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A Simplified Genomic Profiling Approach Predicts Outcome in Metastatic Colorectal Cancer

1
Department of Molecular Medicine, University La Sapienza, 00161 Rome, Italy
2
Department of Medical Oncology Sant’ Andrea Hospital, I-00189 Rome, Italy
3
Department of Physics, University La Sapienza, 00185 Rome, Italy
4
Department of Public Health, University Federico II, 80131 Naples, Italy
5
Department of Radiological Oncological and Pathological Sciences, University La Sapienza, 00161 Rome, Italy
6
Department of Experimental Medicine, University La Sapienza, 00161 Rome, Italy
7
National Institute of Gastroenterology-Research Hospital, IRCCS “S. de Bellis”, Castellana Grotte, 70013 Bari, Italy
8
Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy
9
Institute of Cell Biology and Neurobiology, National Research Council, Campus A. Buzzati-Traverso, 00015 Monterotondo Scalo, Italy
10
Center for Life Nano [email protected], Istituto Italiano di Tecnologia, 00161 Rome, Italy
11
Oncology Unit, Macerata Hospital, 62012 Macerata, Italy
12
Institute for Systems Analysis and Computer Science “Antonio Ruberti”, National Research Council, 00185 Rome, Italy
13
Pasteur Institute-Cenci Bolognetti Foundation, 00161 Rome, Italy
*
Authors to whom correspondence should be addressed.
These two authors equally contributed to the work.
Cancers 2019, 11(2), 147; https://doi.org/10.3390/cancers11020147
Received: 24 December 2018 / Revised: 19 January 2019 / Accepted: 22 January 2019 / Published: 27 January 2019
(This article belongs to the Special Issue Application of Next-Generation Sequencing in Cancers)
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. View Full-Text
Keywords: precision medicine; predictive; NGS; genomic profiling; chemotherapy precision medicine; predictive; NGS; genomic profiling; chemotherapy
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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. A Simplified Genomic Profiling Approach Predicts Outcome in Metastatic Colorectal Cancer. Cancers 2019, 11, 147.

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