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.

Diffusion model comparison identifies distinct tumor sub‐regions and tracks treatment response

“…Using model comparison to identify sub‐regions shares similarities with previous efforts to characterize intra‐tumor heterogeneity using clustering or probabilistic classification of multi‐contrast MR data. While it is beyond the scope of the present work to compare model comparison and multi‐contrast approaches to identifying sub‐regions, the inclusion of additional data beyond DWI would be expected to aid the characterization.…”

“…Spatial correspondence between histology and imaging is also hindered by shrinkage and distortion of histological samples due to fixation and sectioning. Improved methods for comparison of histology and imaging, such as the use of tumor‐specific moulds and image registration, would provide a more comprehensive biological validation. Taken together, the in vivo and in silico results suggest that %MM is related to tumor necrosis, although actual necrotic fractions will be lower than %MM suggests.…”

What Is the Storage Effect, Why Should It Occur in Cancers, and How Can It Inform Cancer Therapy?

“…Longitudinally monitoring tumors and responses to therapy may require more frequent magnetic resonance imaging and other imaging techniques as noninvasive tools for identifying and tracking different tumor habitats based on vascularity, hypoxia, necrosis, and so on. 66,67 Pseudo-Resistance Heterogeneity in quiescence could obscure detection of resistance to treatment through the phenomenon of pseudo-resistance, a situation where an otherwise treatment-sensitive population of cancer cells appears to be resistant. Pseudoresistance could result from temporal variation that selects for cell lineages that maintain a high fraction of cells within a quiescent state.…”

Targeting hypoxic habitats with hypoxia pro-drug evofosfamide in preclinical models of sarcoma

“…In this context, if noninvasive imaging of hypoxia can be developed it would allow longitudinal monitoring of therapy response without a biopsy. We had previously developed a multiparametric MRI (mpMRI) method to identify hypoxic habitats in breast cancers using Gaussian Mixture Models (26). Herein, we take a similar approach to identify hypoxia in sarcoma using a convolutional neural network (CNN).…”

“…To this end, different magnetic resonance imaging (MRI)-and positron emission tomography (PET)-imaging based analyses have been explored to identify tumor hypoxia (23,24) and predict response to HAPs in pre-clinical models (19,25). We have previously reported that multiparametric (mp) MRI can capture subtle difference in the tumor microenvironments, and is able to differentiate viable, necrotic and hypoxic tumor habitats in breast cancer models (26).…”