Identification of prognostic molecular features in the reactive stroma of human breast and prostate cancer

Abstract

Primary tumor growth induces host tissue responses that are believed to support and promote tumor progression. Identification of the molecular characteristics of the tumor microenvironment and elucidation of its crosstalk with tumor cells may therefore be crucial for improving our understanding of the processes implicated in cancer progression, identifying potential therapeutic targets, and uncovering stromal gene expression signatures that may predict clinical outcome. A key issue to resolve, therefore, is whether the stromal response to tumor growth is largely a generic phenomenon, irrespective of the tumor type or whether the response reflects tumor-specific properties. To address similarity or distinction of stromal gene expression changes during cancer progression, oligonucleotide-based Affymetrix microarray technology was used to compare the transcriptomes of laser-microdissected stromal cells derived from invasive human breast and prostate carcinoma. Invasive breast and prostate cancer-associated stroma was observed to display distinct transcriptomes, with a limited number of shared genes. Interestingly, both breast and prostate tumor-specific dysregulated stromal genes were observed to cluster breast and prostate cancer patients, respectively, into two distinct groups with statistically different clinical outcomes. By contrast, a gene signature that was common to the reactive stroma of both tumor types did not have survival predictive value. Univariate Cox analysis identified genes whose expression level was most strongly associated with patient survival. Taken together, these observations suggest that the tumor microenvironment displays distinct features according to the tumor type that provides survival-predictive value.

Figure 1. Tumor-specific stromal responses displayed by breast and prostate cancer.

A, PCA shows that breast and prostate tumors have a distinct stromal reaction to tumor invasion that can be used to classify cancer patients. B, pairwise correlation analysis showing a higher correlation of breast stromal genes with breast data than with prostate data and vice versa.

Figure 2. Representative images of periostin expression in normal and tumor tissues.

A, normal breast tissue. B, breast carcinoma. C, normal prostate tissue. D, prostate carcinoma. E, ovarian carcinoma. F, lung carcinoma. G, colon carcinoma. Magnification: 200×.

Figure 3. Kaplan-Meier survival analysis.

A, Kaplan-Meier survival analysis of early-breast carcinoma patients (van de Vijver et al.) and B, prostate carcinoma patients (Glinsky et al.) obtained using breast and prostate stromal genes respectively (FDR 15%), showing that the two groups of patients differ significantly in their overall survival. Red, poor prognosis group; blue, good prognosis group.