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Introduction: Malignant phyllodes tumors are rare breast malignancies (0.1% of all breast tumors) with limited effective treatment options for recurrent and metastatic disease. Recent trials indicated a potential for anti-angiogenic therapy in soft tissue sarcomas, which led us to investigate these pathways. Materials and Methods: Thirty-five malignant phyllodes tumors (including two cases with matched primary and metastatic tumors) were profiled using gene sequencing (Next-generation and Sanger), gene copy number analysis (in-situ hybridization), whole genome RNA expression, and protein expression (immunohistochemical assay). Results: RNA microarray assay showed consistent over-expression of genes involved in angiogenesis including VEGFA, Angiopoietin2, VCAM1, PDGFRA, PTTG1, and CYP3A5 in all cases analyzed (n=5). No mutations in KDR (VEGFR2) were detected (0/26). EGFR protein overexpression was observed in 25/26 (96%) of cases with amplification of the EGFR gene in 8 cases (33%). EGFR gene mutations were identified in 2 cases (8%) including one case with presumed pathogenic V774M mutation and one case with EGFRvIII mutation. The most common mutations included those of TP53 (50%) and PIK3CA (15%) while other mutations (BRCA1, BRCA2, RET, CDH1, MLH1, ATM) were rare affecting single phyllodes cases. Two cases with matched primary and metastatic cancers harbored the same mutations in both sites (PIK3CA/KRAS and RB1 gene mutations, respectively). Conclusions: Comprehensive multiplatform profiling approach to phyllodes tumors identifies various molecular alterations of which some are potentially actionable. Our data suggests that anti-angiogenic therapy may also be effective in patients with malignant phyllodes tumor. Evaluation of EGFR pathway discovered consistent protein over-expression but rare activating mutations, which necessitates refinement in patient selection targeting these pathways. Citation Format: Gatalica Z, Vranic S, Ghazalpour A, Xiu J, Ocal I, McGill J, Bender R, Discianno E, Sanati S, Reddy S, Pockaj B. Comprehensive multiplatform molecular profiling identifies potentially targetable biomarkers in malignant phyllodes tumors of the breast. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P4-09-19.

Background: Metaplastic breast carcinoma (MBC) is a rare subtype of breast carcinoma less responsive to conventional chemotherapy relative to usual breast carcinomas (UBCs) such as ductal and lobular subtype. In molecular terms MBC usually clusters with triple negative breast cancer (TNBC), but MBCs portray a worse prognosis in comparison to TNBCs. Published studies investigating MBCs for specific biomarkers of therapy response are rare and limited by the methodological approaches. Methods: This study included 132 patients with 38 histologically confirmed MBCs and 94 UBCs. Amongst the 94 UBCSs, 44 were estrogen receptor positive, 33 were triple negative and 17 were HER2 positive. Direct sequencing analysis was performed on genomic DNA isolated from formalin-fixed paraffin-embedded tissue (FFPE) using the Illumina MiSeq Next Generation Sequencing platform (NGS). Immunohistochemistry for PD-L1 (SP142, Spring Bioscience), PD-1 (NAT105, Ventana) and EGFR (31G7, Life Technologies) was performed using automated procedures. Additionally, mutation analysis for EGFRvIII was performed on RNA extracted from FFPE tissue. Results: At the genomic level, numerous cases of MBC had multiple genomic alterations with the most frequent genetic mutation in TP53 gene (14/24), similar to the TNBC controls (17/33). BRCA1 mutations were detected in 2/10 cases. Potentially actionable mutations were rare and included PIK3CA gene. Importantly, PD-L1 expression on cancer cells was detected in significantly higher proportion of MBCs (37%) than in the UBC cohort (6%) or TNBC control (14%) (p=3.7x10-5 and p=0.03, respectively). PD-1 positive tumors infiltrating lymphocytes (TILS) varied greatly in MBC (0 to >50/mm2). Over-expression of EGFR was frequent in MBCs (62%); however no mutations in the gene including EGFRvIII were detected. Conclusion: Comprehensive profiling of a large cohort of this rare carcinoma highlighted predominance of TP53 mutations, wild type EGFR gene expression, a distinct increase in proportion of PD-L1 expression in carcinoma cells, and PD-1 expression in TILS. The latter properties can be exploited in clinical trials utilizing immune check point inhibitors. Citation Format: Gatalica Z, Joneja U, Ghazalpour A, Swensen J, Feldman R, Cai F, Chen W, Xiao N, Reddy S, Palazzo J. Comprehensive profiling of metaplastic breast carcinoma reveals frequent over-expression of PD-L1. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P3-07-47.

Malignant phyllodes tumor is a rare breast malignancy with sarcomatous overgrowth and with limited effective treatment options for recurrent and metastatic cases. Recent clinical trials indicated a potential for anti-angiogenic, anti-EGFR and immunotherapeutic approaches for patients with sarcomas, which led us to investigate these and other targetable pathways in malignant phyllodes tumor of the breast. Thirty-six malignant phyllodes tumors (including 8 metastatic tumors with two cases having matched primary and metastatic tumors) were profiled using gene sequencing, gene copy number analysis, whole genome expression, and protein expression. Whole genome expression analysis demonstrated consistent over-expression of genes involved in angiogenesis including VEGFA, Angiopoietin-2, VCAM1, PDGFRA, and PTTG1. EGFR protein overexpression was observed in 26/27 (96%) of cases with amplification of the EGFR gene in 8/24 (33%) cases. Two EGFR mutations were identified including EGFRvIII and a presumed pathogenic V774M mutation, respectively. The most common pathogenic mutations included TP53 (50%) and PIK3CA (15%). Cases with matched primary and metastatic tumors harbored identical mutations in both sites (PIK3CA/KRAS and RB1 gene mutations, respectively). Tumor expression of PD-L1 immunoregulatory protein was observed in 3/22 (14%) of cases. Overexpression of molecular biomarkers of increased angiogenesis, EGFR and immune checkpoints provides novel targeted therapy options in malignant phyllodes tumors of the breast.

Cancer cells expressing PD-1 ligands (PD-L1/PD-L2) inhibit immune-modulatory T-cell activation facilitating disease progression. Preliminary clinical trials exploring interruption of PD-1/PD-L1 signaling showed benefit in several cancer types. We analyzed the distribution of PD-1–positive tumor-infiltrating lymphocytes (TIL) and cancer cells' expression of PD-L1 in a molecularly profiled cohort of 437 malignancies (380 carcinomas, 33 sarcomas, and 24 melanomas). We showed that the presence of PD-1+ TILs significantly varied among cancer types (from 0% in extraskeletal myxoid chondrosarcomas to 93% in ovarian cancer), and was generally associated with the increased number of mutations in tumor cells (P = 0.029). Cancer cell expression of PD-L1 varied from absent (in Merkel cell carcinomas) to 100% (in chondro- and liposarcomas), but showed the inverse association with the number of detected mutations (P = 0.004). Both PD-1 and PD-L1 expression were significantly higher in triple-negative breast cancers (TNBC) than in non-TNBC (P < 0.001 and 0.017, respectively). Similarly, MSI-H colon cancers had higher PD-1 and PD-L1 expression than the microsatellite stable tumors (P = 0.002 and 0.02, respectively). TP53-mutated breast cancers had significantly higher PD-1 positivity than those harboring other driver mutations (e.g., PIK3CA; P = 0.002). In non–small cell lung cancer, PD-1/PD-L1 coexpression was identified in 8 cases (19%), which lacked any other targetable alterations (e.g., EGFR, ALK, or ROS1). Our study demonstrated the utility of exploring the expression of two potentially targetable immune checkpoint proteins (PD-1/PD-L1) in a substantial proportion of solid tumors, including some aggressive subtypes that lack other targeted treatment modalities. Cancer Epidemiol Biomarkers Prev; 23(12); 2965–70. ©2014 AACR.

