Mepylome: A Point‐of‐Care Tumor Diagnostic Toolkit for Tumor DNA Methylation and Copy Number Analysis

Epigenetic changes play a major role in mediating chemoresistance and relapse in pediatric ALL, and hence in current pilot study, we tried to identify DNA methylation, miRNA expression, and copy number variations (CNVs) in a cohort of relapse pediatric B-ALL cases. DNA methylation, miRNA expression, and CNV analysis were performed in a total of 14, 16, and 18 cases as diagnosis-relapse samples. Briefly, DNA methylation was performed using Infinium HumanMethylation850 chip and data analyzed using RnBeads. miRNA was sequenced on illumina NextSeq500 platform for 20M 75bp SE reads and analyzed by DESeq2. CNVs were assessed by MLPA assay using the ALL P-335 probemix kit and analyzed by coffalyzer.net. On methylation analysis, oncogenes MYCN, MYB, and EGFR and tumor suppressor genes MDM4 & BCL11B were found differentially expressed as compared to controls (p-0.03). In addition, protooncogenes-AXL, HCK, MED12, and ETS2-were hypomethylated/overexpressed in 4 or more cases (P<.05). miRNA analysis revealed significant differential expression of miR-128-2-5p and miR-378C (p-4.4e-15 and p-6.4E-12) in relapse samples. CNV analysis revealed that frequency of good and intermediate/poor risk CNV profile at diagnosis was nearly equal (40% vs 60%). However, CDKN2A/2B and IKZF1 gene CNVs if present in initial diagnostic clone usually persisted in relapse clone. Our pilot study highlights two miRNAs (miR-128-2-5p and miR-378C) as possible candidate biomarkers of relapsed B-ALL. However, these miRNAs and hypomethylated protooncogene signature noted in our data needs validation in a larger series of B-ALL.

Gastrointestinal stromal tumors (GISTs) span a broad clinical spectrum, from indolent neoplasms to life-threatening metastatic tumors. A persistent limitation of current risk stratification systems is that a subset of GISTs is graded as low-risk but nevertheless metastasizes. Therefore, new predictive factors that improve risk stratification are needed. In this exploratory study, we investigated the potential of genome-wide DNA methylation profiling and copy number variation (CNV) analysis as additional prognostic tools for GISTs. We collected a cohort of 28 patients with GIST diagnosed between 2001 and 2022, with available follow-up and molecular data. This included 15 patients without progressive disease (seven low-risk and eight moderate- to high-risk GISTs) and 13 with progressive disease. Among those with progression, eight experienced recurrence or metastasis post-surgery (one low-risk, seven high-risk GISTs), while five had metastatic disease at initial diagnosis. Risk stratification was determined according to Miettinen's criteria. Genome-wide DNA methylation data and CNV plots were generated from imatinib-naïve primary GISTs using the Illumina Infinium MethylationEPIC BeadChip array. Unsupervised cluster analysis revealed distinct DNA methylation patterns predominantly associated with anatomical location and genotype. Differential DNA methylation analysis comparing primary gastric GISTs associated with and without progressive disease showed 8 differentially methylated regions spanning the coding and promoter areas of 6 genes. CNV analysis demonstrated that GISTs associated with progressive disease had the most CNVs, whereas low-risk, non-progressive GISTs had the fewest. Despite the limited sample size, this exploratory study indicates that genome-wide DNA methylation profiling and CNV analysis could enhance GIST risk stratification.

