Gene expression data generated from whole blood via next generation sequencing is frequently used in studies aimed at identifying mRNA-based biomarker panels with utility for diagnosis or monitoring of human disease. These investigations often employ data normalization techniques more typically used for analysis of data originating from solid tissues, which largely operate under the general assumption that specimens have similar transcriptome composition. However, this assumption may be violated when working with data generated from whole blood, which is more cellularly dynamic, leading to potential confounds. In this study, we used next generation sequencing in combination with flow cytometry to assess the influence of donor leukocyte counts on the transcriptional composition of whole blood specimens sampled from a cohort of 138 human subjects, and then subsequently examined the effect of four frequently used data normalization approaches on our ability to detect inter-specimen biological variance, using the flow cytometry data to benchmark each specimens true cellular and molecular identity. Whole blood samples originating from donors with differing leukocyte counts exhibited dramatic differences in both genome-wide distributions of transcript abundance and gene-level expression patterns. Consequently, three of the normalization strategies we tested, including median ratio (MRN), trimmed mean of m-values (TMM), and quantile normalization, noticeably masked the true biological structure of the data and impaired our ability to detect true interspecimen differences in mRNA levels. The only strategy that improved our ability to detect true biological variance was simple scaling of read counts by sequencing depth, which unlike the aforementioned approaches, makes no assumptions regarding transcriptome composition. Currently, diagnostics targeting whole blood gene expression signatures are being assessed for a variety of clinical uses including risk stratification in cancer2, early detection of chronic infectious disease3, diagnosis of various neurologic disorders5, and organ transplant monitoring7. Given the potential, countless biomarker discovery investigations are now performed involving high-throughput gene expression profiling of whole blood, and even more are carried out via secondary analyses and meta-analyses of the resultant data.The advent of whole transcriptome profiling techniques, along with the push towards personalized medical treatments, has led to the clinical emergence of multi-analyte algorithmic molecular diagnostics which target mRNA. The peripheral whole blood is an attractive source of candidate biomarkers to develop such tests, as it is relatively easy to sample, and exhibits altered gene expression in several disease states via the peripheral immune responseBecause the translational goal of these studies is typically development of biomarker panels that could ultimately be measured in the blood with minimal specimen handling and post-analytical data processing in a clinical laboratory setting, often using lower throughput techniques such as qRT-PCR, the focus is largely on identifying diagnostically targetable absolute differences in mRNA levels that are directly observable at the bulk tissue level. This differs in some regards from the studies which use whole blood transcriptomics to investigate physiologic or pathophysiologic mechanisms, in that the latter may be more focused on making inferences about the underlying nuclear transcriptional state of the constitute blood cells as opposed to best quantifying the true RNA levels in a sample. Given this fundamental difference, it is important that the methodology employed in biomarker-centric investigations is specifically tailored to the application.8. While the primary goal of normalization is remove technical variance, if performed incorrectly, normalization can introduce unintended artifacts and impair the ability to detect true differences in RNA levels9. For example, prior studies have demonstrated that the use of different normalization approaches can generate highly discordant results from the same dataset11, and that in some circumstances, normalized data can perform worse for disease diagnosis than the raw data itself12. Thus, selection of an appropriate data normalization strategy is critical to increase the odds of identifying gene expression signatures with true diagnostic utility in RNA-seq based biomarker discovery workflows.A large majority of the data collected or used in transcriptomic biomarker discovery studies of whole blood today is now generated via next generation RNA sequencing (RNA-seq). One of the fundamental steps in the analysis of bulk RNA-seq data generated from any tissue is normalization of the raw read counts13 and EdgeR14 packages for R15. However, many of the assumptions underlying these commonly used normalization strategies may not hold true in whole blood, especially if the goal of analysis is to identify purely diagnostic differences in mRNA levels between disease states. For example, a majority of widely used global scaling normalization techniques, including median-ratio normalization (MRN)13 and trimmed mean of m-values (TMM) normalization14, work under the assumption that a majority of genes are not differentially expressed between specimens16. Furthermore, other commonly used normalization strategies, including quantile normalization, assume that all specimens have similar global distributions of transcript levels16. Both of these key assumptions are likely to be violated when working with data generated from whole blood if there are inter-donor differences in leukocyte counts.Despite the importance of normalization and potential impact on results, many biomarker discovery investigations performed using RNA-seq data generated from whole blood simply employ the default normalization strategies deployed in the most commonly used RNA-seq data analysis tools such as the DESeq217, virtually all of the of the mRNA in whole blood originates from white blood cells; furthermore, a majority of this mRNA is contributed by neutrophils and lymphocytes specifically, as they collectively make up well over 90% of the circulating leukocyte pool18. However, circulating proportions of neutrophils and lymphocytes can differ significantly between donors. For example, the ratio of circulating neutrophils to circulating lymphocytes, commonly referred to as the neutrophil-to-lymphocyte ratio (NLR), ranges from roughly 4:1 (4.0) to 1:2 (0.5) in healthy adults19, with more extreme values observed in a wide variety of disease states26.Unlike solid tissues which are relatively static in terms of cellular composition, the cellular composition of whole blood is highly dynamic and can differ substantially between donors. With the exception of hemoglobin transcripts, which are often selectively depleted prior to gene expression profiling27, any shift in white blood cell differential has the potential to result in dramatic changes in gene expression at the level of whole blood, even in the absence of true transcriptional differences in gene expression at the level of individual cells28; given their outsized contributions to the total pool of whole blood RNA, it is likely that even the normal inter-individual heterogeneity in neutrophil-to-lymphocyte ratio, much less that which is observed in disease states, results in near genome-wide differences in whole blood transcript levels that violate the assumptions of the aforementioned normalization approaches. Thus, the use of these normalization approaches when analyzing whole blood data in mRNA-based biomarker discovery studies has the potential to generate artifactual false positives or obscure true biologic differences in transcript levels that have diagnostic potential.Because the predominate leukocyte subpopulations found in circulation each carry highly unique transcriptomesDespite this potential confound, no studies have formally investigated the effects of inter-donor differences in white blood cell counts on the composition of the whole blood transcriptome within the context of implications for data normalization in RNA-seq based biomarker discovery workflows. In this study, we used RNA sequencing to generate genome-wide gene expression profiles of whole blood specimens collected from a cohort of human subjects recruited in an emergency medicine setting, and used white blood cell differential data collected from the same blood draw with flow cytometry to examine the influence of shifts in neutrophil-to-lymphocyte ratio on global transcriptome composition. Then, we examined the effect of four commonly used data normalization approaches on our ability to detect true inter-specimen biological variance, using the white blood cell differential data to benchmark each specimens true molecular and cellular identity.In order to better understand the potential effects of shifts in neutrophil-to-lymphocyte ratio on the composition of the whole blood transcriptome, we first compared the transcriptomes of the respective cell populations themselves using publically available gene expression data. In particular, we assessed differences in overall distributions of transcript abundance, as well