Archival single-cell genomics reveals persistent subclones during DCIS progression.

Ductal carcinoma in situ (DCIS) is a common precursor of invasive breast cancer. Our understanding of its genomic progression to recurrent disease remains poor, partly due to challenges associated with the genomic profiling of formalin-fixed paraffin-embedded (FFPE) materials. Here, we developed Arc-well, a high-throughput single-cell DNA-sequencing method that is compatible with FFPE materials. We validated our method by profiling 40,330 single cells from cell lines, a frozen tissue, and 27 FFPE samples from breast, lung, and prostate tumors stored for 3-31 years. Analysis of 10 patients with matched DCIS and cancers that recurred 2-16 years later show that many primary DCIS had already undergone whole-genome doubling and clonal diversification and that they shared genomic lineages with persistent subclones in the recurrences. Evolutionary analysis suggests that most DCIS cases in our cohort underwent an evolutionary bottleneck, and further identified chromosome aberrations in the persistent subclones that were associated with recurrence.

Multiclonal Invasion in Breast Tumors Identified by Topographic Single Cell Sequencing.

Ductal carcinoma in situ (DCIS) is an early-stage breast cancer that infrequently progresses to invasive ductal carcinoma (IDC). Genomic evolution has been difficult to delineate during invasion due to intratumor heterogeneity and the low number of tumor cells in the ducts. To overcome these challenges, we developed Topographic Single Cell Sequencing (TSCS) to measure genomic copy number profiles of single tumor cells while preserving their spatial context in tissue sections. We applied TSCS to 1,293 single cells from 10 synchronous patients with both DCIS and IDC regions in addition to exome sequencing. Our data reveal a direct genomic lineage between in situ and invasive tumor subpopulations and further show that most mutations and copy number aberrations evolved within the ducts prior to invasion. These results support a multiclonal invasion model, in which one or more clones escape the ducts and migrate into the adjacent tissues to establish the invasive carcinomas.

Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing.

Triple-negative breast cancer (TNBC) is an aggressive subtype that frequently develops resistance to chemotherapy. An unresolved question is whether resistance is caused by the selection of rare pre-existing clones or alternatively through the acquisition of new genomic aberrations. To investigate this question, we applied single-cell DNA and RNA sequencing in addition to bulk exome sequencing to profile longitudinal samples from 20 TNBC patients during neoadjuvant chemotherapy (NAC). Deep-exome sequencing identified 10 patients in which NAC led to clonal extinction and 10 patients in which clones persisted after treatment. In 8 patients, we performed a more detailed study using single-cell DNA sequencing to analyze 900 cells and single-cell RNA sequencing to analyze 6,862 cells. Our data showed that resistant genotypes were pre-existing and adaptively selected by NAC, while transcriptional profiles were acquired by reprogramming in response to chemotherapy in TNBC patients.

A spatially resolved single-cell genomic atlas of the adult human breast.

The adult human breast is comprised of an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue1-3. Although most previous studies have focused on the breast epithelial system4-6, many of the non-epithelial cell types remain understudied. Here we constructed the comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution. Our single-cell transcriptomics study profiled 714,331 cells from 126 women, and 117,346 nuclei from 20 women, identifying 12 major cell types and 58 biological cell states. These data reveal abundant perivascular, endothelial and immune cell populations, and highly diverse luminal epithelial cell states. Spatial mapping using four different technologies revealed an unexpectedly rich ecosystem of tissue-resident immune cells, as well as distinct molecular differences between ductal and lobular regions. Collectively, these data provide a reference of the adult normal breast tissue for studying mammary biology and diseases such as breast cancer.

Simple oligonucleotide-based multiplexing of single-cell chromatin accessibility.

Microdroplet single-cell ATAC-seq is widely used to measure chromatin accessibility, however, highly scalable and simple sample multiplexing procedures are not available. Here, we present a transposome-assisted single nucleus barcoding approach for ATAC-seq (SNuBar-ATAC) that utilizes a single oligonucleotide adaptor for multiplexing samples during the existing tagmentation step and does not require a pre-labeling procedure. The accuracy and scalability of SNuBar-ATAC was evaluated using cell line mixture experiments. We applied SNuBar-ATAC to investigate treatment-induced chromatin accessibility dynamics by multiplexing 28 mice with lung tumors that received different combinations of chemo, radiation, and targeted immunotherapy. We also applied SNuBar-ATAC to study spatial epigenetic heterogeneity by multiplexing 32 regions from a human breast tissue. Additionally, we show that SNuBar can multiplex single cell ATAC and RNA multiomic assays in cell lines and human breast tissue samples. Our data show that SNuBar is a highly accurate, easy-to-use, and scalable system for multiplexing scATAC-seq and scATAC and RNA co-assay experiments.

