Single-cell DNA Sequencing Data Research Articles

Scuphr: A probabilistic framework for cell lineage tree reconstruction

Cell lineage tree reconstruction methods are developed for various tasks, such as investigating the development, differentiation, and cancer progression. Single-cell sequencing technologies enable more thorough analysis with higher resolution. We present Scuphr, a distance-based cell lineage tree reconstruction method using bulk and single-cell DNA sequencing data from healthy tissues. Common challenges of single-cell DNA sequencing, such as allelic dropouts and amplification errors, are included in Scuphr. Scuphr computes the distance between cell pairs and reconstructs the lineage tree using the neighbor-joining algorithm. With its embarrassingly parallel design, Scuphr can do faster analysis than the state-of-the-art methods while obtaining better accuracy. The method’s robustness is investigated using various synthetic datasets and a biological dataset of 18 cells.

Abstract PL-01: Breast Cancer Evolution, Immune Evasion and Metastasis Driven by Chromosomal Instability

Abstract Chromosomal instability (CIN) is a poor prognostic feature with high prevalence in breast cancer and is responsible for driving Somatic Copy Number Aberrations and Intratumour Heterogeneity (ITH) within subclones during breast cancer evolution. We have observed that SCNAs are enriched in metastatic samples, including gains in chromosome 11q13.3 (encompassing CCND1) in HER2+ breast cancer. However, there have thus far been no prospective, comprehensive studies of intratumor heterogeneity in breast cancer. We will present data from TRACERx Breast/SCANDARE a prospective, multicentre study of triple-negative breast cancer (TNBC) in which multi-region biopsies have been collected from primary TNBC prior to neoadjuvant treatment, and at longitudinal time points including surgery and relapse. We find prevalent intratumor heterogeneity in somatic mutations and SCNAs in the primary tumor prior to treatment. We have leveraged the rich clinical annotation in this cohort to correlate intratumor heterogeneity scores with key clinical outcomes, including treatment response (pathological complete response to neoadjuvant chemotherapy) and survival. We find that CIN manifests as oncogenic amplification on extrachromosomal DNA (ecDNA), which plays a pivotal role in driving drug resistance and tumor evolution. Through analysis of Whole Genome Sequencing data from 2936 breast tumors from the Genomics England breast cancer cohort, we have identified focal amplifications driven by ecDNA in 46.4% of HER2+ breast cancers. EcDNA were enriched in metastatic tumors and was associated with poor clinical outcomes. In addition to oncogenic amplifications such as HER2 derived from ecDNA during cancer evolution, ecDNAs contained immunomodulatory genes associated with reduced T cell infiltration. Further Immune-modulation by CIN results from loss of heterozygosity of the HLA locus (HLA LOH) and is a prevalent subclonal event in breast cancer. CIN is further drives haploid LOH, resulting in the high prevalence of whole genome doubling and further CIN in TNBC. Tumors are heterogeneous compositions of distinct clones with different compliments of SCNAs and varying levels of fitness, hence measuring the proliferation of individual clones to predict future evolutionary outcomes is likely to be critical. We have developed SPRINTER (Single-cell Proliferation Rate Inference in Non-homogeneous Tumours through Evolutionary Routes), a novel computational method to measure proliferation rates in individual tumour clones using single-cell whole-genome DNA sequencing data. We applied SPRINTER to 42,009 triple negative breast cancer cells, demonstrating significant proliferative heterogeneity between distinct tumor clones. Importantly, we show a correlation between higher proliferation rates and increased rates of somatic variants, suggesting proliferation links to clone evolvability. Taken together, these complimentary analyses provide evidence for the importance of CIN in driving ITH, selection, metastasis and immune evasion and provide a framework to determine recurrent evolutionary patterns in breast cancer evolution. Citation Format: C. Swanton. Breast Cancer Evolution, Immune Evasion and Metastasis Driven by Chromosomal Instability [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PL-01.

