Helen Bardwell

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.

Biological and prognostic associations of miR-205 and let-7b in breast cancer revealed by in situ hybridization analysis of micro-RNA expression in arrays of archival tumour tissue.

Micro‐RNAs (miRNAs) are frequently dysregulated in a range of human malignancies, many have been shown to act either as tumour supressors or oncogenes and several have been implicated in breast cancer. However, breast cancer is a diverse disease and little is known about the relationships between miRNA expression, clinical outcome and tumour subtype. We used locked nucleic acid probe in situ hybridization (LNA‐ISH) to visualize, in tissue micro‐arrays (TMAs) of 2919 formalin‐fixed paraffin‐embedded (FFPE) archival breast tumours, the expression of two key miRNAs that are frequently lost in a range of solid malignancies, let‐7b and miR‐205. These miRNAs were also quantified by quantitative reverse transcription PCR in cores of FFPE tissue from 40 of these cases, demonstrating that LNA‐ISH is semi‐quantitative. The tumours in the TMAs were assigned to subtypes based on their immunohistochemical (IHC) staining with ER, PR, HER2, CK5/6 and EGFR. let‐7b expression was shown to be associated with luminal tumours and to have an independent significant positive prognostic value in this group. miR‐205 is associated with tumours of ductal morphology and is of significant positive prognostic value within these tumours. We propose that the expression of miR‐205 may contribute to ductal tumour morphology. Copyright

Integration of genomic, transcriptomic and proteomic data identifies two biologically distinct subtypes of invasive lobular breast cancer

Invasive lobular carcinoma (ILC) is the second most frequently occurring histological breast cancer subtype after invasive ductal carcinoma (IDC), accounting for around 10% of all breast cancers. The molecular processes that drive the development of ILC are still largely unknown. We have performed a comprehensive genomic, transcriptomic and proteomic analysis of a large ILC patient cohort and present here an integrated molecular portrait of ILC. Mutations in CDH1 and in the PI3K pathway are the most frequent molecular alterations in ILC. We identified two main subtypes of ILCs: (i) an immune related subtype with mRNA up-regulation of PD-L1, PD-1 and CTLA-4 and greater sensitivity to DNA-damaging agents in representative cell line models; (ii) a hormone related subtype, associated with Epithelial to Mesenchymal Transition (EMT), and gain of chromosomes 1q and 8q and loss of chromosome 11q. Using the somatic mutation rate and eIF4B protein level, we identified three groups with different clinical outcomes, including a group with extremely good prognosis. We provide a comprehensive overview of the molecular alterations driving ILC and have explored links with therapy response. This molecular characterization may help to tailor treatment of ILC through the application of specific targeted, chemo- and/or immune-therapies.

Erratum: The somatic mutation profiles of 2,433 breast cancers refine their genomic and transcriptomic landscapes (Nature Communications (2016) 7:11479 DOI: 10.1038/ncomms11479)

Bernard Pereira, Suet-Feung Chin, Oscar M. Rueda, Hans-Kristian Moen Vollan, Elena Provenzano, Helen A. Bardwell, Michelle Pugh, Linda Jones, Roslin Russell, Stephen-John Sammut, Dana W.Y. Tsui, Bin Liu, Sarah-Jane Dawson, Jean Abraham, Helen Northen, John F. Peden, Abhik Mukherjee, Gulisa Turashvili, Andrew R. Green, Steve McKinney, Arusha Oloumi, Sohrab Shah, Nitzan Rosenfeld, Leigh Murphy, David R. Bentley, Ian O. Ellis, Arnie Purushotham, Sarah E. Pinder, Anne-Lise Børresen-Dale, Helena M. Earl, Paul D. Pharoah, Mark T. Ross, Samuel Aparicio & Carlos Caldas

