Responsabile scientifico

• LALLI Daniela

Titolo del progetto

• Metabolomics approach for predicting response to immunotherapy in patients with lung cancer

Ente finanziatore

• Università degli studi del Piemonte Orientale Amedeo Avogadro

Programma di finanziamento

• BANDO RICERCA UPO

Bando di riferimento

• Bando Ricerca UPO 2022

Anno di presentazione del progetto

• 2022

Anno di approvazione del progetto

• 2023

Acronimo del progetto

• MetaboLICI

Project ID / numero contratto

• 1083660

Inizio del progetto

• 08/08/2023

Fine del progetto

• 30/06/2025

Ente capofila

• UNIUPO - Universita Degli Studi del Università del Piemonte Orientale "Amedeo Avogadro"

Progetto approvato - Contributo unità di ricerca del Dipartimento

• €48.750,00

Settore ERC

• PE Mathematics, physical sciences, information and communication,engineering, universe and earth sciences
  
  PE4 Physical and Analytical Chemical sciences: analytical chemistry, chemical theory, physical chemistry/chemical physics
  
   PE4_3 Spectroscopic and spectrometric techniques

Stato del progetto

• Approvato

Abstract

• Lung cancer (LC) is one of the most diffused cancers worldwide. Accounting for 2.1 million new cases and 1.8 million deaths every year, it ranks first among all cancer species in terms of morbidity and mortality. Immunotherapy based on immune checkpoint inhibitors (ICIs), such as PD-1 and PD-L1 inhibitors, has revolutionized LC treatment, becoming one of the pillars of therapeutic innovation in the last decade. Despite the great successes achieved, ~80% of the patients do not benefit from this treatment. Therefore, understanding the mechanisms of response and resistance to ICIs is of paramount importance. The present project aims at assessing molecular and genetic predictors of the clinical response to ICIs in LC patients. The identification of such biomarkers may allow clinicians to prospectively pinpoint patients who can benefit from ICIs, thus optimizing the treatment with ICIs. To achieve this goal, we plan to analyze the metabolic profiles of plasma samples collected before treatment from LC patients who responded differently to ICIs. The analytical techniques primarily employed for these analyses are Nuclear Magnetic Resonance (NMR) spectroscopy and Mass Spectrometry (MS), which have proven to be excellent diagnostic platforms to stratify patients for treatment selection, and to identify biomarkers for clinical settings use. The combined use of MS and NMR, usually employed separately, represents an innovative and powerful approach to facilitate metabolite identification, increase the number of detectable metabolites, thus maximizing biomarkers discovery. The complementary information derived from NMR and MS analyses will be integrated into a statistical analysis platform to build a predictive model of response to immunotherapy. Furthermore, a pharmacogenetic study will be performed to potentially identify germline genetic variants as potential predictors of ICIs response in LC patients.