ScGAL: unmask tumor clonal substructure by jointly analyzing independent single-cell copy number and scRNA-seq data

BackgroundAccurately deciphering clonal copy number substructure can provide insights into the evolutionary mechanism of cancer, and clustering single-cell copy number profiles has become an effective means to unmask intra-tumor heterogeneity (ITH). However, copy numbers inferred from single-cell DNA sequencing (scDNA-seq) data are error-prone due to technically confounding factors such as amplification bias and allele-dropout, and this makes it difficult to precisely identify the ITH.ResultsWe introduce a hybrid model called scGAL to infer clonal copy number substructure. It combines an autoencoder with a generative adversarial network to jointly analyze independent single-cell copy number profiles and gene expression data from same cell line. Under an adversarial learning framework, scGAL exploits complementary information from gene expression data to relieve the effects of noise in copy number data, and learns latent representations of scDNA-seq cells for accurate inference of the ITH. Evaluation results on three real cancer datasets suggest scGAL is able to accurately infer clonal architecture and surpasses other similar methods. In addition, assessment of scGAL on various simulated datasets demonstrates its high robustness against the changes of data size and distribution. scGAL can be accessed at: https://github.com/zhyu-lab/scgal.ConclusionsJoint analysis of independent single-cell copy number and gene expression data from a same cell line can effectively exploit complementary information from individual omics, and thus gives more refined indication of clonal copy number substructure.

Abstract SY02-03: Stochastic modeling of clonal evolution in carcinogenesis

Abstract In order to develop strategies that can detect cancer earlier, we need to better understand the dynamics of cancer evolution during the human lifetime. Starting from gestation, this includes measuring both the timing of early somatic mutations that lead to clonal mosaicism as well as the growth rates of mutated clones that carry higher potential for malignant transformation. Continuous observation of this evolving clonal composition across a lifetime is not feasible, therefore mathematical models are needed to integrate data from longitudinal and cross-sectional samples and infer these dynamics. Stochastic models of clonal evolution offer such a framework and provide a means for predicting individual clone trajectories into the future. Furthermore, if measurements of evolution are to be utilized clinically for early detection and patient risk stratification, faster methods to apply these models to data are required. To this end, we derived methods using coalescent theory for single-cell DNA sequencing data that enable instantaneous estimation of the growth rate of a clone along with its confidence intervals. Our tool is available in an open source R package, cloneRate, and provides the first method for constructing valid confidence intervals of growth rates analytically without having to rely on Bayesian inference techniques and complex simulations. When applying our methods to recent datasets derived from normal and neoplastic human blood cells, we quantified increased fitness effects of multi-hit driver mutations and found that higher initial clone growth rates led to decreased time to cancer diagnosis in patients. We can also use these models to estimate other important biological parameters that are difficult to measure in vivo such as bounds for total number of hematopoietic stem cells and expansion rates in early development. Ultimately, the ability to easily and quickly parameterize clonal dynamics in individual patients will benefit future early detection efforts and screening strategies for many cancer types. Citation Format: Kit Curtius. Stochastic modeling of clonal evolution in carcinogenesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr SY02-03.

Abstract 1205: Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics

Abstract Tumors evolve through the acquisition of somatic mutations over time and across space. While the temporal aspects of tumor evolution can be inferred by applying phylogenetic techniques to bulk or single-cell DNA sequencing data, the spatial aspects of tumor evolution remain understudied. Spatially resolved transcriptomics (SRT) measures gene expression at thousands of spatial locations in a tumor, but does not directly measure genetic aberrations. Thus, SRT data has not yet been fully utilized for tumor evolution studies. We introduce CalicoST, an algorithm that simultaneously infers allele-specific copy number aberrations (CNAs) and derives a phylogeographic model of tumor evolution from SRT data. By modeling CNA-induced perturbations in both gene expression and allele frequencies, CalicoST identifies important types of CNAs - including copy-neutral loss of heterozygosity (CNLOH) and mirrored subclonal CNAs - that are invisible to total copy number analysis. On SRT data from nine patients from the Human Tumor Atlas Network (HTAN), CalicoST achieves an average accuracy of 86%, approximately 21% higher than existing methods. On two of these patients with multiple adjacent slices, CalicoST reconstructs a tumor phylogeography that describes the spread of cancerous clones in three-dimensional space. CalicoST analysis of multiple SRT slices from a cancerous prostate organ reveals five spatially coherent clones, with mirrored subclonal CNAs distinguishing clones on the two sides of the prostate, and a bifurcating phylogeography in both genetic and physical space that suggests a polyclonal cancer. Citation Format: Cong Ma, Metin Balaban, Jingxian Liu, Siqi Chen, Li Ding, Benjamin J. Raphael. Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics [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 1205.

