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The underrepresentation of non-Europeans in human genetic studies so far has limited the diversity of individuals in genomic datasets and led to reduced medical relevance for a large proportion of the world’s population. Population-specific reference genome datasets as well as genome-wide association studies in diverse populations are needed to address this issue. Here we describe the pilot phase of the GenomeAsia 100K Project. This includes a whole-genome sequencing reference dataset from 1,739 individuals of 219 population groups and 64 countries across Asia. We catalogue genetic variation, population structure, disease associations and founder effects. We also explore the use of this dataset in imputation, to facilitate genetic studies in populations across Asia and worldwide., Using whole-genome sequencing data from 1,739 individuals, the GenomeAsia 100K Project catalogues genetic variation, population structure and disease associations to facilitate genetic studies in Asian populations and increase representation in genetics studies worldwide.

Metastasis is the principal cause of cancer related deaths. Tumor invasion is essential for metastatic spread. However, determinants of invasion are poorly understood. We addressed this knowledge gap by leveraging a unique attribute of kidney cancer. Renal tumors invade into large vessels forming tumor thrombi (TT) that migrate extending sometimes into the heart. Over a decade, we prospectively enrolled 83 ethnically-diverse patients undergoing surgical resection for grossly invasive tumors at UT Southwestern Kidney Cancer Program. In this study, we perform comprehensive histological analyses, integrate multi-region genomic studies, generate in vivo models, and execute functional studies to define tumor invasion and metastatic competence. We find that invasion is not always associated with the most aggressive clone. Driven by immediate early genes, invasion appears to be an opportunistic trait attained by subclones with diverse oncogenomic status in geospatial proximity to vasculature. We show that not all invasive tumors metastasize and identify determinants of metastatic competency. TT associated with metastases are characterized by higher grade, mTOR activation and a particular immune contexture. Moreover, TT grade is a better predictor of metastasis than overall tumor grade, which may have implications for clinical practice., Tumour thrombi (TT) are intravascular extensions of renal cell carcinomas (RCC) which often lead to distant metastases. Here the authors examine the determinants of vascular invasion and metastasis in a unique cohort of RCC patients with TT using multi-region genomics and in vivo models.

By leveraging tumorgraft (patient-derived xenograft) RNA-sequencing data, we developed an empirical approach, DisHet, to dissect the tumor microenvironment (eTME). We found that 65% of previously defined immune signature genes are not abundantly expressed in renal cell carcinoma (RCC) and identified 610 novel immune/stromal transcripts. Using eTME, genomics, pathology, and medical record data involving >1,000 patients, we established an inflamed pan-RCC subtype (IS) enriched for regulatory T cells, natural killer cells, TH1 cells, neutrophils, macrophages, B cells, and CD8+ T cells. IS is enriched for aggressive RCCs, including BAP1-deficient clear-cell and type 2 papillary tumors. The IS subtype correlated with systemic manifestations of inflammation such as thrombocytosis and anemia, which are enigmatic predictors of poor prognosis. Furthermore, IS was a strong predictor of poor survival. Our analyses suggest that tumor cells drive the stromal immune response. These data provide a missing link between tumor cells, the TME, and systemic factors. Significance: We undertook a novel empirical approach to dissect the renal cell carcinoma TME by leveraging tumorgrafts. The dissection and downstream analyses uncovered missing links between tumor cells, the TME, systemic manifestations of inflammation, and poor prognosis. Cancer Discov; 8(9); 1142–55. ©2018 AACR. This article is highlighted in the In This Issue feature, p. 1047

Recent work has demonstrated that two archaic human groups (Neanderthals and Denisovans) interbred with modern humans and contributed to the contemporary human gene pool. These findings relied on the availability of high-coverage genomes from both Neanderthals and Denisovans. Here we search for evidence of archaic admixture from a worldwide panel of 1,667 individuals using an approach that does not require the presence of an archaic human reference genome. We find no evidence for archaic admixture in the Andaman Islands, as previously claimed, or on the island of Flores, where Homo floresiensis fossils have been found. However, we do find evidence for at least one archaic admixture event in sub-Saharan Africa, with the strongest signal in Khoesan and Pygmy individuals from Southern and Central Africa. The locations of these putative archaic admixture tracts are weighted against functional regions of the genome, consistent with the long-term effects of purifying selection against introgressed genetic material.

