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

Abstract

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 (scRNAseq) 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.

CD69 and SBK1 as potential predictors of responses to PD-1/PD-L1 blockade cancer immunotherapy in lung cancer and melanoma

“…Although PD-1/PD-L1 blockade exhibits remarkable anticancer efficacy and safety, it is estimated that it benefits less than half of applicable cancer patients (8-10). To better predict response to PD-1/PD-L1 blockade and avoid the waste of medical resources, robust biomarkers have been developed using various strategies, including imaging omics (27,28) and patient derived biological materials (29)(30)(31). Furthermore, genomic based biomarkers, like Tumor Mutational Burden (32), Antigen Processing Machinery score (33), Tumor Immune Dysfunction and Exclusion score (34) and Gene Expression Profiles score (35) are reported as prediction biomarkers.…”

Targeting the CD47/thrombospondin-1 signaling axis regulates immune cell bioenergetics in the tumor microenvironment to potentiate antitumor immune response

“…Blood was processed to obtain plasma and PBMC as previously described. 27 PBMC was maintained with RPMI containing 2 mM L-glutamine, 10% FBS, and 1% penicillin-streptomycin at 37°C with 5% CO 2 .…”

“…Once PBMC were isolated, single-cell RNA-sequencing (scRNA-seq) was performed using a 10 x genomics system. 27 PBMC went through single-cell analysis, and results were displayed as clusters based on immune cell population: NK cells (NKG7+, CD56+), T lymphocytes (CD3+, CD28+), B cells (CD19+, CD40+), and monocytes (CD68+, CD163+) ( online supplemental figure 3A ).…”

“…To examine different cell populations within human melanoma patient PBMC and mouse Pmel-1 splenocytes, cell surface flow cytometry was performed as previously described. 27 Human melanoma patient PBMC were stained with the following anti-human antibodies from BD: Fixable Viability Stain 575V, CD3 APC-R700, CD19 APC-H7, CD47 APC, PD-1 BV 421, and CD8 BUV496. Pmel-1 splenocytes were stained with dilutions of the following anti-mouse antibodies from BD: CD19 BV421, Fixable Viability Stain 575V, CD64 BV786, CD8a APC-H7, and CD3 BUV496.…”

Cancer-testis antigen lactate dehydrogenase C4 as a novel biomarker of male infertility and cancer

“…This study supports the rationale to assess LDH-C4; in particular, the HLA-A*0201 restricted LDH-C4 (aa41–55) and LDH-C4 (aa288–303) peptides ( 57 ), as a CTA for targeted immunotherapy. Another study highlights that LDHC expression is upregulated in the responder population among patients with melanoma undergoing treatment with anti–PD-1 therapy, suggesting that LDHC is a potential predictive biomarker of response to immune checkpoint inhibitor therapy ( 72 ).…”