Extraskeletal myxoid chondrosarcoma (EMC) is a rare mesenchymal neoplasm, rarely reported in the genitourinary tract with only 5 cases reported in the vulva. We investigated 2 cases of vulvar sarcomas whose morphologic appearance and immunohistochemical profiles were consistent with EMC using fluorescence in situ hybridization (FISH), reverse-transcription polymerase chain reaction, and a whole genome expression array. FISH and reverse-transcription polymerase chain reaction assays showed no EWSR1 and NR4A3 loci rearrangements. Microarray-based analysis also revealed no changes in NR4A3 and EWSR1 gene transcription levels. Microarray data showed a significant downregulation of the muscle-related genes (eg, myosin heavy chain family, actins, myoglobin, desmin, creatine kinase, troponins) and cytokeratins (KRT6A, 6B, 13, 14, and 78), upregulation of several neuron-specific genes [neural cell adhesion molecule 1 (NCAM-1/CD56), neurofilament (NEFH)], along with some well-characterized tumor biomarkers [carbonic anhydrase IX (CA-9), topoisomerase IIα (TOP2A), matrix metalloproteinases (MMP-7, MMP-9), CDKN2 gene (p16-INK4a), checkpoint homolog 2 (CHEK2)]. Notably, both tumors showed upregulation of the pleomorphic adenoma gene 1 (PLAG1), and in 1 case PLAG1 gene rearrangement was detected by break-apart FISH. Some vulvar tumors with morphologic and immunohistochemical characteristics of EMC may represent a molecular genetic entity separate from EMCs arising in other locations. PLAG1 gene activation appears to be involved in the development of these neoplasms.

Several studies have investigated RNA–DNA differences (RDD), presumably due to RNA editing, with conflicting results. We report a rigorous analysis of RDD in exonic regions in mice, taking into account critical biases in RNA-Seq analysis. Using deep-sequenced F1 reciprocal inbred mice, we mapped 40 million RNA-Seq reads per liver sample and 180 million reads per adipose sample. We found 7300 apparent hepatic RDDs using a multiple-site mapping procedure, compared with 293 RDD found using a unique-site mapping procedure. After filtering for repeat sequence, splice junction proximity, undirectional strand, and extremity read bias, 63 RDD remained. In adipose tissue unique-site mapping identified 1667 RDD, and after applying the same four filters, 188 RDDs remained. In both tissues, the filtering procedure increased the proportion of canonical (A-to-I and C-to-U) editing events. The genomic DNA of 12 RDD sites among the potential 63 hepatic RDD was tested by Sanger sequencing, three of which proved to be due to unreferenced SNPs. We validated seven liver RDD with Sequenom technology, including two noncanonical, Gm5424 C-to-I(G) and Pisd I(G)-to-A RDD. Differences in diet, sex, or genetic background had very modest effects on RDD occurrence. Only a small number of apparent RDD sites overlapped between liver and adipose, indicating a high degree of tissue specificity. Our findings underscore the importance of properly filtering for bias in RNA-Seq investigations, including the necessity of confirming the DNA sequence to eliminate unreferenced SNPs. Based on our results, we conclude that RNA editing is likely limited to hundreds of events in exonic RNA in liver and adipose.

Purpose of reviewWe provide an overview of our current understanding of the genetic architecture of coronary artery disease (CAD) and discuss areas of research that provide excellent opportunities for further exploration.Recent findingsLarge-scale studies in human populations, coupled with rapid advances in genetic technologies over the last decade, have clearly established the association of common genetic variation with risk of CAD. However, the effect sizes of the susceptibility alleles are for the most part modest and collectively explain only a small fraction of the overall heritability. By comparison, evidence that rare variants make a substantial contribution to risk of CAD has been somewhat disappointing thus far, suggesting that other biological mechanisms have yet to be discovered. Emerging data suggests that novel pathways involved in the development of CAD can be identified through complementary and integrative systems genetics strategies in mice or humans. There is also convincing evidence that gut bacteria play a previously unrecognized role in the development of CAD, particularly through metabolism of certain dietary nutrients that lead to proatherogenic metabolites in the circulation. A major effort is now underway to functionally understand the newly discovered genetic and biological associations for CAD, which could lead to the development of potentially novel therapeutic strategies. Other important areas of investigation for understanding the pathophysiology of CAD, including epistatic interactions between genes or with either sex and environmental factors, have not been studied on a broad scope and represent additional opportunities for future studies.

PURPOSE OF REVIEW This article highlights recent advances in the emerging role that gut microbiota play in modulating metabolic phenotypes, with a particular focus on lipid metabolism. RECENT FINDINGS Accumulating data from both human and animal studies demonstrate that intestinal microbes can affect host lipid metabolism through multiple direct and indirect biological mechanisms. These include a variety of signaling molecules produced by gut bacteria that have potent effects on hepatic lipid and bile metabolism and on reverse cholesterol transport, energy expenditure, and insulin sensitivity in peripheral tissues. Additionally, host genetic factors can modulate the abundance of bacterial taxa, which can subsequently affect various metabolic phenotypes. Proof of causality for identified microbial associations with host lipid-related phenotypes has been demonstrated in several animal studies, but remains a challenge in humans. Ultimately, selective manipulation of the gut microbial ecosystem for intervention will first require a better understanding of which specific bacteria, or alternatively, which bacterial metabolites, are appropriate targets. SUMMARY Recent discoveries have broad implications for elucidating bacterially mediated pathophysiological mechanisms that alter lipid metabolism and other related metabolic traits. From a clinical perspective, this newly recognized endocrine organ system can be targeted for therapeutic benefit of dyslipidemia and cardiometabolic diseases.

Summary Many common diseases have an important inflammatory component mediated in part by macrophages. Here we used a systems genetics strategy to examine the role of common genetic variation in macrophage responses to inflammatory stimuli. We examined genome-wide transcript levels in macrophages from 92 strains of the Hybrid Mouse Diversity Panel. We exposed macrophages to control media, bacterial lipopolysaccharide (LPS), or oxidized phospholipids. We performed association mapping under each condition and identified several thousand expression quantitative trait loci (eQTL), gene-by-environment interactions, and eQTL "hot spots" that specifically control LPS responses. We used siRNA knockdown of candidate genes to validate an eQTL hot spot in chromosome 8 and identified the gene 2310061C15Rik as a regulator of inflammatory responses in macrophages. We have created a public database where the data presented here can be used as a resource for understanding many common inflammatory traits that are modeled in the mouse and for the dissection of regulatory relationships between genes.

We have developed an association-based approach using classical inbred strains of mice in which we correct for population structure, which is very extensive in mice, using an efficient mixed-model algorithm. Our approach includes inbred parental strains as well as recombinant inbred strains in order to capture loci with effect sizes typical of complex traits in mice (in the range of 5 % of total trait variance). Over the last few years, we have typed the hybrid mouse diversity panel (HMDP) strains for a variety of clinical traits as well as intermediate phenotypes and have shown that the HMDP has sufficient power to map genes for highly complex traits with resolution that is in most cases less than a megabase. In this essay, we review our experience with the HMDP, describe various ongoing projects, and discuss how the HMDP may fit into the larger picture of common diseases and different approaches.

