Single-cell Genotyping Research Articles

Abstract 1692: Single-cell DNA sequencing from frozen endometrial tumors to address clonal evolution of somatic mutations

Abstract Background: Single-cell DNA sequencing with droplet microfluidics has enabled the characterization of genetic heterogeneity in hematologic malignancies and fresh solid tumors through the direct DNA sequencing of thousands of single cells from individual samples without the need of whole-genome amplification. The application of this technology in banked solid tumor samples has been rather limited to date, however. Here, we describe and validate a method to characterize clonal evolution of somatic mutations from frozen tumor specimens, utilizing endometrial carcinomas (ECs) as an example. Methods: Tissue cores were obtained from frozen ECs and the extracellular matrix was enzymatically digested. A subset of extracted nuclei were used for bulk DNA extraction and targeted sequencing. The remaining nuclei were encapsulated with cell lysis buffer, and the DNA was primed and barcoded with hydrogel beads containing 317 cancer gene amplicons using a Tapestri instrument (Mission Bio). Following droplet PCR, Illumina libraries were generated and sequenced on a NovaSeq6000. Sequencing reads were trimmed and mapped to the reference genome. Barcode sequences were error-corrected and cells were detected from barcodes based on number of reads assigned to each barcode and amplicon read completeness. Genotyping was performed using GATK and the allele drop-out (ADO) rate was calculated. Non-neoplastic cells were defined by the absence of somatic mutations. Allele-specific copy number (ASCN) profiles for each tumor cell were estimated by normalizing mean reads per cell per amplicon to reads of non-neoplastic cells. Results: We performed single-cell genotyping and ASCN profiling on two ECs with matched bulk sequencing data. For EC1, 5,004 tumor and 132 non-neoplastic cells were sequenced, for a 97% tumor purity. For EC2, 2,981 tumor and 165 non-neoplastic cells were obtained, for a 95% tumor purity. The average number of reads per amplicon per cell for EC1 and EC2 was 116 and 146 with an ADO rate of 6% and 8%, respectively. The data obtained from single-cell analysis were concordant with the matched bulk sequencing data in terms of clonal and subclonal mutations, and their respective cancer cell fraction. Single-cell sequencing further revealed the presence of loss of heterozygosity and emergence of minor subclones as late events in tumor evolution, which could not be inferred by bulk sequencing. In EC2, branch mutations displayed a spectrum consistent with microsatellite instability (MSI), reflecting its acquired MSI phenotype. Conclusions: Here we describe a robust method to perform single-cell targeted DNA sequencing with droplet microfluidics of individual nuclei isolated from frozen tissue samples, providing the opportunity to determine the repertoire of somatic mutations at single cell resolution and to infer cancer clonal architecture and dynamics utilizing banked solid cancer tissue samples. Citation Format: Arnaud Da Cruz Paula, Yingjie Zhu, Shirin Issa Bhaloo, Fresia Pareja, Timothy Hoang, Pier Selenica, Etta Hanlon, Thais Basili, Higinio Dopeso, Nadeem Abu-Rustum, Jorge Sergio Reis-Filho, Britta Weigelt. Single-cell DNA sequencing from frozen endometrial tumors to address clonal evolution of somatic mutations [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 1692.

Same-Cell Co-Occurrence of RAS Hotspot and BRAF V600E Mutations in Treatment-Naive Colorectal Cancer.

Mitogen-activated protein kinase pathway-activating mutations occur in the majority of colorectal cancer (CRC) cases and show mutual exclusivity. We identified 47 epidermal growth factor receptor/BRAF inhibitor-naive CRC patients with dual RAS hotspot/BRAF V600E mutations (CRC-DD) from a cohort of 4,561 CRC patients with clinical next-generation sequencing results. We aimed to define the molecular phenotypes of the CRC-DD and to test if the dual RAS hotspot/BRAF V600E mutations coexist within the same cell. We developed a single-cell genotyping method with a mutation detection rate of 96.3% and a genotype prediction accuracy of 92.1%. Mutations in the CRC-DD cohort were analyzed for clonality, allelic imbalance, copy number, and overall survival. Application of single-cell genotyping to four CRC-DD revealed the co-occurrence of both mutations in the following percentages of cells per case: NRAS G13D/KRAS G12C, 95%; KRAS G12D/NRAS G12V, 48%; BRAF V600E/KRAS G12D, 44%; and KRAS G12D/NRAS G13V, 14%, respectively. Allelic imbalance favoring the oncogenic allele was less frequent in CRC-DD (24 of 76, 31.5%, somatic mutations) compared with a curated cohort of CRC with a single-driver mutation (CRC-SD; 119 of 232 mutations, 51.3%; P = .013). Microsatellite instability-high status was enriched in CRC-DD compared with CRC-SD (23% v 11.4%, P = .028). Of the seven CRC-DD cases with multiregional sequencing, five retained both driver mutations throughout all sequenced tumor sites. Both CRC-DD cases with discordant multiregional sequencing were microsatellite instability-high. Our findings indicate that dual-driver mutations occur in a rare subset of CRC, often within the same tumor cells and across multiple tumor sites. Their presence and a lower rate of allelic imbalance may be related to dose-dependent signaling within the mitogen-activated protein kinase pathway.

