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Serum very long-chain fatty acid-containing lipids predict response to immune checkpoint inhibitors in urological cancers

Abstract

Checkpoint inhibitors (CPI) have significantly changed the therapeutic landscape of oncology. We adopted a non-invasive metabolomic approach to understand immunotherapy response and failure in 28 urological cancer patients. In total, 134 metabolites were quantified in patient sera before the first, second, and third CPI doses. Modeling the association between metabolites and CPI response and patient characteristics revealed that one predictive metabolite class  (n = 9/10) were very long-chain fatty acid-containing lipids (VLCFA-containing lipids). The best predictive performance was achieved through a multivariate model, including age and a centroid of VLCFA-containing lipids prior to first immunotherapy (sensitivity: 0.850, specificity: 0.825, ROC: 0.935). We hypothesize that the association of VLCFA-containing lipids with CPI response is based on enhanced peroxisome signaling in T cells, which results in a switch to fatty acid catabolism. Beyond use as a novel predictive non-invasive biomarker, we envision that nutritional supplementation with VLCFA-containing lipids might serve as an immuno sensitizer.

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Abbreviations

CI:

Confidence interval

CPI:

Checkpoint inhibitor

CR:

Complete remission

FAO:

Fatty acid oxidation

FDG-PET:

Fluorodeoxyglucose positron emission tomography

GLC:

Glucose

GPL:

Glycerophospholipid

HR:

Hazard ratio

LCFA:

Long-chain fatty acid

LMM:

Linear mixed effects model

LOD:

Limit of detection

MS:

Mass spectroscopy

NMR:

Nuclear magnetic resonance

PC:

Phosphatidylcholine

PD:

Progressive disease

PPAR-α:

Peroxisome proliferation-activated receptor α

PR:

Partial remission

RCC:

Renal cell carcinoma

ROC:

Receiver operating characteristic

SD:

Stable disease

SLC27A2:

Solute carrier family 27 member 2 gene

sGPL:

Saturated GPL

SM:

Sphingomyelin

SVM:

Support vector machine

TCGA:

The Cancer Genome Atlas

TMB:

Tumor mutational burden

tSNE:

T-distributed stochastic neighbor embedding

UC:

Urothelial carcinoma

uGPL:

Unsaturated GPL

VLCFA:

Very long-chain fatty acid

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Funding

The National Center for Tumor Diseases (NCT) is supported by the German Cancer Research Center (DKFZ), the University Hospital Heidelberg in cooperation with the Medical Faculty Heidelberg, and by the German Cancer Aid (Deutsche Krebshilfe). This project was supported by the NCT Elevator Pitch 2015.

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Authors and Affiliations

Authors

Contributions

Conception of the work: SZ, CG; funding acquisition: CG, DJ; sample collection and processing: RK, EC, MJ, AM; metabolomic analysis: MS, BW, AM; data collection and data analysis: AM, SZ, CG, CH; manuscript writing/editing: AM, SZ, GC; critical revision of the manuscript: DJ, MS, BW, RK, RK, EB, MJ; final approval: all the authors.

Corresponding author

Correspondence to Carsten Grüllich.

Ethics declarations

Conflict of interest

Matthias Scheffler and Barbara Wolf are employed by BIOCRATES Life Sciences AG. The other authors have declared no competing interests.

Ethical approval and ethical standards

The ethics committee of the University of Heidelberg, Germany approved the study design (S023/2016). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

All patients provided written informed consent. Informed consent included the permission to take blood samples for research purposes and the use of their anonymized clinical data. Any patient identifiable information was excluded from further sample handling and data processing.

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Mock, A., Zschäbitz, S., Kirsten, R. et al. Serum very long-chain fatty acid-containing lipids predict response to immune checkpoint inhibitors in urological cancers. Cancer Immunol Immunother 68, 2005–2014 (2019). https://doi.org/10.1007/s00262-019-02428-3

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  • DOI: https://doi.org/10.1007/s00262-019-02428-3

Keywords

  • Cancer immunotherapy
  • Cancer metabolomics
  • Renal cell carcinoma
  • Urothelial cancer