Although mapping quantitative traits in inbred strains is simpler than mapping the analogous traits in humans, classical inbred crosses suffer from reduced genetic diversity compared to experimental designs involving outbred animal populations. Multiple crosses, for example the Complex Trait Consortium's eight-way cross, circumvent these difficulties. However, complex mating schemes and systematic inbreeding raise substantial computational difficulties. Here we present a method for locally imputing the strain origins of each genotyped animal along its genome. Imputed origins then serve as mean effects in a multivariate Gaussian model for testing association between trait levels and local genomic variation. Imputation is a combinatorial process that assigns the maternal and paternal strain origin of each animal on the basis of observed genotypes and prior pedigree information. Without smoothing, imputation is likely to be ill-defined or jump erratically from one strain to another as an animal's genome is traversed. In practice, one expects to see long stretches where strain origins are invariant. Smoothing can be achieved by penalizing strain changes from one marker to the next. A dynamic programming algorithm then solves the strain imputation process in one quick pass through the genome of an animal. Imputation accuracy exceeds 99% in practical examples and leads to high-resolution mapping in simulated and real data. The previous fastest quantitative trait loci (QTL) mapping software for dense genome scans reduced compute times to hours. Our implementation further reduces compute times from hours to minutes with no loss in statistical power. Indeed, power is enhanced for full pedigree data.

Systems genetics relies on common genetic variants to elucidate biologic networks contributing to complex disease-related phenotypes. Mice are ideal model organisms for such approaches, but linkage analysis has been only modestly successful due to low mapping resolution. Association analysis in mice has the potential of much better resolution, but it is confounded by population structure and inadequate power to map traits that explain less than 10% of the variance, typical of mouse quantitative trait loci (QTL). We report a novel strategy for association mapping that combines classic inbred strains for mapping resolution and recombinant inbred strains for mapping power. Using a mixed model algorithm to correct for population structure, we validate the approach by mapping over 2500 cis-expression QTL with a resolution an order of magnitude narrower than traditional QTL analysis. We also report the fine mapping of metabolic traits such as plasma lipids. This resource, termed the Hybrid Mouse Diversity Panel, makes possible the integration of multiple data sets and should prove useful for systems-based approaches to complex traits and studies of gene-by-environment interactions.

Copy number variants (CNVs) are genomic segments which are duplicated or deleted among different individuals. CNVs have been implicated in both Mendelian and complex traits, including immune and behavioral disorders, but the study of the mechanisms by which CNVs influence gene expression and clinical phenotypes in humans is complicated by the limited access to tissues and by population heterogeneity. We now report studies of the effect of 19 CNVs on gene expression and metabolic traits in a mouse intercross between strains C57BL/6J and C3H/HeJ. We found that 83% of genes predicted to occur within CNVs were differentially expressed. The expression of most CNV genes was correlated with copy number, but we also observed evidence that gene expression was altered in genes flanking CNVs, suggesting that CNVs may contain regulatory elements for these genes. Several CNVs mapped to hotspots, genomic regions influencing expression of tens or hundreds of genes. Several metabolic traits including cholesterol, triglycerides, glucose and body weight mapped to three CNVs in the genome, in mouse chromosomes 1, 4 and 17. Predicted CNV genes, such as Itlna, Defcr-1, Trim12 and Trim34 were highly correlated with these traits. Our results suggest that CNVs have a significant impact on gene expression and that CNVs may be playing a role in the mechanisms underlying metabolic traits in mice.

A principal task in dissecting the genetics of complex traits is to identify causal genes for disease phenotypes. We previously developed a method to infer causal relationships among genes through the integration of DNA variation, gene transcription and phenotypic information. Here we have validated our method through the characterization of transgenic and knockout mouse models of genes predicted to be causal for abdominal obesity. Perturbation of eight out of the nine genes, with Gas7, Me1 and Gpx3 being newly confirmed, resulted in significant changes in obesity-related traits. Liver expression signatures revealed alterations in common metabolic pathways and networks contributing to abdominal obesity and overlapped with a macrophage-enriched metabolic network module that is highly associated with metabolic traits in mice and humans. Integration of gene expression in the design and analysis of traditional F(2) intercross studies allows high-confidence prediction of causal genes and identification of pathways and networks involved.

We previously mapped a locus on chromosome 6 with a large effect (LOD > 6) on aortic lesion size in a (C57BL/6J × CAST/Ei) F2 cross and identified arachidonate 5-lipoxygenase (5LO) as a candidate gene in this region. Subsequent studies with the 5LO knockout model showed effects on atherosclerosis and aortic aneurysms. We now report detailed genetic analysis of the chromosome 6 locus. We created a panel of overlapping and reciprocal subcongenic lines from the B6.CAST Ldlr−/− chromosome 6 congenic strain (CON6.Ldlr−/−) and analyzed aortic lesion size in different subcongenic lines. Our results revealed that there are at least two subregions, designated as Ath37 and Ath38 that affect the size of aortic lesions independently of 5LO. We also showed that homozygote 5LO null mice develop smaller atherosclerotic lesions. We conclude that the relation between the mouse chromosome 6 locus and atherosclerosis is complex and is due to at least two genes with large effects within this region. This complexity should be considered when interpreting results of knockout studies.

The goal of systems biology is to define all of the elements present in a given system and to create an interaction network between these components so that the behavior of the system, as a whole and in parts, can be explained under specified conditions. The elements constituting the network that influences the development of atherosclerosis could be genes, pathways, transcript levels, proteins, or physiologic traits. In this review, we discuss how the integration of genetics and technologies such as transcriptomics and proteomics, combined with mathematical modeling, may lead to an understanding of such networks.

The genes that contribute to common, complex forms of atherosclerosis remain largely unknown. Genetic studies in humans have, for the most part, focused on identifying genes that predispose to the traditional risk factors, such as lipid levels and blood pressure, but apart from rare, single-gene disorders, there have been few successes to date. The use of mice to dissect the complex genetic etiology of atherosclerosis offers a viable alternative to human studies, because experimental parameters, such as environment, breeding scheme, and detailed phenotyping, can be controlled. Herein we review how mouse genetics can lead to the identification of genes, some of which would otherwise not have been considered as candidates for atherosclerosis, and provide an overview of the prospects for successful gene discovery in the future.

C57BL/6J mice develop genetically determined age-related hippocampal granular deposits that have some similarities to lesions seen in the brains of human patients with tau protein related neurodegenerative disorders ("tauopathies"). We sought to identify the genetic loci responsible for these in an F2 intercross of inbred mouse strains C57BL/6J and DBA/2J, using quantitative trait locus (QTL) analysis. Hippocampal lesions were shown to be PAS positive, H and E negative, and immunoreactive for tau protein and alpha synuclein, but not to Abeta 1-40 or Abeta 1-42, or for ubiquitin. These were quantitated by histomorphometry, and QTL analysis revealed a locus on chromosome 7 with a lod score of 6.5 as well as two suggestive loci on chromosome 10. The genomic data indicate that the genetic basis is complex, but with one locus playing a major role in lesion formation. These lesions may represent a useful model for investigating dysregulation of tau protein in the hippocampus.

