Genetic and transcriptional contributions to relapse in normal karyotype acute myeloid leukemia

To better understand clonal and transcriptional adaptations after relapse in patients with acute myeloid leukemia (AML), we collected presentation and relapse samples from six normal karyotype AML cases. We performed enhanced whole-genome sequencing to characterize clonal evolution, and deep-coverage single-cell RNA sequencing on the same samples, which yielded 142,642 high-quality cells for analysis. Identifying expressed mutations in individual cells enabled us to discriminate between normal and AML cells, to identify coordinated changes in the genome and transcriptome, and to identify subclone-specific cell states. We quantified the coevolution of genetic and transcriptional heterogeneity during AML progression, and found that transcriptional changes were significantly correlated with genetic changes. However, transcriptional adaptation sometimes occurred independently, suggesting that clonal evolution does not represent all relevant biological changes. In three cases, we identified cells at diagnosis that likely seeded the relapse. Finally, these data revealed a conserved relapse-enriched leukemic cell state bearing markers of stemness, quiescence, and adhesion. SIGNIFICANCE: These data enabled us to identify a relapse-enriched leukemic cell state with distinct transcriptional properties. Detailed case-by-case analyses elucidated the complex ways in which the AML genome, transcriptome, and immune microenvironment interact to evade chemotherapy. These analyses provide a blueprint for evaluating these factors in larger cohorts

CONICS integrates scRNA-seq with DNA sequencing to map gene expression to tumor sub-clones.

Motivation:Single-cell RNA-sequencing (scRNA-seq) has enabled studies of tissue composition at unprecedented resolution. However, the application of scRNA-seq to clinical cancer samples has been limited, partly due to a lack of scRNA-seq algorithms that integrate genomic mutation data. Results:To address this, we present. CONICS:COpy-Number analysis In single-Cell RNA-Sequencing. CONICS is a software tool for mapping gene expression from scRNA-seq to tumor clones and phylogenies, with routines enabling: the quantitation of copy-number alterations in scRNA-seq, robust separation of neoplastic cells from tumor-infiltrating stroma, inter-clone differential-expression analysis and intra-clone co-expression analysis. Availability and implementation:CONICS is written in Python and R, and is available from https://github.com/diazlab/CONICS. Supplementary information:Supplementary data are available at Bioinformatics online

Functional analysis of structural variants in single cells using Strand-seq

Somatic structural variants (SVs) are widespread in cancer, but their impact on disease evolution is understudied due to a lack of methods to directly characterize their functional consequences. We present a computational method, scNOVA, which uses Strand-seq to perform haplotype-aware integration of SV discovery and molecular phenotyping in single cells by using nucleosome occupancy to infer gene expression as a readout. Application to leukemias and cell lines identifies local effects of copy-balanced rearrangements on gene deregulation, and consequences of SVs on aberrant signaling pathways in subclones. We discovered distinct SV subclones with dysregulated Wnt signaling in a chronic lymphocytic leukemia patient. We further uncovered the consequences of subclonal chromothripsis in T cell acute lymphoblastic leukemia, which revealed c-Myb activation, enrichment of a primitive cell state and informed successful targeting of the subclone in cell culture, using a Notch inhibitor. By directly linking SVs to their functional effects, scNOVA enables systematic single-cell multiomic studies of structural variation in heterogeneous cell populations

Single-cell profiling of human dura and meningioma reveals cellular meningeal landscape and insights into meningioma immune response

BACKGROUND: Recent investigations of the meninges have highlighted the importance of the dura layer in central nervous system immune surveillance beyond a purely structural role. However, our understanding of the meninges largely stems from the use of pre-clinical models rather than human samples. METHODS: Single-cell RNA sequencing of seven non-tumor-associated human dura samples and six primary meningioma tumor samples (4 matched and 2 non-matched) was performed. Cell type identities, gene expression profiles, and T cell receptor expression were analyzed. Copy number variant (CNV) analysis was performed to identify putative tumor cells and analyze intratumoral CNV heterogeneity. Immunohistochemistry and imaging mass cytometry was performed on selected samples to validate protein expression and reveal spatial localization of select protein markers. RESULTS: In this study, we use single-cell RNA sequencing to perform the first characterization of both non-tumor-associated human dura and primary meningioma samples. First, we reveal a complex immune microenvironment in human dura that is transcriptionally distinct from that of meningioma. In addition, we characterize a functionally diverse and heterogenous landscape of non-immune cells including endothelial cells and fibroblasts. Through imaging mass cytometry, we highlight the spatial relationship among immune cell types and vasculature in non-tumor-associated dura. Utilizing T cell receptor sequencing, we show significant TCR overlap between matched dura and meningioma samples. Finally, we report copy number variant heterogeneity within our meningioma samples. CONCLUSIONS: Our comprehensive investigation of both the immune and non-immune cellular landscapes of human dura and meningioma at single-cell resolution builds upon previously published data in murine models and provides new insight into previously uncharacterized roles of human dura

