Single-cell Copy Number Profiling Research Articles

Polyploid cancer cells reveal signatures of chemotherapy resistance.

Therapeutic resistance in cancer significantly contributes to mortality, with many patients eventually experiencing recurrence after initial treatment responses. Recent studies have identified therapy-resistant large polyploid cancer cells in patient tissues, particularly in late-stage prostate cancer, linking them to advanced disease and relapse. Here, we analyzed bone marrow aspirates from 44 advanced prostate cancer patients and found the presence of circulating tumor cells with increased genomic content (CTC-IGC) was significantly associated with poorer progression- free survival. Single cell copy number profiling of CTC-IGC displayed clonal origins with typical CTCs, suggesting complete polyploidization. Induced polyploid cancer cells from PC3 and MDA-MB-231 cell lines treated with docetaxel or cisplatin were examined through single cell DNA sequencing, RNA sequencing, and protein immunofluorescence. Novel RNA and protein markers, including HOMER1, TNFRSF9, and LRP1, were identified as linked to chemotherapy resistance. These markers were also present in a subset of patient CTCs and associated with recurrence in public gene expression data. This study highlights the prognostic significance of large polyploid tumor cells, their role in chemotherapy resistance, and their expression of markers tied to cancer relapse, offering new potential avenues for therapeutic development.

NestedBD: Bayesian inference of phylogenetic trees from single-cell copy number profiles under a birth-death model.

Copy number aberrations (CNAs) are ubiquitous in many types of cancer. Inferring CNAs from cancer genomic data could help shed light on the initiation, progression, and potential treatment of cancer. While such data have traditionally been available via "bulk sequencing," the more recently introduced techniques for single-cell DNA sequencing (scDNAseq) provide the type of data that makes CNA inference possible at the single-cell resolution. We introduce a new birth-death evolutionary model of CNAs and a Bayesian method, NestedBD, for the inference of evolutionary trees (topologies and branch lengths with relative mutation rates) from single-cell data. We evaluated NestedBD's performance using simulated data sets, benchmarking its accuracy against traditional phylogenetic tools as well as state-of-the-art methods. The results show that NestedBD infers more accurate topologies and branch lengths, and that the birth-death model can improve the accuracy of copy number estimation. And when applied to biological data sets, NestedBD infers plausible evolutionary histories of two colorectal cancer samples. NestedBD is available at https://github.com/Androstane/NestedBD .

Abstract 4338: A pan-cancer single-cell analysis of intratumoral copy number diversity and evolution

Abstract Aneuploidy is a hallmark of human cancers and studies have shown that cancer patients harbor many differences in their copy number aberrations (CNAs) reflecting interpatient heterogeneity (IPH). While IPH has been studied extensively, the intratumoral heterogeneity (ITH) of CNAs within a tumor remains understudied. Here, we conducted a pan-cancer analysis of 94 human tumor samples at single cell genomic resolution, representing seven major cancer types: bladder, breast, colon, glioblastoma, kidney, lung, and ovary. In total, 62,646 aneuploid cells were analyzed by single cell copy number profiling and bulk exome sequencing was performed. In most cancer types, increased subclonal diversity was associated with higher CNA burden, whole genome doubling (WGD) events, and TP53 mutations. In all tumors, our data show that cancer cells share a clonal set of truncal CNAs or mutations, suggesting these tumors evolved from a single ancestral cell. In 18/94 cases we identified subclonal WGD events, which resulted in a higher number of copy number losses after tetraploidization. Macro-spatial sampling showed that increased clonal diversity also correlated with increased spatial geographical diversity. We found that many cancers evolved CNAs in short punctuated bursts of evolution, and that this was not restricted to a specific cancer type. Collectively, these data show that copy number ITH is universally associated with molecular features across these human cancer types. Citation Format: Hanghui Ye, Thomas McDonald, Emi Sei, Darlan Conterno Minussi, Min Hu, Chenling Tang, Junke Wang, Kaile Wang, Anna Casasent, Hui Chen, Franziska Michor, Nicholas Navin. A pan-cancer single-cell analysis of intratumoral copy number diversity and evolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4338.

Cracking the pattern of tumor evolution based on single-cell copy number alterations.

