Ruminococcus bromii, associated with a favorable outcome. By combining microbiome signature and Immunologic Constant of Rejection, we developed and validated a composite score (mICRoScore), which identifies a group of patients with excellent survival probability. The publicly available multi-omics dataset provides a resource for better understanding colon cancer biology that could facilitate the discovery of personalized therapeutic approaches.The lack of multi-omics cancer datasets with extensive follow-up information hinders the identification of accurate biomarkers of clinical outcome. In this cohort study, we performed comprehensive genomic analyses on fresh-frozen samples from 348 patients affected by primary colon cancer, encompassing RNA, whole-exome, deep T cell receptor and 16S bacterial rRNA gene sequencing on tumor and matched healthy colon tissue, complemented with tumor whole-genome sequencing for further microbiome characterization. A type 1 helper T cell, cytotoxic, gene expression signature, called Immunologic Constant of Rejection, captured the presence of clonally expanded, tumor-enriched T cell clones and outperformed conventional prognostic molecular biomarkers, such as the consensus molecular subtype and the microsatellite instability classifications. Quantification of genetic immunoediting, defined as a lower number of neoantigens than expected, further refined its prognostic value. We identified a microbiome signature, driven by  A large, publicly available dataset integrating RNA, whole-exome, T cell receptor and 16S rRNA sequencing from patients with colon cancer enables the discovery of a prognostic score consisting of tumor, immune and microbial features. MSI is caused by somatic or germline defective of MMR genes and leads to the accumulation of somatic mutations, neoantigens resulting in immune recognition and high density of tumor infiltrating lymphocytes3.Although there has been a substantial amount of research conducted on biomarkers for primary colon cancer, the current clinical guidelines in the USA and Europe  only rely on the tumor-node-metastasis staging and the detection of DNA mismatch repair (MMR) deficiency or microsatellite instability (MSI), in addition to standard clinicopathological variables, to determine treatment recommendations5.The strength of the in situ adaptive immune reaction, as captured for instance by the evaluation of the density and spatial distribution of T cells (Immunoscore), is associated with a reduced risk of relapse and death independently of other clinicopathological variables, including MSI status7, a lack of association between gene-expression-based estimates of immune response and patient survival in The Cancer Genome Atlas (TCGA) colon adenocarcinoma (COAD) cohort has been noted by the research community10. TCGA, for its genomic data richness and curation, represents the preeminent dataset for omics analyses; however, the collecting of comprehensive clinical data, including survival outcomes was neither a primary objective of TCGA nor a practical possibility in view of its worldwide scope and time constraints11. As such, the limited patient follow-up data associated with TCGA-COAD and other TCGA datasets has hindered statistically rigorous survival analyses11. In addition, TCGA did not include dedicated assays for T cell receptor (TCR) repertoire analysis or microbiome characterization, which was later performed using bulk DNA and RNA sequencing (RNA-seq) data and includes only few healthy solid tissue  samples13. Furthermore, as TCGA focused initially on cataloging genomic and molecular changes that occur in cancer cells, sample inclusion criteria based on stringent tumor purity cutoffs were imposed14, potentially biasing the population toward less-immune- or stroma-rich tumor specimens.However, despite the overwhelming evidence of the prognostic effect of the Immunoscore and other immune-related parameters in colon cancer17, knowledge of how their interactions impact patient outcome is fragmentary.In recent years, while quantitative features of primary colon cancer, including those that are cancer cell intrinsic, immunological, stromal or microbial in nature, have been reported to be significantly associated with clinical outcomes, individuallyTo dissect this phenotypic complexity with respect to outcomes, we used orthogonal genomic platforms to rigorously profile a large collection of primary colon cancer specimens (unselected for tumor cell purity) and matched healthy colon tissue, complemented with curated clinical and pathological data annotation and appropriate follow-up.Fresh-frozen tumor samples and matched neighboring healthy colon tissues  from systemic treatment-naive, patients with histological diagnosis of colon carcinoma were profiled with orthogonal genomic platforms. After cross-platform quality control (based on whole-exome sequencing (WES) and RNA-seq data) and inclusion criteria checking, genomic data from 348 patients were retained and used for downstream analyses . The ICR also correlates with response to immunotherapy across multiple cancer types, including breast24, melanoma10 and non-small-cell lung cancer25. The ICR signature includes gene modules that reflect the activation of type 1 T (TH1) cell signaling, expression of CXCR3/CCR5 chemokine ligands, cytotoxicity and counter-activation of immunoregulatory mechanisms21 s21 Fig. .9. We then characterized the immune disposition in relation to Consensus Molecular Subtypes (CMS)16, a well-defined transcriptomic-based classification of colon cancers. CMS categories include CMS1/immune, CMS2/canonical, CMS3/metabolic and CMS4/mesenchymal. Overall, t-distributed stochastic neighbor embedding (t-SNE) plotting of the whole expression data segregated CMS1–CMS3 samples, but a high heterogeneity was observed for CMS4  and depletion (for CMS1)  and leukocyte subsets enrichment scores, showed only a modest decrease from right-to-left colon, with ICR high being more prevalent in cecum versus rectosigmoid tumors  cell and T cell subsets was the highest in the ICR-high immune subtype across all CMS, whereas other leukocyte subsets were more variable TRB gene by immunoSEQ was performed on all samples  with sufficient DNA for this assay. TRB gene sequence information was also extracted from bulk RNA-seq using MiXCR (n = 341)30. Among stromal cell and leukocyte subsets (measured by RNA-seq), the strongest correlation with the number of conventional (αβ) T cells with a productive TCR (immunoSEQ TCR productive DNA templates), was observed for estimates of T cell subsets , substantiating the ability of ICR to capture additional features beyond T cell abundance  as well as with ICR (r = 0.40)   Fig. . Consist40) Fig. , which h40) Fig. . Using t10) Fig. . FurtherAT1 Fig. . The cors31 Fig. .Fig. 2TCr = 0.75, P = 0.019; Fig. For nine patients, immunoSEQ TCR profiles were available on both the tumor and matched healthy colon tissue. This allowed the definition of overlap between T cell clones observed in the tumor and healthy colon sample for each of these patients   of the AC-ICAM dataset was highly comparable to the TCGA-COAD cohort Fig. , as were35 with a mutation frequency of <5% in TCGA-COAD36 and Nurses’ Health Study (NHS)-Health Professionals Follow-up Study (HPFS) cohorts37 that were enriched in AC-ICAM and that had not been previously reported as colon cancer oncogenic mediators38 or cancer driver genes for colorectal cancer32  with ICR score, whereas no mutations were enriched in samples with a lower ICR score , no clear association was observed with ICR immune classification as they were dependent primarily on the mutational load/MSI status and secondarily on the CMS status40 Fig. .Altogether, this analysis identified a relationship between specific cancer-related genes and/or histological characteristics and a lower level of intratumoral immune activation.5.We then proceeded by integrating ICR and TMB data. While hypermutated samples frequently displayed an ICR-high phenotype, a considerable proportion of ICR-high samples (46%) had a low TMB Fig. , which d41. We propose that the combination of the two parameters may more accurately reflect the presence of an active, antitumor immune response. Consistently with this hypothesis, a progressive increase of OS was observed from IES1 to IES4 . The lack of association with a favorable outcome might be explained by the ability of F. nucleatum to inhibit T and NK cell killing of tumor cells by binding and activating the inhibitory receptors TIGIT45 and CEACAM1 (ref. 46) or by induction of IL-8-mediated myeloid activation47 . On the AC-ICAM246, we ran a multivariable elastic-net OS Cox regression model that selected 41 features (taxa) with a coefficient different to zero  1.6, high versus low abundance, FDR = 0.085). C5 was constituted by three taxa, including one MBR-high-risk genera and no MBR-low-risk genera. Overall, these results suggest that clinical outcome is influenced by microbiome diversity, which is captured by the MBR classifier. Consistently, a high α diversity was associated with a prolonged OS FDR < 0.05 for all the α diversity estimates , in our training cohort  was associated with a considerable (85%) reduction of risk of death Fig. . We confRuminococcus 2 (based on 16S data) or R. bromii presence (based on either PCR or WGS data)  with unique antigen processing and presentation capabilities for efficient antigen cross-presentation to CD8+ T cells  and CD103+ signature to CD103− signature ratio (P = 0.001)) , while the reverse was observed for taxa with a negative MBR coefficient  correlation with the MBR score was observed for signatures capturing the prevalence of CD103We then sought to develop a multi-omics parameter that could capture a subgroup of patients with exceptional survival.P < 0.05; Supplementary Table ).Among single-omics parameters that were significant in the univariate Cox regression OS analysis , only ICR and MBR were retained by the multivariable Cox models , the co-presence of ICR high and MBR low (mICRoScore high) identified a subgroup of patients with a 97% 5-year OS, with only three deaths detected at a later follow-up Fig.  that werH1 cell/cytotoxic immune activation, as captured by the ICR, immunoediting, concurrent expansion of TCR clonotypes and specific intratumoral microbiome composition, were associated with a favorable clinical outcome. ICR was associated with OS independently of MSI and CMS, which both lost statistical significance in the multivariate analysis. Its prognostic impact increased when combined with a metric capturing the genetic immunoediting (IES).Our multi-omics approach allowed us to thoroughly examine the molecular characteristics of immune responsiveness in colon cancer and uncover interactions between the microbiome and the immune system. We found that a TUsing deep TCR sequencing in tumor and healthy tissues, we showed that the prognostic effect of ICR could be due to its ability to capture the presence of tumor-enriched and possibly tumor-antigen specific, T cell clones.10 and corroborated by our comparative analyses. While several studies have described associations between response to immunotherapy and the gut microbiome50 and identified cancer-specific microbiome compositions51, comprehensive microbiome analyses focused on patients with primary colon cancer are lacking. By analyzing the tumor microbiome composition using 16S rRNA gene sequencing in AC-ICAM samples, we identified a microbiome signature (MBR risk score) with strong prognostic value. This signature was derived from tumor samples, but there was a strong correlation between the healthy colon and tumor MBR risk scores, suggesting that this signature may capture the patient’s gut microbiome composition.The AC-ICAM addressed the