Breast tumours maintain a reservoir of subclonal diversity during expansion.

Our knowledge of copy number evolution during the expansion of primary breast tumours is limited1,2. Here, to investigate this process, we developed a single-cell, single-molecule DNA-sequencing method and performed copy number analysis of 16,178 single cells from 8 human triple-negative breast cancers and 4 cell lines. The results show that breast tumours and cell lines comprise a large milieu of subclones (7-22) that are organized into a few (3-5) major superclones. Evolutionary analysis suggests that after clonal TP53 mutations, multiple loss-of-heterozygosity events and genome doubling, there was a period of transient genomic instability followed by ongoing copy number evolution during the primary tumour expansion. By subcloning single daughter cells in culture, we show that tumour cells rediversify their genomes and do not retain isogenic properties. These data show that triple-negative breast cancers continue to evolve chromosome aberrations and maintain a reservoir of subclonal diversity during primary tumour growth.

Dissociation protocols used for sarcoma tissues bias the transcriptome observed in single-cell and single-nucleus RNA sequencing.

BACKGROUND: Single-cell RNA-seq has emerged as an innovative technology used to study complex tissues and characterize cell types, states, and lineages at a single-cell level. Classification of bulk tumors by their individual cellular constituents has also created new opportunities to generate single-cell atlases for many organs, cancers, and developmental models. Despite the tremendous promise of this technology, recent evidence studying epithelial tissues and diverse carcinomas suggests the methods used for tissue processing, cell disaggregation, and preservation can significantly bias gene expression and alter the observed cell types. To determine whether sarcomas - tumors of mesenchymal origin - are subject to the same technical artifacts, we profiled patient-derived tumor explants (PDXs) propagated from three aggressive subtypes: osteosarcoma (OS), Ewing sarcoma (ES), desmoplastic small round cell tumor (DSRCT). Given the rarity of these sarcoma subtypes, we explored whether single-nuclei RNA-seq from more widely available archival frozen specimens could accurately be identified by gene expression signatures linked to tissue phenotype or pathognomonic fusion proteins. RESULTS: We systematically assessed dissociation methods across different sarcoma subtypes. We compared gene expression from single-cell and single-nucleus RNA-sequencing of 125,831 whole-cells and nuclei from ES, DSRCT, and OS PDXs. We detected warm dissociation artifacts in single-cell samples and gene length bias in single-nucleus samples. Classic sarcoma gene signatures were observed regardless of the dissociation method. In addition, we showed that dissociation method biases could be computationally corrected. CONCLUSIONS: We highlighted transcriptional biases, including warm dissociation and gene-length biases, introduced by the dissociation method for various sarcoma subtypes. This work is the first to characterize how the dissociation methods used for sc/snRNA-seq may affect the interpretation of the molecular features in sarcoma PDXs.

Advances and applications of single-cell sequencing technologies.

Single-cell sequencing (SCS) has emerged as a powerful new set of technologies for studying rare cells and delineating complex populations. Over the past 5 years, SCS methods for DNA and RNA have had a broad impact on many diverse fields of biology, including microbiology, neurobiology, development, tissue mosaicism, immunology, and cancer research. In this review, we will discuss SCS technologies and applications, as well as translational applications in the clinic.

SiCloneFit: Bayesian inference of population structure, genotype, and phylogeny of tumor clones from single-cell genome sequencing data.

Accumulation and selection of somatic mutations in a Darwinian framework result in intra-tumor heterogeneity (ITH) that poses significant challenges to the diagnosis and clinical therapy of cancer. Identification of the tumor cell populations (clones) and reconstruction of their evolutionary relationship can elucidate this heterogeneity. Recently developed single-cell DNA sequencing (SCS) technologies promise to resolve ITH to a single-cell level. However, technical errors in SCS data sets, including false-positives (FP) and false-negatives (FN) due to allelic dropout, and cell doublets, significantly complicate these tasks. Here, we propose a nonparametric Bayesian method that reconstructs the clonal populations as clusters of single cells, genotypes of each clone, and the evolutionary relationship between the clones. It employs a tree-structured Chinese restaurant process as the prior on the number and composition of clonal populations. The evolution of the clonal populations is modeled by a clonal phylogeny and a finite-site model of evolution to account for potential mutation recurrence and losses. We probabilistically account for FP and FN errors, and cell doublets are modeled by employing a Beta-binomial distribution. We develop a Gibbs sampling algorithm comprising partial reversible-jump and partial Metropolis-Hastings updates to explore the joint posterior space of all parameters. The performance of our method on synthetic and experimental data sets suggests that joint reconstruction of tumor clones and clonal phylogeny under a finite-site model of evolution leads to more accurate inferences. Our method is the first to enable this joint reconstruction in a fully Bayesian framework, thus providing measures of support of the inferences it makes.

