4.7 Article

Circulating Immune Bioenergetic, Metabolic, and Genetic Signatures Predict Melanoma Patients' Response to Anti-PD-1 Immune Checkpoint Blockade

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CLINICAL CANCER RESEARCH
卷 28, 期 6, 页码 1192-1202

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AMER ASSOC CANCER RESEARCH
DOI: 10.1158/1078-0432.CCR-21-3114

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  1. V Foundation V Scholar in Cancer Research Award [V2019-018]
  2. Wake Forest Baptist Comprehensive Cancer Center Pilot Funds [NIH/NCI P30CA012197]
  3. NIGMS NRSA T32 Fellowship [T32GM127261]
  4. American Cancer Society Research Scholar Grant [133727-RSG-19-150-01-LIB]
  5. NIH-NCI R21 [R21CA249349]
  6. NCI's Cancer Center Support Grant [P30CA012197]
  7. Hanes and Wills Professorship in Cancer
  8. National Foundation for Cancer Research

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In this study, the bioenergetics of circulating immune cells and the metabolomic profiles of plasma were compared in melanoma patients treated with anti-PD-1 therapy. It was found that patients who responded to the therapy had higher respiratory capacity and glycolytic activity compared to nonresponders. Metabolomic analysis also revealed differences in metabolic signatures between responder and nonresponder patient samples. The study suggests that blood bioenergetics and metabolomics can be used as predictive biomarkers for patient response to immune checkpoint inhibitor therapy.
Purpose: Immunotherapy with checkpoint inhibitors is improving the outcomes of several cancers. However, only a subset of patients respond. Therefore, predictive biomarkers are critically needed to guide treatment decisions and develop approaches to the treatment of therapeutic resistance. Experimental Design: We compared bioenergetics of circulating immune cells and metabolomic profiles of plasma obtained at baseline from patients with melanoma treated with anti-PD-1 therapy. We also performed single-cell RNA sequencing (scRNA-seq) to correlate transcriptional changes associated with metabolic changes observed in peripheral blood mononuclear cells (PBMC) and patient plasma. Results: Pretreatment PBMC from responders had a higher reserve respiratory capacity and higher basal glycolytic activity compared with nonresponders. Metabolomic analysis revealed that responder and nonresponder patient samples cluster differently, suggesting differences in metabolic signatures at baseline. Differential levels of specific lipid, amino acid, and glycolytic pathway metabolites were observed by response. Further, scRNAseq analysis revealed upregulation of T-cell genes regulating glycolysis. Our analysis showed that SLC2A14 (Glut-14; a glucose transporter) was the most significant gene upregulated in responder patients' T-cell population. Flow cytometry analysis confirmed significantly elevated cell surface expression of the Glut-14 in CD3+, CD8+, and CD4+ circulating populations in responder patients. Moreover, LDHC was also upregulated in the responder population. Conclusions: Our results suggest a glycolytic signature characterizes checkpoint inhibitor responders; consistently, both ECAR and lactate-to-pyruvate ratio were significantly associated with overall survival. Together, these findings support the use of blood bioenergetics and metabolomics as predictive biomarkers of patient response to immune checkpoint inhibitor therapy.

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