Research Article: The Subclonal Architecture of Metastatic Breast Cancer: Results from a Prospective Community-Based Rapid Autopsy Program “CASCADE”

Date Published: December 27, 2016

Publisher: Public Library of Science

Author(s): Peter Savas, Zhi Ling Teo, Christophe Lefevre, Christoffer Flensburg, Franco Caramia, Kathryn Alsop, Mariam Mansour, Prudence A. Francis, Heather A. Thorne, Maria Joao Silva, Nnennaya Kanu, Michelle Dietzen, Andrew Rowan, Maik Kschischo, Stephen Fox, David D. Bowtell, Sarah-Jane Dawson, Terence P. Speed, Charles Swanton, Sherene Loi, Marc Ladanyi

Abstract: BackgroundUnderstanding the cancer genome is seen as a key step in improving outcomes for cancer patients. Genomic assays are emerging as a possible avenue to personalised medicine in breast cancer. However, evolution of the cancer genome during the natural history of breast cancer is largely unknown, as is the profile of disease at death. We sought to study in detail these aspects of advanced breast cancers that have resulted in lethal disease.Methods and FindingsThree patients with oestrogen-receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer and one patient with triple negative breast cancer underwent rapid autopsy as part of an institutional prospective community-based rapid autopsy program (CASCADE). Cases represented a range of management problems in breast cancer, including late relapse after early stage disease, de novo metastatic disease, discordant disease response, and disease refractory to treatment. Between 5 and 12 metastatic sites were collected at autopsy together with available primary tumours and longitudinal metastatic biopsies taken during life. Samples underwent paired tumour-normal whole exome sequencing and single nucleotide polymorphism (SNP) arrays. Subclonal architectures were inferred by jointly analysing all samples from each patient. Mutations were validated using high depth amplicon sequencing.Between cases, there were significant differences in mutational burden, driver mutations, mutational processes, and copy number variation. Within each case, we found dramatic heterogeneity in subclonal structure from primary to metastatic disease and between metastatic sites, such that no single lesion captured the breadth of disease. Metastatic cross-seeding was found in each case, and treatment drove subclonal diversification. Subclones displayed parallel evolution of treatment resistance in some cases and apparent augmentation of key oncogenic drivers as an alternative resistance mechanism. We also observed the role of mutational processes in subclonal evolution.Limitations of this study include the potential for bias introduced by joint analysis of formalin-fixed archival specimens with fresh specimens and the difficulties in resolving subclones with whole exome sequencing. Other alterations that could define subclones such as structural variants or epigenetic modifications were not assessed.ConclusionsThis study highlights various mechanisms that shape the genome of metastatic breast cancer and the value of studying advanced disease in detail. Treatment drives significant genomic heterogeneity in breast cancers which has implications for disease monitoring and treatment selection in the personalised medicine paradigm.

Partial Text: Heterogeneity in the natural history of advanced cancers has long been noted. The advent of cancer genomics has revealed that significant heterogeneity may exist both between and within lesions in the same patient. Since that time, autopsy studies have found a great variety of genomic heterogeneity in multiple cancer types. Evolution over time has also been documented, with varying influences of therapy in shaping the subclonal architecture of advanced disease. At the same time, the clinical significance of heterogeneity is yet to be established.

As breast cancer is relatively treatment responsive compared to other tumour types, we focussed our analysis on understanding subclonal composition and how this evolves over time under the influence of therapy. The first case was recruited in 2013, at which time methods for performing subclonal inference on multiple samples were not mature. By the time the final case was recruited in 2015, the field had advanced considerably, which made this approach feasible, as will be detailed below. All four cases were analysed concurrently.

Three ER-positive cases (denoted ER1, ER2, and ER3) and one triple negative case (denoted TN1) were analysed. Cases were recruited sequentially, and no cases were excluded from the analysis. In all cases, primary tumours were available as FFPE samples. Original pathology reports describing the macro-dissection of the primary tumours were used to select FFPE blocks in spatially distinct regions for each patient. In ER2, ER3, and TN1, metastatic biopsies taken during life were also available for analysis, as well as ctDNA for ER2. To study heterogeneity and evolution in detail, 8 (ER1), 13 (ER2), 16 (ER3), and 15 (TN1) samples were sequenced from each patient. Samples are summarised in S1 Table.

In this study, we analyse four cases chosen to represent difficult clinical scenarios in the contemporary management of advanced breast cancer. We took the approach of extensively sampling metastatic disease at the time of autopsy, performing whole exome sequencing and SNP arrays coupled with high depth validation of discovered mutations. We also analysed samples taken from the primary tumour and, where available, metastatic lesions biopsied whilst patients were still alive. In line with other work utilising the CASCADE program, we found studying advanced disease at the time of death to be highly informative [48]. We found significant heterogeneity present across multiple metastatic sites, and by performing subclonal inference, it was possible to understand the key processes that drive tumour evolution over time. We have shown several novel findings, including how treatment shapes clonal evolution, the importance of mutational processes over a disease course, and augmentation of oncogenic signalling as a mechanism of treatment failure.

Source:

http://doi.org/10.1371/journal.pmed.1002204

 

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