Integrative and comparative genomic analysis of lung squamous cell carcinomas in East Asian patients

PURPOSE

Lung squamous cell carcinoma (SCC) is the second most prevalent type of lung cancer. Currently, no targeted therapeutics are approved for treatment of this cancer, largely because of a lack of systematic understanding of the molecular pathogenesis of the disease. To identify therapeutic targets and perform comparative analyses of lung SCC, we probed somatic genome alterations of lung SCC by using samples from Korean patients.

PATIENTS AND METHODS

We performed whole-exome sequencing of DNA from 104 lung SCC samples from Korean patients and matched normal DNA. In addition, copy-number analysis and transcriptome analysis were conducted for a subset of these samples. Clinical association with cancer-specific somatic alterations was investigated.

RESULTS

This cancer cohort is characterized by a high mutational burden with an average of 261 somatic exonic mutations per tumor and a mutational spectrum showing a signature of exposure to cigarette smoke. Seven genes demonstrated statistical enrichment for mutation: TP53, RB1, PTEN, NFE2L2, KEAP1, MLL2, and PIK3CA). Comparative analysis between Korean and North American lung SCC samples demonstrated a similar spectrum of alterations in these two populations in contrast to the differences seen in lung adenocarcinoma. We also uncovered recurrent occurrence of therapeutically actionable FGFR3-TACC3 fusion in lung SCC.

CONCLUSION

These findings provide new steps toward the identification of genomic target candidates for precision medicine in lung SCC, a disease with significant unmet medical needs.

Abstract PL07-01: Molecular profiling of breast cancer in Mexico: Identification of novel therapeutic targets through whole genome sequencing analysis

Today, more than 55% of the world's breast cancer cases are diagnosed in low and middle-income countries and in 2020, more that 70% of the cases will come from the developing nations. In Mexico, breast cancer-specific mortality doubled during the past 20 years, representing the second-leading cause of death in women between 30 and 59 years and the leading cause of cancer related death in the female population. According to statistics, in Mexico a woman dies due to breast cancer every two hours. Even though breast cancer represents a major public health problem in the developing world, knowledge about the genetic and genomic structure of breast tumors in Mexican or Latin American populations is very limited. In the past four years, we have participated in the Slim Initiative of Genomic Medicine (SIGMA) Project, a collaboration between the Carlos Slim Institute of Health, the Broad Institute, and the National Institute of Genomic Medicine in Mexico city. The goal of the SIGMA project is to characterize the genomic basis of common diseases, including several types of cancer. This effort has focused on the application of whole genome and whole exome sequencing of human tumors. In the case of breast cancer, we have analyzed the whole genomes of 22 tumor/normal tissue pairs and the whole exomes of 103 tumor/normal tissues from Mexican and Vietnamese patients. Sequence analysis led to the novel identification of potential loss of function mutations of the CBFB transcription factor, and deletions of its partner RUNX1, an event which has never been previously reported in breast tumors or in any other epithelial tumor. Of clinical relevance, we also identified a somatic translocation involving MAGI3 and AKT3 in a triple negative breast tumor. Ectopic expression of the fusion transcrip leads to constitutive phosphorylation of downstream GSK and loss of contact inhibition. Most importantly, the activity of the fusion protein can be abrogated by an ATP-competitive small molecule inhibitor of AKT, potentially representing a new therapeutic avenue for these patients. In parallel with sequencing, we have also been working on the analysis of somatic DNA copy number aberrations, messenger RNA expression, and microRNA expression patterns in tumors from Mexican patients. Intrinsic breast cancer sub-typing in 125 tumors from Mexican patients showed that 13.6% of the tumors were basal-like, 16.8% were Her2-enriched, 24.8% Luminal A, 34.4% Luminal B and 10.4 normal-like. With microRNA expression, we have identified a group of microRNAs whose role in breast cancer has not been previously described and are currently analyzing differential microRNA expression across tumor sub-types, in particular triple negative tumors, where we have been able to identify at least three different tumor sub-groups based on microRNA expression patterns.

