Single-cell DNA Sequencing Research Articles

Polyploid cancer cells reveal signatures of chemotherapy resistance.

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

Single nucleus DNA sequencing of flow sorted archived frozen and formalin fixed paraffin embedded solid tumors.

Archived samples, including frozen and formalin fixed paraffin embedded (FFPE) tissues, are a vast resource of clinically annotated materials for the application of high-definition genomics to improve patient management and provide a molecular basis for the delivery of personalized cancer therapeutics. Notably, FFPE tissues are stable, provide repeat sampling of tissues of interest, and can be stored indefinitely at ambient temperature. The development of single cell DNA sequencing (scDNA-seq) technologies provides an unparalleled opportunity for the study of tumor heterogeneity and the identification of often rare subclonal cell populations that drive tumor evolution and progression to advanced therapy resistant disease. However, major limitations to the use of archived tissues for scDNA-seq include the low yields of intact cells in the presence of high levels of subcellular debris in biopsies, and the highly variable quantity and quality of the DNA extracted from samples of interest. The latter is of high significance for the use of FFPE tissues due to the presence of DNA-protein crosslinks. In addition, many samples, notably tumors arising in solid tissues, contain admixtures of reactive stroma, inflammatory cells, and necrosis in immediate contact with tumor cells. To expand their use for translational studies, we optimized flow sorting and sequencing of single nuclei from archived fresh frozen (FF) and FFPE tumor tissues. Our methods, which include isolation of intact nuclei suitable for library preparations, quality control (QC) metrics for each step, and a single cell sequencing bioinformatic processing and analysis pipeline, were validated with flow sorted nuclei from matching FF and FFPE ovarian cancer surgical samples and a sequencing panel of 553 amplicons targeting single nucleotide and copy number variants in genes of interest. Our flow sorting based protocol provides intact nuclei suitable for snDNA-seq from archival FF and FFPE tissues. Furthermore, we have developed QC steps that optimize the preparation and selection of samples for deep single cell clonal profiling. Our data processing pipeline captures rare subclones in tumors with highly variable genomes based on variants in genes of interest.

CopyMix: Mixture model based single-cell clustering and copy number profiling using variational inference

Investigating tumor heterogeneity using single-cell sequencing technologies is imperative to understand how tumors evolve since each cell subpopulation harbors a unique set of genomic features that yields a unique phenotype, which is bound to have clinical relevance. Clustering of cells based on copy number data obtained from single-cell DNA sequencing provides an opportunity to identify different tumor cell subpopulations. Accordingly, computational methods have emerged for single-cell copy number profiling and clustering; however, these two tasks have been handled sequentially by applying various ad-hoc pre- and post-processing steps; hence, a procedure vulnerable to introducing clustering artifacts. We avoid the clustering artifact issues in our method, CopyMix, a Variational Inference for a novel mixture model, by jointly inferring cell clusters and their underlying copy number profile. Our probabilistic graphical model is an improved version of the mixture of hidden Markov models, which is designed uniquely to infer single-cell copy number profiling and clustering. For the evaluation, we used likelihood-ratio test, CH index, Silhouette, V-measure, total variation scores. CopyMix performs well on both biological and simulated data. Our favorable results indicate a considerable potential to obtain clinical impact by using CopyMix in studies of cancer tumor heterogeneity.

Inferring replication timing and proliferation dynamics from single-cell DNA sequencing data

Dysregulated DNA replication is a cause and a consequence of aneuploidy in cancer, yet the interplay between copy number alterations (CNAs), replication timing (RT) and cell cycle dynamics remain understudied in aneuploid tumors. We developed a probabilistic method, PERT, for simultaneous inference of cell-specific replication and copy number states from single-cell whole genome sequencing (scWGS) data. We used PERT to investigate clone-specific RT and proliferation dynamics in >50,000 cells obtained from aneuploid and clonally heterogeneous cell lines, xenografts and primary cancers. We observed bidirectional relationships between RT and CNAs, with CNAs affecting X-inactivation producing the largest RT shifts. Additionally, we found that clone-specific S-phase enrichment positively correlated with ground-truth proliferation rates in genomically stable but not unstable cells. Together, these results demonstrate robust computational identification of S-phase cells from scWGS data, and highlight the importance of RT and cell cycle properties in studying the genomic evolution of aneuploid tumors.

