Clonal Decomposition and DNA Replication States Defined by Scaled Single-Cell Genome Sequencing.

Accurate measurement of clonal genotypes, mutational processes, and replication states from individual tumor-cell genomes will facilitate improved understanding of tumor evolution. We have developed DLP+, a scalable single-cell whole-genome sequencing platform implemented using commodity instruments, image-based object recognition, and open source computational methods. Using DLP+, we have generated a resource of 51,926 single-cell genomes and matched cell images from diverse cell types including cell lines, xenografts, and diagnostic samples with limited material. From this resource we have defined variation in mitotic mis-segregation rates across tissue types and genotypes. Analysis of matched genomic and image measurements revealed correlations between cellular morphology and genome ploidy states. Aggregation of cells sharing copy number profiles allowed for calculation of single-nucleotide resolution clonal genotypes and inference of clonal phylogenies and avoided the limitations of bulk deconvolution. Finally, joint analysis over the above features defined clone-specific chromosomal aneuploidy in polyclonal populations.

Interfaces of Malignant and Immunologic Clonal Dynamics in Ovarian Cancer.

High-grade serous ovarian cancer (HGSC) exhibits extensive malignant clonal diversity with widespread but non-random patterns of disease dissemination. We investigated whether local immune microenvironment factors shape tumor progression properties at the interface of tumor-infiltrating lymphocytes (TILs) and cancer cells. Through multi-region study of 212 samples from 38 patients with whole-genome sequencing, immunohistochemistry, histologic image analysis, gene expression profiling, and T and B cell receptor sequencing, we identified three immunologic subtypes across samples and extensive within-patient diversity. Epithelial CD8+ TILs negatively associated with malignant diversity, reflecting immunological pruning of tumor clones inferred by neoantigen depletion, HLA I loss of heterozygosity, and spatial tracking between T cell and tumor clones. In addition, combinatorial prognostic effects of mutational processes and immune properties were observed, illuminating how specific genomic aberration types associate with immune response and impact survival. We conclude that within-patient spatial immune microenvironment variation shapes intraperitoneal malignant spread, provoking new evolutionary perspectives on HGSC clonal dispersion.

A Biobank of Breast Cancer Explants with Preserved Intra-tumor Heterogeneity to Screen Anticancer Compounds.

The inter- and intra-tumor heterogeneity of breast cancer needs to be adequately captured in pre-clinical models. We have created a large collection of breast cancer patient-derived tumor xenografts (PDTXs), in which the morphological and molecular characteristics of the originating tumor are preserved through passaging in the mouse. An integrated platform combining in vivo maintenance of these PDTXs along with short-term cultures of PDTX-derived tumor cells (PDTCs) was optimized. Remarkably, the intra-tumor genomic clonal architecture present in the originating breast cancers was mostly preserved upon serial passaging in xenografts and in short-term cultured PDTCs. We assessed drug responses in PDTCs on a high-throughput platform and validated several ex vivo responses in vivo. The biobank represents a powerful resource for pre-clinical breast cancer pharmacogenomic studies (http://caldaslab.cruk.cam.ac.uk/bcape), including identification of biomarkers of response or resistance.

The life history of 21 breast cancers.

Cancer evolves dynamically as clonal expansions supersede one another driven by shifting selective pressures, mutational processes, and disrupted cancer genes. These processes mark the genome, such that a cancer's life history is encrypted in the somatic mutations present. We developed algorithms to decipher this narrative and applied them to 21 breast cancers. Mutational processes evolve across a cancer's lifespan, with many emerging late but contributing extensive genetic variation. Subclonal diversification is prominent, and most mutations are found in just a fraction of tumor cells. Every tumor has a dominant subclonal lineage, representing more than 50% of tumor cells. Minimal expansion of these subclones occurs until many hundreds to thousands of mutations have accumulated, implying the existence of long-lived, quiescent cell lineages capable of substantial proliferation upon acquisition of enabling genomic changes. Expansion of the dominant subclone to an appreciable mass may therefore represent the final rate-limiting step in a breast cancer's development, triggering diagnosis.

Mutational processes molding the genomes of 21 breast cancers.