as gene-level differential expression, using genome-wide data generated from isolated human neutrophils and lymphocytes via both next generation sequencing and microarray. Data generated via both technologies was used in order to give the clearest picture of transcriptome differences independent of platform-specific caveats such as differences in dynamic range and composition biases.Expectedly, both sequencing data and microarray data revealed dramatic differences in transcriptome composition between cell types. Across both platforms, distributions of mRNA abundance in neutrophil samples were dominated by a small number of highly expressed transcripts, characterized by higher upper percentiles, lower medians, and a smaller number of contributing genes, whereas distributions of mRNA abundance in lymphocyte samples were more normal and more diverse, characterized by lower upper percentiles, higher medians, and a larger number of contributing genes Fig. A,D.FigurIn terms of gene-level differential expression, we observed near genome-wide differences, as 84.1% and 84.4% of reliably detected genes exhibited at least a 1.5-fold difference in expression levels between cell types in the sequencing and microarray datasets respectively , median-ratio normalization (MRN), trimmed mean of m-values (TMM) normalization, and quantile normalization. The resultant pre and post-normalization read count distributions are depicted in Fig. In order to examine this potential effect further, we assessed the impact of the tested normalization strategies on our ability to detect expected inter-specimen differences in gene-level mRNA expression. Given that our collective prior analyses demonstrated that whole blood mRNA levels are largely dependent on the cellular composition of the specimen, and that specimens with differing cellular composition exhibit inter-specimen gene-level differential expression patterns which are highly reflective of those that exist between their isolated constitute cell populations, we assessed the impact of each normalization strategy on the correlation that we previously observed between the genome-wide fold differences in raw read counts yielded from comparison of whole blood specimens from donors with high and low neutrophil-to-lymphocyte ratios, and those yielded from comparison of isolated neutrophils and lymphocytes. Purely in terms of correlation coefficient, simple scaling of the raw read counts by sequencing depth significantly improved the strength of the observed correlation, however, the use of every other normalization strategy weakened it Fig. A,B. FurtFinally, we assessed the impact of each normalization strategy on our ability to bioinformatically infer the true cellular composition of each whole blood specimen using gene expression data. To do this, we leveraged the same publically available RNA-sequencing dataset of isolated human leukocyte subpopulations that we employed in our prior analyses to identify a list of 126 marker genes  whose expression levels are highly enriched in either neutrophils, lymphocytes, monocytes, or eosinophils Fig. . We thenThe results of these analyses  values by additionally adjusting for transcript length, should be adequate to remove technical variance while leaving the ability to detect true biological variance intact, especially in analyses with modest to large sample sizes29, which is becoming increasingly common in transcriptomic biomarker investigations due to the decreasing cost of bulk sequencing.A majority of the most commonly-employed RNA sequencing data normalization approaches used today operate under at least one of the following two assumptions: that either genome-wide distributions of transcript levels are similar between specimens, or a majority of genes are not differentially expressed between specimens28; given that tissue-level transcript abundance is cumulatively dependent on both factors, if the variance contributed by cell type admixture effects is not accurately quantified in an experiment, then further variance stemming from true transcriptional differences will also be harder to reliably detect. Thus, because MRN, TTM, and quantile normalization dramatically disrupted the baseline global distributions of transcript abundance which were biologically expected based on sample leukocyte composition, we can assume that their use for normalization would also interfere with the ability to detect differences in whole blood mRNA levels driven by underlying cell-level differences in rates of nuclear transcription, not just those driven by cell-type admixture effects.It could be argued that a limitation to this study is that we only experimentally assessed the effects of the tested normalization strategies on our ability to detect biological variance in terms of expected differences in whole blood mRNA levels driven by underlying differences in cellular composition, and not differences in whole blood mRNA levels driven by cell-level differences in nuclear transcription. To directly assess the latter, we would have needed a priori knowledge of true transcriptional differences between specimens, or ideally, to have used a spike-in strategy. However, the absolute mRNA level for any given gene in a bulk sample is collectively determined by the relative proportions of functionally distinct cell populations present in the sample, and the nuclear transcriptional state of said gene within the cell populations themselves31. While such correction may be more warranted in a physiologic experiment aimed at understanding nuclear transcriptional state, given the degree to which the core assumptions of these normalization methods were violated in our analyses, we would still have reservation about their use with whole blood data for any experimental aim. While these methods have exhibited strong performance in prior benchmarking studies which have utilized simulated data or real data generated from various human and non-human solid tissues34, these studies did not evaluate them for specific use with data generated from whole blood as we did here.One obvious caveat to the conclusions we have drawn here lies in that we evaluated the performance of the tested normalization strategies within the context of investigations focused on discovery of RNA-based biomarker panels, where the goal is typically to identify diagnostically targetable absolute differences in mRNA levels that are directly observable at the level of whole blood, as opposed to making inferences about the underlying nuclear transcriptional state of the composite leukocyte pool, which may be the case in studies more focused purely on evaluating physiologic or pathophysiologic mechanisms. Two of the normalization strategies we tested, MRN and TMM normalization, were actually conceptualized with the latter application in mind; they were designed with the intention of helping reveal true nuclear transcriptional state by controlling for potential sequencing-specific biases associated with the influence of the read counts of most highly expressed genes on the read counts of the remainder of the genome given the finite number of total reads that are generated from a sample39, continuing to experimentally illustrate and emphasize this phenomenon as we have done here is important, as an alarming proportion of the vast number of papers currently being published reporting the results of experiments performed with whole blood gene expression data fail to account for potential inter-specimen differences in leukocyte composition in their experimental designs, or to discuss the influence of any such differences on the results; many of these papers interpret the results of their analyses under the flawed assumption that a majority of the observed gene expression differences are driven by true changes in nuclear transcription, ignoring the fact that many are likely a result of underlying changes in specimen cellularity, which is undoubtedly leading to widespread biological misinterpretation of findings. Furthermore, an additional insight our results provide outside of the primary aim of our analyses is that many of the commonly employed data normalization techniques which we evaluated should likely be avoided in studies of whole blood gene expression data using cellular deconvolution methods, as their use could clearly interfere with the accuracy of cell type enumeration.While our findings have direct implications for informing data normalization strategies in biomarker-centric whole blood gene expression analyses, they also shed light on other caveats associated with the analysis of whole blood gene expression data in general. For example, the results of the permutation analysis we performed when assessing gene-level expression differences between specimens from donors with differing leukocyte counts suggest that neutrophil-to-lymphocyte ratio is likely by a wide margin the single largest determinant of the overall pattern of gene expression observed at the level of whole blood. While this result is not necessarily surprising, and aligns well with prior reports by our group and othersOur collective findings highlight the pitfalls associated with the application of widely-used RNA sequencing data normalization strategies