Malignant and Nonmalignant Gene Signatures in Squamous Head and Neck Cancer

Genetic events specific to the pathogenesis of malignancy can offer clues to the tumorigenesis process. The objective of this study was to identify gene alterations that differentiate tumor and nontumor lesions in squamous head and neck cancer (HNSCC). DNA from 220 primary HNSCC with concurrently present tumor and nontumor lesions from the same patient was interrogated for genomic alterations of loss or gain of copy. Conditional logistic regression dealt with tumor and non-tumor records within a patient. Of 113 genes, 53 had univariate effects (P < 0.01), of which 16 genes remained in the multivariable model with P < 0.01. The model had a C-index (ROC) of 0.93. Loss of CDKN2B and gain of BCL6, FGF3, and PTP4A3 predicted tumor. Loss of BAK1 and CCND1 and gain of STCH predicted nontumor. This highly powered model assigned alterations in 16 genes specific for malignant versus nonmalignant lesions, supporting their contribution to the pathogenesis of HNSCC as well as their potential utility as relevant targets for further evaluation as markers of early detection and progression

Delineating an Epigenetic Continuum for Initiation, Transformation and Progression to Breast Cancer

Aberrant methylation of promoter CpG islands is a hallmark of human cancers and is an early event in carcinogenesis. We examined whether promoter hypermethylation contributes to the pathogenesis of benign breast lesions along a progression continuum to invasive breast cancer. The exploratory study cohort comprised 17 breast cancer patients with multiple benign and/or in situ lesions concurrently present with invasive carcinoma within a tumor biopsy. DNA from tumor tissue, normal breast epithelium when present, benign lesions (fibroadenoma, hyperplasia, papilloma, sclerosing adenosis, apocrine metaplasia, atypical lobular hyperplasia or atypical ductal hyperplasia), and in situ lesions of lobular carcinoma and ductal carcinoma were interrogated for promoter methylation status in 22 tumor suppressor genes using the multiplex ligation-dependent probe amplification assay (MS-MLPA). Methylation specific PCR was performed to confirm hypermethylation detected by MS-MLPA. Promoter methylation was detected in 11/22 tumor suppressor genes in 16/17 cases. Hypermethylation of RASSF1 was most frequent, present in 14/17 cases, followed by APC in 12/17, and GSTP1 in 9/17 cases with establishment of an epigenetic monocloncal progression continuum to invasive breast cancer. Hypermethylated promoter regions in normal breast epithelium, benign, and premalignant lesions within the same tumor biopsy implicate RASSF1, APC, GSTP1, TIMP3, CDKN2B, CDKN2A, ESR1, CDH13, RARB, CASP8, and TP73 as early events. DNA hypermethylation underlies the pathogenesis of step-wise transformation along a monoclonal continuum from normal to preneoplasia to invasive breast cancer

MicroRNA methylomes of normal breast tissue from ER negative and ER positive breast cancer identify progression markers specific for estrogen receptor status

