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

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.

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Characterizing the allele- and haplotype-specific copy number landscape of cancer genomes at single-cell resolution with CHISEL

10.1101/837195 ◽

2019 ◽

Cited By ~ 3

Author(s):

Simone Zaccaria ◽

Benjamin J. Raphael

Keyword(s):

Breast Cancer ◽

Single Cell ◽

Copy Number ◽

Single Cells ◽

Neutral Loss ◽

Tumor Evolution ◽

Whole Genome ◽

Copy Numbers ◽

Allele Specific ◽

Total Copy Number

AbstractSingle-cell barcoding technologies have recently been used to perform whole-genome sequencing of thousands of individual cells in parallel. These technologies provide the opportunity to characterize genomic heterogeneity at single-cell resolution, but their extremely low sequencing coverage (<0.05X per cell) has thus far restricted their use to identification of the total copy number of large multi-megabase segments in individual cells. However, total copy numbers do not distinguish between the two homologous chromosomes in humans, and thus provide a limited view of tumor heterogeneity and evolution missing important events such as copy-neutral loss-of-heterozygosity (LOH). We introduce CHISEL, the first method to infer allele- and haplotype-specific copy numbers in single cells and subpopulations of cells by aggregating sparse signal across thousands of individual cells. We applied CHISEL to 10 single-cell sequencing datasets from 2 breast cancer patients, each dataset containing ≈2000 cells. We identified extensive allele-specific copy-number aberrations (CNAs) in these samples including copy-neutral LOH, whole-genome duplications (WGDs), and mirrored-subclonal CNAs in subpopulations of cells. These allele-specific CNAs alter the copy number of genomic regions containing well-known breast cancer genes including TP53, BRCA2, and PTEN but are invisible to total copy number analysis. We utilized CHISEL’s allele- and haplotype-specific copy numbers to derive a more refined reconstruction of tumor evolution: timing allele-specific CNAs before and after WGDs, identifying low-frequency subclones distinguished by unique CNAs, and uncovering evidence of convergent evolution. This reconstruction is supported by orthogonal analysis of somatic single-nucleotide variants (SNVs) obtained by pooling barcoded reads across clones defined by CHISEL.

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SNV identification from single-cell RNA sequencing data

Human Molecular Genetics ◽

10.1093/hmg/ddz207 ◽

2019 ◽

Vol 28 (21) ◽

pp. 3569-3583 ◽

Cited By ~ 3

Author(s):

Patricia M Schnepp ◽

Mengjie Chen ◽

Evan T Keller ◽

Xiang Zhou

Keyword(s):

Dna Sequencing ◽

Single Cell ◽

Rna Sequencing ◽

Single Cells ◽

Specific Gene ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Single Cell Rna Sequencing ◽

Sequencing Studies ◽

Genomic Regions

Abstract Integrating single-cell RNA sequencing (scRNA-seq) data with genotypes obtained from DNA sequencing studies facilitates the detection of functional genetic variants underlying cell type-specific gene expression variation. Unfortunately, most existing scRNA-seq studies do not come with DNA sequencing data; thus, being able to call single nucleotide variants (SNVs) from scRNA-seq data alone can provide crucial and complementary information, detection of functional SNVs, maximizing the potential of existing scRNA-seq studies. Here, we perform extensive analyses to evaluate the utility of two SNV calling pipelines (GATK and Monovar), originally designed for SNV calling in either bulk or single-cell DNA sequencing data. In both pipelines, we examined various parameter settings to determine the accuracy of the final SNV call set and provide practical recommendations for applied analysts. We found that combining all reads from the single cells and following GATK Best Practices resulted in the highest number of SNVs identified with a high concordance. In individual single cells, Monovar resulted in better quality SNVs even though none of the pipelines analyzed is capable of calling a reasonable number of SNVs with high accuracy. In addition, we found that SNV calling quality varies across different functional genomic regions. Our results open doors for novel ways to leverage the use of scRNA-seq for the future investigation of SNV function.

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Construction of thousands of single cell genome sequencing libraries using combinatorial indexing

10.1101/065482 ◽

2016 ◽

Author(s):

Sarah A. Vitak ◽

Kristof A. Torkenczy ◽

Jimi L. Rosenkrantz ◽

Andrew J. Fields ◽

Lena Christiansen ◽

...

