Cancer predictive studies
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Cancer predictive studies Ivano Amelio1* , Riccardo Bertolo1,2 , Pierluigi Bove1,2 , Eleonora Candi1 , Marcello Chiocchi1 , Chiara Cipriani1,2 , Nicola Di Daniele1 , Carlo Ganini1 , Hartmut Juhl3, Alessandro Mauriello1 , Carla Marani1,2 , John Marshall4, Manuela Montanaro1 , Giampiero Palmieri1 , Mauro Piacentini1 , Giuseppe Sica1 , Manfredi Tesauro1 , Valentina Rovella1 , Giuseppe Tisone1 , Yufang Shi1,5,6 , Ying Wang5 and Gerry Melino1*
Abstract The identification of individual or clusters of predictive genetic alterations might help in defining the outcome of cancer treatment, allowing for the stratification of patients into distinct cohorts for selective therapeutic protocols. Neuroblastoma (NB) is the most common extracranial childhood tumour, clinically defined in five distinct stages (1–4 & 4S), where stages 3–4 define chemotherapy-resistant, highly aggressive disease phases. NB is a model for geneticists and molecular biologists to classify genetic abnormalities and identify causative disease genes. Despite highly intensive basic research, improvements on clinical outcome have been predominantly observed for less aggressive cancers, that is stages 1,2 and 4S. Therefore, stages 3–4 NB are still complicated at the therapeutic level and require more intense fundamental research. Using neuroblastoma as a model system, here we herein outline how cancer prediction studies can help at steering preclinical and clinical research toward the identification and exploitation of specific genetic landscape. This might result in maximising the therapeutic success and minimizing harmful effects in cancer patients. Keywords: Neuroblastoma, Microbiota, Precision oncology, Omics
Background Since the revelation of the whole human genome, there has been tremendous advances in sequencing technologies, with reductions of costs and time, allowing for an incredible step forward in the global cancer genomic fruition [1–3]. We moved from The Cancer Genome Atlas (TCGA) to the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium [4–6], which includes most tumor types, matching DNA sequencing and RNA transcripts. Recently, 2658 cancers have been deeply analysed [7, 8] reconstructing the origin and evolution of mutational processes and driver mutation sequences of 38 types of * Correspondence: [email protected]; [email protected]; [email protected] 1 Torvergata Oncoscience Research Centre of Excellence, TOR, Department of Experimental Medicine, University of Rome Tor Vergata, via Montpellier 1, 00133 Rome, Italy Full list of author information is available at the end of the article
cancer, including neuroblastoma. A significant number of driver gene mutations (4–5) was observed, and a fourfold diversification of these drivers and increased genomic instability have been reported at later stages. Since the clinical application of massive sequencing, and moreover the clinical application of omics, each patient can provide an enormous amount of molecular data which can be also implemented in the drug discov
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