Decoding the patterns of clonal dynamics in breast cancer metastasis using single-cell sequencing in patient-derived xenograft models

Introduction: Selection and evolution of tumour cells occurs during cancer progression and metastasis. Understanding the mechanism of clonal dynamics and evolution is an important key to develop new therapeutic strategies for cancer metastasis. This thesis summarizes the development of breast cancer patient-derived xenograft (PDX) metastasis models and measurement of clonal dynamics during metastasis by identifying genomic changes in single cells from primary tumours and metastases. Methods: Tumour cells from untreated primary breast cancer patients were used to develop PDX tumours in immunodeficient mice. Tumours were removed when they reached maximum allowed endpoint size (1,000mm³) during a survival surgery and mice were monitored for metastasis. Immunohistochemical (IHC) staining was performed with 10 markers to characterize tumours. Single cell whole-genome sequencing (scWGS) was used to analyse primary and metastatic tumour cells and copy number alterations (CNAs) were identified which allow us to cluster cells and identify clones. Phylogenetic analysis was performed to identify clonal relationship between primary and metastatic tumour cells. Results: Nine different triple-negative breast cancer PDX lines were tested and 5 developed metastases (SA919, SA535, SA1142, SA605, SA609). We observed that protein marker expression was similar between primary tumour and metastases. Metastatic sites were reproducible over multiple passages in both SA919 and SA535. We also observed that metastatic potential increased with passage number in SA919 while 4 different passages of SA535 showed similar metastases development. From single cell analysis, we observed that the ability to metastasize of primary tumour increases with passage number due to the evolution of clonal population in SA919 and metastatic potential is a property distributed across CNA-defined clones in both SA919 and SA535. We also observed that metastasis to specific anatomical site was not associated with genomic clones and CNA induced genotype and LOH are potential factors that can affect metastatic potential of clones. Conclusion: We established breast cancer metastasis mouse model using patient-derived tissues and were able to capture different patterns of metastases in several PDXs. From the two transplant systems studied in detail, we observed metastatic potential was distributed across many genomic clones and CNAs have potential impact on metastatic potential of tumour cells.Medicine, Faculty ofPathology and Laboratory Medicine, Department ofGraduat

Age-correlated protein and transcript expression in breast cancer and normal breast tissues is dominated by host endocrine effects

The magnitude and scope of intrinsic age-correlated and host endocrine age-correlated gene expression in breast cancer is not well understood. From age-correlated gene expression in 3071 breast cancer transcriptomes and epithelial protein expression of 42 markers in 5001 breast cancers and 537 normal breast tissues, we identified a majority of age-correlated genes as putatively regulated by age-dependent estrogen signaling. Surprisingly these include the chromatin modifier EZH2 with negative age correlation and associated H3K27me3, with an inverse positive age correlation. Among TCGA-lung, thyroid, kidney and prostate transcriptomes, the largest overlap with breast cancer in age-correlated transcripts was in lung cancer, where about 1/3 of overlapping age-correlated transcripts appeared estrogen regulated. Age-quartile stratified outcomes analysis of 3,500 breast cancers using EZH2, H3K27me3, FOXA1 and BCL2 proteins revealed distinct age-related prognostic significance. Age correlation in gene expression may thus be an important factor in ER, EZH2, H3K27me3 and other biomarker assessment and treatment strategies.Medicine, Faculty ofNon UBCCellular and Physiological Sciences, Department ofMedical Genetics, Department ofPathology and Laboratory Medicine, Department ofReviewedFacultyResearcherGraduat

Clonal fitness inferred from time-series modelling of single-cell cancer genomes

Progress in defining genomic fitness landscapes in cancer, especially those defined by copy number alterations (CNAs), has been impeded by lack of time-series single-cell sampling of polyclonal populations and temporal statistical models1-7. Here we generated 42,000 genomes from multi-year time-series single-cell whole-genome sequencing of breast epithelium and primary triple-negative breast cancer (TNBC) patient-derived xenografts (PDXs), revealing the nature of CNA-defined clonal fitness dynamics induced by TP53 mutation and cisplatin chemotherapy. Using a new Wright-Fisher population genetics model8,9 to infer clonal fitness, we found that TP53 mutation alters the fitness landscape, reproducibly distributing fitness over a larger number of clones associated with distinct CNAs. Furthermore, in TNBC PDX models with mutated TP53, inferred fitness coefficients from CNA-based genotypes accurately forecast experimentally enforced clonal competition dynamics. Drug treatment in three long-term serially passaged TNBC PDXs resulted in cisplatin-resistant clones emerging from low-fitness phylogenetic lineages in the untreated setting. Conversely, high-fitness clones from treatment-naive controls were eradicated, signalling an inversion of the fitness landscape. Finally, upon release of drug, selection pressure dynamics were reversed, indicating a fitness cost of treatment resistance. Together, our findings define clonal fitness linked to both CNA and therapeutic resistance in polyclonal tumours.ISSN:0028-0836ISSN:1476-468

