Meta-Analysis of Human Cancer Single-Cell RNA-Seq Datasets Using the IMMUcan Database.

Autor:	Camps J; Biomedical Data Science, Research & Early Development Oncology, Bayer AG, Berlin, Germany., Noël F; Université de Paris, Institut de Recherche Saint-Louis, INSERM U976, Paris, France., Liechti R; Vital-IT group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland., Massenet-Regad L; Université de Paris, Institut de Recherche Saint-Louis, INSERM U976, Paris, France.; Université Paris-Saclay, Saint Aubin, France., Rigade S; Université de Paris, Institut de Recherche Saint-Louis, INSERM U976, Paris, France., Götz L; Vital-IT group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland., Hoffmann C; Institut Curie, INSERM U932 Research Unit, Department of Surgical Oncology, PSL University, Paris, France., Amblard E; Université de Paris, Institut de Recherche Saint-Louis, INSERM U976, Paris, France.; Université de Paris, Centre de Recherches Interdisciplinaires, Paris, France., Saichi M; Université de Paris, Institut de Recherche Saint-Louis, INSERM U976, Paris, France., Ibrahim MM; Biomedical Data Science, Research & Early Development Premedical, Bayer AG, Wuppertal, Germany., Pollard J; Sanofi Research and Development, Cambridge, Massachusetts., Medvedovic J; Université de Paris, Institut de Recherche Saint-Louis, INSERM U976, Paris, France., Roider HG; Oncology Precision Medicine, Research & Early Development Oncology, Bayer AG, Berlin, Germany., Soumelis V; Université de Paris, Institut de Recherche Saint-Louis, INSERM U976, Paris, France.; Assistance Publique-Hôpitaux de Paris (AP-HP), Hôpital Saint-Louis, Laboratoire d'Immunologie, Paris, France.; Owkin, Paris, France.
Jazyk:	angličtina
Zdroj:	Cancer research [Cancer Res] 2023 Feb 03; Vol. 83 (3), pp. 363-373.
DOI:	10.1158/0008-5472.CAN-22-0074
Abstrakt:	The development of single-cell RNA sequencing (scRNA-seq) technologies has greatly contributed to deciphering the tumor microenvironment (TME). An enormous amount of independent scRNA-seq studies have been published representing a valuable resource that provides opportunities for meta-analysis studies. However, the massive amount of biological information, the marked heterogeneity and variability between studies, and the technical challenges in processing heterogeneous datasets create major bottlenecks for the full exploitation of scRNA-seq data. We have developed IMMUcan scDB (https://immucanscdb.vital-it.ch), a fully integrated scRNA-seq database exclusively dedicated to human cancer and accessible to nonspecialists. IMMUcan scDB encompasses 144 datasets on 56 different cancer types, annotated in 50 fields containing precise clinical, technological, and biological information. A data processing pipeline was developed and organized in four steps: (i) data collection; (ii) data processing (quality control and sample integration); (iii) supervised cell annotation with a cell ontology classifier of the TME; and (iv) interface to analyze TME in a cancer type-specific or global manner. This framework was used to explore datasets across tumor locations in a gene-centric (CXCL13) and cell-centric (B cells) manner as well as to conduct meta-analysis studies such as ranking immune cell types and genes correlated to malignant transformation. This integrated, freely accessible, and user-friendly resource represents an unprecedented level of detailed annotation, offering vast possibilities for downstream exploitation of human cancer scRNA-seq data for discovery and validation studies. Significance: The IMMUcan scDB database is an accessible supportive tool to analyze and decipher tumor-associated single-cell RNA sequencing data, allowing researchers to maximally use this data to provide new insights into cancer biology. (©2022 The Authors; Published by the American Association for Cancer Research.)
Databáze:	MEDLINE
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