Background PIK3CA mutations are frequent in human breast cancer. Pik3caH1047R mutant expression in mouse mammary gland promotes tumorigenesis. TP53 mutations co-occur with PIK3CA mutations in human breast cancers. We previously generated a conditionally activatable Pik3caH1047R;MMTV-Cre mouse model and found a few malignant sarcomatoid (spindle cell) carcinomas that had acquired spontaneous dominant-negative Trp53 mutations. Methods A Pik3caH1047R;Trp53R270H;MMTV-Cre double mutant mouse breast cancer model was generated. Tumors were characterized by histology, marker analysis, transcriptional profiling, single-cell RNA-seq, and bioinformatics. Cell lines were developed from mutant tumors and used to identify and confirm genes involved in metastasis. Results We found Pik3caH1047R and Trp53R270H cooperate in driving oncogenesis in mammary glands leading to a shorter latency than either alone. Double mutant mice develop multiple histologically distinct mammary tumors, including adenocarcinoma and sarcomatoid (spindle cell) carcinoma. We found some tumors to be invasive and a few metastasized to the lung and/or the lymph node. Single-cell RNA-seq analysis of the tumors identified epithelial, stromal, myeloid, and T cell groups. Expression analysis of the metastatic tumors identified S100a4 as a top candidate gene associated with metastasis. Metastatic tumors contained a much higher percentage of epithelial–mesenchymal transition (EMT)-signature positive and S100a4-expressing cells. CRISPR/CAS9-mediated knockout of S100a4 in a metastatic tumor-derived cell line disrupted its metastatic potential indicating a role for S100a4 in metastasis. Conclusions Pik3caH1047R;Trp53R270H;MMTV-Cre mouse provides a preclinical model to mimic a subtype of human breast cancers that carry both PIK3CA and TP53 mutations. It also allows for understanding the cooperation between the two mutant genes in tumorigenesis. Our model also provides a system to study metastasis and develop therapeutic strategies for PIK3CA/TP53 double-positive cancers. S100a4 found involved in metastasis in this model can be a potential diagnostic and therapeutic target.

Background Data from the 1000 Genomes project is quite often used as a reference for human genomic analysis. However, its accuracy needs to be assessed to understand the quality of predictions made using this reference. We present here an assessment of the genotyping, phasing, and imputation accuracy data in the 1000 Genomes project. We compare the phased haplotype calls from the 1000 Genomes project to experimentally phased haplotypes for 28 of the same individuals sequenced using the 10X Genomics platform. Results We observe that phasing and imputation for rare variants are unreliable, which likely reflects the limited sample size of the 1000 Genomes project data. Further, it appears that using a population specific reference panel does not improve the accuracy of imputation over using the entire 1000 Genomes data set as a reference panel. We also note that the error rates and trends depend on the choice of definition of error, and hence any error reporting needs to take these definitions into account. Conclusions The quality of the 1000 Genomes data needs to be considered while using this database for further studies. This work presents an analysis that can be used for these assessments. Electronic supplementary material The online version of this article (10.1186/s12864-019-5957-x) contains supplementary material, which is available to authorized users.

We analyzed transcriptomes (n = 211), whole exomes (n = 99) and targeted exomes (n = 103) from 216 malignant pleural mesothelioma (MPM) tumors. Using RNA-seq data, we identified four distinct molecular subtypes: sarcomatoid, epithelioid, biphasic-epithelioid (biphasic-E) and biphasic-sarcomatoid (biphasic-S). Through exome analysis, we found BAP1, NF2, TP53, SETD2, DDX3X, ULK2, RYR2, CFAP45, SETDB1 and DDX51 to be significantly mutated (q-score ≥ 0.8) in MPMs. We identified recurrent mutations in several genes, including SF3B1 (∼2%; 4/216) and TRAF7 (∼2%; 5/216). SF3B1-mutant samples showed a splicing profile distinct from that of wild-type tumors. TRAF7 alterations occurred primarily in the WD40 domain and were, except in one case, mutually exclusive with NF2 alterations. We found recurrent gene fusions and splice alterations to be frequent mechanisms for inactivation of NF2, BAP1 and SETD2. Through integrated analyses, we identified alterations in Hippo, mTOR, histone methylation, RNA helicase and p53 signaling pathways in MPMs.