THERAPEUTIC RATIONALE TO TARGET HIGHLY EXPRESSED CDK7 CONFERRING POOR OUTCOMES IN TRIPLE-NEGATIVE BREAST CANCER

Triple-negative breast cancer (TNBC) patients commonly exhibit poor prognosis and high relapse after treatment, but there remains a lack of biomarkers and effective targeted therapies for this disease. Here, we report evidence highlighting the cell-cycle-related kinase CDK7 as a driver and candidate therapeutic target in TNBC. Using publicly available transcriptomic data from a collated set of TNBC patients (n = 383) and the METABRIC TNBC dataset (n = 217), we found CDK7 mRNA levels to be correlated with patient prognosis. High CDK7 protein expression was associated with poor prognosis within the RATHER TNBC cohort (n = 109) and the METABRIC TNBC cohort (n = 203). The highly specific CDK7 kinase inhibitors, BS-181 and THZ1, each downregulated CDK7-mediated phosphorylation of RNA polymerase II, indicative of transcriptional inhibition, with THZ1 exhibiting 500-fold greater potency than BS-181. Mechanistic investigations revealed that the survival of MDA-MB-231 TNBC cells relied heavily on the BCL-2/BCL-XL signaling axes in cells. Accordingly, we found that combining the BCL-2/BCL-XL inhibitors ABT-263/ABT199 with the CDK7 inhibitor THZ1 synergized in producing growth inhibition and apoptosis of human TNBC cells. Collectively, our results highlight elevated CDK7 expression as a candidate biomarker of poor prognosis in TNBC, and they offer a preclinical proof of concept for combining CDK7 and BCL-2/BCL-XL inhibitors as a mechanism-based therapeutic strategy to improve TNBC treatment. Cancer Res; 77(14); 3834-45. ©2017 AACR.

Shallow whole genome sequencing for robust copy number profiling of formalin-fixed paraffin-embedded breast cancers.

Pathology archives with linked clinical data are an invaluable resource for translational research, with the limitation that most cancer samples are formalin-fixed paraffin-embedded (FFPE) tissues. Therefore, FFPE tissues are an important resource for genomic profiling studies but are under-utilised due to the low amount and quality of extracted nucleic acids. We profiled the copy number landscape of 356 breast cancer patients using DNA extracted FFPE tissues by shallow whole genome sequencing. We generated a total of 491 sequencing libraries from 2 kits and obtained data from 98.4% of libraries with 86.4% being of good quality. We generated libraries from as low as 3.8 ng of input DNA and found that the success was independent of input DNA amount and quality, processing site and age of the fixed tissues. Since copy number alterations (CNA) play a major role in breast cancer, it is imperative that we are able to use FFPE archives and we have shown in this study that sWGS is a robust method to do such profiling.

Abstract A30: RATHER: High-resolution molecular profiling of invasive lobular breast cancers

Introduction: RATHER (Rational Therapy for Breast Cancer) is an international multi-site collaborative effort that aims to use high resolution molecular profiling techniques to identify novel kinase targets for two subtypes of breast cancer, invasive lobular cancers (ILC) and triple negatives (TN) where no targeted therapies are available at present. Experiments: DNA, RNA and protein were extracted from 137 ILC and 155 TN samples with an average of 5 years clinical follow-up. A variety of high resolution molecular profiling methods were used such as copy number analysis (Affymetrix SNP6), gene expression profiling (Agilent 4x44K gene arrays), targeted sequencing (Agilent customized kinome panel & Illumina Nextera Custom Enrichment), whole transcriptomic sequencing and reverse phase protein lysate array (RPPA) analysis. Results: Combining copy number and gene expression data, we have classified the ILC tumors into the intergrative Cluster (IntClust) subgroups that we have previously identified from our large-scale breast cancer study, METABRIC (Molecular Taxonomy of Breast Cancer International Consortium). The ILC tumors were predominantly in IntClust3 (37.2%) and IntClust8 (21.2%). Only two genes were found to be frequently mutated (>10%) ie. CDH1 (40.8%) and PIK3CA (35%ILC). The PI3K pathway has been found to be frequently altered in ILCs by either mutations (PIK3CA and AKT1) or copy number alterations (PTEN). Integrating with transcriptomic and proteomic data, two main subtypes of ILCs were identified: (i) an immune responsive subtype with mRNA up-regulation of PDL1, PD1 and CTLA4 and greater sensitivity to DNA-damaging agents in representative cell line models; (ii) a hormone receptor signalling subtype, associated with Epithelial to Mesenchymal Transition (EMT), and gain of chromosomes 1q and 8q and loss of chromosome 11q. Using somatic mutation rate and eIF4B protein level, we identified three groups with different clinical outcomes, including a group with extremely good prognosis. Conclusion: We provide a comprehensive overview of the molecular alterations driving ILC and have explored links with therapy response. This molecular characterization will help to tailor treatment of ILC through the application of specific targeted, chemo- and/or immune-therapies. Citation Format: Suet-Feung Chin, Magali Michault, Ian Majewski, Tesa M. Severson, Tycho Bismeijer, Leanne De Korning, Justine Peeters, Phillip Schouten, Oscar M. Rueda, Astrid Bosma, Finbarr Tarrant, Yue Fan, BeiLei He, Bernard Pereira, Helen A. Bardwell, Elena Provenzano, Darran P. O9Connor, Sabine Linn, Thierry Dubois, Iris Simon, William Gallagher, Lodewyk Wessels, Rene Bernards, Carlos Caldas. RATHER: High-resolution molecular profiling of invasive lobular breast cancers. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research; Oct 17-20, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(2_Suppl):Abstract nr A30.