limitations of the TCGA colon cancer cohort noted by the scientific communityR. bromii. Correlation analyses between the MBR risk score and immune traits suggest a specific positive modulation of CD103+ dendritic cells, which are critical for antitumor immune responses. We speculate that the identified consortium of bacteria favors optimal T cell priming mediated by CD103+ dendritic cell activation and suppression of the myeloid compartment, leading to the induction of a partially protective antitumor immunity.Additional analysis and technical validation using orthogonal platforms such as WGS and PCR indicated that the detected signal was driven by By combining the ICR and MBR scores, we were able to identify and validate a multi-omics biomarker (mICRoScore) that could predict exceptionally long survival in patients with colon cancer.52, have shown favorable associations with Ruminococcus and or R. bromii and response55. Here, we propose the R. bromii as the possible link between prognostic and predictive microbiome-based signatures. Our findings support the testing of adjuvant microbiota-targeted/dietary interventions57 aimed at decreasing the risk of recurrence and death in patients with colon cancer through the induction of an antitumor response against minimal residual disease. These approaches might also be investigated in the context of neoadjuvant immunotherapy58.Studies on the gut microbiome compositions of patients receiving immunotherapy, including anti-CD19 CAR T cell treatmentR.bromii through the administration of castalagin , resulting in enhanced antitumor immunity, possibly mediated by boosting antigen presentation and T cell response59.For example, data from breast and sarcoma mouse models suggest that the gut microbiome can be enriched with Myrciaria dubia powder concomitantly with immune checkpoint inhibitors is currently being explored in patients with melanoma and non-small-cell lung cancer (NCT05303493).Administration of 60), it remains underpowered for stage-specific survival analysis. For the mICRoScore, we were unable to assess and quantify potential data overfitting as we did not reserve internal samples for this purpose; however, we observed a good performance of the mICRoScore in the external validation cohort (TCGA-COAD), which may be due to the combination of two biologically relevant variables (ICR and MBR) into the model. This combination likely contributed to the model’s impact and suggests that the mICRoScore might be generally applicable. We did not perform in situ spatial profiling, which could reveal more complex spatial immune–microbiome interactions61. Additional research is needed to confirm the validity of the mICRoScore and investigate its potential applications in clinical treatment decision-making. Both the mICRoScore and IES could be tested in the context of cancer immunotherapy as predictive biomarkers. Data from the NIBIT-M4 trial and publicly available datasets suggest that the combination of the genetic immunoediting and ICR (IES) has predictive value in melanoma patients treated with immune checkpoint inhibitors62. The quantification of the immunoediting using WES data is an emerging subject of research64. These scores might also be explored to define a subgroup of patients with stage III tumors that could be eligible for a reduced chemotherapy regimen.Our study has some notable limitations. While the cohort was relatively large and compares favorably with the TCGA-COAD colon cohort  are from patients with colon cancer diagnosed at Leiden University Medical Center, the Netherlands, from 2001 to 2015 that did not object for future use of human tissues for scientific research and that were consented on biospecimen protocol ‘Immunology and Genetic of colon Cancer’ approved by the Committee on Medical Ethics of Leiden University Medical Center (study protocol no. P00.193 (06/2001)). Snap-frozen tumor and healthy colon tissue were stored at −80°C until processing for DNA and RNA extraction. DNA and RNA from those samples were extracted at Leiden University Medical Center and then transferred to Sidra Medicine for sequencing together with de-identified clinicopathological data of the corresponding patients (Sidra Medicine IRB study protocols no. 1768087-1 (04/2016)/1602002725 (06/2022)). All genomic assays  were performed at Sidra Medicine.Patient information was de-identified and patient samples were anonymized and handled according to the medical guidelines described in the Code of Conduct for Proper Secondary Use of Human Tissue of The Federation of Dutch Medical Scientific Societies. This research was performed according to the recommendations outlined in the Helsinki Declaration.For each assay we included all samples that had sufficient material  available at the time of processing considering the need to preserve aliquots for additional/future assays.Snap-frozen tumor and healthy colon tissue were collected from patients with colon cancer who underwent surgical resection of the primary tumor between 2001 and 2015 at Leiden University Medical Center. Patients who received radiotherapy and/or chemotherapy before resection and patients with a primary tumor of non-epithelial origin were excluded. Based on tissue availability, successful nucleic acid extraction and subsequent sequencing quality control (QC), data from 348 patients were retained in the final AC-ICAM cohort , were calculated using R package survival (v.2.41–3). Cox proportional hazard analysis was only computed when both groups of comparison consisted of at least ten patients. Overall P value for comparison of survival between two or more groups was also calculated by log-rank test.Kaplan–Meier curves were generated using ggsurvplot from R package survminer (v.0.4.9). HRs between any two groups of interest and corresponding Multivariate Cox regression was performed using conventional clinical and biological variables, as explained in the specific section. Separate multivariate Cox regression analyses were run including age (continuous), pathological stage , MSI status (binary) and CMS . Additional variables that were found significant in univariate Cox proportional hazard regression analysis were added to these models. These variables included, ICR score (continuous) or ICR cluster , GIE (binary) and MBR group (binary). Forest plots were generated using ‘forestplot’ (v.1.7.2).Tumor and healthy tissue samples (unselected for tumor cell purity) were sectioned in a cryostat until the surface area was sufficient to assess tissue morphology by H&E staining. Non-target tissue was removed by macrodissection, including necrotic or adipose tissue and for tumor tissue samples, healthy colon tissue. When macrodissection was required, an H&E-stained slide was examined after this to confirm removal of unwanted tissue types. Frozen tissue was then sectioned at 20 µm until approximately ~10–15 mg was collected per sample. A final section post-sample processing was made for H&E staining. The collected tissue was stored at −80 °C for a few months until DNA and RNA extraction.QC metrics of RNA and DNA data were superimposable between samples collected over the years  was added to the lysis buffer on the day of use. Lysis was performed by completely submerging the sections in 350 µl lysis buffer. Tubes were rotated for at least 1 h at room temperature to allow complete homogenization. QIAcube AllPrep DNA/RNA Mini kit Standard (v.2) program was run, after which DNA and RNA samples were stored at −80 °C. The same DNA was used for human and microbiome sequencing. Samples were shipped from Leiden University Medical Centre (LUMC), The Netherlands to Sidra Medicine, Qatar under a temperature-controlled environment at −80 °C (for 4 d). Samples from 361 patients were sequenced by WES and RNA-seq. Samples from 13 patients were excluded as they did not pass QC, including concordance between healthy and tumor samples . Sequencing mRNA libraries were constructed from 500 ng of total RNA using the Illumina TruSeqStranded mRNA kit (Illumina). cDNA was synthesized using Superscript IV Reverse Transcriptase (Thermo Fisher) and amplified for 15 cycles after ligating with TruSeq RNA Combinatorial Dual-Index adapters. Clonal amplification and cluster generation was performed using Illumina’s cBot 2 System. Sequencing libraries were run on Illumina HiSeq platforms using 75 bp (93% of samples) or 150 bp (7% of samples) paired-end reads at the Clinical Genomics Laboratory, Sidra Medicine. We targeted a coverage of 20 M reads per sample. Obtained coverage was 18.4 M (s.d. 4.7 M).Data conversion and demultiplexing was performed using bcl2fastq2 conversion software (v.2.20). FastQC was run to perform QC checks on the raw sequence data . Trimming of adaptor sequences was performed using flexbar (v.3.0.3) using Illumina primers FASTA file. Subsequently, reads were aligned to reference genome GRCh38.93 by Hisat2 (v.2.1.0) using SAMtools (v.1.3). After alignment, QC was performed to verify quality of the alignment and paired-end mapping overlap . Finally, the featureCounts function of subreads (v.1.5.1) was used to count paired reads per genes. Gene expression normalization was performed within lanes, to correct for gene-specific effects (including GC content) and between lanes, to correct for sample-related differences (including sequencing depth) using R package EDASeq  (v.2.12.0). The resulting expression values were quantile normalized using R package preprocessCore (v.1.36.0). All downstream analysis of the expression data was performed using R .65. WES  had an on-target sequencing rate of 65–70%. The median (across samples) of the average target coverage (per sample) was 129× (interquartile range (IQR) 18) for tumor samples and 69× (IQR 10) for normal samples  on the FlexStation 3 Microplate reader (Molecular Devices). DNA of both tumor and matched normal samples was available for 294 patients. Whole-exome libraries were constructed with the Agilent SureSelect XT Target enrichment kit and the exonic DNA was captured using the Agilent SureSelect XT Human All Exon V6r2 capture library for 60-Mb exonic regions. Libraries were constructed using 250 ng of DNA and were sequenced on Illumina’s HiSeq 4000 platform using 150 bp paired-end reads (150PE) at the Genomics Core, Sidra Medicine. Reads were mapped to reference genome hs37d5 (1000 Genomes Phase2 Reference Genome Sequence) based on GRCh37/hg19 using BWA (v.0.7.12)11 and survival events OS and progression-free interval (relabeled here as PFS) were used. ICR clustering and calculation of ICR score was performed exactly as described for the AC-ICAM cohort. For the TCGA-COAD cohort, the optimal number of clusters for best segregation based on the Calinski–Harabasz criterion was three. CMS classification of TCGA-COAD samples was performed as described for the AC-ICAM cohort. The Single Sample Predictor by ‘CMSclassifier’ (v.1.0) was used for comparison of CMS classification between AC-ICAM and TCGA-COAD.RNA-seq data (raw counts) from TCGA were downloaded and processed using R package TCGAbiolinks (v.2.18.0). Gene symbols were converted to official HGNC gene symbols and genes without symbol or gene information were excluded. Normalization was performed within lanes, to correct for gene-specific effects (including GC content) and between lanes, to correct for sample-related differences (including sequencing depth) using R package EDASeq (v.2.12.0) and quantile normalized using preprocessCore (v.1.36.0). After normalization, samples were extracted to obtain a single primary tumor tissue (TP) sample per patient. Clinical data were sourced from the TCGA Pan-Cancer Clinical Data ResourceA renormalized matrix of both TCGA-COAD and AC-ICAM datasets was generated by merging the raw counts matrices and performing the EDASeq normalization, as