Breast cancer is a complex and heterogeneous disease with distinct subtypes and molecular profiles corresponding to different clinical outcomes. Mouse models of breast cancer are widely used, but their relevance in capturing the heterogeneity of human disease is unclear. Previous studies have shown the heterogeneity at the gene expression level for the MMTV-Myc model, but have only speculated on the underlying genetics.Tumors from the microacinar, squamous, and EMT histological subtypes of the MMTV-Myc mouse model of breast cancer underwent whole genome sequencing. The genomic data obtained were then integrated with previously obtained matched sample gene expression data and extended to additional samples of each histological subtype, totaling 42 gene expression samples. High correlation was observed between genetic copy number events and resulting gene expression by both Spearman’s rank correlation coefficient and the Kendall rank correlation coefficient. These same genetic events are conserved in humans and are indicative of poor overall survival by Kaplan–Meier analysis. A supervised machine learning algorithm trained on METABRIC gene expression data was used to predict the analogous human breast cancer intrinsic subtype from mouse gene expression data.Herein, we examine three common histological subtypes of the MMTV-Myc model through whole genome sequencing and have integrated these results with gene expression data. Significantly, key genomic alterations driving cell signaling pathways were well conserved within histological subtypes. Genomic changes included frequent, co-occurring mutations in KIT and RARA in the microacinar histological subtype as well as SCRIB mutations in the EMT subtype. EMT tumors additionally displayed strong KRAS activation signatures downstream of genetic activating events primarily ascribed to KRAS activating mutations, but also FGFR2 amplification. Analogous genetic events in human breast cancer showed stark decreases in overall survival. In further analyzing transcriptional heterogeneity of the MMTV-Myc model, we report a supervised machine learning model that classifies MMTV-Myc histological subtypes and other mouse models as being representative of different human intrinsic breast cancer subtypes.We conclude the well-established MMTV-Myc mouse model presents further opportunities for investigation of human breast cancer heterogeneity.The online version contains supplementary material available at 10.1186/s13058-023-01723-3. MYC is a master transcriptional regulator that is amplified in approximately 15–20% of breast cancers and overexpressed in up to 35% of breast cancers . MYC amp−/−, MMTV-Cre/BRCA1fl/fl) [There is a clear, concerted interest to develop mouse models of breast cancer that recapitulate the heterogeneous features of human breast cancer to better understand disease dynamics. In response, numerous mouse models of breast cancer have been created, characterized by a wide variety of genetic drivers including constitutive overexpression of endogenous oncogenes specifically in the mouse mammary gland , conditA1fl/fl) , or overA1fl/fl) ). VariouA1fl/fl) , 19, witA1fl/fl) . PreviouA1fl/fl) . These aA1fl/fl) . ImportaA1fl/fl) .While the contributions of the tumor microenvironment, epigenetics, tumor metabolome, immune composition, and other factors have been highlighted recently for their roles in cancer –23, mostHere, we report our findings of interrogating the heterogeneity within the MMTV-Myc mouse model. We performed WGS on three common, distinct histological subtypes that arose in this model. We hypothesized genomic heterogeneity would be present across subtypes but would be conserved within each histological subtype. Importantly, each of the subtypes have numerous matched and additional tumors that have been previously analyzed for gene expression . IntegraFlash frozen MMTV-Myc tumors  stored aGene expression data used in this study have previously been published  on MMTV-Mus musculus genome using BWA-mem [Raw whole genome sequencing paired end fastq files first underwent initial quality control using FASTQC . Quality BWA-mem  with opt BWA-mem . SAMtool BWA-mem  was thenSomatic mutations were called using the consensus of Mutect2  . Briefly, RNA was extracted from mammary tumors using the Qiagen RNeasy Midi KIT. cDNA was generated using the AppliedBiosystems High-Capacity cDNA Reverse Transcription KIT. Genes were amplified under PCR using the following primers: c-KIT 5′ ATAGACTCCAGCGTCTTCCG 3′ and 5′ GCTCCCAATGTCTTTCCAAAACT 3′; RARA 5′ TTGTGCATCTGAGTCCGGTT 3′ and 5′ TGGGCAAGTACACTACGAACA 3′; APC 5′ CCGCTCGTATTCAGCAGGT 3′ and 5′ CCTGCAGCCTATTCTGTGCT 3′. PCR parameters are standard parameters as listed on New England BioLabs PCR protocol (M0273) V1. PCR fragments were purified using QIAquick PCR Purification KIT or QIAquick Gel Extraction KIT. Sanger sequencing was performed by GENEWIZ/Azenta under standard premixed conditions, with one of the PCR primers used as the sequencing primer. Sequence alignment and visualization were performed with Geneious Prime-2020.0.5 software.Representative mouse Circos plots for each histological subtype were generated using Circos  as a sofWorking from the outermost region of each plot, it begins with a labeled mouse ideogram with chromosomes presented in ascending order. The next innermost rings consist of SNVs color coded to represent low (yellow), moderate (orange), or high (red) predicted impact as determined by Mutect2. Following, the next inner ring depicts copy number variations as whole integer copy number changes, with height corresponding to copy number integers of one or two, determined by CNVKIT. Copy number gains are depicted in red, while copy number losses are depicted in blue. In the final innermost circle, translocations are colored randomly to one of the two chromosomes involved in the translocation event. Inversions are colored in black. Only somatic variants that satisfy the previous requirements for somatic variant calling are included for each tumor.Correlation of copy number and gene expression was done on CNVKIT. After generating copy ratios and copy segments under the “batch” WGS method, discrete copy number segments were generated using the “segment” method. The “cnv_expression_correlate” method was used to generate Kendall rank correlation coefficients and Pearson correlation coefficients on discrete copy number data for all 9 tumors that underwent WGS and gene expression data for all 42 tumors that underwent transcriptomic profiling. Correlation coefficients for each gene were then mapped onto Circos plots, with significant Kendall’s τ coefficients (≥ 0.3) colored blue and in the outermost ring. In the innermost ring, significant Pearson’s r coefficients (≥ 0.7) are colored red. Non-significant values for both metrics were colored black. Correlation coefficients could only be generated for genes with discrete copy number changes at ± 1 of ploidy level or greater differences, so a majority of the genome will show no correlation.All unsupervised hierarchical clustering was performed using the clustermap function as part of the Seaborn Python library. Distance between clusters was computed using the Ward variance minimization algorithm.Principal component analysis was performed using the scikit-learn  library,All heatmaps displaying log2 fold change of copy number segmentation