Functional analysis of structural variants in single cells using Strand-seq

Somatic structural variants (SVs) are widespread in cancer, but their impact on disease evolution is understudied due to a lack of methods to directly characterize their functional consequences. We present a computational method, scNOVA, which uses Strand-seq to perform haplotype-aware integration of SV discovery and molecular phenotyping in single cells by using nucleosome occupancy to infer gene expression as a readout. Application to leukemias and cell lines identifies local effects of copy-balanced rearrangements on gene deregulation, and consequences of SVs on aberrant signaling pathways in subclones. We discovered distinct SV subclones with dysregulated Wnt signaling in a chronic lymphocytic leukemia patient. We further uncovered the consequences of subclonal chromothripsis in T cell acute lymphoblastic leukemia, which revealed c-Myb activation, enrichment of a primitive cell state and informed successful targeting of the subclone in cell culture, using a Notch inhibitor. By directly linking SVs to their functional effects, scNOVA enables systematic single-cell multiomic studies of structural variation in heterogeneous cell populations

EPIGENETIC CONTROL OF NEURAL CREST DEVELOPMENT AND NEURAL CREST DERIVED TUMORS

The neural crest is a multipotent transient cell population that migrates, proliferates, and differentiates in vertebrate embryogenesis and gives rises to a cornucopia of tissue derivatives such as craniofacial bone and cartilage, peripheral nerves, and glia. Congenital malformations including cleft palate and craniosynostosis arise from aberrant development of cranial neural crest cells and their derivatives. Neural crest derived tumors of Schwann cell lineages can arise from single genomic hits in the form of NF2 loss. Considering the low genomic burden of many neural crest derived tumors, we hypothesized that epigenetic regulators, which normally specify cell lineages and maintain cell fates in embryonic development, are mis-regulated in adult neural crest derived tissues, contributing to tumorigenesis and differential tumor radiation responses.Here we use mouse genetics and single cell sequencing to investigate how PRC2, a histone methyltransferase broadly involved in gene repression and maintenance of pluripotency in development, is involved in epigenetic control of post- otic neural crest derivates. We identified Eed, a PRC2 core subunit, as a potent regulator of craniofacial development. Eed deletion in post-migratory neural crest cells was perinatal lethal and knockout embryos presented with severe craniofacial abnormalities consistent with impaired differentiation of osteoblast derivatives. Using targeted primary cell culture gene expression analysis and unbiased scRNA-seq, we discovered changes in transcription factors involved in the proliferation and differentiation of neural crest derivatives including loss of Sox transcription factors and increase in Hox genes. Strikingly, we found an expansion of undifferentiated mesenchymal stem cells and decrease in differentiated osteoblast cells, indicating Eed controls proper differentiation of mesenchymal derivatives that comprise craniofacial structures. Taken together, we establish the embryonic, cellular, and molecular consequences of Eed loss in neural crest derived craniofacial tissues. Next, we investigated more broadly how epigenetic regulation contributes to cancer of neural crest derived cells including the tumorigenesis of schwannomas and schwannoma radiation responses. Using bulk and single-cell bioinformatics, functional genomic approaches, and mechanistic validation, we discovered schwannomas comprise 2 molecular subgroups marked by activation of neural crest signaling pathways or enrichment of immune cells in response to radiotherapy. CRISPRi radiation screening in human schwannoma cells identified the lysine demethylases KDM1A and KDM5C as drivers of radioresistance or radiosensitivity, respectively. Lastly, we integrated single-nuclei ATAC, RNA, and CRISPRi perturbation to identify chromatin accessibility motifs that drive schwannoma cell state evolution and radiation responses