Citation Format: Shantanu Banerji, Kristian Cibulskis, Claudia Rangel-Escareño, Kristin K. Brown, Scott L. Carter, Abbie M. Frederick, Michael S. Lawrence, Andrey Y. Sivachenko, Carrie Sougnez, Lihua Zou, Maria L. Cortes, Juan C. Fernandez-Lopez, Shouyong Peng, Kristin G. Ardlie, Daniel Auclair, Veronica Bautista-Piña, Fujiko Duke, Joshua Francis, Joonil Jung, Antonio Maffuz-Aziz, Robert C. Onofrio, Melissa Parkin, Nam H. Pho, Valeria Quintanar-Jurado, Alex H. Ramos, Rosa Rebollar-Vega, Sergio A. Rodríguez-Cuevas, Sandra L. Romero-Cordoba, Steven E. Schumacher, Nicolas Stransky, Kristin M. Thompson, Laura Uribe-Figueroa, Jose Baselga, Rameen Beroukhim, Kornelia Polyak, Dennis C. Sgroi, Andrea L. Richardson, Gerardo Jimenez-Sánchez, Eric S. Lander, Stacey B. Gabriel, Levi A. Garraway, Todd R. Golub, Jorge Meléndez-Zajgla, Alex Toker, Gad Getz, Matthew Meyerson, Alfredo Hidalgo-Miranda. Molecular profiling of breast cancer in Mexico: Identification of novel therapeutic targets through whole genome sequencing analysis. [abstract]. In: Proceedings of the Fifth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2012 Oct 27-30; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2012;21(10 Suppl):Abstract nr PL07-01.

Sequence analysis of mutations and translocations across breast cancer subtypes

Breast carcinoma is the leading cause of cancer-related mortality in women worldwide, with an estimated 1.38 million new cases and 458,000 deaths in 2008 alone. This malignancy represents a heterogeneous group of tumours with characteristic molecular features, prognosis and responses to available therapy. Recurrent somatic alterations in breast cancer have been described, including mutations and copy number alterations, notably ERBB2 amplifications, the first successful therapy target defined by a genomic aberration. Previous DNA sequencing studies of breast cancer genomes have revealed additional candidate mutations and gene rearrangements. Here we report the whole-exome sequences of DNA from 103 human breast cancers of diverse subtypes from patients in Mexico and Vietnam compared to matched-normal DNA, together with whole-genome sequences of 22 breast cancer/normal pairs. Beyond confirming recurrent somatic mutations in PIK3CA, TP53, AKT1, GATA3 and MAP3K1, we discovered recurrent mutations in the CBFB transcription factor gene and deletions of its partner RUNX1. Furthermore, we have identified a recurrent MAGI3-AKT3 fusion enriched in triple-negative breast cancer lacking oestrogen and progesterone receptors and ERBB2 expression. The MAGI3-AKT3 fusion leads to constitutive activation of AKT kinase, which is abolished by treatment with an ATP-competitive AKT small-molecule inhibitor.

Melanoma genome sequencing reveals frequent PREX2 mutations

Melanoma is notable for its metastatic propensity, lethality in the advanced setting, and association with ultraviolet (UV) exposure early in life¹. To obtain a comprehensive genomic view of melanoma, we sequenced the genomes of 25 metastatic melanomas and matched germline DNA. A wide range of point mutation rates was observed: lowest in melanomas whose primaries arose on non-UV exposed hairless skin of the extremities (3 and 14 per Mb genome), intermediate in those originating from hair-bearing skin of the trunk (range = 5 to 55 per Mb), and highest in a patient with a documented history of chronic sun exposure (111 per Mb). Analysis of whole-genome sequence data identified PREX2 - a PTEN-interacting protein and negative regulator of PTEN in breast cancer² - as a significantly mutated gene with a mutation frequency of approximately 14% in an independent extension cohort of 107 human melanomas. PREX2 mutations are biologically relevant, as ectopic expression of mutant PREX2 accelerated tumor formation of immortalized human melanocytes in vivo. Thus, whole-genome sequencing of human melanoma tumors revealed genomic evidence of UV pathogenesis and discovered a new recurrently mutated gene in melanoma.

