Abstract P4-02-06: Intratumoral and peritumoral MRI signatures of HER2-enriched subtype also predict pathological response to neoadjuvant chemotherapy in HER2+ breast cancers

Abstract

Abstract Background: Applying the PAM50 classifier to targeted RNA-Sequencing data allows HER2+ tumors to be sub-categorized into intrinsic breast cancer subtypes. HER2+ breast cancers belonging to the HER2-enriched [HER2-E] subtype exhibit the highest rate of response to neoadjuvant therapy with combination of HER2-blockade and chemotherapy, as well as dual-HER2 blockade alone. A non-invasive predictor of PAM50 subtype from clinical dynamic contrast-enhanced MRI [DCE-MRI] could provide valuable clinical guidance in the treatment of HER2+ breast cancer. In this work, we identify a set of computer-extracted heterogeneity features computed within the lesion and its surrounding peritumoral region capable of distinguishing HER2-E from other HER2+ breast cancers [Non-HER2-E]. We then demonstrate that this imaging signature of HER2-E is also predictive of pathological complete response [pCR] in an independent HER2+ testing set, consistent with the HER2-E subtype's elevated response to HER2-targeted therapy. Methods: The training set consisted of 42 HER2+ patients with both 1.5 or 3 T DCE-MRI and targeted RNA sequencing collected prior to neoadjuvant treatment from a multicenter trial [BrUOG 211B, n=35] and The Cancer Genome Atlas-Breast Cancer project [TCGA-BRCA, n=7]. Intrinsic subtypes were assigned by unsupervised hierarchical clustering of the PAM50 gene set. 19 patients were determined to belong to the HER2-E subtype, while the remaining 23 represented non-HER2-E subtypes [19 HER2-Luminal, 4 HER2-basal]. Lesion boundaries were annotated by an expertly trained radiologist and expanded to 5 annular peritumoral regions in 3 mm increments out to a maximum radius of 15 mm. Computer-extracted heterogeneity features were computed voxelwise within intratumoral and peritumoral regions by first order statistics. A top HER2-E-associated feature from each region was identified by Wilcoxon feature selection and used to train a diagonal linear discriminant analysis [DLDA] classifier to predict HER2-E in a 3-fold cross-validation setting. This classifier was then applied to pCR prediction from DCE-MRI in a testing set of 28 HER2+ patients with available post neoadjuvant chemotherapy surgical specimens at one institution. 16 patients achieved pCR (ypT0/is), while the remainder had partial or no response (non-pCR). Results: A combination of heterogeneity features within the intratumoral region and annular peritumoral regions out to 12 mm from the tumor yielded optimal results within the training set, with an average HER2-E prediction AUC of .77 +/- .03. When applied to response prediction in an independent testing set, this HER2-E classifier was predictive of pCR (AUC = .72). Conclusions: Computer-extracted heterogeneity features calculated within the tumor and the surrounding peritumoral environment on DCE-MRI were able to distinguish the HER2-E PAM50 intrinsic subtype from other HER2+ breast cancers. HER2-E was characterized by elevated expression of intratumoral and peritumoral heterogeneity features, indicating a more disordered imaging phenotype within and around the tumor. Additional independent validation of these findings is needed. Citation Format: Braman N, Prasanna P, Singh S, Beig N, Gilmore H, Etesami M, Bates D, Gallagher K, Bloch BN, Somlo G, Sikov W, Harris L, Plecha D, Varadan V, Madabhushi A. Intratumoral and peritumoral MRI signatures of HER2-enriched subtype also predict pathological response to neoadjuvant chemotherapy in HER2+ breast cancers [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-02-06.