High-grade serous ovarian cancer (HGSOC) is characterized by widespread genomic instability, with copy number alterations (CNAs) playing a key role in tumor progression and therapy resistance. Single-cell RNA sequencing (scRNA-seq) enables the study of genetic heterogeneity inside tumors at the single-cell level. In this thesis, CNAs inferred from scRNA-seq data with InferCNV, SCEVAN, and Numbat are compared to those derived from bulk whole-genome sequencing data, which serves as the ground truth, in order to evaluate their ability in CNA detection. Based on samples from patients with HGSOC, the analysis demonstrates that SCEVAN is the most accurate method. CNA profiles generated from scRNA-seq-based tools are further used to quantify CNA signature activities and predict platinum-based treatment response. However, the results indicate that these predictions are not consistent when compared with those derived from WGS data. In conclusion, this benchmark provides guidance for selecting the optimal tool for CNA inference when working with scRNA-seq data, being particularly relevant to future studies of chromosomal instability and tumor heterogeneity in HGSOC.

A benchmark of tools for inferring Copy Number Alterations from Single-Cell RNA sequencing data

CIBOLA, ELENA
2023/2024

Abstract

High-grade serous ovarian cancer (HGSOC) is characterized by widespread genomic instability, with copy number alterations (CNAs) playing a key role in tumor progression and therapy resistance. Single-cell RNA sequencing (scRNA-seq) enables the study of genetic heterogeneity inside tumors at the single-cell level. In this thesis, CNAs inferred from scRNA-seq data with InferCNV, SCEVAN, and Numbat are compared to those derived from bulk whole-genome sequencing data, which serves as the ground truth, in order to evaluate their ability in CNA detection. Based on samples from patients with HGSOC, the analysis demonstrates that SCEVAN is the most accurate method. CNA profiles generated from scRNA-seq-based tools are further used to quantify CNA signature activities and predict platinum-based treatment response. However, the results indicate that these predictions are not consistent when compared with those derived from WGS data. In conclusion, this benchmark provides guidance for selecting the optimal tool for CNA inference when working with scRNA-seq data, being particularly relevant to future studies of chromosomal instability and tumor heterogeneity in HGSOC.
2023
A benchmark of tools for inferring Copy Number Alterations from Single-Cell RNA sequencing data
Benchmark
Copy number
Single cell
RNA sequencing
Ovarian cancer
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12608/80517