described above, on this combined matrix. These data were used to calculate ssGSEA scores for deconvoluted immune cell subpopulations, immune signatures and oncogenic pathways, to compare between cohorts.Somatic mutation calls from the TCGA MC3 Project were downloaded using R package TCGAmutations (v.0.3.0) using the function tcga_load with parameters ‘COAD’ for study and ‘MC3’ for source. The downloaded Mutation Annotation Format (MAF) file contained 406 distinct TCGA tumor sample barcodes and 18,183 genes (Hugo Symbol). This file was filtered to only include nonsynonymous mutations , analogous to the variant filter applied to the AC-ICAM somatic mutation calls.13. TCGA-COAD relative abundance matrix was filtered to exclude duplicated samples . Overall, 81 genera were present with a nonzero abundance in at least one of the 117 samples (main matrix). When we applied the same filter as the one used for AC-ICAM 16S RNA gene-sequencing data (presence in at least 10% of the samples with at least 1% relative abundance in one sample), 27 taxa at the genus level were retained.Microbiome genus relative abundance matrix for TCGA-COAD cohort  was downloaded from The Cancer Microbiome Atlas website Somatic mutations in NHS and HPFS Colorectal Cancers were downloaded from the supplementary data of the Giannakis et al. study  genes used by two consortia to define germline genetic variations in pediatric cancers ); (2) genes with at least one pathogenic or likely pathogenic germline variants in the TCGA cohort (n = 99)66; (3) genes classified as driver genes according to the most updated TCGA analysis (n = 299)32; (4) genes included in the MSK-IMPACT (n = 505), MSK-IMPACT HEME (n = 575), Foundation One CDx (n = 324) and Foundation One Heme (n = 593) panels; (5) cancer genes cataloged as tier 1 by the Sanger Cancer Gene Census (n = 576); and (6) cancer genes defined as such by Vogelstein et al.67. Sources 4–6 were downloaded from OncoKB68. Original sources’ gene names were converted into Ensemble GRCh37 gene symbols. The final list included 1,219 unique cancer genes and is provided in the A cancer-related gene list was constructed from using different sources, as previously described:IFNG, IRF1, STAT1, IL12B, TBX21, CD8A, CD8B, CXCL9, CXCL10, CCL5, GZMB, GNLY, PRF1, GZMH, GZMA, CD274/PDL1, PDCD1, CTLA4, FOXP3 and IDO1)21, was applied to the normalized log2-transformed expression matrix using R package ConsensusClusterPlus (v.1.42.0)69 using 5,000 repeats, agglomerative hierarchical clustering with Ward criterion inner and complete outer linkage. The optimal number of clusters allowing for the best segregation of samples was based on the Calinski–Harabasz criterion. Optimal number of clusters used for segregation was three. Colon cancer samples in the cluster with the highest expression of ICR genes were designated as ‘ICR high’, the intermediate cluster as ‘ICR medium’ and the cluster with the lowest expression was designated ‘ICR low’. The mean log2-transformed expression value of the 20 ICR genes is referred to as the ICR score.Consensus clustering based on 20 a priori selected ICR genes (16. The obtained CMS labels (from the column ‘RF.predictedCMS’ in output dataframe) were used for all downstream analyses with the exception of the comparison of CMS subtypes between AC-ICAM and TCGA cohort. To allow between-cohort comparison, we ran the CMSclassifier using the ‘single-sample predictor’ method. This method makes it possible to predict unique samples, with a constant output whether the sample is predicted alone or within a series of samples16 and can therefore be used for comparison across cohorts.Samples were classified according to CMS by R package ‘CMSclassifier’ (v.1.0) using random forest methodt-SNE by ‘Rtsne’ (v.0.15) and visualized using ggplot2 (v.3.3.2). The t-SNE plot was annotated with distinct colors to visualize the distribution of samples of different CMS (using random forest method) in high-dimensional space. The same t-SNE plot was annotated by ICR cluster in a separate panel. A circos plot to visualize the relation between CMS and ICR classifications was generated using the chordDiagram function from R package ‘circlize’ (v.0.4.8).Dimension-reduction of the complete expression matrix was performed using 70 was performed to estimate relative abundancies of specific immune cell subsets from bulk transcriptome data. This method relies on integrated gene sets from multiple sources that have been curated and validated on a per-cancer-type basis, using benchmark datasets and seems to outperform previously published methods70. We applied ConsensusTME using R package ConsensusTME (v.0.0.1.9) using parameters ‘COAD’ to specify cancer type and ‘ssgsea’ as statistical method.Consensus tumor microenvironment cell estimation (ConsensusTME)The median of each ConsensusTME score was calculated per CMS stratified by ICR cluster and was displayed in a dotted heat map using R package ComplexHeatmap (v.2.1.2). The association of each ConsensusTME score with OS and PFS was calculated by Cox proportional hazard regression. HR and corresponding 95% CIs as are displayed as forest plots (forestplot v.2.0.1).To infer estimated levels of overall stromal and immune cell infiltration to the tumor, the ESTIMATE algorithm (v.1.0.13) was applied to the expression data in R. ESTIMATE was run for both TCGA-COAD dataset and the AC-ICAM cohort. The combined ESTIMATE score for both the stromal and immune signature was compared between cohorts and a box-plot was generated using ggplot2 (v.3.3.2).2-transformed, normalized gene expression matrix71 . Gene sets that reflect specific tumor-related pathways were selected from multiple sources as described in detail in Roelands et al.10 and Supplementary Source Data Table Single-sample gene set enrichment analysis (ssGSEA) was applied to the log72. This collection includes 68 gene signatures related to immunomodulatory signaling, including IFN signaling, TGF-β, wound healing (core serum response) and T cell/B cell response73. Gene expression values were median centered and gene symbols were mapped to EntrezIDs (org.Hs.eg.db_3.6.0). Signatures scores were then mean centered and their s.d. values were scaled to one. For all other immune traits, ssGSEA was applied. These included signatures for antigen-presenting machinery (APM1 and APM2) and angiogenesis and nine TCGA-based coexpression signatures (metagene attractors). This collection was supplemented with the tumor inflammation signature9 and two non-overlapping signatures of IFN-stimulated genes (ISGs), including IFNG hallmark gene set IFNG.GS and ISG resistance signature (ISG.RS)74, calculated using ssGSEA. Finally, the deconvoluted immune cell abundancies by ConsensusTME70 and ICR score10 were included among the immune traits. In total we used 103 immune traits (including ConsensusTME)  with hierarchical clustering. Co-clustering immune traits that formed distinct modules were visualized and labeled according to the immune traits’ enrichment. The clustering was compared to previously defined immune trait modules within a pan-cancer setting, by annotation of the correlation matrix with the previously defined clusters in Sayaman et al.We subsampled AC-ICAM hundreds of times in two ways, one was random, the other was on a subgroup of samples with an ESTIMATE distribution that approximates that of the TCGA-COAD. The function ‘approxfun’ in R was used to generate a function to approximate the density of ESTIMATE scores in TCGA-COAD. Cases were sampled from AC-ICAM using the ‘sample’ function in R with prob argument set to sample points with probability distribution of the TCGA-COAD. Each subsampled cohort consisted of 200 samples. The number of subsets in which the Cox proportional regression for ICR score was significant was compared between the two ways of subsampling, statistical significance was determined using a chi-squared test.This sensitive and specific dedicated assay requires high quantity of genomic DNA (>2 µg) and sample selection was exclusively based on DNA availability. TCR sequencing was performed using extracted DNA of 114 primary tissue samples and ten matched healthy colon tissues with sufficient DNA available.−1 using 3.840 μg of DNA as input per sample. The immunoSEQ assay from Adaptive Biotechnologies was used to amplify all possible variable, diversity and joining (VDJ) gene rearrangements of the TCRβ locus (TRB) using a multiplex PCR method. PCR and magnetic bead cleanup were performed according to manufacturer’s instructions. Recommended QC was performed after the first PCR and second PCR amplification steps by running the PCR product on an agarose gel. Purified second PCR amplification products were pooled and the library pool was quantified using Agilent Bioanalyzer 2100. Subsequently, pools were diluted to a concentration of 1 pM and sequenced on Illumina NextSeq 500/550 system with Mid Output kit (150 cycles) and Custom NextSeq Sequencing Primer  . Sequencing was performed using survey resolution (two replicates per sample). A sample manifest was created in immunoSEQ Analyzer and the raw sequencing data were uploaded to the Adaptive Biotechnologies cloud following the manufacturer’s instructions. Data were processed using the company’s proprietary pipeline. Number of total templates analyzed per sample ranged 1,906–95,834 . The average read coverage per sample ranged 11.4–80.6 (median 36.2).DNA samples were normalized to a concentration of 125 ng μlTRB gene). A high T cell clonality implies presence of expanded T cell clones.ImmunoSEQ sample-based output variables, as made available by the immunoSEQ Analyzer, include the total number of templates analyzed, number of productive templates, fraction productive templates, number of total rearrangements, number of productive rearrangements, productive clonality and the maximum productive frequency. Herein, the total number of templates reflects the total number of T cells analyzed, of which only the productive templates can produce a functional protein receptor (rearrangement in the sample are inframe and do not contain a stop codon). The total number of productive rearrangements is the total number of unique T cell clones and clonality is calculated by normalizing the productive entropy using the total number of productive rearrangements and subtracting the result from 1. Values for (productive) clonality range from 0 to 1, with values near 0 reflecting more polyclonal samples and values near 1 representing samples with just few predominant rearrangements dominating the observed T cell repertoire . Similarly, Pearson’s correlation coefficient was calculated between productive clonality and each of the 18,270 genes in the expression matrix. A volcano plot was used to visualize significant results (ggplot2). The top 50 genes with the highest correlation with TCR productive clonality were mapped to the Global Molecular Network and core network analysis was performed using Ingenuity Pathway Analysis software.n = 9) were evaluated and visualized by scatter-plots (ggplot2). Sequences with a productive frequency at least 32-fold higher in the tumor compared to the healthy colon tissue and a tumor productive frequency >0.1% were defined as tumor-enriched sequences, as previously implemented by Beausang et al.75. The fraction of tumor-enriched TCR sequences in the tumor was calculated by dividing the number of productive templates of tumor-enriched sequences by the total number of productive templates per tumor sample. Pearson’s correlation coefficient between the fraction tumor-enriched TCR sequences and ICR score was calculated.Data on all productive rearrangements per sample were exported from the immunoSEQ Analyzer Rearrangement Details View. This file includes the exact nucleotide sequence generated