Assessing the performance of methods for copy number aberration detection from single-cell DNA sequencing data.

Single-cell DNA sequencing technologies are enabling the study of mutations and their evolutionary trajectories in cancer. Somatic copy number aberrations (CNAs) have been implicated in the development and progression of various types of cancer. A wide array of methods for CNA detection has been either developed specifically for or adapted to single-cell DNA sequencing data. Understanding the strengths and limitations that are unique to each of these methods is very important for obtaining accurate copy number profiles from single-cell DNA sequencing data. We benchmarked three widely used methods-Ginkgo, HMMcopy, and CopyNumber-on simulated as well as real datasets. To facilitate this, we developed a novel simulator of single-cell genome evolution in the presence of CNAs. Furthermore, to assess performance on empirical data where the ground truth is unknown, we introduce a phylogeny-based measure for identifying potentially erroneous inferences. While single-cell DNA sequencing is very promising for elucidating and understanding CNAs, our findings show that even the best existing method does not exceed 80% accuracy. New methods that significantly improve upon the accuracy of these three methods are needed. Furthermore, with the large datasets being generated, the methods must be computationally efficient.

Single-cell DNA sequencing reveals a late-dissemination model in metastatic colorectal cancer.

Metastasis is a complex biological process that has been difficult to delineate in human colorectal cancer (CRC) patients. A major obstacle in understanding metastatic lineages is the extensive intra-tumor heterogeneity at the primary and metastatic tumor sites. To address this problem, we developed a highly multiplexed single-cell DNA sequencing approach to trace the metastatic lineages of two CRC patients with matched liver metastases. Single-cell copy number or mutational profiling was performed, in addition to bulk exome and targeted deep-sequencing. In the first patient, we observed monoclonal seeding, in which a single clone evolved a large number of mutations prior to migrating to the liver to establish the metastatic tumor. In the second patient, we observed polyclonal seeding, in which two independent clones seeded the metastatic liver tumor after having diverged at different time points from the primary tumor lineage. The single-cell data also revealed an unexpected independent tumor lineage that did not metastasize, and early progenitor clones with the "first hit" mutation in APC that subsequently gave rise to both the primary and metastatic tumors. Collectively, these data reveal a late-dissemination model of metastasis in two CRC patients and provide an unprecedented view of metastasis at single-cell genomic resolution.

SCMarker: Ab initio marker selection for single cell transcriptome profiling.

Single-cell RNA-sequencing data generated by a variety of technologies, such as Drop-seq and SMART-seq, can reveal simultaneously the mRNA transcript levels of thousands of genes in thousands of cells. It is often important to identify informative genes or cell-type-discriminative markers to reduce dimensionality and achieve informative cell typing results. We present an ab initio method that performs unsupervised marker selection by identifying genes that have subpopulation-discriminative expression levels and are co- or mutually-exclusively expressed with other genes. Consistent improvements in cell-type classification and biologically meaningful marker selection are achieved by applying SCMarker on various datasets in multiple tissue types, followed by a variety of clustering algorithms. The source code of SCMarker is publicly available at https://github.com/KChen-lab/SCMarker.

Tumor evolution: Linear, branching, neutral or punctuated?

Intratumor heterogeneity has been widely reported in human cancers, but our knowledge of how this genetic diversity emerges over time remains limited. A central challenge in studying tumor evolution is the difficulty in collecting longitudinal samples from cancer patients. Consequently, most studies have inferred tumor evolution from single time-point samples, providing very indirect information. These data have led to several competing models of tumor evolution: linear, branching, neutral and punctuated. Each model makes different assumptions regarding the timing of mutations and selection of clones, and therefore has different implications for the diagnosis and therapeutic treatment of cancer patients. Furthermore, emerging evidence suggests that models may change during tumor progression or operate concurrently for different classes of mutations. Finally, we discuss data that supports the theory that most human tumors evolve from a single cell in the normal tissue. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

Clonal evolution in breast cancer revealed by single nucleus genome sequencing.

Sequencing studies of breast tumour cohorts have identified many prevalent mutations, but provide limited insight into the genomic diversity within tumours. Here we developed a whole-genome and exome single cell sequencing approach called nuc-seq that uses G2/M nuclei to achieve 91% mean coverage breadth. We applied this method to sequence single normal and tumour nuclei from an oestrogen-receptor-positive (ER(+)) breast cancer and a triple-negative ductal carcinoma. In parallel, we performed single nuclei copy number profiling. Our data show that aneuploid rearrangements occurred early in tumour evolution and remained highly stable as the tumour masses clonally expanded. In contrast, point mutations evolved gradually, generating extensive clonal diversity. Using targeted single-molecule sequencing, many of the diverse mutations were shown to occur at low frequencies (<10%) in the tumour mass. Using mathematical modelling we found that the triple-negative tumour cells had an increased mutation rate (13.3×), whereas the ER(+) tumour cells did not. These findings have important implications for the diagnosis, therapeutic treatment and evolution of chemoresistance in breast cancer.

Cabazitaxel plus carboplatin for the treatment of men with metastatic castration-resistant prostate cancers: a randomised, open-label, phase 1-2 trial.

BACKGROUND: Taxane-platinum combinations have shown promising activity in metastatic castration-resistant prostate cancers in single-group clinical studies but not in randomised trials. Distinct biological subsets of the disease might derive the greatest benefit from the addition of platinum. We aimed to determine whether adding carboplatin to cabazitaxel would improve the outcomes of men with metastatic castration-resistant prostate cancer. METHODS: We did a phase 1-2, open label, randomised study at two centres in men with progressive metastatic castration-resistant prostate cancer. In phase 1, patients received intravenous cabazitaxel 20-25 mg/m2 and intravenous carboplatin area under the curve (AUC) 3-4 mg/mL per min every 21 days. The maximum tolerated dose was defined as the highest dose cohort studied in which one of six or fewer patients experienced a dose-limiting toxicity. In phase 2, patients were randomly assigned (1:1) centrally by a computerised algorithm to intravenous cabazitaxel 25 mg/m2 with or without intravenous carboplatin AUC 4 mg/mL per min. All patients received growth factor support and oral prednisone 10 mg daily. The primary endpoints were the maximum tolerated dose of the combination in phase 1 and investigator-assessed progression-free survival in phase 2. This trial is registered at ClinicalTrials.gov, number NCT01505868. FINDINGS: Between Aug 17, 2012, and May 11, 2015, nine patients completed phase 1 as planned, and 160 were randomly assigned to cabazitaxel (n=79) or cabazitaxel plus carboplatin (n=81) in phase 2. During phase I, grade 3 adverse events were anaemia (n=2), fatigue (n=1), thrombocytopenia (n=1), hypomagnesaemia (n=1), diarrhoea (n=1), hypokalaemia (n=1), anorexia (n=1), and dehydration (n=1), and no grade 4 adverse events occurred. No dose-limiting toxicities were observed, therefore, a maximum tolerated dose of cabazitaxel of 25 mg/m2 and carboplatin of AUC 4 mg/mL per min was selected for phase 2. At a median follow-up of 31·0 months (IQR 20·5-37·1), the combination improved the median progression-free survival from 4·5 months (95% CI 3·5-5·7) to 7·3 months (95% CI 5·5-8·2; hazard ratio 0·69, 95% CI 0·50-0·95, p=0·018). In the phase 2 study, the most common grade 3-5 adverse events were fatigue (7 [9%] of 79 in the cabazitaxel group vs 16 [20%] of 81 in the combination group), anaemia (3 [4%] vs 19 [23%]), neutropenia (3 [4%] vs 13 [16%]), and thrombocytopenia (1 [1%] vs 11 [14%]). There were no treatment-related deaths. INTERPRETATION: Carboplatin added to cabazitaxel showed improved clinical efficacy compared with cabazitaxel alone for men with metastatic castration-resistant prostate cancer. Although adverse events were more common with the combination, the treatment was safe and generally well tolerated. Our data suggest that taxane-platinum combinations have a clinically beneficial role in advanced prostate cancer and a randomised phase 3 study is planned. FUNDING: Sanofi Genzyme, University of Texas MD Anderson Cancer Center Prostate Cancer Moon Shot Program, and Solon Scott III Prostate Cancer Research Fund.

SCOPIT: sample size calculations for single-cell sequencing experiments.

BACKGROUND: In single cell DNA and RNA sequencing experiments, the number of cells to sequence must be decided before running an experiment, and afterwards, it is necessary to decide whether sufficient cells were sampled. These questions can be addressed by calculating the probability of sampling at least a defined number of cells from each subpopulation (cell type or cancer clone). RESULTS: We developed an interactive web application called SCOPIT (Single-Cell One-sided Probability Interactive Tool), which calculates the required probabilities using a multinomial distribution (www.navinlab.com/SCOPIT). In addition, we created an R package called pmultinom for scripting these calculations. CONCLUSIONS: Our tool for fast multinomial calculations provide a simple and intuitive procedure for prospectively planning single-cell experiments or retrospectively evaluating if sufficient numbers of cells have been sequenced. The web application can be accessed at navinlab.com/SCOPIT.

Monovar: single-nucleotide variant detection in single cells.

Current variant callers are not suitable for single-cell DNA sequencing, as they do not account for allelic dropout, false-positive errors and coverage nonuniformity. We developed Monovar (https://bitbucket.org/hamimzafar/monovar), a statistical method for detecting and genotyping single-nucleotide variants in single-cell data. Monovar exhibited superior performance over standard algorithms on benchmarks and in identifying driver mutations and delineating clonal substructure in three different human tumor data sets.

The first five years of single-cell cancer genomics and beyond.

Single-cell sequencing (SCS) is a powerful new tool for investigating evolution and diversity in cancer and understanding the role of rare cells in tumor progression. These methods have begun to unravel key questions in cancer biology that have been difficult to address with bulk tumor measurements. Over the past five years, there has been extraordinary progress in technological developments and research applications, but these efforts represent only the tip of the iceberg. In the coming years, SCS will greatly improve our understanding of invasion, metastasis, and therapy resistance during cancer progression. These tools will also have direct translational applications in the clinic, in areas such as early detection, noninvasive monitoring, and guiding targeted therapy. In this perspective, I discuss the progress that has been made and the myriad of unexplored applications that still lie ahead in cancer research and medicine.

Genome evolution in ductal carcinoma in situ: invasion of the clones.

Ductal carcinoma in situ (DCIS) is the most frequently diagnosed early-stage breast cancer. Only a subset of patients progress to invasive ductal carcinoma (IDC), and this presents a formidable clinical challenge for determining which patients to treat aggressively and which patients to monitor without therapeutic intervention. Understanding the molecular and genomic basis of invasion has been difficult to study in DCIS cancers due to several technical obstacles, including low tumour cellularity, lack of fresh-frozen tissues, and intratumour heterogeneity. In this review, we discuss the role of intratumour heterogeneity in the progression of DCIS to IDC in the context of three evolutionary models: independent lineages, evolutionary bottlenecks, and multiclonal invasion. We examine the evidence in support of these models and their relevance to the diagnosis and treatment of patients with DCIS. We also discuss how emerging technologies, such as single-cell sequencing, STAR-FISH, and imaging mass spectrometry, are likely to provide new insights into the evolution of this enigmatic disease. Copyright © 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Tumour evolution inferred by single-cell sequencing.

Genomic analysis provides insights into the role of copy number variation in disease, but most methods are not designed to resolve mixed populations of cells. In tumours, where genetic heterogeneity is common, very important information may be lost that would be useful for reconstructing evolutionary history. Here we show that with flow-sorted nuclei, whole genome amplification and next generation sequencing we can accurately quantify genomic copy number within an individual nucleus. We apply single-nucleus sequencing to investigate tumour population structure and evolution in two human breast cancer cases. Analysis of 100 single cells from a polygenomic tumour revealed three distinct clonal subpopulations that probably represent sequential clonal expansions. Additional analysis of 100 single cells from a monogenomic primary tumour and its liver metastasis indicated that a single clonal expansion formed the primary tumour and seeded the metastasis. In both primary tumours, we also identified an unexpectedly abundant subpopulation of genetically diverse 'pseudodiploid' cells that do not travel to the metastatic site. In contrast to gradual models of tumour progression, our data indicate that tumours grow by punctuated clonal expansions with few persistent intermediates.