BackgroundMyxofibrosarcoma (MFS) and undifferentiated soft tissue sarcoma (USTS) are common sarcoma subtypes with overlapping molecular features. Both are treated with neoadjuvant radiotherapy followed by surgery, yet radiotherapy response is variable and unpredictable. This study investigated DNA methylation and copy number variation (CNV) profiles obtained from pre-radiotherapy biopsies as predictive biomarkers of radiotherapy response.Patients and methodsPre-radiotherapy biopsies and post-radiotherapy resections were obtained from 49 patients (27 MFS, 22 USTS). Radiotherapy response was assessed on the resection specimens using the EORTC-STBSG 5-tier system; grades A-C (<10% viable tumor) were classified as responders, D-E (≥10% viable tumor) as non-responders. Genome-wide DNA methylation and CNV data were generated from the pre-radiotherapy biopsies using Illumina MethylationEPIC BeadChips and were correlated with response grades.ResultsDNA methylation profiling yielded evaluable results in 23/49 tumors (15 MFS, 8 USTS), with 9 responders and 14 non-responders. Unsupervised methylation clustering, incorporating public datasets, showed that MFS, USTS, and pleomorphic liposarcomas formed a single, heterogeneous cluster. Similarly, CNV profiles did not distinguish MFS from USTS. Methylation patterns did not significantly differ between responders and non-responders. CNV profiles were largely comparable between responders and non-responders, except of a significantly higher frequency of chromosome 11q24.1 loss in responders compared to non-responders (100% vs 33%; P = 0.0039).ConclusionsOur findings support the concept that MFS and USTS represent a spectrum of the same disease. We could not demonstrate the value of DNA methylation profiling in radiotherapy response prediction. However, 11q24.1 loss may represent a potential predictive biomarker and merits further validation.

Background Liquid biopsies based on cell-free DNA (cfDNA) analysis provide non-invasive clinical diagnostic insights. Recently, cfDNA fragmentomics has emerged as a tool for inferring epigenomic and transcriptional information from tumor-derived cfDNA. We conducted a comprehensive profiling of cfDNA fragmentomics, whole transcriptome sequencing (WTS), and genome-wide DNA methylation on two metastatic castration-resistant prostate cancer (mCRPC) samples to systematically investigate molecular aberrations in mCRPC. Methods Two mCRPC patient plasma samples were extensively profiled using high-depth 150x whole-genome sequencing (WGS), 30x whole-genome methylation, and cfRNA WTS. CfDNA fragmentomics analysis was performed to assess androgen receptor (AR) transcriptional activity. We calculated fragment coverage around AR binding sites (ARBS) and quantified ARBS nucleosome profiling abnormality scores (ARBS scores) by comparing centric fragment coverage with normal plasma background. Additionally, we calculated fragment size entropy around AR promoter and enhancer regions to reflect AR gene expression activity. Fragmentomics-inferred gene activity was validated through promoter-targeted panels and WTS profiling of matched mCRPC samples. Results Both mCRPC plasma samples exhibited significant ARBS nucleosome depletion. Sample A showed a higher ARBS score compared to Sample B. Interestingly, AR enhancer activity inferred from high-depth WGS and targeted panels was high in both samples, while AR promoter activity was high only in Sample A. Genome-wide DNA methylation revealed hypo-methylation in AR enhancer and ARBS regions in both samples, with Sample A showing more pronounced hypo-methylation. Copy number variation analysis indicated AR amplification in Sample A and RB1 loss in Sample B. Collectively, these findings suggest that Sample A may represent androgen receptor-dependent prostate cancer (ARPC), while Sample B may have developed resistance to androgen therapy, possibly indicating neuroendocrine prostate cancer (NEPC). Conclusions This study, to our knowledge, is the first to combine high-depth WGS, promoter-targeted panels, WTS, and genome-wide DNA methylation to systematically study epigenomic and transcriptional dysregulation in mCRPC. The integration of mutation, copy number variation, fragmentomics, and DNA methylation profiling holds potential clinical utility for inferring prostate cancer subtypes, facilitating patient stratification, and guiding treatment selection. Citation Format: Chao Dai, Lisha Zhu, Yong Huang, Giancarlo Bonora, Kemin Zhou, Shidong Jia, Pan Du. Integrated analysis of cfDNA fragmentomics, DNA methylation and cfRNA transcription in metastatic castration-resistant prostate cancer (mCRPC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 940.

