Can the accuracy of timing of luteal phase endometrial biopsies based on urinary ovulation testing be improved by measuring the expression of a small number of genes and a continuous, non-categorical modelling approach?IL2RB, IGFBP1, CXCL14, DPP4, GPX3 and SLC15A2) is sufficient to obtain substantially more accurate timing estimates and to assess the reliability of timing estimates for each sample.Measuring the expression levels of six genes . Pearson correlation between EndoTime estimates for the same sample set but based on RTq-PCR or RNA-sequencing data showed a high degree of congruency between the two . Estimated timings did not differ significantly between control subjects and patients with recurrent pregnancy loss or recurrent implantation failure (P > 0.05).The Wilcoxon rank-sum test was used to confirm that ordering samples by EndoTime estimates yields sharper temporal expression profiles for held-out genes (not used when determining sample timings) than ordering the same expression values by patient-reported times .The RTq-PCR data files are available via the GitHub repository for the EndoTime software at Timing estimates are informed by glandular gene expression and will only represent the temporal state of other endometrial cell types if in synchrony with the epithelium. Methods that estimate the day of ovulation are still required as these data are essential inputs in our method. Our approach, in its current iteration, performs batch correction such that larger sample batches impart greater accuracy to timing estimations. In theory, our method requires endometrial samples obtained at different days in the luteal phase. In practice, however, this is not a concern as timings based on urinary ovulation testing are associated with a sufficient level of noise to ensure that a variety of time points will be sampled.Our method is the first to assay the temporal state of luteal-phase endometrial samples on a continuous domain. It is freely available with fully shared data and open-source software. EndoTime enables accurate temporal profiling of any gene in luteal endometrial samples for a wide range of research applications and, potentially, clinical use.This study was supported by a Wellcome Trust Investigator Award (Grant/Award Number: 212233/Z/18/Z) and the Tommy's National Miscarriage Research Centre. None of the authors have any competing interests. J.L. was funded by the Biotechnology and Biological Sciences Research Council (UK) through the Midlands Integrative Biology Training Partnership . Menstruation is the defining characteristic of the endometrium in humans and higher primates, a trait otherwise found in only a handful of non-primate species . EndometIt is widely accepted that pathological cues that interfere with the sequence of endometrial events leading to a functional implantation window causes reproductive failure, including recurrent implantation failure (RIF) and recurrent pregnancy loss (RPL) , and Tommy’s National Reproductive Health Biobank (REC reference: 18/WA/0356). All samples were obtained with written informed consent and in accordance with The Declaration of Helsinki (2000) guidelines.Endometrial biopsies were obtained from women attending the Implantation Clinic, a dedicated research clinic at University Hospitals Coventry and Warwickshire (UHCW) National Health Service Trust. Surplus tissues from endometrial biopsies obtained for diagnostic purposes were used for this study. Participants were instructed to use over-the-counter urinary ovulation tests and contacted the clinic on the day of a positive test or soon after. An endometrial biopsy was then scheduled 4–12 days after a positive test. The timing of endometrial biopsies was designated as LH+ (day), i.e. the number of days following a positive urinary ovulation test. Following transvaginal ultrasound assessment to exclude overt pelvic pathology, an endometrial biopsy was obtained using a Wallach Endocell™ endometrial sampler. All samples were immediately portioned with one part placed in RNAlater Stabilization Solution  for a minimum of 24 h at 4°C before removal and storage at −80°C, one part snap frozen in liquid nitrogen, and one part fixed in 10% formalin for immunohistochemistry.Two sets of RTq-PCR data were utilized in the development of EndoTime. Data Set I was used for model training, consisting of ΔCT values for 257 endometrial samples, with 96 provided by patients diagnosed with RPL, 81 by patients diagnosed with RIF and 80 acting as controls. Data Set II was applied in tandem with a trained model in order to provide timing estimates for 36 samples independently of patient-reported values. EndoTime was then applied to an independent data set, Data Set III, comprising of 407 LH-timed endometrial biopsies. Demographic information for all three data sets is presented in L19 was used as a reference gene. Primer sequences of marker genes and the endometrial cell type(s) of expression are tabulated in Total RNA was extracted from RNAlater-protected endometrial biopsies using STAT-60 , according to the manufacturer’s instructions. Reverse transcription was performed from 1 µg RNA using the Quantitect Reverse Transcription Kit  and cDNA was diluted to 10 ng/µl equivalent before use in qPCR. Amplification was performed on a Quant5 Real-Time PCR system  in 10 µl reactions using 2× QuantiFast SYBR Green PCR Master Mix containing ROX dye (QIAGEN), with 300 nM each of forward and reverse primers. RNA was purified using RNA STAT-60 (AMS Bio) according to manufacturer’s instructions and treated using Amplification Grade DNase I (Invitrogen) followed by ethanol precipitation and clean-up. Quality control, library preparation and sequencing were performed by the Wellcome Trust Centre for Human Genetics. Libraries were prepared using the Illumina TruSeq Stranded mRNA sample prep kit according to manufacturer’s instructions. Paired-end 75 bp sequencing was performed on Illumina HiSeq4000.Endometrial biopsies were fixed overnight in 10% neutral buffered formalin at 4°C and wax embedded in Surgipath Formula ‘R’ paraffin using the Shandon Excelsior ES Tissue processor (ThermoFisher). Tissues were sliced into 3 μM sections on a microtome and adhered to coverslips by overnight incubation at 60°C. Deparaffinization, antigen retrieval (pH 9), antibody staining, haematoxylin counter stain and 3,3’-diaminobenzidine colour development were fully automated in a Leica BondMax autostainer (Leica BioSystems). Tissue sections were stained for CD56 (a uNK cell-specific surface antigen) using a 1:200 dilution of concentrated CD56 antibody . Stained slides were de-hydrated, cleared and cover-slipped in a Tissue-Tek Prisma Automated Slide Stainer, model 6134 . Bright-field images were obtained on a Mirax Midi slide scanner using a 20× objective lens and opened in Panoramic Viewer v1.15.4 .Expression (ΔCT) values in individual samples in Data Sets I and III were normalized to a scale of zero to one per gene and then adjusted by a batch-specific additive constant as a modest batch effect correction, making mean expression values equal in each batch. Samples in Data Set II were processed for RTq-PCR analysis as a single batch.EndoTime modelling required that reported sample time points be converted from an ordinal to a continuous domain, a process that was undertaken in two steps. First, random noise sampled from a uniform distribution between −0.5 and 0.5 was added to each reported LH+ (rLH+) value. Second, samples were sorted in ascending order according to these updated timings, and the timings were smoothed using linear regression. This procedure allowed for each sample to be spaced evenly throughout the defined time course in a non-discrete manner, but close to its original rLH+ value, an approach that was considered robust in the presence of samples with unusually high or low reported timing values.The approach for modelling via EndoTime relies upon an iteration of temporal gene expression profile refinement followed by the application of these refined profiles to estimate sample timings. Continuous rLH+ values generated during pre-processing were used to form initial expression profiles specific to each gene in the panel, which were then partitioned into windows of equal size . A normaEach sample within the data set was then assessed individually for its likely timing. The expression values for each marker gene within each window were used to generate a probability density curve based on the likelihood that the expression values observed in a sample were drawn from the distribution seen in the windowed data. This resulted in a set of six probability density curves being generated per sample, with each curve representing the results of attempting to estimate timing for the sample based on one gene alone, with associated curve maxima suggesting the time point with the greatest likelihood. Utilizing a shrinking bin size allowed for the first iteration of the process to filter out the majority of noise introduced by unreliable rLH+ into the data. Subsequent iterations refined estimations, while enforcing a minimum bin size ensured that the curves were smooth and presented a single clear maximum .This process of generating six individual probability density curves also allowed for an assessment to be made regarding the congruency of their peak maxima and therefore the consistency with which each gene provided the same timing estimate. Synchronous samples were those wherein the six maxima all suggested a similar estimate, while asynchronous samples were those that presented conflicting estimations; an ‘asynchrony’ score was provided to each sample based on the standard deviation among all six maxima, which describes how coherently the aforementioned probability density curve-based approach provides a singular timing estimate.Consolidating these six curves into a single curve by averaging their scaled densities allowed for the identification of a maximum in a single curve, which was used as the new time point estimation for the sample. A window-based approach was used when consolidating the six individual curves into one, with bin sizes equal to those used when generating the underlying reference profiles of panel gene expression. This process iterated until convergence, with each iteration undertaking both refinement of temporal profiles and time point estimation. The difference between the estimations provided by the preceding and current iteration was measured using the Euclidean distance and convergence was declared once the distance fell below 