Sample multiplexing is often used to reduce cost and limit batch effects in single-cell RNA sequencing (scRNA-seq) experiments. A commonly used multiplexing technique involves tagging cells prior to pooling with a hashtag oligo (HTO) that can be sequenced along with the cells’ RNA to determine their sample of origin. Several tools have been developed to demultiplex HTO sequencing data and assign cells to samples. In this study, we critically assess the performance of seven HTO demultiplexing tools: hashedDrops, HTODemux, GMM-Demux, demuxmix, deMULTIplex, BFF  and HashSolo. The comparison uses data sets where each sample has also been demultiplexed using genetic variants from the RNA, enabling comparison of HTO demultiplexing techniques against complementary data from the genetic ‘ground truth’. We find that all methods perform similarly where HTO labelling is of high quality, but methods that assume a bimodal count distribution perform poorly on lower quality data. We also suggest heuristic approaches for assessing the quality of HTO counts in an scRNA-seq experiment. Improvements in droplet-based single-cell RNA sequencing (scRNA-seq) technologies have prompted growing interest in exploring variation in gene expression at cellular resolution. While costs continue to decrease, it remains expensive to separately capture and sequence individual samples. Batch effects also confound meaningful differences in gene expression between samples, and robust detection of multiplets (droplets containing two or more cells) solely from the transcriptome remains an issue . One solDespite these advantages, it is important to carefully consider the most appropriate multiplexing protocol for the sample type(s) , and wheCell hashing is an alternative multiplexing technique. Prior to pooling, a barcoded label called a hashtag oligo (HTO) is added, one to each sample. The HTOs attach to either antibodies or lipids on the surface of the cells and the HTOs are captured and sequenced in parallel to the RNA. The antibodies bind to ubiquitous cell surface proteins , while tNHTOs × Ndroplets matrix consisting of the read counts for each HTO in each droplet. In an ideal scenario, each droplet contains only one cell and each cell contains only counts for the HTO corresponding to its sample of origin. In this ideal case, the demultiplexing algorithm involves simply identifying the non-empty entries in each column of the HTO count matrix. In practice, the data are noisy; droplets may contain multiple cells, HTO-conjugated antibodies/lipid molecules may not bind well to the cells or may dissociate and bind to cells from another sample in the pooling stage, or unbound HTOs may be present in droplets  and HashSolo. We discuss the details of each in the ‘Materials and Methods’ section. In all cases, the fundamental goal of each method is the same: to examine the counts of each HTO in a droplet and determine the sample of origin of each cell. Conceptually, this is achieved by separating the signal from the oligo bound to the cell in the sample preparation stage (‘positive’ HTOs) from ambient counts that arise due to contamination (‘negative’ HTOs). Droplets with no positive HTOs are classified as ‘negative’ or ‘unknown’. Droplets with more than one positive HTO are classified as doublets/multiplets. Those with only one positive HTO are classified as singlets. Here, we use three data sets to assess the performance of each demultiplexing method by comparing the assignments from HTO demultiplexing to assignments from genetic demultiplexing on the same data. Recent work has shown that performance of HTOs varies between technologies and tissue types ,9, and tg for 5 min at 4°C. Supernatant was discarded and each sample resuspended at 62 500 cells/100 μl following which 100 μl of each sample was pooled into one tube. Pooled cells were centrifuged at 400 × g for 5 min at 4°C, supernatant discarded and resuspended in 25 μl cell staining buffer and 25 μl of TotalSeq-A Human Universal Cocktail v1.0 (BioLegend) for 30 min on ice. This cocktail contains 154 immune-related surface proteins. Cells were washed in 3 ml cell staining buffer and centrifuged at 400 × g for 5 min at 4°C. Following two more washes, cells were resuspended in phosphate-buffered saline + 0.04% bovine serum albumin for Chromium captures. Single-cell captures, library preparation and sequencing were performed as we have described previously  data set is derived from CITE-seq experiments of 24 samples of paediatric BAL. Samples were collected, cryopreserved and thawed as previously described . Live, seviously .Data for the ovarian tumour data set were taken from , and werFor the cell line data set, three human lung cancer cell lines, H1792, H3122 and H358, were labelled with a different 3′ lipid-modified oligo (LMO) as in  . We usedF-score, which is defined asFor each possible HTO assignment, we calculate the true positive rate TP, which is the fraction of cells with that HTO assignment that have the corresponding vireo assignment; the false positive rate FP, which is the fraction of cells with that HTO assignment and a different genetic assignment; and the false negative rate FN, which is the fraction of cells with the corresponding genetic assignment but a different HTO assignment. Our key metric is the F is the harmonic mean of the precision and recall, and can vary between 0 and 1, with a higher F-score implying better performance.hashedDrops, part of the DropletUtils package , is a sik-medoids clustering is performed, with k = NHTOs + 1. For each HTO, cells are identified as positive or negative in a two-step procedure. First, the cluster with the lowest expression count for each HTO is defined as the ‘negative’ cluster, and a negative binomial distribution is fitted to the counts in that cluster. For the droplets outside that cluster, droplets with HTO counts above a user-defined quantile (0.99 by default) are assigned as positive for the HTO. After performing this procedure on all HTOs, droplets that have been assigned positive for more than one HTO are classified as multiplets, droplets with no positive assignments are classified as negative and the droplets assigned positive for only one HTO are classified as singlets. We explore the effect of varying the quantile threshold in Supplementary Figure S4.HTODemux , includeLike HTODemux, GMM-Demux  uses thek-means clustering. Cells with very high counts are marked as outliers, and the non-outliers are fitted to a two-component negative binomial distribution using an expectation–maximization algorithm. demuxmix can also leverage the RNA counts to improve performance, using the number of detected genes in the RNA library as a covariate in the mixture model. Using this additional RNA information with the BAL data set showed no significant improvement on either data set in this paper, so the results presented are based on the HTO counts only.demuxmix  is simildeMULTIplex  uses an raw and BFFcluster. The first mode, BFFraw, smooths the count distribution using a kernel density estimator, much like deMULTIplex. The threshold between positive and negative classifications in this case is the local minimum between the two peaks. The second mode, BFFcluster, is similar, but includes an additional layer of normalization, called bimodal quantile normalization, before finalizing classifications. The level of smoothing on the counts can be selected by the user; however, our results are based on the default, which searches for an optimal value.BFF  also assF-score varying by <1% in all batches.HashSolo  is a BayWe perform our comparison of hashtag demultiplexing methods on six tagging experiments across three data sets, each using different tagging technologies. The first data set, the BAL data set, contains 24 genetically distinct samples of BAL fluid tagged with TotalSeq-A antibody-derived tags (ADTs) . These sThe second data set, the ovarian tumour (OT) data set, contains eight genetically distinct samples of high-grade serous ovarian tumours, tagged with TotalSeq-B ADTs. These samples were processed in two batches of four samples each; batch 1 contains 12 510 droplets and batch 2 contains 9547 droplets  data set, contains samples from three human lung cancer cell lines, which are tagged with MULTI-seq LMOs . The celFor each data set, vireo  is used Throughout this paper, we make a distinction between the labels assigned to cells using vireo and the labels of the corresponding HTOs. The vireo labels are denoted by the data set name followed by an alphabetical suffix, e.g. BAL A, CL B and OT C, while the corresponding HTO labels contain a numeric suffix, e.g. BAL 1, CL 2 and OT 3.To assess the quality of the HTO labelling and sequencing, we suggest using some common qualitative visualizations that can guide overall expectations of the performance of HTO deconvolution. Figure raw and BFFcluster, and we present the output of both. All of the methods we consider have some adjustable parameters that affect output; however, in our exploration, changing the default options does not significantly change the assignments. We discuss the details of each method and their parameters further in the ‘Materials and Methods’ section. The exception is hashedDrops, which uses a simple count threshold to distinguish negatives and singlets. We find that in many cases the default value of this threshold is too high, and performance  is improved by lowering its