Clinical Implications of Transcriptomic Changes After Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer.

Publication Title

Annals of surgical oncology : the official journal of the Society of Surgical Oncology

Authors

Javier I Orozco, Department of Translational Molecular Medicine, John Wayne Cancer Institute at Providence Saint John's Health Center, Santa Monica, USAFollow
Janie Grumley, John Wayne Cancer Institute, Providence Saint John's Health Center, Santa Monica, CA, USAFollow
Chikako Matsuba, Department of Translational Molecular Medicine, John Wayne Cancer Institute at Saint John's Health Center, Providence Health System, Santa Monica, California.Follow
Ayla O Manughian-Peter, Cancer Epigenetics Laboratory, John Wayne Cancer Institute at Providence Saint John's Health Center, Santa Monica, CA, USA
Shu-Ching Chang, Medical Data Research Center, Providence Health & Services, Portland, OR, USAFollow
Grace Chang, Hematology and Oncology Department, Providence Saint John's Health Center, Santa Monica, CA, USAFollow
Francisco E Gago
Matthew P Salomon, Center for Endocrine Tumors and Disorders, John Wayne Cancer Institute at Providence Saint John's Health Center, Santa Monica, CA, USA.Follow
Diego M Marzese, Department of Translational Molecular Medicine, John Wayne Cancer Institute at Providence Saint John's Health Center, Santa Monica, CAFollow

Document Type

Article

Publication Date

7-24-2019

Abstract

BACKGROUND: Pathological response to neoadjuvant chemotherapy (NAC) is critical in prognosis and selection of systemic treatments for patients with triple-negative breast cancer (TNBC). The aim of this study is to identify gene expression-based markers to predict response to NAC.

PATIENTS AND METHODS: A survey of 43 publicly available gene expression datasets was performed. We identified a cohort of TNBC patients treated with NAC (n = 708). Gene expression data from different studies were renormalized, and the differences between pretreatment (pre-NAC), on-treatment (post-C1), and surgical (Sx) specimens were evaluated. Euclidean statistical distances were calculated to estimate changes in gene expression patterns induced by NAC. Hierarchical clustering and pathway enrichment analyses were used to characterize relationships between differentially expressed genes and affected gene pathways. Machine learning was employed to refine a gene expression signature with the potential to predict response to NAC.

RESULTS: Forty nine genes consistently affected by NAC were involved in enhanced regulation of wound response, chemokine release, cell division, and decreased programmed cell death in residual invasive disease. The statistical distances between pre-NAC and post-C1 significantly predicted pathological complete response [area under the curve (AUC) = 0.75; p = 0.003; 95% confidence interval (CI) 0.58-0.92]. Finally, the expression of CCND1, a cyclin that forms complexes with CDK4/6 to promote the cell cycle, was the most informative feature in pre-NAC biopsies to predict response to NAC.

CONCLUSIONS: The results of this study reveal significant transcriptomic changes induced by NAC and suggest that chemotherapy-induced gene expression changes observed early in therapy may be good predictors of response to NAC.

Area of Special Interest

Cancer

Specialty/Research Institute

Oncology