Screening human lung cancer with predictive models of serum magnetic resonance spectroscopy metabolomics
Menée à partir d'échantillons sériques prélevés sur 79 patients avant et après diagnostic de cancer du poumon, cette étude met en évidence l'intérêt d'un modèle prédictif, basé sur l'analyse des métabolites à l'aide de la spectroscopie par résonance magnétique, pour identifier les patients susceptibles d'être atteints de la maladie et estimer la survie
Metabolomics predictive models constructed from high-resolution magic angle spinning (HRMAS) proton magnetic resonance spectroscopy (1H MRS) data measured from 10 μL blood serum of human lung cancer patients collected prior to diagnosis reflect disease status and can be developed into a screening tool to triage patients with suspicious readings for advance imaging tests.The current high mortality of human lung cancer stems largely from the lack of feasible, early disease detection tools. An effective test with serum metabolomics predictive models able to suggest patients harboring disease could expedite triage patient to specialized imaging assessment. Here, using a training-validation-testing-cohort design, we establish our high-resolution magic angle spinning (HRMAS) magnetic resonance spectroscopy (MRS)-based metabolomics predictive models to indicate lung cancer presence and patient survival using serum samples collected prior to their disease diagnoses. Studied serum samples were collected from 79 patients before (within 5.0 y) and at lung cancer diagnosis. Disease predictive models were established by comparing serum metabolomic patterns between our training cohorts: patients with lung cancer at time of diagnosis, and matched healthy controls. These predictive models were then applied to evaluate serum samples of our validation and testing cohorts, all collected from patients before their lung cancer diagnosis. Our study found that the predictive model yielded values for prior-to-detection serum samples to be intermediate between values for patients at time of diagnosis and for healthy controls; these intermediate values significantly differed from both groups, with an F1 score = 0.628 for cancer prediction. Furthermore, values from metabolomics predictive model measured from prior-to-diagnosis sera could significantly predict 5-y survival for patients with localized disease.
Proceedings of the National Academy of Sciences , résumé, 2020