Abstract LB020: Epigenomic tumor evolution modeling with single-cell methylation data profiling

2021 ◽  
Author(s):  
Xuan C. Li ◽  
Yuelin Liu ◽  
Farid Rashidi ◽  
Salem Malikic ◽  
Stephen M. Mount ◽  
...  
Keyword(s):  
Single Cell ◽  
Tumor Evolution ◽  
Methylation Data ◽  
Data Profiling ◽  
Evolution Modeling

scholarly journals Epigenomic tumor evolution modeling with single-cell methylation data profiling

2021 ◽  
Author(s):  
Xuan Cindy Li ◽  
Yuelin Liu ◽  
Farid Rashidi Mehrabadi ◽  
Salem Malikić ◽  
Stephen M. Mount ◽  
...  
Keyword(s):  
Single Cell ◽  
Sequencing Error ◽  
Whole Genome Sequencing Data ◽  
Tumor Evolution ◽  
Data Sets ◽  
Sequencing Data ◽  
Data Profiling ◽  
Colorectal Cancer Cells ◽  
Methylation Patterns ◽  
Evolution Modeling

AbstractRecent studies on the heritability of methylation patterns in tumor cells, suggest that tumor heterogeneity and progression can be studied through methylation changes. To elucidate methylation-based evolution trajectories in tumors, we introduce a novel computational frame-work for methylation phylogeny reconstruction, leveraging single cell bisulfite treated whole genome sequencing data (scBS-seq), additionally incorporating copy number information inferred independently from matched single cell RNA sequencing (scRNA-seq) data, when available. Our framework consists of three components: (i) noise-minimizing site selection, (ii) likelihood-based sequencing error correction, and (iii) pairwise expected distance calculation for cells, all designed to mitigate the effect of noise and uncertainty due to data sparsity commonly observed in scBS-seq data. We validate our approach with the scBS-seq data of multi-regionally sampled colorectal cancer cells, and demonstrate that the cell lineages constructed by our method strongly correlate with original sampling regions. Additionally, we show that the constructed phylogeny can be used to impute missing entries, which, in turn, may help reduce sparsity issues in scBS-seq data [email protected]

scholarly journals Reconstructing single-cell karyotype alterations in colorectal cancer identifies punctuated and gradual diversification patterns

2021 ◽  
Author(s):  
Yannik Bollen ◽  
Ellen Stelloo ◽  
Petra van Leenen ◽  
Myrna van den Bos ◽  
Bas Ponsioen ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Single Cell ◽  
Temporal Dynamics ◽  
De Novo ◽  
Tumor Evolution ◽  
Genome Wide ◽  
Aneuploid Tumor ◽  
Evolutionary Paths ◽  
Tumor Biopsies ◽  
Punctuated Evolution

AbstractCentral to tumor evolution is the generation of genetic diversity. However, the extent and patterns by which de novo karyotype alterations emerge and propagate within human tumors are not well understood, especially at single-cell resolution. Here, we present 3D Live-Seq—a protocol that integrates live-cell imaging of tumor organoid outgrowth and whole-genome sequencing of each imaged cell to reconstruct evolving tumor cell karyotypes across consecutive cell generations. Using patient-derived colorectal cancer organoids and fresh tumor biopsies, we demonstrate that karyotype alterations of varying complexity are prevalent and can arise within a few cell generations. Sub-chromosomal acentric fragments were prone to replication and collective missegregation across consecutive cell divisions. In contrast, gross genome-wide karyotype alterations were generated in a single erroneous cell division, providing support that aneuploid tumor genomes can evolve via punctuated evolution. Mapping the temporal dynamics and patterns of karyotype diversification in cancer enables reconstructions of evolutionary paths to malignant fitness.

IMMU-04. UNVEILING THE TUMOR-METABOLOME-IMMUNITY AXIS OF GLIOMA

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi92-vi92
Author(s):  
Mirco Friedrich ◽  
Lukas Bunse ◽  
Roman Sankowski ◽  
Wolfgang Wick ◽  
Marco Prinz ◽  
...  
Keyword(s):  
T Cell ◽  
Single Cell ◽  
T Cell Activation ◽  
Cell Activation ◽  
Immune Cell ◽  
Myeloid Cell ◽  
Tumor Evolution ◽  
Large Neutral Amino Acid ◽  
Immune Microenvironment ◽  
Idh Mutations