Abstract 5671: Profiling intra-tumor heterogeneity and chromosomal instability in malignant peripheral nerve sheath tumors

Abstract Malignant peripheral nerve sheath tumors (MPNSTs) are rare soft-tissue sarcomas that manifest on peripheral nerves, constituting 2% of all sarcomas. While previous genomic analyses of MPNSTs have been conducted on small cohorts, often limited to single samples per patient and lacking comprehensive resolution, the extent of intra-tumor heterogeneity and the contribution of chromosomal instability to MPNST development remains unclear. To address these limitations, we developed a multi-omics integration pipeline and applied it to bulk Whole Genome Sequencing (WGS), single-cell RNA sequencing(scRNA-seq), single-cell DNA sequencing (scDNA-seq), spatial transcriptomics (ST), and Laser Capture Microsection (LCM) sequencing data. 60 tumor samples were collected from 17 patients with MPNST, and the above five techniques were applied to each sample. Overall, tumors harbor a high degree of chromosomal instability and evidence of whole-genome doubling (WGD). We reconstructed tumor phylogenetic trees from WGS data using Dirichlet Process-based methods. Intriguingly, our findings indicated that for patient 2.3, primary tumor samples PT_1 and PT_2 displayed a close phylogenetic relationship, while local recurrences LR_2 and LR_3 represented a different branch of the phylogenetic tree. Moreover, scRNA-seq analysis of patient 2.16 highlighted an enrichment of immune cells, including macrophages, B cells, and T cells. To infer copy number alterations (CNA) of tumor cells from scRNA-seq data, we leveraged inferCNV, with normal cells identified within the same sample as controls. Significant copy number gains were identified on chromosomes 5, 7, 8, and 15 in patient 2.16. The inferred copy number changes derived from scRNA-seq were validated against copy number profiles obtained through WGS using Battenberg, revealing a remarkable congruence. In the scDNA analysis of patient 2.12, we observed that all tumor cells exhibited biallelic loss of CDKN2A, with a majority showcasing WGD and chromosome 7 amplification. This finding underscored the potential significance of CDKN2A in the pathogenesis of this tumor, in keeping with the observation that CDKN2A is recurrently mutated and lost in MPNSTs. Our research profiled the tumors' evolutionary histories and intra-tumor heterogeneity down to single-cell resolution, highlighting the genotype and phenotype differences between tumor subclones. By shedding light on the genomic landscape of MPNSTs, we aspire to make profound contributions to the field of sarcoma research, ultimately improving patient outcomes. Citation Format: Yixiao Cheng, Haixi Yan, Maxime Tarabichi, Zhihui Zhang, Chunxu Gao, Tom Lesluyes, Adrienne Flanagan, Peter Van Loo. Profiling intra-tumor heterogeneity and chromosomal instability in malignant peripheral nerve sheath tumors [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 5671.

Allele-specific transcriptional effects of subclonal copy number alterations enable genotype-phenotype mapping in cancer cells

Subclonal copy number alterations are a prevalent feature in tumors with high chromosomal instability and result in heterogeneous cancer cell populations with distinct phenotypes. However, the extent to which subclonal copy number alterations contribute to clone-specific phenotypes remains poorly understood. We develop TreeAlign, which computationally integrates independently sampled single-cell DNA and RNA sequencing data from the same cell population. TreeAlign accurately encodes dosage effects from subclonal copy number alterations, the impact of allelic imbalance on allele-specific transcription, and obviates the need to define genotypic clones from a phylogeny a priori, leading to highly granular definitions of clones with distinct expression programs. These improvements enable clone-clone gene expression comparisons with higher resolution and identification of expression programs that are genomically independent. Our approach sets the stage for dissecting the relative contribution of fixed genomic alterations and dynamic epigenetic processes on gene expression programs in cancer.