High Throughput Multi‐omic Characterization of Clonal Hematopoietic Stem Cells During Progression of Myeloid Malignancies

Hematopoietic stem cells (HSCs) are responsible for giving rise to all other lineages of blood cells in the body. Over time, mutations in HSCs can promote the outgrowth of clonal populations that outcompete other HSCs, resulting in a phenomenon called Clonal Hematopoiesis of Indeterminate Potential (CHIP). Though not cancerous in and of itself, CHIP can progress to more serious hematologic disorders, such as the Myelodysplastic Syndromes (MDS) and Acute Myeloid Leukemia (AML). The mechanisms of clonal expansion, by which certain mutant HSCs acquire a competitive advantage over other HSCs, currently remain largely undeciphered, as are the mechanisms by which clonal HSCs drive the initiation of MDS and contribute to the development of AML. Moreover, previous studies have shown that such mutant HSCs are resistant to conventional therapies and may act as reservoirs for disease relapse and progression. In the past, researchers were hindered by bulk cell analyses and the relative rarity of HSCs, but advances in single‐cell omics have now enabled us to explore the molecular heterogeneity of clonal HSCs and identify distinct clonal populations based on genotype and cell surface phenotype. We used single cell RNA sequencing to examine the transcriptional dynamics of purified HSCs from MDS and AML patients before and after treatment, as well as from age matched elderly controls. Interestingly, dimensionality reduction methods such as UMAP and tSNE revealed a reservoir of control HSCs that clustered with MDS HSCs. Upon comparison with other normal control HSCs, we found that genes associated with aging, mitochondrial function, and particular ion channels were strongly upregulated in these “MDS‐like” control HSCs, while genes involved in ribosomal and translation activity, along with certain surface markers, were substantially downregulated. Additionally, ribosomal transcripts were depleted in MDS HSCs from patients who did not respond to treatment. These results support the notion that the most immature HSCs, which impose the strictest constraints on translation, might clonally expand and initiate CHIP and/or MDS upon acquiring driver mutations, also serving as treatment resistant populations that underlie disease relapse. We also subjected purified MDS HSCs to simultaneous single cell targeted DNA sequencing and cell surface phenotyping, which allowed us to correlate the cell surface phenotype and genotype for specific clonal populations and identify cell surface markers that can be used to isolate HSCs with enhanced engraftment ability. Taken together, these results indicate that combined single cell genotyping and phenotyping can be used to track clonal populations across different stages of pathogenesis, providing further insight about the development of CHIP and its progression to MDS and AML.

Construction of a chromosome-level Japanese stickleback species genome using ultra-dense linkage analysis with single-cell sperm sequencing.

It is still difficult to construct the genomes of higher organisms as their genome sequences must be extended to the length of the chromosome by linkage analysis. In this study, we attempted to provide an innovative alternative to conventional linkage analysis by devising a method to genotype sperm using 10× Genomics single-cell genome sequencing libraries to generate a linkage map without interbreeding individuals. A genome was assembled using sperm from the Japanese stickleback Gasterosteus nipponicus, with single-cell genotyping yielding 1 864 430 very dense hetero-SNPs and an average coverage per sperm cell of 0.13×. In total, 1665 sperm were used, which is an order of magnitude higher than the number of recombinations used for conventional linkage analysis. We then improved the linkage analysis tool scaffold extender with low depth linkage analysis (SELDLA) to analyze the data according to the characteristics of the single-cell genotyping data. Finally, we were able to determine the chromosomal location (97.1%) and orientation (64.4%) of the contigs in the 456 Mb genome of G. nipponicus, sequenced using nanopores. This method promises to be a useful tool for determining the genomes of non-model organisms for which breeding systems have not yet been established by linkage analysis.

Single-Cell Quantitative Phenotyping via the Aptamer-Mounted Nest-PCR (Apt-nPCR).

Analyzing single-cell phenotypes is increasingly required in biomedical studies, for non-genetic understanding of cellular activities and the biological significance of rare cell subpopulations. However, as compared to the genotypic analysis, single-cell phenotype analysis is technically more challenging. Herein, a tractable method that allows quantitative phenotyping of single cell is developed in this work, termed as the aptamer-mounted nest-PCR (Apt-nPCR). In specific, only two rounds of PCR reactions are required to complete the analysis, where aptamers (short oligonucleotides that bind to specific target molecules) are used as the recognition elements to bind antigens and also as the templates of nPCR for multiplexed and quantitative detection. So, quantitative information of these target antigens can be revealed by quantitative PCR analysis of these aptamers, which can thus be used to interpret cell phenotypes in a quantitative-to-qualitative way. By addressing two technical issues that are involved in single-cell phenotype analysis─multiplexed detection plus high sensitivity, we have shown the availability of this method for single-cell phenotyping. Therefore, the Apt-nPCR method may represent a tractable method to facilitate the single-cell phenotype analysis, which can be used as a complementary method against these single-cell genotyping methods in our daily research.

