Single-cell Genomic Profiling Research Articles

Mutation Patterns Predict Drug Sensitivity in Acute Myeloid Leukemia.

The inherent genetic heterogeneity of acute myeloid leukemia (AML) has challenged the development of precise and effective therapies. The objective of this study was to elucidate the genomic basis of drug resistance or sensitivity, identify signatures for drug response prediction, and provide resources to the research community. We performed targeted sequencing, high-throughput drug screening, and single-cell genomic profiling on leukemia cell samples derived from patients with AML. Statistical approaches and machine learning models were applied to identify signatures for drug response prediction. We also integrated large public datasets to understand the co-occurring mutation patterns and further investigated the mutation profiles in the single cells. The features revealed in the co-occurring or mutual exclusivity pattern were further subjected to machine learning models. We detected genetic signatures associated with sensitivity or resistance to specific agents, and identified five co-occurring mutation groups. The application of single-cell genomic sequencing unveiled the co-occurrence of variants at the individual cell level, highlighting the presence of distinct subclones within patients with AML. Using the mutation pattern for drug response prediction demonstrates high accuracy in predicting sensitivity to some drug classes, such as MEK inhibitors for RAS-mutated leukemia. Our study highlights the importance of considering the gene mutation patterns for the prediction of drug response in AML. It provides a framework for categorizing patients with AML by mutations that enable drug sensitivity prediction.

The genomic landscape of acute lymphoblastic leukemia with intrachromosomal amplification of chromosome 21

AbstractIntrachromosomal amplification of chromosome 21 defines a subtype of high-risk childhood acute lymphoblastic leukemia (iAMP21-ALL) characterized by copy number changes and complex rearrangements of chromosome 21. The genomic basis of iAMP21-ALL and the pathogenic role of the region of amplification of chromosome 21 to leukemogenesis remains incompletely understood. In this study, using integrated whole genome and transcriptome sequencing of 124 patients with iAMP21-ALL, including rare cases arising in the context of constitutional chromosomal aberrations, we identified subgroups of iAMP21-ALL based on the patterns of copy number alteration and structural variation. This large data set enabled formal delineation of a 7.8 Mb common region of amplification harboring 71 genes, 43 of which were differentially expressed compared with non-iAMP21–ALL ones, including multiple genes implicated in the pathogenesis of acute leukemia (CHAF1B, DYRK1A, ERG, HMGN1, and RUNX1). Using multimodal single-cell genomic profiling, including single-cell whole genome sequencing of 2 cases, we documented clonal heterogeneity and genomic evolution, demonstrating that the acquisition of the iAMP21 chromosome is an early event that may undergo progressive amplification during disease ontogeny. We show that UV-mutational signatures and high mutation load are characteristic secondary genetic features. Although the genomic alterations of chromosome 21 are variable, these integrated genomic analyses and demonstration of an extended common minimal region of amplification broaden the definition of iAMP21-ALL for more precise diagnosis using cytogenetic or genomic methods to inform clinical management.

Single-cell analyses of axolotl telencephalon organization, neurogenesis, and regeneration.

Salamanders are tetrapod models to study brain organization and regeneration; however, the identity and evolutionary conservation of brain cell types are largely unknown. We delineated the cell populations in the axolotl telencephalon during homeostasis and regeneration using single-cell genomic profiling. We identified glutamatergic neurons with similarities to amniote neurons of hippocampus, dorsal and lateral cortex, and conserved γ-aminobutyric acid-releasing (GABAergic) neuron classes. We inferred transcriptional dynamics and gene regulatory relationships of postembryonic, region-specific neurogenesis and unraveled conserved differentiation signatures. After brain injury, ependymoglia activate an injury-specific state before reestablishing lost neuron populations and axonal connections. Together, our analyses yield insights into the organization, evolution, and regeneration of a tetrapod nervous system.