Summary The cellular origin of sporadic pancreatic neuroendocrine tumors (PNETs) is obscure. Hormone expression suggests that these tumors arise from glucagon-producing alpha cells or insulin-producing β cells, but instability in hormone expression prevents linage determination. We utilize loss of hepatic glucagon receptor (GCGR) signaling to drive alpha cell hyperproliferation and tumor formation to identify a cell of origin and dissect mechanisms that drive progression. Using a combination of genetically engineered Gcgr knockout mice and GCGR-inhibiting antibodies, we show that elevated plasma amino acids drive the appearance of a proliferative population of SLC38A5+ embryonic progenitor-like alpha cells in mice. Further, we characterize tumors from patients with rare bi-allelic germline GCGR loss-of-function variants and find prominent tumor-cell-associated expression of the SLC38A5 paralog SLC7A8 as well as markers of active mTOR signaling. Thus, progenitor cells arise from adult alpha cells in response to metabolic signals and, when inductive signals are chronically present, drive tumor initiation., Graphical Abstract, Highlights GCGR inhibition induces an SLC38A5+ alpha cell population in aged mice An SLC38A5+ alpha cell subpopulation initiates pancreatic tumors in aged Gcgr−/− mice Tumors exhibit low mutational burden and response to mTOR inhibition by rapamycin Tumors in GCGR loss-of-function models lack immune cell infiltration, Smith et al. demonstrate that chronic mTOR signaling, driven by elevated serum amino acids, in Gcgr knockout mice promotes tumor initiation from an SLC38A5+ alpha cell subpopulation and supports tumor maintenance. Furthermore, they provide evidence that an SLC7A8+ alpha cell population contains the progenitors for human tumors in GCGR-mutant patients.

Colorectal cancer remains a major unmet medical need, prompting large-scale genomics efforts in the field to identify molecular drivers for which targeted therapies might be developed. We previously reported the identification of recurrent translocations in R-spondin genes present in a subset of colorectal tumours. Here we show that targeting RSPO3 in PTPRK-RSPO3-fusion-positive human tumour xenografts inhibits tumour growth and promotes differentiation. Notably, genes expressed in the stem-cell compartment of the intestine were among those most sensitive to anti-RSPO3 treatment. This observation, combined with functional assays, suggests that a stem-cell compartment drives PTPRK-RSPO3 colorectal tumour growth and indicates that the therapeutic targeting of stem-cell properties within tumours may be a clinically relevant approach for the treatment of colorectal tumours.

Purpose: MAPK pathway inhibitors targeting BRAF and MEK have shown clinical efficacy in patients with RAF- and/or RAS-mutated tumors. However, acquired resistance to these agents has been an impediment to improved long-term survival in the clinic. In such cases, targeting ERK downstream of BRAF/MEK has been proposed as a potential strategy for overcoming acquired resistance. Preclinical studies suggest that ERK inhibitors are effective at inhibiting BRAF/RAS-mutated tumor growth and overcome BRAF or/and MEK inhibitor resistance. However, as observed with other MAPK pathway inhibitors, treatment with ERK inhibitors is likely to cause resistance in the clinic. Here, we aimed to model the mechanism of resistance to ERK inhibitors. Experimental Design: We tested five structurally different ATP-competitive ERK inhibitors representing three different scaffolds on BRAF/RAS-mutant cancer cell lines of different tissue types to generate resistant lines. We have used in vitro modeling, structural biology, and genomic analysis to understand the development of resistance to ERK inhibitors and the mechanisms leading to it. Results: We have identified mutations in ERK1/2, amplification and overexpression of ERK2, and overexpression of EGFR/ERBB2 as mechanisms of acquired resistance. Structural analysis of ERK showed that specific compounds that induced on-target ERK mutations were impaired in their ability to bind mutant ERK. We show that in addition to MEK inhibitors, ERBB receptor and PI3K/mTOR pathway inhibitors are effective in overcoming ERK-inhibitor resistance. Conclusions: These findings suggest that combination therapy with MEK or ERBB receptor or PI3K/mTOR and ERK inhibitors may be an effective strategy for managing the emergence of resistance in the clinic. Clin Cancer Res; 24(16); 4044–55. ©2018 AACR.

