Global mapping of cancers: The Cancer Genome Atlas and beyond. Issue 11 (20th July 2021)

• Record Type: Journal Article
• Authors: Ganini, Carlo; Amelio, Ivano; Bertolo, Riccardo; Bove, Pierluigi; Buonomo, Oreste Claudio; Candi, Eleonora; Cipriani, Chiara; Di Daniele, Nicola; Juhl, Hartmut; Mauriello, Alessandro; Marani, Carla; Marshall, John; Melino, Sonia; Marchetti, Paolo; Montanaro, Manuela; Natale, Maria Emanuela; Novelli, Flavia; Palmieri, Giampiero; Piacentini, Mauro; Rendina, Erino Angelo; Roselli, Mario; Sica, Giuseppe; Tesauro, Manfredi; Rovella, Valentina; Tisone, Giuseppe; Shi, Yufang; Wang, Ying; Melino, Gerry
• Is Part Of: Molecular oncology. Volume 15:Issue 11(2021)
• Page Start: 2823
• Page End: 2840
• Publication Date: 2021-07-20
• Subjects: artificial intelligence -- cancer -- molecular signature -- omics -- whole‐genome sequencing; Cancer -- Molecular aspects -- Periodicals; 616.994005
• DOI: 10.1002/1878-0261.13056
• Languages: English
• ISSNs: 1574-7891

Abstract

Abstract : Cancer genomes have been explored from the early 2000s through massive exome sequencing efforts, leading to the publication of The Cancer Genome Atlas in 2013. Sequencing techniques have been developed alongside this project and have allowed scientists to bypass the limitation of costs for whole‐genome sequencing (WGS) of single specimens by developing more accurate and extensive cancer sequencing projects, such as deep sequencing of whole genomes and transcriptomic analysis. The Pan‐Cancer Analysis of Whole Genomes recently published WGS data from more than 2600 human cancers together with almost 1200 related transcriptomes. The application of WGS on a large database allowed, for the first time in history, a global analysis of features such as molecular signatures, large structural variations and noncoding regions of the genome, as well as the evaluation of RNA alterations in the absence of underlying DNA mutations. The vast amount of data generated still needs to be thoroughly deciphered, and the advent of machine‐learning approaches will be the next step towards the generation of personalized approaches for cancer medicine. The present manuscript wants to give a broad perspective on some of the biological evidence derived from the largest sequencing attempts on human cancers so far, discussing advantages and limitations of this approach and its power in the era of machine learning. Abstract : Since the publication of The Cancer Genome Atlas data in 2013, the advances in the sequencing techniques allowed us to study cancer through whole‐genome sequencing and multiomics approaches. The vast amount of data generated still needs to be thoroughly deciphered, and the advent of machine learning approaches will be the next step towards personalized approaches for cancer medicine.