value. To illustrate this, we present the hashedDrops classifications with both the default value (confident.min = 2) and the value we find maximizes the F-score (confident.min = 0.5). As each batch was processed across two captures, we run the demultiplexing methods on HTO data from each individual capture. However, for simplicity, the results are presented per batch as we do not observe significant variation between captures within a batch.Each of the three batches in our BAL data set contains cells from eight samples, from genetically distinct donors. Each demultiplexing method (including the genetic demultiplexing) can return one of 10 assignments for a cell: singlet, corresponding to one of the eight unique samples; doublet; or negative. We compare seven HTO demultiplexing methods: BFF , deMULTIcluster, which assigns slightly more singlets than vireo in batches 1 and 2, and assigns fewer negative droplets in batches 2 and 3 than in batch 1. HashSolo assigns fewer negative cells than any other method, excluding vireo and BFFcluster, and assigns a similar fraction of doublets in each batch, with the doublet fraction greater than vireo in all batches.In Figure F-score , a statistic that is the harmonic mean of precision and recall. The F-score ranges between 0 and 1, with a higher value indicating better performance. Figure F-score of each method, for each possible singlet assignment, split by batch.We next compare the specific individuals allocated by the singlet assignments of each HTO demultiplexing method to the ‘ground truth’ of genetic assignments from vireo. To quantitatively assess their performance, we calculate the F-score of each sample for each method in all data sets. The BAL data are shown in the second, third and fourth columns. Numerical values of the F-scores for all samples and methods are included in the Supplementary Data. Figure cluster, which has the highest mean F-score in batch 1, has F < 0.1 for all HTOs except BAL 9 in batch 2 and all HTOs except BAL 17, BAL 18, BAL 19 and BAL 20 in batch 3. BFFraw is unable to classify any cells to BAL 10 or BAL 14 in batch 2 or BAL 17, BAL 21 and HTO 14 in batch 3. hashedDrops has higher scores in all batches with optimized parameters than with the default settings, and has the highest mean F-score of all methods in batches 2 and 3. Demuxmix, GMM-Demux, HashSolo and HTODemux show consistent performance across all three batches.Table F-score analysis above. Instead, we perform a separate, complementary analysis of how each HTO demultiplexing method classifies the genetic doublets. For each batch in the BAL data set, we take the droplets assigned as doublets by vireo, and look at which broad category  they are assigned by each of the HTO demultiplexing methods. For this analysis, the best-performing method is the one that minimizes the number of ‘true’ doublets assigned as singlets. Since both negative droplets and doublets are typically excluded from downstream analysis, misclassification of genetic doublets as negatives is relatively unimportant.Next, we investigate doublets in more detail. As shown in Figure cluster, which has the highest F-score for batch 1, has the worst performance on the doublets, assigning more than half of the genetic doublets in that batch as singlets. Second, while adjusting the parameters of hashedDrops from their default values improves the F-score, the number of incorrectly assigned genetic doublets approximately doubles in all batches. Third, the other best-performing methods based on F-score, demuxmix, HTODemux and GMM-demux, perform well on the doublet analysis as well, assigning <20% of genetic doublets as singlets in all batches, though HashSolo performs slightly worse, with ≈30% of genetic doublets identified as singlets.Figure The second data set we analyse consists of eight samples from high-grade serous ovarian carcinoma patients from . Unlike F-scores of all demultiplexing methods on this data set.Genetic demultiplexing with vireo positively assigns >90% of droplets as singlets for both batches . InitialF-scores for each method on each sample and the assignment of genetic doublets as doublets, singlets or negatives by each method. The mean F-scores for each method in each batch are shown in the fifth and sixth columns of Table F-score and doublet classification accuracy. HashSolo, GMM-Demux and HTODemux all perform relatively well, while demuxmix, the method with best overall performance on the BAL data set, falls behind on F-score but makes fewer errors on doublets. How these two factors should be weighed against one another cannot be answered objectively for all cases, especially as the overall fraction of doublets depends on the overall number of cells per batch , we expect the demultiplexing methods to perform similarly on the LMO and ADT data. Figure F-score for each method on each of the three samples in this data set  fail to correctly assign some droplets to their samples of origin. Threshold-based methods, such as hashedDrops, can perform well but make a trade-off between greater recovery of singlets and false positives. More sophisticated methods, such as the clustering-based HTODemux and demuxmix, and the Bayesian estimation-based methods GMM-Demux and HashSolo perform best and most consistently on both high- and low-quality hashtag data.Low-quality hashtag data do not imply low-quality RNA expression data; importantly, the two are largely uncorrelated . We show that the difference between demultiplexing methods becomes more pronounced as the quality of the hashtag data reduces. Therefore, maximizing performance of demultiplexing methods on lower quality hashtag data is particularly important to prevent otherwise good quality cells being excluded in a single-cell analysis.For samples with similar or identical genetic backgrounds, labelling of cells before pooling will continue to be an important strategy and this manuscript provides a benchmark for applying these methods. While genetic demultiplexing has been shown to be accurate for deconvolving samples from outbred populations , there ilqad086_Supplemental_FilesClick here for additional data file.


Alistipes, a common gut commensal, showed the highest network degree centrality and was associated with genes related to metabolism and inflammation. The {exotic} tool can support the discovery of microbes in tumors in a way that leverages the many existing and growing RNA-seq datasets.The microbiome affects cancer, from carcinogenesis to response to treatments. New evidence suggests that microbes are also present in many tumors, though the scope of how they affect tumor biology and clinical outcomes is in its early stages. A broad survey of tumor microbiome samples across several independent datasets is needed to identify robust correlations for follow-up testing. We created a tool called {exotic} for “exogenous sequences in tumors and immune cells” to carefully identify the tumor microbiome within RNA sequencing (RNA-seq) datasets. We applied it to samples collected through the Oncology Research Information Exchange Network (ORIEN) and The Cancer Genome Atlas. We showed how the processing removes contaminants and batch effects to yield microbe abundances consistent with non–high-throughput sequencing–based approaches and DNA-amplicon–based measurements of a subset of the same tumors. We sought to establish clinical relevance by correlating the microbe abundances with various clinical and tumor measurements, such as age and tumor hypoxia. This process leveraged the two datasets and raised up only the concordant (significant and in the same direction) associations. We observed associations with survival and clinical variables that are cancer specific and relatively few associations with immune composition. Finally, we explored potential mechanisms by which microbes and tumors may interact using a network-based approach. The intrinsic tumor microbiome holds great potential for its ability to predict various aspects of cancer biology and as a target for rational manipulation. Here, we describe a tool to quantify microbes from within tumor RNA-seq and apply it to two independent datasets. We show new associations with clinical variables that justify biomarker uses and more experimentation into the mechanisms by which tumor microbiomes affect cancer outcomes. The microbiome affects many aspects of human health, including cancer. Interactions with the immune system affect immune cell activity and the levels of systemic inflammation, and thereby various disease states . For exaBacteroides fragilis biofilms on colon polyps have been found to secrete a toxin that directly damages DNA and leads to colon cancer  that used a similar approach to the {exotic} tool (described here) found bacteria and viruses in all tumors tested . FurtherWe extend this previous work with a computational tool we call {exotic} for exogenous sequences in tumors and immune cells. The process involves stringent alignment parameters and a series of filtering steps. We show how validation on two large, independent datasets—TCGA and Oncology Research Information Exchange Network (ORIEN)—finds microbes with concordant behavior in cancer types.ORIEN is an alliance of 18 U.S. cancer centers established in 2014. All ORIEN alliance members utilize a standard Total Cancer Care (TCC) protocol. As part of the TCC study, participants agree to have their clinical data followed over time, undergo