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A Simplified Genomic Profiling Approach Predicts Outcome in Metastatic Colorectal Cancer
The response of metastatic colorectal cancer (mCRC) to the first-line conventional combination therapy is highly variable, reflecting the elevated heterogeneity of the disease. The genetic alterations underlying this heterogeneity have been thoroughly characterized through omic approaches requiring...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6406354/ https://www.ncbi.nlm.nih.gov/pubmed/30691222 http://dx.doi.org/10.3390/cancers11020147 |
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author | Capalbo, Carlo Belardinilli, Francesca Raimondo, Domenico Milanetti, Edoardo Malapelle, Umberto Pisapia, Pasquale Magri, Valentina Prete, Alessandra Pecorari, Silvia Colella, Mariarosaria Coppa, Anna Bonfiglio, Caterina Nicolussi, Arianna Valentini, Virginia Tessitore, Alessandra Cardinali, Beatrice Petroni, Marialaura Infante, Paola Santoni, Matteo Filetti, Marco Colicchia, Valeria Paci, Paola Mezi, Silvia Longo, Flavia Cortesi, Enrico Marchetti, Paolo Troncone, Giancarlo Bellavia, Diana Canettieri, Gianluca Giannini, Giuseppe |
author_facet | Capalbo, Carlo Belardinilli, Francesca Raimondo, Domenico Milanetti, Edoardo Malapelle, Umberto Pisapia, Pasquale Magri, Valentina Prete, Alessandra Pecorari, Silvia Colella, Mariarosaria Coppa, Anna Bonfiglio, Caterina Nicolussi, Arianna Valentini, Virginia Tessitore, Alessandra Cardinali, Beatrice Petroni, Marialaura Infante, Paola Santoni, Matteo Filetti, Marco Colicchia, Valeria Paci, Paola Mezi, Silvia Longo, Flavia Cortesi, Enrico Marchetti, Paolo Troncone, Giancarlo Bellavia, Diana Canettieri, Gianluca Giannini, Giuseppe |
author_sort | Capalbo, Carlo |
collection | PubMed |
description | The response of metastatic colorectal cancer (mCRC) to the first-line conventional combination therapy is highly variable, reflecting the elevated heterogeneity of the disease. The genetic alterations underlying this heterogeneity have been thoroughly characterized through omic approaches requiring elevated efforts and costs. In order to translate the knowledge of CRC molecular heterogeneity into a practical clinical approach, we utilized a simplified Next Generation Sequencing (NGS) based platform to screen a cohort of 77 patients treated with first-line conventional therapy. Samples were sequenced using a panel of hotspots and targeted regions of 22 genes commonly involved in CRC. This revealed 51 patients carrying actionable gene mutations, 22 of which carried druggable alterations. These mutations were frequently associated with additional genetic alterations. To take into account this molecular complexity and assisted by an unbiased bioinformatic analysis, we defined three subgroups of patients carrying distinct molecular patterns. We demonstrated these three molecular subgroups are associated with a different response to first-line conventional combination therapies. The best outcome was achieved in patients exclusively carrying mutations on TP53 and/or RAS genes. By contrast, in patients carrying mutations in any of the other genes, alone or associated with mutations of TP53/RAS, the expected response is much worse compared to patients with exclusive TP53/RAS mutations. Additionally, our data indicate that the standard approach has limited efficacy in patients without any mutations in the genes included in the panel. In conclusion, we identified a reliable and easy-to-use approach for a simplified molecular-based stratification of mCRC patients that predicts the efficacy of the first-line conventional combination therapy. |
format | Online Article Text |
