Introduction: In hepatocellular carcinoma (HCC), alternative splicing (AS) is related to tumor invasion and progression.Methods: We used HCC data from a public database to identify AS subtypes by unsupervised clustering. Through feature analysis of different splicing subtypes and acquisition of the differential alternative splicing events (DASEs) combined with enrichment analysis, the differences in several subtypes were explored, cell function studies have also demonstrated that it plays an important role in HCC.Results: Finally, in keeping with the differences between these subtypes, DASEs identified survival-related AS times, and were used to construct risk proportional regression models. AS was found to be useful for the classification of HCC subtypes, which changed the activity of tumor-related pathways through differential splicing effects, affected the tumor microenvironment, and participated in immune reprogramming.Conclusion: In this study, we described the clinical and molecular characteristics providing a new approach for the personalized treatment of HCC patients. Hepatocellular carcinoma (HCC) has become the second leading cause of cancer-related deaths worldwide, with more than 800,000 deaths each year . SurgicaAberrant alternative splicing (AS) is the result of splicing regulatory sequence mutations or ectopic RNA binding protein regulation. It plays an indispensable role in cancer and many other diseases . AlthougAll splicing data for liver cancer were downloaded from the cancer genome atlas (TCGA) SpliceSeq database including AS data, expression data, phenotype data, and survival data .We also downloaded the human genome sequence from the TCGA database , the humWe used the R package Rtsne (v0.15), which based on the t-distributed stochastic neighbor embedding (t-SNE) method, to cluster the samples according to their PSI values . BecauseTo further detect the differences in the distribution of age, sex, grade, pathologic T stage, alcohol, hepatitis B, and hepatitis C groups in the AS subtypes, Fisher test was applied .p < 0.05); and 2) Chi-squared test based on the median PSI reaching a significant level (p < 0.05). After DASEs were obtained, those whose average PSI of cancer samples were greater than the average PSI of normal samples were regarded as upregulated, and those whose PSI were less were regarded as downregulated. Next, DASEs between samples of different subtypes and normal samples were collected in the same way, and the number of different AS types in the relevant subtypes was counted, with the results included in a histogram. After obtaining the DASEs between subtypes, the overlap similarity of the upregulated and downregulated DASEs between subtypes was calculated as follows: Overlapping similarity = intersection of two sets/minimum value of the two sets. According to the obtained DASEs, analysis of variance was used to screen differential AS events between the two subtypes, with a threshold of p < 0.05, and then the intersection was taken for subsequent analyses.DASEs of cancer samples and normal samples were called according to the PSI value of AS. DASEs met two conditions: 1) Wilcoxon rank-sum test between groups reached a significant level ) was first used to obtain the reference sequence of each chromosome from the reference genome , and theAll gene sets were downloaded from the MSigDB database , and theThe correlation between the differential gene sets within the subtypes and the AS events was further calculated. First, the AS event was selected according to a PSI interquartile range of greater than 0.05 as the threshold in the samples, and then the screening results were used to calculate the Spearman’s correlation coefficient (coef) for the differential gene set. Alternative splicing-related pathways (SPs) in the MSigDB database were searched using “splice”, “splicing”, and “spliceosome” as keywords, and protein-coding genes in related pathways were used as splicing factors (SFs). After that, Spearman’s correlation coefficients of AS events and SPs and SFs were further calculated, and then the largest |coef. of SP| and |coef. of SF| corresponding to each AS event was selected to construct a scatter plot. Because coef. of SP and coef. of SF had the greatest number of AS events greater than 0.5 at the same time, the relevant AS events were selected for further PSI display, as well as the strongly correlated SP enrichment score of each subtype and the strongly correlated SF differential expression [edgeR (v3.28.1)] . The R pTo predict whether an immunosuppressive agent has a therapeutic effect on different AS subtypes, SubMap was used to map different AS subtype samples to samples with inhibitor processing information , as well50 values, and then the differences in the IC50 value of the samples of different AS subtypes was further counted using Kruskal’s algorithm to detect the significant differences. Next adj.p < 0.05 was used to screen for significantly different drugs, and the IC50 values of bosutinib, dasatinib, midostaurin, elesclomol, pazopanib, bortezomib, sorafenib, docetaxel, and gefitinib were plotted in box plots.The R package pRRophetic (v 0.5) was then to predict the sample’s response to 138 drugs , generatp < 0.05 was used as a threshold to screen significantly related AS events for subsequent analyses.Cancer samples were collected according to the PSI value of differential AS in the previous step combined with OS data, and batch Cox one-way regression analysis was performed on differential AS. After regression analysis, Lasso regression was further performed on the single-factor Cox regression results and a risk scoring model was built. This process mainly relied on the R package glmnet (v4.0–2). In the glmnet function , Y is Sup-value of <0.05 indicating that the difference between the high and low risk groups was significant.Where PSI represents the PSI value of the corresponding AS, β represents the regression coef. of the corresponding gene in the lasso regression result, and RScore represents the PSI value of the significantly related AS event in each sample multiplied by the corresponding AS event. The coef was then calculated, where i is the sample and j is the AS event. On the basis of the risk score of the sample, the high and low risk groups were divided by the median as the node and combined with the overall survival (OS) and disease-free interval (DFI) data to generate a Kaplan–Meier curve, with a Paired tumoral and adjacent normal samples were from patients diagnosed with HCC and accepted surgery at the Department of Transplantation, Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine  after the written informed consent. All these samples were kept and processed as previously described. RT-PCR was implemented using transcripts specific primers by 2 × Green PCR Mix . Splicing specific transcripts were distinguished using agarose gel electrophoresis and grayscale-measured using software ImageJ . PSI of each lane was calculated by the greyscale of the longer transcript divided by the sum greyscale of the longer and the shorter transcripts.HCC AS data  was downloaded from TCGA SpliceSeq database, and 11,179 AS events were obtained, corresponding to 4423 genes, which included 568 AAs, 469 ADs, 968 alternate promoters (APs), 6346 alternate terminators (ATs), 1992 ESs, 29 mutually exclusive exons (MEs), and 807 retained introns (RIs). At the same time, relevant HCC expression data and clinical data were downloaded from the UCSC Xena database, and 370 cancer samples and 50 normal samples were obtained after integration.According to the t-SNE method, the PSI values of all samples were displayed in clusters, and a scatter plot revealed that the cancer samples could be clearly distinguished from the normal samples . Next, uThe survival analysis of the three subtypes was further based on OS, and a Kaplan–Meier curve was generated. The results showed that the survival difference of the three subtypes was significant, and the survival curve of cluster 4 samples dropped faster .The distribution differences of age, sex, grade, pathological T stage, alcohol consumption, hepatitis B, and hepatitis C groupings in AS subtypes were further examined . The resOn the basis of the PSI value of AS, the DASEs of cancer samples and normal samples were retrieved. After threshold screening, 1,777 DASEs were obtained, of which 977 were upregulated and 800 were downregulated, corresponding to 1,005 genes . AccordiNext, the DASEs between the three subtypes and the normal samples were obtained. Cluster 1-normal had 1,681 DASEs, including 948 that were upregulated and 733 that were downregulated; cluster 3-normal had 2,545 DASEs, including 1,275 that were upregulated and 1,270 that were downregulated; and cluster 4-normal had 2,862 DASEs, including 1,565 that were upregulated and 1,297 that were downgraded. . After oThe AS score, GC content, and AS fragment length of the overall DASEs were further analyzed. For the length of AS fragments, the AS length of downregulated AAs was significantly higher than that of upregulated AAs and unchanged AAs, while the AS length of downregulated ADs was significantly lower than that of unchanged ADs, ESs, and RIs. There were no significant differences among the three groups .For GC content, the GC content of downregulated AAs was significantly higher than that of upregulated AAs, and the GC content of downregulated ESs was significantly higher than that of upregulated ESs and unchanged ESs. The GC content of upregulated RIs was significantly higher than that of upregulated RIs .For the AS score, the score of the first 5′ site of upregulated ADs was significantly higher than the score of the first 5′ site of unchanged ADs , and theNext, we analyzed the total DASEs corresponding to 1,005 genes, and drew heat maps based on the expression of related genes. The heat map showed that the overall gene expression of cluster 3 samples was high, while the overall gene expression of cluster 4 samples was low .A total of 32,284 gene sets was downloaded from the MSigDB database. The enrichment score of each sample for all gene sets was obtained and the cumulative distribution curve of GSVA scores according to different subtypes was generated. The score curve showed that compared with normal samples, the GSVA score distribution of samples of other subtypes was relatively. The score distribution of cluster 1 samples among subtypes was the most concentrated, while cluster 3 samples had more scores of less than 0, and cluster 4 had more scores of greater than 0 . After oCorrelations between differential gene sets within subtypes and AS events were further calculated. According to the interquartile range of the sample PSI, 3,662 AS events were screened, and strong correlations were screened from each subtype according to a Spearman’s correlation coefficient greater than 0.6. Eighty-eight splicing events and 326 gene sets in cluster 1 samples had a strong correlation, 519 splicing events in cluster 3 samples and 1,103 gene sets had a strong correlation, and 320 splicing events and 1,187 gene sets in cluster 4 samples were strongly correlated . The abop < 0.01).Twenty-four SPs and 370 SFs were identified from the MSigDB database. In further calculations of the Spearman’s correlation coefficients of AS events and SPs and SFs, a scatter plot showed that the coef. of SP and coef. of SF at the same time were greater than 0.5 AS events, including 138 AS events in each subtype. A heat map of the median of related events showed that the PSI value of cluster 3 samples was higher than that of the other subtypes, and the enrichment score of SPs of cluster 3 samples was higher. For SFs, in the cluster 4 samples, some SFs were significantly downregulated . A Venn p < 0.01). The difference in PSI of PQBP1 was not significant (p = 0.081), but the trend was consistent with the other three SFs. Therefore, the upregulation of PSI in HCC may be responsible for the upregulation of SFs such as PABPN1, CCDC12, ISY1 and PQBP1. Overall, the heterogeneity and homogeneity of splicing changes in HCC-related pathways may suggest a distinct role for alternative splicing in tumorigenesis and maintenance of cancer cell survival. Irregular splicing may regulate isoform switching of genes in cancer biological pathways and mRNA expression to promote HCC infiltration and invasion.The longer spliced isoforms of these SFs were significantly overexpressed in all HCC subtypes and validated by RT-PCR in HCC tissues . These mWe further explored the immune status of AS subtypes. A