which rely on the assumption of homogeneous transcriptome composition across specimens in biomarker discovery studies performed with whole blood. Given our results, such normalization techniques should be avoided in future investigations in lieu of others that are not confounded by inter-specimen differences in transcriptome composition associated with heterogeneity in donor leukocyte counts.40 were retrieved as gene-level summarized TPM values from the Human Protein Atlas (https://www.proteinatlas.org/about/download). Median TPM values observed in neutrophil samples and the total pool of lymphocyte samples were used directly for fold difference calculations.RNA sequencing data generated from isolated human neutrophils, naive CD4+ T cells, naive CD8+ T cells, memory CD4+ T cells, memory CD8+ T cells, naive B cells, and memory B cells originally sampled from the peripheral blood of healthy donors by Uhlen et al.41 were retrieved as raw intensity values with corresponding present-absent calls from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) via accession number GSE3982. Median raw intensity values observed in neutrophil samples and the total pool of lymphocyte samples were used directly for fold difference calculations. Note that raw values were used in this analysis because the most commonly used microarray data-normalization approaches, such as quantile normalization, would have likely masked the true biological structure of the data if applied to samples with dramatically different transcriptome composition, in a similar manner as the RNA sequencing data normalization methods we evaluated.Microarray data originating from isolated human neutrophils, TH1 CD4+ T cells, TH2 CD4+ T cells, memory CD4+ T cells, and B cells originally sampled from the peripheral blood of healthy donors by Mackay et al.2EDTA vacutainers, prior to initiation of any treatment. PAXgene vacutainers were moved to − 80 °C for storage until downstream RNA isolation, while K2EDTA vacutainers were used immediately for hematology analysis. Donor clinical and demographic information was retrospectively retrieved from the electronic medical record. All methods were carried out in accordance with relevant guidelines and regulations, and were approved by the Institutional Review Board of Dell-Seton Medical Center. Written informed consent was obtained from all subjects or their authorized representatives prior to any study procedures.Patients were recruited in the emergency department at Dell-Seton Medical Center  as part of a larger acute care biomarker discovery investigation, and presented with a broad range of medical conditions. Venus whole blood was collected via both PAXgene and KWhite blood cell differential was immediately assessed in EDTA-treated blood via four angle optical flow cytometry on the Cell-Dyn Sapphire automated clinical hematology system using the Cell-Dyn WBC reagent pack . Relative counts of leukocyte subpopulations were generated by dividing absolute subpopulation cell counts by the absolute total leukocyte count. The neutrophil-to-lymphocyte ratio was calculated as relative neutrophil count divided by relative lymphocyte count.Total RNA was isolated from archived PAXgene-stabilized whole blood using the PAXgene IVD Blood RNA Kit . RNA purity was assessed using spectrophotometry , RNA concentration was assessed using fluorometry , and RNA integrity was assessed using chip capillary electrophoresis .15. Low quality reads were filtered, and remaining reads were aligned to human reference genome GRCh38 using the HISAT2 pipeline via the ‘rhisat2’ package42. Counts of mapped reads were summarized at the gene level using the featureCounts function of the ‘Rsubread’ package43.Ribosomal RNA and globin mRNA-depleted cDNA libraries were prepared from 500 ng of total RNA using the illumina TruSeq Stranded Total RNA Ribo-Zero Globin kit . Paired-end 150 bp sequencing was performed on the illumina NovaSeq 6000 platform. Pre-processing of raw data was performed using R 13, using a combination of the DESeq and counts functions under default settings to respectively generate scaling factors and retrieve scaled read counts. TMM normalization was performed via the ‘edgeR’ package14, using a combination of the calcNormFactors and cpm functions under default settings to respectively generate scaling factors and retrieve scaled read counts. Quantile normalization was performed via the ‘preProcess’ package using normalize.quantiles function with ties in read counts being broken at random.All data normalization was carried out using R. Scaling of raw read counts to produce RPM values was performed using basic scripts. MRN normalization was performed via the ‘DEseq2’ package40 were retrieved as gene-level summarized TPM values from the Human Protein Atlas (https://www.proteinatlas.org/about/download) and used to select the WBC4.126 marker gene list used for deconvolution. Final marker genes for neutrophils, total lymphocytes, monocytes, and eosinophils were selected by filtering the total pool of detected genes to retain those which exhibited at least 100-fold higher expression levels in one target cell population relative to the others, and also a median expression level of 1.0 TPM within the target cell population of maximal expression. In said analysis, inter-cell population fold differences were calculated from weighted median TPM values, which were used to avoid bias associated with differing numbers of samples associated with each target cell population.RNA sequencing data generated from isolated human neutrophils, naive CD4+ T cells, naive CD8+ T cells, memory CD4+ T cells, memory CD8+ T cells, naive B cells, memory B cells, monocytes, natural killer cells, and eosinophils originally sampled from the peripheral blood of healthy donors by Uhlen et al.44 for R as described by our group previously45, with cell count values based solely on the elgenvector capturing the most variance. The resultant inferred counts of each cell population were arbitrarily scaled from zero to one using unity-based normalization.For deconvolution, principal components analysis was used to produce an inferred count for each target cell population based on the whole blood read counts of its associated marker genes using the collapserows function of the ‘WGCNA’ package46, and were similarly implemented using basic scripts and the sample function of base R. Sample sizes were arbitrarily determined. In the case of all statistical testing, the null hypothesis was rejected when p < 0.05. The parameters of all statistical tests performed are outlined in detail within the figure legends.All statistics were carried out using the R ‘stats’ package. Mann–Whitney U-test or Kruskal–Wallis rank test was used for the comparison of continuous variables where appropriate. Spearman’s rho was used to assess the strength and significance of correlational relationships. Permutation analyses were carried out using basic scripts and the sample function of base R. Bootstrap statistical comparisons were carried out using the percentile bootstrap methodAll R analyses were carried out using R version 4.3.Supplementary Information.


Tumors are known to shed DNA into the bloodstream, and since the tumor DNA is marked by aberrant methylation patterns, this can be exploited for their detection through a simple blood sample. However, specific methylation biomarkers that efficiently detect a broad range of tumors and are effective at early-stage disease are still lacking. In this study we identify two novel methylation biomarkers and combine these with an already existing biomarker to improve multi-cancer detection. We test their performances as individual and combined markers using large methylation array datasets covering multiple cancer types, mimic blood samples using data from healthy blood cell DNA, and finally test the biomarkers in cancer plasma samples. We find that the combination of markers greatly improves the ability of the test to distinguish between cancer and normal samples, and in addition we provide the research field with a complete workflow for evaluating novel methylation biomarkers based on pre-existing datasets.TLX1 and GALR1. To evaluate their performance as a generalized blood-based screening approach, along with our previously reported methylation biomarker, ZNF154, we rigorously assessed each marker individually or combined. Utilizing TCGA methylation data and applying logistic regression models within each individual cancer type, we found that the three-marker combination significantly increased the average area under the ROC curve (AUC) across the 14 tumor types compared to single markers . Furthermore, we simulated dilutions of tumor DNA into healthy blood cell DNA and demonstrated increased AUC of combined markers across all dilution levels. Finally, we evaluated assay performance in bisulfite sequenced DNA from patient tumors and plasma, including early-stage samples. When combining all three markers, the assay correctly identified nine out of nine lung cancer plasma samples. In patient plasma from hepatocellular carcinoma, ZNF154 alone yielded the highest combined sensitivity and specificity values averaging 68% and 72%, whereas multiple markers could achieve higher sensitivity or specificity, but not both. Altogether, this study presents a comprehensive pipeline for the identification, testing, and validation of multi-cancer methylation biomarkers with a considerable potential for detecting a broad range of cancer types in patient blood samples.The ability to detect several types of cancer using a non-invasive, blood-based test holds the potential to revolutionize oncology screening. We mined tumor methylation array data from the Cancer Genome Atlas (TCGA) covering 14 cancer types and identified two novel, broadly-occurring methylation markers at  The negative impact of COVID-19 resulted in an estimated 10 million missed cancer screenings in the first six months of 2020 . DespiteThe absence of generalized cancer screening combined with a lack of patient adherence to available testing contributes to the problem of a higher prevalence of tumors being detected at late stages, which in turn is known to produce poor survival rates in these patient populations . In addiNew, non-invasive, and affordable screening tests are urgently needed that can reliably detect more cancer types, at stages early enough to provide effective treatment ,9. BloodThe detection of markers with high sensitivity and specificity for widespread cancer screening is the current challenge in the field. Although many cancer detection tests target somatic mutations in cfDNA, with the expectation of exclusivity to tumor cells, differences in driver mutations per individual tumors reduce test sensitivity ,12,13. TZNF154, which is hypermethylated in at least 14 different cancer types in The Cancer Genome Atlas (TCGA) of tumor data [ZNF154 hypermethylation compared to panels of somatic driver mutations, underscoring the utility of a multi-cancer, DNA methylation biomarker [ZNF154 could be improved by combination with additional multi-cancer methylation biomarkers.Previously, we reported a promising multi-cancer methylation biomarker, mor data ,20. Impoiomarker . Utiliziiomarker ,22. HoweGALR1 and TLX1. While novel pan-cancer markers such as these continue to emerge, the need for preliminary validation methods prior to initiating extensive laboratory testing intensifies. Therefore, the objective of this study was not only to validate the three markers individually and combined, but also to explore strategies for testing the biomarkers in a more blood-related context prior to processing plasma samples. The goal of this approach is to detect a broad spectrum of tumor types. Naturally, this calls for additional, more advanced follow-up tests, enabling identification of the tumor tissue of origin, which will not be in the scope of the present study. Exploiting the large number of tumor samples contained in TCGA database, we were able to assess multi-cancer detection performance across 14 cancer types, with cross-validation and validation in four independent cancer datasets, creating a solid foundation for the identified biomarkers. In addition to simulating dilutions of TCGA data with tumor DNA concentrations decreasing from 100–1%, also with cross-validation, we tested different thresholds used in calling a positive test. We evaluated this approach in terms of the analytical performance criteria, sensitivity, and specificity. We also demonstrated the beneficial effect of combining markers on the positive predictive value (PPV). Finally, we tested the marker combinations in two cohorts of patient plasma samples using bisulfite treated cfDNA sequencing data previously generated from lung and liver cancer patient samples. We ultimately provide a complete workflow generating supportive evidence for the further implementation of these three markers into a multi-cancer detection assay.In this study we mined TCGA methylation data from 14 cancer types and identified two additional methylation markers for use in combinatorial testing: https://tcga-data.nci.nih.gov/tcga/) (accessed on 1 October 2013). The data represent the same collection of TCGA DNA methylation values processed for the discovery of ZNF154 DNA methylation [The Cancer Genome Atlas (TCGA) data and ENCODE cell line data were collected as described in Sanchez-Vega et al. . In briehylation ,20 from hylation , and knohylation . Our prehylation , as wellhylation . Our apphylation . Thyroidhylation . Indepenhylation , GSE6669hylation , and GSEhylation ). Illumihylation . Prostathylation . All Illhylation ,34. The hylation . Additiohylation . Liver chylation .p-values from both t-test and Wilcoxon rank sum tests after Bonferroni-correction. The Wilcoxon test constraint was removed for three tumor types , where no probes would otherwise have been identified. The genomic coordinate for the GALR1 probe cg03502002 in hg19 is chr18: 74,962,134. For TLX1, the genomic coordinate for probe cg14861089 in hg19 is chr10: 102,895,044, and the ZNF154 probe cg21790626 is located at chr19:58,220,494. Mean and median values are presented in GALR1 locus window coordinates are chr18:74961138−74962794 and contain 25 CpGs, the TLX1 window coordinates are chr10:102894120−102895708 and contain 9 CpGs, and the ZNF154 window coordinates are chr19:58211993−58220837 and contain 11 CpGs .We assessed differential methylation in all positions on the Illumina 450 K methylation array data, using data from 4050 tumors and 646 normal samples provided by TCGA in 14 cancer types. Processing 485,512 probe positions on the array in one tumor type at a time, we searched for CpG sites whose absolute differences in mean and median methylation beta values between tumors and normal samples were above 0.4, and with significant p-values to compare average AUC values from each of the possible biomarker combinations. We applied two strategies for validating test performance. First, each of the 14 TCGA cohorts  were split randomly 50/50 five times and each time one set was used for training and setting of thresholds, while the other was used for validation. Second, we validated the test performance in independent datasets, using logistic regression coefficients for the corresponding cancer type and probe combinations from TCGA data to predict probabilities in the independent validation cohorts. This was followed by classification using the same cutoff threshold applied to TCGA discovery cohort. The sample code will be made available at https://github.com/elnitskilab/Tumor_classification_bymethylation (accessed on 10 May 2023).Logistic regression models were trained using methylation levels from one, two, or three markers as covariates (additive effect) for all 14 cancer types individually . Methylation data obtained from TCGA tumors were simulated into percentages of total DNA by weighting the average methylation value from a given tumor sample and normal peripheral blood cell sample at the targeted fractions. Simulated circulating tumor DNA (ctDNA) fractions ranged from 1–100%. We did not take into account the fragmentation pattern observed in cell-free DNA  when perGALR1, TLX1, ZNF154, GALR1 + TLX1, GALR1 + ZNF154, TLX1 + ZNF154, GALR1 + TLX1 + ZNF154). Alternatively, we calculated the PPV for each of the assays where thresholds were determined based on a fixed specificity at 95%.Incidence rates were extracted using the SEER*Stat software Version 8.4.0 providing access to the 1975–2019 SEER Research Plus Data (November 2021 Submission). We used the database ‘Incidence—SEER Research Limited-Field Data, 22 Registries, November 2021 Sub (2000–2019)’ and retrieved age-adjusted rates using the 2000 U.S. Std Population (19 age groups—Census P25–1130). Rates were calculated for cases diagnosed from 2010–2019 and with age at diagnosis set to 50–85+, thereby maintaining a focus at the screening-relevant part of the population. We compiled a complete list of tumor subtypes covered by TCGA cohort used for analysis, including the topography code (ICD-O-3 Site) and the morphology code (ICD-O-3 Histology). We then extracted the relevant incidence rates from SEER and summed the subtype-specific rates together to obtain the final incidence rate for each tumor type . In the lung squamous cell carcinoma (LUSC) cohort, a single tumor sample was excluded prior to the retrieval of incidence rates, since the patient was diagnosed with ‘Adenocarcinoma, NOS’ at the ‘upper lobe, lung’, and thus in theory belonged to the lung adenocarcinoma (LUAD) type. A list of incidence rates applied for each TCGA tumor type can be found in The sample cohort covered 40 breast tumors and 8 normal breast samples, each in duplicate. Our primer design assumed all non-CpG cytosines (C) to be converted with sodium bisulfite to thymine (T). The primers were annealed to regions in the genomic DNA sequence devoid of any cytosines in a CpG context. The following PCR primers were used: ZNF154, at genomic position Chr19: 58220404-58220705 (+strand)Forward primer: 5′-GGTTTTTATTTTAGGTTTGA-3′Reverse primer: 5′-AAATCTATAAAAACTACATTACCTAAAATACTCTA-3′Amplicon size (incl. primers) 302 bp (20 CpGs) TLX1, at genomic position Chr10: 102894992-102895165 (+strand)Forward primer: 5′-TTTTTAGTTTAGGTTTTATGGGGTAG-3′Reverse primer: 5′-AAAACCATAACTTCCTTTATAACCC-3′Amplicon size (incl. primers) 174 bp (13 CpGs) GALR1 at genomic position Chr18: 74961979-74962242 (+strand)Forward primer: 5′-GGGAGTTTTTTTTGTAGGAGT-3′Reverse primer: 5′-AAAACACTAAAATCCCCTTCC-3′Amplicon size (incl. primers) 264 bp (27 CpGs)TLX1 and GALR1 amplicons were generated and sequenced at Zymo Research in a multiplex reaction. Zymo’s PCR annealing temperature was 56 degrees, and bisulfite conversion, PCR, and sequencing were performed in the same way as ZNF154.