2.During the modelling process, the absolute values of sample timings could deviate from the desired range as our method was primarily designed to optimize the correct order of samples, rather than retaining the original unit associated with timings. To ensure that EndoTime outputs are in line with original units, the raw timings obtained by modelling were converted following the last iteration such that the overall distribution of patient-reported LH times is approximately matched by the EndoTime output.The six panel genes initially formed part of a set of 15 genes that were measured across the samples of Data Set I , providiRNA-seq libraries were mapped to the hg19 human genome assembly (2014) using Bowtie v. 2.2.3 , (ii) by EndoTime timing estimates (denoted as Ev) and (iii) by expression level, in ascending order if the expression level of the held-out gene increases over time and descending order otherwise (denoted as Gv). As patient-reported times are integer values with a unit of days, breaking ties needed to be resolved in order to compare directly against the other vectors. This was done by ordering samples of the same day in ascending or descending order according to the expression level of the held-out gene. We applied the Wilcoxon rank-sum test to check whether the absolute values of the differences G—vPv were greater than for G—vEv in a single-sided test. A significant P-value indicates that the order of samples provided by EndoTime is closer to the perfect order. In this setting, breaking the ties for patient-reported times as described above yields the largest P-value among all possible resolutions of ties, meaning that statistical significance may be under-stated but not over-stated with this approach as the P-value computed is an upper bound for the P-value that could be obtained if patient-reported times were more finely resolved. This process was repeated six times, holding out one panel gene at a time, and P-values Bonferroni-corrected for multiple testing.To assess the improvement in timing accuracy, we used a cross validation approach. Sample timings were estimated using EndoTime with a panel of only five genes, holding out the expression data for one gene. Expression data for the held-out gene was ordered (i) by patient-reported times  or P-values against control or 0 losses were computed via ordinary one-way ANOVA with Dunnett’s multiple comparisons test (Data Sets II and III). P-values for non-normally distributed data were calculated via Wilcoxon rank-sum test (Data Set I) or Kruskal–Wallis with Dunn’s multiple comparisons test (Data Sets II and III).Patient demographics were assessed for normality via Shapiro–Wilk test. GPX3 can be found in the ER-Map panel. The distribution of expression values for all six panel genes were found to be comparable between clinical cohorts . The other genes, though visually appearing tighter, did not test significantly under the conservative testing our approach, which resolves breaking ties for patient-reported times in a way that maximizes P-values . These genes may also be less tightly regulated in the luteal phase. We concluded that EndoTime arranged samples on the time axis in a biologically more accurate manner than patient-reported times.We applied the EndoTime method on Data Set I, comprising of 257 luteal endometrial samples. We used a leave-one-out cross-validation approach for the set of marker genes used, inferring timings based on five genes while not using the expression data of the held-out, sixth gene. We hypothesized that EndoTime estimates will yield sharper, less noisy temporal profiles for temporally regulated genes. If samples were ordered merely to fit the data of five genes without inferring the true order of samples, then the temporal profile for the held-out gene would not improve. This process was repeated six times, holding out one gene at a time. We found that the temporal profile of each held-out gene became tighter after EndoTime with expression values, deviating less from the temporal trajectory when compared to profiles plotted using patient-reported times . We concluded that meaningful EndoTime estimates can be obtained from RNA-seq data even if there is not enough data to re-train reference profiles.Data Set II consisted of 36 endometrial biopsies for which RTq-PCR as well as RNA-seq data across 33 329 genes was obtained as well as RTq-PCR data for the EndoTime gene panel. We used this set to assess if estimates derived from RTq-PCR data would yield comparable results when EndoTime is applied to measurements of the same six marker genes by RNA-sequencing. This necessitated normalizing the RNA-seq read counts to make these comparable to the normalized RTq-PCR values in terms of means and variances. As reference profiles were fixed by the modelling exercise for both data types, EndoTime was applied only to carry out a single estimation step for sample timings without updating the temporal profiles. A fundamental concern towards reliance upon patient-reported timings provided with clinical biopsies is the potential for inaccuracy. The endometrium is intrinsically dynamic and mistimed samples could confound the diagnosis of underlying pathologies. Histological approaches can provide insights into biopsy timing but require additional processing of samples and appropriate expertise.EndoTime analysis of the 257 biopsies in Data Set I revealed a mean difference between reported and estimated LH timing of 1.29 days, with 48 samples showing an estimated deviance of more than two days and 19 samples a deviance of more than three days. One biopsy was estimated to be 6.22 days later than the rLH+ value. The likelihood of mistiming appeared to be broadly independent of the temporal state of the tissue , with deAppraisal of the influence of time on transcriptomic variability in comparison to other potential sources of variation, such as interpatient variability, was achieved by performing principal component analysis on the RNA-seq data in Data Set II. The two principal components that explained the largest percentage of variance overlaid RTq-PCR-based EndoTime estimations , implyinP > 0.05, Wilcoxon rank-sum test with Bonferroni multiple testing correction). Likewise, with EndoTime estimates, the difference between reported and estimated LH time and the asynchrony scores also did not differ between the groups  or continuously decreasing (one gene) as these are the patterns found for most temporally variable genes in this tissue. Monotonic shapes are most informative as each expression level is only seen once across time, but the EndoTime methodology can be used for any temporal patterns. We believe that EndoTime has a range of applications in research settings as well as broad potential for clinical application as well.EndoTime provides a good degree of transparency to the user, with each panel gene contributing its own estimate of sample timing, which are then aggregated in a single final time estimate. Estimates based on individual genes that appear inconsistent are reported to the user as asynchrony between panel genes, providing a measure of reliability and highlighting estimates with low confidence. Transcriptomic measurements in an individual biopsy sample can be plotted against the normal temporal profiles identified by EndoTime, providing a direct visual representation of the evidence for synchrony or asynchrony. Asynchrony may arise due to both technical errors and/or biological determinants. Although there was no observable correlation between timing errors and any of the three clinical groups that comprise Data Set I, future work could further investigate correlations of asynchrony with reproductive pathologies in larger data sets. Notably in this study, the concept of a biopsy being considered as ‘asynchronous’ relates to the relative congruence of all panel genes towards a singular estimated timepoint (PER2 (EndoTime is able to provide timing estimations of greater accuracy as the size of contributing batches increases due to improved batch effect correction in the underlying transcriptomic data used for modelling. Samples in this study were obtained exclusively between 2 and 6 p.m., which limits the degree to which timing estimations might be influenced by fluctuations imposed by the circadian clock, such as those associated with By transforming RNA-seq measurements to match the distribution of RTq-PCR data prior to modelling via EndoTime, estimates can be obtained that are highly congruent. This conclusion was supported further upon projecting estimated timings over the principal component analysis of RNA-seq data, showing that over 44% of transcriptomic variance between samples can be explained as temporal fluctuations in gene expression. This offers the possibility of applying EndoTime to the transformation and timing estimation of endometrial RNA-seq data. This should broaden the application of EndoTime and identify additional temporally sensitive genes that might further improve the gene panel in the future. This may also provide a foundation for dissecting normal temporal changes from changes related to patient cohorts. In addition, it creates potential for developing methods for adjusting the timing of RNA-seq data sets computationally to make these more comparable across patient cohorts.In summary, EndoTime is a novel open access software which advances the process of timing luteal phase endometrial biopsies along a continuous scale, presenting opportunities for further improvements in terms of its generalization across the entire endometrium. EndoTime’s application to a wider range of transcriptomic measurements and its timing resolution presents potentially far-reaching research and clinical applications.https://www.github.com/AE-Mitchell/EndoTime.The RNA-seq data are available in the Gene Expression Omnibus under accession GSE180485. The RTq-PCR data are available in the GitHub repository for the EndoTime software at deac006_Supplementary_Figure_S1Click here for additional data file.deac006_Supplementary_Figure_S2Click here for additional data file.deac006_Supplementary_Figure_S3Click here for additional data file.deac006_Supplementary_Figure_S4Click here for additional data file.deac006_Supplementary_Figure_S5Click here for additional data file.deac006_Supplementary_Table_S1Click here for additional data file.deac006_Supplementary_Table_S2Click here for additional data file.deac006_Supplementary_Table_S3Click here for additional data file.deac006_Supplementary_Table_S4Click here for additional data file.