Single-cell genomic profiling of human dopamine neurons identifies a population that selectively degenerates in Parkinson’s disease

The loss of dopamine (DA) neurons within the substantia nigra pars compacta (SNpc) is a defining pathological hallmark of Parkinson’s disease (PD). Nevertheless, the molecular features associated with DA neuron vulnerability have not yet been fully identified. Here, we developed a protocol to enrich and transcriptionally profile DA neurons from patients with PD and matched controls, sampling a total of 387,483 nuclei, including 22,048 DA neuron profiles. We identified ten populations and spatially localized each within the SNpc using Slide-seq. A single subtype, marked by the expression of the gene AGTR1 and spatially confined to the ventral tier of SNpc, was highly susceptible to loss in PD and showed the strongest upregulation of targets of TP53 and NR2F2, nominating molecular processes associated with degeneration. This same vulnerable population was specifically enriched for the heritable risk associated with PD, highlighting the importance of cell-intrinsic processes in determining the differential vulnerability of DA neurons to PD-associated degeneration.

Single-Cell Transcriptomics of Human Plasma Cells from HLA-Sensitized Patients Reveals Population Heterogeneity and Drug Resistance Adaptations

Kidney transplantation improves the overall survival of end stage renal disease patients but the presence of preformed human leukocyte antigen (HLA)-specific antibodies often precludes finding an acceptable donor. Recent studies demonstrate that bone marrow plasma cells (BMPCs) are a major source of HLA-specific antibodies in highly sensitized transplant candidates. However, therapeutic strategies that target BMPCs and overcome HLA desensitization are only beginning to emerge. Proteasome inhibitor (PI) therapies effectively eliminate BMPCs, reduce HLA-antibody levels and have shown promise as an effective means of enhancing transplantation outcomes. However, PIs are frequently limited by de novo or therapy-induced drug resistance. In addition, BMPCs exhibit variable sensitivity to PI-based therapy. To identify potential mechanisms of drug resistance in BMPCs, we employed single-cell genomic profiling. Single-cell studies provide a powerful tool that allows us to study rare cell populations, such as BMPCs, and intermediate cell states that cannot be resolved at the population level. Here, we have performed single-cell genomic profiling of human BMPCs isolated from highly HLA-sensitized renal transplant candidates prior to and after desensitization therapy with the irreversible PI carfilzomib. HLA-sensitized transplant candidates received CFZ (20, 27, 36 mg/m2) with BM aspirates performed prior to and after CFZ therapy. CD138+BMPCs were isolated to >98% purity and individual cells analyzed using the recently developed method, Iterative Clustering and Guide-gene Selection (ICGS), which utilizes pair-wise correlation of dynamically expressed genes and iterative clustering with pattern-specific guide genes to delineate coherent gene-expression patterns. Strongly and differentially expressed gene sets were clustered and evaluated using gene enrichment, prior knowledge-based networks and ICGS. Each cluster corresponds to one or a group of closely related cell types that represent spatial or temporal distinct states. Single-cell profiling by the drop-seq method analyzed mRNA expression in 2,000 individual BMPCs isolated from the same HLA-sensitized patient prior to and after CFZ therapy. We used CellHarmony, a new bioinformatic approach that allows us to determine the frequency of all CFZ-treated cells in direct reference to the untreated cells and any altered gene expression profiles. In our first comparison of these cells, eight cell clusters were defined (Fig. 1A). Single-cell analysis of BMPCs obtained post-CFZ therapy indicated qualitative and quantitative changes in gene expression as well differences in the proportionality of existing cell clusters. To determine the frequency and expression of these genes in the CFZ-treated samples, we then performed classification of those samples using a k-nearest neighbor approach using all untreated cells as the reference. For example, protein catabolism, nonsense-mediated decay and integrin signaling were increased (clusters 1, 5 and 6) while clusters enriched in IL-12 signaling, interferon signaling and protein translation genes (2, 3 and 7) were reduced. Importantly, the state associated with the mitotic cell cycle was significantly upregulated in BMPCs that survived CFZ. BMPCs were analyzed from additional patients using a comparable approach identified common gene expression sets in pre-treatment samples as well as recurrent differences in post-treatment samples (Fig. 1B).Conclusions: The results presented here demonstrate the power and promise of single-cell genomics in uncovering functional diversity between cells and in deciphering how cell states respond to therapeutic challenge. Our results indicate a shift toward highly mitotic states in BMPCs that survive CFZ therapy. Importantly, the single cell results also provide internal validation of our prior bulk RNA-seq studies on BMPCs. Our results suggest the feasibility of pathway-based secondary targeting of the recovering cell populations to enhance response. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Abstract LT008: Mapping the evolution of T cell states during response and resistance to adoptive cellular therapy

