Dataset Information

DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma.

ABSTRACT: Therapies based on targeting immune checkpoints have revolutionized the treatment of metastatic melanoma in recent years. Still, biomarkers predicting long-term therapy responses are lacking. A novel approach of reference-free deconvolution of large-scale DNA methylation data enabled us to develop a machine learning classifier based on CpG sites, specific for latent methylation components (LMC), that allowed for patient allocation to prognostic clusters. DNA methylation data were processed using reference-free analyses (MeDeCom) and reference-based computational tumor deconvolution (MethylCIBERSORT, LUMP). We provide evidence that DNA methylation signatures of tumor tissue from cutaneous metastases are predictive for therapy response to immune checkpoint inhibition in patients with stage IV metastatic melanoma. These results demonstrate that LMC-based segregation of large-scale DNA methylation data is a promising tool for classifier development and treatment response estimation in cancer patients under targeted immunotherapy.

SUBMITTER: Filipski K

PROVIDER: S-EPMC8291310 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Publications

DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma.

Filipski Katharina K Scherer Michael M Zeiner Kim N KN Bucher Andreas A Kleemann Johannes J Jurmeister Philipp P Hartung Tabea I TI Meissner Markus M Plate Karl H KH Fenton Tim R TR Walter Jörn J Tierling Sascha S Schilling Bastian B Zeiner Pia S PS Harter Patrick N PN

Journal for immunotherapy of cancer 20210701 7

<h4>Background</h4>Therapies based on targeting immune checkpoints have revolutionized the treatment of metastatic melanoma in recent years. Still, biomarkers predicting long-term therapy responses are lacking.<h4>Methods</h4>A novel approach of reference-free deconvolution of large-scale DNA methylation data enabled us to develop a machine learning classifier based on CpG sites, specific for latent methylation components (LMC), that allowed for patient allocation to prognostic clusters. DNA met ...[more]

PMID: 34281986

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Project description:Despite major improvements in combatting metastatic melanoma since the advent of immunotherapy, the overall survival for patients with advanced disease remains low. Recently, there is a growing number of reports supporting an "obesity paradox," in which patients who are overweight or mildly obese may exhibit a survival benefit in patients who received immune checkpoint inhibitors. We studied the relationship between body mass index and progression-free survival and overall survival in a cohort of 423 metastatic melanoma patients receiving immunotherapy, enrolled and prospectively followed up in the NYU Interdisciplinary Melanoma Cooperative Group database. We analyzed this association stratified by first vs. second or greater-line of treatment and treatment type adjusting for age, gender, stage, lactate dehydrogenase, Eastern Cooperative Oncology Group performance status, number of metastatic sites, and body mass index classification changes. In our cohort, the patients who were overweight or obese did not have different progression-free survival than patients with normal body mass index. Stratifying this cohort by first vs. non-first line immunotherapy revealed a moderate but insignificant association between being overweight or obese and better progression-free survival in patients who received first line. Conversely, an association with worse progression-free survival was observed in patients who received non-first line immune checkpoint inhibitors. Specifically, overweight and obese patients receiving combination immunotherapy had a statistically significant survival benefit, whereas patients receiving the other treatment types showed heterogeneous trends. We caution the scientific community to consider several important points prior to drawing conclusions that could potentially influence patient care, including preclinical data associating obesity with aggressive tumor biology, the lack of congruence amongst several investigations, and the limited reproduced comprehensiveness of these studies.

Dataset Information

DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma.

Publications

DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma.

Similar Datasets

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