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A Serum Metabolomics Classifier Derived from Elderly Patients with Metastatic Colorectal Cancer Predicts Relapse in the Adjuvant Setting

SIMPLE SUMMARY: Around 30–40% of patients with early stage colorectal cancer (eCRC) experience relapse after surgery. Current recommendations for adjuvant therapy are based on suboptimal risk-stratification tools. In elderly patients, risk of relapse assessment is particularly important to ultimatel...

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Autores principales: Di Donato, Samantha, Vignoli, Alessia, Biagioni, Chiara, Malorni, Luca, Mori, Elena, Tenori, Leonardo, Calamai, Vanessa, Parnofiello, Annamaria, Di Pierro, Giulia, Migliaccio, Ilenia, Cantafio, Stefano, Baraghini, Maddalena, Mottino, Giuseppe, Becheri, Dimitri, Del Monte, Francesca, Miceli, Elisangela, McCartney, Amelia, Di Leo, Angelo, Luchinat, Claudio, Biganzoli, Laura
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8199587/
https://www.ncbi.nlm.nih.gov/pubmed/34199435
http://dx.doi.org/10.3390/cancers13112762
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author Di Donato, Samantha
Vignoli, Alessia
Biagioni, Chiara
Malorni, Luca
Mori, Elena
Tenori, Leonardo
Calamai, Vanessa
Parnofiello, Annamaria
Di Pierro, Giulia
Migliaccio, Ilenia
Cantafio, Stefano
Baraghini, Maddalena
Mottino, Giuseppe
Becheri, Dimitri
Del Monte, Francesca
Miceli, Elisangela
McCartney, Amelia
Di Leo, Angelo
Luchinat, Claudio
Biganzoli, Laura
author_facet Di Donato, Samantha
Vignoli, Alessia
Biagioni, Chiara
Malorni, Luca
Mori, Elena
Tenori, Leonardo
Calamai, Vanessa
Parnofiello, Annamaria
Di Pierro, Giulia
Migliaccio, Ilenia
Cantafio, Stefano
Baraghini, Maddalena
Mottino, Giuseppe
Becheri, Dimitri
Del Monte, Francesca
Miceli, Elisangela
McCartney, Amelia
Di Leo, Angelo
Luchinat, Claudio
Biganzoli, Laura
author_sort Di Donato, Samantha
collection PubMed
description SIMPLE SUMMARY: Around 30–40% of patients with early stage colorectal cancer (eCRC) experience relapse after surgery. Current recommendations for adjuvant therapy are based on suboptimal risk-stratification tools. In elderly patients, risk of relapse assessment is particularly important to ultimately avoid unnecessary chemotherapy-related toxicity in this frailer population. Serum metabolomics via NMR spectroscopy may improve risk stratification by identifying patients with residual micrometastases after surgery and thus at higher risk of relapse. We evaluated the serum metabolomic fingerprints of 94 elderly patients with eCRC (65 relapse free and 29 relapsed), and of 75 elderly patients with metastatic disease. Metabolomics efficiently discriminated patients with relapse-free eCRC from those with metastatic disease, correctly predicting relapse in 69% of relapsed eCRC patients. The metabolomic score was strongly and independently associated with prognosis. Our data suggest metabolomics as a valid addition to standard tools to refine risk stratification for eCRC and warrant further investigation. ABSTRACT: Adjuvant treatment for patients with early stage colorectal cancer (eCRC) is currently based on suboptimal risk stratification, especially for elderly patients. Metabolomics may improve the identification of patients with residual micrometastases after surgery. In this retrospective study, we hypothesized that metabolomic fingerprinting could improve risk stratification in patients with eCRC. Serum samples obtained after surgery from 94 elderly patients with eCRC (65 relapse free and 29 relapsed, after 5-years median follow up), and from 75 elderly patients with metastatic colorectal cancer (mCRC) obtained before a new line of chemotherapy, were retrospectively analyzed via proton nuclear magnetic resonance spectroscopy. The prognostic role of metabolomics in patients with eCRC was assessed using Kaplan–Meier curves. PCA-CA-kNN could discriminate the metabolomic fingerprint of patients with relapse-free eCRC and mCRC (70.0% accuracy using NOESY spectra). This model was used to classify the samples of patients with relapsed eCRC: 69% of eCRC patients with relapse were predicted as metastatic. The metabolomic classification was strongly associated with prognosis (p-value 0.0005, HR 3.64), independently of tumor stage. In conclusion, metabolomics could be an innovative tool to refine risk stratification in elderly patients with eCRC. Based on these results, a prospective trial aimed at improving risk stratification by metabolomic fingerprinting (LIBIMET) is ongoing.
