SpringerLink
Log in

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

  • Original Article
  • Published:
Metabolomics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (France)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

Datasets used are in [Metabolomics Workbench (MetWB), RRID:SCR_013794, www.metabolomicsworkbench.org] (Study ST001527).

Abbreviations

HR:

Hazard ratio

NSCLC:

Non-small cell lung cancer

OS:

Overall survival

PC1:

First principal component

PC2:

Second principal component

PC3:

Third principal component

PFS:

Progression free survival

PLS-DA:

Partial least squares discriminant analysis

SPLS-DA:

Sparse partial least squares discriminant analysis

References

  • Abusalamah, H., Reel, J. M., & Lupfer, C. R. (2020). Pyruvate affects inflammatory responses of macrophages during influenza A virus infection. Virus Research, 286, 198088.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Anantharaju, P. G., Reddy, B. D., Padukudru, M. A., Kumari Chitturi, C. M., Vimalambike, M. G., & Madhunapantula, S. V. (2017). Naturally occurring benzoic acid derivatives retard cancer cell growth by inhibiting histone deacetylases (HDAC). Cancer Biology & Therapy, 18, 492–504.

    Article  CAS  Google Scholar 

  • Bamji-Stocke, S., van Berkel, V., Miller, D. M., & Frieboes, H. B. (2018). A review of metabolism-associated biomarkers in lung cancer diagnosis and treatment. Metabolomics, 14, 81.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Berker, Y., Vandergrift, L. A., Wagner, I., Su, L., Kurth, J., Schuler, A., Dinges, S. S., Habbel, P., Nowak, J., Mark, E., Aryee, M. J., Christiani, D. C., & Cheng, L. L. (2019). Magnetic resonance spectroscopy-based metabolomic biomarkers for ty**, staging, and survival estimation of early-stage human lung cancer. Science and Reports, 9, 10319.

    Article  CAS  Google Scholar 

  • Cascino, A., Muscaritoli, M., Cangiano, C., Conversano, L., Laviano, A., Ariemma, S., Meguid, M. M., & Rossi Fanelli, F. (1995). Plasma amino acid imbalance in patients with lung and breast cancer. Anticancer Research, 15, 507–510.

    CAS  PubMed  Google Scholar 

  • Collino, S., Martin, F. P., & Rezzi, S. (2013). Clinical metabolomics paves the way towards future healthcare strategies. British Journal of Clinical Pharmacology, 75, 619–629.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cooke, J. P., & Ghebremariam, Y. T. (2008). Endothelial nicotinic acetylcholine receptors and angiogenesis. Trends in Cardiovascular Medicine, 18, 247–253.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Dercle, L., Fronheiser, M., Lu, L., Du, S., Hayes, W., Leung, D. K., Roy, A., Wilkerson, J., Guo, P., Fojo, A. T., Schwartz, L. H., & Zhao, B. (2020). Identification of non-small cell lung cancer sensitive to systemic cancer therapies using radiomics. Clinical Cancer Research, 26, 2151–2162.

    Article  CAS  PubMed  Google Scholar 

  • Fan, T. W., Lane, A. N., Higashi, R. M., Farag, M. A., Gao, H., Bousamra, M., & Miller, D. M. (2009). Altered regulation of metabolic pathways in human lung cancer discerned by (13)C stable isotope-resolved metabolomics (SIRM). Molecular Cancer, 8, 41.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Girouard, J., Belgorosky, D., Hamelin-Morrissette, J., Boulanger, V., D’Orio, E., Ramla, D., Perron, R., Charpentier, L., van Themsche, C., Eijan, A. M., Berube, G., & Reyes-Moreno, C. (2020). Molecular therapy with derivatives of amino benzoic acid inhibits tumor growth and metastasis in murine models of bladder cancer through inhibition of TNFalpha/NFKappaB and iNOS/NO pathways. Biochemical Pharmacology, 176, 113778.

    Article  CAS  PubMed  Google Scholar 

  • Goldstraw, P., Crowley, J., Chansky, K., Giroux, D. J., Groome, P. A., Rami-Porta, R., Postmus, P. E., Rusch, V., Sobin, L., International Association for the Study of Lung Cancer International Staging, C. & Participating, I. (2007). The IASLC Lung Cancer Staging Project: Proposals for the revision of the TNM stage grou**s in the forthcoming (seventh) edition of the TNM classification of malignant tumours. Journal of Thoracic Oncology, 2, 706–714.

