Abstract P4-01-02: Association of DCE-MRI texture features with molecular phenotypes and neoadjuvant therapy response in breast cancer
Authors: N Banerjee, S Maity, V Varadan, A Janevski, S Kamalakaran, W Sikov, M Abu-Khalaf, V Bossuyt, D Lannin, L Harris, D Cornfeld, N Dimitrova
Journal: Cancer Research (2012)

Abstract Purpose: MRI imaging phenotype features such as volume and morphology are used to characterize tumor heterogeneity and tumor response. Texture-based imaging features are important in lesion characterization but their relationship to molecular phenotypes and response is unclear. Molecular stratification of breast cancer into luminal, basal, ERBB2, and normal-like can be made based on gene expression profiles. We investigate how texture-based imaging features relate to tumor biology, genetic subtypes and neoadjuvant therapy response using MRI, histopathological and RNA-sequencing data. Materials and Methods: Data from 74 Stage IIA to IIIB breast cancer patients enrolled in neo-adjuvant clinical trials NCT00617942 and NCT00723125 were retrospectively reviewed. We evaluated 37 gray-level co-occurrence matrix features (GLCM) on post-contrast T1 fat-suppressed images of 38 HER2− tumors and 35 HER2+ tumors. The texture features included angular second moment, contrast, correlation, first diagonal moment, entropy, regularity, roughness, line likeness and other statistical summaries. We also performed RNA-sequencing on 23 tumors and compared RNAseq-based PAM50 clustering with texture-based clustering. Patients with pCR and RCB class=I were determined to be responders and the rest were labeled non-responders. Wilcoxon signed rank test was used to compare luminal vs. basal, ER+ vs. ER− and PR+ vs. PR- tumors and determine the discriminative power of the texture features. We then performed hierarchical clustering on our patient data set based on the significant texture features and evaluated their association with subtypes, hormone receptor status and response. Statistical significance of clusters was determined by hypergeometric test. Results: We found five MRI texture features to be significantly associated with tumor subtypes: first diagonal moment, contrast range, correlation range and entropy range (p &amp;lt; 0.05). These five features together differentiated basal and luminal PAM50 subtypes with p = 0.001. Our analysis also showed an association between texture features and tumor hormone status. ER− tumors clustered strongly (13 of 20 ER− cases clustered, p = 0.009) with the 23 significant ER-associated texture features. Similarly, the PR- tumors formed tight grouping (15 of 24 PR- cases clustered, p = 0.006) with 26 significant PR-associated texture features in HER2− patients. Interestingly, only two texture features, entropy range and regularity, distinguished between responders and non-responders (p = 0.04). These features will be further evaluated with DCE-MRI data capturing post brief exposure dynamics. Conclusion: Our results indicate that certain texture features from DCE-MRI images do capture biological heterogeneity in tumors and can potentially complement standard clinical evaluations. Texture features have previously been assessed for diagnostic settings but to our knowledge this is the first study that shows association of texture features with breast cancer subtyping and neoadjuvant therapy response. We speculate that this could potentially impact clinical management decisions and therapy selection. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr P4-01-02.