Multiparametric MRI and Coregistered Histology Identify Tumor Habitats in Breast Cancer Mouse Models

Abstract

It is well-recognized that solid tumors are genomically, anatomically, and physiologically heterogeneous. In general, more heterogeneous tumors have poorer outcomes, likely due to the increased probability of harboring therapy-resistant cells and regions. It is hypothesized that the genomic and physiologic heterogeneity are related, because physiologically distinct regions will exert variable selection pressures leading to the outgrowth of clones with variable genomic/proteomic profiles. To investigate this, methods must be in place to interrogate and define, at the microscopic scale, the cytotypes that exist within physiologically distinct subregions ("habitats") that are present at mesoscopic scales. MRI provides a noninvasive approach to interrogate physiologically distinct local environments, due to the biophysical principles that govern MRI signal generation. Here, we interrogate different physiologic parameters, such as perfusion, cell density, and edema, using multiparametric MRI (mpMRI). Signals from six different acquisition schema were combined voxel-by-voxel into four clusters identified using a Gaussian mixture model. These were compared with histologic and IHC characterizations of sections that were coregistered using MRI-guided 3D printed tumor molds. Specifically, we identified a specific set of MRI parameters to classify viable-normoxic, viable-hypoxic, nonviable-hypoxic, and nonviable-normoxic tissue types within orthotopic 4T1 and MDA-MB-231 breast tumors. This is the first coregistered study to show that mpMRI can be used to define physiologically distinct tumor habitats within breast tumor models. SIGNIFICANCE: This study demonstrates that noninvasive imaging metrics can be used to distinguish subregions within heterogeneous tumors with histopathologic correlation.

Figure 1.. Workflow for the 3D-printed tumor mold.

T₂-weighted images were used to segment the tumor contours and create a 3D-printed tumor mold. (A) Tumor volume of interest (VOI) (red) in MRI T₂-weighted image (upper: coronal, bottom: axial, field of view 30 × 30 mm²; image size 256 × 256); (B) 3D tumor reconstruction; (C) Mold designed in SOLIDWORKS and (D) printed in the 3D printer; (E) Tissue dyes were used to help with tumor orientation when inserting tumor into the 3D printed mold; (F) Tumor tissue inside the 3D-printed mold; (G) CT image (H) Tumor was cut in slices of 2 mm in thickness; (I) Slices were placed into individual cassettes; (J) Each tumor slice was cut into histological section of 4 μm and stained with H&E.

Figure 2.. Estimation of the optimal 3D alignment of MRI and histology.

(A) T₂-weighted axial slices (field of view 30 × 30 mm²; image size 256 × 256); (B) 3D representation of the tumor contour obtained by MRI (grey) and overlay of co-registered histological slices; (C) Histology images co-registered to the contours of the tumor on the surface on ‘b’.

Figure 3.

Segmentation of histological tumor sections. (A) Tumor section stained with hematoxylin & eosin (H&E) and respective masked representation of viable (green) and non-viable (blue) cells region; (B) Tumor section stained with pimonidazole and respective masked representation of positive pixels for pimonidazole in viable (magenta) and non-viable (yellow) regions; (C) Tumor section stained with CD-31 and respective masked representation of positive pixels for CD-31 (cyan). (D) Superimposed image classifying 4 habitats in histology, represented as viable cells in green and non-viable cells in blue, hypoxic areas in viable cells regions in magenta and hypoxia areas in non-viable cells regions in yellow. (E) Ground truth habitats map from histology, created using a super-pixel of 117.19 μm × 117.19 μm square grid to downsample the image to an equivalent MRI resolution.

Figure 4.. Representative examples of the corresponding habitat maps from MRI and histology. 4T1 tumor is shown in (A) and MDA-MB-231 is shown in (G).

Six MRI parameter maps were obtained [T₂-map, T₂*-map, Apparent diffusion coefficient (ADC), Slope, Area under the curve (AUC) and time to max (TTM)]. Field of view 30 × 30 mm²; image size 256 × 256; Tumor volume of interest (VOI) is shown in red. (B and H) These six MRI parameter maps were clustered by using a Gaussian Mixture Model (GMM) to create the Habitat Maps. Cluster green shows high enhancement in DCE and cluster region blue shows low enhancement in DCE. Magenta and yellow clusters show moderate enhancement in DCE. Histological images ((C and I) H&E; (D and J) pimonidazole and (E and K) CD31) were used to create downsampled habitat-maps derived from histology (F and L) in the same resolution of MR images. The colors in histology (ground truth) habitat maps are delineated by viable cells region in green, non-viable cells region in blue and hypoxic areas in viable or non-viable regions in magenta and yellow, respectively.

Figure 5.

Mean values of each parameter from habitat maps generated by clustering six MRI-parameters [T₂-map, T₂*-map, Apparent diffusion coefficient (ADC), Slope, Area under the curve (AUC) and time to max (TTM)].Mean values of each parameter were compared between clusters (habitats) for the (A) 4T1 (n=6 samples) and (B) MDA-MB-231 (n=4 samples) tumor samples. Graphs represent mean and SD. p-values were obtained using one-way analysis of variance (ANOVA) followed by the Tukey test for comparison of mean values between regions (a-c indicate p<0.05 between groups).

Figure 6.

Identification of adipocytes in each segmented region (viable, hypoxic viable, hypoxic non-viable, non-viable) in histological samples. (A) Adipocytes observed in non-viable cells region; (B) Adipocytes observed in viable (#) and non-viable (*) cells region; (C and D) Relative area of adipocytes (%) in 4T1 and MDA-MB-231 histological tumor samples. Graphs represent mean and SD. p-values were obtained using one-way analysis of variance (ANOVA) followed by the Tukey test for comparison of mean values between regions (*p<0.05; ** p<0.01; ***p<0.001; ****p<0.0001 (n=6 histological samples for 4T1 and n=8 histological samples for MDA-MB-231). (E) Representative example of a MDA-MB-231 sample showing the adipocytes identified in histology (masked in yellow), which can be observed as a region with low signal in the corresponding ADC Map in (F) and is also visible in the T₂* map in (G) (MR images = field of view 30 × 30 mm²; image size 256 × 256).