Asya Stepansky

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.

Genome-wide copy number analysis of single cells

Copy number variation (CNV) is increasingly recognized as an important contributor to phenotypic variation in health and disease. Most methods for determining CNV rely on admixtures of cells in which information regarding genetic heterogeneity is lost. Here we present a protocol that allows for the genome-wide copy number analysis of single nuclei isolated from mixed populations of cells. Single-nucleus sequencing (SNS), combines flow sorting of single nuclei on the basis of DNA content and whole-genome amplification (WGA); this is followed by next-generation sequencing to quantize genomic intervals in a genome-wide manner. Multiplexing of single cells is discussed. In addition, we outline informatic approaches that correct for biases inherent in the WGA procedure and allow for accurate determination of copy number profiles. All together, the protocol takes ∼3 d from flow cytometry to sequence-ready DNA libraries.

A RSC/nucleosome complex determines chromatin architecture and facilitates activator binding.

How is chromatin architecture established and what role does it play in transcription? We show that the yeast regulatory locus UASg bears, in addition to binding sites for the activator Gal4, sites bound by the RSC complex. RSC positions a nucleosome, evidently partially unwound, in a structure that facilitates Gal4 binding to its sites. The complex comprises a barrier that imposes characteristic features of chromatin architecture. In the absence of RSC, ordinary nucleosomes encroach over the UASg and compete with Gal4 for binding. Taken with our previous work, the results show that both prior to and following induction, specific DNA-binding proteins are the predominant determinants of chromatin architecture at the GAL1/10 genes. RSC/nucleosome complexes are also found scattered around the yeast genome. Higher eukaryotic RSC lacks the specific DNA-binding determinants found on yeast RSC, and evidently Gal4 works in those organisms despite whatever obstacle broadly positioned nucleosomes present.

Rapid Phenotypic and Genomic Change in Response to Therapeutic Pressure in Prostate Cancer Inferred by High Content Analysis of Single Circulating Tumor Cells

Timely characterization of a cancers evolution is required to predict treatment efficacy and to detect resistance early. High content analysis of single Circulating Tumor Cells (CTCs) enables sequential characterization of genotypic, morphometric and protein expression alterations in real time over the course of cancer treatment. This concept was investigated in a patient with castrate-resistant prostate cancer progressing through both chemotherapy and targeted therapy. In this case study, we integrate across four timepoints 41 genome-wide copy number variation (CNV) profiles plus morphometric parameters and androgen receptor (AR) protein levels. Remarkably, little change was observed in response to standard chemotherapy, evidenced by the fact that a unique clone (A), exhibiting highly rearranged CNV profiles and AR+ phenotype was found circulating before and after treatment. However, clinical response and subsequent progression after targeted therapy was associated with the drastic depletion of clone A, followed by the sequential emergence of two distinct CTC sub-populations that differed in both AR genotype and expression phenotype. While AR- cells with flat or pseudo-diploid CNV profiles (clone B) were identified at the time of response, a new tumor lineage of AR+ cells (clone C) with CNV altered profiles was detected during relapse. We showed that clone C, despite phylogenetically related to clone A, possessed a unique set of somatic CNV alterations, including MYC amplification, an event linked to hormone escape. Interesting, we showed that both clones acquired AR gene amplification by deploying different evolutionary paths. Overall, these data demonstrate the timeframe of tumor evolution in response to therapy and provide a framework for the multi-scale analysis of fluid biopsies to quantify and monitor disease evolution in individual patients.

Whole-genome single-cell copy number profiling from formalin-fixed paraffin-embedded samples

A substantial proportion of tumors consist of genotypically distinct subpopulations of cancer cells. This intratumor genetic heterogeneity poses a substantial challenge for the implementation of precision medicine. Single-cell genomics constitutes a powerful approach to resolve complex mixtures of cancer cells by tracing cell lineages and discovering cryptic genetic variations that would otherwise be obscured in tumor bulk analyses. Because of the chemical alterations that result from formalin fixation, single-cell genomic approaches have largely remained limited to fresh or rapidly frozen specimens. Here we describe the development and validation of a robust and accurate methodology to perform whole-genome copy-number profiling of single nuclei obtained from formalin-fixed paraffin-embedded clinical tumor samples. We applied the single-cell sequencing approach described here to study the progression from in situ to invasive breast cancer, which revealed that ductal carcinomas in situ show intratumor genetic heterogeneity at diagnosis and that these lesions may progress to invasive breast cancer through a variety of evolutionary processes.

