Using high-density DNA methylation arrays to profile copy number alterations

Feber, Andrew; Guilhamon, Paul; Lechner, Matthias; Fenton, Tim R.; Wilson, Gareth A.; Thirlwell, Christina; Morris, Tiffany; Flanagan, Adrienne M.; Teschendorff, Andrew E.; Kelly, John D.; Beck, Stephan

doi:10.1186/gb-2014-15-2-r30

Cited by 124 publications

(118 citation statements)

References 44 publications

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“…In order to validate these findings, we took advantage of the recent evidence that the HM450 DNA methylation platform can also be used to determine CNAs by summing the methylated and unmethylated signal intensities of the probes [32, 33]. This analysis provided additional evidence that the T2 and T3 foci were very similar to the PL in patient 41.…”

Section: Resultsmentioning

confidence: 98%

Identifying aggressive prostate cancer foci using a DNA methylation classifier

et al. 2017

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BackgroundSlow-growing prostate cancer (PC) can be aggressive in a subset of cases. Therefore, prognostic tools to guide clinical decision-making and avoid overtreatment of indolent PC and undertreatment of aggressive disease are urgently needed. PC has a propensity to be multifocal with several different cancerous foci per gland.ResultsHere, we have taken advantage of the multifocal propensity of PC and categorized aggressiveness of individual PC foci based on DNA methylation patterns in primary PC foci and matched lymph node metastases. In a set of 14 patients, we demonstrate that over half of the cases have multiple epigenetically distinct subclones and determine the primary subclone from which the metastatic lesion(s) originated. Furthermore, we develop an aggressiveness classifier consisting of 25 DNA methylation probes to determine aggressive and non-aggressive subclones. Upon validation of the classifier in an independent cohort, the predicted aggressive tumors are significantly associated with the presence of lymph node metastases and invasive tumor stages.ConclusionsOverall, this study provides molecular-based support for determining PC aggressiveness with the potential to impact clinical decision-making, such as targeted biopsy approaches for early diagnosis and active surveillance, in addition to focal therapy.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1129-3) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 98%

Identifying aggressive prostate cancer foci using a DNA methylation classifier

et al. 2017

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“…Although the validation dataset separated all anaplastic meningiomas accurately (all WHO grade III meningiomas in the MM-UNFAV arm) (Figures 1 and 2), this model should be interpreted with care when applied to WHO grade III meningiomas. Second, although retrieval of copy number information from the 450k array intensity values has been validated in the literature by multiple molecular platforms and the 450k platform has been identified as having the sensitivity of SNP arrays for copy number alteration detection [49,18,56] the results should be interpreted with care. Third, our prediction analyses based on hypothetical downregulation of the 6 genes presented in Online resource 7, should be interpreted with care having in mind that epigenetic regulation is more complex and involves other regulatory molecules that were not taken into consideration here, such as microRNA expression and histone/chromatin modifications.…”

Section: Discussionmentioning

confidence: 99%

Global epigenetic profiling identifies methylation subgroups associated with recurrence-free survival in meningioma