germline and tumor sequencing, and are contacted by their provider if an appropriate clinical trial or other study becomes available . TCC is Bulk RNA-seq samples with corresponding expression data were obtained from ORIEN and TCGA. Samples from TCGA were identified and downloaded using the R package {GenomicDataCommons} and restricted to samples from patients’ primary tumors. TCGA projects included lung adenocarcinoma (LUAD), LUSC, colorectal (COAD), READ, SARC, THCA, skin (SKCM), kidney renal cell carcinoma (KIRC), and bladder (BLCA). These samples were then classified using Kraken2 with Bracken, and a custom database was built using the available libraries archaea, bacteria, fungi, human, plasmid, protozoa, UniVec, and viral. Microbes with greater than five supporting reads were included in the abundance tables. From here, the R package {decontam} was used to identify contaminants within ORIEN and TCGA samples . These cn = 31) and adjacent normal (n = 31) tissues from 31 patients. Tissues were lysed on a PowerLyzer 24 at 2,000 rpm for 30 seconds, and then DNA was purified using an AllPrep mini kit (QIAGEN). The bacterial rDNA was amplified using V3-V4 primers and KAPA HiFi enzyme . Amplicons were cleaned by magnetic beads, and then sequencing libraries were generated using a QIAseq kit (QIAGEN) following the manufacturer's instructions. Libraries were sequenced on a MiSeq 2 × 300 (600 cycles) using a V3 reagent kit (Illumina). Demultiplexed fastqs were filtered for quality and length (340–440 bp). Taxonomy was assigned by processing through the precontamination filtering steps of {exotic} pipeline.The bacterial 16S rRNA gene was amplified from FF tumor , and all immune cell fractions were tested using Spearman ranked correlation test. Unnormalized expression counts were deconvolved to immune cell fractions using CIBERSORT . SurvivaAll microbes were correlated with all genes using Spearman ranked correlation test in both the TCGA and ORIEN datasets. The ORIEN results were filtered to keep only correlations that had the same direction of effect as the corresponding correlation in TCGA data. The top and bottom 2.5% correlations were then used to define the edges of a network as the most extreme correlations. The degree centrality of this network was calculated as the number of connections formed with each node, and the betweenness centrality was calculated using the R package igraph (RRID:SCR_021238). Microbes with high degree centrality and literature significance were chosen for pathway analysis with R package FGSEA (bioRxiv 060012) using the hallmark pathway database. The microbes were given indicators for each of the correlation analyses, in which they were found to be consistently significant in TCGA and ORIEN datasets. These indicators were then summarized in a table with the degree and betweenness centrality information from the network. The concordant links per node histogram was compared with a random network by creating two datasets each with two matrices of 100 × 100 and 100 × 1,000 to represent microbes and genes, respectively. These two matrix sets were processed by the same method as the tumor microbe and gene data, including correlation, concordance filter, most extreme 5% selection, and then degree centrality calculation.https://github.com/spakowiczlab/exotic. Analysis scripts to regenerate all figures and tables are available at: https://github.com/spakowiczlab/exotic-manuscript.The Ohio State University Institutional Review Board approved data access in an Honest Broker protocol (2015H0185) and Total Cancer Care protocol (2013H0199) in coordination with Aster Insights. The processed data generated in this study are publicly available in Gene Expression Omnibus through the BioProject PRJNA856973. The {exotic} tool is available at: n = 99), SARC (n = 116), COAD (n = 70), KIRC (n = 20), and others. We retrieved and processed by the same method the equivalent cancer types from TCGA. TCGA samples showed similar BMI at the time of collection and genders but were different in the age of collection . In addition, the fraction of samples preserved by freezing was nearly 100% of TCGA samples, whereas roughly one-quarter of the ORIEN samples were FFPE . The fraction of samples from primary tumors was also significantly different between the cohorts, with the ORIEN cohort having a larger perfect from metastatic legions . Finally, the times to last follow-up or death were significantly different, with the ORIEN cohort showing nearly two years longer .We processed tumor RNA-seq from 480 samples through the {exotic} tool . These iexotic} tool to broadly but conservatively identify microbes present in the tumors and remove as many technical artifacts and contaminants from the dataset as possible ] datasets were consistent with published reports using quantitative PCR-based measurements  and sequenced them by 16S DNA amplicon sequencing. Broadly, the fraction of reads belonging to major bacterial phyla, such as Firmicutes, was largely consistent between the two data types , others may be overly stringent. For example, the literature-based filter removes microbes commonly found in sequenced negative controls . UnfortuThe presence or abundance of tumor microbes is associated with various clinical variables, including overall survival, age, BMI, and tumor features such as hypoxia and immune cell composition. While many microbes and taxonomic levels were associated with survival in the ORIEN and TCGA datasets separately, a much smaller number was observed in both, suggesting that caution should be observed with associations between tumor microbes inferred by this method and long-term survival in real-world datasets, particularly if only one dataset is available. Moreover, no microbes are consistently associated with survival in more than one cancer. This suggests microbe-cancer specificity that may provide a biomarker for cancer type or treatment outcomes. These observations are consistent with previous reports that showed machine learning models could predict cancer types with high accuracy . This asRank-based correlations with age and BMI also showed cancer specificity but in a different form than for survival. Many or few associations were found consistently between the two datasets for each cancer, depending on the clinical variable. In particular, age correlated with microbes across both datasets in BLCA and SKCM cancers but not in KIRC, LUAD, COAD, and SARC or all cancers. Older adults are more susceptible to urinary tract infections , while gSimilarly, BMI is associated with BLCA, KIRC, LUAD, and SARC cancers, but not the others. A high BMI has been identified as a risk factor for several cancers, including COAD . Unlike Bifidobacterium correlated with genes in pathways related to IFN signaling and hypoxia. Bifidobacterium is a common gut commensal that was shown to affect the response to immune checkpoint blockade via the secretion of inosine, which is taken up by immune cells via the adenosine receptor A2AAR  more complete microbial genome databases, (ii) thorough contaminant profiling datasets, and (iii) smaller-scale validation studies that increase the confidence in low-abundance organisms. Still, repurposing tumor sequencing data to observe non-human sequences has many challenges. Major barriers to understanding when and how tumor microbes affect cancer are the (i) sparsity of the data and (ii) heterogeneity at the level of patient demographics, tumors, and treatments. In addition, the source of many microbes, whether they originate from the gut, oral cavity, or elsewhere, remains an open question. Geography has been shown to affect the gut and other microbiomes and is expected to drive differences in the tumor microbiome . We propSupplemental Figure S1Example survival analysis using multiple stratification thresholds. The p-values for the separations between survival curves at each quantile are shown, colored by the direction of association (hazard ratio greater or less than 1).Click here for additional data file.Supplemental Figure S2Histogram of the correlations between microbes and genes, from which the most extreme 5% were selected for the network.Click here for additional data file.Supplemental Figure S3The number of edges per node for the nodes of a random network.Click here for additional data file.Supplementary Table S1Individual results for the associations of microbe prevalence with survival .Click here for additional data file.Supplementary Table S2Summary of the microbes associated with survival and the concordance network-based rankings.Click here for additional data file.Supplementary Table S3Ranked list of microbes by degree centrality in a gene-microbe network controlling for cancer type .Click here for additional data file.Supplementary Table S4Ranked list of microbes by closeness centrality in a gene-microbe network controlling for cancer type Click here for additional data file.Supplementary Table S5Ranked list of microbes by betweenness centrality in a gene-microbe network controlling for cancer type Click here for additional data file.Supplementary Table S6Pathways enriched in the genes linked to microbes with the highest degree centrality.Click here for additional data file.Supplementary Table S7Gene set enrichment analysis of the MSigDB Hallmark pathways using genes significantly associated with microbes in the TCGA and ORIEN datasets.Click here for additional data file.