-Forward adapter: 5′-ACACTCTTTCCCTACACGACGCTCTTCCGATCT-3′,-Reverse adapter: 5′-GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT-3′.For purposes of sequencing, the primers contained the following adapters:ZNF154 amplicons were prepared from 20 ng genomic DNA, which was bisulfite converted with the EZ DNA Methylation-Direct Kit .In brief, ZNF154 were obtained from our previous study [TLX1 and GALR1 analysis was carried out using standard Illumina base-calling software and methylation was analyzed using Zymo’s in-house pipeline built on Bismark version 0.7.12. PCR products were sequenced at the NIH Intramural Sequencing Center using the Illumina MiSeq platform with Reagent Kit Version 3  to generate paired-end, 300-bp reads. Post-run processing of data was performed using RTA version 1.18.42 and CASAVA software version 1.8.2 (Illumina Inc.). Sequencing data from PCR-generated amplicons of us study . TLX1 anZNF154 amplicon. Since TLX1 and GALR1 amplicons resulted in lower sequencing reads, the threshold was set proportionally lower for the analyses of these two amplicons (19 reads as cutoff). The duplicate samples were treated as separate sample cohorts in order to check for reproducibility of the results with either sample cohort. The maximum mean methylation level among the normal samples was used to set the threshold for classification of the tumor samples (resulting in 100% specificity). To combine the markers into multi-marker assay, every sample that passed the read count limit for all markers was evaluated for positivity for each of the markers and presented in the Venn diagram.We aligned the full-length fragments to the human genome version GRCh37/hg19 using Bismark version 0.7.12 [min, defined from the fraction of methylated CpG sites within the region for all samples , although this difference was not statistically significant .In accordance with the EpiClass procedure , we set ples see , and (2)mance in , where, As input, we used reads from the whole genome bisulfite sequencing (WGBS) plasma samples that overlapped 500–600 bp regions, flanking the corresponding CpG probe of interest. Samples were aligned using Bismark version 0.7.12  and duplZNF154 probe cg21790626  in Illumina’s 450 K methylation array is significantly hypermethylated in 4050 TCGA tumor vs. 646 normal samples from 14 cancer types [ZNF154) across all tumor samples was high  a. In a ±he array a; howeveZNF154 across multiple cancer types, we reanalyzed TCGA data from the same 14 cancer types. We required mean and median methylation differential values p-values < 0.05 for t-tests and Wilcoxon rank sum tests). This process revealed several CpG sites that displayed strong differential methylation signals in a wide range of tumors. We followed up on two positions as putative multi-cancer markers. At probe cg14861089 in TLX1 , the difference in median beta values between tumor and normal samples across all 14 cancer types ranged from 0.03 in kidney renal clear cell carcinoma (KIRC) to 0.73 in uterine corpus endometrioid carcinoma (UCEC) , showed an average difference in median beta value between tumor and normal of 0.43, with the smallest difference detected in KIRP (0.02) and the largest difference again found in UCEC (0.70). GALR1 also met the 0.40 threshold in nine tumor types: bladder, breast, colon, head and neck, lung, prostate, rectal, stomach, and uterine cancer  or tumor (n = 23) cell karyotypes [p < 0.01, Fisher’s exact test for each marker). The average beta value across all tumor-derived cell lines was very high: 0.87 for TLX1, 0.90 for GALR1, and 0.94 for ZNF154. Conversely, the average beta value across all non-tumor-derived cell lines was <0.1 for each marker.To expand upon cancer types in our methylation analysis in human tissues from TCGA data , we examll lines . Here, wryotypes , and in ZNF154 was unmethylated in all non-tumor-derived cell lines (range 0–0.05) and hypermethylated in all tumor-derived cell lines (range 0.47–0.99), with the exception of Nt2d1 (a teratocarcinoma cell line), which had methylation beta values < 0.1 for all three markers. The low level of methylation seen in this cell line may be related to its pluripotent nature [TLX1 showed high methylation (>0.4) in 18/23 tumor-derived cell lines and two non-tumor cell lines. The two normal cell lines were HEEpic  and NHBE . GALR1 showed high methylation in 18/23 tumor-derived cell lines and one non-tumor-derived cell line, GM12878 .Slight differences existed for each marker. t nature . TLX1 shZNF154, TLX1, and GALR1 methylation were elevated across the majority of tumor-derived cell lines. Notably, the combination of the three markers clustered the known tumor cell lines and normal cell lines into distinct groups, with only two exceptions—cell lines with no information (“Not specified”) clustered with both the tumor and non-tumor groups based on their methylation values. Notably, the tumor cell lines included cell types, which were not included in the 14 cancer types we analyzed from TCGA in Thus, GALR1 and TLX1 showed near perfect performance in classifying colon cancer (AUC of 0.97 for both), ZNF154 only achieved an AUC of 0.81 within this tumor type. On the other hand, ZNF154 showed optimal performance in several other cancers such as uterine corpus endometrial and head and neck cancer , the threshold that allows for a high specificity (92%) comes with a compromise in sensitivity (64%). The same tendency could be observed for this marker in classification of rectal cancer samples, albeit with a better performance (100% specificity and 78% sensitivity). On the contrary, because the GALR1 marker suffered from low levels of methylation in TCGA liver cancer tissue , the specificity of 94% corresponds to a sensitivity of only 46%, making it a poor marker for detection of this cancer type. To summarize each marker, we found the average of the sensitivity values in tissue samples across all 14 cancer types was 78% for GALR1, 81% for TLX1, and 84% for ZNF154. The average specificity across all 14 tissue types was 95% for GALR1, 96% for TLX1, and 97% for ZNF154. Together, these data indicate that no marker has ideal performance in all cancer types and supports the use of multiple markers to complement each other’s performance. While the vast majority of cell-free DNA (cfDNA) in plasma from healthy individuals derives from hematopoietic cells [To classify tumor and normal samples from TCGA data, we trained logistic regression models using methylation beta values from the three biomarkers one tumor type at a time. Next, we determined the best probability thresholds for classification with receiver operating characteristic (ROC) analysis and used the area under the ROC curve (AUC) to measure the performance of each marker. Across the 14 tumor types, the three markers showed varying AUC values ranging from 0.63–1.0 . While Gk cancer . ImportaC values . A good ic cells , certainic cells . Thus, wp-value = 0.0026; pairwise Wilcoxon signed-rank tests with Bonferroni correction). Compared to ZNF154 alone, the addition of TLX1 and GALR1 into a three-marker assay improved the AUC value by more than 0.07 in 4 cancer types   . The senctively) . Importactively) . In summTo assess performance of our model within TCGA dataset, we split each tumor and normal cohort into two equally sized cohorts in a random manner and used only half of the samples for training and the other half for performance testing. We repeated the randomization split over five iterations, and the performance in both training and validation cohorts is reported in GALR1, TLX1, and ZNF154 markers in independent sets of Illumina 450 K methylation array data available for four cancer types: breast cancer , colon cancer , lung adenocarcinoma , and prostate adenocarcinoma . In all of these datasets, the average methylation value was elevated in tumor samples compared to normal tissue samples for all three probes . With only two stage I tumors in the stomach cancer cohort and two normal samples, the sample size was too small to be tested statistically. Again, the three markers displayed complementary strengths. GALR1 and TLX1 provided higher discrimination , improving performance over TLX1 or GALR1 . We also tested the combination of the three markers by logistic regression in stage I tumors for 11 of 14 cancer types with available data . For individual markers, the median AUCs for classification of stage I tumors were 0.91 (GALR1), 0.88 (TLX1), and 0.90 (ZNF154) vs. 1.00 using all three markers together . Although pairwise comparison using Wilcoxon signed-rank test with multiple comparison correction showed no significant difference between any of the markers, a trend towards reduced variability in AUC could be observed when combining more markers  a–c. Addimination b,c of nomay lack a. The ditogether d. We app markers d. Our anp-values < 0.0001; Wilcoxon rank