Speaker
Mr.
Antonio De Falco
(University of Naples Federico II (DIETI) & BIOGEM Institute of Molecular Biology and Genetics, 83031 Ariano Irpino, Italy)
Description
Understanding intratumor heterogeneity and the interactions between tumor cells and the immune system is the critical step in the study of tumor growth and evolution. Typically in these studies a large number of unsorted cells from tumor biopsies are subject to Single-cell RNA sequencing (scRNA-seq) and then classified as malignant cells, stromal cells, and immune cells.
The distinction of malignant from non-malignant cells is a key step in the follow-up analysis of scRNA-seq tumor datasets. The basic idea to solve such a problem relies on estimating common copy number alterations that characterize aneuploidy cells. The copy number profiles are obtained by considering the gene expression profiles of each cell as a function of the genomic coordinates.
The main drawback is that the clusters of reference non-malignant cells require manual identification, and recent work that tries to overcome this problem is severely affected by a wrong identification of normal cells and, similarly to other methods, was not designed to perform a complete automatic identification of the clones, reporting their breakpoints, the specific and shared alteration and a complete clonal deconvolution.
We have developed Single CEll Variational ANeuploidy analysis (SCEVAN). It uses a multichannel segmentation algorithm that exploiting the assumption that all the cells in a given copy number clone share the same breakpoints. Thus, the smoothed expression profile of every individual cell constitutes part of the evidence of the copy number profile in each subclone. SCEVAN exploit a set of stromal and immune signatures and the fact that malignant cells often harbor aneuploid copy number events to automatically discriminate between transformed cells and micro-environment cells. Afterwards, SCEVAN performs a complete downstream analysis to automatically identify tumor subclones, classifying their specific and shared alterations up to a clone phylogeny.
We apply SCEVAN to several datasets encompassing 106 samples and 93,322 cells from different tumors types and technologies. For which SCEVAN exhibits faster and more accurate performance against state-of-the-art methods. Clonal deconvolution extracted from scRNA-seq can also be used to study tumor evolution, for example in glioma tumors has allowed us to confirming that the heterogeneity of glioma subtypes is driven by the clonal architectures and to identify novel drivers of cellular states such as the Proliferative/Progenitor (PPR) subtype.
SCEVAN is available in open source as an R package at the following address \href{https://github.com/AntonioDeFalco/SCEVAN}{https://github.com/AntonioDeFalco/SCEVAN}.
| Department | DIETI |
|---|
Primary authors
Mr.
Antonio De Falco
(University of Naples Federico II (DIETI) & BIOGEM Institute of Molecular Biology and Genetics, 83031 Ariano Irpino, Italy)
Mr.
Antonio Iavarone
(Institute for Cancer Genetics, Columbia University, 1130 St Nicholas Ave, New York, NY 10032, USA)
Ms.
Francesca Caruso
(Department of Electrical Engineering and Information Technology (DIETI) & BIOGEM Institute of Molecular Biology and Genetics, 83031 Ariano Irpino, Italy)
Prof.
Michele Ceccarelli
(University of Naples, Federico II)
Mr.
Xiao-Dong Su
(Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, 5 Yiheyuan Rd, Haidian District, 100871 Beijing, China)