Abstract Immune therapies have transformed the cancer therapeutic landscape but fail to benefit most patients. To elucidate the underlying mechanisms by which T cells mediate elimination of leukemia, we generated a high-resolution map of longitudinal T cell dynamics within the same tumor microenvironment (TME; bone marrow) during response or resistance to donor lymphocyte infusion (DLI), a widely used immunotherapy for relapsed leukemia. We analyzed 87,939 bone marrow-derived single T cell transcriptomes, along with chromatin accessibility and single T cell receptor clonality profiles, by developing novel machine learning tools for integrating longitudinal and multimodal data. We found that pre-treatment enrichment and post-treatment rapid, durable expansion of ‘terminal’ (TEX) and ‘precursor’ (TPEX) exhausted subsets, respectively, defined DLI response. In contrast to the common, shared pathways marking DLI response, a heterogeneous pattern of T cell dysfunction marked DLI resistance. Unexpectedly, TPEX cells that expanded in responders did not arise from the infusion product but instead from both pre-existing and novel clonotypes recruited to the TME. Further, we introduce a Bayesian method, Symphony, to define the T cell regulatory circuitry and master regulators underlying TEX and TPEX subsets that may be broadly relevant to other exhaustion antagonists across cancers. In conclusion, our data implicate the hierarchy of both TEX and TPEX subsets for immunotherapeutic responses in leukemia, extending the scope of their relevance beyond checkpoint blockade to adoptive cellular therapy. Moreover, our results provocatively suggest that immunologic ‘help’ from DLI, rather than direct transfer of anti-leukemic T cells, drove leukemic remission. Finally, we provide a general analysis paradigm for exploiting temporal single-cell genomic profiling for deep understanding of how immune therapies differentially shape the evolutionary trajectories of the TME in accordance with clinical outcome. Citation Format: Pavan Bachireddy, Elham Azizi, Cassandra Burdziak, Vinhkhang Nguyen, Christina Ennis, Zi- Ning Choo, Shuqiang Li, Kenneth Livak, Donna Neuberg, Robert Soiffer, Jerome Ritz, Edwin Alyea, Dana Pe'er, Catherine Wu. Mapping the evolution of T cell states during response and resistance to adoptive cellular therapy [abstract]. In: Proceedings of the AACR Virtual Special Conference on the Evolving Tumor Microenvironment in Cancer Progression: Mechanisms and Emerging Therapeutic Opportunities; in association with the Tumor Microenvironment (TME) Working Group; 2021 Jan 11-12. Philadelphia (PA): AACR; Cancer Res 2021;81(5 Suppl):Abstract nr LT008.