    Article  PubMed  Google Scholar 

  • Gueta, I., Perach Ovadia, Y., Markovits, N., Schacham, Y. N., Epsztein, A., & Loebstein, R. (2020). Is pyroglutamic acid a prognostic factor among patients with suspected infection? A Prospective Cohort Study. Scientific Reports, 10, 10128.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hori, S., Nishiumi, S., Kobayashi, K., Shinohara, M., Hatakeyama, Y., Kotani, Y., Hatano, N., Maniwa, Y., Nishio, W., Bamba, T., Fukusaki, E., Azuma, T., Takenawa, T., Nishimura, Y., & Yoshida, M. (2011). A metabolomic approach to lung cancer. Lung Cancer, 74, 284–292.

    Article  PubMed  Google Scholar 

  • Howlader, N., Noone, A., Krapcho, M., Miller, D., Brest, A., Yu, M., Ruhl, J., Tatalovich, Z., Mariotto, A., Lewis, D. R., Chen, H. S., Feuer, E. J., & Cronin, K. A. (2021). Based on November 2020 SEER data submission, posted to the SEER web site, April 2021. SEER Cancer Statistics Review, 1975–2018. National Cancer Institute, Bethesda, MD.

    Google Scholar 

  • Huang, J., Weinstein, S. J., Moore, S. C., Derkach, A., Hua, X., Mondul, A. M., Sampson, J. N., & Albanes, D. (2019). Pre-diagnostic serum metabolomic profiling of prostate cancer survival. Journals of Gerontology. Series a, Biological Sciences and Medical Sciences, 74, 853–859.

    Article  CAS  Google Scholar 

  • Ji, X., Qian, J., Rahman, S. M. J., Siska, P. J., Zou, Y., Harris, B. K., Hoeksema, M. D., Trenary, I. A., Heidi, C., Eisenberg, R., Rathmell, J. C., Young, J. D., & Massion, P. P. (2018). xCT (SLC7A11)-mediated metabolic reprogramming promotes non-small cell lung cancer progression. Oncogene, 37, 5007–5019.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • **, X., Yun, S. J., Jeong, P., Kim, I. Y., Kim, W. J., & Park, S. (2014). Diagnosis of bladder cancer and prediction of survival by urinary metabolomics. Oncotarget, 5, 1635–1645.

    Article  PubMed  PubMed Central  Google Scholar 

  • Jung, S. Y., Song, H. S., Park, S. Y., Chung, S. H., & Kim, Y. J. (2011). Pyruvate promotes tumor angiogenesis through HIF-1-dependent PAI-1 expression. International Journal of Oncology, 38, 571–576.

    CAS  PubMed  Google Scholar 

  • Kami, K., Fujimori, T., Sato, H., Sato, M., Yamamoto, H., Ohashi, Y., Sugiyama, N., Ishihama, Y., Onozuka, H., Ochiai, A., Esumi, H., Soga, T., & Tomita, M. (2013). Metabolomic profiling of lung and prostate tumor tissues by capillary electrophoresis time-of-flight mass spectrometry. Metabolomics, 9, 444–453.

    Article  CAS  PubMed  Google Scholar 

  • Klavins, K., Drexler, H., Hann, S., & Koellensperger, G. (2014). Quantitative metabolite profiling utilizing parallel column analysis for simultaneous reversed-phase and hydrophilic interaction liquid chromatography separations combined with tandem mass spectrometry. Analytical Chemistry, 86, 4145–4150.

    Article  CAS  PubMed  Google Scholar 

  • Loo, J. M., Scherl, A., Nguyen, A., Man, F. Y., Weinberg, E., Zeng, Z., Saltz, L., Paty, P. B., & Tavazoie, S. F. (2015). Extracellular metabolic energetics can promote cancer progression. Cell, 160, 393–406.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Maas, M. N., Hintzen, J. C., Porzberg, M. R., & Mecinović, J. (2020). Trimethyllysine: From carnitine biosynthesis to epigenetics. International Journal of Molecular Sciences, 21, 9451.

    Article  CAS  PubMed Central  Google Scholar 

  • McShane, L. M., Altman, D. G., Sauerbrei, W., Taube, S. E., Gion, M., Clark, G. M., Statistics Subcommittee of the NCI-EORTC Working Group on Cancer Diagnostics. (2005). REporting recommendations for tumor MARKer prognostic studies (REMARK). Nature Clinical Practice. Urology, 2, 416–422.

    CAS  PubMed  Google Scholar 

  • Medina, V. A., & Rivera, E. S. (2010). Histamine receptors and cancer pharmacology. British Journal of Pharmacology, 161, 755–767.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Miller, H. A., Yin, X., Smith, S. A., Hu, X., Zhang, X., Yan, J., Miller, D. M., van Berkel, V. H., & Frieboes, H. B. (2021). Evaluation of disease staging and chemotherapeutic response in non-small cell lung cancer from patient tumor-derived metabolomic data. Lung Cancer, 156, 20–30.