heat map showed that the StromalScore, ImmuneScore, and ESTIMATEScore of the cluster 3 samples were slightly lower than the other two subtypes. From median data, the tumor purity of cancer samples was significantly higher than that of normal samples, and the immune-related scores were significantly lower than normal samples. There was no significant difference in the expression of immune checkpoints and the enrichment scores of related pathways in each subtype. For most immune cells, the immune cell score of normal samples was significantly higher than that of cancer samples. In addition, cluster 4 samples had a higher activated CD4 T cell enrichment score and a lower activated CD8 T cell enrichment score. For G3–4 and hepatitis B patients, the related differences were also obvious .Kaplan-Meier analysis was further performed within the group for grade grouping and hepatitis B grouping. This showed that for the G3–4 samples, the Kaplan–Meier curves between the internal AS subtypes were significantly different, and for the samples with hepatitis B, the Kaplan–Meier curves between the internal AS subtypes were significantly different . The aboUsing SubMap to predict the possible therapeutic effects of related immunosuppressive agents on different AS subtypes, the results showed that the similarity between cluster 4 samples and CTLA4 response samples reached a significant level, suggesting that CTLA4 inhibitors may have a better effect on cluster 4 samples. .We further predicted the samples’ responses to 138 drugs to obtain predicted IC50 values. The results showed that there were significant differences in the degree of response of 111 drugs among the different subtypes . The IC5According to the obtained PSI values of DASEs, the differential AS events between subtypes were screened. There were 1,109 DASEs between cluster 1 and cluster 3, 1,154 DASEs between cluster 3 and cluster 4, and 1,177 DASEs between cluster 1 and cluster 4. Intersection of the three clusters resulted in a total of 455 DASEs for subsequent analysis . Using cLasso regression was then performed on 111 survival-related DASEs, and 20 AS events were obtained to construct a risk model. Regression coefficients and PSI values were analyzed to obtain the following risk scores: PSI39967 * (−5.5748) + PSI64018 * (−2.0928) + PSI46796 * (−0.5633) + PSI83140 * (−0.3544) + PSI44266 * (0.3162) + PSI85919 * (−0.3101) + PSI50488 * (−0.2602) + PSI19307 * (−0.1502) + PSI17008 * (−0.0950) + PSI19309 * (1.0474) E−13) + PSI50489 * 0.0014 + PSI85920 * 0.0023 + PSI85601 * 0.0223 + PSI58889 * 0.5534 + PSI1730 * 0.6888 + PSI24866 * 1.0308 + PSI61665 * 1.0423 + PSI 82016 * 1.6102 + PSI18599 * 40.6609 + PSI24760 * 51.4456 .According to the risk score of the samples, the high and low risk groups were divided by the median as the node, and a Kaplan–Meier curve was drawn on the basis of the OS data and DFI data. The results showed that the difference between the high and low risk groups was significant (OS, p < 0.0001; DFI, p = 0.0083; HCC is the most common primary liver cancer. Liver cancer is the sixth most common cancer and the second leading cause of cancer-related deaths worldwide. In the past few decades, the incidence of liver cancer and liver cancer-related deaths has increased in many parts of the world, including China . SorafenAlternative splicing can be regulated by many different mechanisms, such as histone modification and DNA methylation, which are usually associated with specific SFs or transcriptional elongation . The latFuture research aims to determine the molecular drivers of the transition from cluster 1 to cluster 4 subtypes. These changes may be triggered by changes in the genome or by epigenetic or transcriptional regulators that have been shown to drive splicing factor changes in other tumor types. Understanding these mechanisms will allow us to determine the development of AS-based HCC treatments. AS research is moving in the direction of making full use of the potential of AS in precision medicine.In this analysis, we identified AS subtypes through unsupervised clustering, analyzed the characteristics of different spliced subtypes, obtained DASEs, and combined the findings with GSVA enrichment analysis to explore the differences in the subtypes. Finally, on the basis of the DASEs of different subtypes, the survival-related AS time was identified, and the PSI value was used to construct a risk proportional regression model to guide prognosis. We systematically described clinical, splicing, transcriptomic, genomic, and immunological characteristics, and identified the underlying regulatory mechanisms of AS in HCC subtypes .Our research shows that the splicing regulation of SFs may play a role in the transformation and survival of HCC cancer cells. We studied AS comprehensively and systematically and used TCGA data to explore possible non-classical regulatory mechanisms in HCC. The data sample size of our research was sufficient, and the results of the verification data are good and have strong statistical significance, covering a wide range of fields. Our findings may provide the foundation for more in-depth research in the future, such as studies of the splicing regulation mechanism, cancer biomarker design, targeted drug screening, and other clinical applications.


Despite its wide-ranging benefits, whole-transcriptome or RNA exome profiling is challenging to implement in a clinical diagnostic setting. The Unified Assay is a comprehensive workflow wherein exome-enriched RNA-sequencing (RNA-Seq) assays are performed on clinical samples and analyzed by a series of advanced machine learning-based classifiers. Gene expression signatures and rare and/or novel genomic events, including fusions, mitochondrial variants, and loss of heterozygosity were assessed using RNA-Seq data generated from 120,313 clinical samples across three clinical indications . Since its implementation, the data derived from the Unified Assay have allowed significantly more patients to avoid unnecessary diagnostic surgery and have played an important role in guiding follow-up decisions regarding treatment. Collectively, data from the Unified Assay show the utility of RNA-Seq and RNA expression signatures in the clinical laboratory, and their importance to the future of precision medicine. Large-scale efforts such as The Cancer Genome Atlas (TCGA) have contributed to the identification of genes and pathways that drive various disease states ,2. Many The clinical utility of DNA markers in diagnosing disease and selecting treatment is undisputable; however, there are limitations to the information that can be derived from DNA alone. Notably, variants present in DNA may not be expressed in the tissue of interest. Available literature describing disease-associated DNA variants varies widely across disease types, and emerging evidence suggests that tissue-restricted, allele-specific, or imprinted expression of certain genes may modify the influence of genetic variants on disease manifestation ,6,7,8,9.The diagnostic shortcoming of DNA variants has led to the widespread development and success of diagnostic algorithms, such as classifiers trained using machine learning on RNA-sequencing (RNA-Seq) derived features ,15,16,17While whole-transcriptome or RNA exome profiling is often performed in research and discovery efforts, such a complex assay platform is more difficult to implement in a clinical diagnostic setting, owing to robustness, cost and/or throughput issues. Furthermore, addition of a separate DNA assay platform to an RNA workflow to derive variant information adds operational complexity and cost. As a result, the use of targeted, closed platforms often prevails.In establishing the Unified Assay, we have implemented a comprehensive workflow wherein exome-enriched RNA-Seq assays are performed on patient specimens submitted to a Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory for diagnostic testing . AlthougHere, we show how the Unified Assay can be successfully deployed as a clinical application, as exemplified in the results from a large repository of small biopsy specimens processed via an automated and clinically validated RNA-Seq assay.® Genomic Sequencing Classifier (GSC), which uses RNA exome sequencing information derived from a fine-needle aspiration (FNA) biopsy to classify cytologically indeterminate thyroid nodules as Afirma GSC Benign or Suspicious [® Genomic Classifier (GC), a diagnostic test that uses a 190-gene expression signature to differentiate between usual interstitial pneumonia (UIP) and non-UIP subtypes in lung transbronchial biopsy (TBB) samples from patients with ILD [Sequencing data were analyzed via the following advanced machine learning-based classifiers  that are applied to the diagnosis of thyroid cancer, interstitial lung disease (ILD), and lung cancer, and may be applicable to other disease indications: the Afirmaspicious ,23,24,25spicious ; the Envwith ILD ; the Perwith ILD ; the Perwith ILD ; and thewith ILD . Beyond Freshly collected biopsy samples were placed into sample tubes containing an RNA preservative  ahead of shipping to the laboratory at refrigerated temperatures (2–8 °C), where they were frozen prior to processing. Sample extraction methods were developed and optimized for each biopsy and tissue type, as described previously ,16,24,25RNA was sequenced using the Illumina TruSeq RNA Access kit . The amount of RNA used for library preparation varied by sample type, i.e., 15 ng for thyroid nodule FNAs and lung TBB samples from ILD patients, and 50 ng for all other lung and nasal samples. Library preparation was performed on a custom, automated Hamilton STAR platform  and sequenced on the NextSeq 500/550 platform (Illumina). The scripts for automated liquid handling were optimized to accurately aspirate and dispense low reagent volumes (as low as 2 μL), thus enabling use of biopsy specimens yielding very low amounts of total RNA (15 ng). All total RNA samples were size fragmented (~200–300 bp) prior to cDNA synthesis and adaptor ligation. Sixteen PCR cycles were used to amplify all cDNA species  using primers complementary to the generic adaptor sequences. The cDNA libraries were then enriched for exon content via two rounds of hybridization to probes designed to capture all human exons. Sequencing was performed using the NextSeq 500/550 platform via 2 × 75 bp paired-end protocols, to an average read depth of 25M paired-end reads per sample.RNA-Seq data were processed through the bioinformatics pipeline as described previously ,14,22,29Expression-level data and variant- and fusion-calling results were passed to the Afirma GSC, Afirma XA, Envisia GC, Percepta GSC, Percepta NS, and Percepta GA, and scores and indication-specific predictions were generated by each classifier and biomarker detector. Data processing and visualization were performed with R . SpecifiFusion calls were annotated by FusionAnnotator (part of the STAR-Fusion tool) and identified as having both splice sites matching a known reference splice site (ONLY_REF_SPLICE) or having one or both splice sites not matching a known reference splice site (INCL_NON_REF_SPLICE). The reference splice sites are reference exon junctions as provided by the reference transcript structure annotation GRCh37_gencode_v19. Fusions were further labeled as being either within chromosomes (intrachromosome) or between chromosomes (interchromosome).Transcripts from all 13 protein-coding mitochondrial genes were captured by the RNA-Seq assay; these were critical input features for the Hürthle Cell Index classifier, an integral component of the Afirma GSC .For each chromosome, the LOH score was calculated as the proportion of variants with variant allele frequency (VAF) < 0.2 or > 0.8 among heterozygous variants, excluding those in human leukocyte antigen (HLA) or immunoglobulin genes. Genome-level LOH scores were calculated similarly using variants across chromosomes 1 to 22.To identify CNVs, we employed jointly regulated blocks (JRBs) methodology, whereby increased and decreased JRB expression identified amplification and deletion events, respectively .The Unified Assay was used to generate RNA-Seq data from deidentified clinical samples collected during the development and implementation of the aforementioned tests ,43,44,45TAOK1-PIPOX and LPIN2-MYOM1, which were each found in 3 samples and have been observed previously in breast and lung cancer TCGA datasets, respectively [BRAF and RET, were also identified.Use of the Unified Assay enables analysis of