id | pubmed-6406354 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-64063542019-03-21 A Simplified Genomic Profiling Approach Predicts Outcome in Metastatic Colorectal Cancer Capalbo, Carlo Belardinilli, Francesca Raimondo, Domenico Milanetti, Edoardo Malapelle, Umberto Pisapia, Pasquale Magri, Valentina Prete, Alessandra Pecorari, Silvia Colella, Mariarosaria Coppa, Anna Bonfiglio, Caterina Nicolussi, Arianna Valentini, Virginia Tessitore, Alessandra Cardinali, Beatrice Petroni, Marialaura Infante, Paola Santoni, Matteo Filetti, Marco Colicchia, Valeria Paci, Paola Mezi, Silvia Longo, Flavia Cortesi, Enrico Marchetti, Paolo Troncone, Giancarlo Bellavia, Diana Canettieri, Gianluca Giannini, Giuseppe Cancers (Basel) Article The response of metastatic colorectal cancer (mCRC) to the first-line conventional combination therapy is highly variable, reflecting the elevated heterogeneity of the disease. The genetic alterations underlying this heterogeneity have been thoroughly characterized through omic approaches requiring elevated efforts and costs. In order to translate the knowledge of CRC molecular heterogeneity into a practical clinical approach, we utilized a simplified Next Generation Sequencing (NGS) based platform to screen a cohort of 77 patients treated with first-line conventional therapy. Samples were sequenced using a panel of hotspots and targeted regions of 22 genes commonly involved in CRC. This revealed 51 patients carrying actionable gene mutations, 22 of which carried druggable alterations. These mutations were frequently associated with additional genetic alterations. To take into account this molecular complexity and assisted by an unbiased bioinformatic analysis, we defined three subgroups of patients carrying distinct molecular patterns. We demonstrated these three molecular subgroups are associated with a different response to first-line conventional combination therapies. The best outcome was achieved in patients exclusively carrying mutations on TP53 and/or RAS genes. By contrast, in patients carrying mutations in any of the other genes, alone or associated with mutations of TP53/RAS, the expected response is much worse compared to patients with exclusive TP53/RAS mutations. Additionally, our data indicate that the standard approach has limited efficacy in patients without any mutations in the genes included in the panel. In conclusion, we identified a reliable and easy-to-use approach for a simplified molecular-based stratification of mCRC patients that predicts the efficacy of the first-line conventional combination therapy. MDPI 2019-01-27 /pmc/articles/PMC6406354/ /pubmed/30691222 http://dx.doi.org/10.3390/cancers11020147 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Capalbo, Carlo Belardinilli, Francesca Raimondo, Domenico Milanetti, Edoardo Malapelle, Umberto Pisapia, Pasquale Magri, Valentina Prete, Alessandra Pecorari, Silvia Colella, Mariarosaria Coppa, Anna Bonfiglio, Caterina Nicolussi, Arianna Valentini, Virginia Tessitore, Alessandra Cardinali, Beatrice Petroni, Marialaura Infante, Paola Santoni, Matteo Filetti, Marco Colicchia, Valeria Paci, Paola Mezi, Silvia Longo, Flavia Cortesi, Enrico Marchetti, Paolo Troncone, Giancarlo Bellavia, Diana Canettieri, Gianluca Giannini, Giuseppe A Simplified Genomic Profiling Approach Predicts Outcome in Metastatic Colorectal Cancer |
title | A Simplified Genomic Profiling Approach Predicts Outcome in Metastatic Colorectal Cancer |
title_full | A Simplified Genomic Profiling Approach Predicts Outcome in Metastatic Colorectal Cancer |
title_fullStr | A Simplified Genomic Profiling Approach Predicts Outcome in Metastatic Colorectal Cancer |
title_full_unstemmed | A Simplified Genomic Profiling Approach Predicts Outcome in Metastatic Colorectal Cancer |
title_short | A Simplified Genomic Profiling Approach Predicts Outcome in Metastatic Colorectal Cancer |
title_sort | simplified genomic profiling approach predicts outcome in metastatic colorectal cancer |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6406354/ https://www.ncbi.nlm.nih.gov/pubmed/30691222 http://dx.doi.org/10.3390/cancers11020147 |
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