When assessed over a large number of samples, bulk RNA sequencing provides reliable data for gene expression at the tissue level. Single-cell RNA sequencing (scRNA-seq) deepens those analyses by evaluating gene expression at the cellular level. Both data types lend insights into disease etiology. With current technologies, scRNA-seq data are known to be noisy. Constrained by costs, scRNA-seq data are typically generated from a relatively small number of subjects, which limits their utility for some analyses, such as identification of gene expression quantitative trait loci (eQTLs). To address these issues while maintaining the unique advantages of each data type, we develop a Bayesian method (bMIND) to integrate bulk and scRNA-seq data. With a prior derived from scRNA-seq data, we propose to estimate sample-level cell type–specific (CTS) expression from bulk expression data. The CTS expression enables large-scale sample-level downstream analyses, such as detection of CTS differentially expressed genes (DEGs) and eQTLs. Through simulations, we show that bMIND improves the accuracy of sample-level CTS expression estimates and increases the power to discover CTS DEGs when compared to existing methods. To further our understanding of two complex phenotypes, autism spectrum disorder and Alzheimer's disease, we apply bMIND to gene expression data of relevant brain tissue to identify CTS DEGs. Our results complement findings for CTS DEGs obtained from snRNA-seq studies, replicating certain DEGs in specific cell types while nominating other novel genes for those cell types. Finally, we calculate CTS eQTLs for 11 brain regions by analyzing Genotype-Tissue Expression Project data, creating a new resource for biological insights. Gene expression quantified at the tissue level, bulk gene expression data, has been a useful resource for understanding the etiology of different diseases. RNA sequencing technology, applied to tissue samples, is mature, and its relatively cost-efficient property allows assessment of tissue from hundreds of samples, thereby producing rich data sets   algorithm to refine the estimation of CTS expression for each bulk sample. As compared to MIND, bMIND not only works for bulk data without multiple measures, but it can also be used to estimate the sample-level CTS expression for each brain region, for instance, thereby enabling a study of heterogeneous CTS expression patterns across brain regions. To provide accurate and reliable estimates in this setting, we propose to use information from scRNA-seq data by incorporating it as prior in a Bayesian analysis. Specifically, we extract informative prior distributions of mean CTS expression and covariance structure for each gene from scRNA-seq data to facilitate the estimation of sample-level CTS expression from bulk data. We adopt a Bayesian approach because it is known to work well and robustly by incorporating prior information to regularize the statistically challenging estimation we aim to achieve in this work. Distinguishing itself from other methods, bMIND is a powerful and flexible tool, suitable for estimation of CTS expression and for testing for differential expression. The approach works best when scRNA-seq data are available, but it also works well without prior information.Here, we introduce the bMIND algorithm and compare it to other state-of-the-art methods. We also show its utility by various analyses, including CTS differential expression analysis of data relevant for autism spectrum disorder (ASD) and Alzheimer's disease (AD). Moreover, analyzing updated Genotype-Tissue Expression Project (GTEx) V8 brain data, we calculate CTS eQTLs for each of 11 brain regions to create a new resource for uncovering the etiologies of complex diseases and other phenotypes.i in gene j, xij, for T ≥ 1 measures, as a product of cell type fraction  and CTS expression  of K cell types in Bayesian mixed-effects models
αj (K × 1) is the expected CTS expression for the jth gene that constitutes the profile matrix, K cell types, i, gene j, and K cell types.To improve the estimation of sample-level CTS expression, we propose a Bayesian algorithm (bMIND) to incorporate prior information from single-cell data  into the model for differential expression analysis. We use a Bayesian framework for testing For comparison, we use four other methods: (1) TCA  across cell types. Furthermore, because bMIND is a Bayesian approach, we evaluated the sensitivity of the estimates to the prior distribution specification by comparing bMIND to bMIND_rp . The bulk data were simulated with measured cell type fractions and sample-level CTS expression derived from snRNA-seq data .The correlation per gene based on bMIND was higher than those from TCA  and thus did not consider it for CTS DE analysis. CIBERSORTx  and power. Using these simulated data, we planned to compare performance of bMIND to csSAM .We assessed FDR and power as a function of effect size, the number of cell types, and sample size. Under all simulation scenarios, bMIND controlled FDR  data for Alzheimer's disease (AD). 2 fold change >0.14) . For comparison, analysis of the 41 snRNA-seq samples produced 513 CTS DEGs , including NRXN1  were among the 102 ASD risk genes discovered by P-value = 0.04) and 49 genes were among the SFARI autism gene list . As compared with the top 50 marker genes for each cell type derived from snRNA-seq data .When comparing CTS DEGs detected by bMIND and snRNA-seq data  enrichment analysis . Similar to the findings based on snRNA-seq of AD .Using bMIND, we estimated sample-level CTS expression and detected CTS DEGs related to AD with FDR <0.05 . To illustrate how genes worked together in Alzheimer's disease at the cell type level, we took advantage of bMIND's estimates of sample-level CTS expression to construct a coexpression network of a subset of genes expressed in excitatory neurons . As expected, enriched terms for DEGs from excitatory neurons were enriched in synaptic and neuronal functions. For example, using REVIGO to assess the large number (683) of enriched GO terms for Mayo data identified several key themes: vesicle-mediated transport in the synapse, regulation of catabolism, and chemical synaptic transmission; and more minor themes of organelle organization and macro- and autophagy. DEGs in microglia, however, were enriched in immune processes . This was somewhat different than the key themes associated with the entire set of ASD DEGs, namely, cell projection organization and neurotransmitter transport. In fact, all these themes are likely important to liability for ASD  ASD DEGs were enriched for 309 GO terms that were not shared with enriched terms for AD DEGs. From these terms, REVIGO identified one key theme, regulation of synapse organization, and two minor themes, amino acid transport and anatomical structure morphogenesis . These themes were quite similar to those identified from the entire list of enriched GO terms for AD DEGs.There were 504 enriched GO terms specific for AD DEGs, from which REVIGO identified key themes of vesicle-mediated transport and regulation of catabolism. More minor themes were organelle organization, macro- and autophagy, and protein/macromolecule modification . The major themes that emerged for ASD were cell projection organization and neurotransmitter transport, quite similar to those for the entire set of enriched GO terms associated with ASD DEGs . For AD, however, the major theme was regulation of cellular component organization, a theme that recurred across the different partitions of enriched GO terms for AD DEGs .Next, we asked how REVIGO interpreted the enriched terms that were shared between ASD and AD. Although 179 GO terms were identical, the FDR https://github.com/randel/bMIND_GTEx8_signif_region_CTS_eQTLs_cis).To generate a resource of inferred CTS eQTLs for various brain regions, we analyzed the latest GTEx brain data  and eQTLs were enriched near the transcriptional start site (TSS), as expected . Next, we hypothesized that many of our region-specific CTS eQTLs would match the GTEx regional analysis of eQTLs using bulk data . Because conserved genes were less likely to be eGenes and ASD genes tended to be very conserved  using CTS expression for each gene with respect to the phenotype of interest and claim a gene as a DEG by Benjamini–Hochberg adjusted P-values by regressing the phenotype on CTS expression in that gene.To find in which cell type a DEG is differentially expressed, we obtain CTS DE P-value is the minimal across cell types in a DEG and less than 0.05/K, where K is the number of cell types . With estimated sample-level CTS expression, we are able to conduct analyses that are previously only available using bulk RNA-seq data, deepening the analyses from tissue level to cell type level. Here, we focus on CTS differential expression (DE) analysis as an example. To control for false discovery rate (FDR) in CTS DE analysis, we propose a stringent testing procedure:
W) for more than 600 samples from ROSMAP data. Then for each gene j = 1, …, 1000, we simulated CTS expression from djk = d denotes the differential effect when gene j is a DEG in cell type k, and djk = 0 if not. There were 200K/3 gene-cell type pairs that were differentially expressed, where K is the number of cell types. The disease status y is simulated as binary with a probability of 0.5. With the error term (ej) and covariates (c(1) and c(2)) generated from standard normal distribution, we simulated the bulk expression for the jth gene as
b is a vector of K × 1 with element bk = 0.1k representing the covariate effect on the kth cell type. We explored multiple simulation scenarios by varying the sample size, number of cell types, and effect size, which is defined as d/sd(X), where X is the bulk expression.We first used estimated cell type fractions (To identify CTS genes expressed differently between ASD and unaffected (control) subjects, we analyzed bulk RNA-seq data from the PsychENCODE UCLA-ASD project  through accession number phs000424.v8.p2. After estimating subject-level region-specific CTS expression, we calculated cis-eQTLs for each brain region and cell type using MatrixEQTL  enrichment analysis  with detailed bMIND tutorials.The R software (


OBJECTIVES/GOALS: Large-scale clinical proteomic studies of cancer tissues often entail complex workflows and are resource-intensive. In this study we analyzed ovarian tumors using an emerging, high-throughput proteomic technology termed SWATH. We compared SWATH with the more widely used iTRAQ workflow based on robustness, complexity, ability to detect differential protein expression, and the elucidated biological information. METHODS/STUDY POPULATION: Proteomic measurements of 103 clinically-annotated high-grade serous ovarian cancer (HGSOC) tumors previously genomically characterized by The Cancer Genome Atlas were conducted using two orthogonal mass spectrometry-based proteomic methods: iTRAQ and SWATH. The analytical differences between the two methods were compared with respect to relative protein abundances. To assess the ability to classify the tumors into subtypes based on proteomic signatures, an unbiased molecular taxonomy of HGSOC was established using protein abundance data. The 1,599 proteins quantified in both datasets were classified based on z-score-transformed protein abundances, and the emergent protein modules were characterized using weighted gene-correlation network analysis and Reactome pathway enrichment. RESULTS/ANTICIPATED RESULTS: Despite the greater than two-fold difference in the analytical depth of each proteomic method, common differentially expressed proteins in enriched pathways associated with the HGSOC Mesenchymal subtype were identified by both methods. The stability of tumor subtype classification was sensitive to the number of analyzed samples, and the statistically stable subgroups were identified by the data from both methods. Additionally, the homologous recombination deficiency-associated enriched DNA repair and chromosome organization pathways were conserved in both data sets. DISCUSSION/SIGNIFICANCE OF IMPACT: SWATH is a robust proteomic method that can be used to elucidate cancer biology. The lower number of proteins detected by SWATH compared to iTRAQ is mitigated by its streamlined workflow, increased sample throughput, and reduced sample requirement. SWATH therefore presents novel opportunities to enhance the efficiency of clinical proteomic studies.


