9IN - The molecular heterogeneity of breast tumors
|Date||30 September 2012|
|Event||ESMO Congress 2012|
|Session||The impact of the cancer genome project and high-throughput analyses on personalised oncology: Today and tomorrow|
|Topics|| Breast Cancer
Breast Cancer (BC) is a complex disease caused by accumulation of genetic alterations leading to a disturbed balance between proliferation and apoptosis, genetic instability and acquisition of an invasive and resistant phenotype. The inherent heterogeneity, including the microenvironment, contributes to each tumors phenotype and dictate tumors molecular portraits. We have generated high-throughput data of tumors from mRNA and miRNA expression, SNP-CGH and DNA-methylation, paired-end sequencing, protein expression by RPPA, and metabolic profiles from HR-MAS MR analyses. We have explored to which extent combining the various profiles derived from each level, can further subdivide the initially discovered expression subclasses and improve prognostic potential. Patterns of genomic aberrations that underlie specific expression subgroups may infer paths of tumor progression and shed light on mechanisms involved. We recently developed two platform-independent algorithms to explore genomic architectural distortion using aCGH data to measure whole-arm gains and losses (WAAI) and complex rearrangements (CAAI), and were able to separate the cases into eight subgroups. Within each subgroup data from expression analyses, sequencing and ploidy indicated that progression occurs along separate paths into more complex genotypes. The results emphasized the relation among structural genomic alterations, molecular subtype, and clinical behavior and showed that objective score of genomic complexity (CAAI) is an independent prognostic marker. By integrating data from the patient's own genotype with multiple layers of data derived from the primary tumor, the different subclones within the tumor, as well as the metastases, we seek to reach a more fundamental understanding of the biological dynamics of breast cancer. This will facilitate identification of risk factors, search for novel cancer diagnostics, prediction of therapeutic effects and prognosis and identification of new targets for therapy, that may lead to a more personalized treatment of BC. Ref: Stephens P et al. Nature. 2009, Russnes HG et al. STM, 2010, Van LP et al. PNAS 2010, Kristensen VN et al, PNAS 2011, Russnes et al JCI, 2011, Curtis et al, Nature, 2012Disclosure
The author has declared no conflicts of interest.