Mutation order in acute myeloid leukemia identifies uncommon patterns of evolution and illuminates phenotypic heterogeneity.

Acute myeloid leukemia (AML) has a poor prognosis and a heterogeneous mutation landscape. Although common mutations are well-studied, little research has characterized how the sequence of mutations relates to clinical features. Using published, single-cell DNA sequencing data from three institutions, we compared clonal evolution patterns in AML to patient characteristics, disease phenotype, and outcomes. Mutation trees, which represent the order of select mutations, were created for 207 patients from targeted panel sequencing data using 1 639 162 cells, 823 mutations, and 275 samples. In 224 distinct orderings of mutated genes, mutations related to DNA methylation typically preceded those related to cell signaling, but signaling-first cases did occur, and had higher peripheral cell counts, increased signaling mutation homozygosity, and younger patient age. Serial sample analysis suggested that NPM1 and DNA methylation mutations provide an advantage to signaling mutations in AML. Interestingly, WT1 mutation evolution shared features with signaling mutations, such as WT1-early being proliferative and occurring in younger individuals, trends that remained in multivariable regression. Some mutation orderings had a worse prognosis, but this was mediated by unfavorable mutations, not mutation order. These findings add a dimension to the mutation landscape of AML, identifying uncommon patterns of leukemogenesis and shedding light on heterogeneous phenotypes.

Abstract PR010: Linking proliferation rate to the evolution of single-cell primary and metastatic tumor clones

Abstract Proliferation is a key phenotypic feature of cancer, with higher rates associated with poorer clinical outcomes. Thus far, proliferation rates have been measured using pathological or experimental techniques on bulk tumor samples. However, tumors are heterogeneous compositions of distinct clones. Measuring the proliferation of individual clones has been unfeasible to date since proliferation and clonal diversity cannot easily be measured for the same set of cells, but it is potentially important as it may allow the identification of clones that develop more aggressive phenotypes (e.g., metastatic potential or treatment resistance). We have developed SPRINTER, a novel algorithm that uses single-cell whole-genome DNA sequencing (scDNA-seq) data to enable the accurate identification of actively replicating cells in both the S and G2 phases of the cell cycle and their assignment to distinct tumor clones, thus providing a proxy to estimate clone-specific proliferation rates. To evaluate SPRINTER’s accuracy, we generated a ground truth dataset of 8,844 diploid and tetraploid cancer cells by coupling scDNA-seq with 5-Ethynyl-2-deoxyuridine (EdU) labeling, and demonstrated that SPRINTER can accurately distinguish clone proliferation rates in contrast to previous approaches. We further generated a longitudinal, primary-metastasis matched dataset of 23,001 cancer cells obtained from 5 samples from the primary tumor and 5 samples from distinct metastases from a patient with non-small cell lung cancer, allowing us to integrate analyses of proliferation and cancer evolution through the metastatic disease course. We revealed widespread heterogeneity in clone proliferation rates both between and within samples, supported by multiple orthogonal analyses including Ki-67 pathology, nuclei microscopy imaging, and patient clinical imaging, with high proliferation seen in fast-growing metastatic lesions. We demonstrated an association between clones with high proliferation and increased metastatic potential, as well as increased shedding of circulating tumor DNA. We further illustrated SPRINTER’s broad applicability on previous datasets of 42,009 breast cancer cells and 19,905 ovarian cancer cells, revealing an association between high proliferation and increased rates of different genetic variants. In conclusion, SPRINTER infers the proliferation rates of distinct tumor clones from scDNA-seq data, allowing the identification of clones with potentially aggressive phenotypes, such as metastatic potential. Citation Format: Olivia Lucas, Sophia Ward, Rija Zaidi, Abigail Bunkum, Alexander M. Frankell, David A. Moore, Mark S. Hill, Wing Kin Liu, Daniele Marinelli, Emilia L. Lim, Sonya Hessey, Cristina Naceur-Lombardelli, Andrew Rowan, Sukhveer Mann, Haoran Zhai, Michelle Dietzen, Boyue Ding, Gary Royle, Nicholas McGranahan, Mariam Jamal-Hanjani, Nnennaya Kanu, Charles Swanton, Simone Zaccaria. Linking proliferation rate to the evolution of single-cell primary and metastatic tumor clones [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr PR010.