Abstract 5380: An integrated platform for the clinical detection and molecular profiling of single circulating tumor cells

Abstract Introduction: Detection of circulating tumor cells (CTCs) and characterization of CTCs at single-cell resolution may benefit the clinical management of cancer patients. To make the CTC analysis a “bench-to-bedside” technology, we developed an integrated platform named ChimeraX®-i120 system for the high-throughput, automated, and clinical applicable CTC sorting and downstream single-cell genomic profiling. Methods: ChimeraX®-i120 system is designed based on negative enrichment strategy, integrating blood sample pre-processing, immunofluorescence staining, artificial intelligence (AI)-assisted CTC identification. CTCs enriched by this system is compatible with downstream single-cell whole genome sequencing (WGS) analysis. Performance of CTCs detection was evaluated by a series of analytical experiments, clinical utility was further verified. We validated our downstream molecular profiling pipeline of single CTC. Results: Analytical experiments showed that this system was featured with high accuracy, repeatability, sensitivity and anti-interference capability for CTC detection, achieving an average recovery rate of the spiked cells in 71.3%-83.2% (R2= 0.99). In clinical samples, this system effectively detected CTCs and CTC clusters (CTM) in 5 mL blood from cancer patients. The proportion of patients that were positive for CTCs (EpCAM+/pan-CK++CD45-+DAPI+), was 59.3% (86/145, avg. cell count 1.38 ± 2.01) for hepatocellular carcinoma (HCC), 63.2% (24/38, 1.42 ± 1.75) for intrahepatic cholangiocarcinoma (ICC), 63.6% (28/44, avg. 2.52 ± 3.93) for breast cancer (BRC), 61.5% (24/39, avg. 1.62 ± 2.11) in colorectal cancer (CRC), 78.9% (15/19, avg. 2.74 ± 4.46) in lung cancer (LC). Only 1 of 125 (0.8%) healthy volunteers had detectable CTC. The prognostic value of the system was assessed in HCC. Preoperative CTC ≥1/5mL blood was significantly associated with a higher 1-year recurrence rate (38.9% vs. 27.6%, P=0.042) in HCC patients after curative surgery. Low-pass WGS analysis was performed on CTCs and paired tumor tissues of 3 ICC, 3 HCC and 4 BRC. We observed concordance in CNAs (10%-61%) between CTCs and tissues. CTC profiling identified diverse intra- and inter-patient heterogeneity in CNAs pattern. Exclusive CNA patterns were identified in CTCs but not tissue, and vice versa. Multiple well-known oncogenes (e.g., BRAF, EGFR, BRCA1/2, MYC and MET) and tumor suppressor genes (e.g., FOXA1 and TRAF3) could be identified from CNAs in CTCs. For example, in one BRC patient, potentially actionable alterations of BRC such as BRAF, EGFR, MET, ERBB2, PIK3CA and MYC were only identified in CTCs but not tissues. Conclusion: Our results highlight the potential clinical utility of ChimeraX®-i120 system in both CTC detection and downstream genomic profiling. Genomic sequencing of CTCs allows blood-based tumor profiling in greater fraction of patients for clinical decision making. Citation Format: Pengxiang Wang, Yunfan Sun, Weixiang Jin, Haixiang Peng, Jian Zhou, Jia Fan, Xinrong Yang. An integrated platform for the clinical detection and molecular profiling of single circulating tumor cells [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 5380.

Integrated Computational Pipeline for Single-Cell Genomic Profiling.

PURPOSECopy-number profiling of multiple individual cells from sparse sequencing may be used to reveal a detailed picture of genomic heterogeneity and clonal organization in a tissue biopsy specimen. We sought to provide a comprehensive computational pipeline for single-cell genomics, to facilitate adoption of this molecular technology for basic and translational research.MATERIALS AND METHODSThe pipeline comprises software tools programmed in Python and in R and depends on Bowtie, HISAT2, Matplotlib, and Qt. It is installed and used with Anaconda.RESULTSHere we describe a complete pipeline for sparse single-cell genomic data, encompassing all steps of single-nucleus DNA copy-number profiling, from raw sequence processing to clonal structure analysis and visualization. For the latter, a specialized graphical user interface termed the single-cell genome viewer (SCGV) is provided. With applications to cancer diagnostics in mind, the SCGV allows for zooming and linkage to the University of California at Santa Cruz Genome Browser from each of the multiple integrated views of single-cell copy-number profiles. The latter can be organized by clonal substructure or by any of the associated metadata such as anatomic location and histologic characterization.CONCLUSIONThe pipeline is available as open-source software for Linux and OS X. Its modular structure, extensive documentation, and ease of deployment using Anaconda facilitate its adoption by researchers and practitioners of single-cell genomics. With open-source availability and Massachusetts Institute of Technology licensing, it provides a basis for additional development by the cancer bioinformatics community.

