DNA methylation markers panel can improve prediction of response to neoadjuvant chemotherapy in luminal B breast cancer

SP1 Gene Methylation in Head and Neck Squamous Cell Cancer in HPV-Negative Patients

There is still much to learn about the epigenetic mechanisms controlling gene expression during carcinogenesis. When researching aberrant DNA methylation, active proliferative tumor cells from head and neck squamous cell cancer (HNSCC) can be used as a model. The aim of the study was to investigate the methylation status of CDKN1, CDKN2A, MYC, Smad3, SP1, and UBC genes in tumor tissue (control-normal tissue) in 50 patients (37 men and 13 women) with HPV-negative HNSCC. Methods: Bisulfite conversion methods and methyl-sensitive analysis of high-resolution melting curves were used to quantify the methylation of genes. In all patients and across various subgroups (tongue carcinoma, laryngeal and other types of carcinomas T2, T3, T4 status; age before and after 50 years; smoking and non-smoking), there are consistent differences in the methylation levels in the SP1 gene in tumor DNA compared to normal. Results: The methylation of the SP1 gene in tumor DNA suppresses its expression, hinders HNSCC cell proliferation regulation, and could be a molecular indicator of malignant cell growth. The study of DNA methylation of various genes involved in carcinogenesis is promising because hypermethylated promoters can serve as potential biomarkers of disease.

Alterations to DNA methylation patterns induced by chemotherapy treatment are associated with negative impacts on the olfactory pathway

BACKGROUND

Exposure to cytotoxic chemotherapy treatment may alter DNA methylation (DNAm) in breast cancer patients.

METHODS

We performed DNAm analysis in 125 breast cancer patients with blood drawn before and after chemotherapy, using the Illumina MethylationEPIC array. DNAm changes of 588,798 individual CpGs (including 41,207 promoter regions) were evaluated using linear regression models adjusted for monocyte proportion. Gene set enrichment analyses (GSEA) were conducted to identify key Gene Ontology (GO) biological processes or Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with chemotherapy. Results were validated in a separate cohort of breast cancer patients who were treated (n = 1273) and not treated (n = 872) by chemotherapy (1808 blood, 337 saliva).

RESULTS

A total of 141 differentially methylated CpGs and 11 promoters were significantly associated with chemotherapy after multiple testing corrections in both the paired sample and single time point analyses. GSEA of promoter regions (pre-ranked by test statistics) identified six suppressed biological processes (p < 4.67e-8) related to sensory perception and detection of chemical stimuli, including smell perception (GO:0007606, GO:0007608, GO:0009593, GO:0050906, GO:0050907, and GO:0050911). The same six biological processes were significantly suppressed in the validation dataset (p < 9.02e-14). The KEGG pathway olfactory transduction (hsa04740) was also found to be significantly suppressed (ppaired-samples = 1.72e-9, psingle-timepoint-blood = 2.03e-15 and psingle-timepoint-saliva = 7.52e-56).

CONCLUSION

The enrichment of imprinted genes within biological processes and pathways suggests a biological mechanism by which chemotherapy could affect the perception of smell.

DNA Methylation Signatures of Response to Conventional Synthetic and Biologic Disease-Modifying Antirheumatic Drugs (DMARDs) in Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a complex condition that displays heterogeneity in disease severity and response to standard treatments between patients. Failure rates for conventional, target synthetic, and biologic disease-modifying rheumatic drugs (DMARDs) are significant. Although there are models for predicting patient response, they have limited accuracy, require replication/validation, or for samples to be obtained through a synovial biopsy. Thus, currently, there are no prediction methods approved for routine clinical use. Previous research has shown that genetics and environmental factors alone cannot explain the differences in response between patients. Recent studies have demonstrated that deoxyribonucleic acid (DNA) methylation plays an important role in the pathogenesis and disease progression of RA. Importantly, specific DNA methylation profiles associated with response to conventional, target synthetic, and biologic DMARDs have been found in the blood of RA patients and could potentially function as predictive biomarkers. This review will summarize and evaluate the evidence for DNA methylation signatures in treatment response mainly in blood but also learn from the progress made in the diseased tissue in cancer in comparison to RA and autoimmune diseases. We will discuss the benefits and challenges of using DNA methylation signatures as predictive markers and the potential for future progress in this area.

