PO-313 Genome-wide analysis of site-specific hotspots in cancer

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IntroductionTumours are continuously evolving through their course of progression and treatment, a major process contributing to resistance to therapy. Increasingly, it becomes clear that unravelling the dynamics of gene expression over time, during stages of cancer progression and therapy is fundamental to interpretation of tumour evolution and resistance mechanisms. We postulate that in order to understand the emergence of resistance it is important to immensely profile matched biopsies from individual patients accompanied with their long-term clinical follow-up.Material and methodsWe collected triplets of archived samples from 33 individual patients that underwent neo-adjuvant (preoperative) treatment. Matched biopsies of tumour pre-treatment, post-treatment and adjacent normal epithelium as well as normal breast tissues from 6 healthy individuals were included. Full transcriptome analysis was performed by mRNA sequencing, after optimising this method for archived samples. Comprehensive clinical and pathological information was collected. A dedicated longitudinal pattern analysis method was developed to follow dynamic expression fluctuations of individual patients. Pathifier was used to calculate pathway deregulation scores.Results and discussionsPrinciple component analysis showed clustering of the samples according to their type. Dynamic fluctuations across the 3 time-points were classified into 8 theoretical patterns, each representing a different scenario through the tumour progression and treatment stages. Genes were divided into two main types: 1. Sharing a common temporal expression pattern across most patients. These genes were associated with tumour progression pathways. 2. Genes that were divided into two or three dominant patterns and this division showed correlation with pathological response score. The dynamic pattern classification enabled to pinpoint genes associated with response that otherwise were difficult to identify using single-time point or two-time points datasets. Furthermore, the dynamics of pathway deregulation scores enabled to detect pathways that were correlated with response to therapy.ConclusionThe longitudinal approach of serial sampling and analysis reveals heterogeneous dynamic behaviour across patients through the course of disease. This individual dynamics has higher sensitivity than single-time point measurements in detecting clinically relevant genes that are associated with resistance to therapy and with tumour progression.