Multi-Omics Integration Through Single-Cell Copy Number Analysis In Cancer

Genetic and epigenetic alterations combine to drive cancer progression. Heterogeneous cell populations within tumors are associated with poor prognosis and outcomes. Copy number aberrations (CNAs), a genetic variant commonly occurring in tumors, are used as markers to detect subclones and reconstruct tumor phylogeny. Multi-omics integration between CNAs and other modalities on tumor subclones facilitates studying the interplay between genome and epigenome, and their effects on transcriptome. So far, there is still a lack of computational methods for the multi-omics integration of different types of single-cell and ST tumor sequencing data. Therefore, the aim of this thesis is to extract (allele-specific) CNA signals in single-cell and ST tumor sequencing data, which enables the integration of multi-omics at the subclone level. We achieved this through the development of two methods — Alleloscope (Chapter 2) and Clonalscope (Chapter 3). Alleloscope is a computational method for profiling allele-specific CNAs in single-cell DNA- and/or transposase-accessible chromatin-sequencing (scDNA-seq, ATAC-seq) data, enabling integrative analysis of allele-specific copy number and chromatin accessibility. On scDNA-seq data from gastric, colorectal and breast cancer samples, with validation using matched linked-read sequencing, Alleloscope finds pervasive occurrence of highly complex, multiallelic CNAs, in which cells that carry varying allelic configurations adding to the same total copy number coevolve within a tumor. On scATAC-seq from two basal cell carcinoma samples and a gastric cancer cell line, Alleloscope detected multiallelic copy number events and copy-neutral loss-of-heterozygosity, enabling dissection of the contributions of chromosomal instability and chromatin remodeling to tumor evolution. To detect genetically different subclones based on CNAs, we also developed Clonalscope, a subclone detection method for different single-cell and ST tumor sequencing data, which leverages prior information from matched bulk DNA-seq data. Clonalscope implements a nested Chinese Restaurant Process to model the evolutionary process in tumors. On scRNA-seq and scATAC-seq data from three gastrointestinal tumor samples, Clonalscope successfully labeled malignant cells and identified genetically different subclones, which were validated in detail using matched scDNA-seq data. On ST data from a basal cell carcinoma and two invasive ductal carcinoma samples, Clonalscope was able to label malignant spots, trace subclones between related datasets, and identify spatially segregated subclones expressing genes associated with drug resistance and survival

An investigation of genomic instability and its impact on cancer development and heterogeneity

Genomic instability (GIN), a genomic state facilitating large scale chromosomal rearrangements, is a hallmark of cancer. GIN can contribute to oncogenesis by disrupting genes, and leading to copy number aberrations (CNAs), the gain or loss of genomic segments. In this thesis I describe two projects linked by the overarching theme of GIN, outlined below: Project 1: Copy-number aberrations (CNAs) contribute to clonal diversity within cancer, with clinical implications. Breast cancer is one such example, but the effect of CNAs on gene expression in intra-tumour subclonal populations has not been properly characterised. Due to sequencing technology limits and lack of computational methods, it is difficult to assess CNAs at a subclonal level. Here, I have benchmarked the ‘InferCNV’ computational method and used it to infer single cell CNA profiles from 14 primary breast cancer single cell RNA-sequencing (scRNA-seq) datasets. I reveal diverse intratumoural heterogeneity involving at least four subclonal populations per tumour. Finally, I identify subclones with expression/CNA profiles indicative of metastatic potential, involving differential regulation of metastasis associated genes such as MUCL1, BST2 and IGFBP5. Project 2: High-grade serous ovarian cancer (HGSOC) is characterised by widespread GIN. Drivers of GIN include deficient DNA repair and amplification of Cyclin E1, however no major cause is known for one third of tumours. Deregulation of repetitive elements may contribute to GIN in HGSOC. It is difficult to investigate repetitive elements from sequencing data as they map to multiple places within the genome. I have quantified repetitive RNA in 99 high-grade serous ovarian cancer (HGSOC) and matched control RNA-seq datasets to determine their potential contribution to GIN. I identified retrotransposons which are deregulated in HGSOC, which may have been active during cancer development. Some of these retrotransposons were enriched at structural variant breakpoints, indicating potential causality. Finally, I identified retrotransposon-associated structural variants in proximity to deregulated oncogenes implicated in homologous DNA repair, which may have modulated their expression and contributed to cancer development. In summary, I have explored both a cause (retrotransposons) and consequence (CNA-based heterogeneity) of GIN in cancer, and shown how GIN can contribute to the modulation of cancer-associated genes which influence cancer development and outcomes