MOLECULAR NETWORKS IN MICROARRAY ANALYSIS

Microarray-based characterization of tissues, cellular and disease states, and environmental condition and treatment responses provides genome-wide snapshots containing large amounts of invaluable information. However, the lack of inherent structure within the data and strong noise make extracting and interpreting this information and formulating and prioritizing domain relevant hypotheses difficult tasks. Integration with different types of biological data is required to place the expression measurements into a biologically meaningful context. A few approaches in microarray data interpretation are discussed with the emphasis on the use of molecular network information. Statistical procedures are demonstrated that superimpose expression data onto the transcription regulation network mined from scientific literature and aim at selecting transcription regulators with significant patterns of expression changes downstream. Tests are suggested that take into account network topology and signs of transcription regulation effects. The approaches are illustrated using two different expression datasets, the performance is compared, and biological relevance of the predictions is discussed.

A framework for variation discovery and genotyping using next-generation DNA sequencing data

Recent advances in sequencing technology make it possible to comprehensively catalog genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious, and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (i) initial read mapping; (ii) local realignment around indels; (iii) base quality score recalibration; (iv) SNP discovery and genotyping to find all potential variants; and (v) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We here discuss the application of these tools, instantiated in the Genome Analysis Toolkit, to deep whole-genome, whole-exome capture and multi-sample low-pass (∼4×) 1000 Genomes Project datasets.

The genomic complexity of primary human prostate cancer

Prostate cancer is the second most common cause of male cancer deaths in the United States. However, the full range of prostate cancer genomic alterations is incompletely characterized. Here we present the complete sequence of seven primary human prostate cancers and their paired normal counterparts. Several tumours contained complex chains of balanced (that is, 'copy-neutral') rearrangements that occurred within or adjacent to known cancer genes. Rearrangement breakpoints were enriched near open chromatin, androgen receptor and ERG DNA binding sites in the setting of the ETS gene fusion TMPRSS2-ERG, but inversely correlated with these regions in tumours lacking ETS fusions. This observation suggests a link between chromatin or transcriptional regulation and the genesis of genomic aberrations. Three tumours contained rearrangements that disrupted CADM2, and four harboured events disrupting either PTEN (unbalanced events), a prostate tumour suppressor, or MAGI2 (balanced events), a PTEN interacting protein not previously implicated in prostate tumorigenesis. Thus, genomic rearrangements may arise from transcriptional or chromatin aberrancies and engage prostate tumorigenic mechanisms.

ProteoLens: a visual analytic tool for multi-scale database-driven biological network data mining

Background

New systems biology studies require researchers to understand how interplay among myriads of biomolecular entities is orchestrated in order to achieve high-level cellular and physiological functions. Many software tools have been developed in the past decade to help researchers visually navigate large networks of biomolecular interactions with built-in template-based query capabilities. To further advance researchers' ability to interrogate global physiological states of cells through multi-scale visual network explorations, new visualization software tools still need to be developed to empower the analysis. A robust visual data analysis platform driven by database management systems to perform bi-directional data processing-to-visualizations with declarative querying capabilities is needed.

Results

We developed ProteoLens as a JAVA-based visual analytic software tool for creating, annotating and exploring multi-scale biological networks. It supports direct database connectivity to either Oracle or PostgreSQL database tables/views, on which SQL statements using both Data Definition Languages (DDL) and Data Manipulation languages (DML) may be specified. The robust query languages embedded directly within the visualization software help users to bring their network data into a visualization context for annotation and exploration. ProteoLens supports graph/network represented data in standard Graph Modeling Language (GML) formats, and this enables interoperation with a wide range of other visual layout tools. The architectural design of ProteoLens enables the de-coupling of complex network data visualization tasks into two distinct phases: 1) creating network data association rules, which are mapping rules between network node IDs or edge IDs and data attributes such as functional annotations, expression levels, scores, synonyms, descriptions etc; 2) applying network data association rules to build the network and perform the visual annotation of graph nodes and edges according to associated data values. We demonstrated the advantages of these new capabilities through three biological network visualization case studies: human disease association network, drug-target interaction network and protein-peptide mapping network.