Peripheral Blood Single-Cell Genotyping and Phenotyping in Multiple Myeloma Reveals Shared Mutations across Multiple Hematopoietic Cell Lineages

Background:Multiple myeloma (MM) is a hematologic malignancy of terminally differentiated plasma cells. While most genomic studies of the disease have focused on characterizing isolated bone marrow plasma cells, few have addressed somatic mutations within the immune microenvironment. We previously demonstrated that somatic mutations are significantly more numerous in unsorted blood compared to bone marrow and that the number of mutations correlated with elevated markers of disease. We also reported that genes implicated in clonal hematopoiesis of indeterminate potential (CHIP) are commonly mutated in the blood from patients with MM.Aim:To integrate single-cell DNA mutation analysis and protein expression to map the clonal relationship between circulating plasma cells and non-plasma cells within the peripheral blood of patients with MM.Methods:Although bulk DNA sequencing can estimate clonal heterogeneity using analytic deconvolution techniques, it is unable to distinguish which mutations occur in the same clone(s) nor can it correlate genotype with immunophenotype from the same cell. To accurately describe the clonal architecture of the blood, we combined targeted single-cell DNA sequencing (scDNA-seq) coupled with protein expression to define mutations specific to cell phenotypes using the Tapestri Platform (Mission Bio). For this analysis, we developed a custom amplicon panel covering 22 frequently mutated genes in MM and/or CHIP to perform scDNA-seq and 4 antibodies targeting CD138, CD19, CD3, and CD33 for immunophenotyping or plasma cells, B cells, T cells, and myeloid cells respectively.Results:Peripheral blood mononuclear cells were isolated from 17 blood samples collected from 12 patients with MM. Three samples were collected at diagnosis before treatment, 8 were collected post-treatment during remission, and 6 were collected at relapse. Serial samples were obtained before and after autologous stem cell transplant from 4 patients. A total of 85,262 cells were sequenced uncovering 154 unique variants in 22 genes after quality filtering. There were 47 somatic variants, defined as those present in less than 40% of cells per sample. There were 52,136 total clones, defined as cells with identical genotypes, within this heterogenous population of cells. The most common non-synonymous somatic mutations were identified in genes SETD2, KMT2C, PPM1D. An immunophenotype was assigned to 82% of cells according to the protein with the most abundant number of reads. Dimensionality reduction of single-cell genotype using t-distributed stochastic neighbor embedding (t-SNE) effectively clustered cells according to sample and by patient in cases where there were multiple samples from the same individual. Patients with active myeloma and known circulating plasma cells detected by clinical flow cytometry were found to have the greatest proportion of CD138+ cells as well as the highest mutational burden. It was rare to detect the same clone in two separate samples collected from the same patient at different time points (median time be collection was 248 days) which may be due to under-sampling or clonal evolution. Surprisingly, all somatic variants detected in plasma cells, were also present in other cell types suggesting they were acquired from a common progenitor cell rather than after differentiation.Conclusions:scDNA-seq coupled with immunophenotyping of the peripheral blood of patients with MM reveals somatic variants are shared across multiple hematopoietic cell lineages including plasma cells indicating these mutational are acquired from a common progenitor. DisclosuresDurruthy-Durruthy: Mission Bio Inc.: Current Employment. Arribas-Layton: Mission Bio, Inc.: Current Employment. Wang: Mission Bio, Inc: Current Employment.

Highly Multiplexed Cell Surface Immunophenotyping with Genotyping and Concurrent Transcriptomic Analysis of NPM1 mutated Acute Myeloid Leukemia

BACKGROUNDThe pathogenesis of acute myeloid leukemia (AML) is often attributed to the presence of somatic allelic variant(s) in hematopoietic stem/progenitor cells. However, malignant clones may have heterogenous cell-surface immunophenotypes including overlap with non-malignant cells. While leukemia-associated immunophenotypes and difference from normal approaches are used for flow cytometric assessment during and after treatment, such analysis may underrepresent true leukemia disease burden. Assessments of AML measurable residual disease (MRD) using flow cytometry and molecular methods have been reported as discrepant. Single-cell RNA sequencing experiments have recently attempted to distinguish malignant cells based on gene expression and/or immunophenotypic profiles alone. We hypothesized that single-cell genotyping of mutated transcript(s) coupled with broad surface proteome and transcriptome profiling could provide an integrated multimodal method for AML characterization. METHODSWe adapted the previously reported “genotyping of transcriptomes” (PMID: 31270458) to identify cells carrying the NPM1 type A mutation commonly seen, and typically stable throughout the disease course, in AML. Healthy human peripheral blood mononuclear cells (PBMC) were mixed with an AML cell line carrying NPM1 type A mutation (OCI-AML3) at 7:3 ratio and labelled with 163 oligo-tagged antibodies. Single cell 3'v3 gene expression- (GEX), antibody derived tag- (ADT) and genotyping of NPM1 (GNPM) -libraries (10X Genomics) were sequenced on the NovaSeq 6000 (Illumina). Results were processed using Seurat 4.0 toolkit. RESULTSIn total, 72% (n=1680) of barcoded cells could be genotyped for NPM1. Of the genotyped cells, 59% (n = 986) were not NPM1 mutated. Visualization using Uniform Manifold Approximation and Projection (UMAP) showed separation of healthy PBMCs and OCI-AML3 cells using protein data, confirmed by annotation using NPM1 genotyping (Figure 1). We found a significant positive correlation between mRNA and corresponding cell surface protein expressions in non-mutated (Pearson's coefficient, r = 0.502, p = 6.87e-11) and NPM1 mutated (r= 0.392, p= 7.5e-7) cells. Compared to non-mutated, NPM1 mutated cells showed nearly 14-fold higher NPM1 transcript levels. In addition, a total 63 proteins were highly expressed on the surface of NPM1 mutated cells (Figure 2). Among these, CD33 and CD36 showed maximum 8-fold increase in expression. Other highly expressed proteins with at least >2.5-fold change were cell adhesion molecules (including CD328, CD155, and CD56), extracellular matrix binding proteins (CD49a/b) and interleukin receptor (CD123). CONCLUSIONOverall, our results demonstrate proof of principle that high-throughput cell surface proteome, transcriptome and genotyping analysis can be simultaneously performed to comprehensively and confidently characterize individual AML cells. Patient-specific multiomics data with broad cell-surface proteomic screening may allow novel target identification for monitoring and/or therapeutic intervention. Ongoing work will now use this methodology to characterize a cohort of NPM1 mutated AML patient samples. [Display omitted] DisclosuresHourigan: Sellas: Research Funding.

