MicroRNAs (miRNAs) show differential expression across breast cancer subtypes, and have both oncogenic and tumour-suppressive roles. Here we report the miRNA expression profiles of 1,302 breast ...tumours with matching detailed clinical annotation, long-term follow-up and genomic and messenger RNA expression data. This provides a comprehensive overview of the quantity, distribution and variation of the miRNA population and provides information on the extent to which genomic, transcriptional and post-transcriptional events contribute to miRNA expression architecture, suggesting an important role for post-transcriptional regulation. The key clinical parameters and cellular pathways related to the miRNA landscape are characterized, revealing context-dependent interactions, for example with regards to cell adhesion and Wnt signalling. Notably, only prognostic miRNA signatures derived from breast tumours devoid of somatic copy-number aberrations (CNA-devoid) are consistently prognostic across several other subtypes and can be validated in external cohorts. We then use a data-driven approach to seek the effects of miRNAs associated with differential co-expression of mRNAs, and find that miRNAs act as modulators of mRNA-mRNA interactions rather than as on-off molecular switches. We demonstrate such an important modulatory role for miRNAs in the biology of CNA-devoid breast cancers, a common subtype in which the immune response is prominent. These findings represent a new framework for studying the biology of miRNAs in human breast cancer.
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Dostopno za:
DOBA, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Solid tumors are heterogeneous tissues composed of a mixture of cancer and normal cells, which complicates the interpretation of their molecular profiles. Furthermore, tissue architecture is ...generally not reflected in molecular assays, rendering this rich information underused. To address these challenges, we developed a computational approach based on standard hematoxylin and eosin-stained tissue sections and demonstrated its power in a discovery and validation cohort of 323 and 241 breast tumors, respectively. To deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy number profiles between samples. We next devised a predictor for survival in estrogen receptor-negative breast cancer that integrated both image-based and gene expression analyses and significantly outperformed classifiers that use single data types, such as microarray expression signatures. Image processing also allowed us to describe and validate an independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative, image-based method could benefit any large-scale cancer study by refining and complementing molecular assays of tumor samples.
High-throughput RNA sequencing (RNA-seq) promises to revolutionize our understanding of genes and their role in human disease by characterizing the RNA content of tissues and cells. The realization ...of this promise, however, is conditional on the development of effective computational methods for the identification and quantification of transcripts from incomplete and noisy data. In this article, we introduce iReckon, a method for simultaneous determination of the isoforms and estimation of their abundances. Our probabilistic approach incorporates multiple biological and technical phenomena, including novel isoforms, intron retention, unspliced pre-mRNA, PCR amplification biases, and multimapped reads. iReckon utilizes regularized expectation-maximization to accurately estimate the abundances of known and novel isoforms. Our results on simulated and real data demonstrate a superior ability to discover novel isoforms with a significantly reduced number of false-positive predictions, and our abundance accuracy prediction outmatches that of other state-of-the-art tools. Furthermore, we have applied iReckon to two cancer transcriptome data sets, a triple-negative breast cancer patient sample and the MCF7 breast cancer cell line, and show that iReckon is able to reconstruct the complex splicing changes that were not previously identified. QT-PCR validations of the isoforms detected in the MCF7 cell line confirmed all of iReckon's predictions and also showed strong agreement (r(2) = 0.94) with the predicted abundances.
Motivation: Next-generation sequencing (NGS) has enabled whole genome and transcriptome single nucleotide variant (SNV) discovery in cancer. NGS produces millions of short sequence reads that, once ...aligned to a reference genome sequence, can be interpreted for the presence of SNVs. Although tools exist for SNV discovery from NGS data, none are specifically suited to work with data from tumors, where altered ploidy and tumor cellularity impact the statistical expectations of SNV discovery. Results: We developed three implementations of a probabilistic Binomial mixture model, called SNVMix, designed to infer SNVs from NGS data from tumors to address this problem. The first models allelic counts as observations and infers SNVs and model parameters using an expectation maximization (EM) algorithm and is therefore capable of adjusting to deviation of allelic frequencies inherent in genomically unstable tumor genomes. The second models nucleotide and mapping qualities of the reads by probabilistically weighting the contribution of a read/nucleotide to the inference of a SNV based on the confidence we have in the base call and the read alignment. The third combines filtering out low-quality data in addition to probabilistic weighting of the qualities. We quantitatively evaluated these approaches on 16 ovarian cancer RNASeq datasets with matched genotyping arrays and a human breast cancer genome sequenced to >40× (haploid) coverage with ground truth data and show systematically that the SNVMix models outperform competing approaches. Availability: Software and data are available at http://compbio.bccrc.ca Contact: sshah@bccrc.ca Supplemantary information: Supplementary data are available at Bioinformatics online.