Background Pancreatic cancer (PC) is a malignant tumor of the digestive tract. It presents with atypical clinical symptoms and lacks specific diagnostic indicators. This study is aimed at exploring the potential biomarkers of PC. Methods TCGA database pancreatic cancer dataset was normalized and used to identify differentially expressed genes (DEGs). Survival, independent prognostic, and clinical correlation analyses were performed on DEGs to screen for key genes. DNA methylation, mutation, and copy number variation (CNV) analyses were used to analyze genetic variants in key genes. GSEA was performed to explore the functional enrichment of the key genes. Based on the expression of key genes, construction of a competing endogenous RNA (ceRNA) network, analysis of the tumor microenvironment (TME), and prediction of chemotherapeutic drug sensitivity were performed. Furthermore, the GEO database was used to validate the reliability of key genes. Results Two key genes (ECT2 and COL17A1) were identified, which were highly expressed in PC. The mRNA expression of ECT2 and COL17A1 was associated with DNA methylation and CNV. The cell cycle, proteasome, and pathways in cancer were enriched in the high-COL17A1 and ECT2 groups. The TME results showed that immune scores were decreased in the high-ECT2 group. CeRNA network results showed that there were eleven miRNAs were involved in the regulation of ECT2 and COL17A1. Moreover, pRRophetic analysis showed that 20 chemotherapeutic drugs were associated with ECT2 and COL17A1 expression. Conclusions Collectively, ECT2 and COL17A1 may be potential biomarkers for PC, providing a new direction for clinical diagnosis and treatment.

The identification of genetic and epigenetic alterations from primary tumor cells has become a common method to identify genes critical to the development and progression of cancer. We provide a bioinformatic analysis of copy number variation and DNA methylation covering the genetic landscape of ovarian cancer tumor cells. We individually examined the copy number variation and DNA methylation for 42 primary ovarian cancer samples using our MOMA-ROMA technology and 379 tumor samples analyzed by The Cancer Genome Atlas. We have identified 346 genes with significant deletions or amplifications among the tumor samples. Utilizing associated gene expression data we predict 156 genes with significantly altered copy number and correlated changes in expression. Among these genes CCNE1, POP4, UQCRB, PHF20L1 and C19orf2 were identified within both datasets. We were specifically interested in copy number variation as our base genomic property in the prediction of tumor suppressors and oncogenes in the altered ovarian tumor. We therefore identify changes in DNA methylation and expression for all amplified and deleted genes. We predicted 615 potential oncogene and tumor suppressor candidates by integrating these multiple genomic and epigenetic data types. Genes with a strong correlation for methylation dependent expression exhibited at varying copy number aberrations include CDCA8, ATAD2, CDKN2A, RAB25, AURKA, BOP1 and EIF2C3. Moreover, well characterized tumor suppressors and oncogenes exhibit varying CNV, methylation and expression features within cancers. We therefore compared significant copy number variation and methylation effects on expression for many known tumor suppressors, oncogenes and cancer related genes in order to identify altered gene function properties specific to primary ovarian tumors. Here, we present the analysis of multiple genomic modalities from two primary ovarian tumor datasets for the identification of candidate genes. We predict genes with epigenetic and genomic tumor suppressor and oncogenic properties having both analyzed and identified known validated ovarian cancer genes and identifying new candidates. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 30. doi:10.1158/1538-7445.AM2011-30

The identification of genetic and epigenetic alterations from primary tumor cells has become a common method to identify genes critical to the development and progression of cancer. We seek to identify those genetic and epigenetic aberrations that have the most impact on gene function within the tumor. First, we perform a bioinformatic analysis of copy number variation (CNV) and DNA methylation covering the genetic landscape of ovarian cancer tumor cells. We separately examined CNV and DNA methylation for 42 primary serous ovarian cancer samples using MOMA-ROMA assays and 379 tumor samples analyzed by The Cancer Genome Atlas. We have identified 346 genes with significant deletions or amplifications among the tumor samples. Utilizing associated gene expression data we predict 156 genes with altered copy number and correlated changes in expression. Among these genes CCNE1, POP4, UQCRB, PHF20L1 and C19orf2 were identified within both data sets. We were specifically interested in copy number variation as our base genomic property in the prediction of tumor suppressors and oncogenes in the altered ovarian tumor. We therefore identify changes in DNA methylation and expression for all amplified and deleted genes. We statistically define tumor suppressor and oncogenic features for these modalities and perform a correlation analysis with expression. We predicted 611 potential oncogenes and tumor suppressors candidates by integrating these data types. Genes with a strong correlation for methylation dependent expression changes exhibited at varying copy number aberrations include CDCA8, ATAD2, CDKN2A, RAB25, AURKA, BOP1 and EIF2C3. We provide copy number variation and DNA methylation analysis for over 11,500 individual genes covering the genetic landscape of ovarian cancer tumors. We show the extent of genomic and epigenetic alterations for known tumor suppressors and oncogenes and also use these defined features to identify potential ovarian cancer gene candidates.