SureTypeSCR: R package for rapid quality control and genotyping of SNP arrays from single cells.

Genotyping of single cells using single nucleotide polymorphism arrays is a cost-effective technology that provides good coverage and precision, but requires whole genome amplification (WGA) due to the low amount of genetic material. Since WGA introduces noise, we recently developed SureTypeSC, an algorithm to minimize genotyping errors. Here, we present SureTypeSCR, an R package that integrates a state-of-the-art algorithm (SureTypeSC) for noise reduction in single cell genotyping and unites all common parts of genotyping workflow in a single tool. SureTypeSCR is built on top of the tidyverse ecosystem, which facilitates common operations over the data and allows users to create and experiment with the genotyping pipeline. Furthermore, the workflow of SureTypeSCR can also be used for standard genotyping of bulk DNA for batch processing in a single pipeline.SureTypeSCR is avaliable from: https://github.com/Meiomap/SureTypeSCR.

Abstract 3051: On the origin of pediatric leukemia: studying somatic mutations in human blood

Abstract The acquisition of oncogenic mutations with age is believed to be rate limiting for carcinogenesis and aging is the biggest risk factor for cancer. However, children can also develop cancers even though their cells are less damaged by age. Here, we studied the cellular origin and time of onset during development of pediatric acute myeloid leukemia (pAML) to increase our understanding of why children can get cancer. We compared by whole genome sequencing individual hematopoietic stem and progenitor cells (HSPCs) and pAML blasts of the same leukemic bone marrow as well as nonmatching healthy HSPCs. We found that the number of clonal mutations in the majority of pAML cases was increased and mutational process contribution altered as compared to normal HSPCs, showing activity of specific processes that are either unique to fetal development or normally not observed in healthy HSPCs. These observations could be explained by altered selection dynamics early in life. Surprisingly, pAML cases with an overall mutation burden comparable to age matched healthy HSPCs showed worse overall and event-free survival. Identifying shared somatic mutations between individual HSPC clones and pAML using single-cell genotyping allowed us to pinpoint the developmental lineage that gave rise to the pAML. Our study provides new insights into the etiology of pAML and underscores the clinical potential of whole genome sequencing in pediatric leukemia. Citation Format: Arianne Brandsma. On the origin of pediatric leukemia: studying somatic mutations in human blood [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3051.

Abstract 2259: Single-cell multi-omics analysis of SNV, CNV, and protein expression

Abstract Recent advancements in precision medicine, while highly promising, presents a major technical challenge to researchers due to disease heterogeneity. The emergence of single-cell technologies has greatly refined the resolution in which sample diversity can be investigated, enhancing the efficiency of selecting appropriate molecular targets. Additionally, applying multi-omics analysis on single cells would further improve the understanding of cell-to-cell heterogeneity by providing unique insights on cellular and genetic composition. Using a two-step droplet microfluidic technology, the Mission Bio Tapestri Platform enables multiplex-PCR based high-throughput targeted DNA sequencing in single cells to obtain single-nucleotide variation (SNV) and copy number variation (CNV) information. By leveraging this technology, a new workflow is developed to detect protein expression in addition to DNA genotype in the same single cells. In this approach, cells are labeled with a pool of oligonucleotide-conjugated antibodies prior to loading the cells into the Tapestri Instrument. Sequencing libraries are then prepared from both antibody oligonucleotides and the amplified DNA sequences, followed by identification of single-cell DNA genotypes and protein signatures from the sequencing readout. The number of protein targets can be in the range from 6 to over 40, which is beyond the limit for a single flow cytometry run. This method has been successfully performed on cell lines, fresh and frozen PBMCs, as well as clinical samples. In an acute myeloid leukemia (AML) sample, combined single-cell SNV, CNV, and protein expression data illustrated the heterogeneity within the sample. The data clearly identified CD3+ T cells and CD19+ B cells without pathogenic SNVs and CNVs. CD34hiCD11blo and CD34loCD11bhi subpopulations were also identified within the cells carrying the same pathogenic SNVs and CNVs. We believe that this novel multi-omics technology will facilitate new discoveries in the complex relationship between genotype and phenotype, enable a better understanding of disease biology, and subsequently improve the design of diagnostics and therapies. Citation Format: Aik Ooi, Dalia Dhingra, Adam Sciambi, Kate Thompson, Jacqueline Marin, Saurabh Parikh, Mani Manivannan, David Ruff. Single-cell multi-omics analysis of SNV, CNV, and protein expression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2259.

