Quantitative analyses to study tumor clones dynamics and tumor heterogeneity

Casiraghi, Nicola (2017) Quantitative analyses to study tumor clones dynamics and tumor heterogeneity. PhD thesis, University of Trento.

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Prostate cancer is a highly heterogeneous disease and its manifestations can vary from indolent localized tumor to widespread metastases. This heterogeneity is also observed at the molecular level both inter- and intra-patient. Intra-patient heterogeneity in the clinical setting of men with castration resistant prostate cancer (CRPC) might be informative in terms of treatment decision. Here I present analytical work on two approaches relevant to the characterization of intra-patient heterogeneity and applied to unpublished CRPC patients sequencing data. The first is based on the genome wide interrogation of multiple metastatic and primary tissue biopsies from single patients. I present genomic analyses to decipher the content of multiple tumor biopsies from CRPC patients and provide comparisons to highlight similarities and differences and to identify alternative patterns of aberrations. The second approach, alternative to tissue biopsies that might under-represent the genomic landscape of the patient’s disease, relies on liquid biopsies, a minimally invasive test that is also amenable to serial sampling. Liquid biopsies contain circulating cell free DNA (cfDNA) released from widespread tumor cells, potentially uncovering the full tumor landscape. By using next generation sequencing on cfDNA obtained from plasma, I developed strategies aimed at systematically tracking the reiterative process of genetic diversification leading to disease evolution and to detect genomic aberrations. I specifically focused on an ad hoc computational procedure (ABEMUS) to detect somatic point mutations that could emerge under treatment pressure and as drug resistance mechanism. The work I present is relevant to the context of precision oncology that exploits detailed patient-specific molecular information to diagnose and follow cancer progression with the ultimate goal of promptly guiding treatment decisions to improve clinical outcome with transdisciplinary strategies. The analytical work I developed can be applied to the study of any tumor type.

Item Type:Doctoral Thesis (PhD)
Doctoral School:Biomolecular Sciences
PhD Cycle:29
Subjects:Area 05 - Scienze biologiche > BIO/11 BIOLOGIA MOLECOLARE
Repository Staff approval on:02 Oct 2017 09:56

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