the number and types of fusions both across and within chromosomes. Fusions containing known reference splice sites are of particular interest. As shown in ectively . Known lTFG-GPR128, which was also common among Afirma CLIA samples (1.8% across both tests). TFG-GPR128 has previously been described to have a prevalence of 2% in a healthy population [GAS7-MYH1 and ZCCHC8-RSRC2, each of which were identified in 4 ILD samples and have been reported previously in cancer samples  [MYH1 has previously been found to be upregulated in the bleomycin mouse model of idiopathic pulmonary fibrosis (IPF) [ILD is not typically associated with the presence of gene fusions. However, the number of fusions identified per sample within the Envisia ILD set was slightly higher than that observed in the Afirma thyroid set. Accordingly, we explored whether these fusions are relevant to ILD or simply alterations present in normal populations. Within the CLIA patient sample set of 3025 Envisia samples, the most common fusion with a known reference splice site was pulation , suggestcancers) . Interesis (IPF) . OverallRET and NTRK) [PAX8-PPARg, with other common fusion partners including RET, NTRK1/3, ALK, and BRAF. However, while the TCGA study was limited to papillary thyroid carcinoma (PTC) [NTRK3, 105 with NTRK1, 112 with ALK, 274 with RET, and 303 with BRAF fusions. A subset of fusions with common fusion partners are shown in RET and BRAF fusions were the most diverse and included a high level of inter- and intrachromosomal rearrangements, consistent with studies in other cancers [BRAF fusion partners. Along with the observed diversity, no one BRAF fusion partner dominated; SND1/BRAF and AGK/BRAF were the 2 most common fusions. RET fusions had the second largest diversity among fusions; however, unlike BRAF fusions, RET fusions had just 2 frequent partners: CCDC6 (RET/PTC1) and NCOA4 (RET/PTC3). Only 5 NTRK3 fusion partners were observed in more than two samples, predominated by ETV6/NTRK3 fusions. Across the different kinase fusions, common partners were observed, with EML4, SQSTM1, ERC1, ETV6, and VIM all being observed to be fused with multiple kinase partners. Each had a dominant kinase partner, but was also observed with secondary, and sometimes tertiary, fusion partners, indicating that some genes can participate in driver fusions across multiple kinase partners  containing 36,862 unique fusion pairs were identified in the Afirma CLIA sample set, comprising 109,912 FNAs. Although the number of fusions with known reference splice sites present in thyroid cancer samples was lower than observed for lung cancer samples, it is widely accepted that gene fusions have an important role in thyroid cancer, and drugs have been approved that target some of these  ,55. Eval cancers ,57; howepartners C. Full dThe capabilities of the Unified Assay extend beyond the measurement of canonical nuclear gene expression, as illustrated by assessment of mitochondrial gene expression across the multitude of patient specimens, revealing both expected and unexpected variations. As shown in We exploited the ability to sequence exonic SNVs to identify blocks in expressed transcripts where observed variant allele frequencies were close to 0 or 1, enabling detection of copy number abnormality in the form of LOH. Examination of 194 lung cancer specimens indicated numerous instances of LOH, with the highest LOH scores observed in chromosomes 9 and 13, and the lowest in chromosomes 2 and 20 A,B. InteWe also found LOH in Hürthle classifier-positive thyroid specimens, consistent with previous findings D 22,59,,59,60. EIn contrast to lung and thyroid samples, high LOH was not observed across 359 ILD samples A; the saB7-H4 (VTCN1) and B7-H7 (HHLA2), showed a large degree of variation across different disease states. Expression of HHLA2 was lower in thyroid cancer and lung cancer samples compared with other respiratory-associated samples, and higher in UIP versus non-UIP ILD samples  and other related genes. PD-L1 (CD274) was expressed in both benign and malignant thyroid and lung cancer samples, as well as within the nasal epithelium (Percepta NS), upper airway (Percepta GSC), and ILD samples (Envisia), with higher expression in benign versus malignant lung cancer samples and in non-UIP versus UIP ILD samples A. Expres samples C.The Unified Assay described herein provides a wealth of data that can be used to support accurate diagnoses and/or identify risk factors in challenging diseases, guide treatment decisions, and have the potential to enable further discoveries that can unlock future advances in patient care and the management of various diseases.Although RNA-Seq has enormous clinical potential, this technique has yet to be widely deployed in clinical laboratories due to both assay and analysis complexity, including the variable nature of sample quality, assay reproducibility, and robustness of the analysis pipeline ,61. NotwSeveral clinical tests across different diseases and biopsy types have been run on the Unified Assay platform, with each test having been analytically and clinically validated, and shown to be robust to normal laboratory variation ,24,25,44The Unified Assay uses an enrichment-based approach whereby, following cDNA amplification, the resulting PCR products are hybridized to a cocktail of oligonucleotide probes designed to enrich for all exons of 26,268 genes. Further customization can be achieved by tiling extra enrichment probes over key exons and adding probes in non-coding regions . Compared with total or nonenriched RNA-Seq, this exon-enrichment approach leads to approximately 5-fold lower sequencing costs and the ability to obtain significantly greater read depth for the key coding regions (exons), while retaining the ability to measure a wide range of genomic features , all from a single test, on a preciously small biopsy sample. Unlike amplicon-based RNA-Seq, which requires locus-specific PCR primer design , this enThe breadth and richness of the data generated by the Unified Assay is exemplified by the Afirma GSC/XA, which uses multiple data features to facilitate diagnosis and treatment decisions for suspected thyroid cancer. For example, detection and quantitation of both nuclear and mitochondrial transcripts increases the information available for diagnostic purposes. Chronic lymphocytic thyroiditis (benign), Hürthle cell adenomas (benign), and Hürthle cell carcinomas  are observed among thyroid nodules and are characterized by increased abundance of mitochondria relative to non-Hürthle nodules . DiffereRET and ALK, and, although the biological significance of many such fusions is yet to be determined, they could potentially become the therapeutic targets of tomorrow.The Unified Assay has also been deployed for other diagnostic tests, including a classifier that can utilize small biopsies to distinguish the UIP pattern associated with IPF from the non-UIP patterns seen in other ILDs ,29. AnalRobust functional testing of reagents and the use of pre-tested control reagents alongside maintained and validated equipment, within a full suite of standard operating procedures, minimizes technical batch effects and enables the combination and analysis of data sets that were run months, and even years, apart and most importantly, across both development and CLIA samples. Additionally, use of a whole-transcriptome assay yields copious data that can be used for quality control analysis to reduce technical noise and improve detection of biological signals. Quality-control methods include identifying gender through expressed genes and use of expressed single-nucleotide polymorphisms , not onlEliminating the need for bridging to targeted assays means all important features from development to commercial use could be preserved. For example, there is no limit to the number of genes that can be used in the Unified Assay, whereas targeted assays are limited, e.g., to tens to hundreds of genes. Each targeted assay type is also different and, when bridging to a new platform, not all genes or features will perform equivalently, thus necessitating further rounds of optimization.KRAS variant positivity and anti-EGFR treatment in colorectal cancer [Most importantly, however, targeted assays may not be able to generate data on all feature types. By contrast, the use of RNA-Seq enables detection of expressed, and therefore clinically relevant, variants and fusions that are more likely to be important to the etiology of the disease compared with non-expressed variants ,65,66,67l cancer ), varianl cancer . Such vaNTRK, RET, ALK and BRAF fusions, representing potentially actionable kinase fusions. Interestingly, rare fusions included some involving RET and NTRK3 that had only been described previously in single-case studies of patients exposed to radiation from the Fukushima nuclear disaster  [ACBD5/RET) [VIM/RET) [Use of the Unified Assay provides new insights into both novel and rare fusions. Analysis of more than 100,000 FNA samples, including both benign and malignant thyroid nodules, identified 895 instances of 1L2/RET)  or atomiBD5/RET) , and in VIM/RET) .Beyond detecting somatic variants that are potential oncogenic drivers, chromosome and genome-wide variants can be used to analyze LOH and CNV in tumors . While lMeasurements of PD-L1 levels are currently used to identify patients most likely to benefit from certain immune checkpoint inhibitors , and theNo individual molecular feature described above is sufficient to describe the complexity of disease biology. For example, in the lung cancer data set, LOH scores were different between pathological variants and between CNV-positive and -negative samples. However, combining data on novel expression signatures with expressed fusion, variant, and LOH data may facilitate development of improved biomarker-based diagnostics without the need for additional biopsies or assays. Realizing this potential will require extensive analysis of these large data sets and demonstration of robust and reproducible performance at all stages of the pipeline.Several key challenges have been identified that contribute to “analysis paralysis” in the development and clinical implementation of RNA-Seq platforms. These include the involvement of complex pipelines encompassing multiple tools that are independently developed, maintained, and licensed, as well as an overabundance of software tools and other options . To addrAn additional challenge associated with the implementation of RNA-Seq platforms is the lack of consensus by regulatory authorities regarding best practices and reference standards for validation . AlthougBRAF mutations, and fusions in RET and NTRK. The diagnosis of ILD and lung cancer in thousands of patients has similarly benefitted from the development of classifiers based on robust expression data.The Unified Assay platform described herein shows the realization of the promise of RNA-Seq in the clinic. Use of RNA-Seq data from thyroid nodules has enabled a complex, multifactorial analysis to examine the molecular nature of individual clinical samples. Notably, of the 109,912 Afirma GSC samples, 75,428 patients received a molecular benign diagnosis, potentially allowing for the prevention of unnecessary surgery and, among the 34,484 patients with a molecular suspicious or high-risk cytopathology result, the use of RNA-Seq helped to identify several important driver events that may inform treatment decisions, including Continued application of multi-modal data that can be accessed from RNA-Seq will help to maximize the information that can be obtained from individual patient biopsies and will ultimately support our scientific understanding of disease biology to expedite the development of new diagnostics and medicines. Indeed, the vast size of this biorepository and the capability of finding associated rare alterations in a large cohort has proven useful to identify patients who may benefit from targeted treatments, in the development of new tests, and in providing enhanced visualization and knowledge to patients, clinicians, and therapeutic partners. Ultimately, we endeavor to enable the biorepository as a platform for interactive research collaboration that will allow for the exploration of additional biological characteristics or genomic signatures beyond the fully developed and validated classifiers. Collectively, these assays show the power and utility of RNA-Seq and RNA expression signatures in the clinical laboratory and their importance to the future of precision medicine.