sum test). GALR1 methylation level in white blood cells was slightly elevated compared to TLX1 and ZNF154  in plasma . To chard ZNF154 a. Using ZNF154 locus to predict the suitability of this marker for liquid biopsy analysis [GALR1 compared to the other two markers, which is likely explained by the elevated methylation level at this locus in WBC DNA   . We did p = 0.016; pairwise Wilcoxon signed-rank test). The best performing assay under these conditions was the triple marker assay (PPV at 8.0%), followed by the TLX1 and ZNF154 combined assay (PPV at 7.7%), and TLX1 alone (PPV at 7.8%). Naturally, and as a consequence of fixing the specificity at 95%, the sensitivities dropped for all of the marker combinations. Sensitivity ranged from 42–61%, except for GALR1, which obtained a sensitivity of only 10%  is the probability that a positive test result indicates the presence of a tumor. Additionally, the PPV provides an indication of the potential for false positive tests, and thus reveals the amount of people that would have to continue through diagnostic follow-up procedures only to confirm the absence of a tumor. The PPV of a screening test can be measured from large-scale population studies enrolling non-symptomatic individuals for screening. However, PPV can also be estimated from the analytical performance parameters, sensitivity, and specificity, calculated from case-control studies. In that case, the frequency of disease within a given population must be considered. The estimated PPV can be calculated using the following formula:database . We appldatabase . While tdatabase a. Thus, database a. Until database b. More sonly 10% .TLX1 (containing 13 CpGs), GALR1 (containing 27 CpGs), and ZNF154 (containing 20 CpGs). We tested the assay on bisulfite-converted DNA extracted from an independent collection of 40 breast tumors and eight normal breast tissue samples, as used in Margolin et al. [ZNF154 reached 82% sensitivity, while TLX1 and GALR1 correctly classified 90% and 95% of the tumor samples, respectively  across all three markers. Interestingly, the combination of ZNF154 with either TLX1 or GALR1 resulted in the successful detection of all tumor samples (100% sensitivity)  d. To tesitivity) . This tiposition . We concTLX1, GALR1, ZNF154) we assessed reads overlapping 600 bp regions flanking either side of the CpG sites of interest  we used EpiClass to determine the methylation density threshold  data from plasma samples belonging to lung cancer (n = 9), and hepatocellular carcinoma (n = 30) patients, along with healthy controls  ,34,35. Nrest see . For eacof 77.8% a. TLX1 h dataset . While tZNF154 and TLX1 (33%) or all three markers (37%), albeit at a lower specificity . In the attempt to include more than one marker without compromising specificity, we set up a different test criterion called “any-two”. Here we considered all three markers, and if two of them exceeded their respective threshold, the sample would be regarded as positive. Applying the any-two criterion increased the stringency of the test, achieving a maximum specificity of 99%, but suffering from an 8% sensitivity , while suffering a decrease in sensitivity which only reached 40%. These results indicate that plasma testing is feasible for hepatocellular carcinoma detection. Because 25 of the 30 samples represented early-stage tumors in this WGBS plasma dataset, we concluded that the results demonstrate potential for use in patient testing. Notably, a variety of combinations of these multi-cancer markers can yield similar overall classification results, although individual thresholds for sensitivity and specificity based on each marker can be selected to meet the user’s preference to exclude false positives.To further investigate the performance of the three-marker panel in a larger dataset, we tested data from the WGBS hepatocellular carcinoma plasma (n = 30) and healthy controls (n = 36). Because the sample size was still too small to divide into actual training and testing sets, we split the data and performed 5-fold cross-validation, randomly assigning half of the cases and controls into two groups (one for training and one for testing) and repeated this sampling in five iterations. We applied the EpiClass approach to each training set, seeking a threshold for the epiallelic fraction that would maximize specificity. We applied these thresholds to the classification of the five testing sets. The resulting sensitivities and specificities for the test sets are plotted as a mean of the five iterations ± 1 standard deviation . AUC valsitivity b. To accectively c. GALR1 ZNF154 methylation detection in ovarian cancer plasma outperformed patient CA-125 measurement , and combined, the two markers showed improvement over ZNF154 alone  [TLX1 and GALR1, respectively, and report the benefit of combining them with ZNF154 as a multi-cancer assay. In addition to the potential as diagnostic markers, the hypermethylation at these locations is consistent with previously reported roles of TLX1, GALR1, and ZNF154 in multiple cancers [ZNF154 was originally reported as a methylation marker specific for bladder cancer [The lack of effective generalized screening tests for most types of cancers means that detection is routinely delayed until the appearance of symptoms, which typically occurs at a late stage of disease with low therapeutic efficacy. Even then, a complex set of tests may be needed to determine the origin of symptoms. An emerging technology to detect more cancers before symptoms appear is through blood-based detection of circulating tumor DNA (ctDNA) . The mosication) . Currentication) ,45,46. Fication) ,49. Whil cancers ,51,52. Fr cancer , later sr cancer ,20.Given the large number of DNA methylation markers that have been published , the likZNF154, TLX1, and GALR1 biomarkers individually or in combinations. Overall, we found a good correlation between the tissue-based and plasma-based analyses, despite the datasets being derived from DNA methylation array and whole genome bisulfite sequencing (WGBS) data, respectively. For example, in TCGA liver cancer tissue data, ZNF154 alone could obtain a sensitivity of 87% and specificity of 98% with an AUC value at 0.92. The two-marker combination, ZNF154 and TLX1, improved performance over ZNF154 alone, increasing sensitivity to 92% with specificity still at 98%, and AUC at 0.95. The inclusion of GALR1 did not improve results in either analysis. In the simulated blood samples with 10% ctDNA, ZNF154 had an AUC of 0.84 in classifying tumor versus normal blood cell samples, which could be increased to 0.88 by adding TLX1. Again, the addition of GALR1 did not improve the results markedly. Using the common threshold for classification of all tumor types as one disease, and then zooming in on the liver cancer cohort, ZNF154 showed the best sensitivity of all single markers at 77%. As expected, the addition of TLX1, either with or without GALR1, improved the sensitivity to 80%. Through all these combinations the specificity emained constant with a range of 80 to 84%. Based on these results, we did not expand beyond three multi-cancer markers.Throughout this study, we have utilized a range of different data sources and techniques to evaluate the performance of the ZNF154 and TLX1 performed with similar sensitivity  when forcing the specificity to its maximum level . If the two markers were combined in a single assay, such that a positive read-out for any of the two markers results in a positive test, the sensitivity increased to 33% along with a drop in specificity to 88%. Adding GALR1 further improved sensitivity to 37% at a cost of 83% specificity. Following this sequential line of analyses from TCGA tissue data to actual plasma samples, it becomes evident how the dilute nature of ctDNA in liquid biopsies challenges assay performance. However, it should be noted that WGBS is likely not the optimal technology for detection of methylation at target loci in cfDNA samples. A natural consequence of the broadness of this approach (targeting the entire genome) is the relatively low coverage that results [ZNF154 and TLX1 markers into a single assay. While GALR1 showed superiority in classifying TCGA tumor versus normal tissue derived from breast, kidney (KIRC) and lung (LUAD), it suffered from increased methylation levels in normal blood cells. Accordingly, GALR1 had markedly worse performance at any simulated dilution level, reaching only 57% specificity in normal blood cell samples when ctDNA concentration was 10%. However, the utility of this marker cannot be ruled out, since the compromise in specificity led to GALR1, reaching the highest sensitivity as a multi-cancer assay within kidney, pancreatic, and lung adenocarcinoma.While the tissue-based analysis using TCGA data depended on the methylation levels at a single CpG probe expressed as a beta value (ranging from 0–1), analysis of whole genome bisulfite sequencing data added another level of complexity. These data provided information on the methylation density across reads overlapping a 1kb region, requiring more advanced computational tools for classification. We have previously developed such an application called the EpiClass tool, which takes into consideration