through V(D)J recombination, corresponding amino acid sequence, number of templates and productive frequency. Overlapping TCR sequences between tumor samples and matched healthy colon tissues , the TCR clonality was calculated as 1 − normalized Shannon entropy  for all samples, except seven cases for which MiXCR failed to identify clones.The software MiXCR (v.3.0.13)76. The OncoPlot function from ComplexHeatmap (v.2.1.2) was used to visualize the most frequent somatic mutations.SNVs were called using mutect (v.1.1.7) and somatic small insertions and deletions (indels) using strelka2 (bcbio-nextgen v.1.1.1). We applied an optimized variant filtering pipeline . For AC-ICAM, the filtered MAF for nonsynonymous mutations was used as input with specified capture size of 40.32 or colon oncogenic mediators38. First, we extracted genes with a nonsynonymous mutation frequency >5% in the AC-ICAM cohort. Subsequently, only genes that are likely involved in cancer development, as described in the section ‘Cancer-related gene annotation’, were retained. All artifact genes , were excluded. Genes that have previously been reported as colon cancer oncogenic mediator38 or cancer driver gene for colorectal cancer (COADREAD)32 were also excluded. Finally, only genes with a mutation frequency <5% in the NHS-HPFS colon cancer cohort37 and <5% in TCGA-COAD36 were maintained. As a final filter, only genes that had a nonsynonymous mutation frequency of at least twofold in AC-ICAM compared to TCGA-COAD were labeled as potentially new in colon cancer.To define mutated genes in the AC-ICAM that were not previously described in colon cancer, we performed a comparison of the most frequently mutated genes in AC-ICAM (>5% of the tumor samples) with frequencies detected in previously published datasets containing colon cancer samples (TCGA-COAD and NHS-HPFS) as well as reported cancer driver genes77. Briefly, a bed file suitable for use by MANTIS was created using RepeatFinder function of the MANTIS tool, to find microsatellites regions within the reference genome (GRCh37). MANTIS was then run for each tumor and matched normal BAM file pair using these detected microsatellite loci. The instability score between the two samples within the pair was used to classify samples either as MSI-H (MANTIS score > 0.4) or MSS (MANTIS score ≤ 0.4).We applied MANTIS (v.1.0.4), a tool for rapid detection of microsatellite instability on our WES datan = 281) as well as within hypermutated (n = 69) and non-hypermutated (n = 212) subgroups separately. The estimate and P value were extracted for each gene and FDR was calculated using the Benjamini–Hochberg method. Significant genes with an FDR < 0.1 and that were mutated in at least five patients in the analysis subgroup were plotted as OncoPrints .We investigated the association of specific somatic alterations, including SNVs and small insertions or deletions (indels) and ICR immune phenotype. Binomial linear regression models were fitted to define which specific mutations associate with ICR score using the glm function with family ‘binomial’ (R). This analysis was performed in the total cohort  genes were compared between hypermutated cases of mucinous histology with hypermutated cases with other histological classifications. An unpaired Student’s t-test was used to compare ICR score between hypermutated cases of mucinous histology with hypermutated cases with other histological classifications.Genes with an inverse association with ICR score within hypermutated colon cancer included genes involved in homologous recombination repair. The frequency of mutations in either of the identified genes . We have used a pipeline using GATK (gatk-package-4. beta.6) to generate each tumor sample’s segmentation file. We performed the below steps:2-transformed segmentation file was loaded in IGV with reference genome GRCh37, including an annotation text file including mutational load category , POLE mutation status, ICR cluster, CMS and MSI status. The samples were ordered consecutively by MSI status, CMS, ICR, POLE and mutational load category. Prevalence of amplification and deletions was visually inspected and compared between groups.We explored the prevalence of SCNAs among ICR clusters and hypermutated and non-hypermutated subgroups by exploration of the segmentation file in IGV. Briefly, the log78. Neoantigen prediction tool pVACseq from pVACtools was run using the following predictors: MHCnuggetsI, NNalign, NetMHC, SMM, SMMPMBEC and SMMalign. The obtained vcfs from our somatic mutation calling pipeline were used as input for pVACseq, along with the predicted HLA type from WES data. Gene expression data aligned to GRCh37 in transcripts per million was annotated to the vcfs using vcf-expression-annotator. Mutant-specific binders, relevant to the restricted HLA-I allele, are referred to as neoantigens, as described in detail by Zhang et al.79. Mutated epitopes with a median IC50 binding affinity across all prediction algorithms used <500 nM, with a corresponding wild-type epitope with a median IC50 binding affinity > 500 nM, were used as criteria to infer neoantigens. Predicted neoantigens were used to calculate the GIE value. We calculated the GIE value by taking the ratio between the number of observed versus the number of expected neoantigens. The expected number of neoantigens was based on the assumption of a linearity between TMB and the number of neoantigens. We therefore assumed that samples that have a lower frequency of neoantigens than expected , display evidence of immunoediting. A higher frequency of neoantigens than expected indicates a lack of immunoediting, see calculations section for details.HLA typing was performed on both WES and RNA-seq data using OptiType (bcbio-nextgen v.1.1.5 in Python v.2.7.0)The IES is a composite score based on both ICR and GIE. Tumors of IES4 are those predicted to be the most immune active, as they are ICR high and display GIE. Tumors of IES1 are expected to be most immune silent, classified both as ICR low and an absence of GIE. Tumors of the intermediate groups IES2 and IES3 reflect ICR-low and GIE and ICR-high and non-GIE tumors, respectively. Mutational load category, MSI status and pathological stage distribution was compared between IES groups using a chi-squared test. The OS was compared between patients with different IES and between GIE and non-GIE tumors in the ICR-medium group using Cox proportional hazard regression analysis. A Cox proportional hazard’s multivariate model was fitted with IES  and pathological stage .The Spearman correlation between IES as ordinal variable and TCR clonality from immunoSEQ as well as MiXCR-based clonality was calculated. We performed several additional analyses to assess whether the relationship between TCR productive clonality and IES was driven by ICR. Multiple regression analysis was performed with ICR score and immunoSEQ TCR clonality as continuous variables to predict productive TCR clonality (immunoSEQ). Second, the data were modeled through local polynomial regression fitting of the productive TCR clonality (immunoSEQ) by IES category .This study complies with the STORM reporting guidelines; the completed checklist can be found in Supplementary Table The 16S rRNA gene sequencing was performed at the Host–microbe Interaction Laboratory, Sidra Medicine.Hypervariable regions V3–V4 of 16S rRNA gene were amplified with PCR using the amplicon primers with Illumina adaptors (underlined):Forward:5′TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG′3Reverse:GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC′3.5′In brief, PCR was performed in a 25-μl reaction mixture containing 5 μl each forward and reverse primer (1 μM), 2.5 μl template DNA for the samples and 12.5 μl 1× Hot Master Mix (Phusion Hot Start Master Mix). No human DNA depletion was used. The amplifications were performed on a Veriti 96-well Thermal Cycler (Thermo Scientific) with the following program: initial denaturation at 95 °C for 2 min, followed by 30 cycles of denaturation at 95 °C for 30 s, primer annealing at 60 °C for 30 s and extension at 72 °C for 30 s, with a final elongation at 72 °C for 5 min. The presence of PCR products was confirmed by electrophoresis in a 1.5% agarose gel conducted at 80 V/cm in Tris-borate–EDTA (TBE) buffer. Amplicons were then purified using AgenCourt AMPure XP magnetic beads (Beckman Coulter) according to the Illumina MiSeq 16S Metagenomic Sequencing Library Preparation protocol. As positive controls, we included DNA from stool samples (extracted with QIAGEN QIAmp Fast DNA Stool Mini kit), using the same input of DNA as the one used for the AC-ICAM samples. We obtained similar 16S rRNA amplicon PCR products across the tissue samples and the positive controls, indicating that the DNA extraction protocol used resulted in enough recovery of the microbial DNA from our specimens.Samples were multiplexed using a dual-index approach with the Nextera XT Index kit (Illumina) according to the manufacturer’s instructions. The concentration of amplicons was determined using the Qubit HS dsDNA assay kit  followed by pooling to achieve an equimolar library concentration. The final pooled product was paired-end sequenced at 2 × 300 bp using a MiSeq Reagent kit v3 on Illumina MiSeq platform (Illumina) at the Sidra Medicine research facility. Sequencing was also performed on 27 empty wells across plates to exclude the occurrence of large-scale cross-contamination among samples during sequencing procedures: the minimum and maximum read counts were 2 and 234, respectively and the average and median reads counts were 37 and 18, respectively. No negative controls for sampling or DNA extractions were included. Samples were aliquoted randomly in the plate.Sequenced data were demultiplexed using MiSeq Control Software. The overall quality of sequencing quality was evaluated using FastQC and the demultiplexed sequencing data were imported into Quantitative Insights into Microbial Ecology  software package. The data were denoised with DADA2, which includes a multi-step process, including read filtering, dereplication and chimera removal. Paired 250-bp reads were trimmed of the initial five low-quality bases and further processed to generate the amplicon sequence variant, interchangeably called operational taxonomic units (OTUs). The data were subsampled at a depth of 22,704 and then normalized using the rarefaction on OTUs count at even depth. Taxonomic classification was performed utilizing 16S rRNA gene database from Silva classifier (silva-132-99-515-806-nb-classifier). The data were imported into R in a Biological Observation Matrix (biom) format, before further evaluation with Phyloseq (v.1.34.0). The 16S rRNA gene sequencing was performed on all samples with sufficient DNA available: 246 tumor samples and 246 matched healthy colon tissues from the same patients (AC-ICAM246) and on additional 42 tumor samples (ICAM42) for which there was no sufficient DNA available from the healthy colon counterpart.The minimum and maximum read counts were 25,868 and 351,069, respectively. The average and median reads counts were 82,506 and 75,668, respectively. No samples were excluded from the analysis.Alpha diversity (within sample community) was assessed by observed OTUs (sum of unique OTUs per sample), Chao1 (Chao 1987) an abundance-based richness estimator that is sensitive to rare OTUs, Shannon (Shannon 1948) and inverse Simpson (InvSimpson) (Simpson 1949), the last one being more dependent on highly abundant OTUs and less sensitive to rare OTUs. Indices were read into R using R package vegan (v.2.5–6).Relative abundance of distinct microbiome elements was determined using the transform_sample_counts function from Phyloseq, such that sum of all abundance values per sample is