Copy number alterations (CNAs) are a key characteristic of tumor development and progression. The accumulation of various CNAs during tumor development plays a critical role in driving tumor evolution. Heterogeneous clones driven by distinct CNAs have different selective advantages, leading to differential patterns of tumor evolution that are essential for developing effective cancer therapies. Recent advances in single-cell sequencing technology have enabled genome-wide copy number profiling of tumor cell populations at single-cell resolution. This has made it possible to explore the evolutionary patterns of CNAs and accurately discover the mechanisms of intra-tumor heterogeneity. Here, we propose a two-step statistical approach that distinguishes neutral, linear, branching and punctuated evolutionary patterns for a tumor cell population based on single-cell copy number profiles. We assessed our approach using a variety of simulated and real single-cell genomic and transcriptomic datasets, demonstrating its high accuracy and robustness in predicting tumor evolutionary patterns. We applied our approach to single-cell DNA sequencing data from 20 breast cancer patients and observed that punctuated evolution is the dominant evolutionary pattern in breast cancer. Similar conclusions were drawn when applying the approach to single-cell RNA sequencing data obtained from 132 various cancer patients. Moreover, we found that differential immune cell infiltration is associated with specific evolutionary patterns. The source code of our study is available at https://github.com/FangWang-SYSU/PTEM.

SCONCE2: jointly inferring single cell copy number profiles and tumor evolutionary distances

BackgroundSingle cell whole genome tumor sequencing can yield novel insights into the evolutionary history of somatic copy number alterations. Existing single cell copy number calling methods do not explicitly model the shared evolutionary process of multiple cells, and generally analyze cells independently. Additionally, existing methods for estimating tumor cell phylogenies using copy number profiles are sensitive to profile estimation errors.ResultsWe present SCONCE2, a method for jointly calling copy number alterations and estimating pairwise distances for single cell sequencing data. Using simulations, we show that SCONCE2 has higher accuracy in copy number calling and phylogeny estimation than competing methods. We apply SCONCE2 to previously published single cell sequencing data to illustrate the utility of the method.ConclusionsSCONCE2 jointly estimates copy number profiles and a distance metric for inferring tumor phylogenies in single cell whole genome tumor sequencing across multiple cells, enabling deeper understandings of tumor evolution.

Identification of epithelial and mesenchymal circulating tumor cells in clonal lineage of an aggressive prostate cancer case

Little is known about the complexity and plasticity of circulating tumor cell (CTC) biology in different compartments of the fluid microenvironment during tumor metastasis. Here we integrated phenomics, genomics, and targeted proteomics to characterize CTC phenotypic and genotypic heterogeneity in paired peripheral blood (PB) and bone marrow aspirate (BMA) from a metastatic prostate cancer patient following the rapid disease progression, using the High-Definition Single Cell Assay 3.0 (HDSCA3.0). Uniquely, we identified a subgroup of genetically clonal CTCs that acquired a mesenchymal-like state and its presence was significantly associated with one subclone that emerged along the clonal lineage. Higher CTC abundance and phenotypic diversity were observed in the BMA than PB and differences in genomic alterations were also identified between the two compartments demonstrating spatial heterogeneity. Single cell copy number profiling further detected clonal heterogeneity within clusters of CTCs (also known as microemboli or aggregates) as well as phenotypic variations by targeted proteomics. Overall, these results identify epithelial and mesenchymal CTCs in the clonal lineage of an aggressive prostate cancer case and also demonstrate a single cell multi-omic approach to deconvolute the heterogeneity and association of CTC phenotype and genotype in multi-medium liquid biopsies of metastatic prostate cancer.

Abstract 6087: Evaluation of intra-tumor heterogeneity by single-cell copy number profiling of circulating multiple myeloma cells using Shannon index

Abstract Tumors develop through an evolutionary process in which genomic lesions accumulate in different coexisting subclones leading to intra-tumor heterogeneity (ITH). ITH allows the tumor to adapt to microenvironments and to resist therapies and has been shown to be a prognostic marker. In this work we show the characterization of ITH in Multiple Myeloma (MM) at single cell resolution from a pool of Circulating Multiple Myeloma Cells (CMMCs) and we study the relationship between number of sampled cells and diversity detected. Peripheral blood was collected from 3 patients affected by MM. Using CellSearch® system, blood was enriched for target CMMCs, with immuno-magnetic selection of CD138+, and immunofluorescently stained cells, positive for CD38-PE, DAPI and negative for CD45/CD19-APC, were enumerated. The samples were then analyzed and CMMCs were isolated with DEPArray platform. The DNA of each single cell (n=261) was amplified with Ampli1™ WGA kit and the WGA output was used for genome-wide single cell copy number alteration analysis with Ampli1 LowPass kit. Copy number profiling revealed from 2 to 3 coexisting subclones per patient, with minor clones size going from 6.5% to 33%. ITH was assessed by determining the Shannon index, which showed a range of values from 0.44 (low diversity) to 1.01 (high diversity) depending on source sample. We evaluated the effect of number of cells/profiles on Shannon index by bootstrapping (200 times) a number of copy number profiles ranging from 2 to the total number of profiles available per patient. Results showed that the mean Shannon index almost reaches a plateau at 15-20 cells/profiles in the case of lower diversity samples and at about 30 cells/profiles for the highest diversity sample. Moreover, as few as 10 cells allowed to differentiate low vs. high diversity samples. Cell enrichment and sorting by CellSearch and DEPArray platforms allow to quantify ITH at diversity level with high precision even from a limited number of initial single-cells and provide a promising method to objectively measure heterogeneity, as required in order to study its clinical implications. Citation Format: Alberto Ferrarini, Claudio Forcato, Valentina del Monaco, Mario Terracciano, Marianna Garonzi, Massimiliano Pellicano, Genny Buson, Francesca Fontana, Paola Tononi, Nicolo Manaresi. Evaluation of intra-tumor heterogeneity by single-cell copy number profiling of circulating multiple myeloma cells using Shannon index [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6087.

Abstract 1957: Dissecting CTC phenotypic heterogeneity for predictive biomarker identification and its association with clonal lineage through single-cell multi-omic profiling

Abstract Background: The mutation, selection, and adaptation of tumor cells along disease progression exhibits a spectrum of phenotypic and genotypic heterogeneity. The importance of distinguishing phenotypic states of CTC in addition to genomic alterations has been addressed for identifying predictive biomarkers and understanding CTC biology. Current liquid biopsy usually relies on only one phenotypic state of CTCs without further genomic validation of cancer cell identity. Here, we developed HDSCA3.0, a multi-omic platform, which could distinguish various phenotypic states of CTCs followed by genomic characterization. Methods: Paired peripheral blood (PB) and bone marrow aspirate (BMA) samples were collected prospectively from 80 metastatic castrate resistant prostate cancer (mCRPC) patients for retrospective analysis. Seventy-nine of them were part of Cabazitaxel With or Without Carboplatin Trial (NCT01505868) and one independent index patient was included with aggressive disease and unfavorable prognosis. CTCs were detected, classified, enumerated through a four-channel immunostaining assay (DAPI|Cytokeratin|Vimentin|CD45/CD31) and a computational pipeline followed by manual curation, and subjected to single-cell copy-number profiling for clonality analysis and aggressive variant prostate cancer molecular signature (AVPC-MS) detection i.e. 2+ defects in PTEN, RB1, and TP53 genes. Results: CTC subtypes were categorized from Cytokeratin-positive rare cell groups based on the presence of mesenchymal features and platelet attachment. Of 79 trial cases, 77 (97.5%) had CTCs, 24 (30.4%) were positive for platelet-coated CTCs (pc.CTCs) and 25 (38.5%) of 65 sequenced patients exhibited AVPC-MS in CTCs. Survival analysis indicated that the presence of pc.CTCs identified the subset of patients who were AVPC-MS-positive with the worst prognosis. In AVPC-MS-negative patients, its presence showed significant survival improvement from combination therapy. In index patient, we uniquely identified genetically clonal mesenchymal-like CTCs (mes.CTCs) and their presence was significantly associated with one subclone emerged along clonal lineage. Meanwhile, differences of CTC abundance and phenotypic diversity were observed between paired PB and BMA as well as genomic variations. Conclusion: Our findings suggest pc.CTCs and AVPC-MS in CTCs as a multi-omic predictive biomarker to stratify mCRPC subpopulations with the worst prognosis and the most significant benefit of additional platinum therapy and illustrate a robust approach to analyze intra-patient CTC genotypic and phenotypic heterogeneity and association. Citation Format: Shoujie Chai, Nicholas Matsumoto, Ryan Storgard, Chen-Ching Peng, Ana Aparicio, Benjamin Ormseth, Kate Rappard, Cunningham Kate, Anand Kolatkar, Rafael Nevarez, Kai Han Tu, Ching-Ju Hsu, Amin Naghdloo, Paymaneh Malihi, Liya Xu, Paul Corn, Amado Zurita-Saavedra, James Hicks, Carmen Ruiz-Velasco, Peter Kuhn. Dissecting CTC phenotypic heterogeneity for predictive biomarker identification and its association with clonal lineage through single-cell multi-omic profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1957.