Identifying and understanding changes in cancer genomes is essential for the development of targeted therapeutics1. Here we analyse systematically more than 70 pairs of primary human colon tumours by applying next-generation sequencing to characterize their exomes, transcriptomes and copy-number alterations. We have identified 36,303 protein-altering somatic changes that include several new recurrent mutations in the Wnt pathway gene TCF7L2, chromatin-remodelling genes such as TET2 and TET3 and receptor tyrosine kinases including ERBB3. Our analysis for significantly mutated cancer genes identified 23 candidates, including the cell cycle checkpoint kinase ATM. Copy-number and RNA-seq data analysis identified amplifications and corresponding overexpression of IGF2 in a subset of colon tumours. Furthermore, using RNA-seq data we identified multiple fusion transcripts including recurrent gene fusions involving R-spondin family members RSPO2 and RSPO3 that together occur in 10% of colon tumours. The RSPO fusions were mutually exclusive with APC mutations, indicating that they probably have a role in the activation of Wnt signalling and tumorigenesis. Consistent with this we show that the RSPO fusion proteins were capable of potentiating Wnt signalling. The R-spondin gene fusions and several other gene mutations identified in this study provide new potential opportunities for therapeutic intervention in colon cancer.

Oncogenic mutations in PIK3CA, which encodes the phosphoinositide-3-kinase (PI3K) catalytic subunit p110α, occur in ∼25% of human breast cancers. In this study, we report the development of a knock-in mouse model for breast cancer where the endogenous Pik3ca allele was modified to allow tissue-specific conditional expression of a frequently found Pik3ca(H1047R) (Pik3ca(e20H1047R)) mutant allele. We found that activation of the latent Pik3ca(H1047R) allele resulted in breast tumors with multiple histological types. Whole-exome analysis of the Pik3ca(H1047R)-driven mammary tumors identified multiple mutations, including Trp53 mutations that appeared spontaneously during the development of adenocarinoma and spindle cell tumors. Further, we used this model to test the efficacy of GDC-0941, a PI3K inhibitor, in clinical development, and showed that the tumors respond to PI3K inhibition.

Deregulated HER2 is a target of many approved cancer drugs. We analyzed 111,176 patient tumors and identified recurrent mutations in HER2 transmembrane domain (TMD) and juxtamembrane domain (JMD) that include G660D, R678Q, E693K, and Q709L. Using a saturation mutagenesis screen and testing of patient-derived mutations we found several activating TMD and JMD mutations. Structural modeling and analysis showed that the TMD/JMD mutations function by improving the active dimer interface or stabilizing an activating conformation. Further, we found that HER2 G660D employed asymmetric kinase dimerization for activation and signaling. Importantly, anti-HER2 antibodies and small-molecule kinase inhibitors blocked the activity of TMD/JMD mutants. Consistent with this, a G660D germline mutant lung cancer patient showed remarkable clinical response to HER2 blockade.

Human cancer cells typically harbour multiple chromosomal aberrations, nucleotide substitutions and epigenetic modifications that drive malignant transformation. The Cancer Genome Atlas ( TCGA) pilot project aims to assess the value of large- scale multi- dimensional analysis of these molecular characteristics in human cancer and to provide the data rapidly to the research community. Here we report the interim integrative analysis of DNA copy number, gene expression and DNA methylation aberrations in 206 glioblastomas - the most common type of primary adult brain cancer - and nucleotide sequence aberrations in 91 of the 206 glioblastomas. This analysis provides new insights into the roles of ERBB2, NF1 and TP53, uncovers frequent mutations of the phosphatidylinositol- 3- OH kinase regulatory subunit gene PIK3R1, and provides a network view of the pathways altered in the development of glioblastoma. Furthermore, integration of mutation, DNA methylation and clinical treatment data reveals a link between MGMT promoter methylation and a hypermutator phenotype consequent to mismatch repair deficiency in treated glioblastomas, an observation with potential clinical implications. Together, these findings establish the feasibility and power of TCGA, demonstrating that it can rapidly expand knowledge of the molecular basis of cancer.

