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DNA methylation–based immune response signature improves patient diagnosis in multiple cancers
Jana Jeschke, Martin Bizet, Christine Desmedt, Emilie Calonne, Sarah Dedeurwaerder, Soizic Garaud, Alexander Koch, Denis Larsimont, Roberto Salgado, Gert Van den Eynden, Karen Willard Gallo, Gianluca Bontempi, Matthieu Defrance, Christos Sotiriou, François Fuks
Jana Jeschke, Martin Bizet, Christine Desmedt, Emilie Calonne, Sarah Dedeurwaerder, Soizic Garaud, Alexander Koch, Denis Larsimont, Roberto Salgado, Gert Van den Eynden, Karen Willard Gallo, Gianluca Bontempi, Matthieu Defrance, Christos Sotiriou, François Fuks
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Clinical Research and Public Health Development Immunology

DNA methylation–based immune response signature improves patient diagnosis in multiple cancers

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Abstract

BACKGROUND. The tumor immune response is increasingly associated with better clinical outcomes in breast and other cancers. However, the evaluation of tumor-infiltrating lymphocytes (TILs) relies on histopathological measurements with limited accuracy and reproducibility. Here, we profiled DNA methylation markers to identify a methylation of TIL (MeTIL) signature that recapitulates TIL evaluations and their prognostic value for long-term outcomes in breast cancer (BC). METHODS. MeTIL signature scores were correlated with clinical endpoints reflecting overall or disease-free survival and a pathologic complete response to preoperative anthracycline therapy in 3 BC cohorts from the Jules Bordet Institute in Brussels and in other cancer types from The Cancer Genome Atlas. RESULTS. The MeTIL signature measured TIL distributions in a sensitive manner and predicted survival and response to chemotherapy in BC better than did histopathological assessment of TILs or gene expression–based immune markers, respectively. The MeTIL signature also improved the prediction of survival in other malignancies, including melanoma and lung cancer. Furthermore, the MeTIL signature predicted differences in survival for malignancies in which TILs were not known to have a prognostic value. Finally, we showed that MeTIL markers can be determined by bisulfite pyrosequencing of small amounts of DNA from formalin-fixed, paraffin-embedded tumor tissue, supporting clinical applications for this methodology. CONCLUSIONS. This study highlights the power of DNA methylation to evaluate tumor immune responses and the potential of this approach to improve the diagnosis and treatment of breast and other cancers. FUNDING. This work was funded by the Fonds National de la Recherche Scientifique (FNRS) and Télévie, the INNOVIRIS Brussels Region BRUBREAST Project, the IUAP P7/03 program, the Belgian “Foundation against Cancer,” the Breast Cancer Research Foundation (BCRF), and the Fonds Gaston Ithier.

Authors

Jana Jeschke, Martin Bizet, Christine Desmedt, Emilie Calonne, Sarah Dedeurwaerder, Soizic Garaud, Alexander Koch, Denis Larsimont, Roberto Salgado, Gert Van den Eynden, Karen Willard Gallo, Gianluca Bontempi, Matthieu Defrance, Christos Sotiriou, François Fuks

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Figure 5

The MeTIL score predicts differences in survival in other types of cancer.

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The MeTIL score predicts differences in survival in other types of cance...
Forest plot showing the log2 value of the HR and CI for the prediction of survival outcomes in univariate (A) or multivariate (B) Cox models for the MeTIL score (orange) or PaTILs (black) in different TCGA cancer types. Red asterisks indicate statistical significance (P < 0.05 by a likelihood ratio test). BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; COREAD, colon and rectum adenocarcinoma; ESCA, esophageal carcinoma; KIRP, kidney renal papillary cell carcinoma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; OV, ovarian serous cystadenocarcinoma; PAAD, pancreatic adenocarcinoma; PRAD, prostate adenocarcinoma; SARC, sarcoma; STAD, stomach adenocarcinoma; TGCT, testicular germ cell tumors; UCEC, uterine corpus endometrial carcinoma. (C) Heatmap displaying the results of an unsupervised hierarchical clustering analysis of TCGA skin cutaneous melanomas based on β values for the MeTIL markers. Note, a hypomethylated, an intermediate methylated, and a hypermethylated cluster appeared, all of which are associated with differences in subtypes, PaTILs, and MeTIL scores. Differences between methylation clusters were assessed by 1-way ANOVA (MeTILs) or χ2 test (PaTILs and subtypes), and P values are shown. (D) Kaplan-Meier survival curves for the 3 methylation clusters defined in the heatmap. (E) MeTIL scores grouped according to 3 melanoma subtypes. Differences in MeTIL scores between melanoma subtypes were assessed by 1-way ANOVA, and the P value is shown. MITF, malenogenesis-associated transcription factor.

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ISSN: 0021-9738 (print), 1558-8238 (online)

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