Prioritizing causal disease genes using unbiased genomic features

Background

Cardiovascular disease (CVD) is the leading cause of death in the developed world. Human genetic studies, including genome-wide sequencing and SNP-array approaches, promise to reveal disease genes and mechanisms representing new therapeutic targets. In practice, however, identification of the actual genes contributing to disease pathogenesis has lagged behind identification of associated loci, thus limiting the clinical benefits.

Results

To aid in localizing causal genes, we develop a machine learning approach, Objective Prioritization for Enhanced Novelty (OPEN), which quantitatively prioritizes gene-disease associations based on a diverse group of genomic features. This approach uses only unbiased predictive features and thus is not hampered by a preference towards previously well-characterized genes. We demonstrate success in identifying genetic determinants for CVD-related traits, including cholesterol levels, blood pressure, and conduction system and cardiomyopathy phenotypes. Using OPEN, we prioritize genes, including FLNC, for association with increased left ventricular diameter, which is a defining feature of a prevalent cardiovascular disorder, dilated cardiomyopathy or DCM. Using a zebrafish model, we experimentally validate FLNC and identify a novel FLNC splice-site mutation in a patient with severe DCM.

Conclusion

Our approach stands to assist interpretation of large-scale genetic studies without compromising their fundamentally unbiased nature.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0534-8) contains supplementary material, which is available to authorized users.     

Allele-specific amplification in cancer revealed by SNP array analysis

Amplification, deletion, and loss of heterozygosity of genomic DNA are hallmarks of cancer. In recent years a variety of studies have emerged measuring total chromosomal copy number at increasingly high resolution. Similarly, loss-of-heterozygosity events have been finely mapped using high-throughput genotyping technologies. We have developed a probe-level allele-specific quantitation procedure that extracts both copy number and allelotype information from single nucleotide polymorphism (SNP) array data to arrive at allele-specific copy number across the genome. Our approach applies an expectation-maximization algorithm to a model derived from a novel classification of SNP array probes. This method is the first to our knowledge that is able to (a) determine the generalized genotype of aberrant samples at each SNP site (e.g., CCCCT at an amplified site), and (b) infer the copy number of each parental chromosome across the genome. With this method, we are able to determine not just where amplifications and deletions occur, but also the haplotype of the region being amplified or deleted. The merit of our model and general approach is demonstrated by very precise genotyping of normal samples, and our allele-specific copy number inferences are validated using PCR experiments. Applying our method to a collection of lung cancer samples, we are able to conclude that amplification is essentially monoallelic, as would be expected under the mechanisms currently believed responsible for gene amplification. This suggests that a specific parental chromosome may be targeted for amplification, whether because of germ line or somatic variation. An R software package containing the methods described in this paper is freely available at http://genome.dfci.harvard.edu/~tlaframb/PLASQ.     

Absolute quantification of somatic DNA alterations in human cancer

We describe a computational method that infers tumor purity and malignant cell ploidy directly from analysis of somatic DNA alterations. The method, named ABSOLUTE, can detect subclonal heterogeneity and somatic homozygosity, and it can calculate statistical sensitivity for detection of specific aberrations. We used ABSOLUTE to analyze exome sequencing data from 214 ovarian carcinoma tumor-normal pairs. This analysis identified both pervasive subclonal somatic point-mutations and a small subset of predominantly clonal and homozygous mutations, which were overrepresented in the tumor suppressor genes TP53 and NF1 and in a candidate tumor suppressor gene CDK12. We also used ABSOLUTE to infer absolute allelic copy-number profiles from 3,155 diverse cancer specimens, revealing that genome-doubling events are common in human cancer, likely occur in cells that are already aneuploid, and influence pathways of tumor progression (for example, with recessive inactivation of NF1 being less common after genome doubling). ABSOLUTE will facilitate the design of clinical sequencing studies and studies of cancer genome evolution and intra-tumor heterogeneity.     

A mathematical methodology for determining the temporal order of pathway alterations arising during gliomagenesis

Human cancer is caused by the accumulation of genetic alterations in cells. Of special importance are changes that occur early during malignant transformation because they may result in oncogene addiction and thus represent promising targets for therapeutic intervention. We have previously described a computational approach, called Retracing the Evolutionary Steps in Cancer (RESIC), to determine the temporal sequence of genetic alterations during tumorigenesis from cross-sectional genomic data of tumors at their fully transformed stage. Since alterations within a set of genes belonging to a particular signaling pathway may have similar or equivalent effects, we applied a pathway-based systems biology approach to the RESIC methodology. This method was used to determine whether alterations of specific pathways develop early or late during malignant transformation. When applied to primary glioblastoma (GBM) copy number data from The Cancer Genome Atlas (TCGA) project, RESIC identified a temporal order of pathway alterations consistent with the order of events in secondary GBMs. We then further subdivided the samples into the four main GBM subtypes and determined the relative contributions of each subtype to the overall results: we found that the overall ordering applied for the proneural subtype but differed for mesenchymal samples. The temporal sequence of events could not be identified for neural and classical subtypes, possibly due to a limited number of samples. Moreover, for samples of the proneural subtype, we detected two distinct temporal sequences of events: (i) RAS pathway activation was followed by TP53 inactivation and finally PI3K2 activation, and (ii) RAS activation preceded only AKT activation. This extension of the RESIC methodology provides an evolutionary mathematical approach to identify the temporal sequence of pathway changes driving tumorigenesis and may be useful in guiding the understanding of signaling rearrangements in cancer development.     