KaryoTap Enables Aneuploidy Detection in Thousands of Single Human Cells.

Investigating chromosomal instability and aneuploidy within tumors is essential for understanding tumorigenesis and developing diagnostic and therapeutic strategies. Single-cell DNA sequencing technologies have enabled such analyses, revealing aneuploidies specific to individual cells within the same tumor. However, it has been difficult to scale the throughput of these methods to detect rare aneuploidies while maintaining high sensitivity. To overcome this deficit, we developed KaryoTap, a method combining custom targeted DNA sequencing panels for the Tapestri platform with a computational framework to enable detection of chromosome- and chromosome arm-scale aneuploidy (gains or losses) and copy number neutral loss of heterozygosity in all human chromosomes across thousands of single cells simultaneously. KaryoTap allows detecting gains and losses with an average accuracy of 83% for arm events and 91% for chromosome events. Importantly, together with chromosomal copy number, our system allows us to detect barcodes and gRNAs integrated into the cells' genome, thus enabling pooled CRISPR- or ORF-based functional screens in single cells. As a proof of principle, we performed a small screen to expand the chromosomes that can be targeted by our recently described CRISPR-based KaryoCreate system for engineering aneuploidy in human cells. KaryoTap will prove a powerful and flexible approach for the study of aneuploidy and chromosomal instability in both tumors and normal tissues.

Mugen-UMAP: UMAP visualization and clustering of mutated genes in single-cell DNA sequencing data

BackgroundThe application of Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and visualization has revolutionized the analysis of single-cell RNA expression and population genetics. However, its potential in single-cell DNA sequencing data analysis, particularly for visualizing gene mutation information, has not been fully explored.ResultsWe introduce Mugen-UMAP, a novel Python-based program that extends UMAP’s utility to single-cell DNA sequencing data. This innovative tool provides a comprehensive pipeline for processing gene annotation files of single-cell somatic single-nucleotide variants and metadata to the visualization of UMAP projections for identifying clusters, along with various statistical analyses. Employing Mugen-UMAP, we analyzed whole-exome sequencing data from 365 single-cell samples across 12 non-small cell lung cancer (NSCLC) patients, revealing distinct clusters associated with histological subtypes of NSCLC. Moreover, to demonstrate the general utility of Mugen-UMAP, we applied the program to 9 additional single-cell WES datasets from various cancer types, uncovering interesting patterns of cell clusters that warrant further investigation. In summary, Mugen-UMAP provides a quick and effective visualization method to uncover cell cluster patterns based on the gene mutation information from single-cell DNA sequencing data.ConclusionsThe application of Mugen-UMAP demonstrates its capacity to provide valuable insights into the visualization and interpretation of single-cell DNA sequencing data. Mugen-UMAP can be found at https://github.com/tengchn/Mugen-UMAP

PTPN11 Mutation Clonal Hierarchy in Acute Myeloid Leukemia.

Mutations in protein tyrosine phosphatase non-receptor type 11 ( PTPN11 ) have been considered late acquired mutations in acute myeloid leukemia (AML) development. To interrogate the ontogeny of PTPN11 mutations, we utilized single-cell DNA sequencing and identified that PTPN11 mutations can occur as initiating events in some AML patients when accompanied by strong oncogenic drivers, commonly NPM1 mutations. The co-driver role of PTPN11 mutations was confirmed in a novel murine model that exhibits an AML phenotype with early expansion of a diverse set of variably differentiated myeloid cells that engrafted into immunodeficient and immunocompetent mice. This immune diversity was reconstituted from early precursor cells when engrafted into immunodeficient mice. Moreover, immune diversity was also observed in the blast component of patient samples with NPM1 and PTPN11 mutations, providing novel antigen targets for immune based approaches in this subset of AML that is resistant to multiple targeted therapies.

Recent progress and applications of single-cell sequencing technology in breast cancer.

