Triple-negative breast cancer: present challenges and new perspectives

Abstract

Triple-negative breast cancers (TNBC), characterized by absence of estrogen receptor (ER), progesterone receptor (PR) and lack of overexpression of human epidermal growth factor receptor 2 (HER2), are typically associated with poor prognosis, due to aggressive tumor phenotype(s), only partial response to chemotherapy and present lack of clinically established targeted therapies. Advances in the design of individualized strategies for treatment of TNBC patients require further elucidation, by combined 'omics' approaches, of the molecular mechanisms underlying TNBC phenotypic heterogeneity, and the still poorly understood association of TNBC with BRCA1 mutations. An overview is here presented on TNBC profiling in terms of expression signatures, within the functional genomic breast tumor classification, and ongoing efforts toward identification of new therapy targets and bioimaging markers. Due to the complexity of aberrant molecular patterns involved in expression, pathological progression and biological/clinical heterogeneity, the search for novel TNBC biomarkers and therapy targets requires collection of multi-dimensional data sets, use of robust multivariate data analysis techniques and development of innovative systems biology approaches.

Figure 1

Lack of correlation between tumor size and nodal status among (A) triple‐negative breast cancers (TNBC) with tumor size smaller than 50 mm (χ2 test for trend: P = 0.47 for TNBC; P < 0.0001 for other breast cancer subtypes) and (B) BRCA1‐associated breast cancers (χ2 test for trend: P = 0.183 for BRCA1‐associated tumors; P < 0.0001 for BRCA2‐associated breast tumors and for breast tumors in non‐carriers). N+ positive nodal status (at least one positive lymph node). Graphs adapted from Dent et al. (2007) and (Foulkes et al. 2003a).

Figure 2

Integration of predictive expression signatures and altered cell signaling patterns in the search for pathway‐driven tumor therapeutics and bioimaging.

Figure 3

The flow, analysis and integration of multi‐dimensional molecular, biological and clinical data from different biomedical sources leading to a new classification system, diagnostic tools and predictive models in breast cancer.