Abstract The glioma microenvironment orchestrates tumor evolution, progression, and resistance to therapy. In high-grade gliomas, microglia and monocyte-derived macrophages constitute up to 70% of the tumor mass. However, the dynamics and phenotypes of intratumoral myeloid cells during tumor progression are poorly understood. Here we define myeloid cellular states in gliomas by longitudinal single-cell profiling and demonstrate their strict control by the tumor genotype. We report the unexpected and clinically highly relevant finding that human as well as murine gliomas with Isocitrate Dehydrogenase (IDH)1-R132H, a key oncogenic driver mutation of glioma, subdue their innate immune microenvironment by prompting a multifaceted reprogramming of myeloid and T cell metabolism. We employed integrated single-cell transcriptomic, time-of-flight mass cytometry and proteomic analyses of human healthy cortex control and glioma samples to identify myeloid cell subsets with distinct fates in IDH-mutated glioma that diverge from canonical trajectories of antigen-presenting cells as a result of a monocyte-to-macrophage differentiation block. Moving beyond single time point assessments, we now longitudinally describe differential immune cell infiltration and phenotype dynamics during glioma progression that are orchestrated by a fluctuating network of resident microglial cells and educated recruited immune cells. IDH mutations in glioma induce a tolerogenic alignment of their immune microenvironment through increased tryptophan uptake via large neutral amino acid transporter (LAT1)-CD98 and subsequent activation of the aryl hydrocarbon receptor (AHR) in educated blood-borne macrophages. In experimental tumor models, this immunosuppressive phenotype was reverted by LAT1-CD98 and AHR inhibitors. Taken together with direct effects on T cell activation, our findings not only link this oncogenic metabolic pathway to distinct immunosuppressive pathways but also provide the rationale and novel molecular targets for the development of immunotherapeutic concepts addressing the disease-defining microenvironmental effects of IDH mutations.

scholarly journals Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data

2018 ◽  
Author(s):  
Camila P.E. de Souza ◽  
Mirela Andronescu ◽  
Tehmina Masud ◽  
Farhia Kabeer ◽  
Justina Biele ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Missing Values ◽  
Clonal Analysis ◽  
Probabilistic Methods ◽  
Methylation Data ◽  
Genome Sequences ◽  
Probabilistic Clustering ◽  
Sequencing Method ◽  
Important Form

AbstractWe present Epiclomal, a probabilistic clustering method arising from a hierarchical mixture model to simultaneously cluster sparse single-cell DNA methylation data and impute missing values. Using synthetic and published single-cell CpG datasets we show that Epiclomal outperforms non-probabilistic methods and is able to handle the inherent missing data feature which dominates single-cell CpG genome sequences. Using a recently published single-cell 5mCpG sequencing method (PBAL), we show that Epiclomal discovers sub-clonal patterns of methylation in aneuploid tumour genomes, thus defining epiclones. We show that epiclones may transcend copy number determined clonal lineages, thus opening this important form of clonal analysis in cancer. Epiclomal is written in R and Python and is available at https://github.com/shahcompbio/Epiclomal.

scholarly journals A statistical test on single-cell data reveals widespread recurrent mutations in tumor evolution

2016 ◽  
Author(s):  
Jack Kuipers ◽  
Katharina Jahn ◽  
Benjamin J. Raphael ◽  
Niko Beerenwinkel
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
Tumor Evolution ◽  
Sequencing Data ◽  
General Validity ◽  
Genomic Deletions ◽  
Single Cell Sequencing ◽  
Statistical Framework ◽  
Recurrent Mutations ◽  
Complex Models

The infinite sites assumption, which states that every genomic position mutates at most once over the lifetime of a tumor, is central to current approaches for reconstructing mutation histories of tumors, but has never been tested explicitly. We developed a rigorous statistical framework to test the assumption with single-cell sequencing data. The framework accounts for the high noise and contamination present in such data. We found strong evidence for recurrent mutations at the same site in 8 out of 9 single-cell sequencing datasets from human tumors. Six cases involved the loss of earlier mutations, five of which occurred at sites unaffected by large scale genomic deletions. Two cases exhibited parallel mutation, including the dataset with the strongest evidence of recurrence. Our results refute the general validity of the infinite sites assumption and indicate that more complex models are needed to adequately quantify intra-tumor heterogeneity.

scholarly journals Reconstructing mutational lineages in breast cancer by multi-patient-targeted single cell DNA sequencing

2021 ◽  
Author(s):  
Nicholas Navin ◽  
Jake Leighton ◽  
Min Hu ◽  
Emi Sei ◽  
Funda Meric-Bernstam
Keyword(s):  
Breast Cancer ◽  
Dna Sequencing ◽  
Single Cell ◽  
Copy Number ◽  
Somatic Mutations ◽  
Single Cells ◽  
Tumor Evolution ◽  
Sequencing Method ◽  
Genomic Regions ◽  
Microfluidics Platform

Single cell DNA sequencing (scDNA-seq) methods are powerful tools for profiling mutations in cancer cells, however most genomic regions characterized in single cells are non-informative. To overcome this issue, we developed a Multi-Patient-Targeted (MPT) scDNA-seq sequencing method. MPT involves first performing bulk exome sequencing across a cohort of cancer patients to identify somatic mutations, which are then pooled together to develop a single custom targeted panel for high-throughput scDNA-seq using a microfluidics platform. We applied MPT to profile 330 mutations across 23,500 cells from 5 TNBC patients, which showed that 3 tumors were monoclonal and 2 tumors were polyclonal. From this data, we reconstructed mutational lineages and identified early mutational and copy number events, including early TP53 mutations that occurred in all five patients. Collectively, our data suggests that MPT can overcome technical obstacles for studying tumor evolution using scDNA-seq by profiling information-rich mutation sites.