data were generated using CNVKIT, both for initial copy number segmentation and copy number ratio file generation, as well as visualization.Mutational burden plots were generated in matplotlib. Mutational signatures were derived using the deconstructSigs  R packagWGS data for MMTV-Neu and MMTV-PyMT previously analyzed in the laboratory and used in mutation plots are available under the NIH SRA with BioProject number PRJNA541842. Processing of Neu and PyMT WGS fastq reads was done according to the same methods as the MMTV-Myc samples.The volcano plot of ssGSEA c6 oncogenic signatures for the EMT subtype compared to both squamous and microacinar signatures was plotted in Python using BioinfoKIT . The logKaplan–Meier plots were made with using the Survminer R package. Publicly available and deidentified TCGA non-redundant breast cancer patient data were used to construct Kaplan–Meier curves using criteria stated for each figure.The t-SNE diagram was created using the t-SNE implementation inside of scikit-learn. t-SNE was performed on the optimized 32 gene subset expression data of the PAM50 gene set as determined by RFECV using a support vector machine classifier. The resulting scatterplot of data is generated using matplotlib and colored according to the legend.The supervised machine learning soft voting classifier implemented using scikit-learn consists of logistic regression, support vector machine, random forest, XGBoost, and multi-layer perceptron classifier probabilities merged together. METABRIC gene expression data for the 32 genes with matched PAM50 subtypes were shuffled and split 70–30 into training and test data sets, respectively. To combat overfitting and training on biased data, PAM50 subtype proportions were kept the same between training and test sets. Probability predictions were averaged over 15 instantiations.Bar chart visualizations and pairplots were generated using Matplotlib, Seaborn, and Yellowbrick Python libraries.Clustering was performed on human breast cancer transcriptional data matched with intrinsic subtypes from the anonymized METABRIC dataset, which is readily available through cBioPortal. Kaplan–Meier analyses were performed on non-redundant human breast cancer patients from all breast cancer studies available in cBioPortal as of time of publication that match the criteria specified in each plot.Unless otherwise stated, statistical tests with p-value displayed are done using a two-sided Student’s t-test.BioinfoKIT-2.1.0, Bokeh-2.4.3, BWA-mem-0.7.17, Circos-0.69.6, CNVKIT-0.9.9, Delly-0.7.8, FASTQC-0.11.7, GATK-4.1.4.1, Lumpy-0.2.13, Matplotlib-3.4.3, Mutect2-2.1, NumPy-1.20.3, Pandas-1.3.4, Panel-0.13.1, Picard-2.18.1, Python-3.9.7, SAMtools-1.9, scikit-learn-0.24.2, SciPy-1.9.0, Seaborn-0.11.2, SnpEff-4.3, ssGSEA-10.0.11, Trimmomatic-0.38, VarScan-2.4.1, and Yellowbrick-1.5.Based on the diverse transcriptional and histological subtypes observed in the MMTV-Myc tumors , we hypoStrikingly, all microacinar tumors sequenced shared the same whole chromosome amplification events on chromosomes 15 and 11 as revealed by copy number segmentation calls Fig. D. Estimar and Kendall’s τ correlation coefficients broadly across the genome for CNV sites . This is unsurprising as mutations typically do not affect self-gene expression, except in the case of truncating mutations .Next we examined wholistic mutation patterns between each histological subtype and compared them to mutation patterns in previously sequenced MMTV-Neu and MMTV-PyMT tumors . We founWe hypothesized these differences in mutational burden could arise from separate oncogenic drivers and mutational processes within each mouse model given the extensive characterization of mutational processes in human cancers , 58. To While overall mutational burden and mutational signatures cannot parse between histological subtypes of the MMTV-Myc tumors, we reasoned that specific mutations may be associated with each subtype. Indeed, we find a small number of conserved (≥ 66% of tumors in each subtype) and impactful mutations within each subtype Fig. C. NotablInterestingly, KIT and RARA mutations were found to be mutually inclusive in the tumors sequenced. Of the 9 tumors sequenced at the genome level, 5 were found to have both the A538E KIT and A255D RARA mutations, while the other 4 tumors contained no discernible mutations whatsoever in either KIT or RARA. To validate our WGS findings and evaluate the extent of these mutations further in other MMTV-Myc tumors, we performed Sanger sequencing on the tumors that previously underwent WGS and an additional two tumors from each histological subtype. From these data, we confirmed that these KIT and RARA mutations were mutually inclusive in a larger population of 7 of the 15 tumors that underwent Sanger sequencing. From the total populations analyzed, these mutations were present in 60% of both the microacinar and squamous subtypes, while only 20% in EMT  compared to the unaltered population with median overall survival at 171.3 months (95% CI: 161.2–182.9)  that can best exemplify a given class (intrinsic subtype). To this end, we employed a machine learning model classifier trained on human METABRIC gene expression data to predict which human breast cancer intrinsic subtype each MMTV-Myc histological subtype best represents.To accomplish this, we first combined the raw METABRIC microarray gene expression data with the MMTV-Myc microarray data (GSE15904), along with additional mouse microarray cohorts for variations of the MMTV-Neu mouse model (GSE42533) and the MMTV-PyMT mouse model (GSE104397) for comparison. PCA of normalized data revealed strong batch effects between datasets that would distort causal biological interpretation  is a well-established scoring metric of gene expression data for 50 genes to stratify breast cancer patients into intrinsic subtypes and offer clinical prognoses . From heThe motivation for developing a supervised machine learning model classifier is evident after examining the distribution of the combined human and mouse gene expression dataset through a scatterplot of t-distributed stochastic neighbor embedding  (t-SNE) F1 scores are often used to evaluate the efficacy of supervised machine learning models. However, it has been shown that these metrics can be overinflated [F1 score of 80.0%, and an MCC score of 74.0% after 15 k-fold stratified and shuffled cross-validation using a 70% train and 30% test split  following from the mouse chromosome 15 amplification .Additional file 3: Human (NP_000213.1) and mouse (NP_001116205.1) KIT protein sequence alignment using Clustal Omega 1.2.4. The percent identity matrix calculated by Clustal 2.1 is shown at the bottom. The percent identity between human and mouse KIT protein is 82.77%.Additional file 4: Human (NP_000955.1) and mouse (NP_001169999.1) RARA protein sequence alignment using Clustal Omega 1.2.4. The percent identity matrix calculated by Clustal 2.1 is shown at the bottom. The percent identity matrix between human and mouse RARA protein is 89.98%.Additional file 5: Fig. S1. Circos plot of Pearson correlation coefficient values for integer copy number and gene expression float value for the EMT histological subtype alone. Red indicates that the correlation value is above 0.7, while being colored black means the correlation value is below 0.7.Additional file 6: Fig. S2. Circos plot of