Cancer predictive studies

• Main Authors: Ivano Amelio, Riccardo Bertolo, Pierluigi Bove, Eleonora Candi, Marcello Chiocchi, Chiara Cipriani, Nicola Di Daniele, Carlo Ganini, Hartmut Juhl, Alessandro Mauriello, Carla Marani, John Marshall, Manuela Montanaro, Giampiero Palmieri, Mauro Piacentini, Giuseppe Sica, Manfredi Tesauro, Valentina Rovella, Giuseppe Tisone, Yufang Shi, Ying Wang, Gerry Melino
• Format: Article
• Language: English
• Published: BMC 2020-10-01
• Series: Biology Direct
• Subjects: Neuroblastoma; Microbiota; Precision oncology; Omics
• Online Access: http://link.springer.com/article/10.1186/s13062-020-00274-3

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.