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spelling pubmed-81995872021-06-14 A Serum Metabolomics Classifier Derived from Elderly Patients with Metastatic Colorectal Cancer Predicts Relapse in the Adjuvant Setting Di Donato, Samantha Vignoli, Alessia Biagioni, Chiara Malorni, Luca Mori, Elena Tenori, Leonardo Calamai, Vanessa Parnofiello, Annamaria Di Pierro, Giulia Migliaccio, Ilenia Cantafio, Stefano Baraghini, Maddalena Mottino, Giuseppe Becheri, Dimitri Del Monte, Francesca Miceli, Elisangela McCartney, Amelia Di Leo, Angelo Luchinat, Claudio Biganzoli, Laura Cancers (Basel) Article SIMPLE SUMMARY: Around 30–40% of patients with early stage colorectal cancer (eCRC) experience relapse after surgery. Current recommendations for adjuvant therapy are based on suboptimal risk-stratification tools. In elderly patients, risk of relapse assessment is particularly important to ultimately avoid unnecessary chemotherapy-related toxicity in this frailer population. Serum metabolomics via NMR spectroscopy may improve risk stratification by identifying patients with residual micrometastases after surgery and thus at higher risk of relapse. We evaluated the serum metabolomic fingerprints of 94 elderly patients with eCRC (65 relapse free and 29 relapsed), and of 75 elderly patients with metastatic disease. Metabolomics efficiently discriminated patients with relapse-free eCRC from those with metastatic disease, correctly predicting relapse in 69% of relapsed eCRC patients. The metabolomic score was strongly and independently associated with prognosis. Our data suggest metabolomics as a valid addition to standard tools to refine risk stratification for eCRC and warrant further investigation. ABSTRACT: Adjuvant treatment for patients with early stage colorectal cancer (eCRC) is currently based on suboptimal risk stratification, especially for elderly patients. Metabolomics may improve the identification of patients with residual micrometastases after surgery. In this retrospective study, we hypothesized that metabolomic fingerprinting could improve risk stratification in patients with eCRC. Serum samples obtained after surgery from 94 elderly patients with eCRC (65 relapse free and 29 relapsed, after 5-years median follow up), and from 75 elderly patients with metastatic colorectal cancer (mCRC) obtained before a new line of chemotherapy, were retrospectively analyzed via proton nuclear magnetic resonance spectroscopy. The prognostic role of metabolomics in patients with eCRC was assessed using Kaplan–Meier curves. PCA-CA-kNN could discriminate the metabolomic fingerprint of patients with relapse-free eCRC and mCRC (70.0% accuracy using NOESY spectra). This model was used to classify the samples of patients with relapsed eCRC: 69% of eCRC patients with relapse were predicted as metastatic. The metabolomic classification was strongly associated with prognosis (p-value 0.0005, HR 3.64), independently of tumor stage. In conclusion, metabolomics could be an innovative tool to refine risk stratification in elderly patients with eCRC. Based on these results, a prospective trial aimed at improving risk stratification by metabolomic fingerprinting (LIBIMET) is ongoing. MDPI 2021-06-02 /pmc/articles/PMC8199587/ /pubmed/34199435 http://dx.doi.org/10.3390/cancers13112762 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Di Donato, Samantha
Vignoli, Alessia
Biagioni, Chiara
Malorni, Luca
Mori, Elena
Tenori, Leonardo
Calamai, Vanessa
Parnofiello, Annamaria
Di Pierro, Giulia
Migliaccio, Ilenia
Cantafio, Stefano
Baraghini, Maddalena
Mottino, Giuseppe
Becheri, Dimitri
Del Monte, Francesca
Miceli, Elisangela
McCartney, Amelia
Di Leo, Angelo
Luchinat, Claudio
Biganzoli, Laura
A Serum Metabolomics Classifier Derived from Elderly Patients with Metastatic Colorectal Cancer Predicts Relapse in the Adjuvant Setting
title A Serum Metabolomics Classifier Derived from Elderly Patients with Metastatic Colorectal Cancer Predicts Relapse in the Adjuvant Setting
title_full A Serum Metabolomics Classifier Derived from Elderly Patients with Metastatic Colorectal Cancer Predicts Relapse in the Adjuvant Setting
title_fullStr A Serum Metabolomics Classifier Derived from Elderly Patients with Metastatic Colorectal Cancer Predicts Relapse in the Adjuvant Setting
title_full_unstemmed A Serum Metabolomics Classifier Derived from Elderly Patients with Metastatic Colorectal Cancer Predicts Relapse in the Adjuvant Setting
title_short A Serum Metabolomics Classifier Derived from Elderly Patients with Metastatic Colorectal Cancer Predicts Relapse in the Adjuvant Setting
title_sort serum metabolomics classifier derived from elderly patients with metastatic colorectal cancer predicts relapse in the adjuvant setting
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8199587/
https://www.ncbi.nlm.nih.gov/pubmed/34199435
http://dx.doi.org/10.3390/cancers13112762
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