Abstract IA025: Decoding breast cancer evolution with single cell genomics

Abstract I will discuss our efforts to develop and apply single cell DNA sequencing technologies to study breast cancer progression and evolution. We have developed a method called Acoustic Cell Tagmentation (ACT) which can provide high-throughput single cell copy number profiling of hundreds of cells in parallel at single-molecule resolution. We have applied this approach to study copy number evolution in Triple-negative breast cancer, which has revealed a ‘transient instability’ model of early copy number evolution, after the acquisition of TP53 mutations and genome doubling. We have also developed a computational toolkit called CopyKit to analyze single cell DNA sequencing data, and applied it to study metastasis in breast and colon cancer, as well as the spatial organization of subclones in metastatic tumors. Collectively these studies and tools provide new insights into the evolution of chromosome aberrations in human tumors. Citation Format: Nicholas Navin. Decoding breast cancer evolution with single cell genomics [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr IA025.

Abstract PO-017: Single-cell copy number heterogeneity tracing enabling cancer gene discovery

Abstract Aneuploidy, the phenomenon that genomes acquire or lose chromosomal fragments, has been causally implicated in a wide variety of human cancer. Defining which copy number alterations (CNAs) are pathogenic is an important goal of cancer research. However, data based on bulk samples cannot fully depict tumor heterogeneity and evolution, which occurs in single cells, and thus have limited power to discover CNAs useful for cancer diagnostics and therapeutics. Recent advances in single-cell DNA sequencing have enabled acquisition of single-cell copy number (SCCN) profiles in tens of thousands of cells, which potentiate reconstruction of copy number evolution lineage and discovery of novel cancer genes. However, current analytical approaches that infer clonal lineages are neither accurate, nor scalable to analyzing molecular features, particularly CNA profiles obtained from thousands of cells. Statistical routines have not been established to leverage lineage tracing results towards identifying cancer-related genes. Here, we present a Minimal Event Distance Aneuploidy Lineage Tree (MEDALT) algorithm that infers the evolution history of a cell population based on single-cell copy number (SCCN) profiles. In a MEDALT, each node represents a cell, each edge represents a kinship between two cells, arrows point towards younger cells, and the root represents a normal diploid cell. We also present a statistical routine named lineage speciation analysis, which facilitates discovery of fitness-associated alterations (FAAs) and genes from SCCN lineage trees. To evaluate MEDALT, we simulated copy number evolution under various CNA mechanisms such as genome doubling, breakage-fusion-bridge (BFB), etc. and spiked in FAAs. We found that MEDALT substantially improved accuracy of FAA identification over GISTIC and conventional phylogenetics methods such as maximum likelihood (ML), maximal parsimony (MP) and neighbor joining (NJ) trees. We applied our methods on the single-cell DNA-sequencing data acquired from 20 triple-negative breast cancer patients (TNBCs), 4 of which had longitudinal pre-, mid- and post- treatment samples. Most of the TNBC samples appeared to have developed through branched evolution via multiple parallel lineages with distinct mutation rates and DNA damage repair (DDR) loss based on the constructed MEDALT. Using our approaches, we discovered novel genes that are predictive of patient survival in TCGA breast cancer data and are functionally more essential than other control gene sets, based on the CRISPR-cas9 knockout screen data obtained from 27 breast cancer cell lines in the DepMap database. Significant benefits in lineage tracing and cancer gene discovery were also achieved, when applying our approaches on the SCCN profiles derived using InferCNV from the single-cell RNA sequencing data of a cohort of 20 multiple myeloma, head and neck cancer, oral squamous cell carcinoma and ovarian cancer patients. The source code of our study is available at https://github.com/KChen-lab/MEDALT. Citation Format: Fang Wang, Qihan Wang, Vakul Mohanty, Shaoheng Liang, Jinzhuang Dou, Jincheng Han, Darlan Minussi, Ruli Gao, Li Ding, Nicholas Navin, Ken Chen. Single-cell copy number heterogeneity tracing enabling cancer gene discovery [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-017.