Single-cell RNA sequencing (scRNA-seq) technology enables the precise analysis of individual cell transcripts with high sensitivity and throughput. When integrated with multiomics technologies, scRNA-seq significantly enhances the understanding of cellular diversity, particularly within the tumor microenvironment. Similarly, single-cell DNA sequencing has emerged as a powerful tool in cancer research, offering unparalleled insights into the genetic heterogeneity and evolution of tumors. In the context of breast cancer, this technology holds substantial promise for decoding the intricate genomic landscape that drives disease progression, treatment resistance, and metastasis. By unraveling the complexities of tumor biology at a granular level, single-cell DNA sequencing provides a pathway to advancing our comprehension of breast cancer and improving patient outcomes through personalized therapeutic interventions. As single-cell sequencing technology continues to evolve and integrate into clinical practice, its application is poised to revolutionize the diagnosis, prognosis, and treatment strategies for breast cancer. This review explores the potential of single-cell sequencing technology to deepen our understanding of breast cancer, highlighting key approaches, recent advancements, and the role of the tumor microenvironment in disease plasticity. Additionally, the review discusses the impact of single-cell sequencing in paving the way for the development of personalized therapies.

Tracking Response and Resistance in Acute Myeloid Leukemia through Single-Cell DNA Sequencing Helps Uncover New Therapeutic Targets.

Acute myeloid leukemia (AML) is an aggressive hematologic neoplasia with a complex polyclonal architecture. Among driver lesions, those involving the FLT3 gene represent the most frequent mutations identified at diagnosis. The development of tyrosine kinase inhibitors (TKIs) has improved the clinical outcomes of FLT3-mutated patients (Pt). However, overcoming resistance to these drugs remains a challenge. To unravel the molecular mechanisms underlying therapy resistance and clonal selection, we conducted a longitudinal analysis using a single-cell DNA sequencing approach (MissionBioTapestri® platform, San Francisco, CA, USA) in two patients with FLT3-mutated AML. To this end, samples were collected at the time of diagnosis, during TKI therapy, and at relapse or complete remission. For Pt #1, disease resistance was associated with clonal expansion of minor clones, and 2nd line TKI therapy with gilteritinib provided a proliferative advantage to the clones carrying NRAS and KIT mutations, thereby responsible for relapse. In Pt #2, clonal architecture was less complex, and 1st line TKI therapy with midostaurin was able to eradicate the leukemic clones. Our results corroborate previous findings about clonal selection driven by TKIs, highlighting the importance of a deeper characterization of individual clonal architectures for choosing the best treatment plan for personalized approaches aimed at optimizing outcomes.

Polyploid cancer cells reveal signatures of chemotherapy resistance.

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

Circulating Multiple Myeloma Cells (CMMCs) as Prognostic and Predictive Markers in Multiple Myeloma and Smouldering MM Patients.

In recent years, liquid biopsy has emerged as a promising alternative to the bone marrow (BM) examination, since it is a minimally invasive technique allowing serial monitoring. Circulating multiple myeloma cells (CMMCs) enumerated using CELLSEARCH® were correlated with patients' prognosis and measured under treatment to assess their role in monitoring disease dynamics. Forty-four MM and seven smouldering MM (SMM) patients were studied. The CMMC medians at diagnosis were 349 (1 to 39,940) and 327 (range 22-2463) for MM and SMM, respectively. In the MM patients, the CMMC count was correlated with serum albumin, calcium, β2-microglobulin, and monoclonal components (p < 0.04). Under therapy, the CMMCs were consistently detectable in 15/40 patients (coMMstant = 1) and were undetectable or decreasing in 25/40 patients (coMMstant = 0). High-quality response rates were lower in the coMMstant = 1 group (p = 0.04), with a 7.8-fold higher risk of death (p = 0.039), suggesting that continuous CMMC release is correlated with poor responses. In four MM patients, a single-cell DNA sequencing analysis on residual CMMCs confirmed the genomic pattern of the aberrations observed in the BM samples, also highlighting the presence of emerging clones. The CMMC kinetics during treatment were used to separate the patients into two subgroups based on the coMMstant index, with different responses and survival probabilities, providing evidence that CMMC persistence is associated with a poor disease course.

High-resolution Inference of Multiplexed Anti-HIV Gene Editing using Single-Cell Targeted DNA Sequencing.

