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An ecological measure of immune-cancer colocalization as a prognostic factor for breast cancer - PubMed

  • ️Thu Jan 01 2015

An ecological measure of immune-cancer colocalization as a prognostic factor for breast cancer

Carlo C Maley et al. Breast Cancer Res. 2015.

Abstract

Introduction: Abundance of immune cells has been shown to have prognostic and predictive significance in many tumor types. Beyond abundance, the spatial organization of immune cells in relation to cancer cells may also have significant functional and clinical implications. However there is a lack of systematic methods to quantify spatial associations between immune and cancer cells.

Methods: We applied ecological measures of species interactions to digital pathology images for investigating the spatial associations of immune and cancer cells in breast cancer. We used the Morisita-Horn similarity index, an ecological measure of community structure and predator-prey interactions, to quantify the extent to which cancer cells and immune cells colocalize in whole-tumor histology sections. We related this index to disease-specific survival of 486 women with breast cancer and validated our findings in a set of 516 patients from different hospitals.

Results: Colocalization of immune cells with cancer cells was significantly associated with a disease-specific survival benefit for all breast cancers combined. In HER2-positive subtypes, the prognostic value of immune-cancer cell colocalization was highly significant and exceeded those of known clinical variables. Furthermore, colocalization was a significant predictive factor for long-term outcome following chemotherapy and radiotherapy in HER2 and Luminal A subtypes, independent of and stronger than all known clinical variables.

Conclusions: Our study demonstrates how ecological methods applied to the tumor microenvironment using routine histology can provide reproducible, quantitative biomarkers for identifying high-risk breast cancer patients. We found that the clinical value of immune-cancer interaction patterns is highly subtype-specific but substantial and independent to known clinicopathologic variables that mostly focused on cancer itself. Our approach can be developed into computer-assisted prediction based on histology samples that are already routinely collected.

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Figures

Fig. 1
Fig. 1

Measuring spatial colocalization of immune and cancer cells through image analysis and spatial statistics. a Example H&E image of a breast tumor. Three sections obtained from different locations of the tumor were stained with H&E. b Automated image analysis was used to identify cell types (cancer, immune and stromal cells including fibroblasts and endothelial cells) in this image. c Density of cancer cells and immune cells per square after applying a square tessellation to this image; squares with less than a predefined amount of tissue would be excluded from analysis. d and e Schematic over an arbitrary spatial plane demonstrating how colocalization statistics can discriminate a highly segregated cell pattern from a highly colocalized cell pattern. f Significant correlation between the Morisita index and visual scoring for immune-cancer colocalization in 40 randomly selected samples (JT-test p = 0.0084); focal (disperse immune cell aggregates), low (mild infiltrate unassociated with cancer), moderate (some spatial association with cancer) and marked (dense infiltrate closely associated with cancer)

Fig. 2
Fig. 2

Association between immune-cancer cell colocalization and disease-specific survival in breast cancer. Kaplan-Meier curves illustrate disease-specific survival probabilities of patient groups in two subsets stratified by the Morisita index and Pearson correlation in the discovery (a) and validation (b) cohorts. The thresholds for dichotomizing two indices were optimized in the discovery cohort and then used without modification in the validation cohort (0.6940734 for the Morisita index and 0.4884236 for Pearson correlation). Numbers in the legend show the number of patients in each group and numbers in brackets show the number of disease-specific deaths

Fig. 3
Fig. 3

The association between immune-cancer cell colocalization and breast cancer prognosis is highly subtype-specific. Kaplan-Meier curves for the validation cohort alone are shown for human epidermal growth factor receptor 2-positive (Her2+) (a), basal (b), luminal A (c), and luminal B (d) Pam50 subtypes. The thresholds for dichotomizing two indices were optimized in the discovery cohort of the subtype and then used for both the discovery and validation cohorts (0.639985 for luminal A, 0.7439517 for luminal B, 0.699701 for basal and 0.7106531 for Her2)

Fig. 4
Fig. 4

Immune-cancer cell colocalization is a strong prognostic factor in the Her2+ subtype, while immune cell abundance is not. Kaplan-Meier curves to show differences in disease-specific survival stratified by the Morisita index, visual and automated scores of immune abundance in human epidermal growth factor receptor 2 (Her2) subtype defined by PAM50 (a) and Her2-amplified (b) samples. Automated immune abundance was estimated as the percentage of cells that are lymphocytes in H&E images using a cutoff of 8 % (“Methods”)

Fig. 5
Fig. 5

Immune-cancer cell colocalization predicts long-term outcome following chemotherapy (CT), radiotherapy (RT) and hormone therapy (HT) in human epidermal growth factor receptor 2-positive (Her2+) breast cancer. Kaplan-Meier curves show differences in disease-specific survival between Her2+ patients with or without CT (a), RT (b) and HT (c). The Morisita index significantly stratifies disease-specific survival for both treated and untreated Her2+ patients, shown by CT treatment and Morisita index (d), RT treatment and Morisita index (e) and HT treatment and Morisita index (f)

Fig. 6
Fig. 6

Immune-cancer cell colocalization predicts long-term outcome following radiotherapy (RT) and hormone therapy (HT) in luminal A breast cancer. Kaplan-Meier curves show differences in disease-specific survival stratified by CT treatment and Morisita index (a), RT treatment and Morisita index (b) and HT treatment and Morisita index (c). (d) Percentage of times where Morisita was statistically significant in univariate and multivariate analysis with different amounts of patient samples that were selected randomly 1000 times. Comparison of Morisita (e) and automated score of immune abundance (f) based on association with disease-specific survival in luminal A cancer

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