The panel of 60 human cancer cell lines (the NCI-60) assembled by the National Cancer Institute for anticancer drug discovery is a widely used resource. We previously sequenced 24 cancer genes in those cell lines. Eleven of the genes were found to be mutated in three or more of the lines. Using a pharmacogenomic approach, we analyzed the relationship between drug activity and mutations in those 11 genes (APC, RB1, KRAS, NRAS, BRAF, PIK3CA, PTEN, STK11, MADH4, TP53, and CDKN2A). That analysis identified an association between mutation in BRAF and the antiproliferative potential of phenothiazine compounds. Phenothiazines have been used as antipsychotics and as adjunct antiemetics during cancer chemotherapy and more recently have been reported to have anticancer properties. However, to date, the anticancer mechanism of action of phenothiazines has not been elucidated. To follow up on the initial pharmacologic observations in the NCI-60 screen, we did pharmacologic experiments on 11 of the NCI-60 cell lines and, prospectively, on an additional 24 lines. The studies provide evidence that BRAF mutation (codon 600) in melanoma as opposed to RAS mutation is predictive of an increase in sensitivity to phenothiazines as determined by 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium, inner salt assay (Wilcoxon P = 0.007). That pattern of increased sensitivity to phenothiazines based on the presence of codon 600 BRAF mutation may be unique to melanomas, as we do not observe it in a panel of colorectal cancers. The findings reported here have potential implications for the use of phenothiazines in the treatment of V600E BRAF mutant melanoma. [Mol Cancer Ther 2008;7(6):1337–46]

International audience; The BioMart Community Portal (www.biomart.org) is a community-driven effort to provide a unified interface to biomedical databases that are distributed worldwide. The portal provides access to numerous database projects supported by 30 scientific organizations. It includes over 800 different biological datasets spanning genomics, proteomics, model organisms, cancer data, ontology information and more. All resources available through the portal are independently administered and funded by their host organizations. The BioMart data federation technology provides a unified interface to all the available data. The latest version of the portal comes with many new databases that have been created by our ever-growing community. It also comes with better support and extensibility for data analysis and visualization tools. A new addition to our toolbox, the enrichment analysis tool is now accessible through graphical and web service interface. The BioMart community portal averages over one million requests per day. Building on this level of service and the wealth of information that has become available, the BioMart Community Portal has introduced a new, more scalable and cheaper alternative to the large data stores maintained by specialized organizations.

To further understand the molecular distinctions between kidney cancer subtypes, we analyzed exome, transcriptome and copy number alteration data from 167 primary human tumors that included renal oncocytomas and non-clear cell renal cell carcinomas (nccRCCs), consisting of papillary (pRCC), chromophobe (chRCC) and translocation (tRCC) subtypes. We identified ten significantly mutated genes in pRCC, including MET, NF2, SLC5A3, PNKD and CPQ. MET mutations occurred in 15% (10/65) of pRCC samples and included previously unreported recurrent activating mutations. In chRCC, we found TP53, PTEN, FAAH2, PDHB, PDXDC1 and ZNF765 to be significantly mutated. Gene expression analysis identified a five-gene set that enabled the molecular classification of chRCC, renal oncocytoma and pRCC. Using RNA sequencing, we identified previously unreported gene fusions, including ACTG1-MITF fusion. Ectopic expression of the ACTG1-MITF fusion led to cellular transformation and induced the expression of downstream target genes. Finally, we observed upregulation of the anti-apoptotic factor BIRC7 in MiTF-high RCC tumors, suggesting a potential therapeutic role for BIRC7 inhibitors.

Tumor-derived cell lines have served as vital models to advance our understanding of oncogene function and therapeutic responses. Although substantial effort has been made to define the genomic constitution of cancer cell line panels, the transcriptome remains understudied. Here we describe RNA sequencing and single-nucleotide polymorphism (SNP) array analysis of 675 human cancer cell lines. We report comprehensive analyses of transcriptome features including gene expression, mutations, gene fusions and expression of non-human sequences. Of the 2,200 gene fusions catalogued, 1,435 consist of genes not previously found in fusions, providing many leads for further investigation. We combine multiple genome and transcriptome features in a pathway-based approach to enhance prediction of response to targeted therapeutics. Our results provide a valuable resource for studies that use cancer cell lines.

