Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome

Abstract

Tumors exhibit genomic and phenotypic heterogeneity, which has prognostic significance and may influence response to therapy. Imaging can quantify the spatial variation in architecture and function of individual tumors through quantifying basic biophysical parameters such as CT density or MRI signal relaxation rate; through measurements of blood flow, hypoxia, metabolism, cell death, and other phenotypic features; and through mapping the spatial distribution of biochemical pathways and cell signaling networks using PET, MRI, and other emerging molecular imaging techniques. These methods can establish whether one tumor is more or less heterogeneous than another and can identify subregions with differing biology. In this article, we review the image analysis methods currently used to quantify spatial heterogeneity within tumors. We discuss how analysis of intratumor heterogeneity can provide benefit over more simple biomarkers such as tumor size and average function. We consider how imaging methods can be integrated with genomic and pathology data, instead of being developed in isolation. Finally, we identify the challenges that must be overcome before measurements of intratumoral heterogeneity can be used routinely to guide patient care.

Figure 1

Quantifying intratumoral heterogeneity: The example liver metastasis from a patient with a colonic primary tumor can be measured in several different ways. (A) Most clinical assessment of tumors is size-based. (B) Functional imaging methods can measure tumor pathophysiology but tend to derive average parameter values, such as median K^trans. (C) Some intratumoral heterogeneity methods quantify overall complexity of a distribution (histograms) or spatial arrangement of data (texture analysis). Other methods identify tumor sub-regions using a priori assumptions (partitioning) or data driven approaches (multispectral analysis).

Figure 2

A GH3 prolactinoma in a rat shows a heterogeneous response to 50mg/kg ZD6126. Histograms analysis quantifies this effect and demonstrates transformation from a unimodal distribution to a bimodal one. Adapted from Robinson et al. (41).

Figure 3

Quantifying tumor sub-regions: A priori methods parcellate tumor regions based on binary classifiers (e.g. non-enhancing (=0) or enhancing (=1) voxels from a K^trans map or threshold classification using a ‘cut point’ defined from previous data, or an arbitrary value such as the median (e.g. of an ADC distribution). Multispectral classification generates clusters based on data driven segmentation from multiple signals (e.g. K^trans and ADC), for example using principal components (PC) analysis (graph shows PC1, PC2 and PC3). The volume or proportional fraction of each region is measured in these methods. Geographical methods are distinct since they define sub-regions based on the location of a pixel rather than its parameter value(s).

Figure 4

Multispectral analysis: (A) ADC, T₂ values and proton density (PD) maps shown for a colorectal cancer murine xenograft. (B) K-means clustering is performed and decision boundaries are formed. (C) Four tissue categories are produced (viable tumor, subcutaneous (SC) fat and two necrotic regions (differentiated from each other by the T₂ signal) that relate to pathology as seen on the H&E section. (D) The anti-vascular effect of G6-31 in treated animals is significant viable tumor tissue but not significant in necrotic regions. Adapted from Carano et al. (76) and Berry et al. (79).