Pre‐existing drug resistance and tumorigenicity of cancer cells are highly correlated with therapeutic failure and tumor growth. However, current cancer models are limited in their application to the study of intratumor functional heterogeneity in personalized oncology. Here, an innovative two‐dimensional (2D) and three‐dimensional (3D) model for patient‐derived cancer cells (PDCCs) and air–liquid interface (ALI) organotypic culture is established from colorectal cancer (CRC). The PDCCs recapitulate the genomic landscape of their parental tumors with high efficiency, high proliferation rate, and long‐term stability, while corresponding ALI organotypic cultures retain histological architecture of their original tumors. Interestingly, both 2D and 3D models maintain the transcriptomic profile of the corresponding primary tumors and display the same trend in response to 5‐Fluoruracil, regardless of their difference in gene expression profiles. Furthermore, single‐cell‐derived clones are efficiently established and pre‐existing drug‐resistant clones and highly tumorigenic clones within individual CRC tumors are identified. It is found that tumorigenic cancer cells do not necessarily possess the stem cells characteristics in gene expression. This study provides valuable platform and resource for exploring the molecular mechanisms underlying the pre‐existing drug resistance and tumorigenicity in cancer cells, as well as for developing therapeutic targets specifically for pre‐existing drug‐resistant or highly tumorigenic clones. Colorectal cancer 2D patient‐derived cancer cells (PDCCs) and 3D air–liquid interface (ALI) organoids are established in this paper. This 2D and 3D model recapitulates histological, genomic and transcriptomic characteristics of parental tumors. Furthermore, the PDCCs show inter‐ and intratumoral heterogeneity of drug sensitivities and tumorigenicity. This 2D and 3D model can be applied to preclinical research more effectively and systematically. Hence, the measurement and modeling inter‐ and intratumoral heterogeneity has become a key clinical issue. Currently, intratumor heterogeneity can be revealed by multiregional sequencing, single‐cell sequencing, imaging, etc. However, current two‐dimensional (2D) and three‐dimensional (3D) cancer models remain limited in their application to study intratumor functional heterogeneity in personalized oncology. Notably, Clevers’ group implemented the first leap forward using single‐cell derived organoids, but only for drug response. Intratumor heterogeneity studies of other important clinically relevant functions, such as tumorigenicity, remain unexplored.Tumor heterogeneity is a major challenge and puzzle in oncology research and clinical practice.1 Intr Traditional 2D cancer cell lines are the most commonly used cancer models. The advantage of 2D cultures is easy of manipulation and observation. However, establishing a new 2D cancer cell line is an extremely inefficient, challenging, and time‐consuming process. Only extremely rare clones have adapted to selection and culture conditions, so they cannot reproduce the complete diversity of primary tumors, not suitable for studying tumor heterogeneity. The more advanced 3D patient‐derived organoids (PDOs) can better recapitulate the physiological features of their parental tumors and capture the inter‐ and intraheterogeneity. However, organoids are heterogeneous in terms of size, shape, and viability even within the same culture, and the relatively rigid Matrigel may limit drug penetration, all of which impedes drug screens. Therefore, the obvious demerits of current 2D and 3D models greatly limit their application in the modern era of individualized oncology study. And the combination of these 2D cancer cell lines and 3D organoids will substantially improve patient‐derived cancer models to meet the needs of personalized treatment.There is a growing consensus that improvement of preclinical models could facilitate the discovery and development more effective therapeutic regimens for patients.1, 8 we previously developed a long‐term and efficient culture of 2D human intestinal stem cell (ISC) clones, as well as air–liquid interface (ALI) cultures of 3D intestinal epithelium. The surface exposure and basal side nutrition supply characteristics make the ALI system very suitable for investigating the interaction between exogenous compounds and cells. In view of these advantages, we need to further explore and verify whether this powerful 2D and 3D model can be used as a personalized cancer model for colorectal cancer.The first challenge is to accomplish the high efficiency of generating patient‐derived cancer cell lines. Until 2015, by optimizing the 3T3 coculture system,13 we Here, we successfully established CRC patient‐derived cancer cells (PDCCs) and ALI organotypic cultures with high efficiency and long‐term stability. This 2D PDCC lines showed extremely high proliferation rate, while this 3D ALI organotypic cultures showed physiologically differentiated tissue architecture with open apical polarity. We also successfully isolated and expanded single‐cell‐derived clones (SC‐PDCCs) from individual tumors to study the intratumoral heterogeneity. This combined 2D and 3D culture system (2D and 3D model) faithfully recapitulates the characteristics of the primary tumors and captures the inter‐ and intratumoral heterogeneity of drug response and tumorigenicity.22.1 efficient primary culture of 2D colorectal cancer cell lines has remained a challenge for decades. We previously established an efficient culture method for 2D human ISCs with the irradiated 3T3‐J2 fibroblasts. Here, we optimized that method to establish a 2D PDCC model . In this, surgically resected tumor tissues and adjacent normal biopsies were obtained from consenting CRC patients  after long‐term cryopreservation.Although patient‐derived colorectal cancer organoids have previously been well established,16 eff3 within 20–50 days, whereas xenografts with relatively low growth rate remained below 500 mm3 beyond 80–90 days  every 12 days to 1:10–1:200 (≈61%) every 7 days, while the passaging ratio of NSCs (1:20) was consistent between patients  and xenografts (n = 2) remained unchanged after long‐term culture of SC‐PDCCs for 10 passages and 20 passages, respectively . The MUC2 and Ki67 staining of spheroids were also consistent with the ALI organotypic cultures derived from 2D PDCCs  was significantly higher than that of parental tumors  on patient‐matched 2D PDCCs, 3D ALI organotypic cultures, and parental tumors from 12 individuals, as well as NSCs from four patients. Here, both 2D PDCCs and 3D ALI organotypic cultures had a high correlation with their corresponding parental tumors in gene expression , and identified their subtypes using consensus molecular CRC classifier Figure .17]17 and transfer to BAM files by Samtools (version 1.3.1) respectively.31 Nex CNAs were called using FACETS (version 0.5.14), which are integer copy number calls that correct for tumor purity, ploidy, and clonal heterogeneity. Tumor purity and ploidy were extracted from the resultant for downstream analysis.Somatic mutations in tumor samples were called by comparison to their matched adjacent normal tissues using Mutect2. The parameters 1) –af‐of‐alleles‐not‐in‐resource was set to 0.0000025 to filter germline variant, and 2) –annotation was set as UniqueAltReadCount. Subsequently, GATK FilterMutectCalls  and FilterByOrientationBias were used to perform second filtration with default parameters. Variants were annotated to the functional consequence using ANNOVAR (version Apr 2018) based on human genome build hg19.32 CNA Differential expression analysis for high abundance genes (mean reads > 10) was performed by DESeq2 (version 1.24.0). Adjusted p value (<0.05) and log2 fold‐change  was used to select significantly DEGs for further downstream pathway analysis. Gene set enrichment analysis (GSEA) and gene ontology term enrichment analysis (GOEA) were performed with clusterProfiler (version 3.12.0).  GOEA was carried out based on DEGs with an adjusted p‐value less than 0.01.RNA was isolated from PDCCs and tissues with Trizol Reagent (Invitrogen) and with RNeasy mini kit (QIAGEN). 2 µg of prepared input RNA was sequenced on the Illumina platform generating 150 bp paired‐end reads. RNA‐seq reads were mapped and quantified using HISAT2 (version 2.0.5)‐StringTi (version 1.3.4) pipeline.36 DifThe variation coefficient (CV) of genes among different samples derived from the same individual were calculated. Next, high abundance genes (mean of read > 50) were selected for downstream analysis. The principal components analysis (PCA) was performed based on the top 10 000 CV genes by the function prcomp of the package ggbiplot (version 0.55) in R. PERMANOVA was carried out using Adonis from the Vegan (version 2.5.6) R package to identify significance between different groups.To assess the overlap of somatic nonsynonymous variants (SNVs) in tissues and matched PDCCs, all sample‐specific mutations were researched among the unfiltered mutations in the compared samples to avoid missing mutations occurring in very few cells. SNVs  located in CRC driver genes were visualized with oncoplot by R package Maftools (version 2.0.16) among 23 paired samples.39 The Spearman and Pearson correlation coefficient were calculated in the R function cor . The corresponding p‐value of the correlation coefficient was calculated in R function cor.test .To assess the gene expression characteristics of the PDCCs and corresponding parental cancer tissues, a Z‐score based method was used to normalize two groups. For each group, the TPM normalized expression data for each gene was scaled to Z‐score among sample. Then, the two groups of scaled expression data were combined and screened using 2042 genes with CV (>1) and mean of TPM (>1) and plotted with the function of heatmap (clustering_method_columns = “ward.D2”) from R package ComplexHeatmap (version 2.0.0).40 The The advantage of this algorithm is that the results are constant, whether it is predicted individually or over a series of samples. To maximize the ability to analyze results, findings and conclusions were presented using the “nearest‐CMS” classification.Our 19 PDCC lines, 17 ALI organotypic cultures and 478 samples from TCGA‐COAD were selected for this analysis. Transcriptome data were used to classify samples into CMS group by R package CMSclassifier with the “single‐sample predictor” algorithm.17 Thet‐test was used to calculate p‐values between two groups. Corresponding statistical significance was denoted with  in the figures and figures legends.Analysis procedures of genome, transcriptome data were provided in the relevant sections of Method Details Statistical analysis was performed with GraphPad Prism and presented as mean values ± SD. Two‐tailed Student's The authors declare no conflict of interest.Y.Z. and B.Z. contributed equally to this work. Y.Z. generated and cultured all of the PDCCs and ALI organotypic cultures performed most of the biological experiments and data analysis, prepared figures, figure legends, and methods. B.Z. analyzed all sequencing data and prepared methods. Y.M. generated 3D spheroids. F.Z. and J.C. provided patient specimens and maintained clinical records. B.W. performed histopathological analysis. H.J, F.Z., J.G., and Q.Z. participated additional data collection and analyses. H.W. supervised tumor tissue collection and generation of spheroids. Q.L. supervised tumor tissue collection and clinical records. F.Z. supervised sequencing data analysis. X.W. supervised project design and wrote manuscript.Supporting InformationClick here for additional data file.