Pearson correlation coefficient values for integer copy number and gene expression float value for the squamous histological subtype alone. Red indicates that the correlation value is above 0.7, while being colored black means the correlation value is below 0.7.Additional file 7: Fig. S3. Circos plot of Pearson correlation coefficient values for integer copy number and gene expression float value for the microacinar histological subtype alone. Red indicates that the correlation value is above 0.7, while being colored black means the correlation value is below 0.7.Additional file 8: Fig. S4. Lollipop plot for human KIT (isoform 1) protein obtained from cBioPortal. Patient samples are from the TCGA PanCancer Atlas. The lollipops represent different mutations, with light green representing missense mutations of unknown pathology. Dark green mutations are confirmed pathologic missense mutations. Gray mutations represent truncating mutations of unknown pathology. Beige mutations represent splice variants of unknown pathology. Brown mutations represent inframe deletions or insertions of unknown pathology. Dark brown mutations represent confirmed pathologic inframe deletions or insertions. The annotation tracks below the plot correspond to the labels designated on the left. Each dot on the annotation track maps back to that location on the protein.Additional file 9: Fig. S5. Lollipop plot for human RARA (isoform 1) protein obtained from cBioPortal. Patient samples are from the TCGA PanCancer Atlas. The lollipops represent different mutations, with light green representing missense mutations of unknown pathology. Dark green mutations are confirmed pathologic missense mutations. Gray mutations represent truncating mutations of unknown pathology. Beige mutations represent splice variants of unknown pathology. Brown mutations represent inframe deletions or insertions of unknown pathology. The annotation tracks below the plot correspond to the labels designated on the left. Each dot on the annotation track maps back to that location on the protein.Additional file 10: Fig. S6. Pairwise correlation plots of MSigDB C1 positional gene sets for EMT, microacinar, and squamous tumors using gene sets (from top to bottom and from left to right): chr22q11, chr8p21, chr17q22, chr11q23, chr8p12, and chr3q12. Generated using the Seabron pairplot function with Python.Additional file 11: Fig. S7. METABRIC gene expression data with 446 random genes selected as the feature set and then undergoing unsupervised hierarchical clustering using Seaborn’s clustermap function within Python. This is the first random instantiation.Additional file 12: Fig. S8. METABRIC gene expression data with 446 random genes selected as the feature set and then undergoing unsupervised hierarchical clustering using Seaborn’s clustermap function within Python. This is the second random instantiation.Additional file 13: Fig. S9. METABRIC gene expression data with 446 random genes selected as the feature set and then undergoing unsupervised hierarchical clustering using Seaborn’s clustermap function within Python. This is the third random instantiation.Additional file 14: Fig. S10. METABRIC gene expression data with 446 random genes selected as the feature set and then undergoing unsupervised hierarchical clustering using Seaborn’s clustermap function within Python. This is the fourth random instantiation.Additional file 15: Fig. S11. METABRIC gene expression data with 446 random genes selected as the feature set and then undergoing unsupervised hierarchical clustering using Seaborn’s clustermap function within Python. This is the fifth random instantiation.Additional file 16: Fig. S12. METABRIC gene expression data with 446 random genes selected as the feature set and then undergoing unsupervised hierarchical clustering using Seaborn’s clustermap function within Python. This is the sixth random instantiation.Additional file 17: Fig. S13. METABRIC gene expression data with 446 random genes selected as the feature set and then undergoing unsupervised hierarchical clustering using Seaborn’s clustermap function within Python. This is the seventh random instantiation.Additional file 18: Fig. S14. METABRIC gene expression data with 446 random genes selected as the feature set and then undergoing unsupervised hierarchical clustering using Seaborn’s clustermap function within Python. This is the eighth random instantiation.Additional file 19: Fig. S15. METABRIC gene expression data with 446 random genes selected as the feature set and then undergoing unsupervised hierarchical clustering using Seaborn’s clustermap function within Python. This is the ninth random instantiation.Additional file 20: Fig. S16. METABRIC gene expression data with 446 random genes selected as the feature set and then undergoing unsupervised hierarchical clustering using Seaborn’s clustermap function within Python. This is the tenth random instantiation.Additional file 21: Fig. S17. PCA plot of Z-scaled data for METABRIC gene expression data, and also MMTV-Myc, MMTV-Neu, and MMTV-PyMT primary mammary mouse tumors and visualized using a matplotlib scatterplot in Python. The first principal component accounts for 44.6% of the variance within the dataset, with three distinct clusters that correspond to different sampling groups appearing. The second principal component accounts for 8.6% of variance within the dataset.Additional file 22: Fig. S18. PCA plot of Z-scaled and batch corrected METABRIC, MMTV-Myc, MMTV-Neu, and MMTV-PyMT primary mammary tumor gene expression data. Batch correction was performed using ComBat with settings defined in the main text methods section. Principal components are plotted using a scatterplot in matplotlib within Python. The first principal component in the batch corrected dataset accounts for 5.9% of variance, while the second principal component accounts for 4.5% of variance.Additional file 23: Fig. S19. Recursive feature elimination with 10-fold cross-validation (RFECV) using a support vector machine (SVM) classifier with radial basis function (RBF) kernel was performed on Z-scored and batch corrected METABRIC gene expression data—all mouse data was removed after batch correction and Z-scoring but before RFECV. Gene expression data was limited to the remaining PAM50 genes after combining human and mouse datasets, which was 45 genes. The vertical dashed line was placed at the optimal number of features, which is the number of features which give the highest average accuracy score over the 10 iterations the test is performed. The light blue shaded area represents one standard deviation away from the mean accuracy score. RFECV was performed using the Yellowbrick package in Python.Additional file 24: Fig. S20. Non-redundant TCGA breast cancer patient data for OBSCN mutation status underwent Kaplan-Meier analysis for overall survival with 95% confidence intervals shown in light blue for the unaltered population and light red for the OBSCN mutated population. KM plots were created using the survminer R package. All breast cancer datasets with mutation data available were analyzed with redundant patients removed from the dataset and the first event occurrence by date for each patient was kept.Additional file 25: Fig. S21. Volcano plot of differentially expressed genes of MMTV-Myc EMT tumors compared to microacinar and squamous tumors. Log fold