Tracking the immune response with single-cell genomics

The immune system is composed of a diverse array of cell types, each with a specialized role in orchestrating the immune response to pathogens or cancer. Even within a single cell ‘type,’ individual cells can access a wide spectrum of differentiation and activation states, which reflect the physiological response of each cell to the tissue environment and immune stimuli. Thus, the cellular diversity of the immune system is inherently quite complex and understanding this complexity has greatly benefited from technologies that measure immune responses at single-cell resolution, in addition to the systems-level response as a whole. In this Commentary, we focus on recent work at the interface of immunology and single-cell genomics and highlight advances in technologies and their application to immune cells. In particular, we highlight recent single-cell genomic profiling studies of T cells, since somatic rearrangements in the T cell receptor (TCR) loci enable the tracking of clonal T cell responses through space and time. Finally, we discuss opportunities for future use of these technologies in understanding vaccination and the basis for effective vaccine-induced immunity.

Abstract 4354: Molecular signature of multiple myeloma progression through single-cell RNA-seq

Abstract Multiple myeloma (MM) is a malignant plasma cell disorder with well-defined primary genetic/cytogenetic abnormalities. Investigating genetic heterogeneity in the MM cells, may have an impact on therapeutic approaches and assessment of response to treatment. Single cell genomic profiling technology provides an opportunity for us to identify genetic heterogeneity during the pathogenesis of MM progression and select the most appropriate clinical interventions. We performed single cell whole transcriptome analysis in 597 CD138 positive cells derived from 15 patients at different stages of MM progression. Using Coefficient of Variation (CV) approach, we identified 790 genes and defined four main groups reflective of increasing levels of aggressiveness at the single cell level. Differential gene expression and gene set enrichment analyses showed that protein homeostasis pathways, unfolded protein response and mTORC1 related gene sets were significantly enriched at the most aggressive level consistent with the MM cell progression and predicts the overall survival. Our results provide novel insights into the cellular heterogeneity within each MM patient and a foundation for therapeutic opportunities including proteasome inhibitors. Citation Format: Jin Sung Jang. Molecular signature of multiple myeloma progression through single-cell RNA-seq [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 4354.

Abstract 3004: Single-cell mutational profiling of clonal evolution in myelodysplastic syndromes (MDS) during therapy and disease progression

