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

Leighton, Jake; Hu, Min; Sei, Emi; Meric‐Bernstam, Funda; Navin, Nicholas E.

doi:10.1016/j.xgen.2022.100215

Cited by 10 publications

(10 citation statements)

References 55 publications

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“…Thus, the breast cancer susceptibility gene 1 ( BRCA1 ), breast cancer susceptibility gene 2 ( BRCA2 ), phosphatase and tensin homolog ( PTEN ), tumor protein 53 ( TP53 ), cadherin 1 ( CDH1 ), and serine/threonine kinase 11 ( STK11/LKB1 ) are known as highly penetrant genes, while the cell cycle checkpoint kinase 2 ( CHEK2 ), BRCA1 interacting helicase/protein 1 ( BRIP1 ), ataxia-telangiectasia mutated gene ( ATM ), and partner and localizer of BRCA2 ( PALB2 ) are known as moderately penetrating genes that suffer mutations, confering up to an 80% and 2–3% lifetime risk of BC, respectively [ 147 ]. A multi-Patient-Targeted (MPT) single-cell DNA sequencing (scDNA-seq) approach reconstructed mutational lineages and identified early mutational and copy number alterations (CNA) in TNBC tissue samples [ 110 ]. Additionally, mitochondrial DNA (mtDNA) germline variants and somatic tumor mutations are also involved in BC development [ 118 ], especially those involved in the OXPHOS system [ 146 ].…”

Section: Breast Cancer Investigation In the Multi-omics Eramentioning

confidence: 99%

“…In addition to mutated p53, the PI3K/AKT pathway is also deregulated in the majority of TNBC, which causes the over-activation of AKT, which leads to cancer development [ 337 ]. A Multi-Patient-Targeted (MPT) single-cell DNA sequencing (scDNA-seq) approach was developed for the identification of early TP53 mutations in TNBC tissue samples [ 110 ]. p53 protein facilitates DNA repair, cell cycle arrest, or apoptosis following DNA damage [ 334 ], orchestrating very diverse cellular responses to different types of stress [ 338 ].…”

Section: Omics-based Investigations Of the Tumoral Suppressor ...mentioning

confidence: 99%

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Omics-Based Investigations of Breast Cancer

Neagu,

Whitham,

Bruno

et al. 2023

Molecules

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Breast cancer (BC) is characterized by an extensive genotypic and phenotypic heterogeneity. In-depth investigations into the molecular bases of BC phenotypes, carcinogenesis, progression, and metastasis are necessary for accurate diagnoses, prognoses, and therapy assessments in predictive, precision, and personalized oncology. This review discusses both classic as well as several novel omics fields that are involved or should be used in modern BC investigations, which may be integrated as a holistic term, onco-breastomics. Rapid and recent advances in molecular profiling strategies and analytical techniques based on high-throughput sequencing and mass spectrometry (MS) development have generated large-scale multi-omics datasets, mainly emerging from the three ”big omics”, based on the central dogma of molecular biology: genomics, transcriptomics, and proteomics. Metabolomics-based approaches also reflect the dynamic response of BC cells to genetic modifications. Interactomics promotes a holistic view in BC research by constructing and characterizing protein–protein interaction (PPI) networks that provide a novel hypothesis for the pathophysiological processes involved in BC progression and subtyping. The emergence of new omics- and epiomics-based multidimensional approaches provide opportunities to gain insights into BC heterogeneity and its underlying mechanisms. The three main epiomics fields (epigenomics, epitranscriptomics, and epiproteomics) are focused on the epigenetic DNA changes, RNAs modifications, and posttranslational modifications (PTMs) affecting protein functions for an in-depth understanding of cancer cell proliferation, migration, and invasion. Novel omics fields, such as epichaperomics or epimetabolomics, could investigate the modifications in the interactome induced by stressors and provide PPI changes, as well as in metabolites, as drivers of BC-causing phenotypes. Over the last years, several proteomics-derived omics, such as matrisomics, exosomics, secretomics, kinomics, phosphoproteomics, or immunomics, provided valuable data for a deep understanding of dysregulated pathways in BC cells and their tumor microenvironment (TME) or tumor immune microenvironment (TIMW). Most of these omics datasets are still assessed individually using distinct approches and do not generate the desired and expected global-integrative knowledge with applications in clinical diagnostics. However, several hyphenated omics approaches, such as proteo-genomics, proteo-transcriptomics, and phosphoproteomics-exosomics are useful for the identification of putative BC biomarkers and therapeutic targets. To develop non-invasive diagnostic tests and to discover new biomarkers for BC, classic and novel omics-based strategies allow for significant advances in blood/plasma-based omics. Salivaomics, urinomics, and milkomics appear as integrative omics that may develop a high potential for early and non-invasive diagnoses in BC. Thus, the analysis of the tumor circulome is considered a novel frontier in liquid biopsy. Omics-based investigations have applications in BC modeling, as well as accurate BC classification and subtype characterization. The future in omics-based investigations of BC may be also focused on multi-omics single-cell analyses.