A statistical learning method for simultaneous copy number estimation and subclone clustering with single-cell sequencing data.

The availability of single-cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in SCS data using various clustering methods, as cells comprising a subpopulation are found to share a genetic profile. However, currently available methods may generate spurious results (e.g., falsely identified variants) in the procedure of CNA detection, thereby diminishing the accuracy of subclone identification within a large, complex cell population. In this study, we developed a subclone clustering method based on a fused lasso model, referred to as FLCNA, which can simultaneously detect CNAs in single-cell DNA sequencing (scDNA-seq) data. Spike-in simulations were conducted to evaluate the clustering and CNA detection performance of FLCNA, benchmarking it against existing copy number estimation methods (SCOPE, HMMcopy) in combination with commonly used clustering methods. Application of FLCNA to a scDNA-seq data set of breast cancer revealed different genomic variation patterns in neoadjuvant chemotherapy-treated samples and pretreated samples. We show that FLCNA is a practical and powerful method for subclone identification and CNA detection with scDNA-seq data.

Integrating SNVs and CNAs on a phylogenetic tree from single-cell DNA sequencing data.

Single-cell DNA sequencing enables the construction of evolutionary trees that can reveal how tumors gain mutations and grow. Different whole-genome amplification procedures render genomic materials of different characteristics, often suitable for the detection of either single-nucleotide variation or copy number aberration, but not ideally for both. Consequently, this hinders the inference of a comprehensive phylogenetic tree and limits opportunities to investigate the interplay of SNVs and CNAs. Existing methods such as SCARLET and COMPASS require that the SNVs and CNAs are detected from the same sets of cells, which is technically challenging. Here we present a novel computational tool, SCsnvcna, that places SNVs on a tree inferred from CNA signals, whereas the sets of cells rendering the SNVs and CNAs are independent, offering a more practical solution in terms of the technical challenges. SCsnvcna is a Bayesian probabilistic model using both the genotype constraints on the tree and the cellular prevalence to search the optimal solution. Comprehensive simulations and comparison with seven state-of-the-art methods show that SCsnvcna is robust and accurate in a variety of circumstances. Particularly, SCsnvcna most frequently produces the lowest error rates, with ability to scale to a wide range of numerical values for leaf nodes in the tree, SNVs, and SNV cells. The application of SCsnvcna to two published colorectal cancer data sets shows highly consistent placement of SNV cells and SNVs with the original study while also supporting a refined placement of ATP7B, illustrating SCsnvcna's value in analyzing complex multitumor samples.

Assessing the performance of methods for cell clustering from single-cell DNA sequencing data.

