Sequence analysis of mutations and translocations across breast cancer subtypes.
ABSTRACT 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.
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ABSTRACT: Human cancers are frequently polyploid, containing multiple aneuploid subpopulations that differ in total DNA content. In this study we exploit this property to reconstruct evolutionary histories, by assuming that mutational complexity increases with time. We developed an experimental method called Ploidy-Seq that uses flow-sorting to isolate and enrich subpopulations with different ploidy prior to next-generation genome sequencing. We applied Ploidy-Seq to a patient with a triple-negative (ER-/PR-/HER2-) ductal carcinoma and performed whole-genome sequencing to trace the evolution of point mutations, indels, copy number aberrations, and structural variants in three clonal subpopulations during tumor growth. Our data show that few mutations (8% to 22%) were shared between all three subpopulations, and that the most aggressive clones comprised a minority of the tumor mass. We expect that Ploidy-Seq will have broad applications for delineating clonal diversity and investigating genome evolution in many human cancers. Electronic supplementary material The online version of this article (doi:10.1186/s13073-015-0127-5) contains supplementary material, which is available to authorized users.Genome Medicine 01/2015; 7(1):6. DOI:10.1186/s13073-015-0127-5 · 4.94 Impact Factor
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ABSTRACT: Tumor genomes are often highly heterogeneous, consisting of genomes from multiple subclonal types. Complete characterization of all subclonal types is a fundamental need in tumor genome analysis. With the advancement of next-generation sequencing, computational methods have recently been developed to infer tumor subclonal populations directly from cancer genome sequencing data. Most of these methods are based on sequence information from somatic point mutations, However, the accuracy of these algorithms depends crucially on the quality of the somatic mutations returned by variant calling algorithms, and usually requires a deep coverage to achieve a reasonable level of accuracy. We describe a novel probabilistic mixture model, MixClone, for inferring the cellular prevalences of subclonal populations directly from whole genome sequencing of paired normal-tumor samples. MixClone integrates sequence information of somatic copy number alterations and allele frequencies within a unified probabilistic framework. We demonstrate the utility of the method using both simulated and real cancer sequencing datasets, and show that it significantly outperforms existing methods for inferring tumor subclonal populations. The MixClone package is written in Python and is publicly available at https://github.com/uci-cbcl/MixClone. The probabilistic mixture model proposed here provides a new framework for subclonal analysis based on cancer genome sequencing data. By applying the method to both simulated and real cancer sequencing data, we show that integrating sequence information from both somatic copy number alterations and allele frequencies can significantly improve the accuracy of inferring tumor subclonal populations.
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ABSTRACT: Morphogenesis is crucial during development to generate organs and tissues of the correct size and shape. During Drosophila late eye development, interommatidial cells (IOCs) rearrange to generate the highly organized pupal lattice, in which hexagonal ommatidial units pack tightly. This process involves the fine regulation of adherens junctions (AJs) and of adhesive E-Cadherin (E-Cad) complexes. Localized accumulation of Bazooka (Baz), the Drosophila PAR3 homolog, has emerged as a critical step to specify where new E-Cad complexes should be deposited during junction remodeling. However, the mechanisms controlling the correct localization of Baz are still only partly understood. We show here that Drosophila Magi, the sole fly homolog of the mammalian MAGI scaffolds, is an upstream regulator of E-Cad-based AJs during cell rearrangements, and that Magi mutant IOCs fail to reach their correct position. We uncover a direct physical interaction between Magi and the Ras association domain protein RASSF8 through a WW domain-PPxY motif binding, and show that apical Magi recruits the RASSF8-ASPP complex during AJ remodeling in IOCs. We further show that this Magi complex is required for the cortical recruitment of Baz and of the E-Cad-associated proteins α- and β-catenin. We propose that, by controlling the proper localization of Baz to remodeling junctions, Magi and the RASSF8-ASPP complex promote the recruitment or stabilization of E-Cad complexes at junction sites. © 2015. Published by The Company of Biologists Ltd.