The Infinium EPIC array measures the methylation status of > 850,000 CpG sites. The EPIC BeadChip uses a two-array design: Infinium Type I and Type II probes. These probe types exhibit different technical characteristics which may confound analyses. Numerous normalization and pre-processing methods have been developed to reduce probe type bias as well as other issues such as background and dye bias.This study evaluates the performance of various normalization methods using 16 replicated samples and three metrics: absolute beta-value difference, overlap of non-replicated CpGs between replicate pairs, and effect on beta-value distributions. Additionally, we carried out Pearson’s correlation and intraclass correlation coefficient (ICC) analyses using both raw and SeSAMe 2 normalized data.The method we define as SeSAMe 2, which consists of the application of the regular SeSAMe pipeline with an additional round of QC, pOOBAH masking, was found to be the best performing normalization method, while quantile-based methods were found to be the worst performing methods. Whole-array Pearson’s correlations were found to be high. However, in agreement with previous studies, a substantial proportion of the probes on the EPIC array showed poor reproducibility (ICC < 0.50). The majority of poor performing probes have beta values close to either 0 or 1, and relatively low standard deviations. These results suggest that probe reliability is largely the result of limited biological variation rather than technical measurement variation. Importantly, normalizing the data with SeSAMe 2 dramatically improved ICC estimates, with the proportion of probes with ICC values > 0.50 increasing from 45.18% (raw data) to 61.35% (SeSAMe 2).The online version contains supplementary material available at 10.1186/s13148-023-01459-z. Epigenetic mechanisms, such as DNA methylation, are known to regulate gene function and phenotypic expression –3. Variagold standard for sequencing and mapping CpG methylation [Whole-genome bisulphite sequencing (WGBS) is the hylation . Howeverhylation , 8. The hylation . Illuminhylation .β) values or logit transformed M-values [The Illumina microarrays are built on the same technology used for genotyping single-nucleotide polymorphisms (SNPs) . The IllM-values , 11.The accuracy of certain probes and methylation output can be influenced by confounding factors such as the presence of SNPs, probe cross-reactivity, cell heterogeneity, between-array biases (batch effects), and within-array biases , 10. WitNormalization procedures currently available for correcting EPIC array methylation output include Quantile normalization (QN) , beta-miReplication studies using Illumina BeadChips (i.e. 450k or EPIC) have shown that at the array level, DNA methylation values are highly correlated , 27–29. This study looks to evaluate the reliability of the EPIC array and identify the best normalization techniques using technical replicate samples (e.g. samples characterized twice using the EPIC array) of elderly individuals from Brazil. We also carried out correlation and ICC analyses to explore the extent to which the best normalization approach improved probe reliability and replicability with respect to the raw data.The whole blood samples used in this paper were obtained from the Health, Well-being and Aging  study cohort. SABE is a cohort of census-withdrawn elderly from the city of São Paulo, Brazil, followed-up every five years since the year 2000, with DNA first collected in 2010, and previously described in genomic studies , 33. SamGenomic DNA was extracted from whole peripheral blood samples collected in EDTA tubes. DNA extraction and purification followed manufacturer’s recommended protocols, using Qiagen AutoPure LS kit with Gentra automated extraction (first time point) or manual extraction (second time point), due to discontinuation of the equipment but using the same commercial reagents. DNA was quantified using Nanodrop spectrometer and diluted to 50 ng/uL. To assess the reproducibility of the EPIC array, a subset of 16 of the 48 samples was randomly selected for technical replicates, for a total of 64 samples submitted for further analyses. Whole-genome sequencing data are also available for the samples described above.Approximately 1000 ng of human genomic DNA was used for bisulphite conversion. Methylation status was evaluated using the MethylationEPIC array at The Centre for Applied Genomics , following protocols recommended by Illumina .Meffil (version 1.1.0), RnBeads (version 2.6.0), minfi (version 1.34.0), and wateRmelon (version 1.32.0) were used to import, process, and perform quality control (QC) analyses on the methylation data. Starting with the 64 samples, we first used Meffil to infer the sex of the 64 samples and compared the inferred sex to reported sex. Utilizing the 59 SNP probes that are available as part of the EPIC array, we calculated concordance between the methylation intensities of the samples and the corresponding genotype calls extracted from their WGS data. We then performed comprehensive sample-level and probe-level QC using the RnBeads QC pipeline. Specifically, we (1) removed probes if their target sequences overlap with a SNP at any base, (2) removed known cross-reactive probes (3) used the iterative Greedycut algorithm to filter out samples and probes, using a detection p value threshold of 0.01, and (4) removed probes if more than 5% of the samples had a missing value. Since RnBeads does not have a function to perform probe filtering based on bead number, we used the wateRmelon package to extract bead numbers from the IDAT files and calculated the proportion of samples with a bead number < 3. Probes with more than 5% of samples having a low bead number (< 3) were removed. For the comparison of normalization methods, we also computed detection p values using out-of-band probes empirical distribution with the pOOBAH function in the SeSAMe (version 1.14.2) R package, with a p value threshold of 0.05, and the combine.neg parameter set to TRUE. In the scenario, where pOOBAH filtering was carried out, it was done in parallel with the previously mentioned QC steps, and the resulting probes flagged in both analyses were combined and removed from the data.The R/Bioconductor packages minfi’s read.metharray.exp function. One sample that was flagged during QC was removed, and further normalization steps were carried out in the remaining set of 63 samples. Prior to all normalizations with minfi, probes that did not pass QC were removed. Noob, SWAN, Quantile, Funnorm, and Illumina normalizations were implemented using minfi. BMIQ normalization was implemented with ChAMP (version 2.26.0), using Raw input data produced by minfi’s preprocessRaw function. In the combination of Noob with BMIQ (Noob + BMIQ), BMIQ normalization was carried out using as input minfi’s Noob normalized data. Noob normalization was also implemented with SeSAMe, using a nonlinear dye-bias correction. For all normalization methods, two scenarios were tested. In the first, which we call version 1 , SeSAMe’s pOOBAH masking was not executed, and the only probes filtered out of the dataset prior to normalization were the ones that did not pass QC in the previous analyses. In the second scenario, which we call version 2 , pOOBAH masking was carried out in the unfiltered dataset, and masked probes were removed. This removal was followed by a further removal of probes that did not pass previous QC and that had not been removed by pOOBAH. Therefore, the version 2 of each method had two rounds of probe removal. Methods were then compared by subsetting the 16 replicated samples and evaluating the effects that the different normalization methods had in the beta-value distributions and in the absolute difference of beta values (|β|) between replicated samples. Results were plotted either with base R, or with the ggplot2 (version 3.3.6) or limma (version 3.52.2) R packages.The normalization methods compared in this study were implemented using different R/Bioconductor packages and are summarized in Fig. β values, including standard deviation (SD), mean, median, and range (maximum β–minimum β). To assess the reproducibility and reliability of probe measures, we used the cor function to compute Pearson’s correlation of the technical replicates at each probe. We then calculated the intraclass correlation coefficients (ICCs) using the icc function from the irr package (version 0.84.1). Since there are many forms of ICCs, we followed the recommendations by Koo and Li [β values (SD and mean).We used R (version 4.2.0) and Bioconductor packages to compute summary statistics and conduct statistical analyses. We first examined the distributions of the raw and normalized o and Li  and calcIn the current paper, we evaluated probe replicability using raw and normalized beta values. The normalization techniques employed reduce technical variability. The normalization methods considered, and the pipelines used, can be found in Fig. β|) for all replicate samples, highlighting the percentage of CpGs surpassing |∆β|= 0.05 or 0.1. Additional file β| value (0.00815) and the lowest proportion of probes with |∆β| > 0.05 and |∆β| > 0.10  and the highest proportion of probes with |∆β| > 0.05. Quantile 1 and BMIQ 1 normalization produced the highest proportions of CpGs with an |∆β| > 0.10 (0.43% and 0.33%).Table β| > 0.10, as well as the number of replication pairs in which the probes exceeded this threshold (1–16). This is also shown in graphical format in the figure. In concordance with the results described above, SeSAMe 2 exhibits the best performance, with a total of 7942 CpGs exceeding |∆β| > 0.10 between replicate pairs. Noob normalization was second in terms of performance, with a total of 13,871 probes, followed by SeSAMe 1 with 14,236 probes. Quantile and BMIQ produced the least favourable results with a total of 27,828 and 21,865 CpGs exceeding |∆β| > 0.10 between replicate pairs, respectively.Additional file All samples produced an expected bimodal beta-value distribution, with most probes having beta values close to either 0 or 1 . SeSAMe 2 continued to be the best performing method when considering the proportion of probes with |∆n = 32 samples (16 replicate pairs) for n = 666,485 probes remaining after quality control filtering.Pearson’s correlation and intraclass correlation coefficient (ICC) results were generated using raw beta values as well as beta values normalized using the SeSAMe 2 normalization method. SeSAMe 2 normalization was used for these detailed analyses as it was deemed the most effective normalization method in this study based on the metrics discussed in the previous section. Correlational methods were performed on the R2 > 0.9975; See Table The whole-array Pearson’s correlation of beta values for each of the 16 replicates pairs was high using both raw and normalized data , overlap of non-replicated CpGs between replicate pairs, and effect on beta-value distributions. We also used Pearson’s correlation and intraclass correlation coefficient (ICC) to evaluate whole-array and probe-level reliability and reproducibility.In this study, we used 16 replicate pairs to systematically assess and compare the performance of raw  data and various normalization techniques  using EPIC array methylation data. After normalization, we used three metrics to evaluate the reproducibility of the 16 technical replicates: absolute beta-value difference , especially BMIQ, had overall worse performance compared to the other normalization methods when considering absolute beta-value difference and overlap of non-replicated CpGs between replicate pairs. In contrast, normalization methods that utilize Noob normalization such as SeSAMe, Noob, and Noob + BMIQ performed more favourably in all three metrics evaluated. Overall, SeSAMe 2 was the best performing normalization method of the methods evaluated. Much of SeSAMe 2’s superior performance can be attributed to the improved QC carried out by pOOBAH, as can be seen by comparing SeSAMe 1 and 2. Using pOOBAH as an additional QC method also improved the performance of the other normalization methods, indicating that it is a beneficial QC step for EPIC array data. However, even without pOOBAH (SeSAMe 1), SeSAMe showed better results when compared to most other methods, being tied in second with Noob + BMIQ 1, with Noob + BMIQ 1 producing better results for the median and Q1, and SeSAMe 1 producing better results for more extreme values. These results differ from some previous studies that found quantile-based methods, such as BMIQ, to outperform other normalization methods using 450k array data , 24, 25,Quantile normalization and Funnorm were the only between-array normalization methods considered in this study. Between-array normalization reduces array-to-array variation through adjusting measures on a global scale . The issβ|) and overlap of non-replicated CpGs between replicate pairs, as it produced only marginally better results when compared to the raw data and BMIQ. Dedeurwaerder et al. [β|) and overlap of non-replicated CpGs between replicate pairs, but also diverged from the expected bimodal distribution  in many sample pairs. Out of a universe of ~850,000 CpGs, the fact that a few have poor replication in multiple sample pairs indicates that there is some underlying problem with their probes. For example, using Funnorm, 363 probes showed |∆β| values higher than 0.1 in 7 or more sample pairs. Even for the best performing approach, SeSAMe 2, there were 78 CpGs that had poor replication in 7 or more sample pairs, pointing to a consistent source of error for these probes. On the other hand, for the majority of CpGs that failed in only one sample pair, there is a higher possibility that these failures were due to random errors.The overlap in non-replicated CpGs shown in Additional file r > 0.99) at the whole-array level for both raw and normalized data. However, previous studies have found that whole-array correlations are also high between different individuals [Replicate samples were used to assess the EPIC array replicability using both Pearson’s correlations and ICC. As with previous research using Illumina arrays , 29, 40,ividuals , suggestividuals , 41.The ICC results of this study are in line with previous studies , 28, 31,Normalizing the data with SeSAMe 2 was found to substantially improve ICC values, through increasing the proportion of probes with an ICC value > 0.50 from 45.18% (raw data) to 61.35% (SeSAMe 2). Other pre-processing and normalization methods, such as background correction and dye-bias correction pre-processing methods, have also been found to increase the number of probes with acceptable reliability . Howevern = 16 replicate pairs, as it has been previously suggested that 30 replicate samples will provide reasonably good agreement with ICC classification [The main limitation of this study is the relatively small sample size of fication . While tβ| observed with Noob + BMIQ 2 is the same as that observed with SeSAMe 2 (0.00815), and the proportion of probes with |∆β| values higher than 0.05 and 0.1 is very similar for Noob 2 and SeSAMe 2. The improvements observed after pOOBAH masking indicate that this would be a useful step to add in QC protocols prior to probe normalization. In general, our study indicates that noob-based normalization methods such as SeSAMe, Noob, and Noob + BMIQ performed well. In contrast, quantile-based normalization methods  were found to exhibit lower performance compared to the noob-based methods. In line with previous studies, the EPIC array was found to be highly reproducible at the whole-array level, but this is not the case at the probe level, with a relatively large number of probes displaying poor reliability (ICC < 0.5). Additionally, in agreement with previous observations, CpGs with low biological variability tend to have low ICC values. Importantly, normalizing the data with SeSAMe 2 substantially improved ICC estimates, with the proportion of probes with ICC values > 0.50 increasing from 45.18% (raw data) to 61.35% (SeSAMe2). These results emphasize the benefit of using normalization methods when analysing EPIC array data.In conclusion, SeSAMe 2  was found to be the best performing normalization method based on the metrics evaluated. It is important to note that the number of probes used in the SeSAMe 2 pipeline is lower than the number of probes used for the other normalization approaches , due to the exclusion of approximately 18,000 probes after pOOBAH masking. We repeated the analyses for all the normalization methods excluding the probes identified by pOOBAH masking, and as expected, there is an improvement in the performance of all the methods. These results are provided in Additional file Additional file 1: Supplementary Figures S1–S19 and Table S1.


Background: Rare endocrine cancers such as Adrenocortical Carcinoma (ACC) present a serious diagnostic and prognostication challenge. The knowledge about ACC pathogenesis is incomplete, and patients have limited therapeutic options. Identification of molecular drivers and effective biomarkers is required for timely diagnosis of the disease and stratify patients to offer the most beneficial treatments. In this study we demonstrate how machine learning methods integrating multi-omics data, in combination with system biology tools, can contribute to the identification of new prognostic biomarkers for ACC.Methods: ACC gene expression and DNA methylation datasets were downloaded from the Xena Browser . A highly correlated multi-omics signature discriminating groups of samples was identified with the data integration analysis for biomarker discovery using latent components (DIABLO) method. Additional regulators of the identified signature were discovered using Clarivate CBDD  network propagation and hidden nodes algorithms on a curated network of molecular interactions (MetaBase™). The discriminative power of the multi-omics signature and their regulators was delineated by training a random forest classifier using 55 samples, by employing a 10-fold cross validation with five iterations. The prognostic value of the identified biomarkers was further assessed on an external ACC dataset obtained from GEO (GSE49280) using the Kaplan-Meier estimator method. An optimal prognostic signature was finally derived using the stepwise Akaike Information Criterion (AIC) that allowed categorization of samples into high and low-risk groups.Results: A multi-omics signature including genes, micro RNA's and methylation sites was generated. Systems biology tools identified additional genes regulating the features included in the multi-omics signature. RNA-seq, miRNA-seq and DNA methylation sets of features