equal to one  x / sum(x))). OTU tables were aggregated by taxonomic ranks including phylum (26 unique phyla), class (48 classes), order (97 orders), family (207 families), genus (562 genera) and species (846 species). As the confidence for annotation of reads decreases with decreasing rank, some reads were only annotated with higher ranks.80). MetaPhlAn2 uses a library of unique clade-specific marker genes to estimate bacterial relative abundance at the species level. The program was run with default parameters except analysis type set to relative abundance and restricted to bacterial organisms only. WGS was targeted to achieve >60× coverage per sample. The median (across samples) of the average target coverage (per sample) was 76× (range of 50–92).Library construction and sequencing was performed at the Sidra Clinical Genomics Laboratory Sequencing Facility. DNA was quantified using the Quant-iT dsDNA Assay (Invitrogen) on the FlexStation 3 (Molecular Devices). The library was constructed from 250 ng of DNA with the Illumina TruSeq DNA Nano kit. Library quality and concentration was assessed using the DNA 1k assay on a PerkinElmer GX2 and qPCR using the KAPA Library quantification kit on a Roche LightCycler 480 II. Genomic libraries were sequenced with paired-end 150 bp on HiSeq X (32% of samples) and Novaseq 6000 (68% of samples) systems (Illumina) following the manufacturer’s recommended protocol to achieve a minimum average coverage 60× for tumor samples. Quality passed reads were aligned to the human reference genome GRCh38 using BWA. Human sequencing reads were removed and unaligned nonhost reads were extracted using SAMtools. Low-quality unaligned reads were trimmed and samples were processed for taxonomic profiling using MetaPhlAn2 , 1.5 × 108  were aligned to bacteria. A total of 132 taxa, at genus level were detected, of which 3 were excluded as possible contaminants 12 (main matrix). When we applied the same filter as the one used for 16S RNA gene-sequencing data (presence in at least 10% of the samples with at least 1% relative abundance in one sample), 54 taxa at the genus level were retained. WGS was performed in all samples with sufficient DNA available (n = 167).Of 3.2 × 1081 using R. bromii 16S rDNA forward primer (GAAGTAGAGATACATTAGGTG) and R. bromii 16S rDNA reverse primer (ACGAGGTTGGACTACTGA). PCR was performed using AmpliTaq Gold 360 Master Mix , 20 ng of sample DNA and 5 nM of each primer. The amplification conditions were one cycle of 95 °C for 10 min, then 35 cycles of 95 °C for 30 s, 50 °C for 30 s and 72 °C for 30 s and finally one cycle of 72 °C for 7 min before storing at 4 °C. PCR products (10 μl each) were separated by electrophoresis in 2% agarose gels  containing ethidium bromide (1 μg ml−1)  using a 100-bp DNA ladder  for size verification. PCR band intensity was defined as negative when intensity was absent or extremely faint. PCR was considered positive if band was gradually more intense (graded from 2 to 4). PCR was performed in all samples from the AC-ICAM246 cohort with sufficient amounts of DNA available (n = 126).PCR was performed based on Wang et al.On tumor samples, microbiome genera were filtered to include genera which are present in at least 10% of the samples with at least 1% relative abundance in one sample; 138 out of 562 were retained. These included 137 genera and the genus labeled ‘unknown’ that reflects all reads for taxa with insufficient confidence at the genus level. The same filtering was applied to normal samples; 129 genera were retained. A total of 120 genera overlapped between normal and tumor samples, 9 genera were unique in normal samples and 18 genera were unique in tumor samples.This set of filtered genera were used for all downstream analysis except for the comparison between tumor and normal pairs. For this analysis we include any genera that passed the filtering approach described above for either normal or tumor groups .82. This list has previously been curated and annotated by Poore et al.12 by manual review of the literature. This curation allowed the discrimination of taxa that are ‘likely contaminants’, ‘potentially pathological or commensal genera’ and ‘mixed evidence’ genera that have been described both as pathogens as well as contaminants. We flagged those taxa that were ‘likely contaminants’ as well as ‘mixed evidence’ for potential exclusion from our 16S rRNA gene-sequencing microbiome abundance matrix.To remove putative contaminants from the 16S rRNA gene-sequencing data, we used a list of microbial taxa that are typically found in negative blank reagents, as described by Salter et al.n = 33) were detected in only fewer than 20 (out of 492) samples. Potential contaminating bacteria that we detected in the highest numbers of samples were Oxalobacter in 39 samples and Micrococcus in 28 samples. Detected putative contaminants and taxa with mixed evidence from the 16S rRNA-sequencing data were removed when we applied the minimal abundance filter (presence in at least 10% of the samples with at least 1% relative abundance in one sample).In total, we detected 25 taxa that were ‘likely contaminants’ and 10 taxa with ‘mixed evidence’ in at least one out of the 492 samples. To evaluate the extent of potential contamination by these 35 taxa, we calculated the sum of these taxa for each sample. On average, only 0.04732% of the total microbial abundance per sample consisted of ‘flagged’ taxa . Furthermore, most of these putative contaminant taxa  was used to determine microbial phyla/genera with significantly different relative abundance between tumor and paired normal samples. FDR was calculated using the Benjamini–Hochberg method. Results were visualized in volcano plots.A paired Mann–Whitney U-test (two-sided) was used to calculate which filtered genera (n = 138) were differentially abundant between ICR-high and ICR-low samples. FDR was calculated using the Benjamini–Hochberg method. Results were visualized in volcano plots.An unpaired Mann–Whitney We performed co-abundance analysis in tumor samples from the AC-ICAM246 cohort. Co-abundance analysis, which involves studying the presence of multiple components within a composition, can be difficult to perform accurately when using relative abundance. This is because the relative abundance of the different components is constrained to sum to 1, which can lead to the appearance of false correlations. To address this issue, techniques such as co-abundance network inference can be used to more accurately infer relationships between the components.83 was used to calculate the co-occurrences between the 137 remaining taxa using centered log-ratio (clr)-transformed OTU counts in tumor samples . A total of 500 inference and 10 exclusion iterations were used to estimate the median correlation of each pairwise. The statistical significance of the correlations was calculated using a bootstrapping procedure to generate 500 simulated data83. For each component pair, pseudo P values (two-sided) were assigned as the proportion of simulated bootstrapped data with a correlation at least as extreme as the one computed for the original data. Benjamini–Hochberg FDR was used for multiple testing correction. All the correlations were then sorted using a statistically significant cutoff (FDR < 0.05) and SparCC correlation coefficient > ±0.3. Clusters among the networks (groups of at least three correlated genera using the cutoffs specified above) were defined via a fast greedy clustering algorithm. All co-occurrence networks were made using the R package ‘NetCoMI (v.1.1.0) – Network Construction and Comparison for Microbiome Data’84 and visualized using Cytoscape (v.3.9.1).Before co-occurrence analysis, the genus labeled ‘unknown’ was excluded. SparCCWithin each cluster, the total relative abundance was calculated by summing up the relative abundance values for genera that positively correlated with each other. For each of the identified clusters, survival analysis was performed by binarizing each sample into high and low abundance based on the median total relative abundance of each cluster.z score using mean and s.d. and treating the normalized abundance matrix as the training set. We built a relaxed multivariable elastic-net OS Cox regression model using the glmnet R package (v.4.1.4) on the training set. The optimal hyper parameters (γ and λ) for the best model were identified through fivefold cross-validation via a grid-search technique using the ‘cv.glmnet’ function. We used the concordance index as a performance metric. The parameters for which the mean cross-validation concordance index was the highest were selected as optimal hyper parameters. Next, the final model was built using these hyper parameters on the complete training set. To calculate risk scores in the training dataset (MBR scores), we passed the training set and best model to the ‘predict’ function. A total of 41 features (genera) were present in the best model with nonzero coefficients; we refer to these features as the ‘MBR classifier’, which represents the final model. A positive or negative coefficient of each genus of the MBR classifier can be binarized into ‘high-risk’ and ‘low-risk’ groups using the cutoff threshold of 0 and attributed to the strength of association with survival. A higher positive coefficient means high hazard risk of death, whereas a negative coefficient corresponds to lower risk of death.We first normalized the genus abundance matrix by converting each genus column into a z scores using the mean and s.d. derived from the training set. These abundance matrices were passed to the ‘predict’ function along with the best model to estimate corresponding risk scores. The risk score (MBR score) of any tested sample is only dependent on the relative abundance of the list of genera that overlap with the ones included in the MBR classifier (the risk score for each sample is not dependent on one of the other samples). Finally, the MBR scores are binarized using the cutoff threshold 0 to categorize the test sample into ‘high-risk’ (>0) and ‘low-risk’ (<0) groups as performed in the training set. Therefore, no cutoff optimization occurred in the validation phase.We validated the final model on two datasets. Both datasets consist of samples that were not used for model training (unseen data). One is an independent internal (ICAM42) dataset, referred to as testing cohort 1 and the other is an external cohort (TCGA-COAD), referred to as testing cohort 2. The ICAM42 consists of 42 samples and TCGA-COAD consists of 117 samples. We processed the two datasets to convert the abundance values for each genus into We tested the concordance index (1) in the training set using the final MBR model; (2) in the training set using the cross-validation of the best MBR model ; and (3) in each test set cohort separately (ICAM42 and TCGA-COAD) and in the full test set (ICAM42 and TCGA-COAD combined) using the final MBR model.To calculate the MBR score in each additional dataset we used taxa that overlapped with the 41 genera of the MBR classifier, which was developed using 16S rRNA gene sequencing on tumor samples.There were 16 of 41 taxa in the TCGA-COAD (WGS data) and 18 of 41 taxa in the AC-ICAM WGS data (tumor sample) main matrices. All the 41 taxa were available in the ICAM42 cohort (tumor samples) and the MBR score for AC-ICAM healthy colon tissue samples was based on 36 genera that passed the applied genus-level filtering for healthy tissue  and the 16S Ruminococcus 2 (16S) relative abundance. In addition, the proportion of WGS reads that mapped to each specific Ruminococcus species was calculated as fraction of all WGS reads that were assigned to the Ruminococcus genus.We characterized the specific species underlying the reads supporting the R. bromii, we performed a