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Quantifying Tumor Heterogeneity via MRI Habitats to Characterize Microenvironmental Alterations in HER2+ Breast Cancer

Anum S. Kazerouni, David A. Hormuth, Tessa Davis, Meghan J. Bloom, Sarah Mounho, Gibraan Rahman, John Virostko, Thomas E. Yankeelov, Anna G. Sorace

2022Cancers54 citationsDOIOpen Access PDF

Abstract

This study identifies physiological habitats using quantitative magnetic resonance imaging (MRI) to elucidate intertumoral differences and characterize microenvironmental response to targeted and cytotoxic therapy. BT-474 human epidermal growth factor receptor 2 (HER2+) breast tumors were imaged before and during treatment (trastuzumab, paclitaxel) with diffusion-weighted MRI and dynamic contrast-enhanced MRI to measure tumor cellularity and vascularity, respectively. Tumors were stained for anti-CD31, anti-ɑSMA, anti-CD45, anti-F4/80, anti-pimonidazole, and H&E. MRI data was clustered to identify and label each habitat in terms of vascularity and cellularity. Pre-treatment habitat composition was used stratify tumors into two "tumor imaging phenotypes" (Type 1, Type 2). Type 1 tumors showed significantly higher percent tumor volume of the high-vascularity high-cellularity (HV-HC) habitat compared to Type 2 tumors, and significantly lower volume of low-vascularity high-cellularity (LV-HC) and low-vascularity low-cellularity (LV-LC) habitats. Tumor phenotypes showed significant differences in treatment response, in both changes in tumor volume and physiological composition. Significant positive correlations were found between histological stains and tumor habitats. These findings suggest that the differential baseline imaging phenotypes can predict response to therapy. Specifically, the Type 1 phenotype indicates increased sensitivity to targeted or cytotoxic therapy compared to Type 2 tumors.

Topics & Concepts

VascularityMagnetic resonance imagingPathologyImaging biomarkerPhenotypeMedicineBreast cancerTrastuzumabTargeted therapyCancer researchOncologyCancerBiologyInternal medicineRadiologyBiochemistryGeneRadiomics and Machine Learning in Medical ImagingMRI in cancer diagnosisMedical Imaging Techniques and Applications