Single-cell mtDNA dynamics in tumors is driven by coregulation of nuclear and mitochondrial genomes.

The extent of cell-to-cell variation in tumor mitochondrial DNA (mtDNA) copy number and genotype, and the phenotypic and evolutionary consequences of such variation, are poorly characterized. Here we use amplification-free single-cell whole-genome sequencing (Direct Library Prep (DLP+)) to simultaneously assay mtDNA copy number and nuclear DNA (nuDNA) in 72,275 single cells derived from immortalized cell lines, patient-derived xenografts and primary human tumors. Cells typically contained thousands of mtDNA copies, but variation in mtDNA copy number was extensive and strongly associated with cell size. Pervasive whole-genome doubling events in nuDNA associated with stoichiometrically balanced adaptations in mtDNA copy number, implying that mtDNA-to-nuDNA ratio, rather than mtDNA copy number itself, mediated downstream phenotypes. Finally, multimodal analysis of DLP+ and single-cell RNA sequencing identified both somatic loss-of-function and germline noncoding variants in mtDNA linked to heteroplasmy-dependent changes in mtDNA copy number and mitochondrial transcription, revealing phenotypic adaptations to disrupted nuclear/mitochondrial balance.

ReMixT: clone-specific genomic structure estimation in cancer.

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 .

BCL11A is a triple-negative breast cancer gene with critical functions in stem and progenitor cells.

Triple-negative breast cancer (TNBC) has poor prognostic outcome compared with other types of breast cancer. The molecular and cellular mechanisms underlying TNBC pathology are not fully understood. Here, we report that the transcription factor BCL11A is overexpressed in TNBC including basal-like breast cancer (BLBC) and that its genomic locus is amplified in up to 38% of BLBC tumours. Exogenous BCL11A overexpression promotes tumour formation, whereas its knockdown in TNBC cell lines suppresses their tumourigenic potential in xenograft models. In the DMBA-induced tumour model, Bcl11a deletion substantially decreases tumour formation, even in p53-null cells and inactivation of Bcl11a in established tumours causes their regression. At the cellular level, Bcl11a deletion causes a reduction in the number of mammary epithelial stem and progenitor cells. Thus, BCL11A has an important role in TNBC and normal mammary epithelial cells. This study highlights the importance of further investigation of BCL11A in TNBC-targeted therapies.

Ordering of mutations in preinvasive disease stages of esophageal carcinogenesis.

Cancer genome sequencing studies have identified numerous driver genes, but the relative timing of mutations in carcinogenesis remains unclear. The gradual progression from premalignant Barrett's esophagus to esophageal adenocarcinoma (EAC) provides an ideal model to study the ordering of somatic mutations. We identified recurrently mutated genes and assessed clonal structure using whole-genome sequencing and amplicon resequencing of 112 EACs. We next screened a cohort of 109 biopsies from 2 key transition points in the development of malignancy: benign metaplastic never-dysplastic Barrett's esophagus (NDBE; n=66) and high-grade dysplasia (HGD; n=43). Unexpectedly, the majority of recurrently mutated genes in EAC were also mutated in NDBE. Only TP53 and SMAD4 mutations occurred in a stage-specific manner, confined to HGD and EAC, respectively. Finally, we applied this knowledge to identify high-risk Barrett's esophagus in a new non-endoscopic test. In conclusion, mutations in EAC driver genes generally occur exceptionally early in disease development with profound implications for diagnostic and therapeutic strategies.

The shaping and functional consequences of the microRNA landscape in breast cancer.

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.

iReckon: simultaneous isoform discovery and abundance estimation from RNA-seq data.

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.

Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling.

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.

Mutation discovery in regions of segmental cancer genome amplifications with CoNAn-SNV: a mixture model for next generation sequencing of tumors.

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.

Functional proteomics can define prognosis and predict pathologic complete response in patients with breast cancer.

PURPOSE: 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). METHODS: 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. RESULTS: 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). CONCLUSION: 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.

SNVMix: predicting single nucleotide variants from next-generation sequencing of tumors.

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 >40x (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.

A consensus prognostic gene expression classifier for ER positive breast cancer.

BACKGROUND: A consensus prognostic gene expression classifier is still elusive in heterogeneous diseases such as breast cancer. RESULTS: Here we perform a combined analysis of three major breast cancer microarray data sets to hone in on a universally valid prognostic molecular classifier in estrogen receptor (ER) positive tumors. Using a recently developed robust measure of prognostic separation, we further validate the prognostic classifier in three external independent cohorts, confirming the validity of our molecular classifier in a total of 877 ER positive samples. Furthermore, we find that molecular classifiers may not outperform classical prognostic indices but that they can be used in hybrid molecular-pathological classification schemes to improve prognostic separation. CONCLUSION: The prognostic molecular classifier presented here is the first to be valid in over 877 ER positive breast cancer samples and across three different microarray platforms. Larger multi-institutional studies will be needed to fully determine the added prognostic value of molecular classifiers when combined with standard prognostic factors.

The link between nutritional status and insulin sensitivity is dependent on the adipocyte-specific peroxisome proliferator-activated receptor-gamma2 isoform.

The nuclear receptor peroxisome proliferator-activated receptor-gamma (PPARgamma) is critically required for adipogenesis. PPARgamma exists as two isoforms, gamma1 and gamma2. PPARgamma2 is the more potent adipogenic isoform in vitro and is normally restricted to adipose tissues, where it is regulated more by nutritional state than PPARgamma1. To elucidate the relevance of the PPARgamma2 in vivo, we generated a mouse model in which the PPARgamma2 isoform was specifically disrupted. Despite similar weight, body composition, food intake, energy expenditure, and adipose tissue morphology, male mice lacking the gamma2 isoform were more insulin resistant than wild-type animals when fed a regular diet. These results indicate that insulin resistance associated with ablation of PPARgamma2 is not the result of lipodystrophy and suggests a specific role for PPARgamma2 in maintaining insulin sensitivity independently of its effects on adipogenesis. Furthermore, PPARgamma2 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 PPARgamma2 is required for the maintenance of normal insulin sensitivity in mice but also raises the intriguing notion that PPARgamma2 may be necessary for the adverse effects of a high-fat diet on carbohydrate metabolism.