All cancers carry somatic mutations. The patterns of mutation in cancer genomes reflect the DNA damage and repair processes to which cancer cells and their precursors have been exposed. To explore these mechanisms further, we generated catalogs of somatic mutation from 21 breast cancers and applied mathematical methods to extract mutational signatures of the underlying processes. Multiple distinct single- and double-nucleotide substitution signatures were discernible. Cancers with BRCA1 or BRCA2 mutations exhibited a characteristic combination of substitution mutation signatures and a distinctive profile of deletions. Complex relationships between somatic mutation prevalence and transcription were detected. A remarkable phenomenon of localized hypermutation, termed "kataegis," was observed. Regions of kataegis differed between cancers but usually colocalized with somatic rearrangements. Base substitutions in these regions were almost exclusively of cytosine at TpC dinucleotides. The mechanisms underlying most of these mutational signatures are unknown. However, a role for the APOBEC family of cytidine deaminases is proposed.

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.

ESQmodel: biologically informed evaluation of 2-D cell segmentation quality in multiplexed tissue images.

MOTIVATION: Single cell segmentation is critical in the processing of spatial omics data to accurately perform cell type identification and analyze spatial expression patterns. Segmentation methods often rely on semi-supervised annotation or labeled training data which are highly dependent on user expertise. To ensure the quality of segmentation, current evaluation strategies quantify accuracy by assessing cellular masks or through iterative inspection by pathologists. While these strategies each address either the statistical or biological aspects of segmentation, there lacks a unified approach to evaluating segmentation accuracy. RESULTS: In this article, we present ESQmodel, a Bayesian probabilistic method to evaluate single cell segmentation using expression data. By using the extracted cellular data from segmentation and a prior belief of cellular composition as input, ESQmodel computes per cell entropy to assess segmentation quality by how consistent cellular expression profiles match with cell type expectations. AVAILABILITY AND IMPLEMENTATION: Source code is available on Github at: https://github.com/Roth-Lab/ESQmodel.

Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups.

The rates and routes of lethal systemic spread in breast cancer are poorly understood owing to a lack of molecularly characterized patient cohorts with long-term, detailed follow-up data. Long-term follow-up is especially important for those with oestrogen-receptor (ER)-positive breast cancers, which can recur up to two decades after initial diagnosis1-6. It is therefore essential to identify patients who have a high risk of late relapse7-9. Here we present a statistical framework that models distinct disease stages (locoregional recurrence, distant recurrence, breast-cancer-related death and death from other causes) and competing risks of mortality from breast cancer, while yielding individual risk-of-recurrence predictions. We apply this model to 3,240 patients with breast cancer, including 1,980 for whom molecular data are available, and delineate spatiotemporal patterns of relapse across different categories of molecular information (namely immunohistochemical subtypes; PAM50 subtypes, which are based on gene-expression patterns10,11; and integrative or IntClust subtypes, which are based on patterns of genomic copy-number alterations and gene expression12,13). We identify four late-recurring integrative subtypes, comprising about one quarter (26%) of tumours that are both positive for ER and negative for human epidermal growth factor receptor 2, each with characteristic tumour-driving alterations in genomic copy number and a high risk of recurrence (mean 47-62%) up to 20 years after diagnosis. We also define a subgroup of triple-negative breast cancers in which cancer rarely recurs after five years, and a separate subgroup in which patients remain at risk. Use of the integrative subtypes improves the prediction of late, distant relapse beyond what is possible with clinical covariates (nodal status, tumour size, tumour grade and immunohistochemical subtype). These findings highlight opportunities for improved patient stratification and biomarker-driven clinical trials.

p53: a new kingpin in the stem cell arena.

Identifying new regulators of the stem cell state offers potential for future gains in biomedicine. Evidence that the tumor suppressor p53 is a key regulator of the stem cell state (Cicalese et al., 2009) suggests a broad role for this protein and its pathways in the control of normal tissue homeostasis and tumor formation.

Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling.