change is displayed along the x-axis and -log base 10 of the p-value determined by a students’ t-test is displayed along the y-axis. Log fold change cut off set at ≥ 3 and negative log base 10 cut off set at ≥ 1.3, which is equivalent to a p-value ≤ 0.05. Select genes are highlighted. The volcano plot is made using bioinfokit as a Python package.Additional file 26: Table S1. Estimated integer copy number gain or loss by gene for all 9 tumors that underwent whole genome sequencing.Additional file 27: Table S2. ssGSEA values for all available EMT, squamous, and microacinar tumors. Includes test statistics, p-values, and log fold changes between groups.Additional file 28: Table S3. Gene expression and estimated integer copy number correlation values by gene with location included in the mm10 genome across all EMT, squamous, and microacinar samples. Includes the Kendall rank correlation coefficient and Pearson correlation coefficient values.Additional file 29: Table S4. Integrative mouse gene set found by taking all genes that were differentially expressed between EMT, squamous, and microacinar histological subtypes that also had copy number differences present from the whole genome sequencing data. The rationale behind this is that if the MMTV-Myc histological subtype preferentially represent different human intrinsic breast cancer subtypes, the combination of genomic and transcriptomic data will limit the number of genes used in unsupervised hierarchical clustering to those that matter most.Additional file 30: Table S5. Contingency table of TCGA breast cancer patient copy number data by select genes available through cBioPortal. An event for each gene is the presence of either a copy number gain or copy number loss. Separate pairwise contingency tables are listed for all possible combinations of genes considered: ERBB2, MYC, COL1A1, KRAS, and FGFR2. Odds ratios and p-values were determined using Fisher’s exact test for each gene combination.Additional file 31: Table S6. Data for constructing the Kaplan-Meier plot of MYC amplified and unaltered breast cancer patients by probability of overall survival in months. Includes bounds for 95% confidence intervals, number at risk, and standard error calculations.Additional file 32: Table S7. Raw data obtained from cBioPortal for all breast cancer patients separated by MYC amplification status. Redundant patient samples have not been removed.Additional file 33: Table S8. Data for constructing the Kaplan-Meier plot of KRAS with amplification/activating mutation and unaltered breast cancer patients by probability of overall survival in months. Includes bounds for 95% confidence intervals, number at risk, and standard error calculations.Additional file 34: Table S9. Raw data obtained from cBioPortal for all breast cancer patients separated by KRAS amplification/mutation status. Redundant patient samples have not been removed.Additional file 35: Table S10. Data for constructing the Kaplan-Meier plot of FGFR2 amplified and unaltered breast cancer patients by probability of overall survival in months. Includes bounds for 95% confidence intervals, number at risk, and standard error calculations.Additional file 36: Table S11. Raw data obtained from cBioPortal for all breast cancer patients separated by FGFR2 amplification status. Redundant patient samples have not been removed.Additional file 37: Table S12. List of the 32 most important genes as a subset of PAM50 for distinguishing between intrinsic subtypes of breast cancer as determined through RFECV using an SVM classifier with RBF kernel.Additional file 38: Table S13. Data for constructing the Kaplan-Meier plot of OBSCN mutated and unaltered breast cancer patients by probability of overall survival in months. Includes bounds for 95% confidence intervals, number at risk, and standard error calculations.Additional file 39: Table S14. Raw data obtained from cBioPortal for all breast cancer patients separated by OBSCN mutation status. Redundant patient samples have not been removed.Additional file 40: Table S15. Sample level enrichment table for genes that are often co-mutated with OBSCN. Samples are ordered from highest to lowest for genes with mutations that occur in the altered group (OBSCN mutated group).Additional file 41: Table S16. Human gene homologs and genomic location that correspond to genes contained within the conserved microacinar tumor amplification events on mouse chromosomes 11 and 15.Additional file 42: Table S17. Fisher exact test of ERBB2 and MYC amplification in human breast cancer patients. MYC and ERBB2 amplification trend toward co-occurrence.


Compartmentalized heterogeneity that traditional methods miss is becoming key to predicting both acquired drug resistance to targeted therapies and patient response to immunotherapy. Here, we describe a novel method for assay‐agnostic spatial profiling and demonstrate its ability to detect immune microenvironment signatures in breast cancer patients that are unresolved by the immunohistochemical (IHC) assessment of programmed cell death ligand‐1 (PD‐L1) on immune cells, which represents the only FDA microenvironment‐based companion diagnostic test that has been approved for triple‐negative breast cancer (TNBC). Two distinct physiological states were found that are uncorrelated to tumor mutational burden (TMB), microsatellite instability (MSI), PD‐L1 expression, and intrinsic cancer subtypes.The role of the tumor microenvironment (TME) in immuno‐oncology has driven demand for technologies that deliver  The immune microenvironment in breast cancer is poorly understood, and consequently, the success rate of immunotherapy is low. This study demonstrates the clinical utility of a novel spatial profiling method and reveals distinct immune microenvironments in triple‐negative breast cancer (TNBC) patients that are unresolved by current clinical indicators such as tumor mutational burden (TMB), microsatellite instability (MSI), and tumor subtype. CDxcompanion diagnosticCTnormalized cycle thresholdDABdiaminobenzidineFFPEformalin‐fixed paraffin‐embeddedIHCimmunohistochemistryISHin situ hybridizationMSImicrosatellite instabilityNGSnext‐generation sequencingPCAprincipal component analysisPCRpolymerase chain reactionPD‐L1programmed cell death ligand‐1QCquality controlROIregion of interestRQrelative quantitySCAsingle‐cell analysisTMBtumor mutational burdenTMEtumor microenvironmentTNBCtriple‐negative breast cancer1in situ, or spatial, molecular information , 2*[positive percentage], and 1*[low‐positive] [qupath   and tumor mutational burden (TMB) were assessed by NGS according to previously published methods , 18.2.4Slides containing FFPE tissue samples were adhered to a LightStream FloCell™ cartridge and attached to the mPrint. Three separate tissue compartments were identified by a pathologist, consisting of (a) Normal—tissue away from the tumor, (b) Interface—area between viable tumor and inflammatory components, and (c) Cancer—viable carcinoma proper (> 90% cancer cells). ROIs were then removed using microfluidic cavitation techniques under fluid flow.2.5m each oligo; 496 total oligos) such that the target concentration of each oligo in the final 50 μL PreLude reaction was approximately 40 nm. After an initial enzyme activation step , the reaction was cycled for a limited number of cycles to increase the target yield 16 times , followed by a hold