Multi-marker detection approach for improving breast cancer treatment tailoring
the majority of patients with early breast cancer receive some form of systemic adjuvant therapy (chemo-, endocrine, and/or targeted therapy). Despite the increase in adjuvant therapy prescription, little progress has been made with respect to assisting oncologists to determine which breast cancer patients, particularly those deemed at “lower risk” of relapse, require chemotherapy or other systemic therapy and which women can safely be treated with loco-regional treatment alone. For these reasons, the identification of prognostic and predictive markers that will assist the clinician in selecting the most suitable form of medical therapy has become very high priority as well as a real challenge in translational research.
Unfortunately, several problems have hampered the identification and/or clinical usefulness of prognostic and predictive markers.
In Chapter 1, we sought to address some of the specific questions regarding prognosis:
- Are gene expression signatures robust and reproducible?
- Do the different gene signatures have similar prognostic performance? Are they concordant in their prediction for the individual patient?
- What is the role of individual genes in a signature and what is their biological interpretation?
- What is the relationship between the molecular classification defined by cluster analysis and the different prognostic signatures?
Through the following specific aims:
1. Independent validation study of a prognostic gene signature derived from microarray technology, to demonstrate its reproducibility, robustness and clinical utility compared with classical breast cancer prognostic factors in an appropriate validation cohort (Chapter 1A);
2. Independent comparison of three prognostic gene signatures (Chapter 1B);
3. Characterization of the biological foundation of the different prognostic signatures and refinement of our knowledge regarding breast cancer prognosis according to the molecular subgroups defined by ER and HER2 through a meta-analysis of publicly available gene expression data (Chapter 1C).
In Chapter 2, we sought to address some specific questions regarding the prediction of response for the most commonly given breast cancer treatments:
- What is the importance of proliferation genes in predicting clinical outcome in patients treated with endocrine therapy?
- What is the value of TOP2A in predicting the efficacy of anthracycline therapy?
- Can we identify a list of genes associated with response to anthracyline therapy?
- What is the best method and cutoff to determine HER2-positive patients eligible for trastuzumab therapy? Would an alternative quantitative method for HER2 expression and homodimerization discriminate patients with significantly different probabilities of clinical outcome following treatment with trastuzumab?
Through the following specific aims:
1. Investigation of molecular markers of response to endocrine therapy in hormono-sensitive patients (Chapter 2A);
2. Prospective evaluation of the predictive value of TOP2A and identification of genes associated with response in a cohort of patients treated with anthracyclines (Chapter 2B);
3. Investigation of the best method to select patients who should be treated by trastuzumab-based therapy and evaluation of a new technique to quantitatively assess HER2 expression (Chapter 2C).
Advisor:Piccart, Martine; Abramowicz, Marc; Sotiriou, Christos; Vassart, Gilbert; Simon, Philippe; Velu, Thierry; André, Fabrice; De Cremoux, Patricia
School:Université libre de Bruxelles
Source Type:Master's Thesis
Keywords:prognosis breast cancer microarray predictive markers
Date of Publication:08/27/2008