Olfactory neuroblastoma (ONB) is a rare, locally aggressive, malignant neoplasm originating in the olfactory epithelium in the nasal vault. The recurrence rate of ONB remains high and there are no specific treatment guidelines for recurrent/metastatic ONBs. This study retrospectively evaluated 23 ONB samples profiled at Caris Life Sciences (Phoenix, Arizona) using DNA sequencing (Sanger/NGS [Illumina], n = 15) and gene fusions (Archer FusionPlex, n = 6), whole genome RNA microarray (HumanHT-12 v4 beadChip, Illumina, n = 4), gene copy number assays (chromogenic and fluorescent in situ hybridization), and immunohistochemistry. Mutations were detected in 63% ONBs including TP53, CTNNB1, EGFR, APC, cKIT, cMET, PDGFRA, CDH1, FH, and SMAD4 genes. Twenty-one genes were over-expressed and 19 genes under-expressed by microarray assay. Some of the upregulated genes included CD24, SCG2, and IGFBP-2. None of the cases harbored copy number variations of EGFR, HER2 and cMET genes, and no gene fusions were identified. Multiple protein biomarkers of potential response or resistance to classic chemotherapy drugs were identified, such as low ERCC1 [cisplatin sensitivity in 10/12], high TOPO1 [irinotecan sensitivity in 12/19], high TUBB3 [vincristine resistance in 13/14], and high MRP1 [multidrug resistance in 6/6 cases]. None of the cases (0/10) were positive for PD-L1 in tumor cells. Overexpression of pNTRK was observed in 67% (4/6) of the cases without underlying genetic alterations. Molecular alterations detected in our study (e.g., Wnt and cKIT/PDGFRA pathways) are potentially treatable using novel therapeutic approaches. Identified protein biomarkers of response or resistance to classic chemotherapy could be useful in optimizing existing chemotherapy treatment(s) in ONBs. Olfactory neuroblastoma (ONB) is a rare, locally aggressive, malignant neoplasm originating in the olfactory epithelium in the nasal vault. The recurrence rate of ONB remains high and there are no specific treatment guidelines for recurrent/metastatic ONBs. This study retrospectively evaluated 23 ONB samples profiled at Caris Life Sciences (Phoenix, Arizona) using DNA sequencing (Sanger/NGS [Illumina], n = 15) and gene fusions (Archer Fusion- Plex, n = 6), whole genome RNA microarray (HumanHT-12 v4 beadChip, Illumina, n = 4), gene copy number assays (chromogenic and fluorescent in situ hybridization), and immunohistochemistry. Mutations were detected in 63% ONBs including TP53, CTNNB1, EGFR, APC, cKIT, cMET, PDGFRA, CDH1, FH, and SMAD4 genes. Twenty-one genes were overexpressed and 19 genes under-expressed by microarray assay. Some of the upregulated genes included CD24, SCG2, and IGFBP-2. None of the cases harbored copy number variations of EGFR, HER2 and cMET genes, and no gene fusions were identified. Multiple protein biomarkers of potential response or resistance to classic chemotherapy drugs were identified, such as low ERCC1 [cisplatin sensitivity in 10/12], high TOPO1 [irinotecan sensitivity in 12/19], high TUBB3 [vincristine resistance in 13/14], and high MRP1 [multidrug resistance in 6/6 cases]. None of the cases (0/10) were positive for PD-L1 in tumor cells. Overexpression of pNTRK was observed in 67% (4/6) of the cases without underlying genetic alterations. Molecular alterations detected in our study (e.g., Wnt and cKIT/ PDGFRA pathways) are potentially treatable using novel therapeutic approaches. Identified protein biomarkers of response or resistance to classic chemotherapy could be useful in optimizing existing chemotherapy treatment(s) in ONBs.

Summary Prior efforts to identify regulators of hematopoietic stem cell physiology have relied mainly on candidate gene approaches with genetically modified mice. Here we used a genome-wide association study (GWAS) strategy with the hybrid mouse diversity panel to identify the genetic determinants of hematopoietic stem/progenitor cell (HSPC) frequency. Among 108 strains, we observed ∼120- to 300-fold variation in three HSPC populations. A GWAS analysis identified several loci that were significantly associated with HSPC frequency, including a locus on chromosome 5 harboring the homeodomain-only protein gene (Hopx). Hopx previously had been implicated in cardiac development but was not known to influence HSPC biology. Analysis of the HSPC pool in Hopx−/− mice demonstrated significantly reduced cell frequencies and impaired engraftment in competitive repopulation assays, thus providing functional validation of this positional candidate gene. These results demonstrate the power of GWAS in mice to identify genetic determinants of the hematopoietic system., Graphical Abstract, Highlights • Genetic variation across mouse strains influences hematopoietic stem cell frequency • This variation can be exploited for genome-wide association studies • Hopx is a regulator of hematopoietic stem/progenitor cell function • This approach can be used to identify genetic determinants of other stem cell systems, In this article, Allayee, Adams, and colleagues use an unbiased strategy in mice to identify the genetic determinants of the hematopoietic system. A genome-wide association study with over 100 inbred mouse strains from the Hybrid Mouse Diversity Panel, followed by functional validation studies, revealed Hopx as a gene that controls the frequency of a sub-population of hematopoietic stem cells.

We profiled and analyzed 283 metabolites representing eight major classes of molecules including Lipids, Carbohydrates, Amino Acids, Peptides, Xenobiotics, Vitamins and Cofactors, Energy Metabolism, and Nucleotides in mouse liver of 104 inbred and recombinant inbred strains. We find that metabolites exhibit a wide range of variation, as has been previously observed with metabolites in blood serum. Using genome‐wide association analysis, we mapped 40% of the quantified metabolites to at least one locus in the genome and for 75% of the loci mapped we identified at least one candidate gene by local expression QTL analysis of the transcripts. Moreover, we validated 2 of 3 of the significant loci examined by adenoviral overexpression of the genes in mice. In our GWAS results, we find that at significant loci the peak markers explained on average between 20 and 40% of variation in the metabolites. Moreover, 39% of loci found to be regulating liver metabolites in mice were also found in human GWAS results for serum metabolites, providing support for similarity in genetic regulation of metabolites between mice and human. We also integrated the metabolomic data with transcriptomic and clinical phenotypic data to evaluate the extent of co‐variation across various biological scales.

We report an analysis of allele-specific expression (ASE) and parent-of-origin expression in adult mouse liver using next generation sequencing (RNA-Seq) of reciprocal crosses of heterozygous F1 mice from the parental strains C57BL/6J and DBA/2J. We found a 60% overlap between genes exhibiting ASE and putative cis-acting expression quantitative trait loci (cis-eQTL) identified in an intercross between the same strains. We discuss the various biological and technical factors that contribute to the differences. We also identify genes exhibiting parental imprinting and complex expression patterns. Our study demonstrates the importance of biological replicates to limit the number of false positives with RNA-Seq data.

Introduction: Caveolin-1 (Cav1) is associated with basal-like triple-negative (ER-/PR-/Her2-) breast cancers (TNBC). Its biological contribution to this subtype has not been fully explored and controversy persists regarding the molecular role of Cav1 in carcinogenesis. Experimental Procedures: Thirty-four TNBC (17 Cav1+/17 Cav1-) patients molecularly-profiled with a commercial assay (Caris Life Sciences, AZ) were evaluated retrospectively. Cav1 status was determined by immunohistochemistry (caveolin-1 polyclonal; ≥2+ ≥50%). The majority of specimens (28/34) used for profiling were from primary breast sites and contained ≥50% neoplastic cells. The transcriptomes were profiled using Illumina's HumanHT-12 microarray (v4). Data were normalized using mean normalization procedure. Differential expression analysis was performed using R's Limma package. Pathway analysis was carried out using R's signaling pathway impact analysis (SPIA) package with 69 cancer, immunity, and cell signaling related KEGG pathways. Results: Using a cutoff of two-fold and adjusted p-value of 0.05, we identified 954 genes differentially expressed between Cav1+/- TNBC patients. Included in these were 31 genes which were found to be up-regulated by over five- fold and 3 genes down-regulated by over five fold in Cav1+ TNBC. Genes of notable interest for their role in cell signaling, cell adhesion, tumor invasion and metastasis, included an up-regulation of TGFBR2, SPARC, integrins (ITGA11, ITGB5, ITGBL1), cell adhesion proteins (LAMB3, COL5A3) and molecules which facilitate tumor invasion (LAMB3, MMP1, MMP2, MMP9). In addition, genes found to be down-regulated in Cav1+ patients and notable for their roles in promoting epithelial-mesenchymal-transition (EMT) included Claudin 3(CLD3) and CA125/MUC16 (Mucin 16). We also detected an approximately two-fold down-regulation of CDKN2A in Cav1+ patients. Using SPIA pathway analysis, 12 pathways were found to be differentially activated in Cav1+ vs. Cav1- TNBC. The most differentially activated pathways were the focal adhesion pathway (p = 4.51E-18), PI3k-Akt signaling pathway (p = 2.01E-6) and TGF-β and MAPK signaling pathways (p = 0.005, 0.014, respectively). Conclusions: Differential gene expression patterns and pathway analyses provide evidence for distinct profiles for gene expression between Cav1+/- TNBC. Cav1+ TNBC patients exhibit up-regulation of genes important for cell signaling, extracellular matrix remodeling and tumor invasion, and down-regulation of genes that may facilitate EMT and loss of cell cycle control. The focal adhesion pathway, as well as TGF-β, PI3K and MAPK signaling pathways, were identified as differentially activated among Cav1+/- TNBC. Taken together, these data support the role of Cav1+ in identifying a subtype of TNBC that may have a greater risk for invasion and metastasis. The correlation of this subtype with prognosis and drug response should be investigated in future studies. Citation Format: Rebecca A. Feldman, Zoran Gatalica, Semir Vranic, Ryan Bender, Sandeep Reddy, Anatole Ghazalpour. Caveolin-1: Beyond a marker for basal-like breast cancers. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3928.