Molecular and Functional Characterization of Disease-Propagating Stem Cells in Juvenile Myelomonocytic Leukemia

Juvenile Myelomonocytic Leukemia (JMML) is a rare, poor prognosis childhood leukemia characterized by aberrant proliferation of dysplastic myeloid cells and germline or somatic RAS-activating mutations. The only curative treatment is hematopoietic stem cell (HSC) transplantation, however, post-transplant relapse is frequent (~35%) and long-term disease-free survival is ~64%. Approximately half of patients show evidence of clonal evolution with acquisition of additional abnormalities, e.g. mutations of epigenetic/spliceosome components which confer a particularly adverse prognosis. The cellular hierarchy in JMML is poorly characterized with little known about the identity of leukemia stem cells (LSCs), a crucial step towards understanding disease evolution and propagation of relapse.We therefore set out to functionally and molecularly characterize LSCs in a cohort of 16 JMML patients enrolled in the UK NIHR Paediatric MDS/JMML study with samples at diagnosis, post-transplant and relapse. All patients underwent genetic analysis using a novel targeted next generation sequencing (NGS) panel confirming typical RAS pathway mutations in all patients and additional mutations in epigenetic/spliceosome genes in 44% of the patients. Based on adult MDS/MPN, we hypothesized that the CD34+ HSC and progenitor cell compartment (HSPC) would be preserved in JMML and that JMML-LSCs may reside within the phenotypic HSC compartment (Lin-CD34+CD38-CD90+CD45RA-). However, FACS analysis revealed disruption of HSPCs with a 2 fold reduction in phenotypic HSCs (Lin-CD34+CD38-CD90+CD45RA-) and an aberrant Lin-CD34+CD38-CD90+CD45RA+ (90+/45RA+) population not present in normal pediatric bone marrow (BM). In single cell and bulk in vitro assays, JMML-HSPCs had myeloid-biased lineage output with reduced and dysplastic erythroid and severely impaired megakaryocyte and lymphoid potential. Using a novel, integrated single cell genotyping technique combining HSPC index-sorting with clonogenic assays we backtracked patient-specific mutations to HSPC populations, establishing that: 1. all somatic mutations could be backtracked to the phenotypic HSC compartment (492 colonies genotyped for 9 different mutations in 4 patients); and 2. clonal evolution events (eg ASXL1 mutation) in JMML occurred within the HSC compartment with RAS pathway mutations as a “first hit”. Moreover, a PTPN11 mutation could be traced back to cells within the HSPC compartment of a post-BMT patient prior to molecular evidence of overt disease relapse in whole BM.We next assessed the stem cell capacity of FACS-purified phenotypic HSPCs in JMML by in vitro long-term culture initiating cell (LTC-IC) assays and in vivo xenotransplantation in NSG mice. LTC-IC assays consistently demonstrated self-renewal capacity in HSCs together with variable LTC potential in the novel 90+45RA+ population. Similarly, transplantation of purified JMML HSCs resulted in higher engraftment at 5, 10, and 16 weeks than 90+45RA+ or GMP populations (16 weeks post BMT: HSC = 91.7% human CD45; 90+/45RA+ = 0.14%; GMP = 0.2%, p < 0.0005 for HSC vs GMP, n = 3). Importantly, mice transplanted with HSCs, but not other HSPC populations, developed myeloid leukemia with 67% mortality (p < 0.01, n = 6). Secondary transplantation of HSCs confirmed LT potential of JMML HSCs.Single cell RNA-seq on Lin-CD34+ HSPCs using the Chromium 10X platform (n > 10,000 single cells) showed co-clustering of JMML and normal cells transcriptionally resembling myeloid progenitors expressing MPO and MS4A3, while a distinct cluster of JMML HSPCs uniquely co-expressed fetal genes (e.g. HMGA2), stem cell transcription factors (e.g. GATA2) and myeloid leukemia-associated genes (e.g HEMGN, HOPX, MEF2C). RNA-seq of FACS-purified JMML HSCs also showed upregulation of stem cell and fetal genes (HLF, MEIS1, ABCB1, MPL, and HMGA2) as well as candidate therapeutic targets e.g. MTOR, PIM1, SLC2A1, and CD96 compared to normal HSCs. We confirmed aberrant expression of CD96 in JMML HSCs by flow cytometry, and experiments inhibiting therapeutic targets in vitro and in vivo are underway. In conclusion, we show that JMML HSPCs are organized as a hierarchy, with counterparts of phenotypic HSCs representing disease propagating LSCs. Single cell molecular profiling of JMML HSCs has identified a number of novel potential therapeutic candidates for future clinical development. Disclosuresde la Fuente:bluebird bio: Consultancy; HCA: Consultancy; Jazz Pharmaceuticals: Honoraria. Mead:BMS: Honoraria; Pfizer: Honoraria; Novartis: Honoraria, Research Funding, Speakers Bureau.