Abstract Myelodysplastic syndromes (MDS) are a collection of clonal diseases with dysfunctional hematopoietic stem cells, characterized by ineffective hematopoiesis, cytopenias, and dysplasia. MDS is increasingly being recognized as a molecularly heterogeneous disease with variability in clinical phenotype, prognosis, and response to treatment. The clonal evolution in response to therapy remains a challenge in the management of MDS. However, resolution of complex clonal architectures is difficult with current bulk sequencing approaches. High-throughput single-cell genomic profiling enables the resolution of tumor heterogeneity, and may improve clinical diagnosis and treatment monitoring by allowing for characterization and early identification of resistant subclonal populations. To enable the characterization of genetic heterogeneity in tumor cell populations, we developed a novel microfluidic approach that barcodes amplified genomic DNA from thousands of individual cells confined to droplets. The barcodes were then used to reassemble the genetic profiles of cells from next-generation sequencing data. We applied this approach to sequential clinical MDS samples, genotyping the most clinically relevant loci across more than 15,000 individual cells. Additionally, to study effects of subclonal mutations on drug sensitivity, ex vivo functional testing was performed on red blood cell-lysed peripheral blood and/or bone marrow aspirate patient samples. Targeted single-cell sequencing was able to recapitulate bulk sequencing data from both peripheral blood and bone marrow aspirate samples. Furthermore, the single-cell nature of our approach enabled definitive determination of mutational co-occurrence within the same cell. For examples in one sample, bulk sequencing identified mutations in JAK2 and NRAS both at 3% variant allele frequency (VAF), where single-cell analysis suggested that these mutations were mutually exclusive, each defining a distinct subclone. Single-cell sequencing allowed for serial monitoring of clonal evolution, with analysis of sequential samples from this same patient showing increase of NRAS clone from 2.5% to 24.7% at time of disease progression after hypomethylating agent therapy. Furthermore, single-cell analysis was able to identify a distinct subclone characterized by a KRAS mutation (0.4% at initiation of therapy and 6.7% at relapse), missed by serial bulk sequencing. Taken together, our results suggest a greater degree of heterogeneity in MDS samples than suggested with bulk sequencing methods alone, and demonstrate utility of single-cell sequencing for sequential monitoring and identification of resistant clones prior to therapy initiation. We show here that this approach is a feasible, scalable, and effective way to identify and track heterogeneous populations of cells in MDS. Citation Format: Alexey Aleshin, Peter L. Greenberg, Bruno C. Medeiros, Diane Heiser, Marianne Santaguida, Sacha Prashad, Robert Durruthy- Durruthy, Dennis J. Eastburn. Single-cell mutational profiling of clonal evolution in myelodysplastic syndromes (MDS) during therapy and disease progression [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 3004.

Abstract P1-06-11: A targetable EGFR-driven tumor-initiating program in breast cancer

Abstract Background: Inter- and intra-tumour heterogeneity underlies variability in therapeutic response. Although targeting of the epidermal growth factor receptor (EGFR) in breast cancer has failed to demonstrate clinical efficacy at the population level, complete and durable responses have been reported at low frequencies. The molecular determinants of these responses are unknown, but are of importance in the era of precision medicine. Results: We performed a patient-derived xenograft (PDX) clinical trial with gefitinib in a breast cancer PDX cohort. Consistent with clinical trial data, gefitinib exhibited limited efficacy across most models. One PDX, however, demonstrated a complete and durable (>6 months) clinical response, and was subject to deep molecular profiling to identify determinants of response. Exome sequencing revealed no single nucleotide variants or copy number alterations in EGFR pathway members. EGFR was differentially expressed between the two major cellular subpopulations identified by single-cell RNAseq and this cellular heterogeneity in EGFR expression was validated immunohistochemically. Fluorescence-activated cell sorting of the EGFRhi subpopulation revealed cells with enhanced stem-like properties, including ALDH activity, sphere-forming capacity in vitro, ability to form tumours in vivo and seeding lung micrometastases from orthotopically transplanted tumours. Tumourspheres derived from EGFRhi cells developed into mixed EGFRhi and EGFRlo subpopulations, as did macrometastases, supporting that EGFRhi subpopulation can self-renew and re-populate. Analysis of expressed SNVs in the single-cell RNAseq data, filtered by variants identified from exome sequencing, showed no clonal segregation, supporting a non-clonal origin of the functionally distinct EGFRhi and EGFRlo subpopulations. This EGFR-driven tumour initiating cell program was observed in independent PDX models, some which showed growth inhibition in response to gefitinib. Conclusions: Using bulk and single-cell genomic profiling, we identified and functionally validated an EGFR-driven tumour-initiating program in a subset of aggressive breast tumours, which may be predictive of gefitinib sensitivity. This contradicts traditional beliefs that good therapeutic targets are homogenously expressed, in that we show that a target displaying intra-tumour heterogeneity can be effective so long that it is expressed in the tumour-initiating population. Citation Format: Savage P, Saleh SMI, Wang Y-C, Revil T, Badescu D, Liu L, Iacucci E, Zuo D, Bertos N, Munoz-Ramos V, Asselah J, Meterissian S, Omeroglu A, Hébert S, Kleinman C, Park M, Ragoussis J. A targetable EGFR-driven tumor-initiating program in breast cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P1-06-11.

