Screening human lung cancer with predictive models of serum magnetic resonance spectroscopy metabolomics

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 118; no. 51; pp. 1 - 8
• Main Authors: Schult, Tjada A., Lauer, Mara J., Berker, Yannick, Cardoso, Marcella R., Vandergrift, Lindsey A., Habbel, Piet, Nowak, Johannes, Taupitz, Matthias, Aryee, Martin, Mino-Kenudson, Mari A., Christiani, David C., Cheng, Leo L.
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 21-12-2021
• Subjects: Aged; Biological Sciences; Diagnosis; Disease detection; Early Detection of Cancer - methods; Female; Humans; Lung cancer; Lung diseases; Lung Neoplasms - blood; Lung Neoplasms - diagnosis; Lung Neoplasms - metabolism; Magnetic Resonance Spectroscopy; Male; Medical diagnosis; Metabolic Networks and Pathways; Metabolomics; Middle Aged; Patients; Prediction models; Predictive control; Predictive Value of Tests; Reproducibility of Results; Resonance; Spectroscopy; Spectrum analysis; Survival; Training; human lung cancer; magnetic resonance spectroscopy; high-resolution magic angle spinning; metabolomics; blood serum
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.2110633118

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 patientswith localized disease.