Elevated heart rate (HR) is a risk factor for cardiovascular diseases. The goal of the study was to map HR trait in mice using quantitative trait locus (QTL) analysis followed by genome-wide association (GWA) analysis. The first approach provides mapping power and the second increases genome resolution. QTL analyses were performed in a C3HeB×SJL backcross. HR and systolic blood pressure (SBP) were measured by the tail-cuff plethysmography. HR was ∼80 beats/min higher in SJL compared with C3HeB. There was a wide distribution of the HR (536–763 beats/min) in N2 mice. We discovered a highly significant QTL (logarithm of odds = 6.7, P < 0.001) on chromosome 7 (41 cM) for HR in the C3HeB×SJL backcross. In the Hybrid Mouse Diversity Panel (58 strains, n = 5–6/strain) we found that HR (beats/min) ranged from 546 ± 12 in C58/J to 717 ± 7 in MA/MyJ mice. SBP (mmHg) ranged from 99 ± 6 in strain I/LnJ to 151 ± 4 in strain BXA4/PgnJ. GWA analyses were done using the HMDP, which revealed a locus (64.2–65.1 Mb) on chromosome 7 that colocalized with the QTL for elevated HR found in the C3HeB×SJL backcross. The peak association was observed for 17 SNPs that are localized within three GABAA receptor genes. In summary, we used a combined genetic approach to fine map a novel elevated HR locus on mouse chromosome 7.

Juxtaglomerular cell tumors (JGCTs) are rare tumors characterized by renin synthesis, hyperaldosteronism and hypertension. A curious immunohistochemical overlap between JGCT and gastrointestinal stromal tumor (GIST) including the expression of vimentin, CD34, CD117, α-smooth muscle actin was previously reported, prompting us to further investigate JGCT and its phenotypic and molecular genetic characteristics. Virtual karyotyping showed gain of chromosomes 3, 4, 10, 13, 17 and 18 in one JGCT, and fluorescence in situ hybridization (FISH) study confirmed this multiple gain pattern. Additionally, loss of chromosome 9 was observed in four of six cases analyzed with FISH. A whole genome expression analysis revealed 415 up-regulated (including renin, and CD117) and 325 down-regulated genes between the 2 cases. The study confirmed earlier reports on the gain of chromosomes 4 and 10, and provided further evidence of up-regulation of the genes located on these 2 chromosomes. For the first time our study indicated the importance of the loss of chromosome 9 and loss of expression of several tumor suppressor genes located on this chromosome as possible pathogenetic events important in development of JGCT.

The relationships between the levels of transcripts and the levels of the proteins they encode have not been examined comprehensively in mammals, although previous work in plants and yeast suggest a surprisingly modest correlation. We have examined this issue using a genetic approach in which natural variations were used to perturb both transcript levels and protein levels among inbred strains of mice. We quantified over 5,000 peptides and over 22,000 transcripts in livers of 97 inbred and recombinant inbred strains and focused on the 7,185 most heritable transcripts and 486 most reliable proteins. The transcript levels were quantified by microarray analysis in three replicates and the proteins were quantified by Liquid Chromatography–Mass Spectrometry using O(18)-reference-based isotope labeling approach. We show that the levels of transcripts and proteins correlate significantly for only about half of the genes tested, with an average correlation of 0.27, and the correlations of transcripts and proteins varied depending on the cellular location and biological function of the gene. We examined technical and biological factors that could contribute to the modest correlation. For example, differential splicing clearly affects the analyses for certain genes; but, based on deep sequencing, this does not substantially contribute to the overall estimate of the correlation. We also employed genome-wide association analyses to map loci controlling both transcript and protein levels. Surprisingly, little overlap was observed between the protein- and transcript-mapped loci. We have typed numerous clinically relevant traits among the strains, including adiposity, lipoprotein levels, and tissue parameters. Using correlation analysis, we found that a low number of clinical trait relationships are preserved between the protein and mRNA gene products and that the majority of such relationships are specific to either the protein levels or transcript levels. Surprisingly, transcript levels were more strongly correlated with clinical traits than protein levels. In light of the widespread use of high-throughput technologies in both clinical and basic research, the results presented have practical as well as basic implications., Author Summary An old dogma in biology states that, in every cell, the flow of biological information is from DNA to RNA to proteins and that the latter act as a working force to determine the organism's phenotype. This model predicts that changes in DNA that affect the clinical phenotype should also similarly change the cellular levels of RNA and protein levels. In this report, we test this prediction by looking at the concordance between DNA variation in population of mouse inbred strains, the RNA and protein variation in the liver tissue of these mice, and variation in metabolic phenotypes. We show that the relationship between various biological traits is not simple and that there is relatively little concordance of RNA levels and the corresponding protein levels in response to DNA perturbations. In addition, we also find that, surprisingly, metabolic traits correlate better to RNA levels than to protein levels. In light of current efforts in searching for the molecular bases of disease susceptibility in humans, our findings highlight the complexity of information flow that underlies clinical outcomes.

Background Our understanding of the genetic basis of learning and memory remains shrouded in mystery. To explore the genetic networks governing the biology of conditional fear, we used a systems genetics approach to analyze a hybrid mouse diversity panel (HMDP) with high mapping resolution. Results A total of 27 behavioral quantitative trait loci were mapped with a false discovery rate of 5%. By integrating fear phenotypes, transcript profiling data from hippocampus and striatum and also genotype information, two gene co-expression networks correlated with context-dependent immobility were identified. We prioritized the key markers and genes in these pathways using intramodular connectivity measures and structural equation modeling. Highly connected genes in the context fear modules included Psmd6, Ube2a and Usp33, suggesting an important role for ubiquitination in learning and memory. In addition, we surveyed the architecture of brain transcript regulation and demonstrated preservation of gene co-expression modules in hippocampus and striatum, while also highlighting important differences. Rps15a, Kif3a, Stard7, 6330503K22RIK, and Plvap were among the individual genes whose transcript abundance were strongly associated with fear phenotypes. Conclusion Application of our multi-faceted mapping strategy permits an increasingly detailed characterization of the genetic networks underlying behavior.

