Dissecting Intra-Tumor Heterogeneity by the Analysis of Copy Number Variations in Single Cells: The Neuroblastoma Case Study

A statistical learning method for simultaneous copy number estimation and subclone clustering with single-cell sequencing data

The availability of single-cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in SCS data using various clustering methods, as cells comprising a subpopulation are found to share a genetic profile. However, currently available methods may generate spurious results (e.g., falsely identified variants) in the procedure of CNA detection, thereby diminishing the accuracy of subclone identification within a large, complex cell population. In this study, we developed a subclone clustering method based on a fused lasso model, referred to as FLCNA, which can simultaneously detect CNAs in single-cell DNA sequencing (scDNA-seq) data. Spike-in simulations were conducted to evaluate the clustering and CNA detection performance of FLCNA, benchmarking it against existing copy number estimation methods (SCOPE, HMMcopy) in combination with commonly used clustering methods. Application of FLCNA to a scDNA-seq data set of breast cancer revealed different genomic variation patterns in neoadjuvant chemotherapy-treated samples and pretreated samples. We show that FLCNA is a practical and powerful method for subclone identification and CNA detection with scDNA-seq data.

A statistical learning method for simultaneous copy number estimation and subclone clustering with single cell sequencing data

The availability of single cell sequencing (SCS) enables us to assess intra-tumor heterogeneity and identify cellular subclones without the confounding effect of mixed cells. Copy number aberrations (CNAs) have been commonly used to identify subclones in SCS data using various clustering methods, since cells comprising a subpopulation are found to share genetic profile. However, currently available methods may generate spurious results (e.g., falsely identified CNAs) in the procedure of CNA detection, hence diminishing the accuracy of subclone identification from a large complex cell population. In this study, we developed a CNA detection method based on a fused lasso model, referred to as FLCNA, which can simultaneously identify subclones in single cell DNA sequencing (scDNA-seq) data. Spike-in simulations were conducted to evaluate the clustering and CNA detection performance of FLCNA benchmarking to existing copy number estimation methods (SCOPE, HMMcopy) in combination with the existing and commonly used clustering methods. Interestingly, application of FLCNA to a real scDNA-seq dataset of breast cancer revealed remarkably different genomic variation patterns in neoadjuvant chemotherapy treated samples and pre-treated samples. We show that FLCNA is a practical and powerful method in subclone identification and CNA detection with scDNA-seq data.

Tumoral heterogeneity in neuroblastoma

Neuroblastoma is a solid, neuroendocrine tumor with divergent clinical behavior ranging from asymptomatic to fatal. The diverse clinical presentations of neuroblastoma are directly linked to the high intra- and inter-tumoral heterogeneity it presents. This heterogeneity is strongly associated with therapeutic resistance and continuous relapses, often leading to fatal outcomes. The development of successful risk assessment and tailored treatment strategies lies in evaluating the extent of heterogeneity via the accurate genetic and epigenetic profiling of distinct cell subpopulations present in the tumor. Recent studies have focused on understanding the molecular mechanisms that drive tumoral heterogeneity in pursuing better therapeutic and diagnostic approaches. This review describes the cellular, genetic, and epigenetic aspects of neuroblastoma heterogeneity. In addition, we summarize the recent findings on three crucial factors that can lead to heterogeneity in solid tumors: the inherent diversity of the progenitor cells, the presence of cancer stem cells, and the influence of the tumor microenvironment.

Intra-Tumour Genetic Heterogeneity and Prognosis in High-Risk Neuroblastoma

Spatial ITH is defined by genomic and biological variations within a tumour acquired by tumour cell evolution under diverse microenvironments, and its role in NB patient prognosis is understudied. In this work, we applied pangenomic techniques to detect chromosomal aberrations in at least two different areas of each tumour and/or in simultaneously obtained solid and liquid biopsies, detecting ITH in the genomic profile of almost 40% of HR-NB. ITH was better detected when comparing one or more tumour pieces and liquid biopsy (50%) than between different tumour pieces (21%). Interestingly, we found that patients with ITH analysed by pangenomic techniques had a significantly better survival rate that those with non-heterogeneous tumours, especially in cases without MYCN amplification. Moreover, all patients in the studied cohort with high ITH (defined as 50% or more genomic aberration differences between areas of a tumour or simultaneously obtained samples) survived after 48 months. These results clearly support analysing at least two solid tumour areas (separately or mixed) and liquid samples to provide more accurate genomic diagnosis, prognosis and therapy options in HR-NB.

Impact of extracellular matrix stiffness on genomic heterogeneity in MYCN-amplified neuroblastoma cell line

Background

Increased tissue stiffness is a common feature of malignant solid tumors, often associated with metastasis and poor patient outcomes. Vitronectin, as an extracellular matrix anchorage glycoprotein related to a stiff matrix, is present in a particularly increased quantity and specific distribution in high-risk neuroblastoma. Furthermore, as cells can sense and transform the proprieties of the extracellular matrix into chemical signals through mechanotransduction, genotypic changes related to stiffness are possible.

Methods

We applied high density SNPa and NGS techniques to in vivo and in vitro models (orthotropic xenograft vitronectin knock-out mice and 3D bioprinted hydrogels with different stiffness) using two representative neuroblastoma cell lines (the MYCN-amplified SK-N-BE(2) and the ALK-mutated SH-SY5Y), to discern how tumor genomics patterns and clonal heterogeneity of the two cell lines are affected.

Results

We describe a remarkable subclonal selection of genomic aberrations in SK-N-BE(2) cells grown in knock-out vitronectin xenograft mice that also emerged when cultured for long times in stiff hydrogels. In particular, we detected an enlarged subclonal cell population with chromosome 9 aberrations in both models. Similar abnormalities were found in human high-risk neuroblastoma with MYCN amplification. The genomics of the SH-SY5Y cell line remained stable when cultured in both models.

Conclusions

Focus on heterogeneous intratumor segmental chromosome aberrations and mutations, as a mirror image of tumor microenvironment, is a vital area of future research.