Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Multicenter Study
. 2021 Mar;9(3):e001458.
doi: 10.1136/jitc-2020-001458.

Tumor DNA methylation profiles correlate with response to anti-PD-1 immune checkpoint inhibitor monotherapy in sarcoma patients

Angelika M Starzer  1   2 , Anna S Berghoff  1   2 , Rainer Hamacher  3 , Erwin Tomasich  1 , Katharina Feldmann  1 , Teresa Hatziioannou  1 , Stefan Traint  1 , Wolfgang Lamm  1 , Iris M Noebauer-Huhmann  4 , Julia Furtner  4 , Leonhard Müllauer  5 , Gabriele Amann  5 , Sebastian Bauer  3 , Hans-Ulrich Schildhaus  6 , Matthias Preusser  1   2 , Gerwin Heller #  7 , Thomas Brodowicz #  1
Affiliations
Multicenter Study

Tumor DNA methylation profiles correlate with response to anti-PD-1 immune checkpoint inhibitor monotherapy in sarcoma patients

Angelika M Starzer et al. J Immunother Cancer. 2021 Mar.

Abstract

Background: Some sarcomas respond to immune checkpoint inhibition, but predictive biomarkers are unknown. We analyzed tumor DNA methylation profiles in relation to immunological parameters and response to anti-programmed cell death 1 (anti-PD-1) immune checkpoint inhibitor (ICI) therapy in patients with sarcoma.

Patients and methods: We retrospectively identified adult patients who had received anti-PD-1 ICI therapy for recurrent sarcoma in two independent centers. We performed (1) blinded radiological response evaluation according to immune response evaluation criteria in solid tumors (iRECIST) ; (2) tumor DNA methylation profiling of >850,000 probes using Infinium MethylationEPIC microarrays; (3) analysis of tumor-infiltrating immune cell subsets (CD3, CD8, CD45RO, FOXP3) and intratumoral expression of immune checkpoint molecules (PD-L1, PD-1, LAG-3) using immunohistochemistry; and (4) evaluation of blood-based systemic inflammation scores (neutrophil-to-lymphocyte ratio, leucocyte-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, platelet-to-lymphocyte ratio). Response to anti-PD-1 ICI therapy was bioinformatically and statistically correlated with DNA methylation profiles and immunological data.

Results: 35 patients (median age of 50 (23-81) years; 18 females, 17 males; 27 soft tissue sarcomas; 8 osteosarcomas) were included in this study. The objective response rate to anti-PD-1 ICI therapy was 22.9% with complete responses in 3 out of 35 and partial responses in 5 out of 35 patients. Adjustment of DNA methylation data for tumor-infiltrating immune cells resulted in identification of methylation differences between responders and non-responders to anti-PD-1 ICI. 2453 differentially methylated CpG sites (DMPs; 2043 with decreased and 410 with increased methylation) were identified. Clustering of sarcoma samples based on these DMPs revealed two main clusters: methylation cluster 1 (MC1) consisted of 73% responders and methylation cluster 2 (MC2) contained only non-responders to anti-PD-1 ICI. Median progression-free survival from anti-PD-1 therapy start of MC1 and MC2 patients was 16.5 and 1.9 months, respectively (p=0.001). Median overall survival of these patients was 34.4 and 8.0 months, respectively (p=0.029). The most prominent DNA methylation differences were found in pathways implicated in Rap1 signaling, focal adhesion, adherens junction Phosphoinositide 3-kinase (PI3K)-Akt signaling and extracellular matrix (ECM)-receptor interaction.

Conclusions: Our data demonstrate that tumor DNA methylation profiles may serve as a predictive marker for response to anti-PD-1 ICI therapy in sarcoma.

Keywords: biomarkers; biostatistics; immunotherapy; sarcoma; tumor.

PubMed Disclaimer

Conflict of interest statement

Competing interests: AS has received travel support from PharmaMar. AB has research support from Daiichi Sankyo and Roche; honoraria for lectures, consultation or advisory board participation from Roche Bristol-Meyers Squibb, Merck, Daiichi Sankyo; as well as travel support from Roche, Amgen, Daiichi Sankyo and AbbVie. MP has received honoraria for lectures, consultation or advisory board participation from the following for-profit companies: Bayer, Bristol-Myers Squibb, Novartis, Gerson Lehrman Group (GLG), CMC Contrast, GlaxoSmithKline, Mundipharma, Roche, BMJ Journals, MedMedia, Astra Zeneca, AbbVie, Lilly, Medahead, Daiichi Sankyo, Sanofi, Merck Sharp & Dome, Tocagen. The following for-profit companies have supported clinical trials and contracted research conducted by MP with payments made to his institution: Böhringer-Ingelheim, Bristol-Myers Squibb, Roche, Daiichi Sankyo, Merck Sharp & Dome, Novocure, GlaxoSmithKline, AbbVie. TB reports personal fees from Roche (lecture fee), personal fees from Amgen (lecture fee, advisory board), personal fees from Bayer (lecture fee, advisory board), personal fees from Novartis (lecture fee, advisory board), personal fees from PharmaMar (lecture fee, advisory board), personal fees from Eisai (lecture fee, advisory board), personal fees from Eli Lilly (lecture fee, advisory board), outside the submitted work. RH is supported by Clinician Scientist Program of the University Medicine Essen Clinician Scientist Academy (UMEA) sponsored by faculty of medicine and Deutsche Forschungsgemeinschaft (DFG). RH has received travel grants from Lilly, Novartis and PharmaMar, as well as fees from Lilly outside of the submitted work. SB reports personal fees from Deciphera, grants from Incyte, grants and personal fees from Blueprint Medicines, personal fees from Lilly, grants and personal fees from Novartis, personal fees from Daichii-Sankyo, personal fees from Plexxikon, personal fees from Exelixis, personal fees from Bayer, other from Pfizer, during the conduct of the study; personal fees from Pharmamar, personal fees from Lilly, personal fees from Roche, personal fees from GSK, outside the submitted work. All other authors report no conflicts of interest concerning this specific publication.