CTC counts as a biomarker of prognosis and response in metastatic castration-resistant prostate cancer (mCRPC) from the CARD trial.

161 Background: In the prospective CARD trial (NCT02485691), cabazitaxel significantly improved radiographic progression-free survival (rPFS) and overall survival (OS) versus abiraterone or enzalutamide in patients with metastatic castration-resistant prostate cancer who had received docetaxel and progressed within 12 months with the alternative androgen-signaling-targeted inhibitor. Here we analyzed circulating tumor cell (CTC) counts as a biomarker of prognosis and response to therapy. Methods: Blood samples collected at screening (SC), Cycle 2 (C2) Day 1, and end of therapy were sent to Epic Sciences for CTC analysis. The association between CTC counts, defined as any cytokeratin-positive, CD45-negative cell, and clinical outcomes were pre-specified and analyzed using a multivariate Cox model for time-to-events (rPFS, OS) or a Χ2 test for categorical outcomes (objective tumor response). Results: Of the 255 patients randomized, 237 (92.9%) had evaluable samples at SC and 213 (83.5%) at C2. CTCs ≥ 1 were detected in 204 samples at SC (86%) and 178 samples at C2 (84%), with a median (interquartile range) count per mL of blood of 4.1 (1.3–14.2) and 5.2 (1.5–17.3), respectively. At baseline, higher CTC counts were associated with higher values of lactate dehydrogenase (P = 0.014), alkaline phosphatase (P &lt; 0.001), and prostate-specific antigen (P &lt; 0.001). High CTC counts, measured as a continuous variable at SC (all arms combined) were associated with a shorter OS (P = 0.03), but not rPFS (P = 0.7). However, favorable changes in absolute CTC count from SC to C2, all arms combined, were associated with longer rPFS (P = 0.03). Conversion from high (≥ 3) to low (&lt; 3) CTC count at C2 or maintenance of a low CTC count at C2 was associated with objective tumor response (P = 0.007). Analysis of the associations of CTC androgen receptor variant 7, chromosomal instability, heterogeneity status (Shannon Index), single-cell genotypes (e.g. retinoblastoma/phosphatase and tensin homolog copy-loss), and the presence of CTCs with small-cell/neuroendocrine morphology is ongoing. Conclusions: This preplanned analysis of the CARD trial confirms that baseline CTC counts, measured by Epic Sciences platform, are prognostic. Moreover, early favorable changes in CTC counts from baseline to C2 are associated with response to therapy. Funding: Sanofi. Clinical trial information: NCT02485691.

Roles of the Immune/Methylation/Autophagy Landscape on Single-Cell Genotypes and Stroke Risk in Breast Cancer Microenvironment.

This study sought to perform integrative analysis of the immune/methylation/autophagy landscape on breast cancer prognosis and single-cell genotypes. Breast Cancer Recurrence Risk Score (BCRRS) and Breast Cancer Prognostic Risk Score (BCPRS) were determined based on 6 prognostic IMAAGs obtained from the TCGA-BRCA cohort. BCRRS and BCPRS, respectively, were used to construct a risk prediction model of overall survival and progression-free survival. Predictive capacity of the model was evaluated using clinical data. Analysis showed that BCRRS is associated with a high risk of stroke. In addition, PPI and drug-ceRNA networks based on differences in BCPRS were constructed. Single cells were genotyped through integrated scRNA-seq of the TNBC samples based on clustering results of BCPRS-related genes. The findings of this study show the potential regulatory effects of IMAAGs on breast cancer tumor microenvironment. High AUCs of 0.856 and 0.842 were obtained for the OS and PFS prognostic models, respectively. scRNA-seq analysis showed high expression levels of adipocytes and adipose tissue macrophages (ATMs) in high BCPRS clusters. Moreover, analysis of ligand-receptor interactions and potential regulatory mechanisms were performed. The LINC00276&MALAT1/miR-206/FZD4-Wnt7b pathway was also identified which may be useful in future research on targets against breast cancer metastasis and recurrence. Neural network-based deep learning models using BCPRS-related genes showed that these genes can be used to map the tumor microenvironment. In summary, analysis of IMAAGs, BCPRS, and BCRRS provides information on the breast cancer microenvironment at both the macro- and microlevels and provides a basis for development of personalized treatment therapy.

