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Prognostic and theranostic 18F-FDG PET biomarkers for anti-PD1 immunotherapy in metastatic melanoma: association with outcome and transcriptomics

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Abstract

Purpose

An imaging-based stratification tool is needed to identify melanoma patients who will benefit from anti Programmed Death-1 antibody (anti-PD1). We aimed at identifying biomarkers for survival and response evaluated in lymphoid tissue metabolism in spleen and bone marrow before initiation of therapy.

Methods

This retrospective study included 55 patients from two institutions who underwent 18F-FDG PET/CT before anti-PD1. Parameters extracted were SUVmax, SUVmean, HISUV (SUV-based Heterogeneity Index), TMTV (total metabolic tumor volume), TLG (total lesion glycolysis), BLR (Bone marrow-to-Liver SUVmax ratio), and SLR (Spleen-to-Liver SUVmax ratio). Each parameter was dichotomized using the median as a threshold. Association with survival, best overall response (BOR), and transcriptomic analyses (NanoString assay) were evaluated using Cox prediction models, Wilcoxon tests, and Spearman’s correlation, respectively.

Results

At 20.7 months median follow-up, 33 patients had responded, and 29 patients died. Median PFS and OS were 11.4 (95%CI 2.7–20.2) and 28.5 (95%CI 13.4–43.8) months. TMTV (>25cm3), SLR (>0.77), and BLR (>0.79) correlated with shorter survival. High TMTV (>25 cm3), SLR (>0.77), and BLR (>0.79) correlated with shorter survival, with TMTV (HR PFS 2.2, p = 0.02, and HR OS 2.5, p = 0.02) and BLR (HR OS 2.3, p = 0.04) remaining significant in a multivariable analysis. Low TMTV and TLG correlated with BOR (p = 0.03). Increased glucose metabolism in bone marrow (BLR) was associated with transcriptomic profiles including regulatory T cell markers (p < 0.05).

Conclusion

Low tumor burden correlates with survival and objective response while hematopoietic tissue metabolism correlates inversely with survival. These biomarkers should be further evaluated for potential clinical application.

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Abbreviations

18F-FDG:

18fluor-fluoro-deoxy-glucose

AJCC:

American joint committee on cancer

BLR:

Bone marrow-to-liver maximum standard uptake value ratio

BM:

Bone marrow

BOR:

Best overall response

CD(4/8):

Cluster of differentiation

CI:

Confidence interval

CR:

Complete response

CT:

Computed tomography

FOXP3:

Forkhead box P3

G-CSF:

Granulocyte-colony stimulating factor

GM-CSF:

Granulocyte-macrophage colony stimulating factor.

HISUV:

Heterogeneity index standard uptake value-based

HR:

Hazard ratio

ICI:

Immune checkpoint inhibitor

IgG:

Immunoglobulin G

IL3RA:

Interleukin 3 receptor subunit alpha

iRECIST:

Immune response evaluation criteria in solid tumors

LDH:

Lactate dehydrogenase

LSO:

Lu2SiO5:Ce (lutetium, orthosilicate, cerium)

LYSO:

Lu1.8Y.2SiO5:Ce (lutetium, yttrium, orthosilicate, cerium)

MDSC:

Myeloid-derived suppressor cell

MRI:

Magnetic resonance imaging

OS:

Overall survival

PET:

Positron emission tomography

PD:

Progression disease

PD-1:

Programmed cell death-1

PERCIST:

Positron emission tomography evaluation response criteria in solid tumors

PFS:

Progression-free survival

PR:

Partial response

RECIST 1.1:

Response evaluation criteria in solid tumors version 1.1

RNA:

Ribonucleic acid

SD:

Stable disease

SLR:

Spleen-to-liver maximum standard uptake value ratio

SUVmax:

Maximum standard uptake value

SUVmean:

Mean standard uptake value

TAM:

Tumor-associated macrophage

TAN:

Tumor-associated neutrophil

TLG:

Total lesion glycolysis

TMTV:

Total metabolic tumor volume

Tregs:

Regulatory T cells

VOI:

Volume of interest

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Acknowledgements

Patients Recruitment (CM, JCS, LHS, YS, CR). Clinical Data Collection (SA, AMP, FZM, GR, LB, LD). Imaging Data Collection (RDS, JSN, RY, LD). Transcriptomics Data Cost (AM, YS). Transcriptomics Data Collection (AM, RG, AB, YS, LD). Data analysis (RDS, LD). Manuscript writing (RDS, LD). Manuscript editing (RDS, JSN, YS, LD). Manuscript final approval (all authors). L Dercle work is funded by a grant from Fondation Philanthropia, Geneva, Switzerland and the Fondation Nuovo-Soldati.

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Author notes

  1. Yvonne Saenger, Caroline Robert, and Laurent Dercle shared co-senior authorship.

Authors and Affiliations

  1. Département d’imagerie Médicale, Gustave Roussy, Université Paris-Saclay, 94800, Villejuif, France

    Romain-David Seban & Samy Ammari

  2. Department of Medicine, Division of Hematology Oncology, New York-Presbyterian Hospital/Columbia University Medical Center, New York, NY, USA

    John S. Nemer, Robyn D. Gartrell, Grace Finkel, Luke Barker & Yvonne Saenger

  3. Department of Radiology, New York Presbyterian Hospital - Columbia University Medical Center, New York, NY, 10039, USA

    John S. Nemer, Randy Yeh, Fatima-Zohra Mokrane, Lawrence H. Schwartz & Laurent Dercle

  4. Drug Development Department (DITEP), Gustave Roussy, Villejuif, France

    Aurélien Marabelle, Eric Deutsch & Amélie E. Bigorgne

  5. UMR1015, Gustave Roussy, Université Paris Saclay, 94800, Villejuif, France

    Aurélien Marabelle, Amélie E. Bigorgne & Laurent Dercle

  6. Inserm U981, Melanoma group, Gustave Roussy Cancer Campus, Villejuif, France

    Antoine Moya-Plana & Caroline Robert

  7. Inserm U1163, Imagine Institute, Paris, France

    Amélie E. Bigorgne

  8. University Paris Descartes, Paris, France

    Amélie E. Bigorgne

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Correspondence to Laurent Dercle.

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Seban, RD., Nemer, J.S., Marabelle, A. et al. Prognostic and theranostic 18F-FDG PET biomarkers for anti-PD1 immunotherapy in metastatic melanoma: association with outcome and transcriptomics. Eur J Nucl Med Mol Imaging 46, 2298–2310 (2019). https://doi.org/10.1007/s00259-019-04411-7

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