Abstract P4-02-07: Radiogenomic analysis of HER2+ breast cancer reveals MRI features correlated with genomic immune index are predictive of neoadjuvant chemotherapy response
Authors: N Braman, P Prasanna, S Singh, N Beig, H Gilmore, M Etesami, D Bates, K Gallagher, BN Bloch, G Somlo, W Sikov, L Harris, D Plecha, V Varadan, A Madabhushi
Journal: Cancer Research (2018)

Abstract Background: It has been previously shown that computer-extracted heterogeneity features calculated within the peritumoral region of a breast cancer from baseline dynamic contrast-enhanced MRI [DCE-MRI] can predict treatment outcome. This approach may due to microenvironmental signatures of elevated immune response, a predictor of favorable response to neoadjuvant chemotherapy [NAC] in HER2+ breast cancer. We assessed the correlation between peritumoral radiomic features and a tissue-derived genomic immune index [II] in HER2+ breast cancer and whether these immune-correlated imaging features are predictive of pathologic response. Methods: 33 HER2+ patients with both 1.5 or 3 T DCE-MRI imaging and targeted RNA sequencing of biopsy samples collected prior to NAC from a multicenter trial [BrUOG 211B, n=26] and The Cancer Genome Atlas-Breast Cancer project [TCGA-BRCA, n=7] were retrospectively analyzed. II was derived from expression of a 140-gene immune signature using the ESTIMATE algorithm. An attending breast radiologist annotated lesion boundaries on the DCE-MRI phase of peak contrast enhancement. Beyond this intratumoral region, 5 annular peritumoral regions in 3 mm increments out to a maximum radius of 15 mm were analyzed. Computer-extracted heterogeneity descriptors computed within the intratumoral and peritumoral regions were summarized by first order statistics. Redundancy was reduced by eliminating correlated imaging features (R2&amp;gt;.6). From the remaining features, the 5 features that were collectively best correlated with II were selected by feed forward, leave-one-out multilinear regression. The regression model was applied to an independent test set of 28 HER2+ patients with post NAC surgical specimens. The estimated II was assessed for its ability to differentiate patients who achieved a pathologic complete response in the breast [pCR, ypT0/is] (n=16) and those who did not (n=12) by 2-sided Wilcoxon rank sum test of median and area under the receiver operating characteristic curve (AUC). Results: The set of top features that significantly correlated (p&amp;lt;.05) with II were entirely within the 3-6 mm and 6-9 mm peritumoral regions. These features characterized uneven patterns of enhancement and heterogeneity of intensity values. Within the testing set, predicted II was significantly higher among responders (pCR: 12.8 +/- .3, non-pCR: 12.5 +/- .3, p&amp;lt;.05) and distinguished the two with an AUC of .73. Conclusions: From a set of quantitative features characterizing heterogeneity within the peritumoral region on DCE-MRI, we identified peritumoral imaging features correlated with a genomic index of immune response in HER2+ breast cancer and were predictive of pathologic response in an independent testing set. Our findings suggest that the predictive capability of peritumoral radiomics may be tied to a patient's immune response to the cancer. In addition to providing insight to the biological basis of peritumoral radiomics, imaging signatures of immune response themselves possess clinical value as a potential means for the non-invasive prediction of HER2+ cancer biology and treatment outcome. Additional independent validation is needed on a larger test set to confirm our preliminary findings. 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. Radiogenomic analysis of HER2+ breast cancer reveals MRI features correlated with genomic immune index are predictive of neoadjuvant chemotherapy response [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 P4-02-07.