Superior breast cancer metastasis risk stratification using an epithelial-mesenchymal-amoeboid transition gene signature.

Autor: Emad A; Department of Electrical and Computer Engineering, McGill University, Montreal, Quebec, Canada.; Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA.; Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA., Ray T; Onconostic Technologies Inc., Champaign, Illinois, USA., Jensen TW; Illinois Health Sciences Institute, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA.; Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA., Parat M; Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA., Natrajan R; The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK., Sinha S; Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA. sinhas@illinois.edu.; Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA. sinhas@illinois.edu.; Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA. sinhas@illinois.edu., Ray PS; Onconostic Technologies Inc., Champaign, Illinois, USA. partha.ray@onconostictechnologies.com.
Jazyk: angličtina
Zdroj: Breast cancer research : BCR [Breast Cancer Res] 2020 Jul 08; Vol. 22 (1), pp. 74. Date of Electronic Publication: 2020 Jul 08.
DOI: 10.1186/s13058-020-01304-8
Abstrakt: Background: Cancer cells are known to display varying degrees of metastatic propensity, but the molecular basis underlying such heterogeneity remains unclear. Our aims in this study were to (i) elucidate prognostic subtypes in primary tumors based on an epithelial-to-mesenchymal-to-amoeboid transition (EMAT) continuum that captures the heterogeneity of metastatic propensity and (ii) to more comprehensively define biologically informed subtypes predictive of breast cancer metastasis and survival in lymph node-negative (LNN) patients.
Methods: We constructed a novel metastasis biology-based gene signature (EMAT) derived exclusively from cancer cells induced to undergo either epithelial-to-mesenchymal transition (EMT) or mesenchymal-to-amoeboid transition (MAT) to gauge their metastatic potential. Genome-wide gene expression data obtained from 913 primary tumors of lymph node-negative breast cancer (LNNBC) patients were analyzed. EMAT gene signature-based prognostic stratification of patients was performed to identify biologically relevant subtypes associated with distinct metastatic propensity.
Results: Delineated EMAT subtypes display a biologic range from less stem-like to more stem-like cell states and from less invasive to more invasive modes of cancer progression. Consideration of EMAT subtypes in combination with standard clinical parameters significantly improved survival prediction. EMAT subtypes outperformed prognosis accuracy of receptor or PAM50-based BC intrinsic subtypes even after adjusting for treatment variables in 3 independent, LNNBC cohorts including a treatment-naïve patient cohort.
Conclusions: EMAT classification is a biologically informed method that provides prognostic information beyond that which can be provided by traditional cancer staging or PAM50 molecular subtype status and may improve metastasis risk assessment in early stage, LNNBC patients, who may otherwise be perceived to be at low metastasis risk.
Databáze: MEDLINE
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