However, some GESs originally developed to represent specific disease processes, primarily with an epithelial cell focus, are being applied to heterogeneous tumor samples where the expression of the genes in the signature may no longer be epithelial-specific. Therefore, unknowingly, even small changes in tumor stroma percentage can directly influence GESs, undermining the intended mechanistic signaling.Precise mechanism-based gene expression signatures (GES) have been developed in appropriate https://confoundr.qub.ac.uk/, that enables users to test the extent of stromal influence on an unlimited number of the genes/signatures simultaneously across colorectal, breast, pancreatic, ovarian and prostate cancer datasets.Using colorectal cancer as an exemplar, we deployed numerous orthogonal profiling methodologies, including laser capture microdissection, flow cytometry, bulk and multiregional biopsy clinical samples, single-cell RNA sequencing and finally spatial transcriptomics, to perform a comprehensive assessment of the potential for the most widely used GESs to be influenced, or confounded, by stromal content in tumor tissue. To complement this work, we generated a freely-available resource, ConfoundR; Findings presented here demonstrate the clear potential for misinterpretation of the meaning of GESs, due to widespread stromal influences, which in-turn can undermine faithful alignment between clinical samples and preclinical data/models, particularly cell lines and organoids, or tumor models not fully recapitulating the stromal and immune microenvironment.Efforts to faithfully align preclinical models of disease using phenotypically-designed GESs must ensure that the signatures themselves remain representative of the same biology when applied to clinical samples. Integrity and robustness in aligning models with human tumors, based on comparable mechanistic and biological signaling representative of specific patient subtypes, is critical to ensure successful translation of preclinical understanding of disease and treatment efficacies into clinical benefit. The versatility and accessibility of transcriptional signatures renders them a fundamental tool in aligning clinical phenotypes and mechanisms across human tumors and preclinical models. Therefore, it is crucial to ensure that the biological meaning of transcriptional signatures remains faithful across different contexts.https://confoundr.qub.ac.uk) that allows users to test the extent to which their gene or signatures of interest might become confounded due to the stromal transcriptome during forward and reverse translation.In this study, we objectively measure the presence and extent to which the biology represented by transcriptional signatures can be altered by differences in tumor microenvironment conditions when moving between the preclinical and clinical settings. Importantly, we provide a freely-available resource (Although the publication of gene expression-based signatures (GES) continues to grow each year in the research setting, these published signatures rarely make any clinical impact . In addiin vitro, in vivo, or ex vivo systems, enabling almost absolute control over the experimental conditions employed during biology-driven GES development , where tumors with a higher tumor stroma percentage (TSP) are associated with poor outcomes . It is in = 7,835 of commonly employed gene sets and signatures in cancer research. Using a combination of discovery and independent validation cohorts, including tissues from laser capture microdissection (LCM), flow cytometry, bulk clinical samples, single-cell RNA sequencing (scRNA-seq) and finally spatial transcriptomics, enabled a detailed interrogation of widely used transcriptomic signatures to enumerate the extent to which stromal composition can confound their classification. The pan-cancer nature of these findings were subsequently assessed across a number of publicly-available LCM datasets derived from pancreatic, breast, ovarian, and prostate cancer. Furthermore, to ensure that our findings can be widely applied, we have developed the freely-available ConfoundR online resource; https://confoundr.qub.ac.uk/, which gives a user the ability to quickly and easily interrogate the potential confounding effects on any individual gene, combination of genes, and GES across colorectal, pancreatic, breast, ovarian, and prostate cancer datasets.To examine if variations in TSP can distort GES results, which in turn could lead to biological misinterpretation, we performed a comprehensive assessment and quantification of the extent that stromal composition in bulk tumors can skew the expression levels of https://www.ncbi.nlm.nih.gov/geo/ using gene series (GSE) codes below:When publicly available, the data were assessed via Gene Expression Omnibus (GEO) and the processed data matrix downloaded. All array data were collapsed using the collapseRows function within weighted gene co-expression network analysis (WGCNA)  R package. In the case of duplicated genes, the probe with the highest mean expression across all samples was used and those genes with no expression across the dataset were removed. All GEO datasets are available via Discovery: LCM GSE35602; matched epithelium and stroma from 13 colorectal tumors transcriptionally profiled using Aligent array. Validation: LCM GSE31279; matched epithelium and stroma from eight colorectal tumors (with both compartments) transcriptionally profiled using Illumina sentrix-8 chip. Validation: FACS GSE39396; Six CRC tumors were sorted by Fluorescence-activated cell sorting (FACS) into four cell populations: epithelial cells (EPCAM+), leukocytes (CD45+), fibroblasts [fibroblast activated protein (FAP+)] and endothelial cells (CD31+). Validation: Breast Cancer GSE14548; matched LCM epithelium and stroma from nine invasive ductal carcinomas transcriptionally profiled using the Affymetrix Human X3P Array. Validation: TNBC GSE81838; matched LCM epithelium and stroma from 10 triple-negative breast cancers (TNBC) transcriptionally profiled using the Affymetrix Human Gene 1.0 ST Array. Validation: PDAC GSE164665; matched LCM epithelium and stroma from 19 pancreatic ductal adenocarcinomas (PDAC) transcriptionally profiled by Illumina NextSeq 500. Validation: Ovarian Cancer GSE9899; matched LCM epithelium and stroma from five ovarian tumors transcriptionally profiled using the Affymetrix Human Genome U133 Plus 2.0 Array. Validation: Prostate Cancer GSE97284; matched LCM epithelium and stroma from 25 prostate tumors, of which 12 were low-grade (Gleason 3 + 3) and 13 were high-grade (Gleason 8 or above) transcriptionally profiled using the Affymetrix Human Gene 1.0 ST Array. Clinical Validation: FOCUS trial GSE156915; The UK Medical Research Council FOCUS  trial involving patients with stage IV primary CRC resection transcriptionally profiled on Almac Xcell chip, only those with matched histology remained for analysis (n = 356). Validation: scRNA-Seq GSE144735; from 6 colorectal patients were single-cell sequenced. Count matrix was aligned to annotation file within Partek Genomics Suite. Genes with an expression less than 1 in at least 99.9% of cells were removed. Data was normalized by counts per million, +1 and log2 transformed. Validation: BOSS biopsy GSE85043; multiple biopsies obtained with different regions of seven CRC resection specimens, profiled on Affymetrix array.+) immunofluorescence (IF) signal were masked for tumor epithelium and extracted, then the complementary inverse segments (PanCK−) was masked and captured corresponding to the TME. For additional information see Supplementary Methods.The whole slide was imaged at 20× magnification using the GeoMx digital spatial profiler  with the integrated software suite then used to select 300 to 600 μM diameter regions of interest (ROI) from which the instrument focuses UV light (385 nm), to cleave the UV-sensitive probes with the subsequent release of the hydridized barcodes. 11 ROIs corresponding to epithelial tumor center, abundant tumor microenvironment (TME) regions and regions representing an interface between tumor and TME and were selected. The DSP software enabled Areas of Interest (AOI) contained in individual ROIs to be defined and selected. Firstly segments containing Pan- Cytokeratin (PanCKN = 356) were scanned at high resolution on an Aperio scanner at a total magnification of 20x. Tissue segmentation was run on H&E images by deep convoluted neural net  from FOCUS (MCP. MCPcounter (v1.2.0) R package was used to generate scores for 10 cell populations. Consensus Molecular Subtype. Consensus Molecular Subtype (CMS) classification utilized “classifyCMS.SSP” function within the CMSclassifier (v1.0.0) R package and the CMScaller (v2.0.1) R package. Single-sample gene set enrichment analysis. Single-sample gene set enrichment analysis (ssGSEA) was performed using gsva  R package with the following nondefault settings: min.sz = 5, verbose = TRUE, method = “ssgsea”, on the HALLMARK, Gene Ontology: Biological Processes and the Kyoto Encyclopedia of Genes and Genomes (KEGG) gene sets. ESCAPE (v1.4) R package was utilized to generated single sample scores for the HALLMARK pathways within the single cell cohort using the “enrichIt” function: groups = 1,000, cores = 2. GSEA. Pair-wise GSEA was performed using fgsea  R package , on the HALLMARK, Gene Ontology: Biological processes and the KEGG gene sets accessed via msigdb . Within the FOCUS validation dataset a median split of DS was used for comparison, followed by Differential gene set enrichment analysis (DGEA). DoRothEA. Transcriptional factor activity was assessed using dorothea (v1.2.2) R package, within the “run_viper” function (filtered for high confidence regulons). Within LCM cohorts, rowTtest with a P < 0.05 was considered significant to obtain consensus LCM transcription factors (TF). Plots in subsequent cohorts include all TF, regardless of significance. ESTIMATE. estimate (v1.0.13) R package was used to generate stromal and immune scores. R studio (v1.3.1073), R Project for Statistical Computing  used for all analysis. All heatmaps were plotted using ComplexHeatmap (RRID:SCR_017270) and all additional plots using ggplot2 (RRID:SCR_014601).https://confoundr.qub.ac.uk. Datasets: The datasets used in the ConfoundR application are described above, along with the preprocessing methods applied to each dataset.The ConfoundR application was created using R version 4.1.2 in combination with the R package shiny  and is running on the Shiny Server (v1.5.17) hosted on the Queen's University Belfast  virtual server . ConfoundR is accessible at U tests are performed using the stat_compare_means function from the ggpubr package  with method = “wilcox.test”. Boxplots for the PDAC dataset (GSE164665) are plotted using normalized counts calculated by DESeq2 , using the size factors calculated by the estimateSizeFactors