Significant advances have been made in the discovery of genes affecting bone mineral density (BMD); however, our understanding of its genetic basis remains incomplete. In the current study, genome-wide association (GWA) and co-expression network analysis were used in the recently described Hybrid Mouse Diversity Panel (HMDP) to identify and functionally characterize novel BMD genes. In the HMDP, a GWA of total body, spinal, and femoral BMD revealed four significant associations (−log10P>5.39) affecting at least one BMD trait on chromosomes (Chrs.) 7, 11, 12, and 17. The associations implicated a total of 163 genes with each association harboring between 14 and 112 genes. This list was reduced to 26 functional candidates by identifying those genes that were regulated by local eQTL in bone or harbored potentially functional non-synonymous (NS) SNPs. This analysis revealed that the most significant BMD SNP on Chr. 12 was a NS SNP in the additional sex combs like-2 (Asxl2) gene that was predicted to be functional. The involvement of Asxl2 in the regulation of bone mass was confirmed by the observation that Asxl2 knockout mice had reduced BMD. To begin to unravel the mechanism through which Asxl2 influenced BMD, a gene co-expression network was created using cortical bone gene expression microarray data from the HMDP strains. Asxl2 was identified as a member of a co-expression module enriched for genes involved in the differentiation of myeloid cells. In bone, osteoclasts are bone-resorbing cells of myeloid origin, suggesting that Asxl2 may play a role in osteoclast differentiation. In agreement, the knockdown of Asxl2 in bone marrow macrophages impaired their ability to form osteoclasts. This study identifies a new regulator of BMD and osteoclastogenesis and highlights the power of GWA and systems genetics in the mouse for dissecting complex genetic traits., Author Summary Osteoporosis is a disease of weak and fracture-prone bones. The characteristic of bone that is most predictive of fractures is low bone mineral density (BMD), a trait primarily controlled by genetics. In recent years, significant advances have been made in the discovery of genes affecting BMD; however, our understanding of its genetic basis is still primitive. In this study, we used genome-wide association in the mouse to identify additional sex combs like-2 (Asxl2) as a novel BMD gene. In confirmation of our genetic analysis, mice deficient in Asxl2 had reduced BMD. To evaluate its function in bone, the expression levels of Asxl2 and tens of thousands of other genes were measured in bone in a large number of inbred mouse strains. Asxl2 demonstrated a pattern of expression indicative of genes that play a critical role in osteoclasts, the cells that are responsible for bone resorption. Further study of Asxl2 may reveal novel therapeutic targets for the treatment and prevention of osteoporosis.

Identifying variations in DNA that increase susceptibility to disease is one of the primary aims of genetic studies using a forward genetics approach. However, identification of disease-susceptibility genes by means of such studies provides limited functional information on how genes lead to disease. In fact, in most cases there is an absence of functional information altogether, preventing a definitive identification of the susceptibility gene or genes. Here we develop an alternative to the classic forward genetics approach for dissecting complex disease traits where, instead of identifying susceptibility genes directly affected by variations in DNA, we identify gene networks that are perturbed by susceptibility loci and that in turn lead to disease. Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome. Three genes in this network, lipoprotein lipase (Lpl), lactamase β (Lactb) and protein phosphatase 1-like (Ppm1l), are validated as previously unknown obesity genes, strengthening the association between this network and metabolic disease traits. Our analysis provides direct experimental support that complex traits such as obesity are emergent properties of molecular networks that are modulated by complex genetic loci and environmental factors. Complex human diseases result from the interplay of many genetic and environmental factors. To build up a picture of the factors contributing to one such disease, obesity, gene expression was evaluated as a quantitative trait in blood and adipose tissue samples from hundreds of Icelandic subjects aged 18 to 85. The results reveal a tendency to certain characteristic patterns of gene activation in the fatty tissues — though to a much lesser extent in the blood — of people with a higher body mass index. A transcriptional network constructed from the adipose tissue data has significant overlap with a network based on mouse adipose tissue data. Experimental support for the idea that complex diseases are emergent properties of molecular networks influenced by genes and environment comes from a study in mice. Mice were examined for disturbances in genetic expression networks that correlate with metabolic traits associated with obesity, diabetes and atherosclerosis. Three genes — Lpl, Lactb and Ppm1l — were identified as previously unknown obesity genes. This 'molecular network' approach raises the prospect that therapies might be directed at whole 'disease networks', rather than at one or two specific genes. Standard approaches to identify the genetic changes that lead to disease are reversed by examination of genetic networks for perturbations that are associated with disease states, and following up candidate genes from there. This begins with three genes in mice that lead to obesity when mutated, demonstrating that complex genetic–environmental traits can be dissected with this new approach.

The molecular pathogenesis and the genetic aberrations that lead to the progression of hepatocellular carcinoma (HCC) are largely unknown. Here, we demonstrate that the thioredoxin interacting protein (Txnip) gene is a candidate tumor suppressor gene in vivo. We previously showed that the recombinant inbred congenic strain HcB-19 has a spontaneous mutation of the Txnip gene, and we now show that the strain has dramatically increased incidence of HCC, and that the HCC cosegregates with the Txnip mutation. Approximately 40% of the Txnip-deficient mice developed hepatic tumors with an increased prevalence in male mice. Visible tumors develop as early as 8 months of age. Histological analysis confirmed the morphology of HCC in the Txnip-deficient mice. Molecular markers of HCC, alpha-fetoprotein and p53, were increased in tumors of Txnip-deficient mice. The upregulation of p53 preceded tumor development; however, bromodeoxyuridine (BrdU) labeling of normal hepatic tissue of Txnip-deficient mice did not reveal increased cell proliferation. Finally, microarray analyses of tumor, non-tumor adjacent, and normal tissue of Txnip-deficient mice highlighted the genetic differences leading to the predisposition and onset of HCC. Our findings suggest that Txnip deficiency is sufficient to initiate HCC and suggest novel mechanisms in hepatocarcinogenesis.

PTPN12 tyrosine phosphatase may play a role in tumor development/progression in triple negative breast cancer patients (TNP). The effects of PTPN12 appear to be mediated through several tyrosine kinase receptors including EGFR, HER2, and PDGFR-beta. We investigated the variability associated with PTPN12 transcript in the microarray gene expression data obtained from 105 TNP as determined by IHC for ER and PR and IHC and FISH for HER2 during our clinical molecular profiling on solid tumors. The mRNA levels of PTPN12 in our cohort was highly variable suggesting a complex genetic regulation of PTPN12 transcription in TNP patients. The highly variable nature of PTPN12 mRNA levels lead us to perform a correlation-based analysis of the transcriptome in TNP samples to gain insight into pathways and cellular processes associated with PTPN12 variation. Specifically, we quantile-normalized and performed two-dimensional hierarchical clustering of the 1000 top correlated genes with PTPN12 expression across the 105 TNP samples. We identified seven distinct gene clusters and three distinct patient subpopulations. The three distinct subtypes of TNP were comprised of low expressing PTPN12 (median log2 expression of 12.3), medium expressing PTPN12 (median log2 expression of 13.1), and high expressing PTPN12 (median log2 expression of 13.4). From the 7 gene clusters identified, 6 were positively correlated and one cluster (cluster 3) was negatively correlated with PTPN12 expression. Upon examining the genes within each cluster, we found that all contain unique set of genes related to cell proliferation, cell death, cell motility, cell cycle regulation and other cancer related pathways. From the 7 clusters, clusters 1, 6 and 7 had the highest fraction of such genes. The gene expression pattern and the gene content of these three clusters is as follows: Cluster 1 (208 genes) were genes that were highly expressed in all three TNP groups and the expression of the genes was highest in high PTPN12 expressing patients. Functional classification of genes by the DAVID bioinformatics tool at NCBI showed several genes related to cancer, including 19 MAP kinase signaling genes, and 15 genes involved in regulation of apoptosis. Cluster 6 (150 genes) contained genes showing very low expression in low-expressing TNPs, moderately expressed in medium expressing TNPs, and highly expressed in high-expressing TNPs. Cluster 6 had the highest fraction of cancer related genes including 7 mitosis and cell cycle check point genes, 6 cytoskeletal genes, 4 phosphatases and 6 kinases including KRAS, 4 DNA repair genes, 8 signal transduction genes, 3 positive regulator of apoptosis, and 22 genes involved in regulation of gene expression. Cluster 7 (166 genes) contained genes that were expressed at low level throughout the three TNP groups but the severity of under-expression was higher in TNP patients with low PTPN12 expression. In cluster 7 there were 5 genes belonging to the EGFR and FGF signaling pathways, and 6 genes involved in mitosis. It is worth noting that EGFR pathway has been postulated to crosstalk with PTPN12 protein. All together our results provide support for the involvement of PTPN12 in cancer development and highlights a promising therapeutic target for TNP patients. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr PD08-09.