The evolution of complex diseases can be modeled as a time-dependent nonlinear dynamic system, and its progression can be divided into three states, i.e., the normal state, the pre-disease state and the disease state. The sudden deterioration of the disease can be regarded as the state transition of the dynamic system at the critical state or pre-disease state. How to detect the critical state of an individual before the disease state based on single-sample data has attracted many researchers’ attention.In this study, we proposed a novel approach, i.e., single-sample-based Jensen-Shannon Divergence (sJSD) method to detect the early-warning signals of complex diseases before critical transitions based on individual single-sample data. The method aims to construct score index based on sJSD, namely, inconsistency index (ICI).This method is applied to five real datasets, including prostate cancer, bladder urothelial carcinoma, influenza virus infection, cervical squamous cell carcinoma and endocervical adenocarcinoma and pancreatic adenocarcinoma. The critical states of 5 datasets with their corresponding sJSD signal biomarkers are successfully identified to diagnose and predict each individual sample, and some “dark genes” that without differential expressions but are sensitive to ICI score were revealed. This method is a data-driven and model-free method, which can be applied to not only disease prediction on individuals but also targeted drug design of each disease. At the same time, the identification of sJSD signal biomarkers is also of great significance for studying the molecular mechanism of disease progression from a dynamic perspective. Complex diseases seriously endanger human health. They often occur as a result of multiple molecular interactions . In the In order to further study the pre-disease state between the normal state and the disease stage, a new type of dynamic network biomarker (DNB) was proposed. DNBs are a set of biomolecules with strong dynamic correlation, and the molecular concentration presents dynamic changes rather than keeping the constant value of the critical state . DNB revWith the advances of bio-experimental technology, especially the widespread application of microarray chip technology, a large number of high-throughput biological data has been generated. These data and information contain the internal correlation between genes and life characteristics, providing an opportunity for further research and understanding of the pathogenesis and development of complex diseases . To furtThis approach has been validated in five real disease datasets, including two genitourinary cancers, i.e., prostate cancer and bladder urothelial carcinoma (BLCA), influenza, Cervical squamous cell carcinoma (CESC) and pancreatic adenocarcinoma (PAAD), in which the prostate cancer dataset (GSE5345) and influenza dataset (GSE30550) are from the NCBI GEO database, BLCA, CESC and PAAD are from the cancer genome atlas (TCGA) database. The critical state or pre-disease state determined by sJSD is consistent with the observation in the real experiment, and the comparison of survival analysis before and after the critical state is significant. Furthermore, the sJSD signal biomarkers have been validated by functional enrichment.http://www.ncbi.nlm.nih.gov/geo) and bladder urothelial carcinoma (BLCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) and pancreatic adenocarcinoma (PAAD) from the cancer genome atlas (TCGA) database (http://cancergenome.nih.gov).The sJSD algorithm proposed in this paper has been applied to five datasets, including prostate cancer dataset (GSE5345), influenza virus infection time series data (GSE30550) from the GEO database (http://www.genecards.org/). The access to Enrichment is through the use of online analysis tools used in the Gene Ontology Consortium , DAVID Bioinformatics Resources 6.8 (https://david.ncifcrf.gov/) and Circos (http://www.circos.ca/). PPI networks are drawn with the use of the online service web page STRING (https://string-db.org/) and the client software Cytoscape (https://cytoscape.org/).The gene function annotation of each dataset is obtained through GeneCards  theory . JSD is JSQ)=D and also JS(PD││Q)=0 if and only if P=Q.Obviously x, instead of the probability itself. While the probability distribution function represents the cumulative probability. In our paper, we use the probability distribution function, which expresses the integral area of the probability density function in the interval , where ijx is the expression of gene ig in the samples j. It not only makes full use of gene expression data, but also reflects the distribution characteristics of genes.Among the applications of JSD, as mentioned in some references and literatures, JSD was used as a measure of the difference between two probability distributions. The probability distributions usually mean the probability density function or the probability distribution function (or the cumulative distribution function). Obviously, the probability density function represents the change rate of the probability for a random variable The sJSD aims to detect critical state or pre-disease state of a complex disease and identify dynamic network biomarkers (DNBs) highly associated with disease deterioration based on single-sample of an individual. Given some reference samples , we can identify a critical state of the disease based on an individual’s single-sample data using the following algorithm Figure 2[Step 1] Give reference samples and case samples.The reference samples may be from healthy individuals, healthy tissues, or samples that have not developed any lesions at the beginning of the diseases. Each case sample refers to the single-sample of an individual during a disease process. In the datasets GSE30550 and GSE5345 taken from the GEO database, the reference samples refer to the samples at the initial time point of the experiment, while each case sample refers to the sample of an individual or subject during the experiment. In the datasets of BLCA, CESC, and PAAD from the TCGA database, reference samples refer to the samples of tumor-adjacent samples , and case samples refer to the samples of tumor tissues at different stages of cancer development.[Step 2] Fit Gaussian distributions of every gene based on reference samples and case samples of an individual.Fit two Gaussian distributions of each gene in terms of the expression respectively from the reference samples and case samples, i.e., reference distribution and perturbed distribution.ig, its reference distribution iP (mean iµ and standard deviation iσ) is fitted based on the n expressions of gi in the reference samples {1S,...,jS,...,nS} and the perturbed distribution iQ x and ijx is the gene expression data of gene i  in the reference samples j , it is the gene expression data of gene ig at time t  of the case samples for an individual.Where [Step 4] Construct the inconsistency index based on JSD.P is compared with the disturbance distribution Q, so as to reveal the critical state or pre-disease state of disease deterioration. The measurement The dynamic differences between reference samples and case samples can be quantified when the reference distribution t is obtained,We select the top 5% genes with the highest score at critical state as sJSD signal biomarkers, which are also the components of the DNBs. During the disease process, the sJSD signal biomarkers will move to the disease state first when the system experiences further disturbances of parameters into the disease stage. The comprehensive ICI score of global genes at time log base e. The higher the value which is in the range  for gression Figure 2https://www.ncbi.nlm.nih.gov/geo/) and BLCA PAAD, CESC from the TCGA database (http://cancergenome.nih.gov). The sJSD method’s effectiveness in quantifying the tipping point before the critical transition into severe disease state was successfully verified by identifying pre-disease states of these datasets.In section 2, we showed the specific algorithm based on sJSD. With a sharp increase in ICI score being treated as a signal of the approaching critical state, we can detect complex disease’s critical state through a single case sample. A single sample with high-throughput data is regarded as the detection target used to identify early warning signals of complex disease’s critical transition. It is of great significance to identify the critical transition through the single sample of an individual since it is difficult to obtain extensive samples from an individual without any symptoms of diseases in the early stage of disease development. To describe how sJSD works, we applied the sJSD method to five real datasets, including influenza dataset (GSE30550), prostate cancer dataset (GSE5345) from GEO database  and alcohol (control group) were used to stimulate human prostate cell line LNCap to explore the effect of synthetic androgen R1881 on the gene expression level. In the original experiment, the case samples were derived from human prostate cancer cell lines stimulated by the synthetic androgen R1881 for 48 hours. The control samples were obtained from human prostate cancer cell lines stimulated by synthetic androgen R1881 for 0 hours. In the experiment, there are seven sampling time points , and there are four samples at each time point (except six samples at the 6th hour) , cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), and pancreatic adenocarcinoma (PAAD) from the cancer genome atlas (TCGA). Each disease dataset includes tumor and tumor-adjacent samples. The tumor samples can be divided into different states according to the samples’ clinical information. The tumor samples of BLCA are divided into four states, tumor samples of CESC are divided into six states, and tumor samples of PAAD are divided into five states. In the three datasets, tumor-adjacent samples are considered reference samples or reference data. The ICI was applied to calculate for each tumor sample based on the proposed algorithm sJSD, every stage’s average ICI score was used to characterize the possible critical state.Through the sJSD algorithm, we successfully detected the critical state and obtained the sJSD signal biomarkers , Stage II (130 samples), Stage III (139 samples), and Stage IV (134 samples) of BLCA. As shown in in vitro . As shown in Through the sJSD algorithm, we successfully detected the early warning signals of critical transition for CESC and identified the sJSD signal biomarkers . The cytPAAD is a highly invasive malignant tumor of the digestive system, and its occurrence and mortality continue to increase . The earFor PAAD, there were 175 tumor samples and four tumor-adjacent samples from TCGA. According to the samples’ clinical information, the tumor-adjacent samples were divided into six stages, i.e., Stage IA, Stage IB, Stage IIA, Stage IIB, Stage III, and Stage IV. In The high-frequency genes in the sJSD signal biomarker groups of PAAD are closely related to pathogenesis In clinical practice and scientific research, differentially expressed genes draw much attention in early diagnosis of disease, screening drug targets, treating diseases, and developing new drugs. However, some non-differential genes in the coding region of DNA are called “Dark Matter” . Based oTo further explore “dark genes”, we focused