Gastric cancer is the second leading cause of worldwide cancer mortality, yet the underlying genomic alterations remain poorly understood. Here we perform exome and transcriptome sequencing and SNP array assays to characterize 51 primary gastric tumours and 32 cell lines. Meta-analysis of exome data and previously published data sets reveals 24 significantly mutated genes in microsatellite stable (MSS) tumours and 16 in microsatellite instable (MSI) tumours. Over half the patients in our collection could potentially benefit from targeted therapies. We identify 55 splice site mutations accompanied by aberrant splicing products, in addition to mutation-independent differential isoform usage in tumours. ZAK kinase isoform TV1 is preferentially upregulated in gastric tumours and cell lines relative to normal samples. This pattern is also observed in colorectal, bladder and breast cancers. Overexpression of this particular isoform activates multiple cancer-related transcription factor reporters, while depletion of ZAK in gastric cell lines inhibits proliferation. These results reveal the spectrum of genomic and transcriptomic alterations in gastric cancer, and identify isoform-specific oncogenic properties of ZAK., The genetic basis of gastric cancer, the fourth most common cancer worldwide, remains poorly understood. Here, the authors sequence and analyse the exomes and transcriptomes of primary gastric tumours and cell lines, and identify a ZAK kinase isoform that may have an oncogenic role in gastric cancer.

Small-cell lung cancer (SCLC) is an exceptionally aggressive disease with poor prognosis. Here, we obtained exome, transcriptome and copy-number alteration data from approximately 53 samples consisting of 36 primary human SCLC and normal tissue pairs and 17 matched SCLC and lymphoblastoid cell lines. We also obtained data for 4 primary tumors and 23 SCLC cell lines. We identified 22 significantly mutated genes in SCLC, including genes encoding kinases, G protein–coupled receptors and chromatin-modifying proteins. We found that several members of the SOX family of genes were mutated in SCLC. We also found SOX2 amplification in ~27% of the samples. Suppression of SOX2 using shRNAs blocked proliferation of SOX2-amplified SCLC lines. RNA sequencing identified multiple fusion transcripts and a recurrent RLF-MYCL1 fusion. Silencing of MYCL1 in SCLC cell lines that had the RLF-MYCL1 fusion decreased cell proliferation. These data provide an in-depth view of the spectrum of genomic alterations in SCLC and identify several potential targets for therapeutic intervention.

International audience; Breast cancers are comprised of molecularly distinct subtypes that may respond differently to pathway-targeted therapies now under development. Collections of breast cancer cell lines mirror many of the molecular subtypes and pathways found in tumors, suggesting that treatment of cell lines with candidate therapeutic compounds can guide identification of associations between molecular subtypes, pathways, and drug response. In a test of 77 therapeutic compounds, nearly all drugs showed differential responses across these cell lines, and approximately one third showed subtype-, pathway-, and/or genomic aberration-specific responses. These observations suggest mechanisms of response and resistance and may inform efforts to develop molecular assays that predict clinical response.

Timely intervention for cancer requires knowledge of its earliest genetic aberrations. Sequencing of tumors and their metastases reveals numerous abnormalities occurring late in progression. A means to temporally order aberrations in a single cancer, rather than inferring them from serially acquired samples, would define changes preceding even clinically evident disease. We integrate DNA sequence and copy number information to reconstruct the order of abnormalities as individual tumors evolve for 2 separate cancer types. We detect vast, unreported expansion of simple mutations sharply demarcated by recombinative loss of the second copy of TP53 in cutaneous squamous cell carcinomas (cSCC) and serous ovarian adenocarcinomas, in the former surpassing 50 mutations per megabase. In cSCCs, we also report diverse secondary mutations in known and novel oncogenic pathways, illustrating how such expanded mutagenesis directly promotes malignant progression. These results reframe paradigms in which TP53 mutation is required later, to bypass senescence induced by driver oncogenes. Significance: Our approach reveals sequential ordering of oncogenic events in individual cancers, based on chromosomal rearrangements. Identifying the earliest abnormalities in cancer represents a critical step in timely diagnosis and deployment of targeted therapeutics. Cancer Discovery; 1(2); 137–43. © 2011 AACR. This article is highlighted in the In This Issue feature, p. 91