Pleiotropy of C1QL proteins across physiological systems and their emerging role in synapse homeostasis

The C1q/TNF superfamily of proteins engages in a pleiotropy of physiological functions associated with various diseases. C1QL proteins demonstrate important protective and regulatory roles in the endocrine, immune, cardiovascular, and nervous systems in both human and rodent studies. Studies in the central nervous system (CNS), adipose, and muscle tissue reveal several C1QL protein and receptor pathways altering multiple cellular responses, including cell fusion, morphology, and adhesion. This review examines C1QL proteins across these systems, summarizing functional and disease associations and highlighting cellular responses based on in vitro and in vivo data, receptor interaction partners, and C1QL-associated protein signaling pathways. We highlight the functions of C1QL proteins in organizing CNS synapses, regulating synapse homeostasis, maintaining excitatory synapses, and mediating signaling and trans-synaptic connections. Yet, while these associations are known, present studies provide insufficient insight into the underlying molecular mechanism of their pleiotropy, including specific protein interactions and functional pathways. Thus, we suggest several areas for more in-depth and interdisciplinary hypothesis testing.

DNA Methylation and Prospects for Predicting the Therapeutic Effect of Neoadjuvant Chemotherapy for Triple-Negative and Luminal B Breast Cancer

Despite advances in the diagnosis and treatment of breast cancer (BC), the main cause of deaths is resistance to existing therapies. An approach to improve the effectiveness of therapy in patients with aggressive BC subtypes is neoadjuvant chemotherapy (NACT). Yet, the response to NACT for aggressive subtypes is less than 65% according to large clinical trials. An obvious fact is the lack of biomarkers predicting the therapeutic effect of NACT. In a search for epigenetic markers, we performed genome-wide differential methylation screening by XmaI-RRBS in cohorts of NACT responders and nonresponders, for triple-negative (TN) and luminal B tumors. The predictive potential of the most discriminative loci was further assessed in independent cohorts by methylation-sensitive restriction enzyme quantitative PCR (MSRE-qPCR), a promising method for the implementation of DNA methylation markers in diagnostic laboratories. The selected most informative individual markers were combined into panels demonstrating cvAUC = 0.83 (TMEM132D and MYO15B markers panel) for TN tumors and cvAUC = 0.76 (TTC34, LTBR and CLEC14A) for luminal B tumors. The combination of methylation markers with clinical features that correlate with NACT effect (clinical stage for TN and lymph node status for luminal B tumors) produces better classifiers, with cvAUC = 0.87 for TN tumors and cvAUC = 0.83 for luminal B tumors. Thus, clinical characteristics predictive of NACT response are independently additive to the epigenetic classifier and in combination improve prediction.

Construction and evaluation of a prognostic risk model of tumor metastasis-related genes in patients with non-small cell lung cancer

BACKGROUND

Lung cancer is a high-incidence cancer, and it is also the most common cause of cancer death worldwide. 80-85% of lung cancer cases can be classified as non-small cell lung cancer (NSCLC).

METHODS

NSCLC transcriptome data and clinical information were downloaded from the TCGA database and GEO database. Firstly, we analyzed and identified the differentially expressed genes (DEGs) between non-metastasis group and metastasis group of NSCLC in the TCGA database, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) were consulted to explore the functions of the DEGs. Thereafter, univariate Cox regression and LASSO Cox regression algorithms were applied to identify prognostic metastasis-related signature, followed by the construction of the risk score model and nomogram for predicting the survival of NSCLC patients. GSEA analyzed that differentially expressed gene-related signaling pathways in the high-risk group and the low-risk group. The survival of NSCLC patients was analyzed by the Kaplan-Meier method. ROC curve was plotted to evaluate the accuracy of the model. Finally, the GEO database was further applied to verify the metastasis‑related prognostic signature.

RESULTS

In total, 2058 DEGs were identified. GO functions and KEGG pathways analysis results showed that the DEGs mainly concentrated in epidermis development, skin development, and the pathway of Neuro active ligand -receptor interaction in cancer. A six-gene metastasis-related risk signature including C1QL2, FLNC, LUZP2, PRSS3, SPIC, and GRAMD1B was constructed to predict the overall survival of NSCLC patients. The reliability of the gene signature was verified in GSE13213. The NSCLC patients were grouped into low-risk and high-risk groups based on the median value of risk scores. And low-risk patients had lower risk scores and longer survival time. Univariate and multivariate Cox regression verified that this signature was an independent risk factor for NSCLC.

CONCLUSION

Our study identified 6 metastasis biomarkers in the NSCLC. The biomarkers may contribute to individual risk estimation, survival prognosis.