Single-cell RNA sequencing has enabled the decomposition of complex tissues into functionally distinct cell types. Often, investigators wish to assign cells to cell types through unsupervised clustering followed by manual annotation or via 'mapping' to existing data. However, manual interpretation scales poorly to large datasets, mapping approaches require purified or pre-annotated data and both are prone to batch effects. To overcome these issues, we present CellAssign, a probabilistic model that leverages prior knowledge of cell-type marker genes to annotate single-cell RNA sequencing data into predefined or de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while controlling for batch and sample effects. We demonstrate the advantages of CellAssign through extensive simulations and analysis of tumor microenvironment composition in high-grade serous ovarian cancer and follicular lymphoma.

The breast cancer oncogene EMSY represses transcription of antimetastatic microRNA miR-31.

Amplification of the EMSY gene in sporadic breast and ovarian cancers is a poor prognostic indicator. Although EMSY has been linked to transcriptional silencing, its mechanism of action is unknown. Here, we report that EMSY acts as an oncogene, causing the transformation of cells in vitro and potentiating tumor formation and metastatic features in vivo. We identify an inverse correlation between EMSY amplification and miR-31 expression, an antimetastatic microRNA, in the METABRIC cohort of human breast samples. Re-expression of miR-31 profoundly reduced cell migration, invasion, and colony-formation abilities of cells overexpressing EMSY or haboring EMSY amplification. We show that EMSY is recruited to the miR-31 promoter by the DNA binding factor ETS-1, and it represses miR-31 transcription by delivering the H3K4me3 demethylase JARID1b/PLU-1/KDM5B. Altogether, these results suggest a pathway underlying the role of EMSY in breast cancer and uncover potential diagnostic and therapeutic targets in sporadic breast cancer.

Gene regulatory network analysis defines transcriptome landscape with alternative splicing of human umbilical vein endothelial cells during replicative senescence.

BACKGROUND: Endothelial cell senescence is the state of permanent cell cycle arrest and plays a critical role in the pathogenesis of age-related diseases. However, a comprehensive understanding of the gene regulatory network, including genome-wide alternative splicing machinery, involved in endothelial cell senescence is lacking. RESULTS: We thoroughly described the transcriptome landscape of replicative senescent human umbilical vein endothelial cells. Genes with high connectivity showing a monotonic expression increase or decrease with the culture period were defined as hub genes in the co-expression network. Computational network analysis of these genes led to the identification of canonical and non-canonical senescence pathways, such as E2F and SIRT2 signaling, which were down-regulated in lipid metabolism, and chromosome organization processes pathways. Additionally, we showed that endothelial cell senescence involves alternative splicing. Importantly, the first and last exon types of splicing, as observed in FLT1 and ACACA, were preferentially altered among the alternatively spliced genes during endothelial senescence. We further identified novel microexons in PRUNE2 and PSAP, each containing 9 nt, which were altered within the specific domain during endothelial senescence. CONCLUSIONS: These findings unveil the comprehensive transcriptome pathway and novel signaling regulated by RNA processing, including gene expression and splicing, in replicative endothelial senescence.

Epiclomal: Probabilistic clustering of sparse single-cell DNA methylation data.

We present Epiclomal, a probabilistic clustering method arising from a hierarchical mixture model to simultaneously cluster sparse single-cell DNA methylation data and impute missing values. Using synthetic and published single-cell CpG datasets, we show that Epiclomal outperforms non-probabilistic methods and can handle the inherent missing data characteristic that dominates single-cell CpG genome sequences. Using newly generated single-cell 5mCpG sequencing data, we show that Epiclomal discovers sub-clonal methylation patterns in aneuploid tumour genomes, thus defining epiclones that can match or transcend copy number-determined clonal lineages and opening up an important form of clonal analysis in cancer. Epiclomal is written in R and Python and is available at https://github.com/shahcompbio/Epiclomal.

Barcoding reveals complex clonal dynamics of de novo transformed human mammary cells.