Abstract 17028: Single Cell Analysis of T Cells Found in Atherosclerotic Plaque

Background: Despite advances in medical therapy, heart attack and stroke remain leading causes of morbidity and mortality. Biological, epidemiological, and clinical studies have shown that many patients (up to 33% in some trials) still have residual inflammatory risk that is associated with plaque rupture, even in the setting of low LDL. Methods and Results: In this study, we analyzed the T cell landscape of coronary atherosclerotic plaque to determine their role in the development of atherosclerosis and its adverse sequelae. We recruited 17 patients undergoing heart transplantation. From each explanted heart, we obtained plaques from the major coronary arteries, digested them to form a single cell suspension, sorted the arteries for CD4 and CD8 T cells into 96 well plates, and submitted them for single cell genotyping and phenotyping ( Figure 1 ). Although peripheral blood from these patients also contains T cell clones, plaque CD8 T cell clones appear to function differently than blood (e.g., producing more BCL6, TNF-, and interferon gamma) further supporting that this is not a bystander effect. We found CD4 and CD8 T cells with identical TCR sequence across the arteries of the same patient, suggesting CD8T cells are purposely recruited to the plaques. When we analyzed the percent clonality based upon location and disease severity, we find that clonality is not dependent on location and is present at all severity levels, especially, in early disease (e.g., AHA Type I-IV lesions. Conclusion: CD8 T cells are clonally expanded in atherosclerotic plaque and appear to function differently than blood, suggesting these cells are purposefully recruited.

Abstract IA10: Uncovering the ecology of glioblastoma with spatial digital profiling and single cell genotyping

Abstract Glioblastoma (GBM) remains one of the biggest challenges in oncology, with median survival of ~15 months. Extreme heterogeneity of the GBM cells within each tumor results in resistance to most of the available therapies. Understanding the relationships between the heterogenous populations of GBM cells and the diverse niches they rely on is key to developing more effective treatment for this aggressive brain tumor. Analysis of the spatial localization of cells with different properties can shed light on their microenvironmental preferences, interactions and potential vulnerabilities. Therefore, we set out to profile 20 GBM cases using methods allowing for single-cell assessment of select genomic features and protein marker expression in intact tissue samples from each tumor. We constructed a tissue microarray (TMA), containing four distant areas from each tumor block to represent the intratumor heterogeneity within each tumor fragment. Based on multicolor immunofluorescent staining with markers of glial cells, immune cells and vasculature, we selected 96 regions of interest, which were then subject to digital spatial profiling for 90 protein markers associated with glioblastoma biology, neurobiology and immunology. Pearson’s correlation and Shannon index calculation based on our profiling results showed that majority of the regions cluster by their tumor of origin, however several highly diverse tumors were also identified. To link the diversity of the protein expression found with the digital spatial profiling with underlying genomic alterations, using the same TMA slide, we performed multiplexed fluorescence in situ hybridization for genes most frequently amplified in GBM. Automated quantification allowed us to collect information from 38,000 individual cells. Based on the presence or absence of the amplifications each cell was assigned a genotype. Since mutation in hTERT promoter has been reported as one of the drivers of GBM, we used STAR-FISH, an in situ single-cell mutation detection assay, to record the frequency and spatial localization of cells harboring this mutation on our TMA. Our datasets provide a unique opportunity to link single-cell genetic diversity to the phenotypic heterogeneity of the tumor cells and their microenvironment. Spatial localization of cells with distinct genotypes allows us to further dissect the interactions between the distinct subclones of GBM cells and uncover their novel dependencies. Citation Format: Kacper A. Walentynowicz, Shreya Yadav, Cristina Vincentelli, Michalina Janiszewska. Uncovering the ecology of glioblastoma with spatial digital profiling and single cell genotyping [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 IA10.

Abstract 5910: Single-cell multiomic analysis of SNV, CNV, and protein expression

Abstract Recent advancements in precision medicine, while highly promising, presents a major technical challenge to researchers due to disease heterogeneity. The emergence of single-cell technologies has greatly refined the resolution in which sample diversity can be investigated, enhancing the efficiency of selecting appropriate molecular targets. Additionally, applying multiomic analysis on single cells would further improve the understanding of cell-to-cell heterogeneity by providing unique insights on cellular and genetic composition. Using a two-step droplet microfluidic technology, the Mission Bio Tapestri Platform enables multiplex-PCR based high-throughput targeted DNA sequencing in single cells to obtain single-nucleotide variation (SNV) and copy number variation (CNV) information. By leveraging this technology, a new workflow is developed to detect protein expression in addition to DNA genotype in the same single cells. In this approach, cells are labeled with a pool of oligonucleotide-conjugated antibodies prior to loading the cells into the Tapestri Instrument for targeted DNA analysis. Sequencing libraries are then prepared from both antibody oligonucleotides and the amplified DNA sequences, followed by identification of single-cell DNA genotypes and protein signatures from the sequencing readout. In a mixed population of four cell lines, single-cell SNV and CNV information from 127 targeted amplicons and the protein data from 10 antibodies independently classified the cells into appropriate clusters. This method has been successfully performed on clinical samples with myeloid malignancies. In an acute myeloid leukemia (AML) sample, combined single-cell SNV, CNV, and protein expression data illustrated the heterogeneity within the sample. The data clearly identified CD3+ T cells and CD19+ B cells without pathogenic SNVs and CNVs. CD34hiCD11blo and CD34loCD11bhi subpopulations were also identified within the cells carrying the same pathogenic SNVs and CNVs. We believe that this novel multiomic technology will enable new discoveries in the complex relationship between genotype and phenotype, leading to a better understanding of disease biology, and subsequently better design of diagnostics and therapies. Citation Format: Aik Ooi, Pedro Mendez, Dalia Dhingra, Nigel Beard, David Ruff. Single-cell multiomic analysis of SNV, CNV, and protein expression [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 5910.

