Epigenetic Profiling for the Molecular Classification of Metastatic Brain Tumors

Abstract

1Optimal treatment of brain metastases is often hindered by limitations in diagnostic 2 capabilities. To meet these challenges, we generated genome-scale DNA methylomes of the 3 three most frequent types of brain metastases: melanoma, breast, and lung cancers (n=96). 4Using supervised machine learning and integration of multiple DNA methylomes from 5 normal, primary, and metastatic tumor specimens (n=1,860), we unraveled epigenetic 6 signatures specific to each type of metastatic brain tumor and constructed a three-step DNA 7 methylation-based classifier (BrainMETH) that categorizes brain metastases according to the 8 Brain metastases (BM) are the most common intracranial neoplasm in adults and are 1 the next frontier for the management of metastatic cancer patients. Large population-based 2 studies have shown that 8-10% of cancer patients develop brain metastases, with this 3 proportion increasing up to 26% when autopsy studies were included 1-5 . Lung cancer, breast 4 cancer, and cutaneous melanoma account for the vast majority (75-90%) of secondary 5 neoplasms in the brain 1-4 . Treatment options for BM include surgery, whole-brain 6 radiotherapy, stereotactic radiosurgery, and systemic-drug therapy, such as immunotherapy 6 . 7While systemic chemotherapy has limited efficacy, targeted therapies have recently shown 8 promise for the management of patients with specific subtypes of cancer 6 . These tailored 9therapies have significantly affected treatment decision making for patients with breast 10 cancer BM (BCBM). For example, patients with human epidermal growth factor receptor 2 11 (HER2)-positive BCBM can be treated with anti-HER2 agents 7 and patients with estrogen 12 receptor (ER)-positive BCBM can be treated with endocrine agents, cyclin dependent 13 kinases 4 and 6 (CDK4/6) inhibitors, and the mechanistic target of rapamycin kinase (mTOR) 14 inhibitors 8 . As such, accurate diagnosis is essential to effectively treat patients with 15 metastatic brain tumors. 16 17 Diagnosis of BM is currently based on neuro-imaging and confirmed by anatomic 18 pathology examination. When appropriate, the diagnostic algorithm begins by distinguishing 19 BM from primary brain tumors using histologic features guided by the clinical and radiologic 20 information 9 . Then, to identify the tissue of origin, morphological evaluation is supplemented 21 by several immunohistochemistry (IHC) markers including thyroid transcription factor (TTF-22 1), chromogranin and synaptophysin for lung cancer BM (LCBM); GATA3 binding protein 23 (GATA3), mammaglobin, gross cystic disease fluid protein 15 (GCDFP-15) and ER for 24 BCBM; and human melanoma black 45 (HMB45), melanoma antigen recognized by T-cells 1 25 4 (Melan A/MART-1), SRY-Box 10 (SOX-10), and the S100 calcium binding proteins (S-100) 1 for melanoma BM (MBM) 9,10 . However, a major limitation in achieving an accurate 2 pathological diagnosis is the often poor differentiation and/or limited availability of metastatic 3 brain tumor tissues to evaluate the complete panel of IHC mark...

DNA hypermethylation is associated with invasive phenotype of malignant melanoma

“…Invasion is a crucial step in metastasis formation in primary tumors including malignant melanoma. Recent advances in epigenome‐wide DNA methylation methods have allowed for the identification of potential biomarkers that could be exploited in clinical settings . However, in the case of early invasion steps in primary melanomas, insufficient data are available regarding the epigenetic mechanisms and especially the functionally relevant DNA methylation changes affecting gene expression patterns.…”

“…Hypermethylation of homeobox genes is frequent in several cancers; however, this higher methylation is not consequently repress their downstream genes, as well as differentially methylated homeobox genes are not shown to be down‐regulated in our invasive cells . Differentially methylated HOXA5 and HOXD11 was found as a specific alteration in melanoma brain metastasis, and hypermethylation of HOXD9 was described in lymph node metastasis with poorer overall survival . It is suggested that methylation pattern of homeobox genes can be specific to melanoma cells, and it is a possible approach to use epigenetic biomarker panels including homeobox genes in diagnosis, prediction and prognosis …”

DIMEimmune: Robust estimation of infiltrating lymphocytes in CNS tumors from DNA methylation profiles

“…Conversely, global DNA methylation analysis has been extensively used for molecular analyses of brain tumors, [20][21][22][23][24] and there is an increasing number of applications in the clinical routine for the diagnostic classification of brain tumors and other neoplasms. [25][26][27][28] Therefore, methods that can robustly quantify immune cells based on DNA methylation are needed and currently much more widely applicable in neuro-oncology than transcriptomic approaches.…”