The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next-generation sequencing data and machine learning for the inference of explanatory models of how the (epi)genomic events are choreographed in cancer initiation and development. However, despite the increasing availability of multiple additional -omics data, this quest has been frustrated by various theoretical and technical hurdles, mostly stemming from the dramatic heterogeneity of the disease. In this paper, we build on our recent work on the "selective advantage" relation among driver mutations in cancer progression and investigate its applicability to the modeling problem at the population level. Here, we introduce PiCnIc (Pipeline for Cancer Inference), a versatile, modular, and customizable pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes. The pipeline has many translational implications because it combines state-of-the-art techniques for sample stratification, driver selection, identification of fitness-equivalent exclusive alterations, and progression model inference. We demonstrate PiCnIc's ability to reproduce much of the current knowledge on colorectal cancer progression as well as to suggest novel experimentally verifiable hypotheses.

Caravagna, G., Graudenzi, A., Ramazzotti, D., Sanz Pamplona, R., De Sano, L., Mauri, G., et al. (2016). Algorithmic methods to infer the evolutionary trajectories in cancer progression. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 113(28), E4025-E4034 [10.1073/pnas.1520213113].

Algorithmic methods to infer the evolutionary trajectories in cancer progression

GRAUDENZI, ALEX;Ramazzotti, D;MAURI, GIANCARLO;ANTONIOTTI, MARCO
Penultimo
;
2016

Abstract

The genomic evolution inherent to cancer relates directly to a renewed focus on the voluminous next-generation sequencing data and machine learning for the inference of explanatory models of how the (epi)genomic events are choreographed in cancer initiation and development. However, despite the increasing availability of multiple additional -omics data, this quest has been frustrated by various theoretical and technical hurdles, mostly stemming from the dramatic heterogeneity of the disease. In this paper, we build on our recent work on the "selective advantage" relation among driver mutations in cancer progression and investigate its applicability to the modeling problem at the population level. Here, we introduce PiCnIc (Pipeline for Cancer Inference), a versatile, modular, and customizable pipeline to extract ensemble-level progression models from cross-sectional sequenced cancer genomes. The pipeline has many translational implications because it combines state-of-the-art techniques for sample stratification, driver selection, identification of fitness-equivalent exclusive alterations, and progression model inference. We demonstrate PiCnIc's ability to reproduce much of the current knowledge on colorectal cancer progression as well as to suggest novel experimentally verifiable hypotheses.
Articolo in rivista - Articolo scientifico
Bayesian structural inference; Cancer evolution; Causality; Next generation sequencing; Selective advantage;
Cancer evolution; Selective advantage; Bayesian Structural Inference
English
28-giu-2016
2016
113
28
E4025
E4034
partially_open
Caravagna, G., Graudenzi, A., Ramazzotti, D., Sanz Pamplona, R., De Sano, L., Mauri, G., et al. (2016). Algorithmic methods to infer the evolutionary trajectories in cancer progression. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 113(28), E4025-E4034 [10.1073/pnas.1520213113].
File in questo prodotto:
File Dimensione Formato  
10281-110995.pdf

accesso aperto

Tipologia di allegato: Publisher’s Version (Version of Record, VoR)
Dimensione 2.44 MB
Formato Adobe PDF
2.44 MB Adobe PDF Visualizza/Apri
PNAS-E4025.full.pdf

Solo gestori archivio

Tipologia di allegato: Publisher’s Version (Version of Record, VoR)
Dimensione 2.43 MB
Formato Adobe PDF
2.43 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/110995
Citazioni
  • Scopus 61
  • ???jsp.display-item.citation.isi??? 50
Social impact