scholarly journals Dhaka: Variational Autoencoder for Unmasking Tumor Heterogeneity from Single Cell Genomic Data

2017 ◽  
Author(s):  
Sabrina Rashid ◽  
Sohrab Shah ◽  
Ziv Bar-Joseph ◽  
Ravi Pandya
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Tumor Heterogeneity ◽  
Genomic Data ◽  
Feature Space ◽  
Marker Genes ◽  
Tumor Evolution ◽  
Evolutionary Trajectory ◽  
Link Type ◽  
Variational Autoencoder

AbstractMotivationIntra-tumor heterogeneity is one of the key confounding factors in deciphering tumor evolution. Malignant cells exhibit variations in their gene expression, copy numbers, and mutation even when originating from a single progenitor cell. Single cell sequencing of tumor cells has recently emerged as a viable option for unmasking the underlying tumor heterogeneity. However, extracting features from single cell genomic data in order to infer their evolutionary trajectory remains computationally challenging due to the extremely noisy and sparse nature of the data.ResultsHere we describe ‘Dhaka’, a variational autoencoder method which transforms single cell genomic data to a reduced dimension feature space that is more efficient in differentiating between (hidden) tumor subpopulations. Our method is general and can be applied to several different types of genomic data including copy number variation from scDNA-Seq and gene expression from scRNA-Seq experiments. We tested the method on synthetic and 6 single cell cancer datasets where the number of cells ranges from 250 to 6000 for each sample. Analysis of the resulting feature space revealed subpopulations of cells and their marker genes. The features are also able to infer the lineage and/or differentiation trajectory between cells greatly improving upon prior methods suggested for feature extraction and dimensionality reduction of such data.Availability and ImplementationAll the datasets used in the paper are publicly available and developed software package is available on Github https://github.com/MicrosoftGenomics/Dhaka.Supporting info and Software: https://github.com/MicrosoftGenomics/Dhaka

scholarly journals HGG-26. SINGLE-CELL RNA-SEQ OF PEDIATRIC HIGH-GRADE GLIOMAS IDENTIFIES COMMON ONCOGENIC PROCESSES AMONG DISTINCT TUMOR HISTOLOGIES

2021 ◽  
Vol 23 (Supplement_1) ◽  
pp. i22-i22
Author(s):  
John DeSisto ◽  
Andrew Donson ◽  
Rui Fu ◽  
Bridget Sanford ◽  
Kent Riemondy ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
Treatment Modalities ◽  
Patient Specific ◽  
Cell Populations ◽  
High Grade ◽  
Methylation Data ◽  
Rna Seq

Abstract Background Pediatric high-grade glioma (PHGG) is a deadly childhood brain tumor that responds poorly to treatment. PHGG comprises two major subtypes: cortical tumors with wild-type H3K27 and diffuse midline gliomas (DMG) that occur in the midline and have characteristic H3K27M mutations. Cortical PHGG is heterogeneous with multiple molecular subtypes. In order to identify underlying commonalities in cortical PHGG that might lead to better treatment modalities, we performed molecular profiling, including single-cell RNA-Seq (scRNA-Seq), on PHGG samples from Children’s Hospital Colorado. Methods Nineteen cortical PHGG tumor samples, one DMG and one normal margin sample obtained at biopsy were disaggregated to isolate viable cells. Fifteen were glioblastomas (GBM), including five with epithelioid and/or giant cell features and five radiation-induced glioblastomas (RIG). There were also four non-GBM PHGG. We performed scRNA-Seq using 10X Genomics v.3 library preparation to enable capture of infiltrating immune cells. We also performed bulk RNA-Seq and DNA methylation profiling. Results After eliminating patient-specific and cell-cycle effects, RIG, epithelioid GBM, and other GBM each formed identifiable subgroups in bulk RNA-Seq and scRNA-Seq datasets. In the scRNA-Seq data, clusters with cells from multiple tumor samples included a PDGFRA-positive population expressing oligodendrocyte progenitor markers, astrocytic, mesenchymal and stemlike populations, macrophage/monocyte immune cells, and a smaller T-cell population. Analyses of DNA methylation data showed PDGFRA and CDK4 amplification and CDKN2A deletion are common alterations among PHGG. Inferred copy number variation analysis of the single-cell data confirmed that individual tumors include populations that both include and lack the molecular alterations identified in the methylation data. RNA velocity studies to define tumor cells of origin and further analyses of the immune cell populations are underway. Conclusions Single-cell analysis of PHGG confirms a large degree of tumor heterogeneity but also shows that PHGG have stemlike, mesenchymal and immune cell populations with common characteristics.

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