Introduction: Immunohistochemical (IHC) analysis of protein expression in clinical specimens has been invaluable in diagnosing, monitoring and guiding treatment in cancer patients. This approach, however, has its limitations including the difficulty to obtain robust and reliable antibodies, the semi quantitative nature of the assay, subjectivity of intensity call, and the lack of specificity to detect clinically relevant isoform expression in tumor specimen. In light of these limitations, RNA expression may be a useful replacement or a complementary approach to IHC in clinical settings. In this study, we compared the concordance between IHC and mRNA expression levels as measured by microarray using a large cohort of cancer patients spanning multiple lineages. Methods: 10246 patient samples that included 40 tumor types were utilized in this study. Transcript and protein expression levels were measured for ESR1, AR, KIT, ERBB2 (Her2), RRM1, and ERCC1. Transcript levels were measured by Illumina's HumanHT-12 microarray (v4), and IHC was performed using the following antibodies: SP1 (ESR1), AR27 (AR), 4B5 (Her2), 8F1 (ERCC1), anti-CD117 polyclonal (KIT), and 10526-1-AP (RRM1). Results: We observed a significant correlation between ERBB2, ESR1, and KIT with IHC data. Of the 3 probes on the microarray measuring ERBB2, only one probe mapping to the 3’ end of the two ERBB2 isoforms showed significant association with IHC data. For KIT, IHC and mRNA expression correlated well across, thymic carcinomas, poorly differentiated neuroendocrine tumors, melanoma, GIST, and head and neck cancer. For ESR1, significant correlations were observed for uterine sarcoma, endometrial carcinoma, and breast adenocarcinoma. Cervical cancers and ovarian surface epithelial carcinomas showed diminished association, and there was a lack of association in NSCLC and neuroendocrine carcinoid tumors. For AR, we observed strong correlation only in prostate tumors. ERCC1 and RRM1 mRNA expressions did not correlate well with protein expression in any of the tumor lineages examined. Conclusion: These observations suggest that measuring mRNA via microarray has the potential to serve as a surrogate for IHC in clinical setting. The mixed associations observed for some of the genes exhibited lineage specificity suggesting that the relation between microarray results and IHC may be driven by the tissue-specific expression of isoforms, differential expression of genes in the stromal cells, and the technical limitations of each platform including the specificity of either the probes used to measure mRNA or antibodies used to measure protein expression. As such, combining the two platforms in the clinical setting may provide a more complete assessment of gene expression in tumor samples. Citation Format: Anatole Ghazalpour, Wangjuh Sting Chen, Wenhsiang Wen, Zoran Gatalica, Ryan Bender. Concordance between protein expression and mRNA expression in the large cohort of cancer patients. [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 LB-171. doi:10.1158/1538-7445.AM2014-LB-171

e22091 Background: PD-1 activation by ligands (PD-L1 and PD-L2) inhibits T-cell activation and facilitates cancer progression. Trials exploring interruption of PD-1/PD-L1 signaling showed benefit i...

Although uveal melanoma represents only 5% of all melanomas, it is the most common primary intraocular malignancy of the adult eye. Approximately 50% of patients will develop metastases which are resistant to medical interventions. There is a great need for improved therapy as the prognosis is poor for advanced-stage disease. Our study was undertaken to investigate the presence of novel therapeutic targets. Methods: We analyzed 49 uveal melanoma patients with immunohistochemistry for 23 markers including cKIT, PDGFR, cMET, PTEN and IGF1R. Furthermore, microarray analysis was performed on 29 samples using the Illumina platform. We also investigated FISH amplification of EGFR and mutational analysis of cKIT, BRAF, KRAS and NRAS on a smaller patient subset. Results: Overexpression of KIT at the protein and RNA level was 74% (28 out of 38) and 45% (13 out of 29), respectively. Expression of cKIT did not correlate with gain-of-function cKIT mutations in any of the 34 samples tested. In our study, MET was overexpressed in 15 out of 17 cases at the RNA level and IGF1R was high in 4 out of 6 patients indicating poor prognosis. PTEN expression by IHC was present in 90% (36 out of 40) patients, indicating the PI3K pathway is not activated in the majority of uveal melanoma patients. BRAF was wild-type in all 42 patients tested. Similarly, no KRAS or NRAS mutations were detected. Protein and RNA expression of PDGFR were low in our patients. MGMT was lost in 16 out of 40 patients at the protein level and 10 out of 29 patients at the RNA level. EGFR expression, copy number and protein levels were low in the patients tested. Conclusions: Our data on cKIT suggests that it is a promising target in uveal melanoma. Low expression of MGMT in about a third of our patients may indicate the likelihood of favorable response to alkylating agents like dacarbazine or temozolomide. There are currently several clinical trials investigating various cKIT inhibitors, as well as temozolomide in advanced uveal melanoma patients. Our findings highlight the importance of molecular profiling uveal melanoma patients. Microarray was also utilized to interrogate the tumor. In all, 88 biomarkers were analyzed by this methodology in 29 tumors. Table 2 and Table 3 (shown below) are graphical illustrations showing the twenty biomarkers with the highest relative overexpression and the twenty biomarkers with the highest relative underexpression, sorted by percentage (from highest to lowest). Overexpressed biomarkers worth mentioning in Table 2 include MET (88.2%), KIT (44.8%), ERCC1 (41.4%), GART (41.4%), SPARC (41.4%), PDGFRB (34.5%), and RRM2B (34.5%). Some of these results are associated with potential benefit to agents like sunitinib (KIT), pemetrexed (GART), and nab-paclitaxel (SPARC), while others are associated with a lack of clinical benefit to agents like cisplatin/carboplatin (ERCC1). By contrast, biomarkers worth mentioning in Table 3 include EGFR (55.2%), TOP2A (51.7%), PDGFRA (48.3%), RRM1 (48.3%), MGMT (34.5%), and TYMS (34.5%). Some of these results are associated with benefit to fluorouracil (TYMS) and temozolomide/ dacarbazine (MGMT). Fluorescent in-situ hybridization (FISH) was also performed, either by reflexing from PTEN or HER2 IHC results or by physician request. Results in Table 4 show that ALK, cMYC, EGFR, HER2, and TOP2A showed no amplification. Note how EGFR by FISH results indicate a lack of clinical benefit to anti-EGFR-targeted therapy, in keeping with EGFR by microarray.