Abstract 4424: Medatree enabling single cell copy number lineage tracing and functional discovery

Abstract Copy number alterations (CNAs) play an important role in cancer development and treatment. CNA profiles in single tumor cells preserve archaeological records of a tumor evolution, enabling retrospective lineage tracing. Although current single-cell sequencing technologies have enabled accurate, high-throughput acquisition of single-cell integer CNA profiles, existing computational methods are neither intelligent, i.e., unaware of CNA evolution mechanisms such as breakage-fusion-bridge, nor scalable to current datasets consisting of thousands of cells. To address this challenge, we developed a novel, efficient computational approach, Medatree (minimal event distance aneuploidy tree) to infer a cell lineage tree from the whole genome CNA profiles of a large number (N>1,000) of cells obtained from patient samples. A tree inferred by Medatree describes the minimal number of single-copy gain or loss events that may have occurred during the evolution, the genealogical relations amongst individual cells, and the significant CNAs associated with lineage expansion. We confirmed through simulation that Medatree substantially improves lineage tracing accuracy (e.g., 0.73 vs. 0.31) over conventional phylogeny methods (e.g., maximum parsimony). We then applied Medatree to single-cell DNA sequencing (scDNA-seq) data obtained from 20 triple-negative breast cancer patients (TNBCs), some of which had longitudinal biopsies through the course of chemotherapy. The trees constructed by Medatree corroborated well with the timing and phenotypes of the biopsies. They also revealed branches with variable CNA rates, likely associated with differentially altered genome instability. We further identified significantly copy-number altered genes through permutation of the single-cell CNA profiles. Compared to results obtained by applying convention methods (phylogenetic trees and GISTIC) on the same datasets, Medatree detected more genes (1.6-fold) that have been functionally associated with breast cancer development and treatment outcome, proving the power of single-cell sequencing. Moreover, it revealed potential convergent CNA (such as gain of IGF1R) occurring in parallel lineages of a tumor sample in 3 out of 20 TNBCs. Similar gains in discovery power were observed when applying Medatree on CNA profiles derived from single-cell RNA sequencing (scRNA-seq) data of 12 multiple myeloma patients. In summary, we developed a first-of-its-kind approach Medatree that enables large-scale retrospective CNA lineage tracing from single-cell copy number profiles obtained from single-cell DNA and RNA-sequencing data of patient samples. Wide application of Medatree is expected to accelerate the study of copy number evolution during tumor development and lead to discovery of novel targets for diagnoses and treatments. The source code of Medatree is available at https://github.com/KChen-lab/Medatree. Citation Format: Fang Wang, Qihan Wang, Jincheng Han, Vakul Mohanty, Shaoheng Liang, Darlan Conterno Minussi, Ruli Gao, Li Ding, Nicholas Navin, Ken Chen. Medatree enabling single cell copy number lineage tracing and functional discovery [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4424.

