Quantification of spatial tumor heterogeneity in immunohistochemistry staining images

Abstract

Motivation: Quantitative immunofluorescence is often used for immunohistochemistry quantification of proteins that serve as cancer biomarkers. Advanced image analysis systems for pathology allow capturing expression levels in each individual cell or subcellular compartment. However, only the mean signal intensity within the cancer tissue region of interest is usually considered as biomarker completely ignoring the issue of tumor heterogeneity.

Results: We propose using immunohistochemistry image-derived information on the spatial distribution of cellular signal intensity (CSI) of protein expression within the cancer cell population to quantify both mean expression level and tumor heterogeneity of CSI levels. We view CSI levels as marks in a marked point process of cancer cells in the tissue and define spatial indices based on conditional mean and conditional variance of the marked point process. The proposed methodology provides objective metrics of cell-to-cell heterogeneity in protein expressions that allow discriminating between different patterns of heterogeneity. The prognostic utility of new spatial indices is investigated and compared to the standard mean signal intensity biomarkers using the protein expressions in tissue microarrays incorporating tumor tissues from 1000+ breast cancer patients.

Supplementary information: Supplementary data are available at Bioinformatics online.

Fig. 1.

Representative examples of Ki67 QIF-IHC images (A–D) with the corresponding marked point patterns (E–H) and estimates of cMean(d) and cVar(d) (I–L). The centers of the plotting circles in (E–H) are at the cancer cell centroids, the radii of the plotting circles are proportional to the Ki67 expression. The green and red lines represent the means and variances of the marginal distribution of log-transformed Ki67 expression, respectively

Fig. 2.

Representative examples of QIF-IHC images of TWIST1 expression (A, B) with the corresponding marked point patterns (C, D) and estimates of cMean(d) and cVar(d) (E, F). The centers of the plotting circles in (C, D) are at the cancer cell centroids, the radii of the plotting circles are proportional to the TWIST1 expression. The green and red lines represent the means and variances of the marginal distribution of log-transformed TWIST1 expression, respectively

Fig. 3.

Ki67 and TWIST1 biomarkers in the test set. (A) associations between MSI, AUcMean and AUcVar of Ki67; (B) Kaplan–Meier estimators of the progression-free survival (PFS) by composite spatial Ki67 marker defined as High AUcMean AND High AUcVar versus Low AUcMean OR Low AUcVar based on the fitted multivariable Cox model; (C) associations between MSI, AUcMean and AUcVar of TWIST1; (D) Kaplan–Meier estimators of the progression-free survival (PFS) by High versus Low AUcVar

Fig. 4.

Continuous spatial biomarkers as predictors of PFS: (A) Ki67 AUcMean, (B) Ki67 AUcVar, (C) TWIST1 AUcMean, (D) TWIST1 AUcVar

Fig. 5.

Representative simulated marked point patterns (MPPs) of typical spatial distributions of cancer cells and protein expression levels, including type A with low CSIs (A, H, O), type B with 10% of high CSIs (B, I, P), type B with 20% of high CSIs (C, J, Q), type C with 10% of high CSIs (D, K, R), type C with 20% of high CSIs (E, L, S), type D with 10% of high CSIs (F, M, T), type D with 20% of high CSIs (G, N, U)

Fig. 6.

Marginal mean distributions (A, C, E) and AUcVar distributions (B, D, F) by typical pattern of high CSI levels: homogeneous (A, B), large clusters (C, D), small clusters (E, F)