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Cancer genomes exhibit extensive chromosomal copy number changes and structural variation, yet how allele specific alterations drive cancer genome evolution remains unclear. Here, through application of a new computational approach we report allele specific copy number alterations in 11,097 single cell whole genomes from genetically engineered mammary epithelial cells and 21,852 cells from high grade serous ovarian and triple negative breast cancers. Resolving single cell copy number profiles to individual alleles uncovered genomic background distributions of gains, losses and loss of heterozygosity, yielding evidence of positive selection of specific chromosomal alterations. In addition specific genomic loci in maternal and paternal alleles were commonly found to be altered in parallel with convergent phenotypic transcriptional effects. Finally we show that haplotype specific alterations trace the cyclical etiology of high level amplifications and reveal clonal haplotype decomposition of complex structures. Together, our results illuminate how allele and haplotype specific alterations, here determined across thousands of single cell cancer genomes, impact the etiology and evolution of structural variations in human tumours.

Structural genome alterations are determinants of cancer ontogeny and therapeutic response. While bulk genome sequencing has enabled delineation of structural variation (SV) mutational processes which generate patterns of DNA damage, we have little understanding of how these processes lead to cell-to-cell variations which underlie selection and rates of accrual of different genomic lesions. We analysed 309 high grade serous ovarian and triple negative breast cancer genomes to determine their mutational processes, selecting 22 from which we sequenced >22,000 single cell whole genomes across a spectrum of mutational processes. We show that distinct patterns of cell-to-cell variation in aneuploidy, copy number alteration (CNA) and segment length occur in homologous recombination deficiency (HRD) and fold-back inversion (FBI) phenotypes. Widespread aneuploidy through induction of HRD throughBRCA1andBRCA2inactivation was mirrored by continuous whole genome duplication in HRD tumours, contrasted with early ploidy fixation in FBI. FBI tumours exhibited copy number distributions skewed towards gains, widespread clone-specific variation in amplitude of high-level amplifications, often impacting oncogenes, and break-point variability consistent with progressive genomic diversification, which we termed serriform structural variation (SSV). SSVs were consistent with a CNA-based molecular clock reflecting a continual and distributed process across clones within tumours. These observations reveal previously obscured genome plasticity and evolutionary properties with implications for cancer evolution, therapeutic targeting and response.

How cell-to-cell copy number alterations that underpin genomic instability

BackgroundSingle-cell RNA sequencing (scRNAseq) is a powerful tool for studying complex biological systems, such as tumour heterogeneity and tissue microenvironments. However, the sources of technical and biological variation in primary solid tumour tissues and patient-derived mouse xenografts for scRNAseq, are not well understood. Here, we used low temperature (6°C) protease and collagenase (37°C) to identify the transcriptional signatures associated with tissue dissociation across a diverse scRNAseq dataset comprising 128,481 cells from patient cancer tissues, patient-derived breast cancer xenografts and cancer cell lines.ResultsWe observe substantial variation in standard quality control (QC) metrics of cell viability across conditions and tissues. From FACS sorted populations gated for cell viability, we identify a sub-population of dead cells that would pass standard data filtering practices, and quantify the extent to which their transcriptomes differ from live cells. We identify a further subpopulation of transcriptomically “dying” cells that exhibit up-regulation of MHC class I transcripts, in contrast with live and fully dead cells. From the contrast between tissue protease dissociation at 37°C or 6°C, we observe that collagenase digestion results in a stress response. We derive a core gene set of 512 heat shock and stress response genes, includingFOSandJUN, induced by collagenase (37°C), which are minimized by dissociation with a cold active protease (6°C). While induction of these genes was highly conserved across all cell types, cell type-specific responses to collagenase digestion were observed in patient tissues. We observe that the yield of cancer and non-cancer cell types varies between tissues and dissociation methods.ConclusionsThe method and conditions of tumour dissociation influence cell yield and transcriptome state and are both tissue and cell type dependent. Interpretation of stress pathway expression differences in cancer single cell studies, including components of surface immune recognition such as MHC class I, may be especially confounded. We define a core set of 512 genes that can assist with identification of such effects in dissociated scRNA-seq experiments.

