate breast cancer risk single nucleotide polymorphisms (SNPs) with transcript abundance in formalin fixed paraffin embedded (FFPE) breast tissue. Identification of expression quantitative loci (eQTLs) may help to better understand the regulatory mechanisms by which these risk variants influence breast cancer susceptibility.

Method: We identified invasive postmenopausal breast cancer cases in the Nurses’ Health Study (NHS) diagnosed from 1990-2004 with GWAS data and sufficient RNA for expression profiling in breast tumor and normal adjacent breast tissue. RNA was extracted using the Qiagen AllPrep RNA isolation kit, amplified using the NuGen Ovation FFPE WTA kit, and profiled using the Affymetrix Human Transcriptome Array (HTA 3.0v1). The HTA includes 6,892,960 features for measuring gene expression, alternative splicing, coding SNPs, and noncoding transcripts. After filtering and removing probes with a low dynamic range in our samples, we included 25,000 gene expression probes in our QTL analyses. We mapped these probes to genes using hg19.

Results: Analyses were conducted separately for 376 breast tumor and 274 normal adjacent samples. We conducted quality control analyses, corrected for assay plate-to-plate variation and included patient's age at diagnosis and year of diagnosis as covariates in the multivariate linear regression model. We identified 11 trans eQTLs in normal adjacent breast tissue, 11 trans eQTLs in estrogen receptor (ER)+ breast tumor samples, and 12 trans eQTLs in ER- breast tumor samples (permutation adjusted p-value<0.05). We also developed a new method, functional quantitative trait loci (fQTL) analysis, to gain additional pathway insight into genetic associations important in breast cancer. In the fQTL analysis we tested for the association between SNPs and the expression of gene functional classes and pathways, evaluating the hypothesis that SNPs may also be associated with regulation of processes in addition to individual genes. Using a cutoff of false discovery rate <10%, we identified 2 SNPs associated with 2 Gene Ontology Molecular Functions in normal adjacent breast tissue, 1 SNP associated with 5 Molecular Functions in ER- tumor samples but no significant SNP associations in ER+ tumor samples. Integrated eQTL and fQTL analyses and variant annotation are ongoing.

Conclusion: In summary, our results provide functional insights on the underlying biology of loci identified in breast cancer GWAS in the specimen type that is most impactful in translation to clinical practice. Identification of gene transcripts that can be measured in FFPE tissue and are associated with breast cancer risk loci is critical in understanding the mechanism by which these variants affect risk and mediate disease processes.

Citation Format: Alejandro Quiroz-Zarate, Benjamin J. Harshfield, Rong Hu, Nick Knoblauch, Andrew H. Beck, Vincent Carey, Susan E. Hankinson, Rulla M. Tamimi, David J. Hunter, John Quackenbush, Aditi Hazra. QTLs in breast tumor and breast normal adjacent FFPE specimens from the Nurses’ Health Study. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3269. doi:10.1158/1538-7445.AM2014-3269

Gene expression variability as a unifying element of the pluripotency network

Heterogeneity is a hallmark of stem cell populations, in part due to the molecular differences between cells undergoing self-renewal and those poised to differentiate. We examined phenotypic and molecular heterogeneity in pluripotent stem cell populations, using public gene expression data sets. A high degree of concordance was observed between global gene expression variability and the reported heterogeneity of different human pluripotent lines. Network analysis demonstrated that low-variability genes were the most highly connected, suggesting that these are the most stable elements of the gene regulatory network and are under the highest regulatory constraints. Known drivers of pluripotency were among these, with lowest expression variability of POU5F1 in cells with the highest capacity for self-renewal. Variability of gene expression provides a reliable measure of phenotypic and molecular heterogeneity and predicts those genes with the highest degree of regulatory constraint within the pluripotency network.

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Gene expression variability is highly concordant with population heterogeneity

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Genes within the pluripotency network have distinct variability profiles

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Expression variability is a network property important for pluripotency

Enhancing reproducibility in cancer drug screening: how do we move forward?