Conclusion

The architectural design of ProteoLens makes it suitable for bioinformatics expert data analysts who are experienced with relational database management to perform large-scale integrated network visual explorations. ProteoLens is a promising visual analytic platform that will facilitate knowledge discoveries in future network and systems biology studies.

Pathway analysis software as a tool for drug target selection, prioritization and validation of drug mechanism

One of the major challenges of drug discovery today is the poor understanding of the detailed molecular mechanisms underlying both disease progression and drug action. Insufficient drug specificity and side effects are often discovered during the late stages of drug development, sometimes after the drug is released on the market. These discoveries result in a high target attrition rate, a slow drug design pipeline and high development costs. Recent advances in systems biology and pathway analysis can help make true rational design a reality through the integration of experimental observations with underlying cellular regulation and metabolic networks. It should enable the formulation of better and more informed testable hypotheses with regard to the most efficient target candidates. In this article, the authors overview the broad and heterogeneous field of molecular interaction databases and pathway analysis tools, and the challenges existing in the field. The authors describe and classify different approaches for data acquisition, storage and navigation, give a detailed description of the integrative technology behind the Pathway Studio software solution, and present a comparison with other integrative pathway analysis platforms suitable for drug discovery tasks.

Mining Alzheimer disease relevant proteins from integrated protein interactome data

Huge unrealized post-genome opportunities remain in the understanding of detailed molecular mechanisms for Alzheimer Disease (AD). In this work, we developed a computational method to rank-order AD-related proteins, based on an initial list of AD-related genes and public human protein interaction data. In this method, we first collected an initial seed list of 65 AD-related genes from the OMIM database and mapped them to 70 AD seed proteins. We then expanded the seed proteins to an enriched AD set of 765 proteins using protein interactions from the Online Predicated Human Interaction Database (OPHID). We showed that the expanded AD-related proteins form a highly connected and statistically significant protein interaction sub-network. We further analyzed the sub-network to develop an algorithm, which can be used to automatically score and rank-order each protein for its biological relevance to AD pathways(s). Our results show that functionally relevant AD proteins were consistently ranked at the top: among the top 20 of 765 expanded AD proteins, 19 proteins are confirmed to belong to the original 70 AD seed protein set. Our method represents a novel use of protein interaction network data for Alzheimer disease studies and may be generalized for other disease areas in the future.

Initial large-scale exploration of protein-protein interactions in human brain

Study of protein interaction networks is crucial to post-genomic systems biology. Aided by high-throughput screening technologies, biologists are rapidly accumulating protein-protein interaction data. Using a random yeast two-hybrid (R2H) process, we have performed large-scale yeast two-hybrid searches with approximately fifty thousand random human brain cDNA bait fragments against a human brain cDNA prey fragment library. From these searches, we have identified 13,656 unique protein-protein interaction pairs involving 4,473 distinct known human loci. In this paper, we have performed our initial characterization of the protein interaction network in human brain tissue. We have classified and characterized all identified interactions based on Gene Ontology (GO) annotation of interacting loci. We have also described the "scale-free" topological structure of the network.

Charged magnetoexcitons in two-dimensions: magnetic translations and families of dark states

We show that optical transitions of charged excitons in semiconductor heterostructures are governed in magnetic fields by a novel exact selection rule, a manifestation of magnetic translations. It is shown that the spin-triplet ground state of the quasi-two-dimensional charged exciton X--a bound state of two electrons and one hole-is optically inactive in photoluminescence at finite magnetic fields. Internal bound-to-bound X- triplet transition has a specific spectral position, below the electron cyclotron resonance, and is strictly prohibited in a translationally invariant system. These results allow one to discriminate between free and disorder-affected charged excitons.