    Article  PubMed  Google Scholar 

  • Peng, F., Liu, Y., He, C., Kong, Y., Ouyang, Q., **e, X., Liu, T., Liu, Z., & Peng, J. (2018). Prediction of platinum-based chemotherapy efficacy in lung cancer based on LC-MS metabolomics approach. Journal of Pharmaceutical and Biomedical Analysis, 154, 95–101.

    Article  CAS  PubMed  Google Scholar 

  • Puchades-Carrasco, L., Jantus-Lewintre, E., Perez-Rambla, C., Garcia-Garcia, F., Lucas, R., Calabuig, S., Blasco, A., Dopazo, J., Camps, C., & Pineda-Lucena, A. (2016). Serum metabolomic profiling facilitates the non-invasive identification of metabolic biomarkers associated with the onset and progression of non-small cell lung cancer. Oncotarget, 7, 12904–12916.

    Article  PubMed  PubMed Central  Google Scholar 

  • Sellers, K., Fox, M. P., Bousamra, M., Slone, S. P., Higashi, R. M., Miller, D. M., Wang, Y., Yan, J., Yuneva, M. O., Deshpande, R., Lane, A. N., & Fan, T. W. (2015). Pyruvate carboxylase is critical for non-small-cell lung cancer proliferation. The Journal of Clinical Investigation, 125, 687–698.

    Article  PubMed  PubMed Central  Google Scholar 

  • Serpa, J. (2020). Cysteine as a carbon source, a hot spot in cancer cells survival. Frontiers in Oncology, 10, 947.

    Article  PubMed  PubMed Central  Google Scholar 

  • Shen, J., Ye, Y., Chang, D. W., Huang, M., Heymach, J. V., Roth, J. A., Wu, X., & Zhao, H. (2017). Circulating metabolite profiles to predict overall survival in advanced non-small cell lung cancer patients receiving first-line chemotherapy. Lung Cancer, 114, 70–78.

    Article  PubMed  Google Scholar 

  • Siegel, R. L., Miller, K. D., Fuchs, H. E., & Jemal, A. (2021). Cancer statistics, 2021. CA: A Cancer Journal for Clinicians, 71, 7–33.

    Google Scholar 

  • Stout, J. T., & Caskey, C. T. (1985). HPRT: Gene structure, expression, and mutation. Annual Review of Genetics, 19, 127–148.

    Article  CAS  PubMed  Google Scholar 

  • Suzuki, T., & Takahashi, E. (1975). Metabolism of xanthine and hypoxanthine in the tea plant (Thea sinensis L.). The Biochemical Journal, 146, 79–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Tanner, J. J., Fendt, S. M., & Becker, D. F. (2018). The proline cycle as a potential cancer therapy target. Biochemistry, 57, 3433–3444.

    Article  CAS  PubMed  Google Scholar 

  • Tian, Y., Wang, Z., Liu, X., Duan, J., Feng, G., Yin, Y., Gu, J., Chen, Z., Gao, S., Bai, H., Wan, R., Jiang, J., Liu, J., Zhang, C., Wang, D., Han, J., Zhang, X., Cai, L., He, J., & Wang, J. (2018). Prediction of chemotherapeutic efficacy in non-small cell lung cancer by serum metabolomic profiling. Clinical Cancer Research, 24, 2100–2109.

    Article  CAS  PubMed  Google Scholar 

  • Tip**, M. E., & Bishop, C. M. (1999). Probabilistic principal component analysis. Journal of the Royal Statistical Society, 61, 611–622.

    Article  Google Scholar 

  • Townsend, M. H., Anderson, M. D., Weagel, E. G., Velazquez, E. J., Weber, K. S., Robison, R. A., & O’Neill, K. L. (2017). Non-small-cell lung cancer cell lines A549 and NCI-H460 express hypoxanthine guanine phosphoribosyltransferase on the plasma membrane. Oncotargets and Therapy, 10, 1921–1932.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • van den Berg, R. A., Hoefsloot, H. C., Westerhuis, J. A., Smilde, A. K., & van der Werf, M. J. (2006). Centering, scaling, and transformations: Improving the biological information content of metabolomics data. BMC Genomics, 7, 142.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Wald, L. L., Nelson, S. J., Day, M. R., Noworolski, S. E., Henry, R. G., Huhn, S. L., Chang, S., Prados, M. D., Sneed, P. K., Larson, D. A., Wara, W. M., McDermott, M., Dillon, W. P., Gutin, P. H., & Vigneron, D. B. (1997). Serial proton magnetic resonance spectroscopy imaging of glioblastoma multiforme after brachytherapy. Journal of Neurosurgery, 87, 525–534.