Figures

Figure 1
Figure 1
Efficacy of anti-programmed cell death 1 (anti-PD-1) immune checkpoint inhibitor (ICI) therapy in patients with sarcoma. (A) Waterfall plot of % change of tumor burden from anti-PD-1 ICI therapy start until best response from center 1 patients. The dashed lines indicate the margins for PD (+20% change of tumor burden) and PR (−30% change of tumor burden). (B) Objective response rate (ORR) and clinical benefit rate (CBR) as % of patients (both centers). (C) Progression-free survival (PFS) and (D) overall survival (OS) of patients with sarcoma from anti-PD-1 ICI therapy start in months (both centers). Vertical lines indicate censored patients at last follow-up. CB, clinical benefit; OR, objective response; PD, progressive disease; PD-1, programmed cell death 1.
Figure 2
Figure 2
Exploratory analysis of EPIC DNA methylation data of tumors from anti-programmed cell death 1 (anti-PD-1) immune checkpoint inhibitor (ICI)-treated patients with sarcoma. (A) Unsupervised hierarchical clustering of 35 patients with sarcoma based on beta values of 704,003 probes. The pink cluster is significantly enriched for responders (dark blue dots) to anti-PD-1 ICI, while the blue cluster contains only one responder to anti-PD-1 ICI. P values were calculated using Fisher’s exact tests; ns, not significant. (B) The volcano plot (middle panel) illustrates differentially methylated CpG sites (DMPs; green, hypomethylated; red, hypermethylated) between non-responders and responders to anti-PD-1 ICI. Each dot represents a unique DMP. The location of these DMPs relative to CpG islands and genomic features are shown in the upper and lower pie charts, respectively.
Figure 3
Figure 3
Impact of DNA methylation on progression-free survival (PFS) and overall survival (OS) of patients with sarcoma treated with anti-programmed cell death 1 (anti-PD-1) immune checkpoint inhibitor (ICI). (A) Heatmap summarizing methylation of 2453 DMPs in 35 sarcoma samples. Patients’ characteristics including response to anti-PD-1 ICI, sarcoma subtype as well as expression of immune markers are displayed according to the color scheme shown at the right-hand side of the figure. Each row represents a unique CpG site and each column represents a unique patient sample. Heatmap colors reflect beta values representing the degree of methylation from low (blue) to high (red). No centering/scaling of beta values was performed. Immune markers were divided into first, second, third and fourth quarter. (B) PFS and OS (C) of patients with sarcoma from anti-PD-1 ICI therapy start in months. Blue and red lines indicate patients included in the methylation clusters MC1 and MC2, respectively. Vertical lines indicate censored patients at last follow-up. P values were calculated using log-rank tests.
Figure 4
Figure 4
Functional implication of differentially methylated CpG sites (DMPs) between responders and non-responders to anti-programmed cell death 1 (anti-PD-1) immune checkpoint inhibitor (ICI) therapy. (A) Results from Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using WebGestalt software (left panel). Each dot represents a unique pathway. (B) Heatmap summarizing methylation of Rap1 signaling genes in 35 sarcoma samples. Genes which appear repeatedly in the heatmap were affected by multiple DMPs. Heatmap colors reflect beta values representing the degree of methylation from low (blue) to high (red). No centering/scaling of beta values was performed.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Fletcher CDM, Hogendoorn P, Mertens F. WHO classification of tumors of soft tissue and bone. Lyon IARC Press, 2013: 321–4.
    1. Casali PG, Abecassis N, Bauer S. Soft tissue and visceral sarcomas: ESMO-EURACAN clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol 2018;29(Suppl 4:iv51–67. 10.1093/annonc/mdy096 - DOI - PubMed
    1. Casali PG, Bielack S, Abecassis N, et al. . Bone sarcomas: ESMO-PaedCan-EURACAN clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol 2018;29:iv79–95. 10.1093/annonc/mdy310 - DOI - PubMed
    1. van der Graaf WTA, Blay J-Y, Chawla SP, et al. . Pazopanib for metastatic soft-tissue sarcoma (PALETTE): a randomised, double-blind, placebo-controlled phase 3 trial. Lancet 2012;379:1879–86. 10.1016/S0140-6736(12)60651-5 - DOI - PubMed
    1. Demetri GD, von Mehren M, Jones RL, et al. . Efficacy and safety of trabectedin or dacarbazine for metastatic liposarcoma or leiomyosarcoma after failure of conventional chemotherapy: results of a phase III randomized multicenter clinical trial. J Clin Oncol 2016;34:786–93. 10.1200/JCO.2015.62.4734 - DOI - PMC - PubMed

Publication types

MeSH terms

Substances