Suet Feung Chin

MicroRNA expression profiling of human breast cancer identifies new markers of tumor subtype

BackgroundMicroRNAs (miRNAs), a class of short non-coding RNAs found in many plants and animals, often act post-transcriptionally to inhibit gene expression.ResultsHere we report the analysis of miRNA expression in 93 primary human breast tumors, using a bead-based flow cytometric miRNA expression profiling method. Of 309 human miRNAs assayed, we identify 133 miRNAs expressed in human breast and breast tumors. We used mRNA expression profiling to classify the breast tumors as luminal A, luminal B, basal-like, HER2+ and normal-like. A number of miRNAs are differentially expressed between these molecular tumor subtypes and individual miRNAs are associated with clinicopathological factors. Furthermore, we find that miRNAs could classify basal versus luminal tumor subtypes in an independent data set. In some cases, changes in miRNA expression correlate with genomic loss or gain; in others, changes in miRNA expression are likely due to changes in primary transcription and or miRNA biogenesis. Finally, the expression of DICER1 and AGO2 is correlated with tumor subtype and may explain some of the changes in miRNA expression observed.ConclusionThis study represents the first integrated analysis of miRNA expression, mRNA expression and genomic changes in human breast cancer and may serve as a basis for functional studies of the role of miRNAs in the etiology of breast cancer. Furthermore, we demonstrate that bead-based flow cytometric miRNA expression profiling might be a suitable platform to classify breast cancer into prognostic molecular subtypes.

High-resolution aCGH and expression profiling identifies a novel genomic subtype of ER negative breast cancer.

BackgroundThe characterization of copy number alteration patterns in breast cancer requires high-resolution genome-wide profiling of a large panel of tumor specimens. To date, most genome-wide array comparative genomic hybridization studies have used tumor panels of relatively large tumor size and high Nottingham Prognostic Index (NPI) that are not as representative of breast cancer demographics.ResultsWe performed an oligo-array-based high-resolution analysis of copy number alterations in 171 primary breast tumors of relatively small size and low NPI, which was therefore more representative of breast cancer demographics. Hierarchical clustering over the common regions of alteration identified a novel subtype of high-grade estrogen receptor (ER)-negative breast cancer, characterized by a low genomic instability index. We were able to validate the existence of this genomic subtype in one external breast cancer cohort. Using matched array expression data we also identified the genomic regions showing the strongest coordinate expression changes (hotspots). We show that several of these hotspots are located in the phosphatome, kinome and chromatinome, and harbor members of the 122-breast cancer CAN-list. Furthermore, we identify frequently amplified hotspots on 8q22.3 (EDD1, WDSOF1), 8q24.11-13 (THRAP6, DCC1, SQLE, SPG8) and 11q14.1 (NDUFC2, ALG8, USP35) associated with significantly worse prognosis. Amplification of any of these regions identified 37 samples with significantly worse overall survival (hazard ratio (HR) = 2.3 (1.3-1.4) p = 0.003) and time to distant metastasis (HR = 2.6 (1.4-5.1) p = 0.004) independently of NPI.ConclusionWe present strong evidence for the existence of a novel subtype of high-grade ER-negative tumors that is characterized by a low genomic instability index. We also provide a genome-wide list of common copy number alteration regions in breast cancer that show strong coordinate aberrant expression, and further identify novel frequently amplified regions that correlate with poor prognosis. Many of the genes associated with these regions represent likely novel oncogenes or tumor suppressors.

The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes

The genomic landscape of breast cancer is complex, and inter- and intra-tumour heterogeneity are important challenges in treating the disease. In this study, we sequence 173 genes in 2,433 primary breast tumours that have copy number aberration (CNA), gene expression and long-term clinical follow-up data. We identify 40 mutation-driver (Mut-driver) genes, and determine associations between mutations, driver CNA profiles, clinical-pathological parameters and survival. We assess the clonal states of Mut-driver mutations, and estimate levels of intra-tumour heterogeneity using mutant-allele fractions. Associations between PIK3CA mutations and reduced survival are identified in three subgroups of ER-positive cancer (defined by amplification of 17q23, 11q13–14 or 8q24). High levels of intra-tumour heterogeneity are in general associated with a worse outcome, but highly aggressive tumours with 11q13–14 amplification have low levels of intra-tumour heterogeneity. These results emphasize the importance of genome-based stratification of breast cancer, and have important implications for designing therapeutic strategies.