PCR specific to R. bromii on the 126 AC-ICAM tumor samples for which sufficient DNA was still available . The concordance between detection of R. bromii in PCR and 16S Ruminococcus 2 was defined as the percentage of samples for which both methods had identical results. The discordant cases were further examined by evaluation of WGS results. Furthermore, the concordance between detection of R. bromii in PCR and R. bromii in WGS was assessed in the 86 samples for which data from both methods were available.To confirm the presence of In view of the individual contribution of analytes extrapolated by individual platforms such as the ICR (RNA-seq data), the GIE (WES data) and the MBR scores (16S data) and TCR clonality (immunoSEQ and MiXCR) we sought to develop a multi-omics parameter that could capture a subgroup of patients with exceptional survival.Each parameter that was significant in the univariate Cox regression analysis , was assessed within a multivariable Cox regression model adjusted for age (as continuous variable), CMS subtypes , stage  and MSI status . The parameters that were retained by the multivariable Cox models were combined into an integrated score. For univariate analysis we used RNA-seq, WES and TCR clonality data from the entire AC-ICAM cohort and MBR score derived from 16S rRNA gene-sequencing data of the AC-ICAM246 cohort.The mICRoScore reflects the co-presence of ICR high and MBR low risk, defined as mICRoScore high. On the AC-ICAM246 (training set), all samples with MBR-high risk and/or in ICR-medium or ICR-low group are defined as mICRoScore low. The survival between patients with mICRoScore high and mICRoScore low was compared using Cox proportional hazard regression analysis and a log-rank test.10) and RNA-seq data available (used for ICR estimation). ICR assignments from this cohort (see section TCGA data) were combined with the MBR classification to classify patients as mICRoScore high and mICRoScore low. The survival between patients with mICRoScore high and mICRoScore low was compared using a log-rank test.We used data from TCGA-COAD as external validation cohort to test the mICRoScore (testing set). The TCGA-COAD cohort includes 107 patients with both tumor microbiome data  and empirical methods have been used by many consortia. Correlation between ICR and survival was declared as a primary objective in the research proposal submitted to the funding agency before any genomic data were generated, representing therefore a prospective–retrospective validation (JSREP07-010-3-005).n = 400), which compared favorably with the sample size of similar studies in the field.In the submitted proposal 2015), we planned to profile 400 tumors for gene expression analysis , for the comparison between ICR high versus ICR low, with 77 OS events detected, our study has a power >80% for an HR of 0.5 with a two-sided α of 0.05. With 154 OS events in the whole cohort, our study has a power of 90% for an HR of 0.59  and a power of 90% for an HR of 0.57  with a two-sided α of 0.05.H) is calculated by a standard Shannon entropy calculation with log base 2. Clonality is the inverse of the normalized entropy calculation. The equations are below:Entropy  sequence x,H) is calculated by a standard Shannon entropy calculation with log base 2. The equations are below:Entropy  calculation:Clonality is calculated as the inverse of the normalized entropy  versus the expected (E) number of neoantigens:in which E is a function of the number of nonsynonymous mutations in that specific sample (x):Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41591-023-02324-5.Supplementary InformationSupplementary Figs. 1–14, Supplementary Table 1 and Supplementary Table 12 (STORMS checklist).Reporting SummarySupplementary Tables 2–11All Supplementary Tables merged into a single Excel file, including separate sheets.Supplementary Source DataData are used across main figures and Extended Data Figs. 1–10 and Supplementary Figs. 1–12. A complete list of all the Source Data is available on sheet 1 of the Supplementary Data workbook, followed by a source data figure location in sheet 2.


Batch effects are notoriously common technical variations in multiomics data and may result in misleading outcomes if uncorrected or over-corrected. A plethora of batch-effect correction algorithms are proposed to facilitate data integration. However, their respective advantages and limitations are not adequately assessed in terms of omics types, the performance metrics, and the application scenarios.As part of the Quartet Project for quality control and data integration of multiomics profiling, we comprehensively assess the performance of seven batch effect correction algorithms based on different performance metrics of clinical relevance, i.e., the accuracy of identifying differentially expressed features, the robustness of predictive models, and the ability of accurately clustering cross-batch samples into their own donors. The ratio-based method, i.e., by scaling absolute feature values of study samples relative to those of concurrently profiled reference material(s), is found to be much more effective and broadly applicable than others, especially when batch effects are completely confounded with biological factors of study interests. We further provide practical guidelines for implementing the ratio based approach in increasingly large-scale multiomics studies.Multiomics measurements are prone to batch effects, which can be effectively corrected using ratio-based scaling of the multiomics data. Our study lays the foundation for eliminating batch effects at a ratio scale.The online version contains supplementary material available at 10.1186/s13059-023-03047-z. Batch effects are notorious technical variations irrelevant to study factors of interests, but are common in transcriptomics –5, proteBatch effects can have a profound negative impact on study outcomes , 17. On Although many batch-effect correction algorithms (BECAs) have been proposed , 26–29, The nature of the datasets used for performance comparison of the BECAs in previous studies is insufficient to determine the actual cause of batch effects. For example, several studies were based on different biological samples , 39, whiMoreover, the levels of confounding between biological and batch factors may greatly influence the validity of BECAs. In a balanced scenario where samples across biological groups of interest are evenly distributed across batch factors, batch effects can be mitigated via diverse BECAs , 34, 40.Here, as part of the Quartet Project for quality control and data integration of multiomics profiling , we compe.g., one chosen Quartet multiomics reference material) along with the study samples in each batch. Expression profiles of each sample can be transformed to ratio-based values using expression data of the reference sample(s) as the denominator, whether in balanced or confounded scenarios , ComBat , Harmonyi.e., similar trend of performance was observed in transcriptomics, proteomics, and metabolomics.We first applied PCA scatter plots to visualize the magnitude of biological and (or) batch effects Fig. a-c. In tThe performance metric of SNR based on PCA was then used to quantify differences between biological sample groups and variations in technical replicates. SNR measures the ability of distinguishing intrinsic biological differences among distinct donors  from technical variations including batch effects of the same donor (“noise”), as mentioned in the accompanying papers , 43. GenAs identifying DEFs is one of the most important tasks for quantitative omics, we compared performance in DEF identification across batches, using the reference FCs and DEFs as the “ground truth” for benchmarking Fig. : Fig. S3i.e., the Pearson correlation coefficient between the FCs of a test dataset for a given pair of donors and the corresponding reference FCs. In addition, we used the “MCC of DEFs” metric, i.e., MCC to measure the consistency of DEFs detected from a test dataset for a given pair of donors with those from the reference DEFs  and MCC of DEFs. Specifically, we introduced the RC metric, EFs Fig. a. Intra-p < 0.001)  Fig. b. Moreov01) Fig. b. Using 01) Fig. c. Indeedi.e., developing a prediction model based on data from one batch and validating its performance based on data from another batch, is another important task in quantitative omics, especially in the context of biomarker discovery for clinical diagnosis, prognosis, and therapeutic action. Thus, we evaluated the impact of BECAs on cross-batch prediction performance.Cross-batch prediction, Frequently, a predictive model was built using some dataset(s), and was further validated using independent dataset(s) , 4. ThesBased on multiple evaluation metrics, we found that under the balanced scenario, the prediction performance of a machine learning method was equally good with or without BECAs, and there were no differences in performance among the BECAs Fig. : Fig. S6As clustering multiomics data has the potential to identify disease subtypes and to reveal systems level insights, it has become one of the most popular applications in integrative analysis. Hence, we further compared performance of these BECAs in terms of ability to accurately clustering cross-batch libraries into their donors  after multiomics data integration. Datasets consisting 36 libraries of three donors derived from 12 batches were randomly selected from the entire dataset in balanced and confounded scenarios  on a feature-by-feature basis and can effectively mitigate the widespread problems of batch effects, in transcriptomics, proteomics, and metabolomics datasets, especially in cases when batch effects are highly confounded with biological factors of interests.I) can be expressed as I = f(C), where C is the concentration or abundance of an analyte and f is assumed to be a linear and fixed relationship between I and C under any experimental conditions [f is fixed or comparable under the same experiment (batch). However, f can vary across batches, due to large variations in experimental design, lab conditions, reagent lots, operators, and other non-biological factors. Hence, the measured value of a gene in batch 1 (I1) and batch 2 (I2) may be largely different and less reproducible. On the contrary, when a common reference sample (r) is profiled along with study samples in the same experiment (batch), ratio-based scaling can be applied and f becomes irrelevant for the ratio data. Thus, the resulting ratio of Is/Ir from each batch will remain reproducible and accurately reflect the ratio of Cs/Cr.Moreover, the effectiveness and broad applicability of the ratio-based method can be explained, at least partially, by the fundamental principles and assumptions behind data representation of omics measurements, as was described in Zheng et al. . Brieflynditions . It shouWe prefer the ratio-based method for three reasons. First, the ratio-based method is easy to implement, platform-independent, and applicable to multiomics quantification, including transcriptomics, proteomics, and metabolomics. Secondly, compared to ComBat or BMC, the ratio-based scaling is less affected by study design of unbalanced distributions of samples in different sample groups between different batches. In clinical applications and large-scale projects, the imbalance of samples across different batches is inevitable. Thirdly, SVA is designed to remove all unwanted sources of variation and retain differences between the specified primary variables  . This maAccording to our results, using two or three replicates of common reference material(s) in each batch and converting expression data to feature-wise ratio-based scaling profiles within each batch can play an important role in making expression levels inherently more comparable and hence resistant to batch effects. As the Quartet multiomics reference materials and the corresponding reference datasets have been successfully developed in our accompanying work –45, whic. [. [Based on the experimental design composed of specific batches of samples representing balanced or confounded distributions of biological groups, we used standard analysis types such as sample clustering, differential analysis and predictive modelling to demonstrate comprehensive evaluations of some widely-used BECAs. Several findings in this study are consistent while some are controversial with previous reports. For example, Luo et al.  focused . [.  