Gene therapy-based HIV cure strategies typically aim to excise the HIV provirus directly, or target host dependency factors (HDFs) that support viral persistence. Cure approaches will likely require simultaneous co-targeting of multiple sites within the HIV genome to prevent evolution of resistance, and/or co-targeting of multiple HDFs to fully render host cells refractory to HIV infection. Bulk cell-based methods do not enable inference of co-editing within individual viral or target cell genomes, and do not discriminate between monoallelic and biallelic gene disruption. Here, we describe a targeted single-cell DNA sequencing (scDNA-seq) platform characterizing the near full-length HIV genome and 50 established HDF genes, designed to evaluate anti-HIV gene therapy strategies. We implemented the platform to investigate the capacity of multiplexed CRISPR-Cas9 ribonucleoprotein complexes (Cas9-RNPs) to simultaneously 1) inactivate the HIV provirus, and 2) knockout the CCR5 and CXCR4 HDF (entry co-receptor) genes in microglia and primary monocyte-derived macrophages (MDMs). Our scDNA-seq pipeline revealed that antiviral gene editing is rarely observed at multiple loci (or both alleles of a locus) within an individual cell, and editing probabilities across sites are linked. Our results demonstrate that single-cell sequencing is critical to evaluate the true efficacy and therapeutic potential of HIV gene therapy.

Improved allele-specific single-cell copy number estimation in low-coverage DNA-sequencing.

Advances in whole-genome single-cell DNA sequencing (scDNA-seq) have led to the development of numerous methods for detecting copy number aberrations (CNAs), a key driver of genetic heterogeneity in cancer. While most of these methods are limited to the inference of total copy number, some recent approaches now infer allele-specific CNAs using innovative techniques for estimating allele-frequencies in low coverage scDNA-seq data. However, these existing allele-specific methods are limited in their segmentation strategies, a crucial step in the CNA detection pipeline. We present SEACON (Single-cell Estimation of Allele-specific COpy Numbers), an allele-specific copy number profiler for scDNA-seq data. SEACON uses a Gaussian Mixture Model to identify latent copy number states and breakpoints between contiguous segments across cells, filters the segments for high-quality breakpoints using an ensemble technique, and adopts several strategies for tolerating noisy read-depth and allele frequency measurements. Using a wide array of both real and simulated datasets, we show that SEACON derives accurate copy numbers and surpasses existing approaches under numerous experimental conditions, and identify its strengths and weaknesses. SEACON is implemented in Python and is freely available open-source from https://github.com/NabaviLab/SEACON and https://doi.org/10.5281/zenodo.12727008.

SCCNAInfer: a robust and accurate tool to infer the absolute copy number on scDNA-seq data.

Copy number alterations (CNAs) play an important role in disease progression, especially in cancer. Single-cell DNA sequencing (scDNA-seq) facilitates the detection of CNAs of each cell that is sequenced at a shallow and uneven coverage. However, the state-of-the-art CNA detection tools based on scDNA-seq are still subject to genome-wide errors due to the wrong estimation of the ploidy. We developed SCCNAInfer, a computational tool that utilizes the subclonal signal inside the tumor cells to more accurately infer each cell's ploidy and CNAs. Given the segmentation result of an existing CNA detection method, SCCNAInfer clusters the cells, infers the ploidy of each subclone, refines the read count by bin clustering, and accurately infers the CNAs for each cell. Both simulated and real datasets show that SCCNAInfer consistently improves upon the state-of-the-art CNA detection tools such as Aneufinder, Ginkgo, SCOPE and SeCNV. SCCNAInfer is freely available at https://github.com/compbio-mallory/SCCNAInfer. Supplementary data are available at Bioinformatics online.

Neoadjuvant targeted therapy in early resectable HER2-mutant lobular breast cancer.