revealed a high power to classify patients from stages I-II and stages III-IV, outperforming previously identified prognostic biomarkers. Using an independent dataset, associations of the genes included in the signature with Overall Survival (OS) data demonstrated that patients with differential expression levels of 8 genes and 4 micro RNA's showed a statistically significant decrease in OS. We also found an independent prognostic signature for ACC with potential use in clinical practice, combining 9-gene/micro RNA features, that successfully predicted high-risk ACC cancer patients.Conclusion: Machine learning and integrative analysis of multi-omics data, in combination with Clarivate CBDD systems biology tools, identified a set of biomarkers with high prognostic value for ACC disease. Multi-omics data is a promising resource for the identification of drivers and new prognostic biomarkers in rare diseases that could be used in clinical practice. Adrenocortical carcinoma (ACC) is a rare and aggressive cancer that originates in the adrenal gland cortex. Among all adrenal tumors, adrenocortical carcinoma is one of the most prevalent cancers with one of the worst prognoses . DespiteIn recent years, several molecular pathways, such as Ghrelin and Wnt signaling, have been described in relation to ACC . AlthougThe limited curative treatments and life expectancy for rare diseases underline the existing caveats in trying to understand the multifactorial nature of these indications. To close this gap, combination of multiple omics has been demonstrated to be previously useful, offering comprehensive insights into the molecular landscape of a specific biological phenomenon . For example, it has been shown to be effective in early blood biomarker characterization for ovarian cancer detection ; as wellBy simultaneously analyzing various molecular layers, biomarkers derived from multi-omics analysis can provide a holistic view of the molecular modifications responsible for the disease. To tackle omics integration complexity, recent developments in machine learning and multivariate methods have helped ease the process . HoweverTo achieve effective patient stratification, it is essential to accurately categorize patients based on the aforementioned data modalities and inferred regulators. In the context of precision medicine, machine learning techniques have been widely employed for this purpose. Notably, among these techniques, the random forest algorithm efficiently and accurately addresses the inherent challenges of integrating diverse data sources .Overall, the combination of multi-omics approaches, network algorithms, and machine learning methods offers a promising framework for enhancing our understanding of ACC and for improving patient stratification for personalized therapeutic strategies. In this study, we proposed a novel machine learning method based on Projection to Latent Structures (PLS) with sparse discriminant analysis, namely, DIABLO, integrating ACC RNA-seq, miRNA-seq and DNA methylation data for biomarker identification. To maximize target identification, we coupled it with Clarivate CBDD  network algorithm pipelines using MetaBase™ molecular network, a high-quality and scientifically validated interactome with more than 300.000 interactions curated by Clarivate experts. Our results show the benefit of the usage of novel machine learning methods coupled with Clarivate bioinformatics workflows and network algorithms.https://xenabrowser.net/datapages/)  datasets , and theapages/) . Three tblock.splsda function from Data Integration Analysis for Biomarker discovery using Latent variable approaches for Omics studies (DIABLO) framework (eigencorplot function from the PCAtools R library (v. 2.12.0) with default parameters.Predictor variables with low variance across all the samples were filtered out from each dataset, selecting features with a median absolute deviation above the third quartile. Datasets from different types of omics were integrated using a multiblock Projection to Latent Structure—Discriminant Analysis (PLS-DA) approach. Specifically, the ramework , availabMetaBase™ (v.4.8.0) a data repository of manually curated molecular interactions and signaling pathways from Clarivate Analytics, was used as the source of biological knowledge in this study. The database includes over 1.500 regulatory and diseases-specific pathway maps, and a network with more than 3.3 million of molecular interactions. Network algorithms were leveraged from the industry-leading systems biology consortium CBDD analytical library (v.17.2.0), developed by Clarivate Analytics. For the network propagation and hidden nodes analyses, network nodes with scores below 0.1 and 20 respectively, were dropped from the analysis.A Random Forest (RF) model with repeated cross-validation (5 times) and 500 trees was trained based on 70% of the samples, using combinations of different features. The number of features randomly selected for each tree (mtry) ranged within 3, 5, 7. Selected division criteria was by “splitrule,” and the minimum node’s size for division ranged within 2, 4, 6. Computations were performed using the caret package (v.6.0–94) in R. Specifically, the “ranger” method and “AUC” metric were used in the training step.coxph function in Survival R package (version 3.5.5) was used to perform univariate and multivariate Cox Proportional-Harzards (CoxPH) regression analyses. Survival curves were drawn using survminer R package (version 0.4.9). Patients with low expression were used as the reference group for all the analyzed features. Weiss score and age (as continuous variable) were included in the multivariate Cox PH model to assess simultaneously the effect of the factors on survival time.Univariate association of the features with overall survival (OS) was estimated by log-rank test and the Kaplan-Meier method. Survival step function with backwards selection from the stats R package was used, in which variable terms were evaluated for dropping at each step. Finally, the optimal prognostic model obtained was defined as risk score using the following equation:i are the ithoptimal multi-omics feature’s regression coefficient obtained from the step analysis. Based on the median risk score, ACC patients were divided into high- and low-risk groups. Next, we assessed the difference in survival between the two groups by using Kaplan-Meier method. We considered p-value <0.05 as statistically significant.Then, we constructed an optimal prognostic signature associated with OS through sequential addition or elimination of features based on their performance using the Akaike Information Criterion (AIC) as proposed by Wagenmakers, Eric-Jan, and Simon Farrell . Specifihttps://www.cortellis.com/drugdiscovery) is a knowledgebase focused on pharma and drug development, developed and maintained by Clarivate Analytics. Biomarker data used in this study was downloaded from CDDI (June 2023). The database integrates biological, chemical, and pharmacological data on more than 620,000 molecules with demonstrated biological activity, and over 440,000 patent family records. Main information about biomarkers include data about biomarker type , development stage, context of their usage , number of related drugs, related literature citations and patents, proof-of-mechanism, proof-of-concept, treatment/safety monitoring, and outcome measurement. Drug targets include annotation about structural data from the Protein Data Bank (PDB) and comprehensive data regarding the development phase  of the associated drugs for different therapeutic areas or indications.CDDI . Integration was performed using sparse Partial Least Squares Discriminant Analysis (sPLS-DA), which enables the selection of the most predictive or discriminative features in the data that assist in the classification of samples. We started generating sPLS-DA models with up to 5 components to estimate the classification error rate between stages with respect to the number of selected variables. This strategy allowed us to tune the number of components and variables from each dataset to be retained in the final model. A 3 × 3 design matrix was used to determine whether the datasets should be connected. The value of the design matrix was set at 0.1 as a default value to prioritize the discriminative ability of the model . A totalWe performed a final N-integrative supervised analysis using the defined features. At the single omics dataset level, the identified features explained a maximum of 30% of the total variance among the five components . DNA metAdditionally, we inspected the correlations of the identified components from the three OMICs datatypes with available clinical data from the TCGA ACC dataset . Statistp-values were adjusted considering all internal nodes. The scores for each node are the obtained adjusted p-values on a -log10 scale. It is a local method able to identify upstream regulators or downstream effectors of the input features. As a complementary approach, a network propagation (NP) algorithm  , which plgorithm  was empllgorithm  include p-value≤0.01) and including at least one of the identified topological regulators. The most enriched important ontologies belong to MetaBase™ pathway maps, a comprehensive ontology of canonical pathways integrating 3-6 signaling pathways which describe biological mechanisms. As shown