A Simplified Genomic Profiling Approach Predicts Outcome in Metastatic Colorectal Cancer

Carlo Capalbo, Francesca Belardinilli, Domenico Raimondo, Edoardo Milanetti, Umberto Malapelle, Pasquale Pisapia, Valentina Magri, Alessandra Prete, Silvia Pecorari, Mariarosaria Colella, Anna Coppa, Caterina Bonfiglio, Arianna Nicolussi, Virginia Valentini, Alessandra Tessitore, Beatrice Cardinali, Marialaura Petroni, Paola Infante, Matteo Santoni, Marco FilettiValeria Colicchia, Paola Paci, Silvia Mezi, Flavia Longo, Enrico Cortesi, Paolo Marchetti, Giancarlo Troncone, Diana Bellavia, Gianluca Canettieri, Giuseppe Giannini

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
JournalCancers
Volume11
Issue number2
DOIs
Publication statusPublished - Jan 27 2019

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Colorectal Neoplasms
Mutation
Genes
Therapeutics
Computational Biology
Costs and Cost Analysis

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Capalbo, C., Belardinilli, F., Raimondo, D., Milanetti, E., Malapelle, U., Pisapia, P., ... Giannini, G. (2019). A Simplified Genomic Profiling Approach Predicts Outcome in Metastatic Colorectal Cancer. Cancers, 11(2). https://doi.org/10.3390/cancers11020147

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.

In: Cancers, Vol. 11, No. 2, 27.01.2019.

Research output: Contribution to journalArticle

Capalbo, C, Belardinilli, F, Raimondo, D, Milanetti, E, Malapelle, U, Pisapia, P, Magri, V, Prete, A, Pecorari, S, Colella, M, Coppa, A, Bonfiglio, C, Nicolussi, A, Valentini, V, Tessitore, A, Cardinali, B, Petroni, M, Infante, P, Santoni, M, Filetti, M, Colicchia, V, Paci, P, Mezi, S, Longo, F, Cortesi, E, Marchetti, P, Troncone, G, Bellavia, D, Canettieri, G & Giannini, G 2019, 'A Simplified Genomic Profiling Approach Predicts Outcome in Metastatic Colorectal Cancer', Cancers, vol. 11, no. 2. https://doi.org/10.3390/cancers11020147
Capalbo C, Belardinilli F, Raimondo D, Milanetti E, Malapelle U, Pisapia P et al. A Simplified Genomic Profiling Approach Predicts Outcome in Metastatic Colorectal Cancer. Cancers. 2019 Jan 27;11(2). https://doi.org/10.3390/cancers11020147
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. In: Cancers. 2019 ; Vol. 11, No. 2.
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AU - Capalbo, Carlo

AU - Belardinilli, Francesca

AU - Raimondo, Domenico

AU - Milanetti, Edoardo

AU - Malapelle, Umberto

AU - Pisapia, Pasquale

AU - Magri, Valentina

AU - Prete, Alessandra

AU - Pecorari, Silvia

AU - Colella, Mariarosaria

AU - Coppa, Anna

AU - Bonfiglio, Caterina

AU - Nicolussi, Arianna

AU - Valentini, Virginia

AU - Tessitore, Alessandra

AU - Cardinali, Beatrice

AU - Petroni, Marialaura

AU - Infante, Paola

AU - Santoni, Matteo

AU - Filetti, Marco

AU - Colicchia, Valeria

AU - Paci, Paola

AU - Mezi, Silvia

AU - Longo, Flavia

AU - Cortesi, Enrico

AU - Marchetti, Paolo

AU - Troncone, Giancarlo

AU - Bellavia, Diana

AU - Canettieri, Gianluca

AU - Giannini, Giuseppe

PY - 2019/1/27

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N2 - 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.

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