Purpose To improve on current standards for breast cancer prognosis and prediction of chemotherapy benefit by developing a risk model that incorporates the gene expression–based “intrinsic” subtypes luminal A, luminal B, HER2-enriched, and basal-like. Methods A 50-gene subtype predictor was developed using microarray and quantitative reverse transcriptase polymerase chain reaction data from 189 prototype samples. Test sets from 761 patients (no systemic therapy) were evaluated for prognosis, and 133 patients were evaluated for prediction of pathologic complete response (pCR) to a taxane and anthracycline regimen. Results The intrinsic subtypes as discrete entities showed prognostic significance (P = 2.26E-12) and remained significant in multivariable analyses that incorporated standard parameters (estrogen receptor status, histologic grade, tumor size, and node status). A prognostic model for node-negative breast cancer was built using intrinsic subtype and clinical information. The C-index estimate for t...

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Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes

Accessories to the Crime: Functions of Cells Recruited to the Tumor Microenvironment

Tumor Metastasis: Molecular Insights and Evolving Paradigms

Cancer is associated with fibroblasts at all stages of disease progression. This Review discusses the pleiotropic actions of cancer-associated fibroblasts (CAFs) on tumour cells and postulates that they are likely to be a heterogeneous and plastic population of cells in the tumour microenvironment. Among all cells, fibroblasts could be considered the cockroaches of the human body. They survive severe stress that is usually lethal to all other cells, and they are the only normal cell type that can be live-cultured from post-mortem and decaying tissue. Their resilient adaptation may reside in their intrinsic survival programmes and cellular plasticity. Cancer is associated with fibroblasts at all stages of disease progression, including metastasis, and they are a considerable component of the general host response to tissue damage caused by cancer cells. Cancer-associated fibroblasts (CAFs) become synthetic machines that produce many different tumour components. CAFs have a role in creating extracellular matrix (ECM) structure and metabolic and immune reprogramming of the tumour microenvironment with an impact on adaptive resistance to chemotherapy. The pleiotropic actions of CAFs on tumour cells are probably reflective of them being a heterogeneous and plastic population with context-dependent influence on cancer.

The biology and function of fibroblasts in cancer

Fibroblast growth factors (FGFs) and their receptors control a wide range of biological functions, regulating cellular proliferation, survival, migration and differentiation. Although targeting FGF signalling as a cancer therapeutic target has lagged behind that of other receptor tyrosine kinases, there is now substantial evidence for the importance of FGF signalling in the pathogenesis of diverse tumour types, and clinical reagents that specifically target the FGFs or FGF receptors are being developed. Although FGF signalling can drive tumorigenesis, in different contexts FGF signalling can mediate tumour protective functions; the identification of the mechanisms that underlie these differential effects will be important to understand how FGF signalling can be most appropriately therapeutically targeted.

Fibroblast growth factor signalling: from development to cancer

Although it is increasingly evident that cancer is influenced by signals emanating from tumor stroma, little is known regarding how changes in stromal gene expression affect epithelial tumor progression. We used laser capture microdissection to compare gene expression profiles of tumor stroma from 53 primary breast tumors and derived signatures strongly associated with clinical outcome. We present a new stroma-derived prognostic predictor (SDPP) that stratifies disease outcome independently of standard clinical prognostic factors and published expression-based predictors. The SDPP predicts outcome in several published whole tumor-derived expression data sets, identifies poor-outcome individuals from multiple clinical subtypes, including lymph node-negative tumors, and shows increased accuracy with respect to previously published predictors, especially for HER2-positive tumors. Prognostic power increases substantially when the predictor is combined with existing outcome predictors. Genes represented in the SDPP reveal the strong prognostic capacity of differential immune responses as well as angiogenic and hypoxic responses, highlighting the importance of stromal biology in tumor progression.

Haiying Chen

Papers

Stromal gene expression predicts clinical outcome in breast cancer