Single-cell genomic variation induced by mutational processes in cancer

How cell-to-cell copy number alterations that underpin genomic instability1 in human cancers drive genomic and phenotypic variation, and consequently the evolution of cancer2, remains understudied. Here, by applying scaled single-cell whole-genome sequencing3 to wild-type, TP53-deficient and TP53-deficient;BRCA1-deficient or TP53-deficient;BRCA2-deficient mammary epithelial cells (13,818 genomes), and to primary triple-negative breast cancer (TNBC) and high-grade serous ovarian cancer (HGSC) cells (22,057 genomes), we identify three distinct ‘foreground’ mutational patterns that are defined by cell-to-cell structural variation. Cell- and clone-specific high-level amplifications, parallel haplotype-specific copy number alterations and copy number segment length variation (serrate structural variations) had measurable phenotypic and evolutionary consequences. In TNBC and HGSC, clone-specific high-level amplifications in known oncogenes were highly prevalent in tumours bearing fold-back inversions, relative to tumours with homologous recombination deficiency, and were associated with increased clone-to-clone phenotypic variation. Parallel haplotype-specific alterations were also commonly observed, leading to phylogenetic evolutionary diversity and clone-specific mono-allelic expression. Serrate variants were increased in tumours with fold-back inversions and were highly correlated with increased genomic diversity of cellular populations. Together, our findings show that cell-to-cell structural variation contributes to the origins of phenotypic and evolutionary diversity in TNBC and HGSC, and provide insight into the genomic and mutational states of individual cancer cells.ISSN:0028-0836ISSN:1476-468

Clonal fitness inferred from time-series modelling of single-cell cancer genomes.

Progress in defining genomic fitness landscapes in cancer, especially those defined by copy number alterations (CNAs), has been impeded by lack of time-series single-cell sampling of polyclonal populations and temporal statistical models <sup>1-7</sup> . Here we generated 42,000 genomes from multi-year time-series single-cell whole-genome sequencing of breast epithelium and primary triple-negative breast cancer (TNBC) patient-derived xenografts (PDXs), revealing the nature of CNA-defined clonal fitness dynamics induced by TP53 mutation and cisplatin chemotherapy. Using a new Wright-Fisher population genetics model <sup>8,9</sup> to infer clonal fitness, we found that TP53 mutation alters the fitness landscape, reproducibly distributing fitness over a larger number of clones associated with distinct CNAs. Furthermore, in TNBC PDX models with mutated TP53, inferred fitness coefficients from CNA-based genotypes accurately forecast experimentally enforced clonal competition dynamics. Drug treatment in three long-term serially passaged TNBC PDXs resulted in cisplatin-resistant clones emerging from low-fitness phylogenetic lineages in the untreated setting. Conversely, high-fitness clones from treatment-naive controls were eradicated, signalling an inversion of the fitness landscape. Finally, upon release of drug, selection pressure dynamics were reversed, indicating a fitness cost of treatment resistance. Together, our findings define clonal fitness linked to both CNA and therapeutic resistance in polyclonal tumours

Clonal fitness inferred from time-series modelling of single-cell cancer genomes

Progress in defining genomic fitness landscapes in cancer, especially those defined by copy number alterations (CNAs), has been impeded by lack of time-series single-cell sampling of polyclonal populations and temporal statistical models1-7. Here we generated 42,000 genomes from multi-year time-series single-cell whole-genome sequencing of breast epithelium and primary triple-negative breast cancer (TNBC) patient-derived xenografts (PDXs), revealing the nature of CNA-defined clonal fitness dynamics induced by TP53 mutation and cisplatin chemotherapy. Using a new Wright-Fisher population genetics model8,9 to infer clonal fitness, we found that TP53 mutation alters the fitness landscape, reproducibly distributing fitness over a larger number of clones associated with distinct CNAs. Furthermore, in TNBC PDX models with mutated TP53, inferred fitness coefficients from CNA-based genotypes accurately forecast experimentally enforced clonal competition dynamics. Drug treatment in three long-term serially passaged TNBC PDXs resulted in cisplatin-resistant clones emerging from low-fitness phylogenetic lineages in the untreated setting. Conversely, high-fitness clones from treatment-naive controls were eradicated, signalling an inversion of the fitness landscape. Finally, upon release of drug, selection pressure dynamics were reversed, indicating a fitness cost of treatment resistance. Together, our findings define clonal fitness linked to both CNA and therapeutic resistance in polyclonal tumours