Background Methylation of CpG islands within the DNA promoter regions is one mechanism that leads to aberrant gene expression in cancer. In particular, the abnormal methylation of CpG islands may silence associated genes. Therefore, using high-throughput microarrays to measure CpG island methylation will lead to better understanding of tumor pathobiology and progression, while revealing potentially new biomarkers. We have examined a recently developed high-throughput technology for measuring genome-wide methylation patterns called mTACL. Here, we propose a computational pipeline for integrating gene expression and CpG island methylation profles to identify epigenetically regulated genes for a panel of 45 breast cancer cell lines, which is widely used in the Integrative Cancer Biology Program (ICBP). The pipeline (i) reduces the dimensionality of the methylation data, (ii) associates the reduced methylation data with gene expression data, and (iii) ranks methylation-expression associations according to their epigenetic regulation. Dimensionality reduction is performed in two steps: (i) methylation sites are grouped across the genome to identify regions of interest, and (ii) methylation profles are clustered within each region. Associations between the clustered methylation and the gene expression data sets generate candidate matches within a fxed neighborhood around each gene. Finally, the methylation-expression associations are ranked through a logistic regression, and their significance is quantified through permutation analysis. Results Our two-step dimensionality reduction compressed 90% of the original data, reducing 137,688 methylation sites to 14,505 clusters. Methylation-expression associations produced 18,312 correspondences, which were used to further analyze epigenetic regulation. Logistic regression was used to identify 58 genes from these correspondences that showed a statistically signifcant negative correlation between methylation profles and gene expression in the panel of breast cancer cell lines. Subnetwork enrichment of these genes has identifed 35 common regulators with 6 or more predicted markers. In addition to identifying epigenetically regulated genes, we show evidence of differentially expressed methylation patterns between the basal and luminal subtypes. Conclusions Our results indicate that the proposed computational protocol is a viable platform for identifying epigenetically regulated genes. Our protocol has generated a list of predictors including COL1A2, TOP2A, TFF1, and VAV3, genes whose key roles in epigenetic regulation is documented in the literature. Subnetwork enrichment of these predicted markers further suggests that epigenetic regulation of individual genes occurs in a coordinated fashion and through common regulators.

Purpose: microRNAs have been shown to be involved in different human cancers. We therefore have performed expression profiles on a panel of pediatric tumors to identify cancer-specific microRNAs. We also investigated if microRNAs are coregulated with their host gene. Experimental Design: We performed parallel microRNAs and mRNA expression profiling on 57 tumor xenografts and cell lines representing 10 different pediatric solid tumors using microarrays. For those microRNAs that map to their host mRNA, we calculated correlations between them. Results: We found that the majority of cancer types clustered together based on their global microRNA expression profiles by unsupervised hierarchical clustering. Fourteen microRNAs were significantly differentially expressed between rhabdomyosarcoma and neuroblastoma, and 8 of them were validated in independent patient tumor samples. Exploration of the expression of microRNAs in relationship with their host genes showed that the expression for 43 of 68 (63%) microRNAs located inside known coding genes was significantly correlated with that of their host genes. Among these 43 microRNAs, 5 of 7 microRNAs in the OncomiR-1 cluster correlated significantly with their host gene MIRHG1 (P < 0.01). In addition, high expression of MIRHG1 was significantly associated with high stage and MYCN amplification in neuroblastoma tumors, and the expression level of MIRHG1 could predict the outcome of neuroblastoma patients independently from the current neuroblastoma risk-stratification in two independent patient cohorts. Conclusion: Pediatric cancers express cancer-specific microRNAs. The high expression of the OncomiR-1 host gene MIRHG1 correlates with poor outcome for patients with neuroblastoma, indicating important oncogenic functions of this microRNA cluster in neuroblastoma biology. (Clin Cancer Res 2009;15(17):5560–8)

Genomic experiments produce multiple views of biological systems, among them are DNA sequence and copy number variation, and mRNA and protein abundance. Understanding these systems needs integrated bioinformatic analysis. Public databases such as Ensembl provide relationships and mappings between the relevant sets of probe and target molecules. However, the relationships can be biologically complex and the content of the databases is dynamic. We demonstrate how to use the computational environment R to integrate and jointly analyze experimental datasets, employing BioMart web services to provide the molecule mappings. We also discuss typical problems that are encountered in making gene-to-transcript-to-protein mappings. The approach provides a flexible, programmable and reproducible basis for state-of-the-art bioinformatic data integration.

