Table 5.

Summary of the gap analysis for disease markers in breast cancer

What do we know?	Patient groups can be successfully stratified in clinical trials using biomarkers.
What are the gaps?	Optimum protocols for pathological assessment of DCIS and sentinel lymph nodes.
	Combining clinical, radiological, pathological and genomic data in trial populations.
	No robust validated markers have yet been developed for predicting response to chemotherapy or radiotherapy.
	There is no consensus for markers indicative of resistance to therapy.
	There is a need for improved prognostic indices based on disease markers.
Problems	New assays must be robust and reproducible.
	There is a need for standardisation of tissue handling.
	The impact of legislation, industrial involvement and academic pressures.
	Networks of collaboration employing systems biology are required.
Translational implications	Accurate recognition of the diversity of breast cancer.
	Identification of patients most likely to benefit.
	Identification of patients least likely to benefit from therapy and hence able to avoid toxicity.
Recommendations	Design innovative trials and translational studies to develop and evaluate predictive and prognostic markers.
	Develop close multidisciplinary collaboration with high-quality histopathology and rigorous scientific assessments to validate new markers important for patient outcome.
	Identify robust markers of resistance or sensitivity to therapy that can be applied across the spectrum of breast disease from screen-detected to metastatic breast cancer.