Florian Markowetz

The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups

The elucidation of breast cancer subgroups and their molecular drivers requires integrated views of the genome and transcriptome from representative numbers of patients. We present an integrated analysis of copy number and gene expression in a discovery and validation set of 997 and 995 primary breast tumours, respectively, with long-term clinical follow-up. Inherited variants (copy number variants and single nucleotide polymorphisms) and acquired somatic copy number aberrations (CNAs) were associated with expression in ∼40% of genes, with the landscape dominated by cis- and trans-acting CNAs. By delineating expression outlier genes driven in cis by CNAs, we identified putative cancer genes, including deletions in PPP2R2A, MTAP and MAP2K4. Unsupervised analysis of paired DNA–RNA profiles revealed novel subgroups with distinct clinical outcomes, which reproduced in the validation cohort. These include a high-risk, oestrogen-receptor-positive 11q13/14 cis-acting subgroup and a favourable prognosis subgroup devoid of CNAs. Trans-acting aberration hotspots were found to modulate subgroup-specific gene networks, including a TCR deletion-mediated adaptive immune response in the ‘CNA-devoid’ subgroup and a basal-specific chromosome 5 deletion-associated mitotic network. Our results provide a novel molecular stratification of the breast cancer population, derived from the impact of somatic CNAs on the transcriptome.

Acetylcysteine for prevention of contrast nephropathy: meta-analysis

BACKGROUND Contrast nephropathy is associated with increased in-hospital morbidity and mortality and leads to extension of hospital stay in patients with chronic renal insufficiency. Acetylcysteine seems to be a safe and inexpensive way to reduce contrast nephropathy. We aimed to assess the efficacy of acetylcysteine to prevent contrast nephropathy after administration of radiocontrast media in patients with chronic renal insufficiency. METHODS We did a meta-analysis of randomised controlled trials comparing acetylcysteine and hydration with hydration alone for preventing contrast nephropathy in patients with chronic renal insufficiency. The trials were identified through a combined search of the BIOSIS+/RRM, MEDLINE, Web of Science, Current Contents Medizin, and The Cochrane Library Databases. We used incidence of contrast nephropathy 48 h after administration of radiocontrast media as an outcome measure. FINDINGS Seven trials including 805 patients were eligible according to our inclusion criteria and were analysed. Overall incidence of contrast nephropathy varied between 8% and 28%. Since significant heterogeneity was indicated by the Q statistics (p=0.016) we used a random-effects model to combine the data. Compared with periprocedural hydration alone, administration of acetylcysteine and hydration significantly reduced the relative risk of contrast nephropathy by 56% (0.435 [95% CI 0.215-0.879], p=0.02) in patients with chronic renal insufficiency. Meta-regression revealed no significant relation between the relative risk of contrast nephropathy and the volume of radiocontrast media administered or the degree of chronic renal insufficiency before the procedure. INTERPRETATION Compared with periprocedural hydration alone, acetylcysteine with hydration significantly reduces the risk of contrast nephropathy in patients with chronic renal insufficiency. The relative risk of contrast nephropathy was not related to the amount of radiocontrast media given or to the degree of chronic renal insufficiency before the procedure.

Poor-prognosis colon cancer is defined by a molecularly distinct subtype and develops from serrated precursor lesions

Colon cancer is a clinically diverse disease. This heterogeneity makes it difficult to determine which patients will benefit most from adjuvant therapy and impedes the development of new targeted agents. More insight into the biological diversity of colon cancers, especially in relation to clinical features, is therefore needed. We demonstrate, using an unsupervised classification strategy involving over 1,100 individuals with colon cancer, that three main molecularly distinct subtypes can be recognized. Two subtypes have been previously identified and are well characterized (chromosomal-instable and microsatellite-instable cancers). The third subtype is largely microsatellite stable and contains relatively more CpG island methylator phenotype–positive carcinomas but cannot be identified on the basis of characteristic mutations. We provide evidence that this subtype relates to sessile-serrated adenomas, which show highly similar gene expression profiles, including upregulation of genes involved in matrix remodeling and epithelial-mesenchymal transition. The identification of this subtype is crucial, as it has a very unfavorable prognosis and, moreover, is refractory to epidermal growth factor receptor–targeted therapy.

Inferring cellular networks - a review

In this review we give an overview of computational and statistical methods to reconstruct cellular networks. Although this area of research is vast and fast developing, we show that most currently used methods can be organized by a few key concepts. The first part of the review deals with conditional independence models including Gaussian graphical models and Bayesian networks. The second part discusses probabilistic and graph-based methods for data from experimental interventions and perturbations.

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.