on their role in cancer prognosis of BLCA, CESC, and PAAD. Firstly, we selected sJSD signal biomarkers (top 5%) genes with the highest ICI score that are not differentially expressed. Secondly, we analyzed the prognosis of these “dark genes” respectively based on gene expression and ICI score by dividing the samples into two groups based on the median of genes expression or ICI score, in which Group 1 is a group with higher value and Group 2 is a group with a lower value. Thirdly, based on the result of prognosis, the “dark genes” could be categorized into two types of molecules as a mutual biomarker for all samples. Those genes with high scores that cause poor prognosis were termed “negative dark genes”, and those genes with high scores that cause good prognosis termed “positive dark genes”. If “negative dark genes” appeared in the sJSD signal biomarkers of a sample, the sample’s prognosis would be more negative than that of other samples. Similarly, if “positive dark genes” appeared in the sJSD signal biomarkers of a sample, the sample’s prognosis would be more positive than others.For BLCA, further analysis showed that “dark genes” were all strongly related to patients’ survival based on ICI score but not expression levels. For CESC, For PAAD, Exploring the warning signals of sudden deterioration is essential for identifying the most complex diseases. The lack of samples makes it difficult to detect the critical state prior to the appearance of obvious symptoms. Identifying the early warning signal of critical state for complex diseases based on an individual’s single-sample data is a crucial problem in current research. In the study, we proposed a sJSD method based on a single sample to quantify the information loss when the reference distribution is used to fit the disturbance distribution. This method converts the molecular expression data into a cumulative area of the molecules’ probability distribution. Based on the sJSD algorithm, the ICI is constructed to quantify the differences between reference distribution and disturbed distribution so as to detect complex diseases’ critical state and reveal “dark genes” in the leading network during disease progression.In this study, JSD is used to detect the critical state by quantifying the difference between reference samples and case samples during disease progression. The case samples are from samples of an individual at multiple time points or stages. Before the sJSD value reaches the peak, it has experienced a period of continuous increase, which corresponds to the process from normal state to critical state of complex disease. The peak in the temporal signals reflects not only the change of the score at one time point or stage, but also the cumulative effect of its phased increase. It is consistent with the progression of the disease. Therefore, the peak in the temporal signals is of great significance in detecting the critical state after a period of potential deterioration. There may be multi-stage deteriorations or multiple tipping points during a cascade deterioration process, which can be also detected by our method. However, we mainly focus on the earliest tipping points in actual clinical applications.th hour) of prostate cancer before distant metastasis in the prostate cell line, the critical state (Stage II) of BLCA before symptoms of hydronephrosis or tumor compression, the critical state (Stage IIB) of CESC before cancer begins to infiltrate the cervical tissue, the critical state (Stage IB) of PAAD before lymph node metastasis. The critical states of cancers were verified by prognostic analysis. If patients are diagnosed before the pre-disease state and get appropriate treatment, they will have a better prognostic effect. Furthermore, prognostic analysis of other stages proves no other critical state, indicating that the detected critical stage is precise and closely related to prognosis. The sJSD signal biomarkers and the high-frequency genes in the sJSD signal biomarker group are closely related to disease development’s biological processes and signal pathways.The sJSD method has been applied to five real datasets. In the influenza infection dataset GSE30550, the method successfully identified warning signals of critical transition in nine symptomatic individuals. The calculated results are consistent with the experimental results, which indicate that the sJSD algorithm can effectively identify the samples’ critical state and accurately detect the early warning signals of an individual infected by the influenza virus. Especially, the obvious change of ICI score indicates the critical state , National Natural Science Foundation of China , Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01) and the Young Backbone Teacher Funding Scheme of Henan (No.2019GGJS079).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Thoracic malignancies are a common type of cancer and area major global health problem. These complex diseases, including lung cancer, esophageal cancer, and breast cancer, etc. have attracted considerable attention from researchers. Potential gene-cancer associations can be explored by demonstrating the association between clinical data and gene expression data. Emerging evidence suggests that the transcriptome plays a particularly critical role as a diagnostic biomarker in pathology and histology studies. Thus, there is an urgent need to develop a platform that allows users to perform a comprehensive prognostic analysis of thoracic cancers. Here, we developed TTSurv, which aims to correlate coding and noncoding genes with cancers by combining high-throughput data with clinical prognosis. TTSurv focuses on the application of high-throughput data to detect ncRNAs, such as lncRNAs and microRNAs, as novel diagnostic and prognostic biomarkers. For a more comprehensive analysis, a large amount of public expression profile data with clinical follow-up information have been integrated into TTSurv. TTSurv also provides flexible methods such as a minimum p-value algorithm and unsupervised clustering methods that can classify thoracic cancer samples into different risk groups. TTSurv will expand our understanding of ncRNAs in thoracic malignancies and provide new insights into their application as potential prognostic/diagnostic biomarkers. Thoracic malignancies are the most common type of cancers , 2, and When undertaking a survival analysis, it is often necessary to group patients according to certain metrics and compare the risk differences between groups, but the different cutoff values have important implications for the outcome, and there is currently a lack of a gold standard for cutoff values. Several databases exist that can be used to conduct a survival analysis of the expression profiling data, such as Kaplan-Meier Plotter, LOGpc, and OncoLnc online tools, but there is still a need for a biologically meaningful cutoff value. Users often differentiate samples using thresholds, such as median/trichotomies/quartiles, which are merely mathematical and do not express biological properties, or they calculate the division values multiple times, which is often time-consuming and lacks a scientific basis. Classifying samples multiple times and performing the Cox test can determine the optimal grouping and find the best separation point among continuous variables. It has been shown to be useful in the analysis of tumor size , 9, cellIn summary, we have developed TTSurv. It is a large collection of cancer-related expression profiles and their associated clinical data that can be found in public databases such as GEO and TCGA. TTSurv also provides a minimum p-value method to calculate the best cutoff value for the user while allowing the user to manually submit the separation value. In addition, we classified the samples according to the expression value of the dataset and performed unsupervised tests. By providing multiple ways to classify samples, we can uncover all possible associations between genes and cancer. In addition, we also provide integration algorithms for multi-gene analysis to integrate the target gene set, which allows the user to analyze the entire gene set as a whole. Finally, the database provides an overall assessment of the prognostic value of genes in multiple datasets, which will gives researchers more experimental opportunities and more valuable analysis results.Expression profiling data and survival data were obtained from the TCGA and GEO public datasets S2. The The minimum p-value method has been validated in clinical trials , and thiFirst, patients were ranked according to the expression values of a given gene. Patients were then divided into two groups (high and low) at all potential cut-off points, and the difference in risk between the two groups was estimated using the log-rank test. The best cut point that gave the most significant p-value (P-minimum) was then selected , n is the number of RNAs in the gene set, and Exp(Ci) is the expression value of RNA Ci in the corresponding sample. Patients were classified into high expression and low expression groups according to the different classifications . The data were visualized using Datatables, echart, highchart, and other plugins, and all statistical analyses were performed using the R framework (V3.6.0) , and we also provide this feature to explore more possibilities . There are already several online tools capable of performing online survival data analyses, including Kaplan-Meier Plotter , GENT 2 and ITTAIn the future, we plan to collect more data samples and provide improved functionality, and we will continue to improve our database in the following areas: (i) collecting more newly released datasets for thoracic tumors; (ii) adding more visualizations: e.g., forest plots based on their HR values in single-gene multi-datasets; and (iii) improving the annotation of probes that cannot be annotated in the current dataset. We believe that TTSurv will be a useful resource for researchers at many stages from target discovery to target validation through continuous updates. We believe that through continuous updates, TTSurv will become an important online survival analysis tool and provide researchers with powerful aid in a variety of ways.TTSurv is unique because it can group samples in multiple ways to find more possible associations between target genes and cancer. We provide an integration algorithm that analyzes the set of user-submitted genes as a whole for a more comprehensive analysis and more valuable conclusions. At the same time, the multiple outcome data we provide offer strong support for the augmentation of gene-cancer associations. We hope that TTSurv will be a useful resource for cancer researchers at multiple stages, from target discovery to target validation.http://www.bio-server.cn/TTSurv.Publicly available datasets were analyzed in this study. This data can be found here: YQ, MX, YZ and QG conceived and designed the experiments. YH, QL, and PW analyzed data. YQ and MX collected the data. YH and PW validated the method and data. YQ and QG wrote this manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the National Natural Science Foundation of China [81902646 and 32070622], Heilongjiang Provincial Natural Science Foundation [LH2020H046]; Postdoctoral Science Foundation of China [2020M670922]; Postdoctoral Foundation