Molecular natural history of breast cancer: Leveraging transcriptomics to predict breast cancer progression and aggressiveness.

ABSTRACT:

Background

Characterizing breast cancer progression and aggressiveness relies on categorical descriptions of tumor stage and grade. Interpreting these categorical descriptions is challenging because stage convolutes the size and spread of the tumor and no consensus exists to define high/low grade tumors.

Methods

We address this challenge of heterogeneity in patient-specific cancer samples by adapting and applying several tools originally created for understanding heterogeneity and phenotype development in single cells (specifically, single-cell topological data analysis and Wanderlust) to create a continuous metric describing breast cancer progression using bulk RNA-seq samples from individual patient tumors. We also created a linear regression-based method to predict tumor

SUBMITTER: Cook DJ 

PROVIDER: S-EPMC7221450 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature