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DNA 5-hydroxymethylcytosine (5hmC) modification is known to be associated with gene transcription and frequently used as a mark to investigate dynamic DNA methylation conversion during mammalian development and in human diseases. However, the lack of genome-wide 5hmC profiles in different human tissue types impedes drawing generalized conclusions about how 5hmC is implicated in transcription activity and tissue specificity. To meet this need, we describe the development of a 5hmC tissue map by characterizing the genomic distributions of 5hmC in 19 human tissues derived from ten organ systems. Subsequent sequencing results enabled the identification of genome-wide 5hmC distributions that uniquely separates samples by tissue type. Further comparison of the 5hmC profiles with transcriptomes and histone modifications revealed that 5hmC is preferentially enriched on tissue-specific gene bodies and enhancers. Taken together, the results provide an extensive 5hmC map across diverse human tissue types that suggests a potential role of 5hmC in tissue-specific development; as well as a resource to facilitate future studies of DNA demethylation in pathogenesis and the development of 5hmC as biomarkers., DNA 5-hydroxymethylcytosine (5hmC) modification is associated with gene transcription and used as a mark of mammalian development. Here the authors report a comprehensive 5hmC tissue map and analysis of 5hmC genomic distributions in 19 human tissues derived from 10 organ systems, thus providing insights into the role of 5hmC in tissue-specific development.

Pancreatic cancer is often detected late, when curative therapies are no longer possible. Here, we present non-invasive detection of pancreatic ductal adenocarcinoma (PDAC) by 5-hydroxymethylcytosine (5hmC) changes in circulating cell free DNA from a PDAC cohort (n = 64) in comparison with a non-cancer cohort (n = 243). Differential hydroxymethylation is found in thousands of genes, most significantly in genes related to pancreas development or function (GATA4, GATA6, PROX1, ONECUT1, MEIS2), and cancer pathogenesis (YAP1, TEAD1, PROX1, IGF1). cfDNA hydroxymethylome in PDAC cohort is differentially enriched for genes that are commonly de-regulated in PDAC tumors upon activation of KRAS and inactivation of TP53. Regularized regression models built using 5hmC densities in genes perform with AUC of 0.92 (discovery dataset, n = 79) and 0.92–0.94 (two independent test sets, n = 228). Furthermore, tissue-derived 5hmC features can be used to classify PDAC cfDNA (AUC = 0.88). These findings suggest that 5hmC changes enable classification of PDAC even during early stage disease., Circulating DNA detected in plasma can be used for diagnostic purposes. Here, the authors show that the 5-hydroxymethyl cytosine biomarker from plasma-derived cell free DNA can be used to detect early stage pancreatic cancer.

The aim of the study was to verify the association of clinical relapses and brain activity with disability progression in relapsing/remitting multiple sclerosis patients receiving disease-modifying treatments in Poland. Disability progression was defined as relapse-associated worsening (RAW), progression independent of relapse activity (PIRA), and progression independent of relapses and brain MRI Activity (PIRMA). Data from the Therapeutic Program Monitoring System were analyzed. Three panels of patients were identified: R0, no relapse during treatment, and R1 and R2 with the occurrence of relapse during the first and the second year of treatment, respectively. In the R0 panel, we detected 4.6% PIRA patients at 24 months (p &lt, 0.001, 5.0% at 36 months, 5.6% at 48 months, 6.1% at 60 months). When restricting this panel to patients without brain MRI activity, we detected 3.0% PIRMA patients at 12 months, 4.5% at 24 months, and varying from 5.3% to 6.2% between 36 and 60 months of treatment, respectively. In the R1 panel, RAW was detected in 15.6% patients at 12 months and, in the absence of further relapses, 9.7% at 24 months and 6.8% at 36 months of treatment. The R2 group was associated with RAW significantly more frequently at 24 months compared to the R1 at 12 months (20.7%, p &lt, 0.05), but without a statistical difference later on. In our work, we confirmed that disability progression was independent of relapses and brain MRI activity.

Background Since 2010, nationwide networks of reference centers for sarcomas (RREPS/NETSARC/RESOS) collected and prospectively reviewed all cases of sarcomas and connective tumors of intermediate malignancy (TIM) in France. Methods The nationwide incidence of sarcoma or TIM (2013–2016) was measured using the 2013 WHO classification and confirmed by a second independent review by expert pathologists. Simple clinical characteristics, yearly variations and correlation of incidence with published clinical trials are presented and analyzed. Results Over 150 different histological subtypes are reported from the 25172 patients with sarcomas (n = 18712, 74,3%) or TIM (n = 6460, 25.7%), with n = 5838, n = 6153, n = 6654, and n = 6527 yearly cases from 2013 to 2016. Over these 4 years, the yearly incidence of sarcomas and TIM was therefore 70.7 and 24.4 respectively, with a combined incidence of 95.1/106/year, higher than previously reported. GIST, liposarcoma, leiomyosarcomas, undifferentiated sarcomas represented 13%, 13%, 11% and 11% of tumors. Only GIST, as a single entity had a yearly incidence above 10/106/year. There were respectively 30, 64 and 66 different histological subtypes of sarcomas or TIM with an incidence ranging from 10 to 1/106, 1–0.1/106, or < 0.1/106/year respectively. The 2 latter incidence groups represented 21% of the patients with 130 histotypes. Published phase III and phase II clinical trials (p−6) are significantly higher with sarcomas subtypes with an incidence above 1/106 per. Conclusions This nationwide registry of sarcoma patients, with exhaustive histology review by sarcoma experts, shows that the incidence of sarcoma and TIM is higher than reported, and that tumors with a very low incidence (16/year) are less likely to be included in clinical trials.

Introduction: Presence of anti-JC-virus antibodies (JCVAbs) is associated with the increased risk of natalizumab (NAT)-related progressive multifocal leukoencephalopathy (PML). Little is known about seroconversion rate and time to seroconversion in relapsing-remitting multiple sclerosis (RRMS) patients treated with NAT in Poland. The aim of the study was to assess the true risk of PML, seroconversion rate, and time to seroconversion in all JCVAb-negative RRMS patients treated with NAT in Poland. Methods: Demographic and clinical data of all Polish RRMS patients treated with NAT reimbursed by National Health Fund (NFZ) were prospectively collected in electronic files using the Therapeutic Programme Monitoring System provided by NFZ. The assessment of JCVAb presence (without collection of JCVAb index value) in serum (Unilabs, STRATIFY JCV: anti-JCV antibody ELISA) was done at the beginning of therapy and then repeated every 6 months. The maximum follow-up time was 4 years. In Poland, since 2013, according to the NFZ drug program guidance, only patients with negative JCVAb test have started treatment with NAT. Results: In all Polish multiple sclerosis centers, 210 negative JCVAb RRMS patients with at least 9 (±3) months of observation (146 females, 64 males, and the median age at baseline: 33 years) were included in the study. During the follow-up period, JCVAb status changed from negative to positive in 34 patients (16.2%). For half of the patients, the seroconversion was diagnosed 1 year after starting NAT treatment. In 4 patients (1.9%) during follow-up, JCVAb status changed again from positive to negative. In Poland, before establishment of NFZ drug program, 4 cases of PML in patients treated with NAT in clinical trials were diagnosed. In the NFZ drug program, since 2013, no patient treated with NAT has been diagnosed with PML. Conclusions: NAT therapy in JCV-seronegative RRMS patients is safe and results in the absence of PML cases. In Poland, JCV seroconversion rate is similar to that observed in other European countries.

Our study employed the detection of 5-hydroxymethyl cytosine (5hmC) profiles on cell free DNA (cfDNA) from the plasma of cancer patients using a novel enrichment technology coupled with sequencing and machine learning based classification method. These classification methods were develoiped to detect the presence of disease in the plasma of cancer and control subjects. Cancer and control patient cfDNA cohorts were accrued from multiple sites consisting of 48 breast, 55 lung, 32 prostate and 53 pancreatic cancer subjects. In addition, a control cohort of 180 subjects (non-cancer) was employed to match cancer patient demographics (age, sex and smoking status) in a case-control study design.Logistic regression methods applied to each cancer case cohort individually, with a balancing non-cancer cohort, were able to classify cancer and control samples with measurably high performance. Measures of predictive performance by using 5-fold cross validation coupled with out-of-fold area under the curve (AUC) measures were established for breast, lung, pancreatic and prostate cancer to be 0.89, 0.84, 0.95 and 0.83 respectively. The genes defining each of these predictive models were enriched for pathways relevant to disease specific etiology, notably in the control of gene regulation in these same pathways. The breast cancer cohort consisted primarily of stage I and II patients, including tumors < 2 cm and these samples exhibited a high cancer probability score. This suggests that the 5hmC derived classification methodology may yield epigenomic detection of early stage disease in plasma. Same observation was made for the pancreatic dataset where >50% of cancers were stage I and II and showed the highest cancer probability score.

Background: Since 2010, presentation to a designated sarcoma tumor board and pathological review by an expert network are mandatory for sarcoma patients in France. NETSARC+ (merging the 3 initial RREPS, RESOS & NETSARC) collected prospectively all cases of reviewed sarcomas and tumors of intermediate malignancy (TIM) nationwide. We report on the incidence of subtypes according to WHO classification from 2013 to 2016. Methods: Sarcoma or TIM confirmed by review of expert sarcoma pathologists were all prospectively integrated in the database; the results using the latest WHO classification are presented for the years 2013 to 2016, including yearly variations. Correlation of the incidence of each histotype with dedicated published clinical trials was conducted. Results: 139 different histological subtypes are reported among the 25172 patients with sarcomas (n = 18710, 64%) or TIM (n = 6460, 36%), respectively n = 5838, n = 6153, n = 6654, and n = 6527 yearly from 2013 to 2016. Over these 4 years, the observed yearly incidence of sarcomas, TIM, and both was therefore 79.7, 24.9 and 95.1/106/year, above that previously reported. GIST, liposarcoma, leiomyosarcomas, undifferentiated sarcomas represented 13%, 13%, 11% and 11% of all sarcomas. Only GIST, as a single entity exceeded a yearly incidence above 10/million per year. There were respectively 30, 63 and 66 different histological subtypes of sarcomas or TIM (single entities or lumped together, e.g. MPNST, or vascular sarcomas...) with an incidence ranging from 10 to 1/106/year, 1-0.1/106 per year, or < 0.1/106/year respectively. The 2 later “incidence groups” included 21% of the patients. The incidence of 8 histotypes varied significantly over this 4 years. Patients with tumors with an incidence above 1/106 per year have significantly higher numbers of dedicated published phase III and phase II clinical trials (p < 10-6). Conclusions: This nationwide registry of sarcoma patients with an histology reviewed by sarcoma experts shows that the incidence of sarcoma and TIM is higher than previously reported, may vary over years for some histotypes, and that tumors with an incidence < 10e6 have a much lower access to clinical trials. Funding Statement: NetSARC (INCA & DGOS) and RREPS (INCA & DGOS), RESOS (INCA & DGOS) and LYRICAN (INCA-DGOSINSERM 12563), Institut Convergence PLASCAN (17-CONV-0002), Association DAM’s, Ensemble contre Le GIST, Eurosarc (FP7-278742), la Fondation ARC, Infosarcome, InterSARC (INCA), LabEx DEvweCAN (ANR-10-LABX-0061), Ligue de L’Ain contre le Cancer, La Ligue contre le Cancer, EURACAN (EC 739521) funded this study. Declaration of Interests: The funders had no role in the study design, data collection, data analysis, data interpretation, or writing of the report.

Aim of study. The aim of this study was to collect and analyse data on relapsing-remitting multiple sclerosis (RRMS) patients receiving disease-modifying therapies (DMTs) in Poland. Material and methods. This observational, multicentre study with prospective data collection included RRMS patients receiving DMTs reimbursed by the National Health Fund (NFZ) in Poland, monitored by the Therapeutic Programme Monitoring System (SMPT). Demographic profiles, disability status, and treatment modalities were analysed. Results. Data from 11,632 RRMS patients was collected (from 15,368 new prescriptions), including 10,649 patients in the first-line and 983 in the second-line therapeutic programme of DMTs. The proportion of females to males was 2.39 in the first-line and 1.91 in the second-line. The mean age at DMTs start was 36.6 years in the first-line and 35.1 in the second-line. The median time from the first symptoms to MS diagnosis was 7.4 months, and from MS diagnosis to treatment it was 18.48 months. A total of 43.4% of MS patients started DMT during the 12 months following diagnosis. There was a positive correlation between the duration from MS diagnosis to the start of DMT and a higher initial EDSS value [correlation 0.296 (p < 0.001)]. About 10% of patients stopped DMTs. In Poland, about one third of all MS patients are treated in both lines, and the choice of first-line treatment depends on the region of the country. Conclusions. In Poland there is a need to increase MS patient access to DMTs by improving the organisation of drug programmes.

Objective The aim of the study was to assess the effectiveness of disease-modifying therapies (DMTs) in relapsing-remitting multiple sclerosis (RRMS) patients treated in MS centres in Poland. Methods Demographic and clinical data of all Polish RRMS patients receiving DMTs were prospectively collected from 2014 to 2018 in electronic files using the Therapeutic Program Monitoring System (SMPT). Results The study included 10,764 RRMS patients treated with DMTs in first-line and 1,042 in second-line programmes. IFNβ more effectively lengthened the times to the first relapse, disability progression, and brain MRI activity than GA. After 2 and 4 years of follow-up, more patients on IFNβ showed no evidence of disease activity (NEDA-3) in comparison to GA (66.3% and 44.3% vs 55.2% and 33.2%, respectively; p

Background: Our feasibility study employed a novel genomic detection methodology that enriches 5-hydroxymethylcytosine (5hmC) loci in cell free DNA (cfDNA) from the plasma of cancer patients using click chemistry coupled with sequencing and machine learning based classification methods. These classification methods were developed to detect the presence of disease in the plasma of cancer and control subjects. Cancer and control patient cfDNA cohorts were accrued from multiple sites consisting of 48 breast, 55 lung, 32 prostate and 2 pancreatic datasets consisting of 41 and 53 cancer subjects (Set 1 and 2). In addition, a control cohort of 260 subjects (non-cancer) was employed to match cancer patient demographics (age, sex and smoking status) in a case-control study design. Methods: Machine learning methods, applied to each cancer case cohort individually, with a balancing non-cancer cohort, were able to classify cancer and control samples. Measures of predictive performance using 5-fold cross validation coupled with out-of-fold Area Under the Receiver Operating Characteristic Curve (AUROC) measures were employed. Gene sets selected as part of biomarker discovery were further analyzed for disease relevance using pathway analysis tools (GSEA, mSigDB). Results: 260 controls and 229 cancers from four disease types (breast, lung, pancreas and prostate) were analyzed; more than 60% of cancer patients had early stage disease (I or II). Predictive performance, employing AUROC measures, was established for breast (0.89), lung (0.84), pancreas (set 1 - 0.95 and 2 - 0.93) and prostate (0.83). The genes defining each of these predictive models were enriched for pathways relevant to disease specific etiology, notably in the control of gene regulation in these same pathways. The breast cancer cohort consisted primarily of stage I and II patients including tumors < 2 cm and these samples exhibited a higher prediction probability score. The prostate cancer cohort consisted of both indolent and aggressive disease sample and prediction performance was equally high for both (AUROC for indolent vs aggressive was 0.81 and 0.77, respectively). Conclusions: These findings suggest that 5hmC changes in cfDNA enable non-invasive detection of early stage breast, pancreatic, prostate, and lung cancers. Furthermore, 5hmC profiling in cfDNA may enable the prediction of clinically relevant features such as tumor size in breast adenocarcinoma or indolent disease in prostate cancer. Finally, this study identified a suite of 5hmC biomarkers that may be further validated in larger, and more diverse, patient cohorts. Citation Format: Anna Bergamaschi, Jeremy Ku, Yuhong Ning, Francois Collin, Chris Ellison, Tierney Phillips, Erin McCarthy, Wendy Wang, Michael Antoine, David Haan, Aaron Scott, Paul Lloyd, Gulfem Guler, Alan Ashworth, Stephen Quake, Samuel Levy. Epigenomic detection of multiple cancers in plasma derived cell free DNA [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 783.

Recent studies have led to considerable advances in the identification of genetic variants associated with type 1 and type 2 diabetes. An approach for converting genetic data into a predictive measure of disease susceptibility is to add the risk effects of loci into a polygenic risk score. In order to summarize the recent findings, we conducted a systematic review of studies comparing the accuracy of polygenic risk scores developed during the last two decades. We selected 15 risk scores from three databases (Scopus, Web of Science and PubMed) enrolled in this systematic review. We identified three polygenic risk scores that discriminate between type 1 diabetes patients and healthy people, one that discriminate between type 1 and type 2 diabetes, two that discriminate between type 1 and monogenic diabetes and nine polygenic risk scores that discriminate between type 2 diabetes patients and healthy people. Prediction accuracy of polygenic risk scores was assessed by comparing the area under the curve. The actual benefits, potential obstacles and possible solutions for the implementation of polygenic risk scores in clinical practice were also discussed. Develop strategies to establish the clinical validity of polygenic risk scores by creating a framework for the interpretation of findings and their translation into actual evidence, are the way to demonstrate their utility in medical practice.

Pancreatic cancers are typically diagnosed at late stage where disease prognosis is poor as exemplified by a 5-year survival rate of 8.2%. Earlier diagnosis would be beneficial by enabling surgical resection or earlier application of therapeutic regimens. We investigated the detection of pancreatic ductal adenocarcinoma (PDAC) in a non-invasive manner by interrogating changes in 5-hydroxymethylation cytosine status (5hmC) of circulating cell free DNA in the plasma of a PDAC cohort (n=51) in comparison with a non-cancer cohort (n=41). We found that 5hmC sites are enriched in a disease and stage specific manner in exons, 3’UTRs and transcription termination sites. Our data show that 5hmC density is reduced in promoters and histone H3K4me3-associated sites with progressive disease suggesting increased transcriptional activity. 5hmC density is differentially represented in thousands of genes, and a stringently filtered set of the most significant genes points to biology related to pancreas (GATA4, GATA6, PROX1, ONECUT1) and/or cancer development (YAP1, TEAD1, PROX1, ONECUT1, ONECUT2, IGF1 and IGF2). Regularized regression models were built using 5hmC densities in statistically filtered genes or a comprehensive set of highly variable 5hmC counts in genes and performed with an AUC = 0.94-0.96 on training data. We were able to test the ability to classify PDAC and non-cancer samples with the Elastic net and Lasso models on two external pancreatic cancer 5hmC data sets and found validation performance to be AUC = 0.74-0.97. The findings suggest that 5hmC changes enable classification of PDAC patients with high fidelity and are worthy of further investigation on larger cohorts of patient samples.

Non-small-cell lung cancer (NSCLC) represents a heterogeneous group of malignancies consisting essentially of adenocarcinoma (ADC) and squamous cell carcinoma (SCC). Although the diagnosis and treatment of ADC and SCC have been greatly improved in recent decades, there is still an urgent need to identify accurate transcriptome profile associated with the histological subtypes of NSCLC. The present study aims to identify the key dysregulated pathways and genes involved in the development of lung ADC and SCC and to relate them with the clinical traits. The transcriptional changes between tumour and normal lung tissues were investigated by RNA-seq. Gene ontology (GO), canonical pathways analysis with the prediction of upstream regulators, and weighted gene co-expression network analysis (WGCNA) to identify co-expressed modules and hub genes were used to explore the biological functions of the identified dysregulated genes. It was indicated that specific gene signatures differed significantly between ADC and SCC related to the distinct pathways. Of identified modules, four and two modules were the most related to clinical features in ADC and SCC, respectively. CTLA4, MZB1, NIP7, and BUB1B in ADC, as well as GNG11 and CCNB2 in SCC, are novel top hub genes in modules associated with tumour size, SUVmax, and recurrence-free survival. Our research provides a more effective understanding of the importance of biological pathways and the relationships between major genes in NSCLC in the perspective of searching for new molecular targets.

Huntington's disease (HD) pathology is well understood at a histological level but a comprehensive molecular analysis of the effect of the disease in the human brain has not previously been available. To elucidate the molecular phenotype of HD on a genome-wide scale, we compared mRNA profiles from 44 human HD brains with those from 36 unaffected controls using microarray analysis. Four brain regions were analyzed: caudate nucleus, cerebellum, prefrontal association cortex [Brodmann's area 9 (BA9)] and motor cortex [Brodmann's area 4 (BA4)]. The greatest number and magnitude of differentially expressed mRNAs were detected in the caudate nucleus, followed by motor cortex, then cerebellum. Thus, the molecular phenotype of HD generally parallels established neuropathology. Surprisingly, no mRNA changes were detected in prefrontal association cortex, thereby revealing subtleties of pathology not previously disclosed by histological methods. To establish that the observed changes were not simply the result of cell loss, we examined mRNA levels in laser-capture microdissected neurons from Grade 1 HD caudate compared to control. These analyses confirmed changes in expression seen in tissue homogenates; we thus conclude that mRNA changes are not attributable to cell loss alone. These data from bona fide HD brains comprise an important reference for hypotheses related to HD and other neurodegenerative diseases.

Background The aim of study was to collect and analyze data on relapsing-remitting multiple sclerosis (RRMS) patients receiving disease modifying therapies (DMTs) in Poland. Material and methods The observational, multi-center with prospective data collection study included all RRMS patients receiving DMTs reimbursed by the National Health Fund (NFZ) in Poland, monitored by the Therapeutic Program Monitoring System (SMPT). The demographic profile, disability status and treatment modalities were analyzed. Results Data from 12,341 patients were collected, including 70.1% women and 29.9% men, therein 11,653 patients in the first-line and 1026 in second-line therapeutic DMTs programme. Proportion female to male was 2.36 in the first-line and 1.9 in the second-line. The mean age was 36.4 years (±SD 10.6) in the first-line and 35.1 years (±SD 9.7) in the second line. The mean time of observation in the first line was 3.7 years (±SD 2.4) and in the second line was 2.8 years (±SD 1.3). The mean time from the first symptoms to MS diagnosis was 24.54 months (median 7.23), and from MS diagnosis to treatment: 39.35 months (median 10.12). There was a positive correlation between time to start treatment and higher the initial EDSS score (R 0.29, p Conclusions In Poland, only about one third of all MS patients is treated with DMTs. There is a need to improve the quality of MS patients healthcare and their access to DMTs.

Pancreatic cancers are typically diagnosed at late stage where disease prognosis is poor as exemplified by a 5-year survival rate of 8.2%. Earlier diagnosis would be beneficial by enabling surgical resection or earlier application of therapeutic regimens. We investigated the detection of pancreatic ductal adenocarcinoma (PDAC) in a non-invasive manner by interrogating changes in 5-hydroxymethylated cytosines (5hmC) in circulating cell free DNA in the plasma of a PDAC cohort (n=51) in comparison with a non-cancer cohort (n=41). 5hmC profiles from PDAC and non-cancer samples were generated using a previously published modified hMe-Seal protocol that utilizes chemical labeling of 5hmC by β-glucosyltransferase and allows detection of cell free 5hmC from small amounts of cfDNA (1). We found that 5hmC sites are enriched in a disease and stage specific manner in exons, 3’UTRs and transcription termination sites. Our data show that 5hmC density is reduced in promoters and histone H3K4me3 associated sites with progressive disease suggesting increased transcriptional activity. 5hmC density is differentially represented in thousands of genes, and a stringently filtered set of the most significant genes points to biology related to pancreas (GATA4, GATA6, PROX1, ONECUT1) and/or cancer development (YAP1, TEAD1, PROX1, ONECUT1, ONECUT2, IGF1 and IGF2). Regularized regression models were built using 5hmC densities in a comprehensive set of genes with the most variable 5hmC counts and performed with an AUC = 0.94 - 0.96 on training data. We tested the ability to classify PDAC and non-cancer samples with the Elastic net and Lasso models on three independent pancreatic cancer 5hmC data sets (n = 26, 23 and 7) compared with corresponding independent non-cancer cohorts (n =103, 53 and 10), and found validation performance to be AUC = 0.74 - 0.97. The findings suggest that 5hmC changes enable classification of PDAC patients with high fidelity and are worthy of further investigation on larger cohorts of patient samples. Reference: 1. Song, C. - X. et al. 5 - Hydroxymethylcytosine signatures in cell-free DNA provide information about tumor types and stages. Cell Res 27, 1231 (2017). Citation Format: Francois Collin, Yuhong Ning, Gulfem D. Guler, Tierney Phillips, Erin McCarthy, Aaron Scott, Chris Ellison, Chin-Jen Ku, Kim Chau, Alan Ashworth, Stephen R. Quake, Samuel Levy. Detection of early stage pancreatic cancer using 5–hydroxymethylcytosine signatures in circulating cell free DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1372.

Combining RNA-seq data from different platforms should increase the power to detect differentially expressed genes, but may not be straightforward. Here we show how RUVs, a recently published method for removing unwanted variation and normalizing RNA-seq data, can combine the counts of single and paired end read libraries from formalin fixed, paraffin embedded tumor samples to permit differential expression analysis. Seven other intra- or inter-platform normalization methods are also described and the results are compared with those from RUVs.

Background: RNA biomarkers discovered by RT-PCR-based gene expression profiling of archival formalin-fixed paraffin-embedded (FFPE) tissue are the basis for very precise and sensitive clinical diagnostic tests, such as the 21 gene Oncotype DX® breast cancer assay. Both inherent limits of technical scalability and the small amounts of patient FFPE RNA available place practical constraints on the number of transcripts that can be interrogated by RT-PCR. We developed new methods for RNA profiling through massively parallel “next generation” sequencing (RNA-Seq) of archival FFPE specimens. We report here the technical performance of this methodology and compare the results to RT-PCR results obtained in one of the studies that were carried out to develop the 21 gene assay. Methods: RNA was extracted in 2002 from 136 invasive breast tumors that were formalin-fixed and paraffin-embedded between 1990 and 1997. RNA-Seq was carried out using minor modifications to methods we have reported previously (Sinicropi et al., Advances in Genome Biology and Technology Conference, p. 170, 2010 and p. 198, 2011). Briefly, 0.1 mg of total RNA was selectively depleted of ribosomal RNA and sequencing libraries were prepared using a modification of the ScriptSeq™ kit from Epicentre. The libraries were sequenced on an Illumina HiSeq 2000 instrument with multiplexing of two libraries per lane for 50 cycles in one direction. The resulting FASTQ sequences were mapped to version hg19 of the human genome using the Illumina CASAVA pipeline. The total number of sequences (reads) that uniquely mapped to all exons of each RefSeq entry was used for quantification of expression levels. Results: On average, there were 43 million reads per sample (range 31 - 58 million; SD=4.6 million) of which 69% uniquely mapped to the human genome. Ribosomal RNA was effectively removed and accounted for Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr PD03-09.

SUMMARY In this paper we report exploratory analyses of high-density oligonucleotide array data from the Affymetrix GeneChip R � system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of five MGU74A mouse GeneChip R � arrays, part of the data from an extensive spike-in study conducted by Gene Logic and Wyeth’s Genetics Institute involving 95 HG-U95A human GeneChip R � arrays; and part of a dilution study conducted by Gene Logic involving 75 HG-U95A GeneChip R � arrays. We display some familiar features of the perfect match and mismatch probe ( PM and MM )v alues of these data, and examine the variance–mean relationship with probe-level data from probes believed to be defective, and so delivering noise only. We explain why we need to normalize the arrays to one another using probe level intensities. We then examine the behavior of the PM and MM using spike-in data and assess three commonly used summary measures: Affymetrix’s (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model-based expression index (MBEI). The exploratory data analyses of the probe level data motivate a new summary measure that is a robust multiarray average (RMA) of background-adjusted, normalized, and log-transformed PM values. We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance and (for MBEI and RMA) model fit. Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spike-in data. We conclude that there is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities. ∗ To whom correspondence should be addressed

Quality of microarray gene expression data has emerged as a new research topic. As in other areas, microarray quality is assessed by comparing suitable numerical summaries across microarrays, so that outliers and trends can be visualized, and poor quality arrays or variable quality sets of arrays can be identified. Since each single array comprises tens or hundreds of thousands of measurements, the challenge is to find numerical summaries which can be used to make accurate quality calls. To this end, several new quality measures are introduced based on probe level and probeset level information, all obtained as a by-product of the low-level analysis algorithms RMA/fitPLM for Affymetrix GeneChips. Quality landscapes spatially localize chip or hybridization problems. Numerical chip quality measures are derived from the distributions of Normalized Unscaled Standard Errors and of Relative Log Expressions. Quality of chip batches is assessed by Residual Scale Factors. These quality assessment measures are demonstrated on a variety of datasets (spike-in experiments, small lab experiments, multi-site studies). They are compared with Affymetrix's individual chip quality report., Comment: 32 pages plus 12 figure pages (17 figures total), correction of typos, conversion of some figures into color

Background: Analytically and clinically validated non-invasive blood tests that quantify breast cancer burden and clinical drug response/resistance are greatly needed. Many groups have successfully detected tumor markers in blood using a variety of technologies, including next generation sequencing (NGS). We performed a comprehensive NGS study on a small number of patients to evaluate the value of global versus individual markers for the quantitation of tumor-derived cell free DNA (cfDNA) in plasma. Methods: DNA isolated from formalin-fixed primary tumor, buffy coat cells, and plasma from 2 patients with metastatic breast cancer were characterized simultaneously for copy number aberrations (CNAs) and differentially methylated regions (DMRs) using whole genome bisulfite sequencing (WBGS), and targeted sequencing-based genotyping of 346 cancer-associated single nucleotide variations (SNVs). CNA and DMR regions were identified from log normalized, GC content corrected counts and DMR data using Poisson and binomial distribution theory and false discovery rate controlling methods. Percent tumor in cfDNA was estimated from the normalized ratio (plasma: primary tumor) of CNA or DMR compared to buffy coat, aggregating over genomic regions. Sample sets from 8 non-metastatic patients were also profiled using the targeted SNV panel in order to compare SNVs between samples and estimate percent tumor cfDNA. Results: WGBS detected tumor specific alterations in each primary tumor compared to buffy coat. By analyzing the genome using 100 Kb bins, we observed over 1000 bins with detectable CNA signal and, among 56 million CpG sites, over 30,000 DMRs. As expected, 5 or fewer informative somatic SNVs were detected in each patient. Analysis of these somatic changes in plasma revealed that the tumor fraction estimated from SNV detected in cfDNA varied widely between sites originally discovered in the patient’s primary tumor. In contrast, similar estimates of tumor fraction in cfDNA were obtained using CNA and DMR profiles within each patient; both methods yielded similar estimates of over 50% in one patient and less than 10% in the other. For the patient with high tumor fraction, both CNA and DMR profiles contained examples of individual large genomic regions that displayed additional clear aberrations in the plasma compared to the original tumor, such as a striking loss of a >25 Mb region of chromosome 4. Conclusions: Although individual somatic SNV in cfDNA can be detected in metastatic disease, calculated allelic fraction based on individual SNVs varies greatly within the same patient. Measuring and integrating CNA or DMR across the genome provided more consistent and reliable estimates of tumor DNA fraction in plasma, and also revealed alterations in plasma from patients with metastatic disease that were not prominent in the primary tumor. Citation Format: Ellen M Beasley, Richard D Abramson, Gregory E Alexander, David Chan, Kristen Bradley, Francois Collin, Michael Crager, Andrew Dei Rossi, Joseph Dorado, Adam Friedman, William J Gibb, Jennie Jeong, Col Jones, C J Ku, Yan Ma, John Morlan, Kunbin Qu, Aibing Rao, Aaron Scott, Haluk Tezcan. Global quantitative measures using next-generation sequencing for breast cancer presence outperform individual tumor markers in plasma [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P4-02-08.