at 4 °C.RNA was extracted from collected samples using the RNeasy FFPE Kit . The FFPE RNA (1 ng) was reverse transcribed using random hexamer priming in a 20 μL reaction using PrimeScript™ 1st strand cDNA Synthesis Kit  following the manufacturer's instructions. Half (10 μL) of the cDNA reaction mix was then combined with 25 μL Prelude™ PreAmp Master Mix  and a pool of 248 oligo pairs  in a 248‐assay × 20 sample MyDesign dispense pattern. The chips were sealed with optical cycling film, spun in an Eppendorf refrigerated centrifuge , and then loaded into the SmartChip Real‐Time PCR Cycler . After an initial enzyme activation step , the chip was cycled 35 times , followed by a melt curve program. Data were analyzed according to the standard settings in the smartchip qpcr software .The analytical qPCR components were assembled and dispensed as described in the SmartChip® MyDesign Kit  user manual. Briefly, the cDNA preamplification reaction mixture described above was diluted 1 : 10 with nuclease‐free water, combined with SmartChip™ TB Green® Gene Expression Master Mix  and aliquoted into the MSND 384‐well sample source plate . Separately, each of the 248 assay pairs was combined with SmartChip TB Green® Master Mix and aliquoted in separate wells of the assay source plate so that each of the final single‐plex reactions in the chip would be present at 300 n2.7CT) values were converted to relative quantity (RQ) values assuming an amplification efficiency value of 2 during on chip qPCR. Hierarchical clustering was performed on the 248 genes across all patients using two‐way Euclidean distance metrics. Clusters were evaluated by average linkage for inter‐cluster separation.Normalized cycle threshold . These latent vectors were clustered based on the K‐Means algorithm using Euclidean distance to ensure that discovered clusters contained all region data and compared with the previous hierarchical clustering. The K‐Means algorithm was run with varying cluster centers, from 2 to 8. Cluster stability over multiple runs and several performance metrics  were used to evaluate goodness of fit. K‐Means clusters at K = 2 were discovered to be similar to single ROI hierarchical clustering (of the interface ROI) at a single‐split level.Building on the groups discovered by the hierarchical clustering, data from all regions were dimensionally reduced via principal component analysis (PCA) from a length of 248 genes × 3 regions = 738 gene–region pairs  process but represented as an arbitrary RQ value. Correlations between gene–region pairs were then calculated based on Pearson correlation at a confidence interval of 95% or greater. Based on thresholds for positive and inverse correlations, subsets of purely intra‐correlated groups of gene–region pairs of arbitrary length in threshold margins were created. Each node color is assigned to a specific pathway, and each edge color indicates a purely intra‐correlated gene–region pair group. Shared nodes between intra‐correlated gene–region groups indicate a shared gene–region pair between groups. These central nodes shared between many groups have a higher importance as they indicate a high degree of potential causal correlations to gene–region expression levels. This process was completed for both the un‐clustered patient data and clustered/split patient data, and for differing missing value thresholds, positive correlation thresholds, and inverse correlation thresholds. These co‐expression graphs were undirected and drawn in a circular layout. Visualization of analysis was performed using python‐based Matplotlib (open source).Based on RQ values across the 33.1The initial goal of this study was to validate the mPrint workflow and demonstrate that samples collected using the mPrint Fig.  were botR2 values was 0.74 for CD3, 0.56 for CD8, and 0.88 for CD20  to the expression of CD8a, as well as other genes involved in immune response PIK3CG (R = 0.74) and CCL3 (R = 0.96). However, in the downregulated patient group, PD‐1 expression in the interface did not correspond to any other genes involved in the immune response. This could represent an area for future study and possible targets for immunotherapy screening beyond the currently used PD‐L1.Modified gene co‐expression networks Fig.  were gen3.4While the mPrint method presents a novel approach to spatial profiling with promising applications, it is important to acknowledge its limitations. Spatial technologies are generally characterized by their degree of multiplexing (measured by the number of targets or ‘omics measured and their resolution (sub‐cellular vs. cellular). The mPrint is a high‐plex, cellular‐resolution technology. However, the current embodiment of its microfluidic design is not ideally suited for high‐throughput single‐cell analysis (SCA), where thousands or millions of cells are individually analyzed. As resolution requirements for spatial technologies vary by application, the mPrint is currently more compatible with translational and clinical applications, rather than discovery efforts that commonly employ high‐throughput SCA.Looking ahead, the design of this novel platform lends itself to integration with other methods like auto‐stainers and molecular tools such as sequencers to provide automated sample‐to‐results solutions. Beyond tissue collection, the unique results generated by the mPrint function as an ideal input for novel informatics approaches as well. It is currently unknown what spatial resolution is necessary to extract clinically relevant information from patient samples. Ongoing preliminary studies by the authors using machine learning and generative AI have demonstrated promise in extracting clinically relevant markers from high‐resolution spatial data sets and identifying those same clinical markers in low‐resolution data sets such as those produced by the mPrint. High‐resolution datasets are costly to generate, typically only performed on physically small specimens, and computationally expensive to analyze since their files range from gigabytes to terabytes in size for a single specimen. Further development of these AI/ML methods would make companion diagnostic (CDx) development less costly and more readily translated to the clinic.4The mPrint represents an effective, innovative approach to gaining spatially dependent insights such as the compartmentalized heterogeneity within the tumor microenvironment. Building on this study to narrow down prognostic indicators will be a focus of future work, as the patients in this study were still undergoing treatment at the time of this work, and consequently, outcome data is not currently available to determine which microenvironment factors contribute to therapeutic response. In the meantime, results from this study suggest that patient stratification based on molecular characteristics of the tumor microenvironment differs from existing methods of patient stratification that rely on molecular subtyping of the entire tumor and has the potential as an additional variable to improve patient treatment selection.The authors declare no conflict of interest.CZ, AM, AF, ZG, DR, and DND conceived and designed the project. CZ, AM, SA, PM, EC, BW, ZG, DR, and DND acquired the data. CZ, AM, PM, IS, CD, AF, SV, ZG, DR, and DND analyzed and interpreted the data. CZ, SA, PM, CD, SV, DR wrote the paper. All authors contributed to the manuscript review.