Somatic evolution of malignant cells produces tumors composed of multiple clonal populations, distinguished in part by rearrangements and copy number changes affecting chromosomal segments. Whole ...genome sequencing mixes the signals of sampled populations, diluting the signals of clone-specific aberrations, and complicating estimation of clone-specific genotypes. We introduce ReMixT, a method to unmix tumor and contaminating normal signals and jointly predict mixture proportions, clone-specific segment copy number, and clone specificity of breakpoints. ReMixT is free, open-source software and is available at http://bitbucket.org/dranew/remixt .
The Link Between Nutritional Status and Insulin Sensitivity Is Dependent on the Adipocyte-Specific Peroxisome Proliferator–Activated
Receptor-γ2 Isoform
Gema Medina-Gomez 1 ,
Sam Virtue 1 ,
...Christopher Lelliott 1 ,
Romina Boiani 2 ,
Mark Campbell 1 ,
Constantinos Christodoulides 1 ,
Christophe Perrin 3 ,
Mercedes Jimenez-Linan 1 ,
Margaret Blount 1 ,
John Dixon 4 ,
Dirk Zahn 4 ,
Rosemary R. Thresher 4 ,
Sam Aparicio 4 ,
Mark Carlton 4 ,
William H. Colledge 1 ,
Mikko I. Kettunen 5 ,
Tuulikki Seppänen-Laakso 6 ,
Jaswinder K. Sethi 1 ,
Stephen O’Rahilly 1 ,
Kevin Brindle 5 ,
Saverio Cinti 2 ,
Matej Orešič 6 ,
Remy Burcelin 3 and
Antonio Vidal-Puig 1
1 Department of Clinical Biochemistry, Histopathology, Physiology and Oncology, University of Cambridge/Addenbrooke’s Hospital,
Cambridge, U.K.
2 Institute of Normal Human Morphology, Faculty of Medicine, Ancona University, Ancona, Italy
3 Centre National de la Recherche Scientifique-UMR 5018, Paul Sabatier University, Toulouse, France
4 Paradigm Therapeutics, Cambridge, U.K.
5 Department of Biochemistry, University of Cambridge, Cambridge, U.K.
6 VTT: Technical Research Centre of Finland, VTT Biotechnology, Espoo, Finland
Address correspondence and reprint requests to Antonio Vidal-Puig, Department of Clinical Biochemistry, University of Cambridge/Addenbrooke’s
Hospital, Hills Road, Cambridge CB2 2QR, U.K. E-mail: ajv22{at}cam.ac.uk
Abstract
The nuclear receptor peroxisome proliferator–activated receptor-γ (PPARγ) is critically required for adipogenesis. PPARγ exists
as two isoforms, γ1 and γ2. PPARγ2 is the more potent adipogenic isoform in vitro and is normally restricted to adipose tissues,
where it is regulated more by nutritional state than PPARγ1. To elucidate the relevance of the PPARγ2 in vivo, we generated
a mouse model in which the PPARγ2 isoform was specifically disrupted. Despite similar weight, body composition, food intake,
energy expenditure, and adipose tissue morphology, male mice lacking the γ2 isoform were more insulin resistant than wild-type
animals when fed a regular diet. These results indicate that insulin resistance associated with ablation of PPARγ2 is not
the result of lipodystrophy and suggests a specific role for PPARγ2 in maintaining insulin sensitivity independently of its
effects on adipogenesis. Furthermore, PPARγ2 knockout mice fed a high-fat diet did not become more insulin resistant than
those on a normal diet, despite a marked increase in their mean adipocyte cell size. These findings suggest that PPARγ2 is
required for the maintenance of normal insulin sensitivity in mice but also raises the intriguing notion that PPARγ2 may be
necessary for the adverse effects of a high-fat diet on carbohydrate metabolism.