Olar

Wani

Wilson³

et al. 2017

Acta Neuropathol

157

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Meningioma is the most common primary brain tumor and carries a substantial risk of local recurrence. Methylation profiles of meningioma and their clinical implications are not well understood. We hypothesized that aggressive meningiomas have unique DNA methylation patterns that could be used to better stratify patient management. Samples (n=140) were profiled using the Illumina HumanMethylation450 BeadChip. Unsupervised modeling on a training set (n=89) identified 2 molecular methylation subgroups of meningioma (MM) with significantly different recurrence free survival (RFS) times between the groups: a prognostically unfavorable subgroup (MM-UNFAV) and a prognostically favorable subgroup (MM-FAV). This finding was validated in the remaining 51 samples and led to a baseline meningioma methylation classifier (bMMC) defined by 283 CpG loci (283-bMMC). To further optimize a recurrence predictor, probes subsumed within the baseline classifier were subject to additional modeling using a similar training/validation approach, leading to a 64-CpG loci meningioma methylation predictor (64-MMP). After adjustment for relevant clinical variables [WHO grade, mitotic index, Simpson grade, sex, location, and copy number aberrations (CNA)] multivariable analyses for RFS showed that the baseline methylation classifier was not significant (p=0.0793). The methylation predictor however was significantly associated with tumor recurrence (p<0.0001). CNA were extracted from the 450k intensity profiles. Tumor samples in the MM-UNFAV subgroup showed an overall higher proportion of CNAs compared to the MM-FAV subgroup tumors and the CNAs were complex in nature. CNAs in the MM-UNFAV subgroup included recurrent losses of 1p, 6q, 14q and 18q, and gain of 1q, all of which were previously identified as indicators of poor outcome. In conclusion, our analyses demonstrate robust DNA methylation signatures in meningioma that correlate with CNAs and stratify patients by recurrence risk.

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“…The "FinalReport" files generated from Illumina Genome Studio were processed by using R packages "lumi" and/or "methylumi," similar to previously described (Feber et al 2014). Briefly, the total intensities from both methylated and unmethylated probes were calculated and then normalized by using a simple scaling normalization method (SSN implemented in lumi R package).…”

Section: Genomic Copy Number Derivation From Methylation Array Datamentioning

confidence: 99%

Imprints and DPPA3 are bypassed during pluripotency- and differentiation-coupled methylation reprogramming in testicular germ cell tumors

et al. 2016

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Testicular germ cell tumors (TGCTs) share germline ancestry but diverge phenotypically and clinically as seminoma (SE) and nonseminoma (NSE), the latter including the pluripotent embryonal carcinoma (EC) and its differentiated derivatives, teratoma (TE), yolk sac tumor (YST), and choriocarcinoma. Epigenomes from TGCTs may illuminate reprogramming in both normal development and testicular tumorigenesis. Herein we investigate pure-histological forms of 130 TGCTs for conserved and subtype-specific DNA methylation, including analysis of relatedness to pluripotent stem cell (ESC, iPSC), primordial germ cell (PGC), and differentiated somatic references. Most generally, TGCTs conserve PGC-lineage erasure of maternal and paternal genomic imprints and DPPA3 (also known as STELLA); however, like ESCs, TGCTs show focal recurrent imprinted domain hypermethylation. In this setting of shared physiologic erasure, NSEs harbor a malignancy-associated hypermethylation core, akin to that of a diverse cancer compendium. Beyond these concordances, we found subtype epigenetic homology with pluripotent versus differentiated states. ECs demonstrate a striking convergence of both CpG and CpH (non-CpG) methylation with pluripotent states; the pluripotential methyl-CpH signature crosses species boundaries and is distinct from neuronal methyl-CpH. EC differentiation to TE and YST entails reprogramming toward the somatic state, with loss of methyl-CpH but de novo methylation of pluripotency loci such as NANOG. Extreme methyl-depletion among SE reflects the PGC methylation nadir. Adjacent to TGCTs, benign testis methylation profiles are determined by spermatogenetic proficiency measured by Johnsen score. In sum, TGCTs share collective entrapment in a PGC-like state of genomic-imprint and DPPA3 erasure, recurrent hypermethylation of cancer-associated targets, and subtype-dependent pluripotent, germline, or somatic methylation.

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Using high-density DNA methylation arrays to profile copy number alterations

Cited by 124 publications

References 44 publications

Identifying aggressive prostate cancer foci using a DNA methylation classifier

Identifying aggressive prostate cancer foci using a DNA methylation classifier

Global epigenetic profiling identifies methylation subgroups associated with recurrence-free survival in meningioma

Imprints and DPPA3 are bypassed during pluripotency- and differentiation-coupled methylation reprogramming in testicular germ cell tumors

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