Most human breast cancers have diversified genomically and biologically by the time they become clinically evident. Early events involved in their genesis and the cellular context in which these events occur have thus been difficult to characterize. Here we present the first formal evidence of the shared and independent ability of basal cells and luminal progenitors, isolated from normal human mammary tissue and transduced with a single oncogene (KRAS(G12D)), to produce serially transplantable, polyclonal, invasive ductal carcinomas within 8 weeks of being introduced either subrenally or subcutaneously into immunodeficient mice. DNA barcoding of the initial cells revealed a dramatic change in the numbers and sizes of clones generated from them within 2 weeks, and the first appearance of many 'new' clones in tumours passaged into secondary recipients. Both primary and secondary tumours were phenotypically heterogeneous and primary tumours were categorized transcriptionally as 'normal-like'. This system challenges previous concepts that carcinogenesis in normal human epithelia is necessarily a slow process requiring the acquisition of multiple driver mutations. It also presents the first description of initial events that accompany the genesis and evolution of malignant human mammary cell populations, thereby contributing new understanding of the rapidity with which heterogeneity in their properties can develop.

Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution.

Human cancers, including breast cancers, comprise clones differing in mutation content. Clones evolve dynamically in space and time following principles of Darwinian evolution, underpinning important emergent features such as drug resistance and metastasis. Human breast cancer xenoengraftment is used as a means of capturing and studying tumour biology, and breast tumour xenografts are generally assumed to be reasonable models of the originating tumours. However, the consequences and reproducibility of engraftment and propagation on the genomic clonal architecture of tumours have not been systematically examined at single-cell resolution. Here we show, using deep-genome and single-cell sequencing methods, the clonal dynamics of initial engraftment and subsequent serial propagation of primary and metastatic human breast cancers in immunodeficient mice. In all 15 cases examined, clonal selection on engraftment was observed in both primary and metastatic breast tumours, varying in degree from extreme selective engraftment of minor (<5% of starting population) clones to moderate, polyclonal engraftment. Furthermore, ongoing clonal dynamics during serial passaging is a feature of tumours experiencing modest initial selection. Through single-cell sequencing, we show that major mutation clusters estimated from tumour population sequencing relate predictably to the most abundant clonal genotypes, even in clonally complex and rapidly evolving cases. Finally, we show that similar clonal expansion patterns can emerge in independent grafts of the same starting tumour population, indicating that genomic aberrations can be reproducible determinants of evolutionary trajectories. Our results show that measurement of genomically defined clonal population dynamics will be highly informative for functional studies using patient-derived breast cancer xenoengraftment.

Higher serum PD-L1 level predicts increased overall survival with lapatinib versus trastuzumab in the CCTG MA.31 phase 3 trial.

BACKGROUND: The purpose of this retrospective biomarker study of the Canadian Cancer Trials Group (CCTG) MA.31 randomized phase 3 trial (lapatinib vs trastuzumab) of HER2-positive metastatic breast cancer (MBC) was to evaluate the prognostic and predictive biomarker utility of pretreatment serum programmed death ligand 1 (PD-L1) levels. METHODS: CCTG MA.31 accrued 652 HER2-positive patients; 387 had serum available (185 in the trastuzumab arm and 202 in the lapatinib arm). The Ella immunoassay platform (ProteinSimple, San Jose, California) was used to quantitate serum PD-L1 levels. Stepwise forward Cox multivariable analyses were performed for progression-free survival and overall survival (OS). RESULTS: In the whole trial population, continuous pretreatment serum PD-L1 levels were not associated with OS. However, within the trastuzumab arm, a higher continuous pretreatment serum PD-L1 level was significant for shorter OS (hazard ratio [HR], 3.85; P = .04), but within the lapatinib arm, pretreatment serum PD-L1 was not associated with OS (P = .37). In the whole trial, in a multivariable analysis for OS, serum PD-L1 (median cut point) remained a significant independent covariate (HR, 2.38; P = .001). There was a significant interaction between treatment arm and continuous serum PD-L1 (bootstrap method; P = .0025): at or above 214.2 pg/mL (the 89th percentile), serum PD-L1 was associated with significantly shorter OS with trastuzumab treatment versus lapatinib treatment. CONCLUSIONS: In the CCTG MA.31 trial, serum PD-L1 was a significant predictive factor: a higher pretreatment serum PD-L1 level was associated with shorter OS with trastuzumab treatment but with longer OS with lapatinib treatment. Immune evasion may decrease the effectiveness of trastuzumab therapy. Further evaluation of elevated serum PD-L1 in advanced breast cancer is warranted to identify patients with HER2-positive MBC who may benefit from novel immune-targeted therapies in addition to trastuzumab.