Spatial and Temporal Heterogeneity in High-Grade Serous Ovarian Cancer: A Phylogenetic Analysis

Background The major clinical challenge in the treatment of high-grade serous ovarian cancer (HGSOC) is the development of progressive resistance to platinum-based chemotherapy. The objective of this study was to determine whether intra-tumour genetic heterogeneity resulting from clonal evolution and the emergence of subclonal tumour populations in HGSOC was associated with the development of resistant disease. Methods and Findings Evolutionary inference and phylogenetic quantification of heterogeneity was performed using the MEDICC algorithm on high-resolution whole genome copy number profiles and selected genome-wide sequencing of 135 spatially and temporally separated samples from 14 patients with HGSOC who received platinum-based chemotherapy. Samples were obtained from the clinical CTCR-OV03/04 studies, and patients were enrolled between 20 July 2007 and 22 October 2009. Median follow-up of the cohort was 31 mo (interquartile range 22–46 mo), censored after 26 October 2013. Outcome measures were overall survival (OS) and progression-free survival (PFS). There were marked differences in the degree of clonal expansion (CE) between patients (median 0.74, interquartile range 0.66–1.15), and dichotimization by median CE showed worse survival in CE-high cases (PFS 12.7 versus 10.1 mo, p = 0.009; OS 42.6 versus 23.5 mo, p = 0.003). Bootstrap analysis with resampling showed that the 95% confidence intervals for the hazard ratios for PFS and OS in the CE-high group were greater than 1.0. These data support a relationship between heterogeneity and survival but do not precisely determine its effect size. Relapsed tissue was available for two patients in the CE-high group, and phylogenetic analysis showed that the prevalent clonal population at clinical recurrence arose from early divergence events. A subclonal population marked by a NF1 deletion showed a progressive increase in tumour allele fraction during chemotherapy. Conclusions This study demonstrates that quantitative measures of intra-tumour heterogeneity may have predictive value for survival after chemotherapy treatment in HGSOC. Subclonal tumour populations are present in pre-treatment biopsies in HGSOC and can undergo expansion during chemotherapy, causing clinical relapse.

Cancer Evolution: Mathematical Models and Computational Inference

Cancer is a somatic evolutionary process characterized by the accumulation of mutations, which contribute to tumor growth, clinical progression, immune escape, and drug resistance development. Evolutionary theory can be used to analyze the dynamics of tumor cell populations and to make inference about the evolutionary history of a tumor from molecular data. We review recent approaches to modeling the evolution of cancer, including population dynamics models of tumor initiation and progression, phylogenetic methods to model the evolutionary relationship between tumor subclones, and probabilistic graphical models to describe dependencies among mutations. Evolutionary modeling helps to understand how tumors arise and will also play an increasingly important prognostic role in predicting disease progression and the outcome of medical interventions, such as targeted therapy.

Diverse epigenetic strategies interact to control epidermal differentiation

It is becoming clear that interconnected functional gene networks, rather than individual genes, govern stem cell self-renewal and differentiation. To identify epigenetic factors that impact on human epidermal stem cells we performed siRNA-based genetic screens for 332 chromatin modifiers. We developed a Bayesian mixture model to predict putative functional interactions between epigenetic modifiers that regulate differentiation. We discovered a network of genetic interactions involving EZH2, UHRF1 (both known to regulate epidermal self-renewal), ING5 (a MORF complex component), BPTF and SMARCA5 (NURF complex components). Genome-wide localization and global mRNA expression analysis revealed that these factors impact two distinct but functionally related gene sets, including integrin extracellular matrix receptors that mediate anchorage of epidermal stem cells to their niche. Using a competitive epidermal reconstitution assay we confirmed that ING5, BPTF, SMARCA5, EZH2 and UHRF1 control differentiation under physiological conditions. Thus, regulation of distinct gene expression programs through the interplay between diverse epigenetic strategies protects epidermal stem cells from differentiation.

Master regulators of FGFR2 signalling and breast cancer risk

The fibroblast growth factor receptor 2 (FGFR2) locus has been consistently identified as a breast cancer risk locus in independent genome-wide association studies. However, the molecular mechanisms underlying FGFR2-mediated risk are still unknown. Using model systems we show that FGFR2-regulated genes are preferentially linked to breast cancer risk loci in expression quantitative trait loci analysis, supporting the concept that risk genes cluster in pathways. Using a network derived from 2,000 transcriptional profiles we identify SPDEF, ERα, FOXA1, GATA3 and PTTG1 as master regulators of fibroblast growth factor receptor 2 signalling, and show that ERα occupancy responds to fibroblast growth factor receptor 2 signalling. Our results indicate that ERα, FOXA1 and GATA3 contribute to the regulation of breast cancer susceptibility genes, which is consistent with the effects of anti-oestrogen treatment in breast cancer prevention, and suggest that fibroblast growth factor receptor 2 signalling has an important role in mediating breast cancer risk.

HTSanalyzeR: an R/Bioconductor package for integrated network analysis of high-throughput screens

Motivation: High-throughput screens (HTS) by RNAi or small molecules are among the most promising tools in functional genomics. They enable researchers to observe detailed reactions to experimental perturbations on a genome-wide scale. While there is a core set of computational approaches used in many publications to analyze these data, a specialized software combining them and making them easily accessible has so far been missing. Results: Here we describe HTSanalyzeR, a flexible software to build integrated analysis pipelines for HTS data that contains over-representation analysis, gene set enrichment analysis, comparative gene set analysis and rich sub-network identification. HTSanalyzeR interfaces with commonly used pre-processing packages for HTS data and presents its results as HTML pages and network plots. Availability: Our software is written in the R language and freely available via the Bioconductor project at http://www.bioconductor.org. Contact: florian.markowetz@cancer.org.uk