The unique structure and function of normal tissues is known to be regulated by epigenetic mechanisms. Understanding how normal cells in their respective tumor milieus might affect their susceptibility to become not only malignant but acquire breast cancer (BC) subtype-specific phenotypes, may determine tumor clinical behavior outcomes. The goal was to compare genome wide methylation profiles of non-coding miRNAs of breast cancer tissue and normal breast epithelium from estrogen receptor (ER) negative (-) and ER positive (+) tumors, and assess their miRNA methylomes in the context of tumor ER phenotypes as either ER- or ER+. BC tissue from 79 patients (39 ER- and 40 ER+) and normal tissue from 39 of these patients (19 ER- and 20-ER+) were assayed using the Illumina 450K bead array. A sub analysis focused on 2249 miRNA CpGs assigned to 615 unique miRNAs. M-values were computed as a logit function [(log (beta/ (1-beta))] of the methylation beta values. T-tests were used to compare the means of the M-values for the ER+ and ER- groups. The t-test p-values were used to generate adaptive FDR (aFDR) levels and aFDRs of 0.05 or lower were considered to be statistically significant (Tier 1). Tier 1 CpGs were subsequently filtered to select only those with a mean beta ratio between ER+ and ER- of under 0.5 or over 2.0 (Tier 2). The Tier 2 CpGs were further filtered to select only those with a mean beta difference of 0.2 or more (Tier 3). In the tumor cohort, 1224/2249 (54%) CpGs were differentially methylated between ER- and ER+ BC at Tier 1. Of the 1224, 963 (78.7%) were hypermethylated, and 1035 (84.6%) were in promoter regions. The 1224 CpGs at Tier 1, the 24 at Tier 2, and 2 CpGs at Tier 3 were associated with 379, 22 and 2 genes respectively. When the same analysis was performed on normal tissue only (19 ER- and 20-ER+), 76 of the 2249 CpGs had significant aFDR values and none of those met the Tier 2 or Tier 3 criteria. Seventy-one of the 76 (93.4%) were hypermethylated, and 65 (85.5%) were in promoter regions. The 76 significant Tier 1 (aFDR) differentially methylated CpGs were associated with 48 genes of which 43 were common to tumor Tier 1 differentially methylated miRNA genes, 10 were common to tumor Tier 2 genes, and 5 were restricted to normal tissue only. Normal epithelial tissues demonstrated similar differential methylation directionality as their respective tumor counterparts, favoring promoter region localization. Accordingly, the recognition of normal breast tissue-specific epigenetic propensities that align with their tumor phenotypes, suggest the possibility of progression markers specific for ER status as well as markers not associated with progression. This provides insights into our view of possible links between epigenetic programming, progression continuums, and how hormonal receptor subtypes may be determined

Network integration of epigenomic data: Leveraging the concept of master regulators in ER negative breast cancer

Background: There has been relatively little advancement in changing the management of women with estrogen receptor (ER) negative breast cancer (BC), mainly due to a dearth of actionable therapeutic targets. Therefore, understanding the underlying biology of such a complex disease is necessary for bringing new therapeutic treatments to light. A key question in cancer genomics is how to distinguish \u27driver\u27 or essential alterations, which contribute to tumorigenesis, from functionally neutral or \u27passenger\u27 alterations that go along for the ride. The majority of published studies investigating driver genes have focused primarily on genomic mutations which have led to novel study designs (basket trials) where patients with a rare mutation, regardless of tumor histology, are matched to a drug expected to work through the mutated pathway. This dominant focus on mutations has overshadowed consideration of inclusion of epigenetic information. This study illustrates network integration of epigenomic data to prioritize ER negative specific methylated genes as potential epigenetic drivers of aggressive disease. Methods: Causal Networks are small hierarchical networks of regulators whose activity can be modulated by the expression of downstream target genes to enhance understanding of the effect of upstream master regulators on disease or function. A master regulator is a gene or drug positioned as the central or master hub that has the ability to command or influence downstream events. Causal Network Analysis (CNA) was used to find networks that connect upstream master regulators with a 16 candidate methylation gene signature differentiating ER negative from ER positive BC. The 16 ER-negative specific gene methylation signature (AHNAK, ALPL, ANXA2R, CCND1, CIRBP, CPQ, DST, EGFR, ESR1, GPRC5B, HERC5, IL22RA2, MITF, OBSL1, POU3F3, RB1CC1) was identified via our drill-down approach starting from a discovery approach (Illumina 450k BeadChip) followed by expression verification, significant rankings in biological pathways (Ingenuity Pathway Analysis), confirmation by targeted sequencing using Illumina MiSeq, and additional filtering in 450K TCGA data sets. Results: CNA software identified 4 hierarchical networks and their corresponding master regulatory molecules, diethylstilbestrol, transcription regulator SP1, MSH2, and 15-ketoprotaglandin E2. Diethylstilbestrol and SP1 had direct regulatory influence (depth level 1) to the candidate molecules ALPL, CCND1, EGFR, ESR1 and CCND1, CIRBP, EGFR, ESR1, respectively. Conclusion: In this study, direct regulatory influence, noted for 5/16 candidate genes indicates additional rationale for further consideration and validation of ALPL, CCND1, CIRBP, EGFR, ESR1 as potential epigenetic driver targets in ER negative BC. As cancer therapies become increasingly more specific and begin to move past cytotoxic agents, determining the molecular features of a tumor that predict response to a given drug has become increasingly essential to match patients with optimal therapy. Currently epigenetic therapy in the form of hypomethylating agents (e.g: decitabine) exhibit clinical efficacy in patients with AML and MDS including those patients not responding to cytotoxic therapy

MicroRNA methylomes of normal breast tissue from ER negative and ER positive breast cancer identify progression markers specific for estrogen receptor status.