TPS630 Background: Invasive lobular carcinoma (ILC) accounts for approximately 10-15% of all breast cancers and is characterized by hormone receptor positivity (HR+), low Ki-67 score, and loss of cell adhesion receptors such as E-cadherin. While these tumors tend to be indolent initially, late recurrences and distant metastasis are common, and ILC has very low rates of pathologic complete response (pCR) to both neoadjuvant chemotherapy and endocrine therapy (ET). HER2 ( ERBB2) mutations are enriched in about 10% of unselected ILC, with some reports showing the prevalence as high as 27% in pleomorphic and higher grade ILCs. HER2 mutations in ILC are associated with ET resistance and a worse prognosis. Neratinib is a HER2 tyrosine kinase inhibitor (TKI) currently FDA-approved for adjuvant treatment of HER2-amplified (HER2+) early-stage breast cancer. It has shown safety and efficacy in combination with ET in metastatic HER2-mutant HR+ breast cancer. We hypothesize that neoadjuvant treatment with neratinib and ET will improve pathologic responses in newly diagnosed HER2-mutant HR+ ILC. Methods: This study (NCT05919108) is an open label multi-site Phase II clinical trial for 30 patients with Stage I-III ILCs and a HER2 activating mutation. Patients will be identified by next generation sequencing (NGS) of the diagnostic biopsy and randomized to 4 weeks of ET +/- neratinib; a biopsy will be done at the end of the lead-in phase. All patients will then receive ET and neratinib for 20 weeks prior to proceeding to surgery. To optimize tolerability, the first cycle of neratinib will include a dose escalation and antidiarrheal prophylaxis. The primary objective of this study will be the pre-operative endocrine prognostic index (PEPI) score, which will be compared to a historical PEPI-0 rate for patients with HR+ breast cancer treated with neoadjuvant ET. Secondary objectives will include pCR, residual cancer burden, and rates of breast conserving surgery. Correlative studies will include biomarkers of tumor cell cycle arrest, apoptosis, HER2 pathway activation, and gene expression on the pre- and on-treatment (4 week) biopsy from the lead-in phase to document molecular synergy between neratinib and ET. Patient-derived organoids from the 4-week biopsies will be established and subjected to single-cell RNA, ATAC seq, and DNA sequencing to study tumor evolution and elucidate epigenetic programs and somatic alterations that allow breast cancer survival despite continuous HER2-directed therapy. This is a first-in-kind, mechanism-based, molecularly targeted combination of a HER2 TKI and ET in HER2-mutant ILC. We posit results from this study may generate a clear signal of clinical activity leading to a randomized phase III trial aimed at incorporating HER2-directed therapies as a treatment for operable, early stage ILC. This signal may also suggest for the first time the added value of NGS in patients with newly diagnosed HR+ breast cancer. Clinical trial information: NCT05919108 .

Migratory Tumor Cells Cooperate with Cancer Associated Fibroblasts in Hormone Receptor-Positive and HER2-Negative Breast Cancer.

Hormone receptor-positive and HER2-negative breast cancer (HR+/HER2-BC) is the most common type with a favorable prognosis under endocrine therapy. However, it still demonstrates unpredictable progression and recurrences influenced by high tumoral diversity and microenvironmental status. To address these heterogeneous molecular characteristics of HR+/HER2-BC, we aimed to simultaneously characterize its transcriptomic landscape and genetic architecture at the same resolution. Using advanced single-cell RNA and DNA sequencing techniques together, we defined four distinct tumor subtypes. Notably, the migratory tumor subtype was closely linked to genomic alterations of EGFR, related to the tumor-promoting behavior of IL6-positive inflammatory tumor-associated fibroblast, and contributing to poor prognosis. Our study comprehensively utilizes integrated analysis to uncover the complex dynamics of this breast cancer subtype, highlighting the pivotal role of the migratory tumor subtype in influencing surrounding cells. This sheds light on potential therapeutic targets by offering enhanced insights for HR+/HER2-BC treatment.

Disentangling oncogenic amplicons in esophageal adenocarcinoma

Esophageal adenocarcinoma is a prominent example of cancer characterized by frequent amplifications in oncogenes. However, the mechanisms leading to amplicons that involve breakage-fusion-bridge cycles and extrachromosomal DNA are poorly understood. Here, we use 710 esophageal adenocarcinoma cases with matched samples and patient-derived organoids to disentangle complex amplicons and their associated mechanisms. Short-read sequencing identifies ERBB2, MYC, MDM2, and HMGA2 as the most frequent oncogenes amplified in extrachromosomal DNAs. We resolve complex extrachromosomal DNA and breakage-fusion-bridge cycles amplicons by integrating of de-novo assemblies and DNA methylation in nine long-read sequenced cases. Complex amplicons shared between precancerous biopsy and late-stage tumor, an enrichment of putative enhancer elements and mobile element insertions are potential drivers of complex amplicons’ origin. We find that patient-derived organoids recapitulate extrachromosomal DNA observed in the primary tumors and single-cell DNA sequencing capture extrachromosomal DNA-driven clonal dynamics across passages. Prospectively, long-read and single-cell DNA sequencing technologies can lead to better prediction of clonal evolution in esophageal adenocarcinoma.

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.

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

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