Large-scale pharmacogenomic high-throughput screening (HTS) studies hold great potential for generating robust genomic predictors of drug response. Two recent large-scale HTS studies have reported results of such screens, revealing several known and novel drug sensitivities and biomarkers. Subsequent evaluation, however, found only moderate interlaboratory concordance in the drug response phenotypes, possibly due to differences in the experimental protocols used in the two studies. This highlights the need for community-wide implementation of standardized assays for measuring drug response phenotypes so that the full potential of HTS is realized. We suggest that the path forward is to establish best practices and standardization of the critical steps in these assays through a collective effort to ensure that the data produced from large-scale screens would not only be of high intrastudy consistency, so that they could be replicated and compared successfully across multiple laboratories.

Analyzing networks of phenotypes in complex diseases: methodology and applications in COPD

Background

The investigation of complex disease heterogeneity has been challenging. Here, we introduce a network-based approach, using partial correlations, that analyzes the relationships among multiple disease-related phenotypes.

Results

We applied this method to two large, well-characterized studies of chronic obstructive pulmonary disease (COPD). We also examined the associations between these COPD phenotypic networks and other factors, including case-control status, disease severity, and genetic variants. Using these phenotypic networks, we have detected novel relationships between phenotypes that would not have been observed using traditional epidemiological approaches.

Conclusion

Phenotypic network analysis of complex diseases could provide novel insights into disease susceptibility, disease severity, and genetic mechanisms.

Relevance of different prior knowledge sources for inferring gene interaction networks

When inferring networks from high-throughput genomic data, one of the main challenges is the subsequent validation of these networks. In the best case scenario, the true network is partially known from previous research results published in structured databases or research articles. Traditionally, inferred networks are validated against these known interactions. Whenever the recovery rate is gauged to be high enough, subsequent high scoring but unknown inferred interactions are deemed good candidates for further experimental validation. Therefore such validation framework strongly depends on the quantity and quality of published interactions and presents serious pitfalls: (1) availability of these known interactions for the studied problem might be sparse; (2) quantitatively comparing different inference algorithms is not trivial; and (3) the use of these known interactions for validation prevents their integration in the inference procedure. The latter is particularly relevant as it has recently been showed that integration of priors during network inference significantly improves the quality of inferred networks. To overcome these problems when validating inferred networks, we recently proposed a data-driven validation framework based on single gene knock-down experiments. Using this framework, we were able to demonstrate the benefits of integrating prior knowledge and expression data. In this paper we used this framework to assess the quality of different sources of prior knowledge on their own and in combination with different genomic data sets in colorectal cancer. We observed that most prior sources lead to significant F-scores. Furthermore, their integration with genomic data leads to a significant increase in F-scores, especially for priors extracted from full text PubMed articles, known co-expression modules and genetic interactions. Lastly, we observed that the results are consistent for three different data sets: experimental knock-down data and two human tumor data sets.

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are extracted from computed tomography data of 1,019 patients with lung or head-and-neck cancer. We find that a large number of radiomic features have prognostic power in independent data sets of lung and head-and-neck cancer patients, many of which were not identified as significant before. Radiogenomics analysis reveals that a prognostic radiomic signature, capturing intratumour heterogeneity, is associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.

An individual tumour is often heterogeneous and its various features can be visualised noninvasively using medical imaging. Here, the authors analyse large computed tomography data sets using radiomic algorithms to identify heterogeneity, and find that some of these tumour features have prognostic value across cancer types.

Comparative genome-wide transcriptional analysis of human left and right internal mammary arteries

In coronary artery bypass grafting (CABG), the combined use of left and right internal mammary arteries (LIMA and RIMA) - collectively known as bilateral IMAs (BIMAs) provides a survival advantage over the use of LIMA alone. However, gene expression in RIMA has never been compared to that in LIMA. Here we report a genome-wide transcriptional analysis of BIMA to investigate the expression profiles of these conduits in patients undergoing CABG. As expected, in comparing the BIMAs to the aorta, we found differences in pathways and processes associated with atherosclerosis, inflammation, and cell signaling - pathways which provide biological support for the observation that BIMA grafts deliver long-term benefits to the patients and protect against continued atherosclerosis. These data support the widespread use of BIMAs as the preferred conduits in CABG.