Single-cell genomic profiling of acute myeloid leukemia for clinical use: A pilot study.

Although bulk high-throughput genomic profiling studies have led to a significant increase in the understanding of cancer biology, there is increasing awareness that bulk profiling approaches do not completely elucidate tumor heterogeneity. Single-cell genomic profiling enables the distinction of tumor heterogeneity, and may improve clinical diagnosis through the identification and characterization of putative subclonal populations. In the present study, the challenges associated with a single-cell genomics profiling workflow for clinical diagnostics were investigated. Single-cell RNA-sequencing (RNA-seq) was performed on 20 cells from an acute myeloid leukemia bone marrow sample. Putative blasts were identified based on their gene expression profiles and principal component analysis was performed to identify outlier cells. Variant calling was performed on the single-cell RNA-seq data. The present pilot study demonstrates a proof of concept for clinical single-cell genomic profiling. The recognized limitations include significant stochastic RNA loss and the relatively low throughput of the current proposed platform. Although the results of the present study are promising, further technological advances and protocol optimization are necessary for single-cell genomic profiling to be clinically viable.

Abstract A34: Single cell genomic profiling of circulating tumor cells (CTCs) in metastatic disease to characterize disease heterogeneity

Abstract Background: Inter- and intra-patient tumor heterogeneity can have a drastic impact on the efficacy of targeted therapy without accurate patient stratification. Emerging literature suggests wide-spread prevalence of intra-tumor heterogeneity (ITH) across most major solid tumor types driven by separate subclonal genomic alterations, thus highlighting the need to understand the proportion of heterogeneous tumor subclonal populations with clinically relevant genomic alterations. Circulating tumor cells (CTCs) have been shown to reflect the active metastatic populations, and Epic Sciences' non-enrichment CTC analysis platform allows for single cell resolution and a more accurate estimate genomic heterogeneity within the population. Here, we demonstrate Epic Sciences CTC Platform capabilities to characterize individual CTCs from a simple blood draw for known markers of therapeutic sensitivity related to genome wide instability (PARP inhibitors, immune check-point inhibitor sensitivity), amplification or deletion of clinically relevant tumor suppressors or oncogenes (PI3K inhibitors or AR targeted therapy), and/or single nucleotide and INDEL variants (Tyrosine Kinase Inhibitor sensitivity). Methods: Healthy donor blood was spiked with well-characterized cancer cell lines to create mock test samples for downstream single-cell genomic analysis. CTCs were enumerated and characterized using the Epic Sciences Platform. Identified single CTCs were recovered, and their whole genome amplified and characterized for the presence of genome wide instability, specific tumor suppressor loss, and oncogene amplification or point mutations by whole genome sequencing and/or mutation specific PCR. Large-scale state transitions (LSTs) and percent genome alterations (PGAs) were measured as surrogates of genomic instability. Z-scores were calculated in specific genomic loci containing tumor suppressor or oncogenes to characterize their amplification or deletion. Results: In healthy donor blood spiked with prostate cancer cell lines (PC3, VCaP or LnCaP), overall genomic instability was observed across a wide dynamic range. Loss of the PTEN tumor suppressor, associated with sensitivity to PI3K inhibitors, was confirmed in PC3 cells. Amplification of the androgen receptor (AR), associated with resistance to AR targeted therapies, was confirmed in VCaP cells. In blood spiked with lung adenocarcinoma the T790M EGFR point mutation, associated with resistance to TKIs, was also detected reproducibly. Genomic profiling of CTCs from prostate cancer patients have shown significant intra-patient heterogeneity in copy number alterations, genomic instabilities and number of mutations. Conclusions: The data shown here demonstrate the feasibility of detecting actionable genomic alterations at the single cell level using the Epic CTC platform. Understanding CTC heterogeneity has great potential for more accurate patient stratification prior to treatment with targeted therapies. Citation Format: Stephanie Greene, Angel Rodriguez, Jerry Lee, Laura Leitz, Mark Landers, Adam Jendrisak, Ryon Graf, Shannon Werner, Yipeng Wang, Ryan Dittamore, Dena Marrinucci. Single cell genomic profiling of circulating tumor cells (CTCs) in metastatic disease to characterize disease heterogeneity. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr A34.