This chapter covers quality assessment for Affymetrix GeneChip data. The focus is on procedures available from the affy and affy-PLM packages. Initially some exploratory plots provided by the affy package, including images of the raw probe-level data, boxplots, histograms, and M vs A plots are examined. Next methods for assessing RNA degradation are discussed, specifically we compare the standard procedures recommended by Affymetrix and RNA degradation plots. Finally, we investigate how appropriate probe-level models yield good quality assessment tools. Chip pseudo-images of residuals and weights obtained from fitting robust linear models to the probe level data can be used as a visual tool for identifying artifacts on GeneChip microarrays. Other output from the probe-level modeling tools provide summary plots that may be used to identify aberrant chips.

We have used RNA-seq to profile and compare normal and cancerous human breast tissue. FFPE breast specimens from a total of 24 patients, 12 normal (N) and 12 tumor (T) specimens from surgical resections, were analyzed on an Illumina's GA IIx sequencer. Whole transcriptome RNA-Seq libraries were prepared after depletion of ribosomal RNA by a protocol developed at Genomic Health Inc. (GHI). The analysis was multiplexed across two flow cells using barcoding, with two specimens per sequencing lane (1 T and 1 closely age-matched N library from a different patient). FFPE tissue archive times ranged from 10 to 13 years and they were also closely matched within each lane. To evaluate reproducibility, triplicate libraries were created from 4 of the specimens and analyzed within and across flow cells. Libraries yielded, on average, 19 million 51 bp sequences. R2 values obtained from replicate libraries prepared from the same patient RNA were > 0.9 within and between flow cells. More than 80% of known genes in the human genome were detected in all patients. Several thousand intergenic transcripts were identified by an algorithm developed at GHI. A negative binomial model with tag-wise estimates of dispersion was applied to the known genes and intergenic regions. Inter-patient count variance is generally higher in the set of intergenic sequences than in the set of gene (RefSeq) sequences. Thousands of gene (RefSeq) and intergenic sequences were found to be differentially expressed between T and N tissues. We sought to build classifiers based on flow cell #1 data that could stratify T and N tissues when applied to flow cell #2 data. Sets of genes and intergenic regions were selected for analysis based on high inter-patient count variance. Support vector machine classifiers were trained and then applied to the data from flow cell #2, and also to another GHI tumor/normal RNA-Seq study. Either a set of 100 genes (RefSeq), or a set of 70 intergenic sequences accurately distinguished tumor and normal tissues. Our results offer further evidence of the potential of RNA-Seq for discovery of biomarkers. 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 4859. doi:10.1158/1538-7445.AM2011-4859

The standard practice in hospital pathology laboratories is to preserve patient clinical tissue specimens as fixed, paraffin-embedded (FPE) tissue. FPE specimens are used for routine pathologic examination, immunohistochemistry (IHC) studies, and a variety of molecular diagnostic assays. The results of these studies assist in determining the patient's clinical status and in therapeutic decision making. However, the methodology for tissue fixation is not standardized across laboratories and a number of different tissue fixatives are currently commercially available. The use of different tissue fixatives may significantly affect the performance of IHC and molecular diagnostic assays. The results of nine common tissue fixatives and their effects on both IHC- and RNA-based molecular assays are reported. Using human placenta as a model tissue system, nine common fixatives (B5, Bouin's, ethyl alcohol 70%, formalin, Hollandes, Penfix, Prefer, Zenker's, and zinc formalin) were compared for effects on six IHC assays and a panel of 42 gene targets by RT-PCR assays, as well as performance relative to fresh (RNAlater®) or frozen (OCT) unfixed tissue. The 42-gene panel assessed by RT-PCR included the six genes assessed by IHC. The IHC assays were scored using a semiquantitative method. For RT-PCR, raw assay scores were derived and subsequently normalized. Different fixatives resulted in varying effects on IHC and molecular assay performance. Per gene across each fixative, mRNA expression levels assessed by RT-PCR assays demonstrated wide variation, which could be largely corrected for by normalization. Variation in immunoreactivity as a function of tissue fixative was also observed with IHC assays. Compared to IHC, RT-PCR assays demonstrated greater sensitivity and were able to detect lower levels of gene expression, when the IHC assay gave negative results. Interestingly, fixative related effects were not always similar between IHC and RT-PCR assays. Therefore, it is recommended that the effects of tissue fixation be taken into consideration when performing data analysis and making comparisons between IHC and molecular diagnostic assays. Citation Information: Clin Cancer Res 2010;16(14 Suppl):B48.