Inference and validation of predictive gene networks from biomedical literature and gene expression data

Although many methods have been developed for inference of biological networks, the validation of the resulting models has largely remained an unsolved problem. Here we present a framework for quantitative assessment of inferred gene interaction networks using knock-down data from cell line experiments. Using this framework we are able to show that network inference based on integration of prior knowledge derived from the biomedical literature with genomic data significantly improves the quality of inferred networks relative to other approaches. Our results also suggest that cell line experiments can be used to quantitatively assess the quality of networks inferred from tumor samples.

Tumor associated seizures in glioblastomas are influenced by survival gene expression in a region-specific manner: a gene expression imaging study

Tumor associated seizures (TAS) are common and cause significant morbidity. Both imaging and gene expression features play significant roles in determining TAS, with strong interactions between them. We describe gene expression imaging tools which allow mapping of brain regions where gene expression has significant influence on TAS, and apply these methods to study 77 patients who underwent surgical evaluation for supratentorial glioblastomas. Tumor size and location were measured from MRI scans. A 9-set gene expression profile predicting long-term survivors was obtained from RNA derived from formalin-fixed paraffin embedded tissue. A total of 32 patients (42%) experienced preoperative TAS. Tumor volume was smaller (31.1 vs. 58.8 cubic cm, p<0.001) and there was a trend toward median survival being higher (48.4 vs. 32.7 months, p=0.055) in patients with TAS. Although the expression of only OLIG2 was significantly lower in patients with TAS in a groupwise analysis, gene expression imaging analysis revealed regions with significantly lower expression of OLIG2 and RTN1 in patients with TAS. Gene expression imaging is a powerful technique that demonstrates that the influence of gene expression on TAS is highly region specific. Regional variability should be evaluated with any genomic or molecular markers of solid brain lesions.

Inconsistency in large pharmacogenomic studies

Two large-scale pharmacogenomic studies were published recently in this journal. Genomic data are well correlated between studies; however, the measured drug response data are highly discordant. Although the source of inconsistencies remains uncertain, it has potential implications for using these outcome measures to assess gene-drug associations or select potential anticancer drugs on the basis of their reported results.

A genome-wide aberrant RNA splicing in patients with acute myeloid leukemia identifies novel potential disease markers and therapeutic targets

PURPOSE

Despite new treatments, acute myeloid leukemia (AML) remains an incurable disease. More effective drug design requires an expanded view of the molecular complexity that underlies AML. Alternative splicing of RNA is used by normal cells to generate protein diversity. Growing evidence indicates that aberrant splicing of genes plays a key role in cancer. We investigated genome-wide splicing abnormalities in AML and based on these abnormalities, we aimed to identify novel potential biomarkers and therapeutic targets.

EXPERIMENTAL DESIGN

We used genome-wide alternative splicing screening to investigate alternative splicing abnormalities in two independent AML patient cohorts [Dana-Farber Cancer Institute (DFCI) (Boston, MA) and University Hospital de Nantes (UHN) (Nantes, France)] and normal donors. Selected splicing events were confirmed through cloning and sequencing analysis, and than validated in 193 patients with AML.

RESULTS

Our results show that approximately 29% of expressed genes genome-wide were differentially and recurrently spliced in patients with AML compared with normal donors bone marrow CD34(+) cells. Results were reproducible in two independent AML cohorts. In both cohorts, annotation analyses indicated similar proportions of differentially spliced genes encoding several oncogenes, tumor suppressor proteins, splicing factors, and heterogeneous-nuclear-ribonucleoproteins, proteins involved in apoptosis, cell proliferation, and spliceosome assembly. Our findings are consistent with reports for other malignances and indicate that AML-specific aberrations in splicing mechanisms are a hallmark of AML pathogenesis.

CONCLUSIONS

Overall, our results suggest that aberrant splicing is a common characteristic for AML. Our findings also suggest that splice variant transcripts that are the result of splicing aberrations create novel disease markers and provide potential targets for small molecules or antibody therapeutics for this disease.

