BIOLOGICALLY MEANINGFUL AND CLINICALLY RELEVANT GENE EXPRESSION PROFILE FOR OPTIMAL TREATMENT PLANNING IN BREAST CANCER.
Tesi di Dottorato
Data di Pubblicazione:
2013
Citazione:
BIOLOGICALLY MEANINGFUL AND CLINICALLY RELEVANT GENE EXPRESSION PROFILE FOR OPTIMAL TREATMENT PLANNING IN BREAST CANCER / V. Musella ; coordinatore: A. Gianni ; relatore: C. Carlo-Stella ; tutor: V. Cappelletti. Università degli Studi di Milano, 2013 Jan 31. 25. ciclo, Anno Accademico 2012. [10.13130/musella-valeria_phd2013-01-31].
Abstract:
ABSTRACT
Breast cancer is a highly heterogeneous disease from the molecular and clinical point of view. Optimal treatment with the currently available drugs depends therefore on the ability to individually predict treatment. Presently available outcome prediction models are however suboptimal, single predictive variables have limited accuracy, and the actual clinical outcomes remain heterogeneous in any given prognostic group. ER status and HER-2 status are helpful in identifying patients who are not eligible for endocrine or trastuzumab therapies by virtue of their high negative predictive values (NPVs) and high sensitivities. However, only a minority of ER-positive or HER-2-positive patients respond to receptor-targeted therapy. The positive predictive values (PPVs) of these tests are <50%. Moreover, currently there are no accepted molecular predictors of response to various chemotherapeutic drugs. These limitations have driven biomarker research to develop more accurate molecular predictors of clinical outcome.
In the present thesis work we report data on an innovative strategy to predict treatment response to conventional therapies by combining in a hierarchical way genomic predictors related to prognosis and to treatment sensitivity and resistance. The strategy takes into account the intrinsic molecular heterogeneity of breast tumors by distinctly developing genomic predictors for each of the three main tumor subtypes defines as: ER+/Her2-, Her2+ and ER-/Her2-. The considered genomic predictors are built based on literature data and results previously obtained at the INT (as in the case of the prognostic role of ISG genes) and are treated as metagenes mainly to facilitate cross-platform comparisons and to stick to pathways with s clear biological role. An important part of the development of the prediction strategy deals with analytical approaches which were optimized to gain more accurate information from FFPE samples.
Gene expression profiles used in such approach were obtained from public database, but also from expression studies carried out at INT on two types of samples; fresh frozen and formalin fixed samples. A detailed comparative analysis of technical solutions (Illumina HT 12, Illumina Ref8, Illumina DASL and Affymetrix HG Plus2.0 chips) for optimizing gene expression profiles in FFPE samples with heavily degraded and chemically-modified RNA is reported. A new robust protocol was developed based on linear amplification of RNA under conditions minimizing rRNA amplification, and on use of the Affymetrix HG Plus 2.0 chips. The protocol was tested in a pilot study on 60 samples to estimate the actual percentage of archived FFPE clinical samples which could yield technically acceptable gene expression profiles and to evaluate the biological reliability of gene expression profiles obtained from fixed samples. Reliability was tested by comparing ER-status classifiers developed using FF-derived expression data and testing the classifier on predicting ER status in FFPE and FF dataset. Prediction accuracy between the two types of samples was comparable (FF Cohen’s k 0,92, FFPE Cohen’s k 0.89).
With the various tools developed as described above, it was possible to identify a priori in the ER+/Her2- a subset of patients who were predicted (and confirmed by external validation) to have 95% 5-year disease free survival. Such accurate (and validated) prediction was achieved by combining optimized metagenes with clear biological roles in proliferation, ER signaling and immunity and separately analyzing prognostic and predictive information.
Work is still in progress for the other molecular subtypes (Her2+ and ER-/Her2-).
The role of immune genes was particularly interesting as it definitely added important inform
Tipologia IRIS:
Tesi di dottorato
Keywords:
breast ; cancer ; microarray ; FFPE ; FF ; IFN ; prognosis
Elenco autori:
V. Musella
Link alla scheda completa: