Metabolomics to Assess Response to Immune Checkpoint Inhibitors in Patients with Non-Small-Cell Lung Cancer.

immune checkpoint inhibitors metabolomics non-small cell lung cancer nuclear magnetic resonance

Journal

Cancers
ISSN: 2072-6694
Titre abrégé: Cancers (Basel)
Pays: Switzerland
ID NLM: 101526829

Informations de publication

Date de publication:
30 Nov 2020
Historique:
received: 02 11 2020
revised: 25 11 2020
accepted: 27 11 2020
entrez: 3 12 2020
pubmed: 4 12 2020
medline: 4 12 2020
Statut: epublish

Résumé

In the treatment of advanced non-small cell lung cancer (NSCLC), immune checkpoint inhibitors have shown remarkable results. However, not all patients with NSCLC respond to this drug treatment or receive durable benefits. Thus, patient stratification and selection, as well as the identification of predictive biomarkers, represent pivotal aspects to address. In this framework, metabolomics can be used to support the discrimination between responders and non-responders. Here, metabolomics was used to analyze the sera samples from 50 patients with NSCL treated with immune checkpoint inhibitors. All the samples were collected before the beginning of the treatment and were analyzed by NMR spectroscopy and multivariate statistical analyses. Significantly, we show that the metabolomic fingerprint of serum acts as a predictive "collective" biomarker to immune checkpoint inhibitors response, being able to predict individual therapy outcome with > 80% accuracy. Metabolomics represents a potential strategy for the real-time selection and monitoring of patients treated with immunotherapy. The prospective identification of responders and non-responders could improve NSCLC treatment and patient stratification, thus avoiding ineffective therapeutic strategies.

Identifiants

pubmed: 33265926
pii: cancers12123574
doi: 10.3390/cancers12123574
pmc: PMC7760033
pii:
doi:

Types de publication

Journal Article

Langues

eng

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Auteurs

Veronica Ghini (V)

Cirmmp, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Florence, Italy.
Magnetic Resonance Center, CERM, University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Florence, Italy.

Letizia Laera (L)

Sandro Pitigliani, Department of Medical Oncology, Hospital of Prato, via Suor Niccolina Infermiera, 20/22, 59100 Prato, Italy.
Department of Oncology, Miulli hospital, Acquaviva delle Fonti, 70021 Bari, Italy.

Beatrice Fantechi (B)

Sandro Pitigliani, Department of Medical Oncology, Hospital of Prato, via Suor Niccolina Infermiera, 20/22, 59100 Prato, Italy.

Francesca Del Monte (FD)

Sandro Pitigliani, Department of Medical Oncology, Hospital of Prato, via Suor Niccolina Infermiera, 20/22, 59100 Prato, Italy.

Matteo Benelli (M)

Bioinformatics Unit, Hospital of Prato, via Suor Niccolina Infermiera, 20/22, 59100 Prato, Italy.

Amelia McCartney (A)

Sandro Pitigliani, Department of Medical Oncology, Hospital of Prato, via Suor Niccolina Infermiera, 20/22, 59100 Prato, Italy.

Tenori Leonardo (T)

Magnetic Resonance Center, CERM, University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Florence, Italy.
Department of Chemistry, University of Florence, via della Lastruccia 3, 50019 Sesto Fiorentino, Florence, Italy.

Claudio Luchinat (C)

Magnetic Resonance Center, CERM, University of Florence, Via Luigi Sacconi 6, 50019 Sesto Fiorentino, Florence, Italy.
Department of Chemistry, University of Florence, via della Lastruccia 3, 50019 Sesto Fiorentino, Florence, Italy.

Daniele Pozzessere (D)

Sandro Pitigliani, Department of Medical Oncology, Hospital of Prato, via Suor Niccolina Infermiera, 20/22, 59100 Prato, Italy.

Classifications MeSH