Quantitative Image Analysis of Cellular Heterogeneity in Breast Tumors Complements Genomic Profiling

Image analysis of breast cancer tissue improves and complements genomic data to predict patient survival. Digitizing Pathology for Genomics The tumor microenvironment is a complex milieu that includes not only the cancer cells but also the stromal cells, immune cells, and even normal, healthy cells. Molecular analysis of tumor tissue is therefore a challenging task because all this “extra” genomic information can muddle the results. Conversely, biopsy tissue staining can provide a spatial and cellular readout (architecture and content), but it is mostly qualitative information. In response, Yuan and colleagues have developed a quantitative, computational approach to pathology. When combined with molecular analyses, the authors were able to uncover new knowledge about breast tumor biology and, in turn, predict patient survival. Yuan et al. first collected histopathology images, gene expression data, and DNA copy number variation data for 564 breast cancer patients. Using a portion of the images (the “discovery set”), they developed an image processing approach that automatically classified cells as cancer, lymphocyte, or stroma on the basis of their size and shape. This approach was validated on the remaining samples, and any errors in this analysis were digitally corrected before obtaining a plot of tumor cellular heterogeneity. With exact knowledge of the tumor’s cellular composition, the authors were able to correct copy number data to more accurately reflect HER2 status compared with uncorrected data. Yuan and colleagues combined their digital pathology with genomic information to devise an integrated predictor of survival for estrogen receptor (ER)–negative patients. Higher number of infiltrating lymphocytes (immune cells) as quantified by their image analysis platform were found in a subset of patients with better clinical outcome than the rest of ER-negative patients, and this outcome difference was significantly enhanced with the addition of gene expression. The quantitative and objective nature of this integrated predictor could benefit diagnosis and prognosis in many areas of cancer by using the rich combination of tumor cellular content and genomic data. 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.

Germline CDH1 deletions in hereditary diffuse gastric cancer families

Germline CDH1 point or small frameshift mutations can be identified in 30–50% of hereditary diffuse gastric cancer (HDGC) families. We hypothesized that CDH1 genomic rearrangements would be found in HDGC and identified 160 families with either two gastric cancers in first-degree relatives and with at least one diffuse gastric cancer (DGC) diagnosed before age 50, or three or more DGC in close relatives diagnosed at any age. Sixty-seven carried germline CDH1 point or small frameshift mutations. We screened germline DNA from the 93 mutation negative probands for large genomic rearrangements by Multiplex Ligation-Dependent Probe Amplification. Potential deletions were validated by RT–PCR and breakpoints cloned using a combination of oligo-CGH-arrays and long-range-PCR. In-silico analysis of the CDH1 locus was used to determine a potential mechanism for these rearrangements. Six of 93 (6.5%) previously described mutation negative HDGC probands, from low GC incidence populations (UK and North America), carried genomic deletions (UK and North America). Two families carried an identical deletion spanning 193 593 bp, encompassing the full CDH3 sequence and CDH1 exons 1 and 2. Other deletions affecting exons 1, 2, 15 and/or 16 were identified. The statistically significant over-representation of Alus around breakpoints indicates it as a likely mechanism for these deletions. When all mutations and deletions are considered, the overall frequency of CDH1 alterations in HDGC is ∼46% (73/160). CDH1 large deletions occur in 4% of HDGC families by mechanisms involving mainly non-allelic homologous recombination in Alu repeat sequences. As the finding of pathogenic CDH1 mutations is useful for management of HDGC families, screening for deletions should be offered to at-risk families.

Genomic architecture characterizes tumor progression paths and fate in breast cancer patients

This study demonstrates the relation among structural genomic alterations, molecular subtype, and clinical behavior and shows that an objective score of genomic complexity can provide independent prognostic information in breast cancer. Form and Malfunction Breast cancer is an iniquitous disease with a panoply of predisposing genetic and environmental causes, the details of which have yet to be fully understood. One of every four women will be diagnosed with breast cancer, hence the early and accurate identification of specific tumor features that may affect overall survival is imperative in achieving an optimal prognosis. A widely appreciated taxonomy in the breast cancer field has enabled the molecular discernment of five pathological subtypes; however, as research dives deeper into the chromosomal underpinnings of the disease, new classifiers are needed to augment what is known with key structural details to create a more vivid tumor landscape. Now, Russnes and colleagues have generated new algorithms that can estimate the specific genomic region as well as the architectural type of rearrangement—gains or losses of chromosome arms. A cohort of breast tumors was scored using this method, and all tumors with complex rearrangements had more whole chromosome arms affected than those without complex rearrangement. Moreover, there was an overlapping correlation with the molecular subtyping features of the tumors, and the score could confer prognostic power. Distinct molecular subtypes of breast carcinomas have been identified, but translation into clinical use has been limited. We have developed two platform-independent algorithms to explore genomic architectural distortion using array comparative genomic hybridization data to measure (i) whole-arm gains and losses [whole-arm aberration index (WAAI)] and (ii) complex rearrangements [complex arm aberration index (CAAI)]. By applying CAAI and WAAI to data from 595 breast cancer patients, we were able to separate the cases into eight subgroups with different distributions of genomic distortion. Within each subgroup data from expression analyses, sequencing and ploidy indicated that progression occurs along separate paths into more complex genotypes. Histological grade had prognostic impact only in the luminal-related groups, whereas the complexity identified by CAAI had an overall independent prognostic power. This study emphasizes the relation among structural genomic alterations, molecular subtype, and clinical behavior and shows that objective score of genomic complexity (CAAI) is an independent prognostic marker in breast cancer.