function, accessed via the counts function with normalized = TRUE. Plots for each of the datasets can be downloaded in png format using the Save Plot button.The Expression Boxplots module allows the user to enter the gene symbol for a single gene into the input box. Boxplots in the Expression Boxplots module are created using ggplot2  and Mann–Whitney Z-scores using the scale function  prior to plotting heatmaps. Heatmaps of the gene expression Z-scores are plotted using the ComplexHeatmap package  with the samples grouped by the respective cell/tissue types to aid visual comparison between groups.The Expression Heatmap module enables users to enter a list of gene symbols with each gene symbol on a new line. For the RNA-seq dataset, variance stabilizing transformed counts, calculated using the vst function (blind = FALSE), from the DESeq2 package , are used as the gene expression values for samples. The gene expression values for each user selected gene in each dataset are converted to The GSEA module enables users to select an existing gene set from established gene set collections using dropdown menus or to enter a custom user-defined gene set by entering a list of gene symbols with each symbol on a new line. The existing gene sets available to the user are the Hallmark, KEGG, Reactome, BioCarta, and Pathway Interactions Database (PID) gene sets as curated by the Molecular Signatures Database  and accessed via the msigdbr package (v7.4.1).t-statistic (limma) or Wald statistic (DESeq2). Preranked GSEA is performed by the GSEA function from the clusterProfiler package  using the fgseaSimple method with 10,000 permutations  and a random seed of 123. Plots of GSEA results are produced using a modified version of the gseaplot2 function from the enrichplot package.In order to perform preranked GSEA, differential analysis is performed for each of the datasets, comparing stromal samples with epithelial samples. For the GSE39396 dataset the user can specify the cell types  to compare using the input boxes provided. Differential analysis is performed using limma  for microarray datasets  and using DESeq2  for the RNA-seq dataset (GSE164665). Following differential analysis, genes are ranked according to the The ConfoundR app uses the following R packages: shiny (v1.7.1), shinydashboard (v0.7.2), dashboardthemes (v1.1.5), shinyFeedback (v0.4.0), shinybusy (v0.2.2), shinyccssloaders (v1.0.0), msigdbr (v7.4.1), ggplot2 (v3.3.5), cowplot(v1.1.1), ggbeeswarm (v0.6.0), ggpubr (v0.4.0), ComplexHeatmap (v2.10.0), limma (v3.50.0), DESeq2 (v1.34.0), clusterProfiler (v4.2.1), fgsea (v1.20.0), enrichplot (v1.14.1) and RColorBrewer (v1.1–2). Schematics drawn using BioRender.https://www.github.com/Dunne-Group/ConfoundR. All scripts to perform the analyses outlined in this paper are available on our lab website www.Dunne-Lab.com.The data analyzed in this study were obtained from GEO at GSE35602, GSE31279, GSE39396, GSE81838, GSE14548, GSE9899, GSE97284, GSE164665, GSE156915, GSE144735, and GSE85043. The source code for the ConfoundR app is available at n = 26 samples from n = 13 tumors; GSE35602) and validation  and immune lineages  cells, monocytic lineages, myeloid dendritic cells, neutrophils] to each individual sample (+), leukocytes (CD45+), fibroblasts (FAP+), and endothelial cells (CD31+), or that the signatures cannot be used for precise enumeration of these lineages in CRC tissue.To confirm the purity of these datasets, we utilized the microenvironment cell population MCP)-counter algorithm  to assigl sample –D. In thl sample  and C. AP-counterl sample  and C. Il sample . While TA number of studies including our own have identified the stromal and immune contributions to the CRC CMS , in partWhen the epithelium was examined, with the exception of two samples, we observed a strong tendency for classification of CMS2 and CMS3, both well-characterized epithelial-rich subtypes, across the LCM and FACS cohorts –G. In con = 50 “Hallmarks” . By performing these analyses in both LCM cohorts in tandem, we observed that n = 21 Hallmarks were significantly  and consistently associated with either stroma (n = 17) or epithelium (n = 4) across both LCM cohorts  signatures, where again we found widespread stromal influence in 50 of 186  and 949 of 7,481 (Supplementary Table S1) signatures consistently in both cohorts, validated within the FACS cohort .While individual studies have highlighted the stromal origins of a number of key genes/proteins, using methods similar to MCP, it remains unknown how influential the stromal transcriptome is on some of the most widely employed GESs. To investigate this, we performed pair-wise GSEA  comparin cohorts . These fulations . Despiten = 118 defined regulons within the Dorothea algorithm.(28) These analyses revealed the extent to which numerous seemingly epithelial-specific cancer-associated TFs are influenced by stromal content, across both LCM cohorts . ConfoundR enables users, regardless of their bioinformatics skillset, to examine individual genes, combinations of genes, and GES of interest to identify if they could be susceptible to the same stromal confounding issues we have identified in this study. This freely available online resource enables users to interrogate the CRC, breast cancer, TNBC, PDAC, ovarian cancer, and prostate cancer datasets through three analysis modules: gene expression boxplots, gene expression heatmaps, and GSEA , KEGG (n = 186), Reactome , BioCarta (n = 292), and PID (n = 196). In addition, as many researchers will be interested in assessing their own bespoke or unpublished gene signatures, ConfoundR also provides the end-user with complete control to input and generate GSEA results from an unlimited number of custom gene sets  and ESTIMATE , ranked by DS content, also correlated to DS% .To test these findings further in bulk tumor datasets, we utilized transcriptional data from al trial , alongsiESTIMATE  stromal P < 2.2e-16), KRAS SIGNALING UP , and MYC TARGETS V2 , KRAS SIGNALING UP , alongside negative correlation with the MYC TARGETS V2  signature  when compared with the profiling in our other cohorts; we demonstrated that the reduced total number of genes still represent excellent surrogates for the whole transcriptome by assessing ssGSEA scores of the full signatures alongside the reduced genes available in the ST data. Using data from the FOCUS cohort, we observed excellent concordance in ssGSEA scores of the full MSigDB Hallmark EMT signature  and MYC Targets V2 signature , when assessed using the corresponding reduced signatures that were present on the GeoMx panel , which again revealed a significant enrichment for the EMT Hallmark signaling cascade in the stromal (PanCK−) regions  can alleviate some of the confounding variations in transcriptional signaling and inaccurate interpretation of findings when using bulk data, we profiled platform . While tgnatures . These f regions . While bIn this study, we provide a comprehensive characterization of the tumor transcriptome, stratified primarily into epithelium and stroma using LCM and ST, alongside a more granular assessment of individual lineages using FACS and scRNA-seq analysis. These analyses provide insight into the extent of the stroma's contribution to some of the most widely-employed signatures in cancer research, and also the potential for biological misinterpretation of resulting data when extrapolating biology from preclinical models that do not contain a full tumor microenvironment . Furtherin vitro, and the actual biology they can represent when applied to bulk tumor datasets. This is primarily due to the fact that while lineage purity is fixed in such preclinical models, a tumor mass is composed of a milieu of lineages (in vitro systems, when applied to tumor data they are more likely to become highly-accurate tumor stromal percentage estimates, rather than measures of subtle epithelial transitions (Preclinical models, particularly epithelial-rich systems where near-complete control over lineage purity and environmental conditions can be achieved and reproduced, represent ideal systems to develop transcriptional signatures that correlate with phenotypes of interest. We and others have previously highlighted discrepancies between the nomenclature used for such published signatures , when nalineages , the pronsitions . Our stuData presented here gives researchers the opportunity to assess the extent of the stroma's influences on specific genes/signatures of interest to them. The stromal influence on the general transcriptome in bulk tumor data has been demonstrated by Isella and colleagues  who idenData presented here do not challenge the use or value of using signatures to interpret data from bulk tumors, but present unambiguous intelligence around the caution that should be applied when interpreting what these signature scores can reflect, despite what the signature name suggests. An inaccurate biological conclusion in itself will not have major consequences for prognostic/predictive signatures in terms of their statistical correlation to identify tumors that are most likely to relapse/respond. However, an issue arises when signatures are being used to describe precise mechanisms across sample types, or when inaccurate biological interpretations of such results are being used as the basis for ongoing clinical therapeutic developments, which themselves are also potentially being tested in models that bear little relevance to the patient tumor samples they are derived from. This is particularly important, as most users are reliant on utilizing existing molecular datasets, where there is no control over the initial profiling steps, or indeed representative histological images that precisely align to the tumor region used for nucleic acid extraction. As such, while the signatures presented here represent large collections of experimentally-validated genes associated with specific phenotypes or biological cascades, our findings support the conclusion that unless users adjust their interpretation based on the extent to which genes/signatures can be influenced by TSP, there is potential for widespread misconceptions when interpreting the meaning underlying transcriptional signatures in tumor studies, given the discordance between their development and application.Genes can have many functions, and in some cases the genes that strongly demark a specific phenotype in a preclinical model system can also be expressed and perform entirely different mechanistic signaling roles in stromal lineages that make up a TME. If the magnitude of expression of these genes is low in the stromal/immune lineages, this may cause minimal impact when interpreting the precise nature of the transcriptional signature in bulk tumor data. However, if the genes within these signatures are expressed at higher levels in non-epithelial lineages, they can become strong surrogate markers for levels of TME components, rather than reflecting any of the mechanistic biology that they were designed to identify. It is this potential for misinterpretation that our paper wishes to highlight, thereby enabling researchers to assess the potential for their mechanistic signature of choice to be confounded in bulk tumor data using our ConfoundR resource. While data presented here identifies a potential issue in the interpretation of GESs, there is no singular solution given that each gene, and near infinite combination of genes that can be generated to make up signatures, will be influenced to different extents in every individual lineage present in every individual tumor. However, the accompanying ConfoundR resource will allow researchers to make a more informed interpretation of the true biology underlying the signature in these tumor samples, and to adjust their analyses based on the extent to which the TME can influence gene expression of their GES of interest when compared with controlled preclinical models.Although the use of scRNA-seq analysis can provide high quality lineage-specific transcriptional data, this comes at the expense of spatial information . ConversSupplementary FigureClick here for additional data file.Supplementary FigureClick here for additional data file.Supplementary FigureClick here for additional data file.Supplementary FigureClick here for additional data file.Supplementary DataClick here for additional data file.Supplementary TableClick here for additional data file.