Predictive biomarkers for personalized medicine in breast cancer

Breast cancer is one of the most frequent malignancies among women worldwide. Based on clinical and molecular features of breast tumors, patients are treated with chemotherapy, hormonal therapy and/or radiotherapy and more recently with immunotherapy or targeted therapy. These different therapeutic options have markedly improved patient outcomes. However, further improvement is needed to fight against resistance to treatment. In the rapidly growing area of research for personalized medicine, predictive biomarkers - which predict patient response to therapy - are essential tools to select the patients who are most likely to benefit from the treatment, with the aim to give the right therapy to the right patient and avoid unnecessary overtreatment. The search for predictive biomarkers is an active field of research that includes genomic, proteomic and/or machine learning approaches. In this review, we describe current strategies and innovative tools to identify, evaluate and validate new biomarkers. We also summarize current predictive biomarkers in breast cancer and discuss companion biomarkers of targeted therapy in the context of precision medicine.

DNA methylation changes in response to neoadjuvant chemotherapy are associated with breast cancer survival

BACKGROUND

Locally advanced breast cancer is a heterogeneous disease with respect to response to neoadjuvant chemotherapy (NACT) and survival. It is currently not possible to accurately predict who will benefit from the specific types of NACT. DNA methylation is an epigenetic mechanism known to play an important role in regulating gene expression and may serve as a biomarker for treatment response and survival. We investigated the potential role of DNA methylation as a prognostic marker for long-term survival (> 5 years) after NACT in breast cancer.

METHODS

DNA methylation profiles of pre-treatment (n = 55) and post-treatment (n = 75) biopsies from 83 women with locally advanced breast cancer were investigated using the Illumina HumanMethylation450 BeadChip. The patients received neoadjuvant treatment with epirubicin and/or paclitaxel. Linear mixed models were used to associate DNA methylation to treatment response and survival based on clinical response to NACT (partial response or stable disease) and 5-year survival, respectively. LASSO regression was performed to identify a risk score based on the statistically significant methylation sites and Kaplan-Meier curve analysis was used to estimate survival probabilities using ten years of survival follow-up data. The risk score developed in our discovery cohort was validated in an independent validation cohort consisting of paired pre-treatment and post-treatment biopsies from 85 women with locally advanced breast cancer. Patients included in the validation cohort were treated with either doxorubicin or 5-FU and mitomycin NACT.

RESULTS

DNA methylation patterns changed from before to after NACT in 5-year survivors, while no significant changes were observed in non-survivors or related to treatment response. DNA methylation changes included an overall loss of methylation at CpG islands and gain of methylation in non-CpG islands, and these changes affected genes linked to transcription factor activity, cell adhesion and immune functions. A risk score was developed based on four methylation sites which successfully predicted long-term survival in our cohort (p = 0.0034) and in an independent validation cohort (p = 0.049).

CONCLUSION

Our results demonstrate that DNA methylation patterns in breast tumors change in response to NACT. These changes in DNA methylation show potential as prognostic biomarkers for breast cancer survival.

Isolation of a Human Betaretrovirus from Patients with Primary Biliary Cholangitis

A human betaretrovirus (HBRV) has been linked with the autoimmune liver disease, primary biliary cholangitis (PBC), and various cancers, including breast cancer and lymphoma. HBRV is closely related to the mouse mammary tumor virus, and represents the only exogenous betaretrovirus characterized in humans to date. Evidence of infection in patients with PBC has been demonstrated through the identification of proviral integration sites in lymphoid tissue, the major reservoir of infection, as well as biliary epithelium, which is the site of the disease process. Accordingly, we tested the hypothesis that patients with PBC harbor a transmissible betaretrovirus by co-cultivation of PBC patients’ lymph node homogenates with the HS578T breast cancer line. Because of the low level of HBRV replication, betaretrovirus producing cells were subcloned to optimize viral isolation and production. Evidence of infection was provided by electron microscopy, RT-PCR, in situ hybridization, cloning of the HBRV proviral genome and demonstration of more than 3400 integration sites. Further evidence of viral transmissibility was demonstrated by infection of biliary epithelial cells. While HBRV did not show a preference for integration proximal to specific genomic features, analyses of common insertion sites revealed evidence of integration proximal to cancer associated genes. These studies demonstrate the isolation of HBRV with features similar to mouse mammary tumor virus and confirm that patients with PBC display evidence of a transmissible viral infection.