Many cancer genomes have been known to contain more than one subclone inside one tumor, the phenomenon of which is called intra-tumor heterogeneity (ITH). Characterizing ITH is essential in designing treatment plans, prognosis as well as the study of cancer progression. Single-cell DNA sequencing (scDNAseq) has been proven effective in deciphering ITH. Cells corresponding to each subclone are supposed to carry a unique set of mutations such as single nucleotide variations (SNV). While there have been many studies on the cancer evolutionary tree reconstruction, not many have been proposed that simply characterize the subclonality without tree reconstruction. While tree reconstruction is important in the study of cancer evolutionary history, typically they are computationally expensive in terms of running time and memory consumption due to the huge search space of the tree structure. On the other hand, subclonality characterization of single cells can be converted into a cell clustering problem, the dimension of which is much smaller, and the turnaround time is much shorter. Despite the existence of a few state-of-the-art cell clustering computational tools for scDNAseq, there lacks a comprehensive and objective comparison under different settings. In this paper, we evaluated six state-of-the-art cell clustering tools-SCG, BnpC, SCClone, RobustClone, SCITE and SBMClone-on simulated data sets given a variety of parameter settings and a real data set. We designed a simulator specifically for cell clustering, and compared these methods' performances in terms of their clustering accuracy, specificity and sensitivity and running time. For SBMClone, we specifically designed an ultra-low coverage large data set to evaluate its performance in the face of an extremely high missing rate. From the benchmark study, we conclude that BnpC and SCG's clustering accuracy are the highest and comparable to each other. However, BnpC is more advantageous in terms of running time when cell number is high (> 1500). It also has a higher clustering accuracy than SCG when cluster number is high (> 16). SCClone's accuracy in estimating the number of clusters is the highest. RobustClone and SCITE's clustering accuracy are the lowest for all experiments. SCITE tends to over-estimate the cluster number and has a low specificity, whereas RobustClone tends to under-estimate the cluster number and has a much lower sensitivity than other methods. SBMClone produced reasonably good clustering (V-measure > 0.9) when coverage is > = 0.03 and thus is highly recommended for ultra-low coverage large scDNAseq data sets.

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.

Single-cell phylogenies reveal changes in the evolutionary rate within cancer and healthy tissues

Cell lineages accumulate somatic mutations during organismal development, potentially leading to pathological states. The rate of somatic evolution within a cell population can vary due to multiple factors, including selection, a change in the mutation rate, or differences in the microenvironment. Here, we developed a statistical test called the Poisson Tree (PT) test to detect varying evolutionary rates among cell lineages, leveraging the phylogenetic signal of single-cell DNA sequencing (scDNA-seq) data. We applied the PT test to 24 healthy and cancer samples, rejecting a constant evolutionary rate in 11 out of 15 cancer and five out of nine healthy scDNA-seq datasets. In six cancer datasets, we identified subclonal mutations in known driver genes that could explain the rate accelerations of particular cancer lineages. Our findings demonstrate the efficacy of scDNA-seq for studying somatic evolution and suggest that cell lineages often evolve at different rates within cancer and healthy tissues.

De novo detection of somatic mutations in high-throughput single-cell profiling data sets.

Characterization of somatic mutations at single-cell resolution is essential to study cancer evolution, clonal mosaicism and cell plasticity. Here, we describe SComatic, an algorithm designed for the detection of somatic mutations in single-cell transcriptomic and ATAC-seq (assay for transposase-accessible chromatin sequence) data sets directly without requiring matched bulk or single-cell DNA sequencing data. SComatic distinguishes somatic mutations from polymorphisms, RNA-editing events and artefacts using filters and statistical tests parameterized on non-neoplastic samples. Using >2.6 million single cells from 688 single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) data sets spanning cancer and non-neoplastic samples, we show that SComatic detects mutations in single cells accurately, even in differentiated cells from polyclonal tissues that are not amenable to mutation detection using existing methods. Validated against matched genome sequencing and scRNA-seq data, SComatic achieves F1 scores between 0.6 and 0.7 across diverse data sets, in comparison to 0.2-0.4 for the second-best performing method. In summary, SComatic permits de novo mutational signature analysis, and the study of clonal heterogeneity and mutational burdens at single-cell resolution.

Abstract A49: Characterizing the order of mutation acquisition in acute myeloid leukemia using large-scale single-cell sequencing