The unique structure and function of normal tissues is known to be regulated by epigenetic mechanisms. Understanding how normal cells in their respective tumor milieus might affect their susceptibility to become not only malignant but acquire breast cancer (BC) subtype-specific phenotypes, may determine tumor clinical behavior outcomes. The goal was to compare genome wide methylation profiles of non-coding miRNAs of breast cancer tissue and normal breast epithelium from estrogen receptor (ER) negative (-) and ER positive (+) tumors, and assess their miRNA methylomes in the context of tumor ER phenotypes as either ER- or ER+. BC tissue from 79 patients (39 ER- and 40 ER+) and normal tissue from 39 of these patients (19 ER- and 20-ER+) were assayed using the Illumina 450K bead array. A sub analysis focused on 2249 miRNA CpGs assigned to 615 unique miRNAs. M-values were computed as a logit function [(log (beta/ (1-beta))] of the methylation beta values. T-tests were used to compare the means of the M-values for the ER+ and ER- groups. The t-test p-values were used to generate adaptive FDR (aFDR) levels and aFDRs of 0.05 or lower were considered to be statistically significant (Tier 1). Tier 1 CpGs were subsequently filtered to select only those with a mean beta ratio between ER+ and ER- of under 0.5 or over 2.0 (Tier 2). The Tier 2 CpGs were further filtered to select only those with a mean beta difference of 0.2 or more (Tier 3). In the tumor cohort, 1224/2249 (54%) CpGs were differentially methylated between ER- and ER+ BC at Tier 1. Of the 1224, 963 (78.7%) were hypermethylated, and 1035 (84.6%) were in promoter regions. The 1224 CpGs at Tier 1, the 24 at Tier 2, and 2 CpGs at Tier 3 were associated with 379, 22 and 2 genes respectively. When the same analysis was performed on normal tissue only (19 ER- and 20-ER+), 76 of the 2249 CpGs had significant aFDR values and none of those met the Tier 2 or Tier 3 criteria. Seventy-one of the 76 (93.4%) were hypermethylated, and 65 (85.5%) were in promoter regions. The 76 significant Tier 1 (aFDR) differentially methylated CpGs were associated with 48 genes of which 43 were common to tumor Tier 1 differentially methylated miRNA genes, 10 were common to tumor Tier 2 genes, and 5 were restricted to normal tissue only. Normal epithelial tissues demonstrated similar differential methylation directionality as their respective tumor counterparts, favoring promoter region localization. Accordingly, the recognition of normal breast tissue-specific epigenetic propensities that align with their tumor phenotypes, suggest the possibility of progression markers specific for ER status as well as markers not associated with progression. This provides insights into our view of possible links between epigenetic programming, progression continuums, and how hormonal receptor subtypes may be determined

Network integration of epigenomic data: Leveraging the concept of master regulators in ER negative breast cancer.

