American Association for Cancer Research
10780432ccr162415-sup-172423_2_supp_3802236_vvyp8y.docx (1.45 MB)

Supplementary Materials from Unsupervised Clustering of Quantitative Image Phenotypes Reveals Breast Cancer Subtypes with Distinct Prognoses and Molecular Pathways

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posted on 2023-03-31, 19:24 authored by Jia Wu, Yi Cui, Xiaoli Sun, Guohong Cao, Bailiang Li, Debra M. Ikeda, Allison W. Kurian, Ruijiang Li

Supplementary Methods Figure S1. The top ranked 25 imaging features in characterizing each imaging subtypes, after significance analysis of microarrays (SAM) analysis Figure S2. Validation of four selected quantitative imaging features (that are significant correlated with imaging subtypes in the discovery cohort) in the TCGA cohort. Table S1. Demographic data in imaging subtype discovery and validation cohorts. Table S2. Details of 110 Quantitative Image Features Extracted from DCE-MRI Table S3. Contingency table of the discovered imaging subtypes and intrinsic molecular subtypes in the validation cohort (TCGA, n=96). Table S4. Contingency table of the discovered imaging subtypes and clinicopathologic features including IHC expression of ER, PR, Her2 in the validation cohort (TCGA, n=96). Table S5. KEGG pathways significant associated (FDR <25%) with three imaging subtypes with Gene Set Enrichment Analysis (GSEA). Name ES NES





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-rank P = 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-rank P from <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.

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