Single-Cell Copy Number Analysis of Prostate Cancer Cells Captured with Geometrically Enhanced Differential Immunocapture Microdevices

Limited access to tumor tissue makes repeated sampling and real-time tracking of cancer progression infeasible. Circulating tumor cells (CTCs) provide the capacity for real-time genetic characterization of a disseminating tumor cell population via a simple blood draw. However, there is no straightforward method to analyze broadscale genetic rearrangements in this heterogeneous cell population at the single cell level. We present a one-step controllable chemical extraction of whole nuclei from prostate cancer cells captured using geometrically enhanced differential immunocapture (GEDI) microdevices. We have successfully used copy number profile analysis to differentiate between two unique cancer cell line populations of metastatic origin (LNCaP and VCaP) and to analyze key mutations important in disease progression.

Corrigendum: Genome-wide copy number analysis of single cells.

Nat. Protoc. 7, 1024–1041 (2012); published online 3 May 2012; corrected after print 24 February 2016 In the version of this article initially published, the units for the concentration of NaCl in the NST buffer described in the Reagent Setup section were incorrect. The correct unit should be mM. The error has been corrected in the HTML and PDF versions of the article.

Utility of Single-Cell Genomics in Diagnostic Evaluation of Prostate Cancer

A distinction between indolent and aggressive disease is a major challenge in diagnostics of prostate cancer. As genetic heterogeneity and complexity may influence clinical outcome, we have initiated studies on single tumor cell genomics. In this study, we demonstrate that sparse DNA sequencing of single-cell nuclei from prostate core biopsies is a rich source of quantitative parameters for evaluating neoplastic growth and aggressiveness. These include the presence of clonal populations, the phylogenetic structure of those populations, the degree of the complexity of copy-number changes in those populations, and measures of the proportion of cells with clonal copy-number signatures. The parameters all showed good correlation to the measure of prostatic malignancy, the Gleason score, derived from individual prostate biopsy tissue cores. Remarkably, a more accurate histopathologic measure of malignancy, the surgical Gleason score, agrees better with these genomic parameters of diagnostic biopsy than it does with the diagnostic Gleason score and related measures of diagnostic histopathology. This is highly relevant because primary treatment decisions are dependent upon the biopsy and not the surgical specimen. Thus, single-cell analysis has the potential to augment traditional core histopathology, improving both the objectivity and accuracy of risk assessment and inform treatment decisions.Significance: Genomic analysis of multiple individual cells harvested from prostate biopsies provides an indepth view of cell populations comprising a prostate neoplasm, yielding novel genomic measures with the potential to improve the accuracy of diagnosis and prognosis in prostate cancer. Cancer Res; 78(2); 348-58. ©2017 AACR.

Abstract 4599: Sequential monitoring of single-cell copy number variation in metastatic prostate cancer.

Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DC The high-definition circulating tumor cell (HD-CTC) assay provides for an enrichment-free approach to CTC identification. Here, we utilized the HD-CTC approach to study androgen receptor (AR) expression combined with single-nucleus sequencing for genome-wide analysis of copy number variation (CNV) in sequential CTCs samples obtained from a patient with metastatic prostate cancer treated with abiraterone acetate (an androgen synthesis inhibitor). At baseline, before initiation of abiraterone treatment, we observed a balanced proportion of AR-negative and AR-positive CTCs. During a brief period of clinical response (marked with a decreased serum PSA and decreased pain) the proportion of AR-positive CTCs declined followed by a rapid increase associated with clinical progression (increased PSA and pain). CNV analysis of single CTCs revealed multiple genomic rearrangements, such as AR amplification along with the chromosomal gains and losses typical of prostate cancer, in multiple cells at baseline. During treatment response, the frequency of CNV alterations significantly declined, followed by a reemergence to a pattern of multiple, complex alterations associated with clinical progression. Detailed analysis of the CNV profiles revealed that many abnormalities were commonly shared between the CTC populations, but a number were unique to the AR+ resistant/hormone refractory CTC population including increased MYC amplification alteration and the AR amplicon that include additional adjacent genes. Remarkably, the reconstruction of tumor lineage history based on the CTC genomic profiles enables us to trace and identify the precise treatment time point where the putative therapy-resistant CTC clone emerges, under therapeutic pressure, until it eventually expanded to become the AR+ resistant CTC population at the point of therapeutic relapse. Overall, our results demonstrated that the integration of the HD-CTC enumeration technology with protein expression and single cell genomic analyses could successfully be applied to real time monitoring of ADT therapy emergent change in a prostate cancer patient, and may provide a direct roadmap for personalized cancer medicine in the near future. Citation Format: Peter Kuhn, Angel E. Dago, Asya Stepansky, Anders Carlsson, Natalie Felch, Madelyn Luttgen, Anand Kolatkar, James Hicks, Mitchell E. Gross. Sequential monitoring of single-cell copy number variation in metastatic prostate cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4599. doi:10.1158/1538-7445.AM2013-4599

Abstract POSTER-TECH-1114: Single-cell molecular profiling of fluid biopsies of epithelial ovarian cancer

The immediate goal of this project is to identify rare circulating tumor cells (CTC) in the bloodstream of epithelial ovarian cancer (EOC) patients and to apply high content, single cell assays that will yield information on morphology, protein expression and genomic structure. The ultimate goal is to develop a minimally invasive “fluid biopsy” for ovarian patients that can provide detailed information on the patient’s response to therapy, early warnings of potential resistance or relapse, and genetic predictors for the most appropriate therapy. The onset of pharmacogenomics has opened the door to a future of precision-guided therapies for cancer. Although as yet not implemented in all cancers, the idea of matching therapy to the molecular genetic characteristics of an individual cancer has been realized for specific classes of breast, lung and prostate cancers. For metastatic ovarian cancer patients the most efficient and convenient way to monitor the continuous state of the patient is through the presence of circulating epithelial cells (CTCs) that are shed from metastatic sites into the bloodstream. The difficulty, of course, is detecting and isolating these cells from the tens of millions of endothelial leukocytes (white blood cells) in a standard blood draw. We have created the HD-CTC assay for visualizing these rare CTC from blood samples and measuring their morphology, protein expression and genomic structure. Briefly, after initial processing to remove red blood cells, the blood samples are deposited on glass slides, stained and examined with a high-speed optical scanner. Epithelial (cancer) cells are distinguished from white blood cells by the presence of a complex of cytokeratin molecules in the cell. Each epithelial cell is imaged and its position recorded along with morphometric data including overall cell and nuclear size and shape. The positional information is used to retrieve individual cells for DNA extraction and DNA sequencing. The DNA sequence is initially converted into a genomic profile of copy number variation (CNV) and can be further sequenced for single nucleotide mutations. With support from the Rivkin Foundation we will obtain blood samples from up to 20 Stage 4 epithelial ovarian cancer patients at sequential times during their cycles of treatment and perform full HD-CTC assays including CTC enumeration, protein expression and genomic analysis of individual cells in order to determine the specific genetic alterations present in each cell and the degree of variation present within each case. Coupled with clinical information, including C125 levels and response to therapy, plus genetic information from bulk primary and metastatic tissue, we expect to establish the landscape of the circulating cell compartment in EOC and begin to understand the relationship of CTC to the metastatic behavior and patient prognosis. Citation Format: Carmen Ruiz Velasco, Asya Stepansky, Angel Dago, Anders Carlson, Anand Kolatkar, Jude Kendall, James Hicks , Peter Kuhn. Single-cell molecular profiling of fluid biopsies of epithelial ovarian cancer [abstract]. In: Proceedings of the 10th Biennial Ovarian Cancer Research Symposium; Sep 8-9, 2014; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(16 Suppl):Abstract nr POSTER-TECH-1114.