of Heilongjiang Province [LBH-Z19077 and LBH-Q20047].The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In-silico classification of the pathogenic status of somatic variants is shown to be promising in promoting the clinical utilization of genetic tests. Majority of the available classification tools are designed based on the characteristics of germline variants or the combination of germline and somatic variants. Significance of somatic variants in cancer initiation and progression urges for development of classifiers specialized for classifying pathogenic status of cancer somatic variants based on the model trained on cancer somatic variants. We established a gold standard exclusively for cancer somatic single nucleotide variants (SNVs) collected from the catalogue of somatic mutations in cancer. We developed two support vector machine (SVM) classifiers based on genomic features of cancer somatic SNVs located in coding and non-coding regions of the genome, respectively. The SVM classifiers achieved the area under the ROC curve of 0.94 and 0.89 regarding the classification of the pathogenic status of coding and non-coding cancer somatic SNVs, respectively. Our models outperform two well-known classification tools including FATHMM-FX and CScape in classifying both coding and non-coding cancer somatic variants. Furthermore, we applied our models to predict the pathogenic status of somatic variants identified in young breast cancer patients from METABRIC and TCGA-BRCA studies. The results indicated that using the classification threshold of 0.8 our “coding” model predicted 1853 positive SNVs  from the TCGA-BRCA dataset, and 500 positive SNVs (out of 1882) from the METABRIC dataset. Interestingly, through comparative survival analysis of the positive predictions from our models, we identified a young-specific pathogenic somatic variant with potential for the prognosis of early onset of breast cancer in young women. Leverage of high-throughput technologies has given rise to an ever-increasing list of sequenced genes, exomes, transcriptomes and genomes. However, availability of a great amount of raw data would not be valuable without being translated into useful information . GenomicOur understanding of the pathogenicity of any given genomic variant falls into a spectrum between almost certainly pathogenic to almost certainly benign for a disease . A princIn-silico experimentation approaches combine mathematical strategies with expert opinion to interpret the biological significance of genomic data  in an efTo date, many computational tools have been developed to classify pathogenic status of genomic variants using different training approaches and datasets. Most classification tools have been only benchmarked for classifying the type of variants (e.g. germline variants) included in their original training dataset , while bConsidering the importance of both coding and noncoding somatic point mutations in cancer initiation and progression, as well as the increasing emergence of cancer sequence databases such as the international Cancer Genome Consortium , The CanDespite the low occurrence rate of breast cancer in individuals under the age of 40 (7% in developed world and 25% in developing world) compared to their older counterparts, they suffer from more severe presentation, lower survival rate, and higher risks of disease relapse . VariousA detailed workflow of the methods and materials used in this study is delineated in Our gold standard dataset involves somatic SNVs identified in coding and non-coding regions of the DNA from either cancer patients or healthy individuals. Generally, mutations occurring in coding and non-coding regions of the genome share the same basic characteristics such as the innate changes they introduce into the DNA sequence. However, coding mutations can be studied from additional aspects in terms of affected genes, transcripts, and proteins. Accordingly, we split our gold standard into coding and non-coding subsets to train each set individually based on their relevant features. Labels assigned to the pathogenic status of variants in the gold standard form the foundation of model development. Therefore, it is of critical importance that maximum precautions be taken in labeling the pathogenic (positive) and non-pathogenic (negative) examples.Our positive (pathogenic) examples are cancer somatic SNVs extracted from COSMIC database  which asOur negative (non-pathogenic) dataset includes SNVs from COSMIC chosen based on information from dbSNP database  and 1,00One goal of our classification models is to learn the discrepancies and similarities between pathogenic and non-pathogenic cancer somatic SNVs. To this aim, we used genomic features characterizing mutations through criteria such as sequence characteristics, genomic content of the mutation sites, and functional and structural consequences of the mutations. Genomic features defined in this study are mostly annotations from different projects such as ENCODE , CADD R, and ENSLike most data collection-based studies, missing data were inevitable in our study. Our sample size would shrink significantly (more than 5%) in the event of removing all the SNVs that have any missing values. Therefore, the MICE (Multivariate imputation by chained equations) imputation approach was used for estimating the potential values of the missing data. MICE is applicable to datasets with missing values in multiple variables , and is Data normalization or scaling is known to be beneficial in improving the performance of some classifiers such as support vector machine (SVM). To investigate the effect of data normalization on our models we used Python Scale package from sklearn library  to norma1) Lasso (least absolute shrinkage and selection operator) regression model is a type of linear regression that sums up penalty scores equal to the absolute value of the coefficients resulting in elimination of features with large penalty scores . The fin2) SVM is a powerful classification method capable of predicting labels of two classes based on their defined features . SVM disIn this study we have used two relatively popular classification methods with demonstrated capability of dealing with cancer genomic data:We used two strategies to evaluate our model’s performance. In the first approach, we used 2/3 of the total samples for training and the remaining 1/3 for testing. In the second approach, we performed 10-fold cross-validation (10F CV) of each of the gold standards. We used area under the ROC (receiver operating characteristic) curve as an estimation of the discrimination power of our classification models .We studied the landscape of somatic SNVs in young breast cancer patients by applying our trained models to the data from two cohort studies including METABRIC   and TCGAMETABRIC is based on exome sequencing of 173 previously established risk genes from 2,433 primary breast cancer samples. From a total of 32,476 SNVs identified in METABRIC, we were able to define the genomic features for 13,942 somatic SNVs of which 1882 were from 326 patients younger than 45 years of age at diagnosis. TCGA-BRCA characterizes human breast tumors using five molecular assessment platforms involving Affymetrix SNP arrays, Illumina Infinium DNA methylation chips, Agilent mRNA expression microarrays, whole exome sequencing and microRNA sequencing. From a total of 80,227 somatic SNVs from 976 patients, we were able to define the genomic features for 8,647 somatic SNVs from 142 young patients (<45 years old at diagnosis). Regarding the data collection approaches followed by TCGA-BRCA study, 6,910 somatic SNVs were from coding regions  and 1737 somatic SNVs were from noncoding regions (e.g. provided by microRNA sequencing) of the genome.1) SNV-level survival analysis, which regards the recurrence of a given mutation in a given patient as the event of interest, was conducted for the somatic SNVs from METABRIC and TCGA-BRCA that were predicted to be pathogenic by our models. We were particularly interested in the predicted somatic pathogenic mutations with the most frequency . SNV-level survival analysis assesses whether the survival time of the patients who harbor a specific mutation in their tumour genome is significantly different from the patients whose tumours do not harbor the mutation. We identified the mutations that were significantly associated with the survival time of young patients and compared the results with outcomes from SNV-level survival analysis of old patients.2) Gene-level survival analysis, when considering a given gene as mutated or not as the event of interest, was conducted for the genes from METABRIC and TCGA-BRCA data sets. Consistent with the purpose of our study we were only interested in those genes whose mutation status was significantly associated with the survival time of young patients but not the survival time of the older patients. We defined a gene as mutated if it was affected by at least one predicted pathogenic SNV in a given sample.Based on different events of interests we conducted two types of survival analysis in our study.a priori known set of genes or proteins  in ± 100 window from the mutation, transverison/transition identity of the nucleotide change, and GC percentage in a ± 75 window from the mutation), “genomic conservation features” (e.g. scores from Phylop), and “epigenetic features” (e.g. methylation modifications to protein histone H3 (H3K36me3) at 36th lysine residue). Interestingly, unlike the coding model, no features had the coefficient of zero in the non-coding Lasso model. Hence, For the Lasso models, 78 and 65 features were used in the models for coding and non-coding regions, respectively. However, for the SVM models, no features were filtered out, and 80 and 65 features were used in the final models for the coding and non-coding regions, respectively.We applied our modeling methods [LASSO and SVM with radial basis function (SVM-rbf)] to both normalized and non-normalized data and evaluated their performance . Using thttp://fathmm.biocompute.org.uk/), we applied FATHMM-XF and Cscape to the same coding and noncoding test data sets used for evaluating our SVM models. Please note that we did not use 10F CV for the model benchmarking, instead, we used the same test data sets used in the train/test strategy for evaluating and comparing the model performance  are shown in We further investigated the biological aspects of prediction results by identifying the genes that were affected by the most frequent pathogenic SNVs  predicted by our models. As indicated in p-value<0.05) associated with the survival experience of patients younger than 45 years of age, while not significantly associated with survival of older patients in the METABRIC data set. However, we did not replicate this finding in the TCGA-Breast data set, which may be due to the small sample size of the young women with breast cancer in the cohort. Following our subsequent investigations, it was found that “17:7674220_C > T” SNV, which is regarded as a “hotspot mutation” in the literature, is an arginine to glutamine single nucleotide substitution occurring at 248th residue of TP53 , and survival outcome of the patients. We grouped the patients based on their age (Age <45 and Age ≥ 45) and compared the survival outcomes between the two groups. The