SNP panel identification assay (SPIA): a genetic-based assay for the identification of cell lines

Translational research hinges on the ability to make observations in model systems and to implement those findings into clinical applications, such as the development of diagnostic tools or targeted therapeutics. Tumor cell lines are commonly used to model carcinogenesis. The same tumor cell line can be simultaneously studied in multiple research laboratories throughout the world, theoretically generating results that are directly comparable. One important assumption in this paradigm is that researchers are working with the same cells. However, recent work using high throughput genomic analyses questions the accuracy of this assumption. Observations by our group and others suggest that experiments reported in the scientific literature may contain pre-analytic errors due to inaccurate identities of the cell lines employed. To address this problem, we developed a simple approach that enables an accurate determination of cell line identity by genotyping 34 single nucleotide polymorphisms (SNPs). Here, we describe the empirical development of a SNP panel identification assay (SPIA) compatible with routine use in the laboratory setting to ensure the identity of tumor cell lines and human tumor samples throughout the course of long term research use.     

Development of the Drosophila entero-endocrine lineage and its specification by the Notch signaling pathway

In this paper we have investigated the developmental–genetic steps that shape the entero-endocrine system of Drosophila melanogaster from the embryo to the adult. The process starts in the endoderm of the early embryo where precursors of endocrine cells and enterocytes of the larval midgut, as well as progenitors of the adult midgut, are specified by a Notch signaling-dependent mechanism. In a second step that occurs during the late larval period, enterocytes and endocrine cells of a transient pupal midgut are selected from within the clusters of adult midgut progenitors. As in the embryo, activation of the Notch pathway triggers enterocyte differentiation and inhibits cells from further proliferation or choosing the endocrine fate. The third step of entero-endocrine cell development takes place at a mid-pupal stage. Before this time point, the epithelial layer destined to become the adult midgut is devoid of endocrine cells. However, precursors of the intestinal midgut stem cells (pISCs) are already present. After an initial phase of symmetric divisions which causes an increase in their own population size, pISCs start to spin off cells that become postmitotic and express the endocrine fate marker, Prospero. Activation of Notch in pISCs forces these cells into an enterocyte fate. Loss of Notch function causes an increase in the proliferatory activity of pISCs, as well as a higher ratio of Prospero-positive cells.     

Ultrastructure of the 5-Hydroxytryptamine3 Receptor

Abstract: We have determined the ultrastructure of 5-hydroxytryptamine₃ (5-HT₃) serotonin receptors purified from NG108-15 mouse neuroblastoma × rat glioma cells by electron microscopic examination of receptor particles embedded in uranyl acetate stain and metal replicas of rapidly frozen receptors. The 5-HT₃ receptor can be modelled as a cylinder 11 nm in length and 8 nm in diameter with a closed end and a central cavity 3 nm in diameter. Analysis of the rotational symmetry of single receptor particles indicates that the 5-HT₃ receptor is composed of five subunits arranged symmetrically around a central cavity. Together with evidence obtained for related proteins in other studies using ultrastructural, biochemical, or electrophysiological methods, our observations suggest that all members of the ligand-gated ion channel superfamily may possess a pentameric quaternary structure.     

Cancer vulnerabilities unveiled by genomic loss

Due to genome instability, most cancers exhibit loss of regions containing tumor suppressor genes and collateral loss of other genes. To identify cancer-specific vulnerabilities that are the result of copy number losses, we performed integrated analyses of genome-wide copy number and RNAi profiles and identified 56 genes for which gene suppression specifically inhibited the proliferation of cells harboring partial copy number loss of that gene. These CYCLOPS (copy number alterations yielding cancer liabilities owing to partial loss) genes are enriched for spliceosome, proteasome, and ribosome components. One CYCLOPS gene, PSMC2, encodes an essential member of the 19S proteasome. Normal cells express excess PSMC2, which resides in?a complex with PSMC1, PSMD2, and PSMD5 and acts as a reservoir protecting cells from PSMC2 suppression. Cells harboring partial PSMC2 copy number loss lack this complex and die after PSMC2 suppression. These observations define a distinct class of cancer-specific liabilities resulting from genome instability.     

Oncogenic PIK3CA-driven mammary tumors frequently recur via PI3K pathway-dependent and PI3K pathway-independent mechanisms

PIK3CA gain-of-function mutations are a common oncogenic event in human malignancy, making phosphatidylinositol 3-kinase (PI3K) a target for cancer therapy. Despite the promise of targeted therapy, resistance often develops, leading to treatment failure. To elucidate mechanisms of resistance to PI3K-targeted therapy, we constructed a mouse model of breast cancer conditionally expressing human PIK3CA(H1047R). Notably, most PIK3CA(H1047R)-driven mammary tumors recurred after PIK3CA(H1047R) inactivation. Genomic analyses of recurrent tumors revealed multiple lesions, including focal amplification of Met or Myc (also known as c-Met and c-Myc, respectively). Whereas Met amplification led to tumor survival dependent on activation of endogenous PI3K, tumors with Myc amplification became independent of the PI3K pathway. Functional analyses showed that Myc contributed to oncogene independence and resistance to PI3K inhibition. Notably, PIK3CA mutations and c-MYC elevation co-occur in a substantial fraction of human breast tumors. Together, these data suggest that c-MYC elevation represents a potential mechanism by which tumors develop resistance to current PI3K-targeted therapies.