in the generated integrative network of the obtained results  included 157 features: 46 genes, 46 micro RNA’s, 65 methylation sites. A second model (REG-Model), corresponding to the identified regulators, was built using 53 genes. A third model (MOREG-Model) combined the features from two previous ones. Finally, a last model (BIOM-Model) was built using known disease biomarkers available in the literature , accountThe biomarkers from the identified multi-omics signature were searched across the CDDI database. Since DNA methylation biomarkers consisted of CpG-gene pairs or trios, we scanned these across our database separately for CpGs and their associated genes, making up a total of 266 input biomarkers for our search.DICER1 and KRAS as specific ACC biomarkers that were identified using systems biology tools. The presence of DICER1 mutations in endocrine cancers calls for more research given the lack of effective treatments. In addition, novel therapeutic strategies, including targeted therapies such as tyrosine kinase inhibitors (TKIs), are starting to be studied for ACC.The search led to the identification of 28 ACC biomarkers, 167 cancer biomarkers and 12 ACC targets overlapping with the features from the identified multi-omics signature . Out of Interestingly, 99 features from the multi-omics signature did not overlap with CDDI data and could represent potential new disease biomarkers and targets. In addition, the 12 features from our multi-omics signature that overlap with ACC targets are particularly interesting. These findings emphasize the applicability of CDDI to identify known biomarkers and/or targets for which novel drugs are approved or are under study, where there is potential for drug repositioning for these rare and understudied tumors. Details about the multi-omics signature features included in CDDI are available in The prognostic value of the features identified in our analysis was further validated using data from an external dataset with available multi-omics data (GSE49280). Specifically, the selected study contains omics data from gene expression (transcripts and micro RNA’s) and DNA methylation, with associated clinical data including survival information. Some samples were not matched across omics datasets: DNA methylation was available for 81 patients; micro RNA data was available for 78 patients and mRNA data was available for 44 patients. For the former data type, only 3 methylation sites from the identified signature were found to overlap with the methylation data from the external dataset, due to the low throughput of the platform used in GSE49277 study (27.578 CpG sites) compared to the platform from the GDC TCGA ACC dataset (450.000 CpG sites).HAUS8, PLXNA1, SHB, UBE2S, DDX39A, DCAF15, and TSPYL4 showed the most statistically significant associations with a lower survival probability , whereas N4BP2L1 was the only gene for which a low expression level was associated with higher mortality. For the analyzed multi-omics biomarkers corresponding to micro RNA’s, high expression levels of microRNA 376c, microRNA 504 and microRNA 615 were associated with lower survival probability , and only microRNA 1258 conferred a protective effect at lower expression levels. Any of the 3 CpG sites screened showed statistically significant association with OS. Interestingly, most of these features are found in the generated integrative network  were still significant  for HAUS8, PLXNA1, DDX39A and microRNA 1258. In contrast, only a new feature, corresponding to a methylation site, achieved a statistically significant HR after adjustment: cg25836301 (MEG3). The results are presented in We generated individual Kaplan-Meier curves for each gene, micro RNA and DNA methylation sites identified in the multi-omics signature. The obtained curves are available as  network . The samEXP GATA4]+[-22.285 x EXP N4BP2L1]+[1.087 x EXP KCNJ14]+[3.157 x EXP SNORD1143]+[2.271 x EXP UBE2S]+[20.908 x EXP microRNA 615]+[-1.173 x EXP microRNA 1179]+[-19.938 x EXP microRNA 217]+[-20.976 x EXP microRNA 5690]. According to the median OptMultiSig risk score of 165.575, we classified patients into low and high-risk groups comprising 22 samples each. As shown in p-value = 2.1 × 10−4; HR = 1.13; 95% CI 0,04–0.46), highlighting the utility of the 9 features as an independent prognostic factor of poor prognosis for ACC.With the aim of building a prognostic signature of ACC disease including the most predictive features obtained from multi-omics integration, and thus with potential application in clinical practice, we run a model to identify the combination of the previous features significantly associated with overall survival. By calculating and comparing the AIC scores of the several possible models, we selected the best-fit model as the one explaining the greatest amount of variation using the fewest possible variables and lower AIC. A total of 9 features involving 5 genes and 4 micro RNAs showed the optimal AIC value and were subsequently used to establish a risk score using the formula: OptMultiSig risk score = [1.958 x Rare diseases encompass a wide range of disorders, many of which are characterized by limited treatment options, delayed diagnoses, and poor prognoses. ACC, as a rare and aggressive malignancy, exemplifies the challenges faced by patients and healthcare professionals in diagnosing, managing, and treating rare diseases. Prognostic evaluation is critical to determine disease progression and inform treatment decisions. Biomarkers can play a vital role in predicting the clinical course of ACC, stratifying patients into risk groups, and enabling personalized treatment approaches. The identification of staging biomarkers associated with tumor aggressiveness and metastatic potential assists in tailoring surveillance strategies and implementing early interventions in high-risk patients.ALDH1A3, BHMT, DMGDH, EN1, ICAM5, KCNC3 and PPFIA4 biomarkers coming from DNA methylation data, CHID1, GATA4, MEG3 and microRNA 770 from RNA expression data, and DICER1, H19 and KRAS from network analysis. Specifically, ALDH1A3, BHMT, CHID1, EN1, GATA4, ICAM5 and KCNC3 appeared as prognostic biomarkers for ACC in experimental stages, and DICER1 in early studies in humans. The higher amount of DNA methylation data derived biomarkers associated with ACC disease and the methylation association with other processes  might be behind the high amount of variance explained by the five components obtained using DNA methylation data during the sPLS-DA model construction.Integrative analysis of multi-omics data allows to capture cellular regulation at different layers, thus bringing the possibility to build more robust classifiers of biological samples and to discover new molecular interactions of the underlying diseases. In this study, we integrated multi-omics data from different origin: RNA-seq gene expression, miRNA-seq expression and DNA methylation belonging to the GDC TCGA Adrenocortical Carcinoma (ACC) datasets . The avaC11orf1 and the cg12138102 methylation site) belong to the genomic location where the imprinted differentially methylated region MEG3:TSS-DMR is located   . UBE2S wal stage . In addi pathway , consist pathway .GATA4, identified as an ACC biomarker, is thought to influence proliferation by regulating transcription in several cancer types (KCNJ14 is a biomarker for cancer progression and development (SNORD1143 was observed in AML and Acute Promyelocytic Leukemia (APL)  . Regardier types . MicroRN of E2F5 . MicroRNing ATD2 ; and micarcinoma .Our proposed workflow is key for rare diseases such as ACC due of the limited number of studies. Using the transversal power that the use of multiple OMICS can bring, we could combine different little evidence from scarce data, resulting in prognostic value and potential translation into clinical research and diagnostics. Although subtle correlations were found between the identified multi-omics components and important ACC clinical parameters, the obtained coefficients are affected by the limited sample size of the study, and a larger cohort can undoubtedly strengthen such associations. In addition, we showed that network analysis can expand the discovery of important molecular players in diseases.These results demonstrate the usefulness of combining Clarivate´s systems biology tools with molecular signatures derived from multi-omics experiments to identify biologically meaningful biomarkers. Given the low prevalence of ACC, large and comprehensive studies are missing to fully understand the molecular alterations and the relevant signaling pathways altered in these patients. The use of multi-omics and systems biology methods can identify new targets or biomarkers that could be clinically relevant in the form of molecular diagnostic tools such as quantitative polymerase chain reaction (qPCR). Owing to the high sensitivity and specificity of qPCR, together with its multiplexing capacity, the identified prognostic multi-omics signature could be used in clinical practice to tailor clinical decisions to individual ACC patient prognostic profiles, based on accurate characterization of the expression profile of included genes and microRNAs. The presented workflow contributes to improving disease stratification and treatment decision support not only for ACC, but also for other rare diseases with a limited amount of data available.