Single-cell analysis based dissection of clonality in myelofibrosis

Cancer development is an evolutionary genomic process with parallels to Darwinian selection. It requires acquisition of multiple somatic mutations that collectively cause a malignant phenotype and continuous clonal evolution is often linked to tumor progression. Here, we show the clonal evolution structure in 15 myelofibrosis (MF) patients while receiving treatment with JAK inhibitors (mean follow-up 3.9 years). Whole-exome sequencing at multiple time points reveal acquisition of somatic mutations and copy number aberrations over time. While JAK inhibition therapy does not seem to create a clear evolutionary bottleneck, we observe a more complex clonal architecture over time, and appearance of unrelated clones. Disease progression associates with increased genetic heterogeneity and gain of RAS/RTK pathway mutations. Clonal diversity results in clone-specific expansion within different myeloid cell lineages. Single-cell genotyping of circulating CD34 + progenitor cells allows the reconstruction of MF phylogeny demonstrating loss of heterozygosity and parallel evolution as recurrent events.

Accurate and efficient cell lineage tree inference from noisy single cell data: the maximum likelihood perfect phylogeny approach

Cells in an organism share a common evolutionary history, called cell lineage tree. Cell lineage tree can be inferred from single cell genotypes at genomic variation sites. Cell lineage tree inference from noisy single cell data is a challenging computational problem. Most existing methods for cell lineage tree inference assume uniform uncertainty in genotypes. A key missing aspect is that real single cell data usually has non-uniform uncertainty in individual genotypes. Moreover, existing methods are often sampling based and can be very slow for large data. In this article, we propose a new method called ScisTree, which infers cell lineage tree and calls genotypes from noisy single cell genotype data. Different from most existing approaches, ScisTree works with genotype probabilities of individual genotypes (which can be computed by existing single cell genotype callers). ScisTree assumes the infinite sites model. Given uncertain genotypes with individualized probabilities, ScisTree implements a fast heuristic for inferring cell lineage tree and calling the genotypes that allow the so-called perfect phylogeny and maximize the likelihood of the genotypes. Through simulation, we show that ScisTree performs well on the accuracy of inferred trees, and is much more efficient than existing methods. The efficiency of ScisTree enables new applications including imputation of the so-called doublets. The program ScisTree is available for download at: https://github.com/yufengwudcs/ScisTree. Supplementary data are available at Bioinformatics online.

Optimized CRISPR/Cas9-mediated in vivo genome engineering applicable to monitoring dynamics of endogenous proteins in the mouse neural tissues

To analyze the expression, localization, and functional dynamics of target proteins in situ, especially in living cells, it is important to develop a convenient, versatile, and efficient method to precisely introduce exogenous genes into the genome, which is applicable for labeling and engineering of the endogenous proteins of interest. By combining the CRISPR/Cas9 genome editing technology with an electroporation technique, we succeeded in creating knock-in alleles, from which GFP (RFP)-tagged endogenous proteins are produced, in neurons and glial cells in vivo in the developing mouse retina and brain. Correct gene targeting was confirmed by single-cell genotyping and Western blot analysis. Several gene loci were successfully targeted with high efficiency. Moreover, we succeeded in engineering the mouse genome to express foreign genes from the endogenous gene loci using a self-cleaving 2A peptide. Our method could be used to monitor the physiological changes in localization of endogenous proteins and expression levels of both mRNA and protein at a single cell resolution. This work discloses a powerful and widely applicable approach for visualization and manipulation of endogenous proteins in neural tissues.

Abstract LB-214: Imputation of single cell cancer genotypes

Abstract Recent advances in sequencing technologies and emerging single cell sequencing promise to profoundly impact cancer studies. One of the most important problems is reconstruction of history of cancer mutations. Existing methods model cells DNA as haplotypes and reconstruct perfect (or almost perfect) phylogeny of mutations from single cell DNA sequencing data. In this paper we propose to reconstruct genotypes (rather than haplotypes) which in absence of repeated and omitted mutations is equivalent to reconstruction of the mutation phylogeny. Single cell genotype reconstruction is proposed to perform in two steps - first the sequencing data are used for imputation of cell genotypes treated as individual human genomes and then the resulted genomes are fitted to perfect phylogeny by eliminating 4-gamete rule violations using integer linear programs. Our simulation results show advantage of single cell genotype imputation over existing methods. Note: This abstract was not presented at the meeting. Citation Format: Alex Zelikovsky, Igor Mandric, Pavel Skums, Ion Mandoiu, Bogdan Pasaniuc, Pramod Srivastava. Imputation of single cell cancer genotypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-214.