Abstract 2177: Sensitive single cell copy number profiling using a novel microfluidic droplet based platform

Abstract Intratumoral genetic heterogeneity may relate to aggressiveness and treatment-resistance. However, the genomic abnormalities underlie this heterogeneity remain poorly characterized. To better describe the picture of tumour heterogeneity, single cell approaches to accommodate larger number of cells are highly in demand. Here, we present results of single cell DNA sequencing by using a novel microfluidic droplet system. We applied this approach to discover copy number profiles in two common and therapeutically challenging cancers, isocitrate dehydrogenase wild-type glioblastoma (GBM) and breast cancer. The GBM cells were extracted from patients operated on site and cultured for 2 months in restricted media to isolate glioma stem cells. The breast cancer cells were extracted from patient-derived xenografts (PDXs). A novel microfluidic droplet based platform (10x GenomicsTM) was used to perform sensitive copy number profiling of thousands of single cells simultaneously. Briefly, the platform integrates single cell encapsulation, cell lysis, genomic amplification, and barcoding with oligonucleotides. The barcoded genomic DNA fragments from individual cells were pooled and converted into libraries that are compatible with Illumina sequencers. Thus far, we sequenced one glioma stem cell sample and two breast cancer PDXs. Uniquely mapped reads were counted in variable-bins with GC bias corrected by lowess fit. The Circular Binary Segmentation was performed and the integer copy number was estimated by least square fitting. The single cell copy number profiles were clustered by hierarchical clustering. We achieved approximately 2% and 6% coverage of the genome per cell for the glioblastoma and the breast tumors, respectively. The uniformity of coverage was comparable to other whole genome amplification and single cell multiplex approaches, while requiring 700k-2.1mi reads/single cell with the median absolute deviation of pair-wise differences in read counts between neighboring bins between 0.17-0.21 vs 0.16)1. We identified two major clonal subpopulations in the glioblastoma sample. One subclone had chr1q amplification and the other subclone had a 9.8Mb deletion on chr4. The clonal CNVs observed were validated using single-cell RNA-seq-based CNV analysis, and the frequent CNVs using fluorescent in situ hybridization. We also identified the HER2 copy number profile within a basal like triple-negative breast cancer as 40% of 2.5N and 25% of 3N. This was previously validated by fluorescence in situ hybridization2. By applying this new microfluidic system, our work will provide more insights into genomic heterogeneity for malignant properties of cancers. 1. Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90-94 (2011). 2. Savage P. et al. A Targetable EGFR-Dependent Tumor-Initiating Program in Breast Cancer. Cell Rep. 21, 1140-1149 (2017). Citation Format: Rui Li, Charles Couturier, Paul Savage, Jean Monlong, Guillaume Bourque, Kevin Petrecca, Morag Park, Jiannis Ragoussis. Sensitive single cell copy number profiling using a novel microfluidic droplet based platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2177.

Abstract 418: Adaptive resistance to chemotherapy in triple-negative breast cancer revealed by single cell DNA and RNA sequencing

Abstract Triple-negative breast cancer (TNBC) is an aggressive subtype that displays extensive intratumor heterogeneity and frequently (46%) develops resistance to neoadjuvant chemotherapy (NAC). Currently, the genomic basis of chemoresistance remains poorly understood. An important question is whether resistance to chemotherapy is driven by the selection of rare pre-existing subclones with genomic mutations and transcriptional programs that confer resistance to chemotherapy (adaptive resistance) or by the spontaneous induction of new mutations and expression changes that confer a resistant phenotype (acquired resistance). To investigate this question we applied single cell DNA and RNA sequencing methods and deep-exome sequencing to longitudinal time-point samples collected from a cohort of 20 TNBC patients. Deep-exome sequencing of the cohort at three time points revealed a random death model, wherein multiple clones were targeted, as opposed to the selection of specific somatic mutations. In contrast, single cell copy number profiling of ~800 cells from 8 patients identified an adaptive resistance model, wherein minor subclones from the pre-treatment tumors were selected and expanded in response to NAC. Similarly, single cell RNA sequencing of ~8000 cells from 8 patients identified subclones with chemoresistant phenotypes that were selected in response to NAC, resulting in the expansion of the resistant tumor mass. These data suggest that chemoresistance evolves through the selection of copy number changes and expression changes in signaling pathways associated with chemoresistance, rather than point mutations. This adaptive resistance model has important translational implications in clinical diagnostics, by suggesting that resistant clones can be detected in TNBC patients prior to the administration of chemotherapy. Citation Format: Charissa Kim, Ruli Gao, Emi Sei, Rachel Brandt, Nicola Crosetto, Theodoros Foukakis, Nicholas Navin. Adaptive resistance to chemotherapy in triple-negative breast cancer revealed by single cell DNA and RNA sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 418. doi:10.1158/1538-7445.AM2017-418

