Molecular natural history of breast cancer: Leveraging transcriptomics to predict breast cancer progression and aggressiveness
Journal article, 2020

Cancer Medicine published by John Wiley & Sons Ltd. 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.

systems medicine

patient heterogeneity

RNA-seq

disease dynamics

Author

Daniel John Cook

Wallenberg Center for Protein Research (WCPR)

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Jonatan Kallus

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Rebecka Jörnsten

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Jens B Nielsen

Novo Nordisk Foundation Center for Biosustainability

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

BioInnovation Institute

Wallenberg Center for Protein Research (WCPR)

Cancer Medicine

2045-7634 (eISSN)

Vol. 9 10 3551-3562

Subject Categories

Medical Genetics

Cancer and Oncology

Genetics

DOI

10.1002/cam4.2996

More information

Latest update

6/18/2020