Open AccessGlobal mapping of cancers: The Cancer Genome Atlas and beyond
Author(s) -
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
Publication year - 2021
Publication title -
molecular oncology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.332
H-Index - 88
eISSN - 1878-0261
pISSN - 1574-7891
DOI - 10.1002/1878-0261.13056
Subject(s) - genome , cancer genome sequencing , computational biology , exome sequencing , dna sequencing , exome , biology , human genome , genomics , whole genome sequencing , personalized medicine , deep sequencing , genetics , gene , mutation
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.