both the methylation density  and the concentration of each of these differentially methylated epialleles . Applyin® has a PPV of 3.7% in detecting colorectal cancer [® test, the authors extrapolate the PPV from their case control study using SEER cancer incidence rates in the 50–79 year age group [https://grail.com/press-releases/grail-announces-final-results-from-the-pathfinder-multi-cancer-early-detection-screening-study-at-esmo-congress-2022/) (accessed on 11 September 2022).We utilized the performance parameters, sensitivity, and specificity, obtained from diluted TCGA tumor methylation signals in a normal white blood cell background to estimate the putative positive predictive value (PPV) for our triple marker combination in a pan-cancer setting. PPV is a key metric used to evaluate harm-benefit in cancer testing, however it is most commonly calculated from large-scale clinical studies enrolling non-symptomatic individuals. As an example, we compared commercially available tests; the Cologuardl cancer , while ll cancer . Pan-cange group . With a ® test is based on enrichment and sequencing of a methylation-specific panel covering 1,166,720 CpGs, whereas our technology focuses on reducing complexity in downstream analysis by targeting only three CpGs. While three markers represent a drastically reduced set, the benefits include a binary testing procedure producing only a yes or no answer. Other potential benefits include cost-effective applications and ease of use in laboratory testing. A three-marker test has been previously demonstrated to show high sensitivity and specificity in detecting colorectal cancer [A major factor affecting the PPV is the prevalence of the disease being detected in the tested population. In line with this, our data also demonstrate how grouping individual cancer types into one disease detected by a single assay drastically improves the PPV. Considering all seven possible marker combinations, we obtained an estimated PPV of 0.4% for colon and 0.3% for lung cancer, respectively. However, when considering all 14 cancer types combined, average PPV increased to 3.2%. Additionally, by setting a fixed specificity level at 95%, we managed to obtain a PPV of 8% for the triple marker assay. Comparing estimated PPV values head-to-head between studies should be conducted with the utmost caution. The study by Klein et al. is considered a clinical validation of pan-cancer testing of >4000 plasma samples from cancer patients and controls . In the l cancer .Utilizing TCGA data for pre-screening of potential cancer biomarkers has several limitations. First, a large number of TCGA samples analyzed here represented late-stage cancer diagnoses. Nonetheless, we were able to detect the presence of significant hypermethylation at the three biomarkers in the majority of stage I TCGA tumors compared to normal controls. Second, the undiluted TCGA methylation data represent an optimal sensitivity, specificity and AUC, , which iIn summary, we present a three-marker combination of DNA methylation sites that, if further validated, could potentially be used as a first-pass cancer detection method, after which more specific biomarker testing procedures could be applied to reach a diagnosis. As an economical test, with a sufficiently high sensitivity and low false positive rate, this combination could be especially useful to complement existing screening methods, especially when screening for a given type of cancer does not currently exist.


Imbalances in gut microbes have been implied in many human diseases, including colorectal cancer (CRC), inflammatory bowel disease, type 2 diabetes, obesity, autism, and Alzheimer's disease. Compared with other human diseases, CRC is a gastrointestinal malignancy with high mortality and a high probability of metastasis. However, current studies mainly focus on the prediction of colorectal cancer while neglecting the more serious malignancy of metastatic colorectal cancer (mCRC). In addition, high dimensionality and small samples lead to the complexity of gut microbial data, which increases the difficulty of traditional machine learning models.To address these challenges, we collected and processed 16S rRNA data and calculated abundance data from patients with non-metastatic colorectal cancer (non-mCRC) and mCRC. Different from the traditional health-disease classification strategy, we adopted a novel disease-disease classification strategy and proposed a microbiome-based multi-view convolutional variational information bottleneck (MV-CVIB).The experimental results show that MV-CVIB can effectively predict mCRC. This model can achieve AUC values above 0.9 compared to other state-of-the-art models. Not only that, MV-CVIB also achieved satisfactory predictive performance on multiple published CRC gut microbiome datasets.Finally, multiple gut microbiota analyses were used to elucidate communities and differences between mCRC and non-mCRC, and the metastatic properties of CRC were assessed by patient age and microbiota expression. The human intestine is one of the most important organs in the digestive system, which maintains the normal life activities of the human body through metabolism  patients are more worried about mCRC with higher mortality  model to specifically address the prediction problem of mCRC. The variational information bottleneck (VIB) can extract all the judgmental information that is helpful for disease prediction while filtering out redundant information  Current studies mainly focus on the prediction of CRC while neglecting the more severe mCRC. We are the first to apply deep learning to the microbiome-based mCRC prediction problem and achieve excellent prediction results.2) Compared with the traditional health-disease classification strategy, we adopt a new disease-disease classification strategy, which identifies more severe diseases among sick patients. Identifying more complex diseases from diseases is more conducive to mining the underlying nature of disease exacerbations.3) We compute the nearest neighbor information for the relative abundance data and feed it together as a view into the VIB with convolution and pooling modules. The VIB can extract all the judgmental information that is helpful for disease prediction while filtering out redundant information. Since we introduce convolution and pooling operations into the model, the data in the input stream become smoother, which increases the robustness and generalization ability of the model and avoids overfitting.n = 9) and non-metastatic colorectal cancer  from the National Center for Biotechnology Information (NCBI) . To evaluate and analyze predictive models, we collected 16S rRNA data from the gut microbiota of patients with metastatic colorectal cancer , and de-redundant these sequences. The purpose is to obtain the signature table and reference sequence.Finally, we used Qiime2 to analyze the composition of microbial communities from the denoised data. Among them, the feature table represents the relative abundance of species and serves as the input feature vector of the proposed model.In this study, to explore the internal structural characteristics of the pre-processed data and the degree of cognitive difference between mCRC and non-mCRC, we used various types of dimensionality reduction analysis methods . There is a difference between the groups in the CRC3 dataset but not significant , which may be related to the fact that the samples included in the dataset are early CRC patients and late CRC patients.Considering the small number of samples and more feature information in mCRC and non-mCRC, we used four different types of dimensionality reduction methods to mine the distribution rules of samples in the dimensional space. According to the results, the spatial distribution of mCRC and non-mCRC is different, but some samples overlap in space. However, compared with non-mCRC, mCRC is formed by further deterioration on the basis of non-mCRC, so there are differences in microbial abundance features. In addition, to further show the significance index of the difference between the groups, we performed an analysis of similarities (ANOSIM)  , which comes from the deep neural network of the intermediate layers in the upstream part of the model. Furthermore, in the rest of this study, X, Y, and U are represented as random variables, and x, y, and u are their multidimensional instances, respectively. θ is a parameter vector, and θ is a function parameterized by θ. S is a set.We set U, so as to maximize the information provided to Y and maximize the compression to X. Therefore, maximizing the mutual information I between U and Y can be written as follows:Referring to the information bottleneck theory  can make U to predict Y, and βI is a constraint that U is the minimal sufficient statistic for X.where We refer to the solution process by Alemi et al.  for the N is denoted as the auxiliary Gaussian noise variable, and KL is the Kullback–Leibler divergence. It is worth noting that f is originally an encoding function, but in this study, it is a neural network. The introduction of f has a re-parameterization trick dx = p(ε)dε, where u = f can be regarded as a deterministic variable, in particular, considering