BAT, brown adipose tissue
GTT, glucose tolerance test
HFD, high-fat diet
ITT, insulin tolerance test
IRS1, insulin receptor substrate 1
LC/MS, liquid chromatography/mass spectrometry
MRI, magnetic resonance imaging
PPARγ, peroxisome proliferator–activated receptor-γ
RPA, ribonuclease protection assay
SREBP1c, sterol regulatory element–binding protein 1c
WAT, white adipose tissue
Footnotes
Additional information for this article can be found in an online appendix at http://diabetes.diabetesjournals.org .
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore
be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Accepted February 21, 2005.
Received November 22, 2004.
DIABETES
To determine whether functional proteomics improves breast cancer classification and prognostication and can predict pathological complete response (pCR) in patients receiving neoadjuvant taxane and ...anthracycline-taxane-based systemic therapy (NST).
Reverse phase protein array (RPPA) using 146 antibodies to proteins relevant to breast cancer was applied to three independent tumor sets. Supervised clustering to identify subgroups and prognosis in surgical excision specimens from a training set (n = 712) was validated on a test set (n = 168) in two cohorts of patients with primary breast cancer. A score was constructed using ordinal logistic regression to quantify the probability of recurrence in the training set and tested in the test set. The score was then evaluated on 132 FNA biopsies of patients treated with NST to determine ability to predict pCR.
Six breast cancer subgroups were identified by a 10-protein biomarker panel in the 712 tumor training set. They were associated with different recurrence-free survival (RFS) (log-rank p = 8.8 E-10). The structure and ability of the six subgroups to predict RFS was confirmed in the test set (log-rank p = 0.0013). A prognosis score constructed using the 10 proteins in the training set was associated with RFS in both training and test sets (p = 3.2E-13, for test set). There was a significant association between the prognostic score and likelihood of pCR to NST in the FNA set (p = 0.0021).
We developed a 10-protein biomarker panel that classifies breast cancer into prognostic groups that may have potential utility in the management of patients who receive anthracycline-taxane-based NST.
Next generation sequencing has now enabled a cost-effective enumeration of the full mutational complement of a tumor genome-in particular single nucleotide variants (SNVs). Most current computational ...and statistical models for analyzing next generation sequencing data, however, do not account for cancer-specific biological properties, including somatic segmental copy number alterations (CNAs)-which require special treatment of the data. Here we present CoNAn-SNV (Copy Number Annotated SNV): a novel algorithm for the inference of single nucleotide variants (SNVs) that overlap copy number alterations. The method is based on modelling the notion that genomic regions of segmental duplication and amplification induce an extended genotype space where a subset of genotypes will exhibit heavily skewed allelic distributions in SNVs (and therefore render them undetectable by methods that assume diploidy). We introduce the concept of modelling allelic counts from sequencing data using a panel of Binomial mixture models where the number of mixtures for a given locus in the genome is informed by a discrete copy number state given as input. We applied CoNAn-SNV to a previously published whole genome shotgun data set obtained from a lobular breast cancer and show that it is able to discover 21 experimentally revalidated somatic non-synonymous mutations in a lobular breast cancer genome that were not detected using copy number insensitive SNV detection algorithms. Importantly, ROC analysis shows that the increased sensitivity of CoNAn-SNV does not result in disproportionate loss of specificity. This was also supported by analysis of a recently published lymphoma genome with a relatively quiescent karyotype, where CoNAn-SNV showed similar results to other callers except in regions of copy number gain where increased sensitivity was conferred. Our results indicate that in genomically unstable tumors, copy number annotation for SNV detection will be critical to fully characterize the mutational landscape of cancer genomes.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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