Expression quantitative trait locus (eQTL) analysis has emerged as an important tool in elucidating the link between genetic variants and gene expression, thereby bridging the gap between risk SNPs and associated diseases. We recently identified and validated a specific case where the methylation of a CpG site influences the relationship between the genetic variant and gene expression.Here, to systematically evaluate this regulatory mechanism, we develop an extended eQTL mapping method, termed DNA methylation modulated eQTL (memo-eQTL). Applying this memo-eQTL mapping method to 128 normal prostate samples enables identification of 1063 memo-eQTLs, the majority of which are not recognized as conventional eQTLs in the same cohort. We observe that the methylation of the memo-eQTL CpG sites can either enhance or insulate the interaction between SNP and gene expression by altering CTCF-based chromatin 3D structure.This study demonstrates the prevalence of memo-eQTLs paving the way to identify novel causal genes for traits or diseases associated with genetic variations.The online version contains supplementary material available at 10.1186/s13059-023-03130-5. Genome-wide association studies (GWAS) have identified over 300,000 SNP-trait associations . HoweverIn a recent study, we identified prostate cancer rSNP, rs11986220, as a novel eQTL for the oncogenic gene MYC in a subset of samples with a high level of methylation at a CpG site located approximately 10 kilobase pairs (kbp) upstream of the MYC promoter. We demonstrated that high DNA methylation at this site prevents CTCF binding and the formation of a chromatin loop, which would allow for a long-range interaction between this rSNP and MYC . Unlike To systematically evaluate the DNA methylation modulated relationship between genetic variant and gene expression, we introduced a novel eQTL analysis method named DNA methylation modulated eQTL (memo-eQTL). The modulation effect was statistically characterized as the interaction between the SNP and methylated CpG site (SNP × meCpG), and its significance was determined by comparing the multiple regression models with and without the interaction , 11. AnaOur previous study demonstrated that a meCpG can modulate the interaction between genetic variant and gene expression by altering CTCF binding . BuildinAlthough a single CTCF binding site might contain multiple meCpG sites  via a moderate model (M3). Subsequently, it requires comparisons against the covariate model M2) and the standard eQTL model (M1) to determine the statistical significance of the modulation effect  cohort, which comprised matched whole-genome sequencing (WGS), RNA sequencing (RNA-seq), and WGBS data for 128 benign prostate samples  Fig. 2A”. For mep-values less than 0.05 , but also surpassed canonical eQTL models in performance (M3 versus M1)  and low (relL) groups 1000 times to simulate the modulation effects of meCpG “”. RemarkIn conclusion, these results suggest that memo-eQTL mapping complements canonical eQTL and meQTL analyses, uncovering previously uncaptured relationships between genetic variant and gene expression modulated by DNA methylation.Among the 1063 memo-eQTLs, there are 352, 749, and 847 unique meCpGs, genes, and SNPs, respectively. Hereafter, they are termed as eCpGs, eGenes, and eSNPs , revealing a mere 5% overlap detected in both cohorts. This variance is likely primarily attributed to the inherent demographic differences between the two cohorts. The CPGEA cohort we utilized comprises 128 matched normal samples obtained from Chinese prostate cancer patients, all aged over 50 . In contConsidering the potential impact of DNA methylation variations from different cell types within the tissue samples, it is important to conduct cell type estimation to assess this influence. In this study, we turned to gene expression-based tools such as EPIC  and xCelSince most of the eCpGs are negatively associated with CTCF binding, we assumed that the sigHigh memo-eQTLs would preferentially partially intersect with CTCF looping, as a result, inhibiting the CTCF-based loop formation can blockade the interplay between eSNP and eGene. Conversely, sigLow memo-eQTLs would be more likely fully located in the CTCF loop, allowing for increased eSNP and eGene interactions since the loop would bring them physically closer. However, we did not observe clear preference between sigHigh and sigLow memo-eQTLs regarding the overlapping pattern with eCpG-CTCF loops. One possible explanation is that the vast majority of memo-eQTL eSNP-eCpG-eGene regions overlapped with multiple CTCF loops, complicating the task of distinguishing 3D structure differences among various memo-eQTLs groups. In addition, it is worth noting that we had successfully validated the meCpG modulated insulation mechanism at MYC locus in our previous study , which pThe memo-eQTL method offers a valuable tool for identifying DNA methylation modulated eQTLs that are often missed by canonical eQTL analysis, thereby allowing for the discovery of novel target genes that are associated with genetic variants and diseases. We found that DNA methylation modulated CTCF-based chromatin 3D organization can either insulate or enhance the cross-talk between genetic variant and gene expression. We anticipate that as more 3D chromatin data becomes available, our understanding of these regulatory mechanisms will continue to improve. Overall, our findings suggest that the memo-eQTL method, coupled with the study of chromatin 3D organization, presents a complementary framework for identifying and understanding the complex regulatory processes underlying genetic variation and gene expression.p-value for each CpG site’s methylation levels and corresponding CTCF binding intensity were calculated, and the p-values were adjusted for multiplicity using the BenJamini–Hochberg procedure. Lastly, a significant association between meCpG and CTCF binding was defined when the absolute SCC was greater than 0.5 and the Padj was smaller than 0.05 . Given the potentially functional capability, we focused on 19,895 pruned SNPs located in 14,374 ATAC-seq distal peak regions that were identified in prostate cancer [Using the processed WGS, WGBS, and RNA-seq data for 128 benign prostate samples from the CPGEA cohort , we extre cancer   on top of M2. These models were built for all 48,348 SNP-meCpG-Gene combinations.p-value threshold of 0.05 was used to determine statistical significance.For memo-eQTLs, first, we required that M3 is significant, and the contribution of the SNP × meCpG interaction is also significant by comparing the M3 and M2 models , 11. To p-value-based method and canonical FDR-based method in identifying significant memo-eQTLs, we devised an artificial dataset for thorough analysis. This dataset employed the identical SNP and meCpG data for all tested SNP-CpG-Gene combinations from our presented study, while the gene express data (y) was deliberately generated through randomization using the following formula:β values combination were randomly selected from the Additional file N = 128). Subsequently, we conducted the memo-eQTL mapping for all SNP-CpG-Gene combinations using this artificial dataset. The real memo-eQTL was defined as when p-value-based and FDR-based methods. Lastly, both F1 scores (as defined below) and the count of the True Positives were examined to evaluate the performance of both methods.To assess the performance of using our entclass1pt{minimap-value-based method (F1 = 0.34). However, it is noteworthy that the p-value-based method yields a higher number of true positive memo-eQTLs  when compared to the FDR-based method.If we were to apply the FDR correction solely to the M3 versus M2 to ascertain memo-eQTLs, we achieved a comparable F1 score (F1 = 0.31) to that achieved by our proposed p-value < 0.05) in either or both relH and relL subsamples.To visualize the effects of SNP × meCpG for memo-eQTL, we dichotomized the 128 samples based on the optimal threshold of meCpG levels that distinguished the M1 models in meCpG relatively high (relH) and low (relL) subsamples the most. This threshold was searched within the range between the lower (Q1) and higher (Q3) quartiles of meCpG levels Fig. A. Specifp-value threshold of less than 0.05. Furthermore, to enhance credibility of the memo-eQTL, we conducted the permutation tests. These tests involved randomly dividing 128 GTEx samples into two groups 1000 times for each sigHigh and sigLow memo-eQTLs, assuming one group with relatively high (relH) DNA methylation levels and the other with relatively low levels (relL) to mimic the modulation effects of meCpG. Notably, the partition of the relH and relL groups is consistent with the group sizes of the corresponding memo-eQTLs in each simulation.Given the specific prerequisites outlined in Fig. http://gusevlab.org/projects/fusion/), where we leveraged pre-computed gene expression weights derived from GTEx prostate data and GWAS summary statistics from two distinct prostate cancer GWAS studies [To evaluate the inherent variability of DNA methylation levels and variations across different cell types, both EPIC  and xCel studies , 21.www.cog-genomics.org/plink/1.9/) was employed to scan all SNP pairs within the query SNP’s centralized 1 million base pair window, and SNPs with an R2 value greater than 0.8 were considered in high LD.All significant SNP-trait associations were downloaded from the GWAS catalog , and onlTo determine the linear distance between the eSNP, eCpG, and eGene, we calculated the pairwise distances between any two of them. The distance to eGene was measured based on the transcription start site (TSS). All distances were measured strandless since meCpG is strandless, and negative distance indicates the location of the former element on the left side. To explore the chromatin 3D structures, we called CTCF-associated chromatin loops from CTCF HiChIP data in VCaP and 22Rv1 cells (GSE172498) using HiCUP (v0.7.2) and hichipper (v0.7.7) pipelines. Additionally, we utilized CTCF ChIA-PET data from the normal prostate cell line (RWPE-1)  and focup-value was less than 0.05.The comparison of continuous variables between groups was conducted using the Wilcoxon rank-sum two-sided test or the Kolmogorov–Smirnov test, while the comparison of categorical variables was conducted using chi-squared test. All statistical significance was provided, and the results were considered significant if the Additional file 1: Table S1. Significantly correlated meCpG-CTCF pairs. All significantly correlated meCpG-CTCF pairs (sheet 1) and the most significantly correlated meCpG-CTCF pair per each CTCF binding site (sheet 2).Additional file 2: Table S2. memo-eQTL mapping results and assessment. Different β values combinations used for memo-eQTL mapping simulation (sheet 1). Meta information for 1,063 memo-eQTLs (sheet 2). The estimated cell type proportions by EPIC (sheet 3) and xCell (sheet 4) using gene expression data.Additional file 3: Table S3. memo-eQTL eSNPs that overlap with GWAS risk SNPs.Additional file 4: Table S4. Linear and spatial patterns for memo-eQTLs. Pairwise linear distances between eSNP, eCpG and eGene for 1,031 memo-eQTLs (sheet 1). The spatial overlapping patterns between the eSNP-eCpG-eGene loci and eCpG-CTCF loops derived from 22Rv1(sheet 2), VCaP (sheet 3) HiChIP data and RWPE-1 ChIA-PET data (sheet 4).Additional file 5:Figure S1-S4.Additional file 6: Peer review history.