results from disease specific survival analysis  and over of TP53 . We also of TP53 . The str of TP53 .Furthermore, we investigated the biochemical properties of arginine and glutamine to unravel the impact of their substitution on proteins structure and function. The positively charged guanidinium group in arginine makes it a hydrophilic amino-acid appropriate for being located on the surface of proteins in an aqueous environment . ArgininTo select the candidate genes for conducting gene-level survival analysis we computed the number of positive SNVs affecting each gene in the prediction datasets. To be consistent with the frequency threshold we defined for labeling positive examples in our gold standards at the very first steps of the model building process, we selected the genes harboring at least four and two pathogenic SNVs in coding (METABRIC and TCGA-BRCA coding) and noncoding (TCGA-BRCA) datasets, respectively. As inferred from p-value < 0.05) affected the survival experience of the young patients but did not have a significant  impact on the survival of the older patients.Through gene-level survival analysis of the selected genes we investigated whether the fact that a given gene is mutated or not, can significantly affect the survival experience of the patients. We conducted the gene-level survival analysis for both young (<45 years old) and old (≥45 years old) groups of patients and compared the results from the two groups. The results revealed that mutated “Muc16” gene significantly  were obtained for the genes from TCGA noncoding dataset. The results of GSEA for the genes from coding datasets are represented in We conducted GSEA of the genes affected by the pathogenic SNVs from both coding (METABRIC and TCGA coding) and noncoding (TCGA noncoding) prediction datasets. No significant results  and TP53 pathway  frequently appeared among the most significant results. Interestingly, consistent with the purpose of our analysis, the results from “KEGG 2019” database identified breast cancer as one of the cancers most significantly associated with the input gene lists. Accordingly, the GSEA results fully support the association between the input gene lists  and breast cancer development.Computational models for classifying the pathogenic status of cancer somatic variants located in coding and noncoding regions of the genome were developed in this study. The novelty of the proposed classification models is the development of a bi-dimensional threshold that spans the recurrent frequency of a given somatic SNV across the whole dataset as well as the number of cancer types the SNV is identified in. Furthermore, both the pathogenic somatic SNVs and benign negative somatic SNVs included in the gold standard datasets are exclusively cancer somatic variants distinguishing our models from the currently available classifiers.The developed models outperform the most powerful available classification tools which is evidence to support the robustness of the discrimination capabilities of our models in terms of classifying cancer somatic variants. The high classification accuracy of the developed models is promising in terms of predicting the pathogenic status of a set of cancer somatic variants whose pathogenicity has not been assigned previously. The potential application of the computational models in identifying novel candidate target genes and biomarkers is also suggested through our study. Our survival analysis on pathogenic cancer somatic SNVs (predicted by our models) and their related genes highlighted the age-specific prognostic significance of a SNV and a gene impacting the survival time of young (<45 years old) breast cancer patients more than their older counterparts.Our classification models are designed based on a robust labelling process defined for the first time in this study. However, labels from clinical wet lab experiments are assumed more reliable. To date there is no major dataset available that includes the clinical significance of cancer somatic variants based on the validated results from wet lab experiments. A higher number of features can usually positively affect the classification power of a computational model. The genomic features we used for training our models are annotations from the limited available annotating tools. A greater number of annotation tools potentially for generating more genomic features can considerably elevate the discrimination power of computational models.As future directions, firstly, more advanced machine learning models such as deep learning could be tested to further improve the performance of the prediction systems. However, the interpretability of these advanced machine learning models might be a concern as the pathogenic prediction problem is related to patient healthcare, which requires that results are interpretable. Secondly, majority of the existing tools used for predicting pathogenic status of the SNVs were trained based on the mixture of somatic and germline pathogenic SNVs. Here we explored to train the models based on only somatic pathogenic SNVs. Although we have shown the advantages of our new tool over the two state-of-the-art existing tools, more work is still needed to compare the new tool to other existing tools. Thirdly, conducting further analysis on the prediction results from applying our models to somatic mutations from breast cancer datasets can lead to identification of novel therapeutic targets and biomarkers. Our model could also be applied to explore other cancer types, but not limited to breast cancer. Furthermore, experimental validation of the results from our model predictions can provide a strong proof of the classification performance of our computational models.


Recently, orders-of-magnitude sparser gene-panel-sequencing data have become increasingly available in the clinic. To deal with such sparse data, we suggest a novel mixture model, Mix. In application to simulated and real gene-panel sequences, Mix is shown to outperform current approaches and yield mutational signatures and patient stratifications that are in higher agreement with the literature. We further demonstrate its utility in several clinical settings, successfully predicting therapy benefit and patient groupings from MSK-IMPACT pan-cancer data. Availability: The online version contains supplementary material available at (10.1186/s13073-021-00988-7). Each cancer genome is shaped by a combination of processes that introduce mutations over time , 2. The Statistical models for discovering and characterizing mutational signatures are crucial for realizing their potential as biomarkers in the clinic. A broad catalog of mutational signatures in cancer genomes was only recently revealed through computational analysis of mutations in thousands of tumors. Alexandrov et al. , 4 were These previous methods are applicable for whole-genome or even whole-exome sequencing (WGS or WXS). However, they cannot handle very sparse data as obtained routinely in targeted (gene panel) sequencing assays . There iwithout pre-training on rich data. Our model simultaneously clusters the samples and learns the mutational landscape of each cluster, thereby overcoming the sparsity problem. Using synthetic and real targeted sequencing data, we show that our method is superior to current non-sparse approaches in signature discovery, signature refitting and patient stratification. We further demonstrate the utility of our model in several clinical settings.Here, we present the first model that can handle sparse targeted sequencing data M=96 categories, denoting the mutation identity and its flanking bases [K (a hyper-parameter) mutational processes, each of which is associated with a signature n by We follow previous work and assume that somatic mutations in cancer fall into ng bases . These mS1,…,SK and their relative exposure vector π, where πi is the prior probability for the ith signature to emit any given mutation.The basic multinomial mixture model we use was presented in , 25 and O and the unobserved signatures Z, the model’s likelihood is: In the following exposition, we assume for simplicity a single sample to facilitate the generalization to our suggested model presented in the next section. Given the observed mutations Vj=|{t|ot=j}| the number of times the jth category appears in the data, the likelihood can be rewritten as: i emitted mutation category j is computed by The expected number of times that signature i was used is computed by Similarly, the expected number of times signature Denoting by These expectations are normalized (to probabilities) in the M-step to yield a new set of parameters until convergence.π. While it is possible to learn a unique exposure vector per sample, as done by existing methods, the number of parameters then grows linearly with the number of samples, which may lead to overfitting in a sparse data scenario.One obvious weakness of this model is that given a collection of samples, we cannot expect all of them to have the same exposures L indicating the number of clusters, denote by e, so as to maximize the model’s likelihood: In order to cope with the problem of sparse data, our approach is to cluster the samples and learn exposures per cluster rather than per sample. To this end, we propose a mixture model and a scheme to optimize its likelihood, leading to simultaneous optimization of sample (soft) clustering, exposures and signatures Fig. . Given aE-step: Compute for every i,j,n,ℓ: M-step: Compute for every i,j,ℓ: We can generalize the EM algorithm of the previous MMM model to Mix as follows: e is skipped and the initial value for it is set to the given signatures. Each EM iteration can be completed in N samples, L clusters and K signatures. The EM algorithm is run until it converges to a local maximum and up to 1000 iterations. To avoid being trapped in poor local maxima, we train the model ten times with different random seeds and choose the one that yields the highest likelihood.A detailed derivation of the algorithm is given in Additional file L,K), we use the Bayesian information criterion (BIC) to weigh the tradeoff between model fit and the number of parameters. We train the model on a range of choices for L and K, and choose the hyper parameters to be: size is the number of parameters in the model, n is the number of data points (number of mutations), and Mix.prob is the probability of the data given the trained model. The total number of learned parameters in Mix is given by (L−1)+L(K−1)+K(M−1), where M is the number of mutation categories.To estimate the hyper parameters of Mix  mutational signatures in COSMIC [https://cancer.sanger.ac.uk/cosmic/signatures_v2.tt) that are known to be active in the cancer type being analyzed.We present below both de-novo experiments, in which we learn mutational signatures, as well as n COSMIC   targeted sequencing data from https://www.cbioportal.org/. The MSK-IMPACT dataset contains 11,369 pan-cancer patients’ sequencing samples across 410 target genes. We restrict our analysis to the 18 cancer types with more than 100 samples, which results in a dataset of 5931 samples and an average of 6.8 mutations per sample. According to COSMIC [K-IMPACT , 28 Pan-o COSMIC  there arICGC breast cancers (BRCA). We downloaded mutations for 560 breast cancer patients [patients  with whopatients  