Keyword(s):

Single Cell ◽

Genome Sequencing ◽

Copy Number ◽

Low Cost ◽

Single Cells ◽

Copy Number Variant ◽

Tumor Evolution ◽

Somatic Variation ◽

Cell Genome ◽

Single Cell Genome

AbstractSingle cell genome sequencing has proven to be a valuable tool for the detection of somatic variation, particularly in the context of tumor evolution and neuronal heterogeneity. Current technologies suffer from high per-cell library construction costs which restrict the number of cells that can be assessed, thus imposing limitations on the ability to quantitatively measure genomic heterogeneity within a tissue. Here, we present Single cell Combinatorial Indexed Sequencing (SCI-seq) as a means of simultaneously generating thousands of low-pass single cell libraries for the purpose of somatic copy number variant detection. In total, we constructed libraries for 16,698 single cells from a combination of cultured cell lines, frontal cortex tissue from Macaca mulatta, and two human adenocarcinomas. This novel technology provides the opportunity for low-cost, deep characterization of somatic copy number variation in single cells, providing a foundational knowledge across both healthy and diseased tissues.

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Remarkably stable copy-number profiles in osteosarcoma revealed using single-cell DNA sequencing

10.1101/2021.08.30.458268 ◽

2021 ◽

Author(s):

Sanjana Rajan ◽

Simone Zaccaria ◽

Matthew V. Cannon ◽

Maren Cam ◽

Amy C. Gross ◽

...

Keyword(s):

Dna Sequencing ◽

Single Cell ◽

Genomic Instability ◽

Copy Number ◽

Fitness Landscape ◽

Clonal Evolution ◽

Response To Therapy ◽

Tumor Evolution ◽

Sequencing Data ◽

Recurrent Mutations

AbstractOsteosarcoma is an aggressive malignancy characterized by high genomic complexity. Identification of few recurrent mutations in protein coding genes suggests that somatic copy-number aberrations (SCNAs) are the genetic drivers of disease. Models around genomic instability conflict-it is unclear if osteosarcomas result from pervasive ongoing clonal evolution with continuous optimization of the fitness landscape or an early catastrophic event followed by stable maintenance of an abnormal genome. We address this question by investigating SCNAs in 12,019 tumor cells obtained from expanded patient tissues using single-cell DNA sequencing, in ways that were previously impossible with bulk sequencing. Using the CHISEL algorithm, we inferred allele- and haplotype-specific SCNAs from whole-genome single-cell DNA sequencing data. Surprisingly, we found that, despite extensive genomic aberrations, cells within each tumor exhibit remarkably homogeneous SCNA profiles with little sub-clonal diversification. Longitudinal analysis between two pairs of patient samples obtained at distant time points (early detection, relapse) demonstrated remarkable conservation of SCNA profiles over tumor evolution. Phylogenetic analysis suggests that the bulk of SCNAs was acquired early in the oncogenic process, with few new events arising in response to therapy or during adaptation to growth in distant tissues. These data suggest that early catastrophic events, rather than sustained genomic instability, drive formation of these extensively aberrant genomes. Overall, we demonstrate the power of combining single-cell DNA sequencing with an allele- and haplotype-specific SCNA inference algorithm to resolve longstanding questions regarding genetics of tumor initiation and progression, questioning the underlying assumptions of genomic instability inferred from bulk tumor data.

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Sweepstake evolution revealed by population-genetic analysis of copy-number alterations in single genomes of breast cancer

Royal Society Open Science ◽

10.1098/rsos.171060 ◽

2017 ◽

Vol 4 (9) ◽

pp. 171060 ◽

Cited By ~ 8

Author(s):

Mamoru Kato ◽

Daniel A. Vasco ◽

Ryuichi Sugino ◽

Daichi Narushima ◽

Alexander Krasnitz

Keyword(s):

Breast Cancer ◽

Single Cell ◽

Cancer Cells ◽

Copy Number ◽

Population Genetic ◽

Single Cells ◽

Genetic Alterations ◽

Tumour Mass ◽

Breast Cancers ◽

Single Cell Sequencing

Single-cell sequencing is a promising technology that can address cancer cell evolution by identifying genetic alterations in individual cells. In a recent study, genome-wide DNA copy numbers of single cells were accurately quantified by single-cell sequencing in breast cancers. Phylogenetic-tree analysis revealed genetically distinct populations, each consisting of homogeneous cells. Bioinformatics methods based on population genetics should be further developed to quantitatively analyse the single-cell sequencing data. We developed a bioinformatics framework that was combined with molecular-evolution theories to analyse copy-number losses. This analysis revealed that most deletions in the breast cancers at the single-cell level were generated by simple stochastic processes. A non-standard type of coalescent theory, the multiple-merger coalescent model, aided by approximate Bayesian computation fit well with the data, allowing us to estimate the population-genetic parameters in addition to false-positive and false-negative rates. The estimated parameters suggest that the cancer cells underwent sweepstake evolution, where only one or very few parental cells produced a descendent cell population. We conclude that breast cancer cells successively substitute in a tumour mass, and the high reproduction of only a portion of cancer cells may confer high adaptability to this cancer.