Pathological conditions may result in certain genes having expression variance that differs markedly from that of the control. Finding such genes from gene expression data can provide invaluable candidates for therapeutic intervention. Under the dominant paradigm for modeling RNA-Seq gene counts using the negative binomial model, tests of differential variability are challenging to develop, owing to dependence of the variance on the mean.Here, we describe clrDV, a statistical method for detecting genes that show differential variability between two populations. We present the skew-normal distribution for modeling gene-wise null distribution of centered log-ratio transformation of compositional RNA-seq data.Simulation results show that clrDV has false discovery rate and probability of Type II error that are on par with or superior to existing methodologies. In addition, its run time is faster than its closest competitors, and remains relatively constant for increasing sample size per group. Analysis of a large neurodegenerative disease RNA-Seq dataset using clrDV successfully recovers multiple gene candidates that have been reported to be associated with Alzheimer’s disease. Finding patterns of gene expression variation that are associated with a biological condition of interest is the first step towards elucidating the molecular basis underlying a biological process. Currently, bulk tissue mRNA collected under specific biological conditions through RNA-sequencing (RNA-Seq) technologies remains an important approach for studying gene expression patterns. Typically, genes that show statistically and biologically meaningful difference in mean expression between conditions are of interest. Indeed, pathological conditions frequently manifest as gene sets with altered mean mRNA expression levels. The identification of these genes is important for understanding how the functions of normal molecular pathways are perturbed . Hence, To obtain a more complete picture of patterns of gene expression variation, we need to look beyond genes with significantly different mean expression (DE genes) between conditions . We refeThe general idea of conducting a test of differential variability for RNA-Seq data involves testing the equality of variances  between two populations. The variance parameter is embedded in some probability distribution that approximates the distribution of gene  counts, assuming the null hypothesis is correct. The standard approach models RNA- Seq data as a discrete random variable.etc. After this, a model that accounts for overdispersion commonly seen in RNA-Seq data  is used, but alternative models are possible  , the medpossible . Statistσ2/μ) from a generalized linear model with NB1 parametrization as a measure for variability. Accordingly, the variance is represented as the product of the mean µand the dispersion ϕ parameter, σ2 = ϕμ. The parameter µis viewed as a technical component, whereas ϕ is treated as a biological component and interpreted as a parameter for gene expression variability. σ2 = μ + ϕμ2. Recently, ϕ, which is treated as a log-normal prior. Subsequently, test of difference in dispersion between two conditions is based on the posterior distribution simulated using Markov Chain Monte Carlo (MCMC).To date, only a few methods are available for finding DV genes using RNA-Seq data. In contrast, even in 2015, there were 22 methods for detecting DE genes . For tese.g., DESEq2, edgeR) are all based on discrete count models such as the NB model. In recent years, there has been an increasing call towards adopting a compositional data analysis (CoDA) framework for improving the analysis of RNA-Seq data. Compositional data analysis originated from the study of chemical, mineral, and fossil compositions of rocks and sediments . In thisIn this article, we propose clrDV , a novel method for detecting DV genes between two conditions in RNA-Seq data that is based on a compositional data analysis framework. The method involves a log-ratio transformation  of the ri.e., each sample is represented by a vector of relative frequencies of the genes, which sums to unity. The key step in processing compositional data involves a log-ratio transformation, for which several variants are available. The simplest is the centered log-ratio (CLR) transformation, first proposed by Xgi be the read count for gene g and sample i, where g = 1, 2, …, G and i = 1, 2, …, n. For a G-component composition {xi1, xi2, …, xGi}, the CLR- transformation of Xgi is given by g′ = 1, 2, …, G. We call CLR(Xgi) the relative gene expression, or CLR-transformed count, of gene g and sample i. A pseudo-value 0.5 is added if xgi = 0 for any i. Thus, the main challenge for using CLR-transformed data to develop a test for differential variability is modeling them using a tractable probability distribution for which estimation of the variance parameter is practical.We begin with the assumption that RNA-seq data are compositional, e.g., as a prior distribution in Bayesian analysis by The skew-normal distribution is a three-parameter continuous probability distribution that generalizes the normal distribution. Its additional skewness parameter enables it to model skewed data on the real line, thus making it more flexible than the normal distribution, while retaining the usual mean and variance parameters. Historically, the skew-normal model was arrived at by several different authors in other contexts , that is, Ygi ∼ SNC,  where μg is the mean, σg is the standard deviation, and γg is the skewness parameter, g = 1, 2, …, G and i = 1, 2, …, n. The parameter vector θgC) has parameter space ℝ × ℝ+ × , where γg = 0 results in a normal distribution with mean μg and variance Denote the relative gene expression from gene sn  as a result of conducting multiple hypothesis tests across genes, we applied the Benjamini-Yekutieli procedure , which aGSE123658) contains whole blood RNA-Seq data from from 39 Type 1 diabetes patients and 43 healthy donors (GSE150318) contains longitudinal gene expression data from 114 short-lived killfish Nothobranchius furzeri measured at 10 weeks and 20 weeks of age  and probability of Type II error, it is important to simulate the null distribution using realistic parameter values. For this purpose, we used two real RNA-Seq datasets that have modest to large sample sizes to enable reliable estimation of the parameters of the NB2 model. The first dataset , Alzheimer’s disease , progressive supranuclear palsy  and pathologic aging (n = 30). We chose to compare the control group against the AD and the PSP group respectively, since the sample sizes in these groups are reasonably large and balanced. The large sample sizes per group of this dataset enables reliable estimation of the variance parameter in the skew-normal model. In addition, since Alzheimer’s disease is a well-researched neurodegeneratve disease, literature support for association with Alzheimer’s disease may be more readily found for the DV genes detected, thus enhancing interpretability of the analysis results.For empirical assessment, we used the Mayo RNASeq dataset , which cFor gene filtering, we removed a gene if it has average count-per-million (CPM) below 0.5, or its count is zero in at least 85% of the samples. After filtering, a total of 12,283 and 16,670 transcripts were left for the Valentim dataset and the Kelmer dataset, respectively. In the Mayo RNA-Seq dataset, after removing samples with missing class labels, 78, 82, and 84 samples were left for the control, AD and PSP groups, respectively. For the AD-control comparison, a total of 18,664 transcripts were left; for the PSP-control comparison, 18, 636 transcripts were left. For MDSeq, diffVar, GAMLSS, and DiffDist, we normalized the raw counts using TMM normalization.Our simulation study has two objectives: (1) evaluation of the goodness-of-fit of the skew-normal distribution on CLR-transformed data; (2) comparison of the performance of clrDV against MDSeq, diffVar, GAMLSS (Benjamini–Hochberg (BH) and Benjamini–Yekutieli (BY) variants), and DiffDist with respect to FDR and probability of Type II error.polyester