Abstract Introduction: Acute myeloid leukemia (AML) has a poor prognosis, despite aggressive therapies and a recent expansion in the array of treatments. Treatment is often determined by mutations, which risk-stratify a patient’s leukemia or can identify mutations that serve as therapeutic targets. Although common mutations in AML have been extensively studied, less research has formally characterized the order by which mutations tend to occur and how this order relates to properties of the disease. Methods: We leveraged published, single-cell DNA sequencing data from three institutions to model the clonal evolution of AML. Sequencing had been performed using targeted sequencing panels covering 19 to 37 genes, and driver mutations were identified using conservative filters. Mutation trees were created using Single Cell Inference of Tumor Evolution (SCITE). Clonal evolution patterns, including across clinical timepoints, were identified and compared to patient characteristics, disease phenotype, and outcomes. The BeatAML dataset was leveraged to explore associations between mutation order and gene expression. Results: Using 835 driver mutations from 276 samples and 209 patients, we identified 223 total unique mutation patterns. Branched evolution primarily involves FLT3 and RAS pathway mutations, whereas certain mutation pairs, such as NPM1 and FLT3, always occurred linearly in the same clone. Although some mutation pairs, such as those related to DNA methylation versus the RAS pathway, tended to occur in a specific order, several cases exhibited atypical orderings. Early signaling gene mutations were associated with younger patient age and increased signaling mutation homozygosity while NRAS-first cases were associated with increased monocyte counts. NRAS-first cases were also associated with distinct gene expression patterns in the BeatAML dataset. Paired diagnosis and relapse samples revealed novel associations between mutations gained at relapse and their clonal context, and these analyses supported a relative fitness advantage for signaling mutations in clones containing mutations in NPM1 or genes affecting DNA methylation. Conclusions: Clonal evolution in AML can be reconstructed at scale using single-cell DNA sequencing data, and different mutation acquisition patterns are associated with distinct leukemia and patient characteristics, despite cases having similar co-mutation patterns. Citation Format: Matthew Schwede, Katharina Jahn, Linde A Miles, Robert L Bowman, Jack Kuipers, Troy M Robinson, Asiri Ediriwickrema, Andrew J Gentles, Ross Levine, Niko Beerenwinkel, Koichi Takahashi, Ravindra Majeti. Characterizing the order of mutation acquisition in acute myeloid leukemia using large-scale single-cell sequencing [abstract]. In: Proceedings of the AACR Special Conference: Acute Myeloid Leukemia and Myelodysplastic Syndrome; 2023 Jan 23-25; Austin, TX. Philadelphia (PA): AACR; Blood Cancer Discov 2023;4(3_Suppl):Abstract nr A49.

Abstract 4277: Measuring proliferation rates of distinct tumour clones using single-cell DNA sequencing

Abstract Background: Tumour proliferation rate is a key phenotypic feature of cancer, with higher rates linked to poorer clinical outcomes. Thus far, proliferation rates have been measured using pathological or experimental techniques on bulk tumour samples. However, while tumours are heterogeneous compositions of distinct clones with varying levels of fitness, measuring the proliferation of individual clones has not been possible to date. We hypothesise that enabling the identification of the most proliferative clones would reveal genomic hallmarks of aggressive clones, or the prediction of their potential phenotype, e.g., metastatic potential. Methods: We have developed SPRINTER (Single-cell Proliferation Rate Inference in Neoplasms Through Evolutionary Routes), a novel computational method to measure proliferation rates in individual tumour clones using single-cell whole-genome DNA sequencing. To assess the accuracy and power of SPRINTER, we have also developed an experimental approach to DNA sequence >18,000 single cells, accurately separated in different DNA-replication phases. We have sequenced and applied SPRINTER to >10,000 non-small cell lung cancer cells from longitudinal and metastatic tumour samples within the TRACERx study and PEACE autopsy programme. We have further analysed published data from >10,000 breast cancer cells. Results: We demonstrate that SPRINTER can accurately identify subpopulations of cells with different proliferation rates using relatively small numbers of cells, in contrast to previous preliminary approaches. While our estimates are concordant with previous bulk experimental studies (5-40%), we importantly have identified clonal heterogeneity in proliferation rates. Using bulk analysis, we have identified patterns of dissemination of tumour clones in non-small cell lung cancer. Integrating this with single-cell data, our results indicate that more widely disseminating tumour clones have higher proliferation rates, suggesting a link between proliferation and dissemination potential. We additionally find that clones are more proliferative in the metastatic versus the primary setting. Furthermore, we have identified high proliferation clones that may have a selective advantage in a breast tumour, and have inferred that they likely arose recently in cancer evolution. Conclusions: We have developed a novel method that enables accurate identification of proliferation rates of individual tumour clones using single-cell DNA sequencing data, allowing the investigation of genomic hallmarks in highly proliferative clones that might lead to higher fitness. Citation Format: Olivia Lucas, Sophie Ward, Rija Zaidi, Mark Hill, Emilia Lim, Haoran Zhai, Abigail Bunkum, Sonya Hessey, Michelle Dietzen, Andrew Rowan, Cristina Naceur-Lombardelli, Nnenna Kanu, Mariam Jamal-Hanjani, Charles Swanton, Simone Zaccaria. Measuring proliferation rates of distinct tumour clones using single-cell DNA sequencing. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4277.