DNA methylation profiles differ in responders versus non-responders to anti-PD-1 immune checkpoint inhibitors in patients with advanced and metastatic head and neck squamous cell carcinoma

BACKGROUND

Biomarkers for response prediction to anti-programmed cell death 1 (PD-1) immune checkpoint inhibitors (ICI) in patients with head and neck squamous cell carcinoma (HNSCC) are urgently needed for a personalized therapy approach. We investigated the predictive potential of inflammatory parameters and DNA methylation profiling in patients with HNSCC treated with anti-PD-1 ICI.

METHODS

We identified patients with HNSCC that were treated with anti-PD-1 ICI therapy in the recurrent or metastatic setting after progression to platinum-based chemotherapy in two independent centers. We analyzed DNA methylation profiles of >850.000 CpG sites in tumor specimens of these patients by Infinium MethylationEPIC microarrays, immune cell density in the tumor microenvironment (CD8, CD3, CD45RO, forkhead box P3 (FOXP3), CD68), PD-1 and programmed cell death ligand 1 (PD-L1) expression by immunohistochemistry, and blood inflammation markers (platelet-to-lymphocyte ratio, leucocyte-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, neutrophil-to-lymphocyte ratio). DNA methylation profiles and immunological markers were bioinformatically and statistically correlated with radiological response to anti-PD-1 ICI.

RESULTS

37 patients with HNSCC (median age of 62 years; range 49-83; 8 (21.6%) women, 29 (78.4%) men) were included (Center 1 N=26, 70.3%; Center 2 N=11, 29.7%). Median number of prior systemic therapies was 1 (range 1-4). Five out of 37 (13.5%) patients achieved an objective response to ICI. Median progression-free survival and median overall survival times were 3.7 months (range 0-22.9) and 9.0 months (range 0-38.8), respectively. Microarray analyses revealed a methylation signature including both hypomethylation and hypermethylation which was predictive for response to ICI and included several genes involved in cancer-related molecular pathways. Over-represented differentially methylated genes between responders and non-responders were associated with 'Axon guidance', 'Hippo signaling', 'Pathways in cancer' and 'MAPK signaling'. A statistically significant correlation of PD-L1 expression and response was present (p=0.0498).

CONCLUSIONS

Our findings suggest that tumor DNA methylation profiling may be useful to predict response to ICI in patients with HNSCC.

Tissue-Specific Methylation Biosignatures for Monitoring Diseases: An In Silico Approach

Tissue-specific gene methylation events are key to the pathogenesis of several diseases and can be utilized for diagnosis and monitoring. Here, we established an in silico pipeline to analyze high-throughput methylome datasets to identify specific methylation fingerprints in three pathological entities of major burden, i.e., breast cancer (BrCa), osteoarthritis (OA) and diabetes mellitus (DM). Differential methylation analysis was conducted to compare tissues/cells related to the pathology and different types of healthy tissues, revealing Differentially Methylated Genes (DMGs). Highly performing and low feature number biosignatures were built with automated machine learning, including: (1) a five-gene biosignature discriminating BrCa tissue from healthy tissues (AUC 0.987 and precision 0.987), (2) three equivalent OA cartilage-specific biosignatures containing four genes each (AUC 0.978 and precision 0.986) and (3) a four-gene pancreatic β-cell-specific biosignature (AUC 0.984 and precision 0.995). Next, the BrCa biosignature was validated using an independent ccfDNA dataset showing an AUC and precision of 1.000, verifying the biosignature's applicability in liquid biopsy. Functional and protein interaction prediction analysis revealed that most DMGs identified are involved in pathways known to be related to the studied diseases or pointed to new ones. Overall, our data-driven approach contributes to the maximum exploitation of high-throughput methylome readings, helping to establish specific disease profiles to be applied in clinical practice and to understand human pathology.

Multiplexed magnetic beads-assisted amperometric bioplatforms for global detection of methylations in nucleic acids

This work reports the first electrochemical bioplatform developed for the multidetection of 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC) in DNA, DNA N6-methyladenine (6mA) and RNA N6-methyladenosine (m6A) methylations at global level. Direct competitive immunoassays were implemented on the surface of magnetic beads (MBs) and optimized for the single amperometric determination of different targets varying in length, sequence and number of methylations on screen-printed carbon electrodes. After evaluating the sensitivity and selectivity of such determinations and the confirmation of no cross-reactivity, a multiplexed disposable platform allowing the simultaneous determination of the mentioned four methylation events in only 45 min has been prepared. The multiplexed bioplatform was successfully applied to the determination of m6A in cellular total RNA and of 5-mC, 5-hmC and 6mA in genomic DNA extracted from tissues. The developed bioplatform showed its usefulness to discriminate the aggressiveness of cancerous cells and between healthy and tumor tissues of colorectal cancer patients.