Introduction: Despite the widespread use of prostate specific antigen screening for early detection, prostate cancer remains the second leading cause of cancer related death among men in the US. Metastatic, hormone refractory prostate cancer (HRPCa) is the end stage, lethal form of the disease. Defining the molecular mechanisms underlying the transition of an androgen responsive prostate cancer represents an important clinical problem. Currently, no effective therapies exist for end stage, hormone refractory disease. In this study, we investigated the differentially expressed genes in primary prostate tumor vs. hormone refractory prostate tumor in order to identify potentially important therapeutic targets. Methods: Formalin fixed paraffin embedded tumor samples from 14 cases (8 primary, chemotherapy-naïve and 6 locally recurrent, hormonal therapy-refractory carcinomas) were analyzed for the whole genome RNA (29,285 transcripts) microarray analysis using Illumina cDNA-mediated annealing, selection, extension and ligation (DASL) process with the HumanHT-12 v4 beadChip (Illumina Inc., San Diego, CA). Additionally, a select number of chemotherapy-predictive (theranostic) biomarkers were analyzed using immunohistochemical methods. Results: Transcriptome analysis identified 299 genes highly differentially expressed between the primary and recurrent groups (p-value < 0.01, Student's t-test). Using these 299 genes, we performed two dimensional hierarchical clustering and were able to discover 8 clusters corresponding to 5 general patterns: Pattern 1-underexpressed in both groups but more so in recurrent than primary (2 clusters with 28 and 22 genes respectively); Pattern 2-slightly overexpressed in primary and underexpressed or no change in recurrent (1 cluster with 65 genes); Pattern 3-slightly under expressed/ no change in primary and overexpressed in recurrent (1 cluster, 12 genes); Pattern 4-overexpressed in primary and no change in recurrent (1 cluster, 25 genes); Pattern 5 - overexpressed in both groups but more so in recurrent than primary (3 clusters with 58, 32, and 57 genes in each cluster). Individual gene analysis revealed upregulation of androgen receptor (AR) mRNA in recurrent HRPCa. This was, however, accompanied by a down regulation of regulator of G-protein signaling 2 (RGS2) and ras responsive element binding protein 1 (RREB1). Consistent down regulation of excision repair cross-complementing group 1 (ERCC1) and thymidylate synthase (TS) proteins were also observed in HRPCa. Conclusion: Up-regulation of AR in HRPCa is associated with down regulation of androgen receptor signaling co-regulators (RGS2 and RREB1), providing a mechanism of androgen-independent activation of AR in HRPCa. Platinum based drugs may have potential benefit in treating of HRPCa due to the down regulation of nucleotide excision repair protein ERCC1, while fluoropyrimidines are potentially beneficial due to the lack of TS protein expression in HRPCa. The heat map derived signature readily distinguishes between prostate cancer specimens from men who were treatment naïve vs. hormone refractory cancers with 5 different expression patterns. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr B92.

Quantitative trait locus (QTL) analysis is a powerful tool for mapping genes for complex traits in mice, but its utility is limited by poor resolution. A promising mapping approach is association analysis in outbred stocks or different inbred strains. As a proof of concept for the association approach, we applied whole-genome association analysis to hepatic gene expression traits in an outbred mouse population, the MF1 stock, and replicated expression QTL (eQTL) identified in previous studies of F2 intercross mice. We found that the mapping resolution of these eQTL was significantly greater in the outbred population. Through an example, we also showed how this precise mapping can be used to resolve previously identified loci (in intercross studies), which affect many different transcript levels (known as eQTL “hotspots”), into distinct regions. Our results also highlight the importance of correcting for population structure in whole-genome association studies in the outbred stock., Author Summary In rodents, as in humans, traits such as obesity or diabetes are under the influence of many genes spread throughout the genome. Using linkage analysis, the locations of the major contributing genes can be mapped only to very large regions of chromosomes, usually encompassing hundreds of genes. This has made it difficult to identify the underlying genes and mutations. Another approach, analogous to genome-wide association in human populations, is to use association analyses among outbred stocks of mice. In this proof-of-principle article, we make use of common variations that locally perturb gene expression to demonstrate the greatly improved mapping resolution of association in mice. Our results indicate that association analyses in mice are a powerful approach to the dissection of complex traits and their underlying molecular networks.

Systems biology approaches that are based on the genetics of gene expression have been fruitful in identifying genetic regulatory loci related to complex traits. We use microarray and genetic marker data from an F2 mouse intercross to examine the large-scale organization of the gene co-expression network in liver, and annotate several gene modules in terms of 22 physiological traits. We identify chromosomal loci (referred to as module quantitative trait loci, mQTL) that perturb the modules and describe a novel approach that integrates network properties with genetic marker information to model gene/trait relationships. Specifically, using the mQTL and the intramodular connectivity of a body weight–related module, we describe which factors determine the relationship between gene expression profiles and weight. Our approach results in the identification of genetic targets that influence gene modules (pathways) that are related to the clinical phenotypes of interest., Synopsis Obesity is a major pub lic health concern in many developed countries. While some people appear to stay lean no matter what or how much they eat, others appear to be genetically predisposed to obesity. The genetic similarity between mouse and human makes the mouse a promising mammalian model system to study obesity. Advantages of mouse models include the ability to control diet/environment and easy access to relevant tissues for gene expression studies. Mouse cross studies have implicated dozens of chromosomal regions that contain weight-predisposing genes, and gene expression studies have yielded hundreds of body weight–related genes. In this study, the authors use a gene network–based approach for integrating clinical traits, genetic marker data, and gene expression data. Instead of focusing on individual genes, the authors provide a systems-level view of a module of genes related to body weight. The resulting model allows them to characterize weight-related genes utilizing network concepts (intramodular connectivity) and genetic concepts (module quantitative trait locus). This integrative genomics approach provides new insights into the relationship between gene expression and body weight.

Background A segregating population of (C57BL/6J × DBA/2J)F2 intercross mice was studied for obesity-related traits and for global gene expression in liver. Quantitative trait locus analyses were applied to the subcutaneous fat-mass trait and all gene-expression data. These data were then used to identify gene sets that are differentially perturbed in lean and obese mice.

Systems-oriented genetic approaches that incorporate gene expression and genotype data are valuable in the quest for genetic regulatory loci underlying complex traits. Gene coexpression network analysis lends itself to identification of entire groups of differentially regulated genes—a highly relevant endeavor in finding the underpinnings of complex traits that are, by definition, polygenic in nature. Here we describe one such approach based on liver gene expression and genotype data from an F2 mouse intercross utilizing weighted gene coexpression network analysis (WGCNA) of gene expression data to identify physiologically relevant modules. We describe two strategies: single-network analysis and differential network analysis. Single-network analysis reveals the presence of a physiologically interesting module that can be found in two distinct mouse crosses. Module quantitative trait loci (mQTLs) that perturb this module were discovered. In addition, we report a list of genetic drivers for this module. Differential network analysis reveals differences in connectivity and module structure between two networks based on the liver expression data of lean and obese mice. Functional annotation of these genes suggests a biological pathway involving epidermal growth factor (EGF). Our results demonstrate the utility of WGCNA in identifying genetic drivers and in finding genetic pathways represented by gene modules. These examples provide evidence that integration of network properties may well help chart the path across the gene–trait chasm. Electronic supplementary material The online version of this article (doi: 10.1007/s00335-007-9043-3) contains supplementary material, which is available to authorized users.

SummaryHeritable epigenetic factors can contribute to complex disease etiology. Here we examine the contribution of DNA methylation to complex traits that are precursors to heart disease, diabetes, and osteoporosis. We profiled DNA methylation in the liver using bisulfite sequencing in 90 mouse inbred strains, genome-wide expression levels, proteomics, metabolomics, and 68 clinical traits and performed epigenome-wide association studies (EWAS). We found associations with numerous clinical traits including bone density, insulin resistance, expression, and protein and metabolite levels. A large proportion of associations were unique to EWAS and were not identified using GWAS. Methylation levels were regulated by genetics largely in cis, but we also found evidence of trans regulation, and we demonstrate that genetic variation in the methionine synthase reductase gene Mtrr affects methylation of hundreds of CpGs throughout the genome. Our results indicate that natural variation in methylation levels contributes to the etiology of complex clinical traits.