Characterizing Trastuzumab-Induced Alterations in Intratumoral Heterogeneity with Quantitative Imaging and Immunohistochemistry in HER2+ Breast Cancer

Abstract

The purpose of this study is to investigate imaging and histology-based measurements of intratumoral heterogeneity to evaluate early treatment response to targeted therapy in a murine model of HER2+ breast cancer. BT474 tumor-bearing mice (N = 30) were treated with trastuzumab or saline and imaged longitudinally with either dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) or 18F-fluoromisonidazole (FMISO) positron emission tomography (PET). At the imaging study end point (day 4 for MRI or 7 for PET), each tumor was excised for immunohistochemistry analysis. Voxel-based histogram analysis was performed on imaging-derived parametric maps (i.e., Ktrans and ve from DCE-MRI, SUV from 18F-FMISO-PET) of the tumor region of interest to measure heterogeneity. Image processing and histogram analysis of whole tumor slice immunohistochemistry data were performed to validate the in vivo imaging findings. Trastuzumab-treated tumors had increased heterogeneity in quantitative imaging measures of cellularity (ve), with a mean Kolmogorov-Smirnov (K-S) distance of 0.32 (P = .05) between baseline and end point distributions. Trastuzumab-treated tumors had increased vascular heterogeneity (Ktrans) and decreased hypoxic heterogeneity (SUV), with a mean K-S distance of 0.42 (P < .01) and 0.46 (P = .047), respectively, between baseline and study end points. These observations were validated by whole-slice immunohistochemistry analysis with mean interquartile range of CD31 distributions of 1.72 for treated and 0.95 for control groups (P = .02). Quantitative longitudinal changes in tumor cellular and vascular heterogeneity in response to therapy may provide evidence for early prediction of response and guide therapy for patients with HER2+ breast cancer.

Multiparametric Analysis of Longitudinal Quantitative MRI Data to Identify Distinct Tumor Habitats in Preclinical Models of Breast Cancer

“…Otsu thresholding [ 83 ] was used for automated segmentation of proliferative cell nuclei from anti-Ki-67 stained sections and microvessels from anti-CD31 stained sections. Further details of the automated segmentation techniques are described in previous work [ 26 ]. Masks of whole tissue for each stained tissue slice were semi-automatically generated using gray (non-tissue) area masks and manually drawn ROIs ( Figure S7c ).…”

“…Approaches to quantify intratumoral heterogeneity from imaging data include histogram and texture analysis [ 22 ]. Histogram analysis involves quantitative assessment of parameter map distributions, demonstrating value over whole-tumor summary statistics [ 23 , 24 ], and allowing for the quantification of longitudinal alterations in intratumoral heterogeneity [ 25 , 26 ]. However, this approach forgoes the spatial information provided in imaging data.…”

Spatiotemporal Extracellular Matrix Modeling for in Situ Cell Niche Studies

“…In order to study the expression of cell surface markers and spatial distribution of cells over time, we developed an algorithm that automatically bins nuclei segmented images and generates density maps in the StEMM. Interestingly, when collating density maps from histological sections, the bin size is often set manually in the range of 50 to 150 μm 54‐56 . In contrast, by the use of our algorithm, bin size was automatically set between 45 and 115 μm.…”

Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association

“…This particular study showed that quantitative data from in vivo imaging is consistent with data derived from quantitative studies in histologic sections to study intratumoral heterogeneity. 54 …”