Abstract A35: Single cell genomic profiling of circulating tumor cells (CTCs) from metastatic colorectal cancer (mCRC) identify tumor heterogeneity and rare somatic driver alterations

Abstract Background: Mostly asymptomatic until late stage, colorectal cancer is driven by the successive accumulation of genetic alterations resulting in genomic instability within subclonal tumor populations. mCRC often progresses as a subclonally diverse multifocal disease due to selective therapeutic pressure, the surrounding tumor microenvironment, and underlying genomic heterogeneity. Targeted therapies against EGFR, VEGF or BRAF have shown increased response in a subset of patients; however, patient stratification using standard population analysis of DNA markers from tumor biopsy, (i.e. chromosomal instability, microsatellite instability, promoter methylation, resistance mutations), is problematic due to tumor heterogeneity. CTCs reflect the active metastatic subclonal populations at any given time, making single cell analysis of DNA markers a more accurate, real-time picture of cumulative metastatic diversity. Using Epic's enrichment-free CTC analysis platform, we characterized individual CTCs from a mCRC patient to understand the extent of intra-patient genomic heterogeneity, including the presence genomic instability and point mutations. We compared the prevalence of clinically relevant subclonal alterations within patient CTCs to CRC TCGA data, offering insights into identification of therapeutic opportunities and potential mechanisms of resistance. Methods: Blood was collected from a heavily pretreated mCRC patient and was processed for CTC analysis using the Epic Platform. 34 CTCs were individually recovered, lysed, whole genome amplified, constructed into shotgun libraries and target enriched for all coding regions of 500 pan-cancer genes. Enriched libraries were sequenced to an average depth of 697X coverage by 2×150 PE sequencing. Sequences were aligned and somatic mutations were determined using VarScan with the patient's WBC as germline reference. Variants were filtered for functional gain- or loss-of-function mutations by SIFT/PolyPhen2 and selected based on low frequency in 1000g database. Genomic instability and loss of heterozygosity (LOH) was also assessed. Somatic variants deriving from the patient CTC cohort and TCGA CRC cohort of 302 patients were annotated, analyzed, and compared using GenePool™ software (Station X). Results: MLL3 alterations, frequently observed in primary CRC biopsies (14%), were identified in 70% of all CTCs sequenced. Previously cited somatic variants were detected in minor subclonal populations of CTCs, including APC (12%), BRCA1/2 (8%), KRAS (6%), PI3KCA (6%) and TP53 (6%). A wide range of genomic instabilities and LOH was also observed across CTCs. Conclusions: The Epic CTC platform is suited to identify subclonal population of CTCs harboring clinically relevant genomic alterations on a single cell level, which can inform clonal drift, identify rare clonal populations, and enable patient stratification at higher resolution. Citation Format: Stephanie Greene, Jerry Lee, Mark Landers, Sandeep Sanga, Adam Jendrisak, Ryon Graf, Jessica Louw, Shannon Werner, Yipeng Wang, Ryan Dittamore, Dena Marrinucci. Single cell genomic profiling of circulating tumor cells (CTCs) from metastatic colorectal cancer (mCRC) identify tumor heterogeneity and rare somatic driver alterations. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr A35.