Abstract 2394: Scalable, rapid and affordable low-pass whole genome sequencing method for single-cell copy-number profiling on LM-PCR based WGA products

Abstract Introduction: Genome-wide profiling of copy-number alterations (CNA) in single cells enables the characterization and investigation of genomic heterogeneity and evolution in tumor cell populations such as individual circulating tumor cells (CTCs), or disaggregated solid tumors. Ampli1™ WGA is a commercially available kit for whole-genome amplification from single cells suitable for array comparative genomic hybridization (aCGH), which is currently the gold standard for genetic diagnosis of CNA. However, labor and reagent costs limit aCGH applicability. Here we introduce for the first time a rapid, high-throughput low-pass Whole Genome Sequencing (WGS)-based method for CNA analysis (LPCNA) on single cells, enabling simultaneous profiling of multiple cells at lower cost and effort. Methods: Six single cells from NCI-H23, NCI-H661 and NCI-H1650 cell lines and 19 white blood cells (WBCs) were amplified using Ampli1™ WGA Kit. Only 5μl (1/10th of total WGA product) were used as sample input for LPCNA. Purified WGA products were processed for each sample in a single-tube, one-step reaction resulting in IonTorrent-compatible indexed libraries. Samples were pooled and size-selected to recover fragments with a length ranging from 300 to 400 bp. Following purification and template preparation, pools were sequenced on Ion 318™v2 Chips. Array CGH was performed for comparison, using Agilent SurePrint 4×180k arrays as previously reported. CNA analysis was performed using Control-FREEC for low-pass WGS data, or Agilent Genome Workbench for aCGH. Results: By exploiting the deterministic nature of Ampli1™ whole genome amplification, we devised a method to generate IonTorrent compatible libraries in a single reaction, drastically reducing time of bench work and cost. Method accuracy has been assessed by comparing the copy number profiles generated by our approach with about 870,000 reads/sample (average genome coverage of 0.064x, range 0.051-0.073) with those obtained by a validated aCGH method. Very good agreement in gain and loss profiles was achieved for all single tumor cells analyzed. As expected WBC analysis resulted in normal copy-number profiles with autosomes copy number = 2, with minimal false-positive rate (<0.6% at a resolution of 760kb with 618k reads). Highlights: This innovative low-pass sequencing approach for CNA detection on single cells enables high level multiplexing on different NGS platforms. The approach is highly scalable and flexible. From few (6-8) samples (IonTorrent PGM, 318™v2 Chip), up to 96 samples (Ion Proton, Ion PI™ Chip v3) can be processed in a single run. This approach meets the need for rapid results (<7h turnaround time for library preparation) and affordable cost required for massively parallel single-cell analysis such as multiple tumor cells or CTCs. This work has been carried out within IMI CANCER-ID consortium (www.cancer-id.eu). Citation Format: Genny Buson, Paola Tononi, Claudio Forcato, Francesca Fontana, Gianni Medoro, Rui Neves, Birte Möhlendick, Nikolas Stoecklein, Nicolò Manaresi. Scalable, rapid and affordable low-pass whole genome sequencing method for single-cell copy-number profiling on LM-PCR based WGA products. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2394.

Abstract 4735: Single-cell sequencing from formalin-fixed paraffin-embedded breast cancers: a powerful tool to address intratumor genetic heterogeneity

