Lung cancer metabolomic data from tumor core biopsies enables risk-score calculation for progression-free and overall survival

Hunter A Miller; Shesh N Rai; Xinmin Yin; Xiang Zhang; Jason A Chesney; Victor H van Berkel; Hermann B Frieboes

doi:10.1007/s11306-022-01891-x

Lung cancer metabolomic data from tumor core biopsies enables risk-score calculation for progression-free and overall survival

Metabolomics. 2022 May 14;18(5):31. doi: 10.1007/s11306-022-01891-x.

Authors

Hunter A Miller¹, Shesh N Rai^{1

2

3}, Xinmin Yin⁴, Xiang Zhang⁴, Jason A Chesney^{1

3

5

6}, Victor H van Berkel^{3

7}, Hermann B Frieboes^{8

9

10

11}

Affiliations

¹ Department of Pharmacology and Toxicology, University of Louisville, Louisville, USA.
² Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, USA.
³ James Graham Brown Cancer Center, University of Louisville, Louisville, USA.
⁴ Department of Chemistry, University of Louisville, Louisville, USA.
⁵ Division of Medical Oncology and Hematology, Department of Medicine, University of Louisville, Louisville, USA.
⁶ Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, USA.
⁷ Department of Cardiovascular and Thoracic Surgery, University of Louisville, Louisville, USA.
⁸ Department of Pharmacology and Toxicology, University of Louisville, Louisville, USA. hbfrie01@louisville.edu.
⁹ James Graham Brown Cancer Center, University of Louisville, Louisville, USA. hbfrie01@louisville.edu.
¹⁰ Department of Bioengineering, University of Louisville, Lutz Hall 419, Louisville, KY, 40292, USA. hbfrie01@louisville.edu.
¹¹ Center for Predictive Medicine, University of Louisville, Louisville, USA. hbfrie01@louisville.edu.

Abstract

Introduction: Metabolomics has emerged as a powerful method to provide insight into cancer progression, including separating patients into low- and high-risk groups for overall (OS) and progression-free survival (PFS). However, survival prediction based mainly on metabolites obtained from biofluids remains elusive.

Objectives: This proof-of-concept study evaluates metabolites as biomarkers obtained directly from tumor core biopsies along with covariates age, sex, pathological stage at diagnosis (I/II vs. III/VI), histological subtype, and treatment vs. no treatment to risk stratify lung cancer patients in terms of OS and PFS.

Methods: Tumor core biopsy samples obtained during routine lung cancer patient care at the University of Louisville Hospital and Norton Hospital were evaluated with high-resolution 2DLC-MS/MS, and the data were analyzed by Kaplan-Meier survival analysis and Cox proportional hazards regression. A linear equation was developed to stratify patients into low and high risk groups based on log-transformed intensities of key metabolites. Sparse partial least squares discriminant analysis (SPLS-DA) was performed to predict OS and PFS events.

Results: Univariable Cox proportional hazards regression model coefficients divided by the standard errors were used as weight coefficients multiplied by log-transformed metabolite intensity, then summed to generate a risk score for each patient. Risk scores based on 10 metabolites for OS and 5 metabolites for PFS were significant predictors of survival. Risk scores were validated with SPLS-DA classification model (AUROC 0.868 for OS and AUROC 0.755 for PFS, when combined with covariates).

Conclusion: Metabolomic analysis of lung tumor core biopsies has the potential to differentiate patients into low- and high-risk groups based on OS and PFS events and probability.

Keywords: Lung cancer; Metabolomics; Overall survival; Progression free survival; Risk score calculator; Tumor core biopsy.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Biopsy
Disease-Free Survival
Humans
Lung Neoplasms* / diagnosis
Metabolomics
Risk Factors
Tandem Mass Spectrometry*

Grants and funding

R15 CA203605/CA/NCI NIH HHS/United States