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Section: Breast Cancer Investigation In the Multi-omics Eramentioning

confidence: 99%

Section: Omics-based Investigations Of the Tumoral Suppressor ...mentioning

confidence: 99%

Omics-Based Investigations of Breast Cancer

Neagu,

Whitham,

Bruno

et al. 2023

Molecules

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“…This biotechnological approach, by individually analyzing targeted cells, facilitates a more precise analysis of CNVs and captures the pathogenic evolution of tumor subclones [13,14]. In contrast to bulk sequencing, which characterizes the genomic landscape at the population level, scDNA-seq avoids the averaging effect that could obscure distinctive CNV profiles [15][16][17]. While single-cell RNA sequencing (scRNA-seq) primarily offers insights into the expressed regions of the genome rather than its entirety, scDNA-seq investigates the complete genome, offering an accurate depiction of CNVs [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

CNVeil enables accurate and robust tumor subclone identification and copy number estimation from single-cell DNA sequencing data

Yuan,

Luo,

et al. 2024

Preprint

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Single-cell DNA sequencing (scDNA-seq) has significantly advanced cancer research by enabling precise detection of chromosomal aberrations, such as copy number variations (CNVs), at a single-cell level. These variations are crucial for understanding tumor progression and heterogeneity among tumor subclones. However, accurate CNV inference in scDNA-seq has been constrained by several factors, including low coverage, sequencing errors, and data variability. To address these challenges, we introduce CNVeil, a robust quantitative algorithm designed to accurately reveal CNV profiles while overcoming the inherent noise and bias in scDNA-seq data. CNVeil incorporates a unique bias correction method using normal cell profiles identified by a PCA-based Gini coefficient, effectively mitigating sequencing bias. Subsequently, a multi-level hierarchical clustering, based on selected highly variable bins, is employed to initially identify coarse subclones for robust ploidy estimation and further identify fine subclones for segmentation. To infer the CNV segmentation landscape, a novel change rate-based across-cell breakpoint identification approach is specifically designed to diminish the effects of low coverage and data variability on a per-cell basis. Finally, a consensus segmentation is utilized to further standardize read depth for the inference of the final CNV profile. In comprehensive benchmarking experiments, where we compared CNVeil with seven state-of-the-art CNV detection tools, CNVeil exhibited exceptional performance across a diverse set of simulated and real scDNA-seq data in cancer genomics. CNVeil excelled in subclone identification, segmentation, and CNV profiling. In light of these results, we anticipate that CNVeil will significantly contribute to single-cell CNV analysis, offering enhanced insights into chromosomal aberrations and genomic complexity.

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“…The recent advancement of single-cell sequencing technology has revolutionized our understanding of how cancer evolves [35, 11]. It enables the sequencing of hundreds to thousands of cells from a single tumor, e.g., [19, 9], offering an unparalleled level of detail. However, single-cell DNA sequencing data (scDNAseq) also presents significant computational challenges chief among them the prohibitive computational requirements of evolutionary analyses [14].…”

Section: Introductionmentioning

confidence: 99%

A Divide-and-Conquer Approach to Large-Scale Evolutionary Analysis of Single-Cell DNA Data

Liu,

Nakhleh

2024

Preprint

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Single-cell sequencing technologies are producing large data sets, often with thousands or even tens of thousands of single-cell genomic data from an individual patient. Evolutionary analyses of these data sets help uncover and order genetic variants in the data as well as elucidate mutation trees and intra-tumor heterogeneity (ITH) in the case of cancer data sets. To enable such large-scale analyses computationally, we propose a divide-and-conquer approach that could be used to scale up computationally intensive inference methods. The approach consists of four steps: 1) partitioning the dataset into subsets, 2) constructing a rooted tree for each subset, 3) computing a representative genotype for each subset by utilizing its inferred tree, and 4) assembling the individual trees using a tree built on the representative genotypes. Besides its flexibility and enabling scalability, this approach also lends itself naturally to ITH analysis, as the clones would be the individual subsets, and the ``assembly tree" could be the mutation tree that defines the clones. To demonstrate the effectiveness of our proposed approach, we conducted experiments employing a range of methods at each stage. In particular, as clustering and dimensionality reduction methods are commonly used to tame the complexity of large datasets in this area, we analyzed the performance of a variety of such methods within our approach.

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

Cited by 10 publications

References 55 publications

Omics-Based Investigations of Breast Cancer

Omics-Based Investigations of Breast Cancer

CNVeil enables accurate and robust tumor subclone identification and copy number estimation from single-cell DNA sequencing data

A Divide-and-Conquer Approach to Large-Scale Evolutionary Analysis of Single-Cell DNA Data

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