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Copy number motifs expose genome instability type and predict driver events and disease outcome in breast cancer

10.1101/769356 ◽

2019 ◽

Author(s):

Arne V. Pladsen ◽

Gro Nilsen ◽

Oscar M. Rueda ◽

Miriam R. Aure ◽

Ørnulf Borgan ◽

...

Keyword(s):

Breast Cancer ◽

Copy Number ◽

Genome Instability ◽

Prognostic Index ◽

Tumor Evolution ◽

Cancer Specific Survival ◽

Allele Specific ◽

Low Dimensional ◽

Genomic Regions ◽

Tumor Genome

AbstractTumor evolution is dependent on and constrained by the genotypes emerging from genome instability. We hypothesized that non-site-specific copy number motifs would correlate with underlying replication defects and also with tumor and patient fate. Six feature detectors were defined to characterize and score the local spatial behaviour of a copy number profile. By accumulating scores across genomic regions, a low-dimensional representation of the tumor genome was obtained. The proposed Copy Aberration Regional Mapping Analysis (CARMA) algorithm was applied to 2384 breast tumors from three breast cancer cohorts, revealing distinct copy number motifs in established molecular subtypes. A prognostic index combining the features predicted breast cancer specific survival better than both the genomic instability index (GII) and all commonly used clinical stratifications. CARMA offers effective comparison of tumor subgroups and extracts biologically and clinically relevant features from allele-specific copy number profiles.

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Clonal approaches to understanding the impact of mutations on hematologic disease development

Blood ◽

10.1182/blood-2018-11-835405 ◽

2019 ◽

Vol 133 (13) ◽

pp. 1436-1445 ◽

Cited By ~ 6

Author(s):

Jyoti Nangalia ◽

Emily Mitchell ◽

Anthony R. Green

Keyword(s):

Somatic Mutations ◽

Clonal Selection ◽

Single Cells ◽

Driver Mutations ◽

Great Promise ◽

Tumor Evolution ◽

Disease Development ◽

Hematopoietic Tissue ◽

The Impact

Abstract Interrogation of hematopoietic tissue at the clonal level has a rich history spanning over 50 years, and has provided critical insights into both normal and malignant hematopoiesis. Characterization of chromosomes identified some of the first genetic links to cancer with the discovery of chromosomal translocations in association with many hematological neoplasms. The unique accessibility of hematopoietic tissue and the ability to clonally expand hematopoietic progenitors in vitro has provided fundamental insights into the cellular hierarchy of normal hematopoiesis, as well as the functional impact of driver mutations in disease. Transplantation assays in murine models have enabled cellular assessment of the functional consequences of somatic mutations in vivo. Most recently, next-generation sequencing–based assays have shown great promise in allowing multi-“omic” characterization of single cells. Here, we review how clonal approaches have advanced our understanding of disease development, focusing on the acquisition of somatic mutations, clonal selection, driver mutation cooperation, and tumor evolution.

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Single Cell Genetic Profiling of Tumors of Breast Cancer Patients Aged 50 Years and Older Reveals Enormous Intratumor Heterogeneity Independent of Individual Prognosis

Cancers ◽

10.3390/cancers13133366 ◽

2021 ◽

Vol 13 (13) ◽

pp. 3366

Author(s):