So far, multiple approaches were described. Here, we investigated the impacts of different data fusion strategies on classification accuracy and feature selection stability, which allow the costs of diagnostic tests to be reduced. (2) Methods: We used molecular features (gene expression) combined with a feature extracted from the independent clinical data describing a patient’s sample. We considered the dependencies between selected features in two data fusion strategies (early fusion and late fusion) compared to classification models based on molecular features only. We compared the best accuracy classification models in terms of the number of features, which is connected to the potential cost reduction of the diagnostic classifier. (3) Results: We show that for thyroid cancer, the extracted clinical feature is correlated with (but not redundant to) the molecular data. The usage of data fusion allows a model to be obtained with similar or even higher classification quality (with a statistically significant accuracy improvement, a  In cancer diagnostics, many different methods and approaches are used independently. New diagnostic tools are implemented into common use if they are characterized by higher diagnostic abilities  and (possibly) lower costs. Construction of such tools typically begins with massive data obtained from radiology and medical imaging, or high-throughput molecular methods, including microarrays, next generation sequencing, and protein mass spectrometry. These technologies are good sources of data for machine learning (ML); supervised learning and sample type prediction are common techniques applied in diagnostics for a wide range of diseases . Since tDesigning a diagnostic test from molecular data involves many challenges due to high-dimensionality and  a limited number of samples. During the process of molecular test construction, the sample collection must be split into multiple subsets, such as test set and learning set. The latter must be split again for comparison of learning techniques using cross-validation methods, such as leave-one-out, bootstrap, or k-fold. Consequently, the number of samples available for training the model shrinks, which may influence the dimensionality reduction process if there are multiple intrinsic subgroups already present in the datasets. The molecular features obtained with high throughput techniques showed multiple dependencies. The relations between such a number of features measured with, e.g., correlation, mutual information, or distance measures, are hard to present/interpret in a readable form, and necessitate dimensionality reduction, which may remove clusters of differential features.One popular method in the context of data integration is feature extraction, which reduces the feature space by creation of new features basing on the existing ones. This is a useful tool to decrease the number of genomic features  . Some noIn order to improve the quality of classification models, data from multiple sources may be used. For the analysis and classification of data from multiple sources, such as -omic, clinical, and others, data integration is a useful practice. Data integration is a broad topic and is linked to data warehousing , data miData integration may be performed with multiple methods, as described in the literature. Data fusion could be considered a stage of data integration , and tecWe hypothesize that some clinical data representing the phenotype of a disease may contain complementary data to those of gene expression in terms of features for the classifier, and we investigated the impact of data fusion on the feature selection process. The proper selection of features has become a science of its own and has been widely described, for example in . Based oIn the present study, we investigated if a molecular classifier may benefit from data fusion resulting in a reduced number of features (lowering the cost of gene expression test) while keeping the accuracy comparable to a molecular test using only gene expression data. Here, we focused on the use of data fusion between high-dimensional gene expression data and low-dimensional clinical data, whose costs are already included in the basic diagnostics process. The inclusion of clinical data in the model may reduce the number of necessary molecular features, decreasing the cost of the whole test. The method we propose allows for assessing the usefulness of fused features and presents the most beneficial feature combination.We demonstrate the effectiveness of our method using data from thyroid cancer (in which overtreatment is a growing concern). A fine needle aspiration biopsy (FNAB) is the main tool for diagnosis of thyroid nodules . The samReferences ,22,23. GMolecular markers, as shown in the studies presented above, are promising for cancer diagnostics. A reduction in the number of molecular markers without any significant losses in accuracy will lower the costs of the test and make it more available for a wider group of patients. In this study, we compare the different integration strategies using sets of microarray gene expression profiles and sets of the most common clinical factors claimed to be risk indicators . We compare the results of data fusion for two popular feature selection methods in terms of classification accuracy and feature ranking stability. Because the aim of genomic feature selection is to find the optimal set of molecular markers, whose expressions may be measured with different methods, such as RT-qPCR , the presented study excludes the use of feature extraction techniques for molecular data.Instead of using the clinical data in raw form, we integrated all non-genomic data into one model to obtain one numeric value—the probability of a nodule’s malignancy. We used a Bayesian network; the dependent variable is represented by the node we called Risk. The graph for the created network is presented in The graph presents the connections that occur most frequently between variables. Malignancy risk is independent from sex and tumor size but we have found connections between malignancy risk and both age and Bethesda. The strength of the connection between Bethesda and malignancy is 10 times stronger than between age and malignancy. This should be interpreted as follows: the sex and size of the lesions show nothing special about the malignancy risk, but knowing the patient’s age, we can speculate about the probability of the malignant type of nodules. The risk of having a malignant nodule increases with age. The lack of connections between sex and age may be a result of a low number of samples and internal bootstrapping.We used two sets of molecular features previously published as markers for genomic classifiers, which we called Microarray_163 and Microarray_40. We estimated the similarity between features, because we considered that they may influence the stability of feature selection. We calculated Spearman’s correlation for each gene pair in genomic feature sets and for each gene and malignancy risk. For those pairs, we also estimated mutual information.We checked the relations between each feature pair in the given genomic feature sets. The dataset differences  are presented in Then, we compared the malignancy risk with each molecular feature for both molecular feature sets. For the Microarray_163 feature set and clinical risk, the correlation coefficients were in the range between moderate negative correlation and moderate positive correlation. For this feature set, most pairs had correlation coefficient close to 0. The smaller dataset showed bimodal distributions for molecular feature correlations and molecular features versus malignancy risk. Eventually, the number of moderate or highly correlated pairs of molecular featured and malignancy risks was comparable in both datasetsWe calculated the mutual information (MI) between the molecular features . Then we compared the reference models (molecular features only) and models with data fusion. We tested a wide range of selected features for each feature selection method. For the Microarray_163 feature set, we tested a whole range of features, but the accuracy decreased with the increasing number of predictors, so we analyzed only the range of 1–41 features. For each reference model, we observed a maximum accuracy for models with 15–16 features. For models with higher numbers of features, the accuracy slowly decreased. For both feature sets, the models with fusion had higher accuracies than the reference model for the small number of features. The results for models based on Microarray_163 are presented in The differences between feature selection algorithms are weak, but models with data fusions have higher accuracies than the best reference model with 15 features. Models with features form Microarray_40 are presented on For the Wilcoxon feature selection, the accuracy results were slightly higher for the late fusion strategy, but the confidence intervals overlapped.These results suggest that data fusion allows for increased accuracy with a reduction of the necessary features. In this case, late fusion seems to be more beneficial. We compared the best reference model with the best models of fusion and the p-values, presented in p-values and the differences between the model with fusion and the expression-only model for the nFeatures range of 1–15 are presented in the We checked if the similarity of the early fusion to the late fusion strategy meant that the malignancy risk was selected as often as in late fusion (in each model). For such a case, the model’s accuracy and feature selection stability would be similar for both data fusion strategies. The exact values of the While the performance of a model (in terms of classification accuracy) is crucial for its applicability, molecular markers can also provide valuable insight through pathway and functional analyses. Therefore, feature selection should also be evaluated for its consistency. We used the Kuncheva stability index , which aAs expected, slight differences were observed. The early fusion strategy seemed to have higher Kuncheva index scores than the reference model and the