R package (For objective (1), we randomly chose 10,000 of the filtered genes and modeled the distribution of RNA-Seq counts for each of them using the NB2 model. This was done by first estimating the parameters of the NB2 model using samples from one group , followed by simulation of 500 biological replicates from the NB2 models with estimated parameters. We used the  package  to perfopolyester R package. Thus, in a two-group comparison setting, both groups have identical NB2 model parameters. For 10% of the genes , we simulated them as DV genes by multiplying their estimated size parameter (1/ϕ) in one of the two groups with a random value x, where x ∈ ∪. For DiffDist, a random subset of a quarter of the DV(50/200) and non-DV genes  were used, since the MCMC step in DiffDist is computationally expensive. Subsequently, biological replicates of size 50, 100, 150, 200 per group were drawn from these NB2 models. The minimum sample size considered was 50 because for smaller values, estimation of the variance parameter of the skew-normal model is unreliable , we first filtered out genes and samples using the mean CPM <0.5 and proportion of zero count ≥0.85 criteria. We then randomly selected 2,000 of the remaining genes and estimated the parameters of the NB2 model for each gene, again using the reliable . A totalWe applied clrDV to the Mayo RNA-Seq dataset to assess its capacity for detecting DV genes that are contextually meaningful. For the genes after filtering, we assessed the fit of the skew-normal model to their CLR-transformed count data using the KS-test. Comparisons were made to close competitors as indicated from the results of the simulation study. Volcano plots were used to inspect the biological effect size and statistical significance of all genes tested. Venn diagrams were used to identify sets of genes that are identically recovered by all three methods, by combinations of two methods, or uniquely recovered by a single method. Violin plots of selected DV genes were made to verify computational results.Computational tasks were done in a computer with a 1.80 GHz i5-8265U CPU and an 8GB RAM processor. R version 4.2.1  operatinp-value of KS tests for the fit of the skew-normal model on distribution of the CLR-transformed count data. For the majority of the genes , the fit is good .For the skew-normal model to adequately capture variation in the CLR-transformed RNA-Seq data, it is necessary to demonstrate goodness-of-fit between model and observed data. Next, to be a viable alternative to existing methods for detecting differential variability, clrDV should have comparable or superior performance with respect to FDR and the probability of Type II error across sample size scenarios appropriate for the testing of differential variability. For the analysis of simulated data from the Kelmer dataset, we find clrDV to have comparable mean FDR and mean probability of Type II error  as MDSeqp-value greater than 0.05 Similar to our findings from simulated data, the skew-normal model also appropriately captures variation in the CLR-transformed counts of the genes for the Mayo RNA-Seq dataset . Almost p-value for each tested gene. For the control vs. AD comparison, we detected a set of 4,754 DV genes  were considered for empirical assessment since the results of the simulation study indicate that they are the only reasonable competitors of clrDV with respect to appropriate control of FDR control and probability of Type II error at sample sizes close to 100.vs. AD comparison (see 2(SD ratio) that is relatively large (>0.5). About 99.8%  of DV genes detected by clrDV are also identified by MDSeq or GAMLSS-BH; 92.0%  are detected by both MDSeq and GAMLSS-BH; about 0.2%  are uniquely identified by clrDV. GAMLSS-BH identified very large numbers of DV genes in this dataset, but the majority of these are probably false positives, given its relatively poorer control of FDR as shown in the results of the simulation studies. Moreover, these DV genes have estimated log2(SD ratio) with small magnitude, suggesting lack of bioloigcal significance  and 89.8%  of the DV genes detected by one method are detected by all three. About 97.0%  of DV genes detected by clrDV are identified by one of other two methods, and 3.0%  of DV genes detected by clrDV are unique.vs. PSP comparison is similar (The result of the control  similar . GAMLSS-2(SD ratio), which is a ssociated with biological significance, is generally larger than 0.5. For those genes uniquely called by GAMLSS or MDSeq, the order of magnitude is generally below 0.5. With respect to run time, for the control vs. AD comparison, clrDV took about 7.5 min, compared to 6 min for MDSeq, and 13 min for GAMLSS; for the control vs. PSP comparison, clrDV took about 7 min, while MDSeq used 6 min, and GAMLSS used 15 min.The violin plots  and 7 suvs. AD comparison that have the largest estimated SD ratio above 1 are LTBP2, SLPI, C2orf40, and SLC47A1  fibril deposits and less neurodegeneration in Aβ42-transgenic flies.We detected 74 genes from the SLC family in the high confidence DV gene set, including four members of the SLC39 family. ormation . Inhibitβ, a neurodegenerative kinase signaling pathway in the brain (β is implicated in Alzheimer’s disease as a key mediator of cell death (β), a subunit of the GPIb-IX-V protein complex on the surface of platelet cells. Amyloid beta peptides are known to be actively released by platelets  are also found in the high confidence gene sets from the control vs. PSP comparison . Altogether, 2,149 DV genes are common to both comparisons. This observation is consistent with recent findings that transcriptomic changes are in AD and PSP relative to control are strongly correlated  equal variance, different mean; (ii) equal mean, different variance; (iii) different mean, different variance. Although clrDV cannot differentiate genes of the second and the third type, inspection of violin plots should be useful for ascertaining whether the DV genes also appear to differ significantly in the mean of their relative expression level.From a practical perspective, the skew-normal model is an ideal choice since it is mathematically tractable and has mature computational support through the vs. AD and control vs. PSP, a majority of the DV genes identified by clrDV (between 86.1% and 92.0%) were already included in the high-confidence gene set, where the estimated log2(SD ratio) has relatively larger magnitude compared to non-DV genes , clrDV’s good control of FDR and probability of Type II error, and its ease of implementation via the clrDV R package make it a good choice for complementing DE tests with DV tests.Variability of gene expression at aberrant levels is one of the hallmarks of disrupted or dysregulated biological processes. Hence, detection of genes with differential variability should accompany routine differential expression analysis to expand the pool of potential therapeutic intervention targets. clrDV offers a novel approach for identifying DV genes in RNA-seq data. By modeling the null distribution of centered log-ratio transformed RNA-Seq data using a skew-normal distribution, clrDV can detect genes with expression variance that differs significantly between two populations. Simulation results demonstrate that clrDV has a comparable or superior false discovery rate and probability of Type II error compared to its close competitors, while also having a faster run time for larger sample sizes per group. Our analysis of the Mayo RNA-seq dataset revealed that four genes associated with AD pathogenesis  showed significantly higher variance in the AD group. On the other hand, GP1BB and PELP1, which are also linked to AD pathogenesis, showed significantly lower variance in the AD group. In summary, for large RNA-Seq studies (10.7717/peerj.16126/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.16126/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.16126/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.16126/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.16126/supp-5Supplemental Information 5Click here for additional data file.