Identification of a selective DDX3X inhibitor with newly developed quantitative high-throughput RNA helicase assays.

The DEAD-box family of RNA helicases plays essential roles in both transcriptional and translational mRNA degradation; they unwind short double-stranded RNA by breaking the RNA-RNA interactions. Two DEAD-box RNA helicases, eukaryotic translation initiation factor 4A3 (eIF4A3) and DEAD-box helicase 3 (DDX3X), show high homology in the ATP-binding region and are considered key molecules for cancer progression. Several small molecules that target eIF4A3 and DDX3X have been reported to inhibit cancer cell growth; however, more potent compounds are required for cancer therapeutics, and there is a critical need for high-throughput assays to screen for RNA helicase inhibitors. In this study, we developed novel fluorescence resonance energy transfer-based high-throughput RNA helicase assays for eIF4A3 and DDX3X. Using these assays, we identified several eIF4A3 allosteric inhibitors whose inhibitory effect on eIF4A3 ATPase showed a strong correlation with inhibitory effect on helicase activity. From 102 compounds that exhibited eIF4A3 ATPase inhibition, we identified a selective DDX3X inhibitor, C1, which showed stronger inhibition of DDX3X than of eIF4A3. Small-molecule helicase inhibitors can be valuable for clarifying the molecular machinery of DEAD-box RNA helicases. The high-throughput quantitative assays established here should facilitate the evaluation of the helicase inhibitory activity of compounds.

Scalable whole-genome single-cell library preparation without preamplification.

Single-cell genomics is critical for understanding cellular heterogeneity in cancer, but existing library preparation methods are expensive, require sample preamplification and introduce coverage bias. Here we describe direct library preparation (DLP), a robust, scalable, and high-fidelity method that uses nanoliter-volume transposition reactions for single-cell whole-genome library preparation without preamplification. We examined 782 cells from cell lines and triple-negative breast xenograft tumors. Low-depth sequencing, compared with existing methods, revealed greater coverage uniformity and more reliable detection of copy-number alterations. Using phylogenetic analysis, we found minor xenograft subpopulations that were undetectable by bulk sequencing, as well as dynamic clonal expansion and diversification between passages. Merging single-cell genomes in silico, we generated 'bulk-equivalent' genomes with high depth and uniform coverage. Thus, low-depth sequencing of DLP libraries may provide an attractive replacement for conventional bulk sequencing methods, permitting analysis of copy number at the cell level and of other genomic variants at the population level.

Clonal genotype and population structure inference from single-cell tumor sequencing.

Single-cell DNA sequencing has great potential to reveal the clonal genotypes and population structure of human cancers. However, single-cell data suffer from missing values and biased allelic counts as well as false genotype measurements owing to the sequencing of multiple cells. We describe the Single Cell Genotyper (https://bitbucket.org/aroth85/scg), an open-source software based on a statistical model coupled with a mean-field variational inference method, which can be used to address these problems and robustly infer clonal genotypes.

Signatures of mutational processes in human cancer.

All cancers are caused by somatic mutations; however, understanding of the biological processes generating these mutations is limited. The catalogue of somatic mutations from a cancer genome bears the signatures of the mutational processes that have been operative. Here we analysed 4,938,362 mutations from 7,042 cancers and extracted more than 20 distinct mutational signatures. Some are present in many cancer types, notably a signature attributed to the APOBEC family of cytidine deaminases, whereas others are confined to a single cancer class. Certain signatures are associated with age of the patient at cancer diagnosis, known mutagenic exposures or defects in DNA maintenance, but many are of cryptic origin. In addition to these genome-wide mutational signatures, hypermutation localized to small genomic regions, 'kataegis', is found in many cancer types. The results reveal the diversity of mutational processes underlying the development of cancer, with potential implications for understanding of cancer aetiology, prevention and therapy.