RT-PCR-based gene expression profiling in archival FFPE tissues with associated clinical records is clinically important in oncology, as evidenced by the wide use of the breast cancer prognostic and predictive Oncotype DX® 21-gene test1. The strength of evidence underlying this test relies on the use of landmark clinical trial patient cohorts and other valuable clinical specimens used for its development and validation. However, the limited amount of RNA available from these FFPE specimens restricts the number of candidate biomarkers that can be tested in discovery studies. To compensate for this limitation, we have developed a method to amplify FFPE RNA which preserves the pre-amplified RNA expression profiles. We have now applied this method to evaluate more than 300 previously untested candidate genes in two of the key clinical cohorts used to develop the Oncotype DX assay. In total, 214 patient RNA samples were amplified and their expression levels for new prognostic markers were analyzed. Genes discovered to be associated with prognosis in the original studies were included in the amplified RNA study as positive controls to confirm that gene expression profiles in the amplified RNA remained consistent with those in the original RNA extracts.2,3 New genes were found that correlate with the risk of breast cancer recurrence across both clinical populations. A number of these genes co-express in a “metabolism-related” gene signature which includes ENO1, the gene encoding enolase 1. After stratifying the patients into ER+ and ER- cohorts, additional prognostic gene expression biomarkers were identified in the ER- patients that are consistent with a TGFbeta-related “stromal response” signature. Newly identified genes were confirmed for association with clinical outcome using publicly available microarray-based data sets.4 Most of the newly identified genes and the previously validated biomarker genes were confirmed as being significantly associated with patient outcome using these data. It will be important to confirm these findings in additional separate patient cohorts.1. Paik S, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004;351:28172. Cobleigh, Ml, etl. al.. (2005) Tumor gene expression predicts distant recurrence-free survival in breast cancer patients with 10 or more positive nodes: High throughput RT-PCR assay of paraffin-embedded tumor tissues. Clin Cancer Res 11: 86233. Cronin, M, et. al., (2004) Measurement of Gene Expression in Archival Paraffin-embedded Tissues: Development and Performance of a 92 Gene RT-PCR Assay. American Journal of Pathology, 164: 354. Wirapati, P., et. al., (2008) Meta-Anlayis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures. Breast Cancer Research, 10: R65 (doi:10.1186/bcr2124) Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2160.

Background Gene expression microarray experiments are expensive to conduct and guidelines for acceptable quality control at intermediate steps before and after the samples are hybridised to chips are vague. We conducted an experiment hybridising RNA from human brain to 117 U133A Affymetrix GeneChips and used these data to explore the relationship between 4 pre-chip variables and 22 post-chip outcomes and quality control measures. Results We found that the pre-chip variables were significantly correlated with each other but that this correlation was strongest between measures of RNA quality and cRNA yield. Post-mortem interval was negatively correlated with these variables. Four principal components, reflecting array outliers, array adjustment, hybridisation noise and RNA integrity, explain about 75% of the total post-chip measure variability. Two significant canonical correlations existed between the pre-chip and post-chip variables, derived from MAS 5.0, dChip and the Bioconductor packages affy and affyPLM. The strongest (CANCOR 0.838, p < 0.0001) correlated RNA integrity and yield with post chip quality control (QC) measures indexing 3'/5' RNA ratios, bias or scaling of the chip and scaling of the variability of the signal across the chip. Post-mortem interval was relatively unimportant. We also found that the RNA integrity number (RIN) could be moderately well predicted by post-chip measures B_ACTIN35, GAPDH35 and SF. Conclusion We have found that the post-chip variables having the strongest association with quantities measurable before hybridisation are those reflecting RNA integrity. Other aspects of quality, such as noise measures (reflecting the execution of the assay) or measures reflecting data quality (outlier status and array adjustment variables) are not well predicted by the variables we were able to determine ahead of time. There could be other variables measurable pre-hybridisation which may be better associated with expression data quality measures. Uncovering such connections could create savings on costly microarray experiments by eliminating poor samples before hybridisation.

High density oligonucleotide array technology is widely used in many areas of biomedical research for quantitative and highly parallel measurements of gene expression. Affymetrix GeneChip arrays are the most popular. In this technology each gene is typically represented by a set of 11–20 pairs of probes. In order to obtain expression measures it is necessary to summarize the probe level data. Using two extensive spike-in studies and a dilution study, we developed a set of tools for assessing the effectiveness of expression measures. We found that the performance of the current version of the default expression measure provided by Affymetrix Microarray Suite can be significantly improved by the use of probe level summaries derived from empirically motivated statistical models. In particular, improvements in the ability to detect differentially expressed genes are demonstrated.

Publisher Summary This chapter reviews the design and low-level analysis of microarray experiments. Microarray experiments are used to quantify and compare gene expression on a large scale. Such experiments can be costly in terms of equipment, consumables, and time. Thus, careful design is important. Low-level analysis is carried out between the image analysis phase and interrogation of gene expression data. The goal of low-level analysis is to take raw data from the scanner, without any biological interpretation, and process it to produce cleaner and more meaningful gene expression measures. This is in contrast to higher level analysis, where questions of more biological nature are addressed. Such high-level questions include: detecting differential expression in treatment and control tissues, gene function, pathway analysis, and changes in gene expression over time. Improved low-level analysis aids the downstream data investigations. The chapter focuses on low-level analysis methods, such as (1) normalization that reduces or removes the sources of nonbiological variability (for both complementary DNA [cDNA] and Affymetrix arrays), (2) summarization, where one combines multiple probes to produce a gene expression measure (for Affymetrix-like arrays), (3) data quality (assessed by image analysis for cDNA arrays and from probe-level modeling of Affymetrix arrays), and (4) the detection of an absolute expression.