signaturTCGA ovarian cancers (OV). We downloaded mutations from whole-exome-sequencing data of 411 ovarian cancer patients from The Cancer Genome Atlas [me Atlas . There ame Atlas  signaturIn addition, we analyzed mutation data sets for which we had clinical information on homologous recombination deficiency (HRD) status or immunotherapy response:Whole Genome Sequencing of Triple Negative breast cancers. Triple negative whole genome breast cancers data along with their HRDetect-predicted labels from Staaf et al. [f et al. . The outMSK-IMPACT sequencing of Non-small cell lung cancer (NSCLC) data treated by CTLA-4/PD-L1 [-4/PD-L1 . We down-4/PD-L1 , 34. TheMSK-IMPACT sequencing of pan-cancer patients data treated by CTLA4, PD-1, and/or PD-L1 [or PD-L1 . Last, wor PD-L1 , 34 1583w, signature exposures π per cluster and the signatures themselves e). We use these estimates as a baseline for data simulation. In the simulations, we vary the number of clusters L from 5 to 9, by sampling clusters without replacement using the distribution w. We then assign the clusters their corresponding weights from w, normalizing the sum to 1 K=4 signatures with probabilities We simulated data according to our model as follows. We start by learning Mix on MSK-IMPACT panel data to obtain realistic estimates for the model’s hyperparameters (10 clusters and 6 signatures using BIC) and parameters  and then randomly sample ni mutations from the mutation set Oi without replacement.For evaluation purposes, we wished to simulate targeted sequencing panels from higher coverage datasets. We use two downsampling strategies: (i) downsampling WGS/WXS data by constraining the samples to target regions of MSK-IMPACT , 28 and full dataset using relative exposures inferred on a downsampled dataset. For ease of comparison, we fix the signature matrix S to consist of known signatures from COSMIC [V be an N×M matrix where Vij is the number of times mutation category j is observed in tumor i in the full dataset, and let V ~ be the matrix V normalized so that each row sums to one. Given the N×K relative exposure matrix Ed computed on the downsampled data, we define the reconstruction error as RE:=|V ~−Ed·S|1, where |·|1 is the L1 norm.To compare methods in their ability to learn mutational signature exposures on sparse datasets, we compare the reconstruction error (RE) obtained by each method on a m COSMIC . Since tV and the signature matrix in the cancer type S, we learn the “true”  exposures E using NNLS, which is a common method used to learn exposures from rich data. Again, let Ed be the relative exposures computed on the downsampled data, and let E ~ be the E normalized so that each row sums to one, we define ERE:=|E ~−Ed|1. This measure is better suited to the case were we would like to know the exposures rather than the mutations, as the mutations can be noisy and we do not expect mutational signatures to be able to reconstruct them with no error.Another reconstruction error measure we use to compare signature learning from sparse data is exposure reconstruction error (ERE). Using the mutation matrix https://github.com/itaysason/Mix-MMM [Mix is implemented in Python 3 using numpy . For NMF/Mix-MMM .We developed the Mix algorithm for elucidating the mutational signature landscape of input samples from their (sparse) targeted sequencing data. We tested our algorithm on synthetic data, downsampled whole-genome/whole-exome data, and gene-panel data and compared its performance to existing approaches. First, we applied Mix to learn parameters from synthetic data it generated. Second, we used Mix to reconstruct mutational signature exposures from downsampled ICGC breast cancer  and TCGAIn the field of mutational signatures, NMF-based methods like SigProfiler  are usedTo solve the sparsity problem, we developed the Mix model. Mix is a probabilistic model that simultaneously learns signatures and soft clusters patients, learning exposures per cluster instead of per sample. Then, to obtain a unique exposure for each new patient, Mix soft-clusters the patient’s mutations and takes a linear combination of all exposures according to their probability. With this, Mix also solves another problem of existing methods, where adding a new patient requires learning a new exposure vector for it. Mix is trained using Expectation Maximization and selects hyperparameters using the Bayesian information criterion. A formal description of Mix, model evaluation strategies and information about the datasets used are given in the “As a first test case of our model, we applied it to synthetic data created to have similar characteristics as the MSK-IMPACT dataset , 28. We To evaluate Mix in a more realistic, yet controlled setting, we applied it to simulated, downsampled data that is derived from whole-genome or whole-exome sequencing see the “” sectiond ranging from 3 to 18, and the MSK-IMPACT region (panel) mutations, with an average of 5.6 and 4.5 mutations for WGS BRCA [We focus the experiments on exposure learning (refitting scenario) and fix the signatures to be the known active COSMIC  signaturWGS BRCA  downsampWGS BRCA  downsampV and signatures H, NNLS extracts (non-negative) exposures W that minimize ∥V−WH∥2. The results are shown in Fig. We compare the performance of Mix against the widely-used non-negative least squares (NNLS) approach. Given a mutation count matrix Although they were not developed to tackle such task, we also compared to three current refitting tools—deconstructSigs , SigLassNext, we wished to compare between Mix and SigMA, the only previous method for analyzing panel data. To this end, we trained Mix using a panel downsampling version of the BRCA data , with thWe applied both models to panel downsampling of 224 WGS triple negative breast cancer samples , clusterNext, we evaluated the clustering produced by Mix and SigMA. To this end, we focused on the clustering produced by the hard clustering variant of Mix and compared to the “categ” output of SigMA. For each method, we randomly drew 200 intra-cluster sample pairs and 200 inter-cluster sample pairs and compared the distributions of similarities they induce. Specifically, for each pair, we computed the cosine similarity between their exposures in the WGS data, obtained by NNLS with the 12 known COSMIC  signaturL of clusters ranging from 1 to 15 and number K of signatures ranging from 1 to 12  score Fig. C. We notL=1−20 clusters; for de-novo Mix we choose the number of signatures for each value of L using BIC. As the clustering of specific samples depends on their sparsity, we also report AMI scores when focusing on samples with at least 10 mutations. Figure We compared our results to those obtained by KMeans clustering of the original mutation count vectors as well as to a refined variant where we first apply NNLS to the data using the 17 active COSMIC  signaturAs another challenge, we wished to test the utility of Mix in additional clinical scenarios in which signature analysis is less abundant. Specifically, we applied Mix to 150 LUAD samples  to prediTumor mutational burden (TMB) is one of the most widely known and analyzed genomic correlates with immunotherapy response. In addition, Rizvi et al.  found thWe evaluated the performance of each method using the area under the ROC curve (AUC). Specifically, we report the AUC score of Signature 4 at predicting the treatment response. For Mix in the de-novo setting, we report the signature which is most similar to signature 4, with cosine similarity 0.924. The AUC scores are 0.64, 0.63, 0.63, 0.6 for refitting-Mix, de-novo-Mix, NNLS, and TMB, respectively. The results suggest a slight advantage for Mix over alternative approaches in this setting.An important application of Mix is for predicting the potential benefit of drug treatments based on the inferred activities of relevant mutational signatures. In particular, we have shown the utility of our model for predicting HRD status and hence the benefit of treatment with PARP inhibitors. However, our results were on a downsampled whole-genome sequencing dataset where “ground truth” was determined by the HRDetect algorithm . While HBeyond the prediction of signature exposures, Mix has the advantage of clustering the patients to potentially clinically relevant groups. To showcase this relevance, we conducted a survival analysis of 1583 pan-cancer patients from  whose mup value of 0.027. Our analysis indicates that the dominant signature in this cluster with exposure of 0.82 is Signature 7, which is associated with UV-radiation. At the same time, 131/204 patients in the cluster are Cutaneous Melanoma (SKCM) patients, which agrees well with the previous finding that Signature 7 is correlated with better SKCM survival [p value of 0.03. This cluster is again dominated by Signature 7 (exposure of 0.9) and 149/243 of its members are SKCM patients  and 617 (39%) of the samples, respectively, spanning all cancer types. They are characterized by samples with the lowest average patient TMB (among the different clusters) with a median value of 4, as is expected for patients treated with immunotherapy [In addition, we find that patients in refitting cluster 1 and de-novo cluster 10 have significantly worse survival Fig. C, D, witotherapy  given thWhile we have shown promising results for Mix, one limitation of our model is that the number of clusters should be larger than the number of signatures or else a possible solution would be to assign each cluster only one signature, which will serve as the “average” signature across samples in that cluster. This property could in fact become an advantage if we only wish to cluster samples, in which case we could set the number of clusters to be equal to the number of signatures and require a single signature with an exposure of 1 in each cluster.Sparse mutation data, as characteristic of targeted sequencing assays, is becoming increasingly available in the clinical setting with important applications in diagnosis and therapy. In this paper, we have presented a novel algorithm to model such data and derive the underlying mutational signatures, exposures, and clinically relevant predictions. Our model is the first to directly capture sparse data without the need for pre-training on rich datasets. We have shown its utility in a range of tasks as well as its favorable performance in comparison to existing methods.Importantly, we have shown the clinical relevance of our model for predicting HRD status in breast cancer, predicting immunotherapy response in lung cancer and patient stratification. Nevertheless, our model is only a first step in such an analysis that should be followed by specific predictors for the tasks at hand that can take additional data (beyond signature exposure) into account. Our model can be further strengthened by making use of WGS/WXS data, when such are available, to improve the signature discovery step.Additional file 1Supplementary methods. Table S1 Model reconstruction from synthetic data. Table S2: Reconstruction Error (RE) comparison in BRCA and OV. Table S3: Exposure Reconstruction Error (ERE) comparison in BRCA and OV. Figure S1: De-novo signatures from MSK-IMPACT. Figure S2: Clusters learned from MSK-IMPACT. Figure S3: Signature discovery from MSK-IMPACT. Figure S4: Signature discovery comparison with SomaticSignatures.