that this formula can make the noise variable independent. Thus, backpropagation is used to optimize the gradient of the objective function of equation (3). Overall, the calculation will be easier. Furthermore, a multivariate Gaussian distribution with a diagonal covariance structure u = f is the target of the variational approximation posterior, and u = μ + σε is re-parameterized.where ε ~ X as a multi-view random variable can be expressed as X. The p(U|x) of equation (3) can be expressed as p, with the joint of V available data views as the condition. We refer to the method in multimodal variational autoencoder (MVAE)  with q(U|xi) = q~(U|xi)p(U) is assumed. p(U) is a prior. Therefore, the product of multiple single-view posteriors can be considered equivalent to the joint posterior, which can be written as follows:Since our model has multi-view input, we take into account the nearest neighbor information between each sample. Therefore, we can further improve the objective function of Deep VIB in equation (3), andEquation (4) can be considered the product of experts (PoE). Considering that the product of Gaussian experts is itself a Gaussian distribution . For the data of both the above views, we used the SiLU  during training.To avoid overfitting, dropout and early stopping are applied in this study. Dropout greatly reduces the size of the neural network, allowing the neural network to learn local features in the data. Early stopping can stop training early when overfitting occurs. We used the dedicated stochastic encoder q(y|u) = σ(fd(u)) with a logistic sigmoid function in the decoder and y models two diagnostic CRC labels, such as mCRC and non-mCRC. Furthermore, for other published CRC datasets in this study, y models two diagnosed disease labels, such as CRC or healthy. In addition, in equation (5) mentioned above, r(U) and r(U) are spherical Gaussian distributions with K dimensions, where r(U) = p(U) = N. We set K = 256 and β = 10−5.We used a logistic regression model https://github.com/cuizhensdws.All experiments were performed under Windows 10 with NVIDIA GTX 1650 GPU and CUDA 10.2 installed, where the machine's processor is AMD Ryzen7 4800H. The source code and data are available at: To evaluate the classification performance of the model more accurately and comprehensively, inspired by DeepMicro, we design a similar evaluation scheme. The ratio of the training set and test set in the mCRC dataset is adjusted to 8:2. It is worth noting that the random partition seed is also set the same as DeepMicro, which guarantees a fair random training-test split. Furthermore, for the published CRC dataset, we also adopted the same dataset partitioning scheme. The above settings can further reduce the information leakage and improve the efficiency of the response model more accurately.We used a stratified 5-fold cross-validation applied to the training set and calculated the AUC score through the validation set, and the epoch with the best parameters among all epochs was selected. The AUC can be written as follows:M is the number of positive samples, and Nis the number of negative samples.where To evaluate the performance of MV-CVIB, we compared the existing advanced methods, including MVIB  which uses only microbiome abundance data. For PopPhy-CNN, we modified the source code so that it is roughly consistent with our model framework. The original PopPhy-CNN provided different experimental procedures. To ensure the consistency of the verification test, we made corresponding framework adjustments. For SVM and RF, we used the same grid search to set the hyperparameters, referring to MetAML  was 0.814, both of which were superior to other advanced models. Interestingly, RF and PopPhy-CNN both have the same AUC of 0.803. The AUC of MVIB is only 0.78, and the performance of the model is mediocre. In DeepMicro, the AUC value of AE + MLP was 0.799. The predictive effect of CAE+MLP was slightly lower than that of AE + MLP, and the AUC was 0.791.Second, on the Colorectal-EMBL dataset, as shown in Finally, on the Early-Colorectal-EMBL dataset, as shown in Considering that we have introduced multi-view, convolution, and pooling modules in MV-CVIB, to verify the impact of this module on the overall performance of the method, we set up multiple ablation experiments on the mCRC dataset and three CRC datasets. Overall, according to the results of the ablation experiments, we speculate that when the number of samples is small, multi-view may be easier to take advantage of the prediction performance; when the number of samples is large, the convolution module may be more likely to take advantage of the prediction performance.The stacked bar graphs of the microbiota (phylum level) of the non-mCRC group and the mCRC group are shown in As shown in Propionibacterium acnes. Not only that, we also used the LDA effect size (LEfSe)  The composition of the human microbial community is very complex, and the boundaries between the bacterial communities are fuzzy. (2) There are various ways to generate microbial community characteristic data, which leads to data heterogeneity. (3) Human health status is dynamic rather than fixed, and healthy samples are not absolute, which may increase data noise and outliers. (4) Conventional microbiome-based disease prediction mostly adopts the health-disease classification method, ignoring the deterioration process of the disease. We adopted a new classification schema: disease-disease instead of health-disease. We focussed on identifying more severe diseases, especially cancer and cancer metastases, from disease samples. This facilitates exploration and reveals the underlying properties of disease exacerbation. It is meaningful for non-mCRC patients. We can obtain potential biomarkers through the analysis of differences in the bacterial flora of patients and explore the biological process and development rules of CRC metastasis on the basis of the microbiome. This is conducive to further expanding the treatment options for non-mCRC patients and improving the prognosis of non-mCRC patients.We employed a variety of microbiome analysis methods to explore the diversity of non-mCRC and mCRC. From the experimental results, the communities of non-mCRC and mCRC were quite different, the distribution of the flora was complex and diverse, and the flora composition of different samples was different. Compared with the state-of-the-art methods for microbiome-based disease prediction, the proposed method MV-CVIB achieved higher AUC values on the mCRC dataset. In addition, in order to more comprehensively evaluate MV-CVIB and verify its generalization ability, we collected datasets from three published studies and conducted experiments. The number of samples in the three public CRC datasets does not exceed 200, which belongs to high-dimensional small sample data. This limitation may affect the experimental results. Therefore, in future, we can use transfer learning or data augmentation. The experimental results show that MV-CVIB achieves higher AUC values on two of the three datasets. Based on the predicted results, we performed a statistical analysis of potential biomarkers in non-mCRC and mCRC. Finally, we modeled the risk score, explored the age trend of the risk score, and screened out those bacterial orders that had positive and negative effects on patients.In this study, we propose a deep learning approach based on a multi-view convolutional variational information bottleneck for the prediction of mCRC. The multi-view contains species abundance data and sample field information, where the sample neighborhood information is obtained based on the species abundance data, which ensures that our method will not introduce additional noise when inputting multi-view data, and has a better time and space complexity. Our results demonstrate that the method has good predictive performance. However, on the Colorectal-EMBL dataset, all deep learning methods are not as effective as RF, which may be related to the internal structural characteristics of the dataset. We explored the degree of difference between non-mCRC and mCRC from various perspectives, analyzed those statistically significant differences in flora, and constructed an age risk assessment model to explore the rules of age and cancer metastasis. Of course, there are also some deficiencies in this study, mainly two points. First, due to concerns about patient privacy and medical ethics, the number of samples obtained is small, which may cause the model to fall into overfitting to small samples. Second, the prediction model is only for CRC data and does not consider other disease data, which may lead to a lack of generalizability of the model. We will also develop more effective methods for more complex microbiome-based disease phenotype prediction and improve the scalability of the prediction method as much as possible.https://www.ncbi.nlm.nih.gov/, PRJNA531761.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: ZC proposed and designed the algorithm. YW and D-SH demonstrated the effectiveness of the method and analyzed the experimental data. Q-HZ, S-GW, and YH drafted the manuscript. All authors contributed to the article and approved the submitted version.