demonstr. [.  simulateWhile the ratio-based method performed favorably in both balanced and confounded scenarios, it is not free of limitations. First, some features cannot be successfully corrected, for example, features that are not expressed in the reference material, including cell-specific features in numerator or male-specific feature when using a female material as the dominator. However, ratio-based scaling can successfully mitigate batch effect from numerous features when expressed in both the study samples (numerator) and reference material(s). Secondly, a reference-based method is possible when the introduction of a reference sample can be decided as part of the experimental design. It is not applicable when combining already existing dataset as the reference sample may not exist or be possible. Thirdly, applying ratio-based scaling needs to include reference samples in each batch, which means additional cost even if limited. Take RNAseq as an example, users can apply two or three samples/replicates per batch of 96 libraries for ratio-based expression profiling, resulting in a reasonable additional cost of 3.2% (3/(96–3)). Additionally, when well-established reference materials are used, such as Quartet multiomics reference materials, users can monitor batch quality based on pre-defined ‘ground truth’ in reference materials for proficiency testing and quality control, which can take full use of reference samples. Fourthly, the performance of the ratio-based scaling can be affected by the quality of the reference sample. If the quality of the reference sample is not good, the power of ratio-based method will be compromised. One possible solution is to use multiple  replicates of reference samples and take the averaged value as the denominator. Finally, the ratio method assumes that there is no interaction between batch and the reference sample, that is, the reference sample is invariant across batches or times. Here, considering the concurrent availability of large amounts of the Quartet multiomics reference materials with demonstrated short- and long-term stability, we therefore recommend using the Quartet multiomics reference materials for monitoring and correcting batch effects. The limitations and caveats of our reference materials and the ratio approach warrant further investigations.Furthermore, our study bears some limitations. First, the number of samples used for developing predictive models was small and the biological endpoints (sex and age) were relatively easy to predict. For example, the MAQC/SEQC Consortia comprehensively assessed the prediction performances of classification models from multiple analysis teams based on microarray and RNAseq datasets, and found that sex was the easiest to predict MCC: 0.839 ~ 0.973) compared to clinically relevant endpoints (MCC: 0.129 ~ 0.748) 9 ~ 0.973. AdditioIn summary, multiomics measurements are prone to batch effects, which, fortunately, can be effectively corrected by using ratio-based scaling of the multiomics data. Profiling common reference materials concurrently with study samples can enhance data comparability of multi-batch studies, especially for large-scale multiomics studies, helping the discovery and validation of omics-based biomarkers for precision medicine.Quartet multiomics reference materials were derived from the same batch of immortalized Epstein-Barr Virus (EBV) infected B-lymphoblastoid cell lines (LCLs) from a four-member Chinese Quartet family, including two monozygotic twin daughters (D5 and D6), their father (F7), and their mother (M8). Cell line authentication was conducted and described by Zheng et al. . BrieflyMultiomics reference materials, including DNA, RNA, protein, and metabolite, were established from the same batch of cultured cells. Large amounts of the reference materials with demonstrated short- and long-term stability were obtained, providing material basis for the batch monitoring and correction. More information of reference materials was detailed in the accompanying papers of Quartet Project, including the overall study , DNA 4242, RNA , proteinImportantly, Quartet DNA and RNA reference materials have been certified by China’s State Administration for Market Regulation as the First Class of National Reference Materials and are extensively being utilized for proficiency testing and method validation. The certified reference material numbers are GBW09900 (DNA of F7), GBW09901 (DNA of M8), GBW09902 (DNA of D5), GBW09903 (DNA of D6), GBW09904 (RNA of F7), GBW09905 (RNA of M8), GBW09906 (RNA of D5), and GBW09907 (RNA of D6).Reference materials were then distributed to multiple labs for generating multiomics profiling data. According to the Quartet Project study design, in each omics type, 12 samples were used as a standard sample set, consisting of 12 tubes with each representing one of the triplicates of a donor . The highttp://chinese-quartet.org/) and described in an accompanying paper by Yang et al. [Finally, a large quantities of multiomics datasets were obtained, comprising of 252 RNAseq profiles from 21 batches , 384 LC–g et al. .Here, we provide a brief description of data generation, analysis, and normalization of transcriptomics, proteomics, and metabolomics data. Detailed description can be found in accompanying papers –46.Transcriptomics datasets from the Quartet RNA reference materials were collected, consisting of 252 RNAseq libraries from 21 batches generated in eight labs using two library construction protocols (poly(A) selection and RiboZero) and two sequencing platforms (Illumina NovaSeq and MGI DNBSEQ-T7). Detailed information was described in the accompanying RNA paper .https://genome-idx.s3.amazonaws.com/hisat/grch38_snptran.tar.gz) and gene model from Ensembl (http://ftp.ensembl.org/pub/release-93/gtf/homo_sapiens/Homo_sapiens.GRCh38.93.gtf.gz) were used for read mapping and gene quantification. The read count and normalized data in Fragments Per Kilobase of transcript per Million mapped reads (FPKM) were obtained. A floor value of 0.01 was added to the FPKM value of each gene, and log2 transformation was then conducted.RNAseq reads were aligned using HISAT2 and genes were quantified using StringTie followed by Ballgown . ReferenTwo batches of Quartet protein reference materials, in the form of dried tryptic peptide mixtures, were generated from the same batch of cultured cells. In this study, proteomics datasets from the first batch were collected, including 312 LC–MS/MS based profiling under a data-dependent acquisition mode (DDA) using different platforms and instruments at different labs. Detailed information was described in the accompanying protein paper , 44. Thernorm function with a mean of zero and a standard deviation of 0.01. A floor value of 0.01 was then added to the value of each protein, and log2 transformation was conducted.MS raw files were searched against the National Center for Biotechnology Information’s (NCBI) human Refseq protein database  using Firmiana 1.0 enabled with Mascot 2.3 (Matrix Science Inc). False dQuartet metabolite reference materials were established in the form of dried cell extracts. A total of 264 LC–MS/MS based profiling were generated from 22 batches in five labs. Non-targeted and targeted metabolomics profiling were then conducted. More information was detailed in the accompanying metabolite paper . In briernorm function with a mean of zero and a standard deviation of one) when applying ComBat, RUVg and RUVs, and with zero when dealing with raw data without batch correction and applying BECAs including BMC, Harmony, SVA and ratio-based scaling. A floor value of 1 was then added to the value of each metabolite, and log2 transformation was conducted.Raw data were extracted, peak-identified and QC processed using the in-house methods in each lab. Compound identification was conducted using in-house library based on the retention time/index (RI), mass to charge ratio (m/z), and MS spectral data for each metabolite. Metabolite quantification was conducted using area-under-the-curve or the concentration calculated by calibration curve using standards of each metabolite. Similar with treatment of proteomics data, we replaced the missing values with a random value approximately zero . Therefore, 180 libraries (12 libraries per batch × 15 batches) were included in full datasets at each omics level. The full datasets were used for calculating intra-batch fold changes (FC) and differentially expressed features (DEFs).We then employed a subset of datasets from the full datasets to create balanced and confounded scenarios for assessing the pros and cons of the BECAs. Here, we arbitrarily selected D6 as the common reference material, leaving the rest three as the study groups . In the balanced experiment scenario, one replicate was selected for each study group from each of 15 batches. This was done independently for each omics type. In the confounded experiment scenario, five batches were randomly assigned to each study group  for each omics type to extract all three replicates for the assigned study group. For both scenarios, all three replicates for the selected reference sample (D6) in each batch were retained for reference-sample-based BECAs. Therefore, 45 study samples and 45 reference samples in balanced and confounded scenarios were employed at each omics level. The experimental design ensured the consistent number of libraries included in the balanced and confounded scenarios, as well as the separation of study samples from the reference samples for objective evaluation of the impact of BECAs. Expression matrix of full datasets, balanced subsets, and confounded subsets were deposited in figshare .Expression profiles without batch correction were defined as “raw” expressions.Mean-centering per feature per batch is to set the mean of each feature across all the samples within each batch to zero. This approach is applied based on log2-transformed expressions.ComBat function in the sva 3.42.0 package [ComBat_seq function in the ComBat-seq package [ComBat is one of the most popular BECA tools . It appl package  was impl package  was implHarmonyMatrix function in the harmony 0.1.0 package [Harmony uses an iterative clustering-correction procedure based on soft clustering to correct for sample differences. The algorithm first combines the batches and projects the data into a dimensionally reduced space using PCA, and then uses an iterative procedure to remove the batch effects. The  package  was implsva function in the sva 3.42.0 package [SVA is able to remove unwanted sources of variation while protecting the contrasts due to the primary variables specified in the function call. The  package  was implRUVg and RUVs functions in the RUVSeq 1.28.0 package [RUV uses a subset of the data to estimate the factors of unwanted variation adjusts for nuisance technical effects. We applied two modes for estimating the factors of unwanted variation, including: (1) RUVg, using negative control genes, assumed not to be differentially expressed with respect to the covariates of interest; and (2) RUVs, using reference sample (D6) for