Background: There has been relatively little advancement in changing the management of women with estrogen receptor (ER) negative breast cancer (BC), mainly due to a dearth of actionable therapeutic targets. Therefore, understanding the underlying biology of such a complex disease is necessary for bringing new therapeutic treatments to light. A key question in cancer genomics is how to distinguish \u27driver\u27 or essential alterations, which contribute to tumorigenesis, from functionally neutral or \u27passenger\u27 alterations that go along for the ride. The majority of published studies investigating driver genes have focused primarily on genomic mutations which have led to novel study designs (basket trials) where patients with a rare mutation, regardless of tumor histology, are matched to a drug expected to work through the mutated pathway. This dominant focus on mutations has overshadowed consideration of inclusion of epigenetic information. This study illustrates network integration of epigenomic data to prioritize ER negative specific methylated genes as potential epigenetic drivers of aggressive disease. Methods: Causal Networks are small hierarchical networks of regulators whose activity can be modulated by the expression of downstream target genes to enhance understanding of the effect of upstream master regulators on disease or function. A master regulator is a gene or drug positioned as the central or master hub that has the ability to command or influence downstream events. Causal Network Analysis (CNA) was used to find networks that connect upstream master regulators with a 16 candidate methylation gene signature differentiating ER negative from ER positive BC. The 16 ER-negative specific gene methylation signature (AHNAK, ALPL, ANXA2R, CCND1, CIRBP, CPQ, DST, EGFR, ESR1, GPRC5B, HERC5, IL22RA2, MITF, OBSL1, POU3F3, RB1CC1) was identified via our drill-down approach starting from a discovery approach (Illumina 450k BeadChip) followed by expression verification, significant rankings in biological pathways (Ingenuity Pathway Analysis), confirmation by targeted sequencing using Illumina MiSeq, and additional filtering in 450K TCGA data sets. Results: CNA software identified 4 hierarchical networks and their corresponding master regulatory molecules, diethylstilbestrol, transcription regulator SP1, MSH2, and 15-ketoprotaglandin E2. Diethylstilbestrol and SP1 had direct regulatory influence (depth level 1) to the candidate molecules ALPL, CCND1, EGFR, ESR1 and CCND1, CIRBP, EGFR, ESR1, respectively. Conclusion: In this study, direct regulatory influence, noted for 5/16 candidate genes indicates additional rationale for further consideration and validation of ALPL, CCND1, CIRBP, EGFR, ESR1 as potential epigenetic driver targets in ER negative BC. As cancer therapies become increasingly more specific and begin to move past cytotoxic agents, determining the molecular features of a tumor that predict response to a given drug has become increasingly essential to match patients with optimal therapy. Currently epigenetic therapy in the form of hypomethylating agents (e.g: decitabine) exhibit clinical efficacy in patients with AML and MDS including those patients not responding to cytotoxic therapy

Methylome differences in differentiated thyroid cancers and benign Adenomas.

Many recent studies examining aberrant DNA methylation in thyroid cancer are restricted to either candidate genes or genome-wide methylation in specific thyroid tumor subtypes. The goal of this study was to identify differentially methylated genes globally and their association with molecular pathways and signaling networks. Common mutations in papillary thyroid cancer (PTC), follicular thyroid cancer (FTC), follicular adenoma (FA) and normal thyroid were also examined. Genome-wide methylation profiling using the Infinium HumanMethylation450FFPE (formalin fixed paraffin embedded) BeadChip Array was performed on 24 thyroid cases (8 PTC, 8 FTC, 4 FA and 4 normal thyroid). Ingenuity Pathway Analysis (IPA) was utilized to assess the roles of significantly differentially methylated genes in biological functions, signaling/metabolic pathways, and networks. Common mutations in 4 genes (BRAF, NRAS, HRAS, and KRAS) were assessed using TaqMan Mutation Detection assay. Twelve genes were significantly differentially methylated among 4 comparison groups: cancer vs normal, cancer vs adenoma, PTC vs normal, and PTC follicular variant (PTC-FV) vs PTC-Classic. CTU1 and HLA-DPB1 were significantly hypermethylated and AARS2, TMSB10, RNF216L, KIF15, KIAA1143, and SLC2A13 were significantly hypomethylated between cancer and normal. Significant differential hypermethylation was noted for PNPLA7 and NPC1L1 in cancer vs adenoma and SNX6 in PTC-FV vs PTC-Classic. NT5C1B was hypermethylated and AARS2 was hypomethylated in PTC vs normal. IPA identified 2 gene networks, involving 11/12 genes, characterized by 1) Cellular development, Cellular growth and proliferation, Connective tissue development and function and 2) Drug metabolism, Cell-mediated immune response, Cellular development. NT5C1B was involved in all 4 highly ranked canonical nucleotide degradation pathways. Several significant bio-functions involved NPC1L1. Mutations of NRAS codon 61 were identified in 1 sample each of FTC-Classic, PTC-FV and FA and BRAF V600E in one PTC-Classic sample. Differential methylation of AARS2, CTU1, HLA-DPB1, SLC2A13, PNPLA7, NPC1L1, NT5C1B and SNX6 suggest potential markers for discriminating thyroid cancers from adenomas and normal. NT5C1B was noted in highly ranked canonical pathways, suggesting a role in nucleotide degradation. Pathway analysis of differentially methylated genes support important biological processes in thyroid cancer pathogenesis