    Article  CAS  PubMed  Google Scholar 

  • Wikoff, W. R., Grapov, D., Fahrmann, J. F., Defelice, B., Rom, W. N., Pass, H. I., Kim, K., Nguyen, U., Taylor, S. L., Gandara, D. R., Kelly, K., Fiehn, O., & Miyamoto, S. (2015). Metabolomic markers of altered nucleotide metabolism in early stage adenocarcinoma. Cancer Prevention Research (philadelphia, Pa.), 8, 410–418.

    Article  CAS  Google Scholar 

  • Wilson, J. M., Tarr, G. E., & Kelley, W. N. (1983). Human hypoxanthine (guanine) phosphoribosyltransferase: An amino acid substitution in a mutant form of the enzyme isolated from a patient with gout. Proceedings of the National Academy of Sciences USA, 80, 870–873.

    Article  CAS  Google Scholar 

  • **e, H., Hou, Y., Cheng, J., Openkova, M. S., **a, B., Wang, W., Li, A., Yang, K., Li, J., Xu, H., Yang, C., Ma, L., Li, Z., Fan, X., Li, K., & Lou, G. (2017). Metabolic profiling and novel plasma biomarkers for predicting survival in epithelial ovarian cancer. Oncotarget, 8, 32134–32146.

    Article  PubMed  PubMed Central  Google Scholar 

  • Yu, H., **a, H., Tang, Q., Xu, H., Wei, G., Chen, Y., Dai, X., Gong, Q., & Bi, F. (2017). Acetylcholine acts through M3 muscarinic receptor to activate the EGFR signaling and promotes gastric cancer cell proliferation. Scientific Reports, 7, 40802.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yue, P., Lopez-Tapia, F., Paladino, D., Li, Y., Chen, C. H., Namanja, A. T., Hilliard, T., Chen, Y., Tius, M. A., & Turkson, J. (2016). Hydroxamic acid and benzoic acid-based STAT3 inhibitors suppress human glioma and breast cancer phenotypes in vitro and in vivo. Cancer Research, 76, 652–663.

    Article  CAS  PubMed  Google Scholar 

  • Zhang, X., Wang, Y., Li, X., Wu, J., Zhao, L., Li, W., & Liu, J. (2021). Dynamics-based discovery of novel, potent benzoic acid derivatives as orally bioavailable selective estrogen receptor degraders for ERalpha+ breast cancer. Journal of Medicinal Chemistry, 64, 7575–7595.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Authors acknowledge Melissa Hall, Andrei Smolenkov, and Danyelle Clark for James Graham Brown Cancer Center Biorepository support; Andrea Spencer, Lauren Whelan, Dr. John Hamm, and Dr. Stephen Wyatt for collection of samples at Norton Hospital.

Funding

This work was supported by National Institutes of Health/National Cancer Institute Grant R15CA203605 (Frieboes).

Author information

Authors and Affiliations

  1. Department of Pharmacology and Toxicology, University of Louisville, Louisville, USA

    Hunter A. Miller, Shesh N. Rai, Jason A. Chesney & Hermann B. Frieboes

  2. Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, USA

    Shesh N. Rai

  3. James Graham Brown Cancer Center, University of Louisville, Louisville, USA

    Shesh N. Rai, Jason A. Chesney, Victor H. van Berkel & Hermann B. Frieboes

  4. Department of Chemistry, University of Louisville, Louisville, USA

    **nmin Yin & **ang Zhang

  5. Division of Medical Oncology and Hematology, Department of Medicine, University of Louisville, Louisville, USA

    Jason A. Chesney

  6. Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, USA

    Jason A. Chesney

  7. Department of Cardiovascular and Thoracic Surgery, University of Louisville, Louisville, USA

    Victor H. van Berkel

  8. Department of Bioengineering, University of Louisville, Lutz Hall 419, Louisville, KY, 40292, USA

    Hermann B. Frieboes

  9. Center for Predictive Medicine, University of Louisville, Louisville, USA

    Hermann B. Frieboes

Authors
  1. Hunter A. Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shesh N. Rai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. **nmin Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. **ang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jason A. Chesney
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Victor H. van Berkel
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hermann B. Frieboes
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hermann B. Frieboes.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Research involving human and animals participants

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by Internal Review Board protocols at University of Louisville Hospital (IRB 05.0523) and Norton Hospital (IRB 18.0264).

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Electronic supplementary material 1 (PDF 1369 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Miller, H.A., Rai, S.N., Yin, X. et al. Lung cancer metabolomic data from tumor core biopsies enables risk-score calculation for progression-free and overall survival. Metabolomics 18, 31 (2022). https://doi.org/10.1007/s11306-022-01891-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11306-022-01891-x

Keywords

Search

Navigation