Gene expression signature of normal cell-of-origin predicts ovarian tumor outcomes

The potential role of the cell-of-origin in determining the tumor phenotype has been raised, but not adequately examined. We hypothesized that distinct cells-of-origin may play a role in determining ovarian tumor phenotype and outcome. Here we describe a new cell culture medium for in vitro culture of paired normal human ovarian (OV) and fallopian tube (FT) epithelial cells from donors without cancer. While these cells have been cultured individually for short periods of time, to our knowledge this is the first long-term culture of both cell types from the same donors. Through analysis of the gene expression profiles of the cultured OV/FT cells we identified a normal cell-of-origin gene signature that classified primary ovarian cancers into OV-like and FT-like subgroups; this classification correlated with significant differences in clinical outcomes. The identification of a prognostically significant gene expression signature derived solely from normal untransformed cells is consistent with the hypothesis that the normal cell-of-origin may be a source of ovarian tumor heterogeneity and the associated differences in tumor outcome.

Histone deacetylase 6-mediated selective autophagy regulates COPD-associated cilia dysfunction

Chronic obstructive pulmonary disease (COPD) involves aberrant airway inflammatory responses to cigarette smoke (CS) that are associated with epithelial cell dysfunction, cilia shortening, and mucociliary clearance disruption. Exposure to CS reduced cilia length and induced autophagy in vivo and in differentiated mouse tracheal epithelial cells (MTECs). Autophagy-impaired (Becn1+/- or Map1lc3B-/-) mice and MTECs resisted CS-induced cilia shortening. Furthermore, CS increased the autophagic turnover of ciliary proteins, indicating that autophagy may regulate cilia homeostasis. We identified cytosolic deacetylase HDAC6 as a critical regulator of autophagy-mediated cilia shortening during CS exposure. Mice bearing an X chromosome deletion of Hdac6 (Hdac6-/Y) and MTECs from these mice had reduced autophagy and were protected from CS-induced cilia shortening. Autophagy-impaired Becn1-/-, Map1lc3B-/-, and Hdac6-/Y mice or mice injected with an HDAC6 inhibitor were protected from CS-induced mucociliary clearance (MCC) disruption. MCC was preserved in mice given the chemical chaperone 4-phenylbutyric acid, but was disrupted in mice lacking the transcription factor NRF2, suggesting that oxidative stress and altered proteostasis contribute to the disruption of MCC. Analysis of human COPD specimens revealed epigenetic deregulation of HDAC6 by hypomethylation and increased protein expression in the airways. We conclude that an autophagy-dependent pathway regulates cilia length during CS exposure and has potential as a therapeutic target for COPD.

CXCR4 pathway associated with family history of melanoma

PURPOSE

Genetic predisposition plays a major role in the etiology of melanoma, but known genetic markers only account for a limited fraction of family-history-associated melanoma cases. Expression microarrays have offered the opportunity to identify further genomic profiles correlated with family history of melanoma. We aimed to distinguish mRNA expression signatures between melanoma cases with and without a family history of melanoma.

METHODS

Based on the Nurses' Health Study, family history was defined as having one or more first-degree family members diagnosed with melanoma. Melanoma diagnosis was confirmed by reviewing pathology reports, and tumor blocks were collected by mail from across the USA. Genomic interrogation was accomplished through evaluating expression profiling of formalin-fixed paraffin-embedded tissues from 78 primary cutaneous invasive melanoma cases, on either a 6K or whole-genome (24K) Illumina gene chip. Gene set enrichment analysis was performed for each batch to determine the differentially enriched pathways and key contributing genes.

RESULTS

The CXC chemokine receptor 4 (CXCR4) pathway was consistently up-regulated within cases of familial melanoma in both platforms. Leading edge analysis showed four genes from the CXCR4 pathway, including MAPK1, PLCG1, CRK, and PTK2, were among the core members that contributed to the enrichment of this pathway. There was no association between the enrichment of CXCR4 pathway and NRAS, BRAF mutation, or Breslow thickness of the primary melanoma cases.

CONCLUSIONS

We found that the CXCR4 pathway might constitute a novel susceptibility pathway associated with family history of melanoma in first-degree relatives.

Differentiating progressive from nonprogressive T1 bladder cancer by gene expression profiling: applying RNA-sequencing analysis on archived specimens

OBJECTIVES

To identify gene signatures in transitional cell carcinoma that can differentiate high-grade T1 nonprogressive (T1NP) bladder cancer (BCa) from those T1 progressive (T1P) tumors that progress to muscularis propria-invasive T2 tumors.