The identification of genetic and epigenetic alterations from primary tumor cells has become a common method to discover genes critical to the development, progression, and therapeutic resistance of cancer. We seek to identify those genetic and epigenetic aberrations that have the most impact on gene function within the tumor. First, we perform a bioinformatics analysis of copy number variation (CNV) and DNA methylation covering the genetic landscape of ovarian cancer tumor cells. We were specifically interested in copy number variation as our base genomic property in the prediction of tumor suppressors and oncogenes in the altered ovarian tumor. We identify changes in DNA methylation and expression specifically for all amplified and deleted genes. We statistically define tumor suppressor and oncogenic gene function from integrative analysis of three modalities: copy number variation, DNA methylation, and gene expression. Our method (1) calculates the extent of genomic and epigenetic alterations of defined tumor suppressor and oncogenic features for the functional prediction of significant ovarian cancer gene candidates and (2) identifies the functional activity or inactivity of known tumor suppressors and oncogenes in ovarian cancer. We applied our protocol on 42 primary serous ovarian cancer samples using MOMA-ROMA representational array assays. Additionally, we provide the basis for incorporating epigenetic profiles of ovarian tumors for the purposes of platinum-free survival prediction in the context of TCGA data.

The polybrominated diphenyl ether flame retardants decabromodiphenyl ether (BDE-209) and bisphenol A (BPA) are environmental contaminants that can cross the placenta and exert toxicity in the developing fetal nervous system. Copy number variants (CNVs) play a role in a number of genetic disorders and may be implicated in BDE-209/BPA teratogenicity. In this study, we found that BDE-209 and/or BPA exposure decreased neural differentiation efficiency of human embryonic stem cells (hESCs), although there was a >90% induction of neuronal progenitor cells (NPCs) from exposed hESCs. However, the mean of CNV numbers in the NPCs with BDE-209 + BPA treatment was significantly higher compared to the other groups, whereas DNA methylation was lower and DNA methyltransferase(DNMT1 and DNMT3A) expression were significantly decreased in all of the BDE-209 and/or BPA treatment groups compared with the control groups. The number of CNVs in chromosomes 3, 4, 11, 22, and X in NPCs with BDE-209 and/or BPA exposure was higher compared to the control group. In addition, CNVs in chromosomes 7, 8, 14, and 16 were stable in hESCs and hESCs-derived NPCs irrespective of BDE-209/BPA exposure, and CNVs in chromosomes 20 q11.21 and 16 p13.11 might be induced by neural differentiation. Thus, BDE-209/BPA exposure emerges as a potential source of CNVs distinct from neural differentiation by itself. BDE-209 and/or BPA exposure may cause genomic instability in cultured stem cells via reduced activity of DNA methyltransferase, suggesting a new mechanism of human embryonic neurodevelopmental toxicity caused by this class of environmental toxins.