SIEVE: joint inference of single-nucleotide variants and cell phylogeny from single-cell DNA sequencing data

We present SIEVE, a statistical method for the joint inference of somatic variants and cell phylogeny under the finite-sites assumption from single-cell DNA sequencing. SIEVE leverages raw read counts for all nucleotides and corrects the acquisition bias of branch lengths. In our simulations, SIEVE outperforms other methods in phylogenetic reconstruction and variant calling accuracy, especially in the inference of homozygous variants. Applying SIEVE to three datasets, one for triple-negative breast (TNBC), and two for colorectal cancer (CRC), we find that double mutant genotypes are rare in CRC but unexpectedly frequent in the TNBC samples.

CNAViz: An interactive webtool for user-guided segmentation of tumor DNA sequencing data.

Copy-number aberrations (CNAs) are genetic alterations that amplify or delete the number of copies of large genomic segments. Although they are ubiquitous in cancer and, thus, a critical area of current cancer research, CNA identification from DNA sequencing data is challenging because it requires partitioning of the genome into complex segments with the same copy-number states that may not be contiguous. Existing segmentation algorithms address these challenges either by leveraging the local information among neighboring genomic regions, or by globally grouping genomic regions that are affected by similar CNAs across the entire genome. However, both approaches have limitations: overclustering in the case of local segmentation, or the omission of clusters corresponding to focal CNAs in the case of global segmentation. Importantly, inaccurate segmentation will lead to inaccurate identification of CNAs. For this reason, most pan-cancer research studies rely on manual procedures of quality control and anomaly correction. To improve copy-number segmentation, we introduce CNAViz, a web-based tool that enables the user to simultaneously perform local and global segmentation, thus overcoming the limitations of each approach. Using simulated data, we demonstrate that by several metrics, CNAViz allows the user to obtain more accurate segmentation relative to existing local and global segmentation methods. Moreover, we analyze six bulk DNA sequencing samples from three breast cancer patients. By validating with parallel single-cell DNA sequencing data from the same samples, we show that by using CNAViz, our user was able to obtain more accurate segmentation and improved accuracy in downstream copy-number calling.

Sgcocaller and comapr: personalised haplotype assembly and comparative crossover map analysis using single-gamete sequencing data.

Profiling gametes of an individual enables the construction of personalised haplotypes and meiotic crossover landscapes, now achievable at larger scale than ever through the availability of high-throughput single-cell sequencing technologies. However, high-throughput single-gamete data commonly have low depth of coverage per gamete, which challenges existing gamete-based haplotype phasing methods. In addition, haplotyping a large number of single gametes from high-throughput single-cell DNA sequencing data and constructing meiotic crossover profiles using existing methods requires intensive processing. Here, we introduce efficient software tools for the essential tasks of generating personalised haplotypes and calling crossovers in gametes from single-gamete DNA sequencing data (sgcocaller), and constructing, visualising, and comparing individualised crossover landscapes from single gametes (comapr). With additional data pre-possessing, the tools can also be applied to bulk-sequenced samples. We demonstrate that sgcocaller is able to generate impeccable phasing results for high-coverage datasets, on which it is more accurate and stable than existing methods, and also performs well on low-coverage single-gamete sequencing datasets for which current methods fail. Our tools achieve highly accurate results with user-friendly installation, comprehensive documentation, efficient computation times and minimal memory usage.