Introduction Breast cancer is the most common cancer in female and triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer which shows high rate of recurrence and metastasis. Malignant cells that comprise primary tumor are heterogeneous and during disease progression selection of tumor cells occur as a mean to adapt and survive. Tumor heterogeneity can be studied by grouping cells as clones which refer to a group of cells related to each other by descent from a unitary origin. Understanding the mechanism of clonal dynamics and selection during cancer evolution is important to develop new therapeutic strategies for cancer metastasis. Moreover, it is imperative to be able to detect rare subclones that are responsible for metastasis. Thus, single-cell analysis is critical to identify cellular heterogeneity of cancer and molecular basis of metastatic phenotype in depth. Measuring genome and transcriptome in single-cell level will enable us to discover clonal dynamics during cancer metastasis and infer molecular determinants of metastasis fitness. Here, we propose to investigate the clonal dynamics, genomic, and transcriptomic profiles of human breast tumor metastasis using TNBC patient-derived xenograft (PDX) model to understand the mechanism of breast cancer progression and metastasis. Methods Tumor cells from previously established PDXs were transplanted into mammary fat pad of mice. Tumors were removed when it reached maximum allowed endpoint size (1,000mm3) and mice were monitored and allowed to grow metastasis. PDXs that developed primary tumor and metastasis were selected for subsequent single cell analysis. Single-cell whole-genome sequencing (scWGS) was performed using direct library preparation (DLP+) method which is preamplification-free single-cell genome sequencing approach. scWGS data was used to call cell-specific copy number events, which allows us to cluster cells and identify clones. Hierarchical clustering was used to identify clonal structure of each sample. Phylogenetic tree was computed using copy number data of samples from each PDX. Results Nine different TNBC PDX lines were tested and 6 PDX lines developed metastasis. We focused on 2 PDX lines of which metastatic tissues were available for single cell sequencing. SA919 developed metastatic mass near cervical or lumbar spine area. SA535 developed metastasis to lung, axillary or inguinal area and tumor recurrence at mammary fat pad site. scWGS for SA919 and SA535 was carried out and we generated libraries from single cells of primary tumor and its metastases. Hierarchical clustering using copy number data revealed clonal structure of primary tumor and metastases. SA919 showed oligoclonal primary tumor and metastasis whereas SA535 showed polyclonal metastasis from polyclonal primary tumor. scWGS data of primary tumor and its metastases in each PDX were merged to compute phylogenetic trees. Phylogenetic analysis revealed that not all clones of primary tumor contributed to metastasis in both PDXs. Different metastatic lesions of SA535 harbored different clones from primary tumor. For example, clones consisting left axillary metastasis and right axillary metastasis in SA535 were different from each other, however they both originated from primary tumor clones. Metastatic lesions from SA919 and SA535 also had newly appearing clones that were not present in primary tumor inferring clonal evolution during metastasis. Conclusion We were able to capture different patterns of metastasis in several PDXs. Phylogenetic analysis using scWGS data revealed clonal relationship between primary tumor and metastases. Further analysis of single cell genomic and transcriptomic profiles will provide deeper understanding of metastasis in breast cancer. Citation Format: Hakwoo Lee, Farhia Kabeer, Ciara O'Flanagan, Jazmine Brimhall, Justina Biele, Biexi Wang, Teresa Ruiz Algara, So Ra Lee, Daniel Lai, Michael Yuen, Simong Song, Patricia Ye, Jenifer Pham, Richard Moore, Andy J Mungall, Sohrab P Shah, Samuel Aparicio. Single-cell analysis of breast cancer metastasis using patient-derived xenograft model reveals clonal relationship between primary tumor and its metastases [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD8-05.