Mapping breast cancer malignancy using single cell transcriptomic data

• Grant number: 22/06678-8
• Support Opportunities: Scholarships abroad - Research Internship - Master's degree
• Start date: October 01, 2022
• End date: March 31, 2023
• Field of knowledge: Health Sciences - Medicine - Medical Clinics
• Principal Investigator: Tathiane Maistro Malta Pereira
• Grantee: Maycon Marção
• Supervisor: Jasmine Plummer
• Host Institution: Faculdade de Ciências Farmacêuticas de Ribeirão Preto (FCFRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
• Institution abroad: St. Jude Children's Research Hospital, United States
• Associated to the scholarship: 21/00283-9 - Mapping tumor epigenomic signatures in gliomas: correlating glioma stem cells with clinical and molecular data, BP.MS

Abstract

This fellow will work on identifying cell subpopulation in breast cancer responsible for tumor malignancy using computational tools to correlate with clinical outcome. This person will take advantage of public single-cell transcriptome (scRNA-seq) data from human breast cancer and non-tumor breast tissue, machine learning models, and open source R/bioconductor tools. After organizing, processing, and normalizing the scRNA-seq data, the machine learning model (One Class Logistic Regression) - generated and validated by the group of Malta et al. (2018) - will be used to identify cell subpopulations in breast cancer with potential tumor malignancy phenotype. Other strategies for converging results regarding cell type identification in scRNA-seq will also be employed such as the Sc-Type algorithm and transcriptomic and epigenomic data from unpublished cell lines. Next, a new model based on the subpopulation identified in breast cancer scRNA-seq will be generated to refine tumor malignancy prediction in bulk RNA-seq data from other cohorts. Finally, the model refined with scRNAseq will be applied to bulk RNA-seq data from The Cancer Genome Atlas Program (TCGA) database in order to classify prognosis in different breast cancer subtypes and their clinical outcome. (AU)