Background Biological studies involve a growing number of distinct high-throughput experiments to characterize samples of interest. There is a lack of methods to visualize these different genomic datasets in a versatile manner. In addition, genomic data analysis requires integrated visualization of experimental data along with constantly changing genomic annotation and statistical analyses. Results We developed GenomeGraphs, as an add-on software package for the statistical programming environment R, to facilitate integrated visualization of genomic datasets. GenomeGraphs uses the biomaRt package to perform on-line annotation queries to Ensembl and translates these to gene/transcript structures in viewports of the grid graphics package. This allows genomic annotation to be plotted together with experimental data. GenomeGraphs can also be used to plot custom annotation tracks in combination with different experimental data types together in one plot using the same genomic coordinate system. Conclusion GenomeGraphs is a flexible and extensible software package which can be used to visualize a multitude of genomic datasets within the statistical programming environment R.

Loss of 1p36 heterozygosity commonly occurs with MYCN amplification in neuroblastoma tumors, and both are associated with an aggressive phenotype. Database searches identified five microRNAs that map to the commonly deleted region of 1p36 and we hypothesized that the loss of one or more of these microRNAs contributes to the malignant phenotype of MYCN-amplified tumors. By bioinformatic analysis, we identified that three out of the five microRNAs target MYCN and of these miR-34a caused the most significant suppression of cell growth through increased apoptosis and decreased DNA synthesis in neuroblastoma cell lines with MYCN amplification. Quantitative RT-PCR showed that neuroblastoma tumors with 1p36 loss expressed lower level of miR-34a than those with normal copies of 1p36. Furthermore, we demonstrated that MYCN is a direct target of miR-34a. Finally, using a series of mRNA expression profiling experiments, we identified other potential direct targets of miR-34a, and pathway analysis demonstrated that miR-34a suppresses cell-cycle genes and induces several neural-related genes. This study demonstrates one important regulatory role of miR-34a in cell growth and MYCN suppression in neuroblastoma.

Background First-generation molecular profiles for human breast cancers have enabled the identification of features that can predict therapeutic response; however, little is known about how the various data types can best be combined to yield optimal predictors. Collections of breast cancer cell lines mirror many aspects of breast cancer molecular pathobiology, and measurements of their omic and biological therapeutic responses are well-suited for development of strategies to identify the most predictive molecular feature sets. Results We used least squares-support vector machines and random forest algorithms to identify molecular features associated with responses of a collection of 70 breast cancer cell lines to 90 experimental or approved therapeutic agents. The datasets analyzed included measurements of copy number aberrations, mutations, gene and isoform expression, promoter methylation and protein expression. Transcriptional subtype contributed strongly to response predictors for 25% of compounds, and adding other molecular data types improved prediction for 65%. No single molecular dataset consistently out-performed the others, suggesting that therapeutic response is mediated at multiple levels in the genome. Response predictors were developed and applied to TCGA data, and were found to be present in subsets of those patient samples. Conclusions These results suggest that matching patients to treatments based on transcriptional subtype will improve response rates, and inclusion of additional features from other profiling data types may provide additional benefit. Further, we suggest a systems biology strategy for guiding clinical trials so that patient cohorts most likely to respond to new therapies may be more efficiently identified.

SummaryThe human epidermal growth factor receptor (HER) family of tyrosine kinases is deregulated in multiple cancers either through amplification, overexpression, or mutation. ERBB3/HER3, the only member with an impaired kinase domain, although amplified or overexpressed in some cancers, has not been reported to carry oncogenic mutations. Here, we report the identification of ERBB3 somatic mutations in ∼11% of colon and gastric cancers. We found that the ERBB3 mutants transformed colonic and breast epithelial cells in a ligand-independent manner. However, the mutant ERBB3 oncogenic activity was dependent on kinase-active ERBB2. Furthermore, we found that anti-ERBB antibodies and small molecule inhibitors effectively blocked mutant ERBB3-mediated oncogenic signaling and disease progression in vivo.