title: Unsupervised Clustering of Quantitative Image Phenotypes Reveals Breast Cancer Subtypes with Distinct Prognoses and Molecular Pathways.
abstract: Purpose:To identify novel breast cancer subtypes by extracting quantitative imaging phenotypes of the tumor and surrounding parenchyma and to elucidate the underlying biologic underpinnings and evaluate the prognostic capacity for predicting recurrence-free survival (RFS).Experimental Design:We retrospectively analyzed dynamic contrast-enhanced MRI data of patients from a single-center discovery cohort (n= 60) and an independent multicenter validation cohort (n= 96). Quantitative image features were extracted to characterize tumor morphology, intratumor heterogeneity of contrast agent wash-in/wash-out patterns, and tumor-surrounding parenchyma enhancement. On the basis of these image features, we used unsupervised consensus clustering to identify robust imaging subtypes and evaluated their clinical and biologic relevance. We built a gene expression-based classifier of imaging subtypes and tested their prognostic significance in five additional cohorts with publically available gene expression data but without imaging data (n= 1,160).Results:Three distinct imaging subtypes, that is, homogeneous intratumoral enhancing, minimal parenchymal enhancing, and prominent parenchymal enhancing, were identified and validated. In the discovery cohort, imaging subtypes stratified patients with significantly different 5-year RFS rates of 79.6%, 65.2%, 52.5% (log-rankP= 0.025) and remained as an independent predictor after adjusting for clinicopathologic factors (HR, 2.79;P= 0.016). The prognostic value of imaging subtypes was further validated in five independent gene expression cohorts, with average 5-year RFS rates of 88.1%, 74.0%, 59.5% (log-rankPfrom <0.0001 to 0.008). Each imaging subtype was associated with specific dysregulated molecular pathways that can be therapeutically targeted.Conclusions:Imaging subtypes provide complimentary value to established histopathologic or molecular subtypes and may help stratify patients with breast cancer.Clin Cancer Res; 23(13); 3334-42. ©2017 AACR.
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
authors.forename: Jia
authors.initials: J
authors.lastname: Wu
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.|Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education (GI-CoRE), Hokkaido University, Proton Beam Therapy Center, Sapporo, Hokkaido, Japan.
authors.forename: Yi
authors.initials: Y
authors.lastname: Cui
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.|Radiotherapy Department, the First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China.
authors.forename: Xiaoli
authors.initials: X
authors.lastname: Sun
authors.affiliation: Department of Radiology, International Hospital of Zhejiang University, Hangzhou, Zhejiang, China.
authors.forename: Guohong
authors.initials: G
authors.lastname: Cao
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
authors.forename: Bailiang
authors.initials: B
authors.lastname: Li
authors.affiliation: Department of Radiology, Stanford University School of Medicine, Advanced Medicine Center, Stanford, California.|Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California.
authors.forename: Debra M
authors.initials: DM
authors.lastname: Ikeda
authors.affiliation: Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California.|Department of Medicine, Stanford University School of Medicine, Stanford, California.|Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California.
authors.forename: Allison W
authors.initials: AW
authors.lastname: Kurian
authors.affiliation: Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California. rli2@stanford.edu.|Stanford Cancer Institute, Stanford University School of Medicine, Stanford, California.
authors.forename: Ruijiang
authors.initials: R
authors.lastname: Li
mesh_terms: D000328:Adult; D000368:Aged; D014408:Biomarkers, Tumor / Q000235:genetics*; D001943:Breast Neoplasms / Q000145:classification / Q000000981:diagnostic imaging* / Q000235:genetics / Q000473:pathology*; D016000:Cluster Analysis; D003287:Contrast Media / Q000627:therapeutic use; D005260:Female; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics; D006801:Humans; D008279:Magnetic Resonance Imaging; D008875:Middle Aged; D011379:Prognosis; D015398:Signal Transduction / Q000235:genetics
chemical_list: D014408:Biomarkers, Tumor; D003287:Contrast Media