MATERIALS AND METHODS

We performed a high-throughput RNA sequencing (RNA-Seq) on formalin-fixed and paraffin-embedded BCa specimens with clinical pathologic characteristics best representing the general clinical development of the disease. For the T1NP group, only patients with long-term follow-up (6-17y) and periodic examinations (average of 4 resections and 9 cytology tests) were selected. For the T1P group, only patients in whom a complete resection was performed after a minimum of 8 months after the initial T1 diagnosis were selected, therefore eliminating the possibility of underdiagnosis. Only samples in which muscularis propria was present and uninvolved were included, further assuring a correct diagnosis. The RNA-Seq reads were mapped to the human genome build NCBI 36 (hg18) using TopHat with no mismatch. After alignment to the transcriptome and expression quantification, a linear statistical model was built using Limma between T1NP and T1P samples to identify differentially expressed genes.

RESULTS

Overall, 5,561 genes were mapped to all samples and used for RNA-Seq analysis to identify a gene signature that was significantly and differentially expressed between patients with T1NP BCa and patients with T1P BCa. Signature-based stratification indicated the gene signature correlated notably with the time of T1 development to T2 tumor, suggesting that the molecular signature might be used as an independent predictor for the pace of high-grade T1 BCa progression.

CONCLUSIONS

This is the first demonstration that RNA-Seq can be applied as a powerful tool to study BCa using formalin-fixed and paraffin-embedded specimens. We identified a gene signature that can distinguish patients diagnosed with high-grade T1 BCas that remain as non-muscle invasive tumors from those patients with cancers progressing to muscle-invasive tumors. Our findings will make future large-scale clinical cohort studies and clinical trial-based studies possible and help the development of prognostic tools for accurate prediction of T1 BCa progression that may considerably influence the clinical decision-making process, treatment regimen, and patient survival.

Passing messages between biological networks to refine predicted interactions

Regulatory network reconstruction is a fundamental problem in computational biology. There are significant limitations to such reconstruction using individual datasets, and increasingly people attempt to construct networks using multiple, independent datasets obtained from complementary sources, but methods for this integration are lacking. We developed PANDA (Passing Attributes between Networks for Data Assimilation), a message-passing model using multiple sources of information to predict regulatory relationships, and used it to integrate protein-protein interaction, gene expression, and sequence motif data to reconstruct genome-wide, condition-specific regulatory networks in yeast as a model. The resulting networks were not only more accurate than those produced using individual data sets and other existing methods, but they also captured information regarding specific biological mechanisms and pathways that were missed using other methodologies. PANDA is scalable to higher eukaryotes, applicable to specific tissue or cell type data and conceptually generalizable to include a variety of regulatory, interaction, expression, and other genome-scale data. An implementation of the PANDA algorithm is available at www.sourceforge.net/projects/panda-net.

Gene expression analysis uncovers novel hedgehog interacting protein (HHIP) effects in human bronchial epithelial cells

Hedgehog interacting protein (HHIP) was implicated in chronic obstructive pulmonary disease (COPD) by genome-wide association studies (GWAS). However, it remains unclear how HHIP contributes to COPD pathogenesis. To identify genes regulated by HHIP, we performed gene expression microarray analysis in a human bronchial epithelial cell line (Beas-2B) stably infected with HHIP shRNAs. HHIP silencing led to differential expression of 296 genes; enrichment for variants nominally associated with COPD was found. Eighteen of the differentially expressed genes were validated by real-time PCR in Beas-2B cells. Seven of 11 validated genes tested in human COPD and control lung tissues demonstrated significant gene expression differences. Functional annotation indicated enrichment for extracellular matrix and cell growth genes. Network modeling demonstrated that the extracellular matrix and cell proliferation genes influenced by HHIP tended to be interconnected. Thus, we identified potential HHIP targets in human bronchial epithelial cells that may contribute to COPD pathogenesis.

Abstract 1347: CXCR4 pathway associated with family history of melanoma

Background & Aims: Inherited genetic variants involved in the etiology of melanoma predispose towards elevated risk of disease, but known allele variants only account for a limited fraction of family history-associated melanoma cases. Hence, there is a need to determine contributing allele variants associated with augmented risk and surrogate biomarkers, which can be used to discern family history-associated melanoma.

Methods: Using data from the Nurses’ Health Study cohort, family history was defined as having one or more immediate family members diagnosed with melanoma. Secondary confirmation of melanoma cases were reviewed by pathology reports and tumor blocks collected by mail from across the United States. Genomic interrogation was accomplished through evaluating formalin-fixed paraffin-embedded tissues from 78 primary cutaneous invasive melanoma cases, using expression profiling on either a 6K or whole-genome (24K) Illumina gene chip. Gene Set Enrichment Analysis was performed separately for each batch to determine the differentially enriched pathways and key contributing genes.

Results: The CXC chemokine receptor 4 (CXCR4) pathway was consistently up-regulated within cases of familial melanoma in both platforms. Leading edge analysis showed four genes from the CXCR4 pathway, including MAPK1, PLCG1, CRK, and PTK2, were among the core members that contributed to the enrichment of this pathway. There was no association between the enrichment of CXCR4 pathway and NRAS, BRAF status, or Breslow thickness of the primary melanoma cases.

Conclusion: We found that the CXCR4 pathway may constitute a novel susceptibility pathway associated with family history of melanoma in first-degree relatives.

Citation Format: Wenqing LI, Jiali Han, Hans Widlund, Mick Correll, Yaoyu Wang, John Quackenbush, Martin Mihm, Alvaro Laga Canales, Todd Golub, Yujin Hoshida, David Hunter, George Murphy, Thomas Kupper, Abrar Qureshi. CXCR4 pathway associated with family history of melanoma. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1347. doi:10.1158/1538-7445.AM2013-1347

Next-generation sequencing and microarray-based interrogation of microRNAs from formalin-fixed, paraffin-embedded tissue: preliminary assessment of cross-platform concordance

Next-generation sequencing is increasingly employed in biomedical investigations. Strong concordance between microarray and mRNA-seq levels has been reported in high quality specimens but information is lacking on formalin-fixed, paraffin-embedded (FFPE) tissues, and particularly for microRNA (miRNA) analysis. We conducted a preliminary examination of the concordance between miRNA-seq and cDNA-mediated annealing, selection, extension, and ligation (DASL) miRNA assays. Quantitative agreement between platforms is moderate (Spearman correlation 0.514-0.596) and there is discordance of detection calls on a subset of miRNAs. Quantitative PCR (q-RT-PCR) performed for several discordant miRNAs confirmed the presence of most sequences detected by miRNA-seq but not by DASL but also that miRNA-seq did not detect some sequences, which DASL confidently detected. Our results suggest that miRNA-seq is specific, with few false positive calls, but it may not detect certain abundant miRNAs in FFPE tissue. Further work is necessary to fully address these issues that are pertinent for translational research.

Sequential binary gene ratio tests define a novel molecular diagnostic strategy for malignant pleural mesothelioma

PURPOSE

To develop a standardized approach for molecular diagnostics, we used the gene expression ratio bioinformatic technique to design a molecular signature to diagnose malignant pleural mesothelioma (MPM) from among other potentially confounding diagnoses and differentiate the epithelioid from the sarcomatoid histologic subtype of MPM. In addition, we searched for pathways relevant in MPM in comparison with other related cancers to identify unique molecular features in MPM.

EXPERIMENTAL DESIGN

We conducted microarray analysis on 113 specimens including MPMs and a spectrum of tumors and benign tissues comprising the differential diagnosis of MPM. We generated a sequential combination of binary gene expression ratio tests able to discriminate MPM from other thoracic malignancies. We compared this method with other bioinformatic tools and validated this signature in an independent set of 170 samples. Functional enrichment analysis was conducted to identify differentially expressed probes.

RESULTS

A sequential combination of gene expression ratio tests was the best molecular approach to distinguish MPM from all the other samples. Bioinformatic and molecular validations showed that the sequential gene ratio tests were able to identify the MPM samples with high sensitivity and specificity. In addition, the gene ratio technique was able to differentiate the epithelioid from the sarcomatoid type of MPM. Novel genes and pathways specifically activated in MPM were identified.

CONCLUSIONS

New clinically relevant molecular tests have been generated using a small number of genes to accurately distinguish MPMs from other thoracic samples, supporting our hypothesis that the gene expression ratio approach could be a useful tool in the differential diagnosis of cancers.