which the covariates of interest are constant . The RUV2-transformed expression profiles of a feature by the mean of log2-transformed expression profiles of the three replicates of reference sample (D6) in the same batch.Ratio-based scaling is to convert expression profiles to relative-scale profiles within each batch on a feature-by-feature basis. Ratio-based scaling data were obtained by subtracting logThe number of original features was 58,395, 8,150, and 984 for transcriptomics, proteomics, and metabolomics, respectively. For transcriptomics, a gene was considered detectable if the FPKM value was equal or higher than 0.1 in over 30% of the libraries. For proteomics, a protein was considered detectable if the normalized FOT value was equal or higher than 0.1 in over 30% of libraries. For metabolomics, a metabolite was considered detectable if the normalized value was equal or higher than 1 in over 70% of libraries. Features that were detected in both balanced and confounded datasets were included for further analysis. After filtering, the number of features considered to be detected across multiple batches and used in further analysis in each omics type was 26,907, 3,489, and 71 for transcriptomics, proteomics, and metabolomics, respectively.Intra-batch differential expressions, including intra-batch FC and DEF/non-DEF, were calculated based on full datasets which consisted of four donors with three replicates per donor in each batch.p < 0.05 were used for further analysis.Intra-batch FCs of three group-pairs  were calculated. In each batch, the three replicates were first averaged and then the FCs were computed for three group-pairs. In order to improve the reliability of the FCs, features that were satisfied with thresholds of t-test p value cutoff with a sufficient FC could be used to identify differentially expressed genes. In this study, a feature was considered as a DEF or non-DEF in each batch using the following criteria: up-regulated DEFs (student’s t-test p < 0.05 and FC > 2 for transcriptomics and proteomics or > 1.5 for metabolomics), down-regulated DEFs (p < 0.05 and FC < 0.5 for transcriptomics and proteomics or < 0.667 for metabolomics), and non-DEFs (the remaining features).Intra-batch DEFs/non-DEFs were then identified. According to recommendations from MAQC/SEQC Consortia , 60, a nReference datasets of differential expressions, including reference FC and DEF/non-DEF, were constructed based on a consensus-based strategy and could be used as “ground truth” for benchmarking.p < 0.05 in at least three batches across the two donors.The reference FC between each pair of donors for a feature was provided in the format of an average over the 15 intra-batch FCs. The reference FCs were retained for those features that had t-test A list of reference DEFs and non-DEFs were then identified based on intra-batch DEFs and non-DEFs using voting for consensus. Specifically, within each batch in each group-pair, a feature can be classified into one of the three groups, i.e. up-regulated DEF, down-regulated DEF and non-DEF. The choice with the most first-preference votes from 15 votes from 15 batches is the final classification of the feature. Features receiving more than one first-preference vote were not included in reference datasets.Cross-batch differential expressions, including FC and DEF/non-DEF, were calculated based on subset datasets in balanced and confounded scenarios. In each comparison in balanced scenario, to ensure that three replicates of each donor were included, three batches of datasets were randomly selected in the balanced scenario. For example, three replicates of D5 from batch X, Y and Z were compared with three replicates of F7 from batch X, Y and Z. Cross-batch FC and DEF/non-DEF were further calculated. On the other hand, in confounded scenario, as each batch included three replicates in one donor, one batch of each donor was randomly selected. For example, three replicates of D5 from batch X were compared with three replicates of F7 from batch Y. Similar methods and criteria with intra-batch FC and DEF/non-DEF were used for calculating cross-batch FC and DEF/non-DEF. In each group-pair, the process was repeated 15 times for eliminating potential selection bias.Frequently, a predictive model was built using some dataset(s), and was further validated using independent dataset(s). These datasets were probably confounded with batch effects. To simulate the clinical context, age and sex corresponding to the donor of each library were used as biological endpoints to assess the reliability of cross-batch prediction.train and predict functions in the caret 6.0.90 package were implemented, using default parameter settings (https://github.com/topepo/caret).Prediction models were developed and validated using a two-layer validation strategy . BrieflyN = 45) and further used for multiomics integration. In order to eliminate selection bias, this process was repeated ten times. Three integrative tools were used, including iClusterBayes from iClusterPlus 1.30.0 package [Expression profiles from 36 samples of three donors derived from 12 batches in each omics type were randomly selected from the dataset  from the average distance among technical replicates of the same (e.g. D5-1 vs D5-2). Based on principal component analysis (PCA), distances of two samples in the space defined by the first two PCs were used to represent distances between the two samples. SNR was calculated as Eq. th principal component of variances.th component values of replicate SNR is defined as the ratio of the average distance among different donors e.g. D5- vs F7-1 We then developed several quality metrics, including, relative correlation (RC) of FCs and MCC of DEFs, for the evaluation of BECA methods in terms of differential expression.RC was calculated based on the Pearson correlation coefficient between the FCs for a given pair of donors and the corresponding reference FC values. It is referred to as the “relative correlation” metric, representing the numerical consistency with the “ground truth”. To improve reliability, the mean of the three replicates of each donor was calculated before performing ratio-based expression analysis. FC were transformed using log2 scaling.Moreover, we compared cross-batch DEFs with reference DEFs and non-DEFs, and calculated the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). Matthews Correlation Coefficient (MCC) was further calculated to measure the consistency of DEFs detected from cross batches for a given group-pair with “ground truth”. This metric is called the “MCC of DEFs”. MCC was computed using the Eq. In each comparison, three replicates of each donor were included, making them suitable for statistical analysis. Specifically, three batches (each batch contained one replicate of a donor) of datasets were randomly selected in balanced scenario, while one batch (each batch contained three replicates of a donor) from each of two donors under comparison was randomly selected in confounded scenario. Cross-batch RC and MCC of DEFs between cross-batch values with reference values were then computed. To eliminate potential selection bias, this process was repeated 15 times. Moreover, since three comparisons were possible, the RC was calculated in each comparison. A total of 45 (15 repeats × 3 group-pairs) RC and MCC of DEFs values under each BECA method in each scenario and each omics type were obtained. Mean with standard error (s.e.) of RC and MCC of DEFs were further calculated, representing performance of BECAs in identifying differential expression.Moreover, the primary goal of BECAs is to make cross-batch expression profiles like intra-batch expression profiles. Hence, intra-batch RC and MCC of DEFs were used as the positive controls and were calculated as the correlation values between intra-batch values with the reference values. However, due to lab proficiency and/or technical limitations, variations existed in each batch and led to variations in intra-batch values across 15 batches. Hence, intra-batch RC and MCC of DEFs were not equal to 1.https://github.com/topepo/caret).Model performances were assessed using multiple performance metrics, including F1 score, MCC, sensitivity, specificity, Pos.Pred.Value , Neg.Pred.Value , precision, and accuracy. Model construction and assessment was implement using caret package 6.0.90  was used to measure consistency of clustering after multiomics integration with true group labeling. The Rand Index (RI) computes a similarity measure between clusters by considering all pairs of samples and counting pairs that are assigned in the same or different clusters in the predicted and true clusters. The raw RI score is then “adjusted for chance” into the ARI score as follows:rnorm function in R package with mean zero and SD equal to different folds of SD that were calculated from three replicates of reference samples in the batch. Different folds of SD ranging from 0.1 to 10 were used to mimic different levels of noises in the reference. Ratio-based method was further evaluated using the average of the three “modified” replicates of reference samples as the denominator.We used two strategies for simulation of low-quality reference samples to evaluate the consistency of ratio-based approach under different data quality scenarios. First, we artificially introduced different levels of noise to the reference samples to evaluate the consistency of ratio-based approach under different data quality scenarios. Specifically, for each feature in each batch, we randomly generated three values of “modified” reference samples using Secondly, we artificially reduced the expression level of some genes to simulate reference samples with low-quality in transcriptomics data. For each replicate of the reference sample, we randomly selected a certain percentage of genes and artificially reduced their read counts to 1/2, 1/4, or 1/8 of the original levels. Using this simulation strategy, the affected genes across different replicates in the same batch were probably different, which was as expected. Modified read counts in each library were then normalized to Count Per Million (CPM). Ratio-based profiles were further conducted based on log2-transformed CPM using the average of the three replicates of reference samples as the denominator. https://www.r-project.org). Student’s t-test was used to compare continuous variables. PCA was conducted with the univariance scaling, using the prcomp function. tSNE was conducted using the R package Rtsne 0.15. Data visualization was implemented using the R packages ggplot2 3.3.5 (https://ggplot2.tidyverse.org/), GGally 2.1.2 , and ggsci 2.9 (https://github.com/nanxstats/ggsci).All statistical analyses and data visualization were implemented using R statistical packages 4.1.2 (Additional file 1: Fig. S1. Diversity of quality of original datasets. Fig. S2. tSNE plots based on different batch-effect correction methods. Fig. S3. Workflow of construction of reference fold change and reference differentially expressed features. Fig. S4. Evaluation of the performances of BECAs using sensitivity, specificity, precision, and Jaccard Index of identification of differentially expressed features. Fig. S5. Matthews Correlation Coefficient of identification of differentially expressed features using with or without controlling multi-testing methods. Fig. S6. Evaluation of the performances of BECAs based on model prediction results. Fig. S7. SNR of the ratio-based method under different data quality scenarios by introducing different levels of noise to the reference samples. Fig. S8. SNR of the ratio-based method under different data quality scenarios by artificially reducing expression level of some genes in the reference samples.Additional file 2. Review history.