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. 2019 Jan 27;11(2):147.
doi: 10.3390/cancers11020147.

A Simplified Genomic Profiling Approach Predicts Outcome in Metastatic Colorectal Cancer

Carlo Capalbo  1   2 , Francesca Belardinilli  3 , Domenico Raimondo  4 , Edoardo Milanetti  5 , Umberto Malapelle  6 , Pasquale Pisapia  7 , Valentina Magri  8 , Alessandra Prete  9 , Silvia Pecorari  10 , Mariarosaria Colella  11 , Anna Coppa  12 , Caterina Bonfiglio  13 , Arianna Nicolussi  14 , Virginia Valentini  15 , Alessandra Tessitore  16 , Beatrice Cardinali  17 , Marialaura Petroni  18 , Paola Infante  19 , Matteo Santoni  20 , Marco Filetti  21 , Valeria Colicchia  22 , Paola Paci  23 , Silvia Mezi  24 , Flavia Longo  25 , Enrico Cortesi  26 , Paolo Marchetti  27 , Giancarlo Troncone  28 , Diana Bellavia  29 , Gianluca Canettieri  30   31 , Giuseppe Giannini  32   33
Affiliations

A Simplified Genomic Profiling Approach Predicts Outcome in Metastatic Colorectal Cancer

Carlo Capalbo et al. Cancers (Basel). .

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.

Keywords: NGS; chemotherapy; genomic profiling; precision medicine; predictive.

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Conflict of interest statement

The authors declared no conflict of interest.

Figures

Figure 1
Figure 1
Mutation frequency for each gene of the panel in the study cohort.
Figure 2
Figure 2
Heat-map representation of hierarchical clustering analysis of mCRC data. Columns represent the 77 samples of mCRC patients and rows represent the 22 different mutational markers. Three separate clusters were generated by this analysis. Each cluster corresponds to a distinct genetic profile. The color assigned to a cell in the heatmap grid indicates mutational condition (yellow for mutation, blue for wild-type gene) of a particular gene in a given patient sample.
Figure 3
Figure 3
Kaplan–Meier plot showing the impact of p53/RAS Group (PRG; green), All Genes Group (AGG; red) and No Mutations Group (NMG; blue) group stratification on progression free survival (PFS) in the mCRC cohort.
See this image and copyright information in PMC

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