Molecular natural history of breast cancer: Leveraging transcriptomics to predict breast cancer progression and aggressiveness. (23rd March 2020)

• Record Type: Journal Article
• Authors: Cook, Daniel J.; Kallus, Jonatan; Jörnsten, Rebecka; Nielsen, Jens
• Is Part Of: Cancer medicine. Volume 9:Number 10(2020)
• Page Start: 3551
• Page End: 3562
• Publication Date: 2020-03-23
• Subjects: disease dynamics -- patient heterogeneity -- RNA‐seq -- systems medicine; 616.994005
• DOI: 10.1002/cam4.2996
• Languages: English
• ISSNs: 2045-7634

Abstract

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 aggressiveness in vivo from bulk RNA‐seq data. Results: We found that breast cancer proceeds along three convergent phenotype trajectories: luminal, HER2‐enriched, and basal‐like. Furthermore, 31 296 genes (for luminal cancers), 17 827 genes (for HER2‐enriched), and 18 505 genes (for basal‐like) are dynamically differentially expressed during breast cancer progression. Across progression trajectories, our results show that expression of genes related to ADP‐ribosylation decreased as tumors progressed (while PARP1 and PARP2 increased or remained stable), suggesting the potential for a differential response to PARP inhibitors based on cancer progression. Additionally, we developed a 132‐gene expression regression equation to predict mitotic index and a 23‐gene expression regression equation to predict growth rate from a single breast cancer biopsy. Conclusion: Our results suggest that breast cancer dynamically changes during disease progression, and growth rate of the cancer cells is associated with distinct transcriptional profiles. Abstract : Breast cancer is a continuous disease and RNA expression dynamically changes with disease progression. Additionally, cellular growth rate is encoded in RNA expression.