Multi-Omics Model Applied to Cancer Genetics

In this review, we focus on bioinformatic oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. Before providing a deeper insight into the bioinformatics approach and utilities involved in oncology, we must understand what is a system biology framework and the genetic connection, because of the high heterogenicity of the backgrounds of people approaching precision medicine. In fact, it is essential to providing general theoretical information on genomics, epigenomics, and transcriptomics to understand the phases of multi-omics approach. We consider how to create a multi-omics model. In the last section, we describe the new frontiers and future perspectives of this field.

Clinical epigenomics for cardiovascular disease: Diagnostics and therapies

The study of epigenomics has advanced in recent years to span the regulation of a single genetic locus to the structure and orientation of entire chromosomes within the nucleus. In this review, we focus on the challenges and opportunities of clinical epigenomics in cardiovascular disease. As an integrator of genetic and environmental inputs, and because of advances in measurement techniques that are highly reproducible and provide sequence information, the epigenome is a rich source of potential biosignatures of cardiovascular health and disease. Most of the studies to date have focused on the latter, and herein we discuss observations on epigenomic changes in human cardiovascular disease, examining the role of protein modifiers of chromatin, noncoding RNAs and DNA modification. We provide an overview of cardiovascular epigenomics, discussing the challenges of data sovereignty, data analysis, doctor-patient ethics and innovations necessary to implement precision health.

Deciphering the Methylation Landscape in Breast Cancer: Diagnostic and Prognostic Biosignatures through Automated Machine Learning

DNA methylation plays an important role in breast cancer (BrCa) pathogenesis and could contribute to driving its personalized management. We performed a complete bioinformatic analysis in BrCa whole methylome datasets, analyzed using the Illumina methylation 450 bead-chip array. Differential methylation analysis vs. clinical end-points resulted in 11,176 to 27,786 differentially methylated genes (DMGs). Innovative automated machine learning (AutoML) was employed to construct signatures with translational value. Three highly performing and low-feature-number signatures were built: (1) A 5-gene signature discriminating BrCa patients from healthy individuals (area under the curve (AUC): 0.994 (0.982-1.000)). (2) A 3-gene signature identifying BrCa metastatic disease (AUC: 0.986 (0.921-1.000)). (3) Six equivalent 5-gene signatures diagnosing early disease (AUC: 0.973 (0.920-1.000)). Validation in independent patient groups verified performance. Bioinformatic tools for functional analysis and protein interaction prediction were also employed. All protein encoding features included in the signatures were associated with BrCa-related pathways. Functional analysis of DMGs highlighted the regulation of transcription as the main biological process, the nucleus as the main cellular component and transcription factor activity and sequence-specific DNA binding as the main molecular functions. Overall, three high-performance diagnostic/prognostic signatures were built and are readily available for improving BrCa precision management upon prospective clinical validation. Revisiting archived methylomes through novel bioinformatic approaches revealed significant clarifying knowledge for the contribution of gene methylation events in breast carcinogenesis.

Tumor DNA methylation profiles correlate with response to anti-PD-1 immune checkpoint inhibitor monotherapy in sarcoma patients

BACKGROUND

Some sarcomas respond to immune checkpoint inhibition, but predictive biomarkers are unknown. We analyzed tumor DNA methylation profiles in relation to immunological parameters and response to anti-programmed cell death 1 (anti-PD-1) immune checkpoint inhibitor (ICI) therapy in patients with sarcoma.

PATIENTS AND METHODS

We retrospectively identified adult patients who had received anti-PD-1 ICI therapy for recurrent sarcoma in two independent centers. We performed (1) blinded radiological response evaluation according to immune response evaluation criteria in solid tumors (iRECIST) ; (2) tumor DNA methylation profiling of >850,000 probes using Infinium MethylationEPIC microarrays; (3) analysis of tumor-infiltrating immune cell subsets (CD3, CD8, CD45RO, FOXP3) and intratumoral expression of immune checkpoint molecules (PD-L1, PD-1, LAG-3) using immunohistochemistry; and (4) evaluation of blood-based systemic inflammation scores (neutrophil-to-lymphocyte ratio, leucocyte-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, platelet-to-lymphocyte ratio). Response to anti-PD-1 ICI therapy was bioinformatically and statistically correlated with DNA methylation profiles and immunological data.

RESULTS

35 patients (median age of 50 (23-81) years; 18 females, 17 males; 27 soft tissue sarcomas; 8 osteosarcomas) were included in this study. The objective response rate to anti-PD-1 ICI therapy was 22.9% with complete responses in 3 out of 35 and partial responses in 5 out of 35 patients. Adjustment of DNA methylation data for tumor-infiltrating immune cells resulted in identification of methylation differences between responders and non-responders to anti-PD-1 ICI. 2453 differentially methylated CpG sites (DMPs; 2043 with decreased and 410 with increased methylation) were identified. Clustering of sarcoma samples based on these DMPs revealed two main clusters: methylation cluster 1 (MC1) consisted of 73% responders and methylation cluster 2 (MC2) contained only non-responders to anti-PD-1 ICI. Median progression-free survival from anti-PD-1 therapy start of MC1 and MC2 patients was 16.5 and 1.9 months, respectively (p=0.001). Median overall survival of these patients was 34.4 and 8.0 months, respectively (p=0.029). The most prominent DNA methylation differences were found in pathways implicated in Rap1 signaling, focal adhesion, adherens junction Phosphoinositide 3-kinase (PI3K)-Akt signaling and extracellular matrix (ECM)-receptor interaction.

CONCLUSIONS

Our data demonstrate that tumor DNA methylation profiles may serve as a predictive marker for response to anti-PD-1 ICI therapy in sarcoma.

Molecular Pap Smear: Validation of HPV Genotype and Host Methylation Profiles of ADCY8, CDH8, and ZNF582 as a Predictor of Cervical Cytopathology

Primary high-risk Human Papillomavirus (hrHPV) screening has recently become an accepted standalone or co-test with conventional cytology. Unfortunately, hrHPV singularly lacks specificity for cytopathological grade. However, mechanisms and markers of evolving virus-host interactions at the epigenome level may be harnessed as a better predictor of carcinogenesis. This study aimed to validate and expand the clinical performance of a multiparametric biomarker panel, referred to as the "Molecular Pap smear" based, on HPV genotype and ADCY8, CDH8 and ZNF582 CpG-methylation as a predictive classifier of cervical cytology. This prospective, cross-sectional study used an independent cohort of residual liquid-based cytology for HPV genotyping and epigenetic analysis. Extracted DNA underwent parallel PCR using 3 primer sets for HPV DNA amplification. HPV-infected samples were genotyped by Sanger sequencing. Promoter methylation levels of 3 tumor suppressor genes were quantified by bisulfite-pyrosequencing of genomic DNA on the newest high-resolution PyroMark Q48 platform. Logistic model performance was compared, and model parameters were used to predict and classify binary cytological outcomes. A total of 883 samples were analyzed. HPV DNA positivity correlated with worsening grade: 125/237 (53%) NILM; 136/235 (58%) ASCUS; 222/229 (97%) LSIL; and 157/182 (86%) HSIL samples. The proportion of carcinogenic HPV-types in PCR-positive sequenceable samples correlated with worsening grade: NILM 34/98 (35%); ASCUS 50/113 (44%); LSIL 92/214 (43%); HSIL 129/152 (85%). Additionally, ADCY8, CDH8, and ZNF582 methylation levels increased in direct correlation with worsening grade. Overall, the multi-marker modeling parameters predicted binarized cytological outcomes better than HPV-type alone with significantly higher area under the receiver operator curve (AUC)s, respectively: NILM vs. > NILM (AUC 0.728 vs. 0.709); NILM/ASCUS vs. LSIL/HSIL (AUC 0.805 vs. 0.776); and Collapse

Key Words

• DNA methylation
• carcinogenesis
• epigenetic modification
• evolution
• host-pathogen interactions
• human papillomavirus infection
• pap smear
• pyrosequencing

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Affiliation(s)

Jane Shen-Gunther Gynecologic Oncology & Clinical Investigation, Department of Clinical Investigation, Brooke Army Medical Center, Fort Sam Houston, TX, United States Department of Molecular Medicine, Cancer Therapy and Research Center, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
Qingqing Xia Department of Clinical Investigation, Brooke Army Medical Center, Fort Sam Houston, TX, United States
Winfred Stacey Department of Clinical Investigation, Brooke Army Medical Center, Fort Sam Houston, TX, United States
Heisy B. Asusta Department of Obstetrics and Gynecology, Brooke Army Medical Center, Fort Sam Houston, TX, United States

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