A Serum Metabolomics Classifier Derived from Elderly Patients with Metastatic Colorectal Cancer Predicts Relapse in the Adjuvant Setting
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 hypoth...
| Main Authors: | , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-06-01
|
| Series: | Cancers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-6694/13/11/2762 |
| _version_ | 1797531659701059584 |
|---|---|
| author | Samantha Di Donato Alessia Vignoli Chiara Biagioni Luca Malorni Elena Mori Leonardo Tenori Vanessa Calamai Annamaria Parnofiello Giulia Di Pierro Ilenia Migliaccio Stefano Cantafio Maddalena Baraghini Giuseppe Mottino Dimitri Becheri Francesca Del Monte Elisangela Miceli Amelia McCartney Angelo Di Leo Claudio Luchinat Laura Biganzoli |
| author_facet | Samantha Di Donato Alessia Vignoli Chiara Biagioni Luca Malorni Elena Mori Leonardo Tenori Vanessa Calamai Annamaria Parnofiello Giulia Di Pierro Ilenia Migliaccio Stefano Cantafio Maddalena Baraghini Giuseppe Mottino Dimitri Becheri Francesca Del Monte Elisangela Miceli Amelia McCartney Angelo Di Leo Claudio Luchinat Laura Biganzoli |
| author_sort | Samantha Di Donato |
| collection | DOAJ |
| description | 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 (<i>p</i>-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. |
| first_indexed | 2024-03-10T10:46:53Z |
| format | Article |
| id | doaj.art-7a1379ac08da45c0b64da938640e6cd5 |
| institution | Directory Open Access Journal |
| issn | 2072-6694 |
| language | English |
| last_indexed | 2024-03-10T10:46:53Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cancers |
| spelling | doaj.art-7a1379ac08da45c0b64da938640e6cd52023-11-21T22:31:23ZengMDPI AGCancers2072-66942021-06-011311276210.3390/cancers13112762A Serum Metabolomics Classifier Derived from Elderly Patients with Metastatic Colorectal Cancer Predicts Relapse in the Adjuvant SettingSamantha Di Donato0Alessia Vignoli1Chiara Biagioni2Luca Malorni3Elena Mori4Leonardo Tenori5Vanessa Calamai6Annamaria Parnofiello7Giulia Di Pierro8Ilenia Migliaccio9Stefano Cantafio10Maddalena Baraghini11Giuseppe Mottino12Dimitri Becheri13Francesca Del Monte14Elisangela Miceli15Amelia McCartney16Angelo Di Leo17Claudio Luchinat18Laura Biganzoli19Department of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, ItalyMagnetic Resonance Center, University of Florence, 50019 Sesto Fiorentino, ItalyBioinformatics Unit, Medical Oncology Department, New Hospital of Prato S. Stefano, 59100 Prato, ItalyDepartment of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, ItalyDepartment of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, ItalyMagnetic Resonance Center, University of Florence, 50019 Sesto Fiorentino, ItalyDepartment of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, ItalyDepartment of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, ItalyDepartment of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, Italy“Sandro Pitigliani” Translational Research Unit, New Hospital of Prato, Stefano, 59100 Prato, ItalyDepartment of Surgery, New Hospital of Prato S. Stefano, 59100 Prato, ItalyDepartment of Surgery, New Hospital of Prato S. Stefano, 59100 Prato, ItalyDepartment of Geriatrics, New Hospital of Prato S. Stefano, 59100 Prato, ItalyDepartment of Geriatrics, New Hospital of Prato S. Stefano, 59100 Prato, ItalyDepartment of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, ItalyDepartment of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, ItalyDepartment of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, ItalyDepartment of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, ItalyMagnetic Resonance Center, University of Florence, 50019 Sesto Fiorentino, ItalyDepartment of Medical Oncology, New Hospital of Prato S. Stefano, 59100 Prato, ItalyAdjuvant 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 (<i>p</i>-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.https://www.mdpi.com/2072-6694/13/11/2762metabolomicscolorectal cancerrecurrenceprognosisNMR spectroscopyelderly |
| spellingShingle | Samantha Di Donato Alessia Vignoli Chiara Biagioni Luca Malorni Elena Mori Leonardo Tenori Vanessa Calamai Annamaria Parnofiello Giulia Di Pierro Ilenia Migliaccio Stefano Cantafio Maddalena Baraghini Giuseppe Mottino Dimitri Becheri Francesca Del Monte Elisangela Miceli Amelia McCartney Angelo Di Leo Claudio Luchinat Laura Biganzoli A Serum Metabolomics Classifier Derived from Elderly Patients with Metastatic Colorectal Cancer Predicts Relapse in the Adjuvant Setting Cancers metabolomics colorectal cancer recurrence prognosis NMR spectroscopy elderly |
| 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 | metabolomics colorectal cancer recurrence prognosis NMR spectroscopy elderly |
| url | https://www.mdpi.com/2072-6694/13/11/2762 |
| work_keys_str_mv | AT samanthadidonato aserummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT alessiavignoli aserummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT chiarabiagioni aserummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT lucamalorni aserummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT elenamori aserummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT leonardotenori aserummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT vanessacalamai aserummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT annamariaparnofiello aserummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT giuliadipierro aserummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT ileniamigliaccio aserummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT stefanocantafio aserummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT maddalenabaraghini aserummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT giuseppemottino aserummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT dimitribecheri aserummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT francescadelmonte aserummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT elisangelamiceli aserummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT ameliamccartney aserummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT angelodileo aserummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT claudioluchinat aserummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT laurabiganzoli aserummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT samanthadidonato serummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT alessiavignoli serummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT chiarabiagioni serummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT lucamalorni serummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT elenamori serummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT leonardotenori serummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT vanessacalamai serummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT annamariaparnofiello serummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT giuliadipierro serummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT ileniamigliaccio serummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT stefanocantafio serummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT maddalenabaraghini serummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT giuseppemottino serummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT dimitribecheri serummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT francescadelmonte serummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT elisangelamiceli serummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT ameliamccartney serummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT angelodileo serummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT claudioluchinat serummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting AT laurabiganzoli serummetabolomicsclassifierderivedfromelderlypatientswithmetastaticcolorectalcancerpredictsrelapseintheadjuvantsetting |