BACKGROUND Patients with a suspected neurooncological disease on radiographic images and no histopathological evidence of a tumor on the surgically retrieved tissue, pose a great challenge for clinicians and neuropathologists. DNA methylation-based classifications and copy number variation (CNV) analysis allow robust brain tumor classification. Here, we aim to evaluate their diagnostic properties in histopathologically unspecifiable or negative cases. MATERIAL AND METHODS We screened all neurosurgical cases, which underwent surgery at our institution between 2009 and 2021, with suspected neurooncological, but negative or unspecific histopathological diagnosis. We differentiated two groups: cases with cell-enriched, reactive tissue (+/- single tumor cells), insufficient to fully classify the lesion according to criteria of WHO for CNS tumors (group 1) and cases without histological evidence of tumor cells (group 2). In both groups, DNA methylation profiling was applied. Endpoints were diagnostic results of DNA methylation profiling (classifier Score ≥0.9) and evidence/no evidence of tumorigenic CNV alterations and eventually feasibility to make a diagnosis according to WHO 2021 criteria for brain tumors. RESULTS 23 cases with unspecifiable histopathological diagnosis were identified, out of whom 5 (21.7%) were confirmed as healthy brain tissue via DNA-methylation profiling, whereas the eventual diagnosis of tumorigenic tissue was provided in 18 (78.3%) cases. 14 of these were assigned to group 1, 4 cases to group 2. In 6 cases of group 1 (42.9%) DNA-methylation profiling resulted in a successful tumor classification (score ≥0.9) compared to 1 case (25%) in group 2. CNV indicated tumorigenic alterations in 9 (64.3%) patients in group 1 and 2 (50%) patients in group 2. Specific tumor alterations in the CNV were found in 8 (57.1%) patients in group 1 and in 1 (25%) patient in group 2. Combining CNV and DNA-methylation profiling, a final tumor diagnosis according to WHO 2021 criteria was feasible in 9 (64.3%) cases in group 1 and 3 (75%) cases in group 2. Median time from surgery to histopathological diagnosis was 6 and 7 days and 32.5 and 31 days for integrated diagnosis (group 1 vs. group 2). CONCLUSION Molecular diagnostics with DNA-methylation profiling and CNV analysis substantially increases the likelihood of a definitive diagnosis according to 2021 WHO criteria for these challenging cases.

Biological network inference is a crucial problem to solve in Bioinformatics as most of biological process are based on bio molecular interactions. Many researchers have worked on especially the inference of gene regulatory networks where a node and edge represent a gene and regulation relationship respectively assuming that a gene can regulate another gene indirectly. However, a gene expression level can be influenced by not only genes and proteins but also other biological factors. Therefore, the inference could be more effective if those factors are considered in gene regulatory network inferences. In this paper, we propose an integrative approach to infer gene regulatory networks where a gene can be regulated by not only gene and but also DNA Methylation and copy number variation. It is assumed that a gene can be directly regulated by a single DNA Methylation and copy number variation at most. The simulation results show that our method outperforms popular and state-of-the-art methods of biological network inference. In addition, we applied the proposed method to psychiatric disorder data. The inferred networks provide the relationships within a set of genes that are more likely to be regulated by DNA Methylation and copy number variation of the genes.

Next Generation Sequencing (NGS) technologies provide rapid genomic analyses of single nucleotide variants, RNA expression and DNA copy number. Application of these technologies to material isolated from formalin fixed paraffin embedded (FFPE) tissue and even degraded frozen material could provide powerful replication samples but remains challenging. We tested the nanoString platform to validate deep sequence analysis of gene expression and DNA copy number in degraded and FFPE material. Firstly, RNA from the Universal Human Reference RNA and a breast cancer cell line (MDA-MB-436) was artificially degraded to different degrees (RIN 1.2-6.8). We used the nanoString platform to simultaneously measure RNA expression across 226 genes in each degraded sample and the corresponding undegraded RNA. Secondly we isolated RNA and DNA from matched fresh frozen and FFPE tissues from nine breast cancer patients (3 HER2+/ER+/PR+, 2 HER2+/ER-/PR-, 2 HER2+/ER+/PR-, 2 HER2-/ER+/PR+) using the nanoString platform to compare expression and copy number across 226 and 86 genes respectively. Finally, we correlated expression and copy number data generated by nanoString with Illumina transcriptome and whole genome sequencing (WGS). NanoString log2 expression fold-change between all artificially degraded samples and their undegraded counterpart showed extremely high correlation (r2&amp;gt;0.91). NanoString DNA copy number between matched fresh-frozen and FFPE showed a high degree of correlation (r2=0.71). All gene amplifications with copy number ≥ 5 in DNA from fresh-frozen material (N=9) were successfully identified in DNA from FFPE material. We also observed good correlation of gene expression between whole transcriptome sequencing and the nanoString platform (r2 0.59 - 0.72) in FFPE and artificially degraded material and for DNA copy number between WGS and nanoString in DNA isolated from cancer cell lines (r2=0.96). The nanoString platform provides reliable data from highly degraded and FFPE material and correlates with sequence analysis of both expression and copy number from NGS platforms demonstrating potential for large-scale replication studies in FFPE material. Citation Format: Nadine Norton, Edith A. Perez, Yan W. Asmann, Jennifer M. Carr, Brian M. Necela, Jennifer M. Kachergus, Jin Jen, Bruce W. Eckloff, E Aubrey Thompson. Analysis of gene expression and copy number variation in breast tumors using both sequencing and hybridization-based platforms. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2005. doi:10.1158/1538-7445.AM2013-2005