Abstract Background: At diagnosis, tumors often consist of multiple, genotypically distinct cell populations. Genome sequencing of single cells has opened new avenues of investigation, yet the application of this technology remains limited to fresh/frozen tissue samples. Here, we describe and validate a method to perform single-cell massively parallel sequencing using DNA extracted from individual nuclei from FFPE tumor samples. Methods: Tissue sections are deparaffinized and the area of interest microdissected, DNA is reverse-crosslinked and the extracellular matrix is digested. Extracted single nuclei are then FACS-sorted, lysed and the whole genome repaired using a broad-spectrum DNA-repair enzyme cocktail. Repaired DNA is whole genome amplified (WGA) using GenomePlex WGA4 (Sigma-Aldrich) with modifications for heavily damaged genomic templates. Illumina sequencing libraries are generated using standard approaches followed by multiplex sequencing on an Illumina HiSeq2000. To obtain copy number (CN) profiles, single-cell sequencing data are mapped to the reference genome with PCR duplicates removed. Uniquely mapped reads are allocated and counted in genomic intervals of variable length (bins) with CN states roughly proportional to the number of allocated sequencing reads. Bin counts are then normalized on the basis of GC-content, segmented and transformed to CN values to identify long contiguous regions of equivalent CN. Results: As a proof-of-principle we performed single-cell CN profiling on two aneuploid synchronous ductal carcinomas in situ (DCIS)/invasive ductal carcinomas (IDCs), where both FFPE and frozen material was available. Using our novel methodology, 24 single nuclei were multiplexed/lesion. An average of 3 million reads/cell (average coverage of 0.1X/cell) was obtained, providing sufficient data to infer CN profiles of single cells accurately. These data were successfully employed to identify non-neoplastic cells and distinct clonal lineages of neoplastic cells within each lesion. The data obtained from the analysis of FFPE samples were concordant with those obtained from the analysis of matched frozen samples. Conclusions: We developed a robust procedure to perform single-cell massively parallel sequencing of individual nuclei isolated from FFPE samples, providing the opportunity to unlock pathology archives for studies aiming to catalog and dissect the biological and clinical relevance of intra-tumor genetic heterogeneity. Citation Format: Luciano G. Martelotto, Rita A. Sakr, Timour Baslan, Linda Rodgers, Hilary Cox, Jude Kendall, Tari A. King, Britta Weigelt, James Hicks, Jorge S. Reis-Filho. Single-cell sequencing from formalin-fixed paraffin-embedded breast cancers: a powerful tool to address intratumor genetic heterogeneity. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4735. doi:10.1158/1538-7445.AM2015-4735

Abstract LB-310: Single cell genome sequencing reveals clonal stability and diversity in breast cancer

Abstract Human breast cancers often display intratumor genomic heterogeneity. This clonal diversity confounds the clinical diagnosis and basic research of cancer, because single samples may not represent that tumor as a whole. Sequencing breast tumor cohorts en masse has identified many prevalent mutations, but has limited ability for resolving subclonal diversity. Here, we developed a whole-genome and exome single-cell sequencing approach (Nuc-Seq) using G2/M cells. To validate our method, we applied Nuc-Seq to sequence the whole genomes of two single cells from a genetically monoclonal breast cancer cell line (SK-BR-3) at high coverage depth (61X ± 5 sem, n=2) and breadth (83.70% ± 3.40 sem, n=2) to detect somatic mutations. Our analysis suggests that Nuc-Seq generates low allelic dropout rates (9.73% ± 2.19%) and low false positive error rates for point mutations (FPR = 1.24e-6). We then applied this method to sequence single normal and tumor cells from an estrogen-receptor positive breast cancer and a triple-negative ductal carcinoma at base-pair resolution. In parallel, we performed single cell copy number profiling. In both tumors, we observed a large number of rare variants that were not detected by sequencing the bulk tumor en masse. In contrast, we find that single cell copy number profiles are highly similar. Our data suggest that aneuploid rearrangements occurred early in tumor evolution and remained highly stable as the tumor mass expanded. In contrast we find that point mutations evolved gradually, generating extensive clonal diversity. Many of the diverse mutations were shown to occur at low frequencies (0.03 -10%) in the tumor mass by targeted duplex sequencing. Mathematical modeling suggests that the triple-negative tumor cells have an increased mutation rate (13.3X), while the ER+ tumor cells do not. These findings have important implications for the diagnosis, therapeutic treatment and evolution of chemoresistance in breast cancer. Citation Format: Yong Wang, Nicholas Navin, Jill Waters, Marco Leung, Anna Unruh, Xiuqing Shi, Whijae Roh, Ken Chen, Paul Scheet, Selina Vattathil, Han Liang, Asha Multani, Hong Zhang, Funda Meric-Bernstam, Franziska Michor, Rui Zhao. Single cell genome sequencing reveals clonal stability and diversity in breast cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr LB-310. doi:10.1158/1538-7445.AM2014-LB-310