Copynumber: Efficient algorithms for single- and multi-track copy number segmentation

BackgroundCancer progression is associated with genomic instability and an accumulation of gains and losses of DNA. The growing variety of tools for measuring genomic copy numbers, including various types of array-CGH, SNP arrays and high-throughput sequencing, calls for a coherent framework offering unified and consistent handling of single- and multi-track segmentation problems. In addition, there is a demand for highly computationally efficient segmentation algorithms, due to the emergence of very high density scans of copy number.ResultsA comprehensive Bioconductor package for copy number analysis is presented. The package offers a unified framework for single sample, multi-sample and multi-track segmentation and is based on statistically sound penalized least squares principles. Conditional on the number of breakpoints, the estimates are optimal in the least squares sense. A novel and computationally highly efficient algorithm is proposed that utilizes vector-based operations in R. Three case studies are presented.ConclusionsThe R package copynumber is a software suite for segmentation of single- and multi-track copy number data using algorithms based on coherent least squares principles.

Regulation of p53 tetramerization and nuclear export by ARC

Inactivation of the transcription factor p53 is central to carcinogenesis. Yet only approximately one-half of cancers have p53 loss-of-function mutations. Here, we demonstrate a mechanism for p53 inactivation by apoptosis repressor with caspase recruitment domain (ARC), a protein induced in multiple cancer cells. The direct binding in the nucleus of ARC to the p53 tetramerization domain inhibits p53 tetramerization. This exposes a nuclear export signal in p53, triggering Crm1-dependent relocation of p53 to the cytoplasm. Knockdown of endogenous ARC in breast cancer cells results in spontaneous tetramerization of endogenous p53, accumulation of p53 in the nucleus, and activation of endogenous p53 target genes. In primary human breast cancers with nuclear ARC, p53 is almost always WT. Conversely, nearly all breast cancers with mutant p53 lack nuclear ARC. We conclude that nuclear ARC is induced in cancer cells and negatively regulates p53.

TP53 mutation spectrum in breast cancer is subtype specific and has distinct prognostic relevance.

Purpose: In breast cancer, the TP53 gene is frequently mutated and the mutations have been associated with poor prognosis. The prognostic impact of the different types of TP53 mutations across the different molecular subtypes is still poorly understood. Here, we characterize the spectrum and prognostic significance of TP53 mutations with respect to the PAM50 subtypes and integrative clusters (IC). Experimental Design: TP53 mutation status was obtained for 1,420 tumor samples from the METABRIC cohort by sequencing all coding exons using the Sanger method. Results: TP53 mutations were found in 28.3% of the tumors, conferring a worse overall and breast cancer-specific survival [HR = 2.03; 95% confidence interval (CI), 1.65–2.48, P < 0.001], and were also found to be an independent marker of poor prognosis in estrogen receptor-positive cases (HR = 1.86; 95% CI, 1.39–2.49, P < 0.001). The mutation spectrum of TP53 varied between the breast cancer subtypes, and individual alterations showed subtype-specific association. TP53 mutations were associated with increased mortality in patients with luminal B, HER2-enriched, and normal-like tumors, but not in patients with luminal A and basal-like tumors. Similar observations were made in ICs, where mutation associated with poorer outcome in IC1, IC4, and IC5. The combined effect of TP53 mutation, TP53 LOH, and MDM2 amplification on mortality was additive. Conclusion: This study reveals that TP53 mutations have different clinical relevance in molecular subtypes of breast cancer, and suggests diverse roles for TP53 in the biology underlying breast cancer development. Clin Cancer Res; 20(13); 3569–80. ©2014 AACR.

ESR1 gene amplification in breast cancer: a common phenomenon?

Lindsay A Brown1,2, Jeremy Hoog3, Suet-Feung Chin4,5, Yu Tao3, Abd Alnaser Zayed1,2, Koei Chin6, Andrew E Teschendorff4,5, John F Quackenbush7, John C Marioni8, Samuel Leung9,10, Charles M Perou11, Torsten O Neilsen9,10, Matthew Ellis3, Joe W Gray6, Philip S Bernard7, David G Huntsman1,2,9,10,12, and Carlos Caldas4,5,12 1Center for Translational and Applied Genomics, and the British Columbia Cancer Agency, Vancouver, British Columbia V5Z 4E6, Canada.