Dedifferentiated and Undifferentiated Ovarian Carcinoma: An Aggressive and Molecularly Distinct Ovarian Tumor Characterized by Frequent SWI/SNF Complex Inactivation

Large-scale somatic genomic copy number variations (CNV) accumulate during cancer progression, resulting in a tumor comprised of collections of cells, or clones, with distinct CNV profiles. Untangling intra-tumor heterogeneity and inferring clone- and haplotype-specific CNV profiles is important for cancer research and can help inform treatment. We present a computational workflow to infer clone- and haplotype-specific cancer CNV profiles by processing long nanopore reads obtained with high-throughput bulk sequencing of a tumor and matching normal sample. The workflow first identifies and phases heterozygous germline single nucleotide polymorphisms (SNPs) in the normal sample. Nanopore reads from the tumor sample are then haplotagged with presence/absence of the phased germline SNPs. Both the overall and the haplotype-specific read counts from the tumor are then tallied over fixed-size bins tiled across the reference genome. Finally, we use the state-of-the art HATCHet matrix factorization algorithm to process the total- and allele-specific read counts and get integer clone- and allele-specific copy number profiles, as well as the clonal cellular fractions of the tumor sample. To test the proposed approach, we have nanopore sequenced to 100x average read-depth coverage a COLO829 tumor and a COLO829BL matching normal cell line. Previous NGS-based bulk and single-cell analyses of the COLO829 cell-line have revealed it to be highly aneuploid and heterogeneous. We show that the proposed nanopore-based workflow identifies clone- and haplotype-specific cancer CNV profiles in concordance with previously published results, with regions of heterogeneity in full agreement with earlier bulk and single-cell studies. Inferred CNV profiles and their clonal fractions are further supported by observed allele-frequencies of somatic SNPs. We demonstrate the stability of the obtained results across lower tumor sample sequencing coverage levels, with CNVs remaining consistent down to 40x tumor coverage, putting the proposed approach on par with the industry standard NGS-based experiments. Notably, because of the unique ability of long nanopore reads to retain single-molecule methylation signals, we were further able to identify haplotype-specific differentially methylated regions both within the tumor sample, as well as in a tumor vs normal comparison, thus shedding light on acquisition/loss of DNA modifications during tumor growth. These results show how nanopore sequencing can be used to resolve some of the complexity that characterizes structurally aberrant heterogeneous cancer samples, while also revealing the previously inaccessible dimension of haplotype-specific tumor methylation. Oxford Nanopore Technologies products are not intended for use for health assessment or to diagnose, treat, mitigate, cure, or prevent any disease or condition. Citation Format: Sergey Aganezov, John Beaulaurier, Eoghan Harrington, Sissel Juul. Resolving clone-and haplotype-specific copy number variation and DNA methylation in heterogeneous tumors with nanopore sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2704.

Pediatric BCOR-Altered Tumors From Soft Tissue/Kidney Display Specific DNA Methylation Profiles