late fusion strategy. The unexpected result was that the early data fusion strategy and ReliefF feature selection helped increase the feature selection stability. We analyzed the feature rankings, and indeed, the selected feature lists were shorter for the early data fusion strategy.For the Microarray_163 feature set, the Kuncheva index is presented in The Kuncheva indexes for the Microarray_40 feature sets showed higher differences as the feature selections reached the maximum of the available features. The maximum values of each model reached the maximum stability  and are omitted in For a low number of features (where accuracy was highest), early fusion increased the stability of the feature selection for both ReliefF and Wilcoxon methods. The malignancy risk was selected, appeared on the top positions of the feature ranking, and enhanced the stability for the selection of molecular features. Higher stability is desired for feature selection, making the early fusion strategy a better solution for a given dataset, because this method produces a similar (or slightly lower) quality performance.Summarizing this study, we compared two data fusion strategies for the two types of feature sets: high-dimensional molecular data and clinical data represented by one extracted feature—malignancy risk. We used two popular ranking-based feature selection methods—ReliefF and the Wilcoxon test. We observed the influence on classification accuracy (using bootstrap) and the stability of feature selection.We added one continuous feature to the preselected subset of features and obtained a higher accuracy than the best subsets of the given molecular features and the malignancy risk itself.For the analyzed dataset, the accuracies of both methods were comparable, but they differed in the stability of feature selection. We should highlight that the comparison of data fusion strategies is far more informative than the analysis of feature dependencies. We observed some correlations between features but mutual information showed that the dependencies between malignancy risk and gene expression were rather weak. The analysis of dependencies between features may influence the feature selection method so a preliminary analysis of the data is still an important step in the process of feature selection. The dependencies between features are important to choose the classification method. Some models based on, e.g., logistic regression, may not work well when features are redundant. In this study, we used SVM with a linear kernel but the influence on classification accuracy may be different for other classification methods.The Kuncheva index is a very sensitive tool to investigate the differences between fusion strategies, especially for the number of features close to the minimum and maximum available features. According to , this inThe data fusion strategies presented here resulted in the increase in the maximum classification quality or kept it on a similar level as the best reference model, but with a considerable reduction in the number of predictors. However, the effect of data fusion was relevant to the interrelation between features. The stability index and comparison of the two data fusion strategies showed the influence of the added features. The effect of adding one feature in the fusion process was easy to detect in the feature rankings, but for multiple models and multiple features, it became a tedious task.The dependencies between features in the data fusion influenced the classification’s accuracy; however, a feature not correlated enough with the classifier outcome (or other features) may have a negative influence on the model’s quality. The data fusion of the other data types may improve the classification of the specimen, and cause the features collected for the lesions from different diagnostic procedures to set different points of view on the specimen. The features used for fusion may be related (such as in genotype—expression—phenotype relation); for example, the cytological picture of the lesion is caused by the changes in the molecular mechanisms in the cells. The data fusion may extend the list of significant features with new molecular markers of cancer.p-values are made from independent tests for each feature . For the other real data, use of the preselection algorithm may be necessary .In this study, the Wilcoxon test achieved slightly higher stability but the ReliefF method seemed to stabilize the selection after fusion of genomic features and malignancy risk. The steps to take following this research would be the comparison of more feature selection algorithms. We expected some differences; although they are both ranking methods, the ReliefF algorithm is more complex and uses internal shuffling of the sample and feature subsets in calculations of the feature weights while the ranking based on the Wilcoxon test In the present study, the data fusions were tested on one type of variable (continuous variables) and the simple combination of fusion and the feature selection, which showed a statistically significant accuracy improvement for models with few features in comparison to the homogenous feature set.The methods presented are not limited to thyroid cancer, but they need further investigations. We see a possibility of using the data fusion of molecular data with results in other diagnostic procedures, such as in the analysis of mutations, checking immunohistochemical markers, and imaging techniques, such as tomography, ultrasounds, magnetic resonance imaging, positron emission tomography, or others. Some of them may use advanced methods of feature extraction  but the methods are well described in the literature and the data fusion may be involved in studies after preprocessing of the features. The success of data fusion depends on the relationship between features. By the present study, the presence of a strong correlation and high mutual information with a few molecular features is beneficial to reduce the number of features. In the case of data fusion with the features represented , some other measures of similarities may be used, such as other implementations of mutual information  or the Kruskal–Wallis test for mixtures of continuous and discrete variables. The method of data fusion may have some limitations in the case of cancer heterogeneity; however, more excessive research should be performed in this field.The thyroid dataset consisted of 200 samples provided by Maria Sklodowska-Curie National Research Institute of Oncology Gliwice Branch. Biological materials for the microarray experiments were extracted from the biopsy of patients’ thyroid nodules. To each microarray, a label (acquired from histopathology studies) was assigned, indicating cancer (77 samples) or benign lesions (123 samples). The samples were assessed with a cytopathological test and fell into Bethesda categories II (benign) to VI , but the majority of samples were from III to V. Gene expression data was obtained using Gene Chip Affymetrix human transcriptome 2.0 arrays . GeneChip WT Pico Reagent Kit  were used for microarray preparation. All reactions including DNA target preparation, target hybridization, fluidics setup and array scanning were performed according to the user guide P/N 703262 rev 4. Skanning of the arrays were performed in GC30007G scanner.The preprocessing of the raw microarray dataset was conducted using the Aroma tool implemented in the R/Bioconductor environment . For bacTo avoid problems with continuous data resulting from unknown types of distributions of clinical features , we discThe correlations and mutual information were calculated using R. Mutual information was calculated using Kraskov’s method , using RIn this paper, we used the Bayesian framework as part of a supervised analysis. As described in , the netThe analysis was based on two gene sets (indicated as molecular markers for thyroid cancer). The first set published by Fujarewicz et al.  containeFrom each patient, the clinicians collected the data they deemed necessary to make decisions about the patient’s treatment, such as age, sex, tumor size, and the grade of disease progress assessed by the specialist. The summary of the used feature sets is presented in We compared the accuracies of the supervised learning models, varying in terms of parameters: (1) applied data fusion strategy, (2) feature selection method. As a reference, we used separate models for each feature subset .The clinical data were fused with genomic data by one of the following strategies: (1) data fusion performed after genomic feature selection (late fusion), (2) data fusion performed before feature selection (early fusion). Late fusion allowed simply adding all variables to the preselected genomic features and then we could measure the effect on classification accuracy in comparison to the one source of the data—the gene expression data only. Early fusion allowed combining information and selecting only the meaningful features from both data sources. In order to unify the data obtained by the classifier function for each strategy, the z-score normalization was performed as follows: (1) the whole dataset was normalized after fusion (and before classification) for late fusion; (2) the whole dataset was normalized before feature selection for early fusion. All fusion strategies are summarized in To validate the classification results, we used the bootstrap technique with 500 iterations. The stages of fusion, feature selection, normalization, and classification were inside the bootstrap loop to avoid an information leak. For each bootstrap iteration, we calculated accuracy based on a test set. Median values for the models based on all bootstrap iterations are presented in the tables and figures. Calculations were performed using SPICY project and the R environment .p-values (Benjamini–Hochberg procedure), and the first N features with the smallest values were selected for classification. For the ReliefF feature selection, we implemented the CORElearn package [The feature selection methods were implemented in the SPICY package. The Wilcoxon test features were ranked based on corrected  package .Classification accuracy is a common metric for the evaluation of the feature selection methods . In the S = {S1, S2, …, KS } containing sets of features, obtained by performing feature selection on K different samplings of a dataset, the Kuncheva stability index κ (S) is defined as:k is the number of selected features , n is the number of all features, and |.| denotes the set cardinality. The high stability of feature selection ensures that all markers are indeed relevant.The index is calculated as follows. For a sequence KS ). This approach allows comparing stability results for general purposes and use with any other combination of data fusion. Thus, the expected results of the comparisons of both fusion strategies were the higher accuracy for late fusion, because at least one feature appeared in each list of features before classification.We compared the stability between early and late fusion. For early data fusion method, the use of the stability index was intuitive, but for late data fusion, where the fusion came after the feature selection, we gathered all features involved in classification for each bootstrap model (as