Anna-Sophie Liegmann ◽

Kerstin Heselmeyer-Haddad ◽

Annette Lischka ◽

Daniela Hirsch ◽

Wei-Dong Chen ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Copy Number ◽

Genome Instability ◽

Single Cells ◽

Gene Mutations ◽

Intratumor Heterogeneity ◽

Breast Cancer Patients ◽

Luminal A ◽

Pik3ca Mutations

Purpose: Older breast cancer patients are underrepresented in cancer research even though the majority (81.4%) of women dying of breast cancer are 55 years and older. Here we study a common phenomenon observed in breast cancer which is a large inter- and intratumor heterogeneity; this poses a tremendous clinical challenge, for example with respect to treatment stratification. To further elucidate genomic instability and tumor heterogeneity in older patients, we analyzed the genetic aberration profiles of 39 breast cancer patients aged 50 years and older (median 67 years) with either short (median 2.4 years) or long survival (median 19 years). The analysis was based on copy number enumeration of eight breast cancer-associated genes using multiplex interphase fluorescence in situ hybridization (miFISH) of single cells, and by targeted next-generation sequencing of 563 cancer-related genes. Results: We detected enormous inter- and intratumor heterogeneity, yet maintenance of common cancer gene mutations and breast cancer specific chromosomal gains and losses. The gain of COX2 was most common (72%), followed by MYC (69%); losses were most prevalent for CDH1 (74%) and TP53 (69%). The degree of intratumor heterogeneity did not correlate with disease outcome. Comparing the miFISH results of diploid with aneuploid tumor samples significant differences were found: aneuploid tumors showed significantly higher average signal numbers, copy number alterations (CNAs) and instability indices. Mutations in PIKC3A were mostly restricted to luminal A tumors. Furthermore, a significant co-occurrence of CNAs of DBC2/MYC, HER2/DBC2 and HER2/TP53 and mutual exclusivity of CNAs of HER2 and PIK3CA mutations and CNAs of CCND1 and PIK3CA mutations were revealed. Conclusion: Our results provide a comprehensive picture of genome instability profiles with a large variety of inter- and intratumor heterogeneity in breast cancer patients aged 50 years and older. In most cases, the distribution of chromosomal aneuploidies was consistent with previous results; however, striking exceptions, such as tumors driven by exclusive loss of chromosomes, were identified.

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MEDALT: single-cell copy number lineage tracing enabling gene discovery

Genome Biology ◽

10.1186/s13059-021-02291-5 ◽

2021 ◽

Vol 22 (1) ◽

Cited By ~ 2

Author(s):

Fang Wang ◽

Qihan Wang ◽

Vakul Mohanty ◽

Shaoheng Liang ◽

Jinzhuang Dou ◽

...

Keyword(s):

Breast Cancer ◽

Single Cell ◽

Copy Number ◽

Speciation Analysis ◽

Population Based ◽

Lineage Tracing ◽

Breast Cancer Patients ◽

Lineage Tree ◽

History Of ◽

A Cell

AbstractWe present a Minimal Event Distance Aneuploidy Lineage Tree (MEDALT) algorithm that infers the evolution history of a cell population based on single-cell copy number (SCCN) profiles, and a statistical routine named lineage speciation analysis (LSA), whichty facilitates discovery of fitness-associated alterations and genes from SCCN lineage trees. MEDALT appears more accurate than phylogenetics approaches in reconstructing copy number lineage. From data from 20 triple-negative breast cancer patients, our approaches effectively prioritize genes that are essential for breast cancer cell fitness and predict patient survival, including those implicating convergent evolution.The source code of our study is available at https://github.com/KChen-lab/MEDALT.

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Genomic Heterogeneity and Clonal Evolution in Gastroesophageal Junction Cancer Revealed by Single Cell DNA Sequencing

Frontiers in Oncology ◽

10.3389/fonc.2021.672020 ◽

2021 ◽

Vol 11 ◽

Author(s):

Qingke Duan ◽

Chao Tang ◽

Zhao Ma ◽

Chuangui Chen ◽

Xiaobin Shang ◽

...

Keyword(s):

Lymph Node ◽

Lymph Node Metastasis ◽

Lymph Nodes ◽

Dna Sequencing ◽

Single Cell ◽

Single Cells ◽

Clonal Evolution ◽

Gastroesophageal Junction ◽

Common Mutation ◽

Node Metastasis

Gastroesophageal junction (GEJ) cancer is a tumor that occurs at the junction of stomach and esophagus anatomically. GEJ cancer frequently metastasizes to lymph nodes, however the heterogeneity and clonal evolution process are unclear. This study is the first of this kind to use single cell DNA sequencing to determine genomic variations and clonal evolution related to lymph node metastasis. Multiple Annealing and Looping Based Amplification Cycles (MALBAC) and bulk exome sequencing were performed to detect single cell copy number variations (CNVs) and single nucleotide variations (SNVs) respectively. Four GEJ cancer patients were enrolled with two (Pt.3, Pt.4) having metastatic lymph nodes. The most common mutation we found happened in the TTN gene, which was reported to be related with the tumor mutation burden in cancers. Significant intra-patient heterogeneity in SNVs and CNVs were found. We identified the SNV subclonal architecture in each tumor. To study the heterogeneity of CNVs, the single cells were sequenced. The number of subclones in the primary tumor was larger than that in lymph nodes, indicating the heterogeneity of primary site was higher. We observed two patterns of multi-station lymph node metastasis: one was skip metastasis and the other was to follow the lymphatic drainage. Taken together, our single cell genomic analysis has revealed the heterogeneity and clonal evolution in GEJ cancer.

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