Lung cancer subtyping based on gene expression data is important for identifying patient subgroups with differing survival prognosis to facilitate customized treatment strategies for each subtype of patients. Unsupervised clustering methods are the traditional approach for clustering patients into subtypes. However, since those methods cluster patients based only on gene expression data, the resulting clusters may not always be relevant to the survival outcome of interest. In recent years, semi-supervised and supervised methods have been proposed, which leverage the survival outcome data to identify clusters more relevant to survival prognosis. This paper aims to compare the performance of different clustering methods for identifying clinically prognostic lung cancer subtypes based on two lung adenocarcinoma datasets. For each method, we clustered patients into two clusters and assessed the difference in patient survival time between clusters. Unsupervised methods were found to have large logrank p-values and no significant results in most cases. Semi-supervised and supervised methods had improved performance over unsupervised methods and very significant p-values. These results indicate that unsupervised methods are not capable of identifying clusters with significant differences in survival prognosis in most cases, while supervised and semi-supervised methods can better cluster patients into clinically useful subtypes.The online version contains supplementary material available at 10.1186/s13104-023-06604-8. The leading cause of cancer death for both men and women is lung cancer . HoweverTraditionally, there have been various unsupervised clustering algorithms used for this purpose. Unsupervised clustering methods treat all features equally, without considering their significance to the clinical outcome. As data typically contain multiple underlying structures, e.g. a set of documents may be clustered based on authorship, topic, or writing style , the resIn this paper, we compare the performance of these three types of clustering algorithms for identifying prognostic subtypes in lung cancer, providing a guidance on future applications of these approaches. The two datasets used are from patients with lung adenocarcinoma, one of the most common types of lung cancer . We willTwo datasets were considered. The first dataset used was the lung adenocarcinoma dataset from The Cancer Genome Atlas Research Network (TCGA-LUAD) , which iThe clustering methods tested were unsupervised methods, semi-supervised methods, and outcome-guided methods. For unsupervised methods, k-means (KM) , GaussiaFor semi-supervised methods, feature selection methods were first used to select features, and these selected features were then used by clustering methods to cluster patients. Specifically, feature selection methods included Cox proportional-hazards regression  and randFor supervised methods, survClust (SC)  was consAll methods were set to cluster into 2 clusters. The number of clusters was selected to evaluate the ability of the methods to separate patients into good and bad prognostic subtypes. Method parameters that were changed from defaults are provided in Supplementary Table To evaluate the performance of the methods, Kaplan-Meier survival curves  were ploUtilizing the survival curves, p-values based on logrank tests were calculated to characterize the performance of the methods. A lower p-value indicates a better-performing method. A significance threshold of p = 0.05 was set to indicate the best performing methods. Since all methods were run 200 times, the average p-value is used to represent the performance of a method. Furthermore, Adjusted Rand Index (ARI)  was usedWe applied unsupervised, semi-supervised, and supervised clustering methods to the TCGA-LUAD  and ShedUnsupervised methods presented non-significant differences  in patient survival time between clusters in most cases, except for the AC and Consensus methods on TCGA-LUAD data and the KM method on Shedden data Table ; Fig. 1.As for semi-supervised methods, both random forest- and Cox Regression-selected models yielded highly significant differences in survival time between clusters for both datasets Table ; Fig. 1.The supervised clustering survClust method also presented a highly significant difference between clusters for both datasets Table ; Fig. 1.To assess the consistency in clustering results across different methods, we calculated the ARI for each pair of methods. As shown in Fig. The analyses above performed clustering using PCs, which is a frequently used approach to reduce the dimension of features but may introduce biases. To make a more unbiased evaluation, we performed a separate set of analyses using all the original features instead of PCs based on TCGA-LUAD data. Results are provided in Supplementary Tables Our study showed that unsupervised clustering methods, regardless of the specific clustering algorithm, were unable to identify lung cancer subtypes with different prognosis in many cases. In contrast, semi-supervised and supervised clustering methods were able to identify subtypes with significant difference in patient survival time. Therefore, when the purpose of clustering is to identify prognostic subtypes, supervised and semi-supervised methods would be preferred.The supervised method, survClust, is unable to converge with large amounts of features, and some measure of feature reduction, such as PCA or a feature selection algorithm, may be required before clustering. It is also more computationally expensive than the other methods tested. Both supervised and semi-supervised methods require the outcome of patients to cluster, so unsupervised methods may be required in a study without outcomes already available, although the clinical interpretation of the clustering results can be ambiguous.Another item of note is that PCA can improve clustering results. Based on our analysis of TCGA-LUAD data, the analyses based on PCs Table ; Fig. 1 Our study focuses on gene expression features for the identification of lung cancer prognostic subtypes. It has been well-documented that other molecular features, such as single nucleotide variants, copy number variations, and fusions, are also associated with lung cancer prognosis , 28. FutBelow is the link to the electronic supplementary material.Supplementary Material 1


Transcriptional classification has been used to stratify colorectal cancer (CRC) into molecular subtypes with distinct biological and clinical features. However, it is not clear whether such subtypes represent discrete, mutually exclusive entities or molecular/phenotypic states with potential overlap. Therefore, we focused on the CRC Intrinsic Subtype (CRIS) classifier and evaluated whether assigning multiple CRIS subtypes to the same sample provides additional clinically and biologically relevant information.2CRIS) was developed for single-sample classification.A multi-label version of the CRIS classifier (multiCRIS) was applied to newly generated RNA-seq profiles from 606 CRC patient-derived xenografts (PDXs), together with human CRC bulk and single-cell RNA-seq datasets. Biological and clinical associations of single- and multi-label CRIS were compared. Finally, a machine learning-based multi-label CRIS predictor (ML2CRIS classifier was validated for retaining the same biological and clinical associations also in the context of single-sample classification.Surprisingly, about half of the CRC cases could be significantly assigned to more than one CRIS subtype. Single-cell RNA-seq analysis revealed that multiple CRIS membership can be a consequence of the concomitant presence of cells of different CRIS class or, less frequently, of cells with hybrid phenotype. Multi-label assignments were found to improve prediction of CRC prognosis and response to treatment. Finally, the MLThese results show that CRIS subtypes retain their biological and clinical features even when concomitantly assigned to the same CRC sample. This approach could be potentially extended to other cancer types and classification systems.The online version contains supplementary material available at 10.1186/s13073-023-01176-5. Despite the advancements in translational oncology, colorectal carcinoma (CRC) is still the second leading cause of cancer death over the world . To imprDespite their great potential for clinical applications, transcriptional classifiers tend to summarize biological traits into separate, non-overlapping subtypes. However, as already shown , subtypeTo tackle these issues, we employed a large collection of human tumors and experimental models to explore the heterogeneity of CRC phenotypes, by fuzzy assignments and single-sample class prediction in bulk and single-cell profiling, unraveling the basis of multiple continuous phenotypes of CRC. These are the foundations for future clinical translation into transcriptome-based nomograms.We used raw counts of RNA-seq expression data from the Colon adenocarcinoma (COAD) and Rectum adenocarcinoma (READ) projects of The Cancer Genome Atlas . We downSELECT (gdc__project__project_id =  = "TCGA-COAD" OR.gdc__project__project_id =  = "TCGA-READ")The dataset includes 698 primary tumor samples aligned to the GRCh38 human assembly, each with 58,387 profiled genes. Furthermore, we collected molecular and survival annotations for the TCGA dataset –23.All the samples were obtained from patients treated by liver metastasectomy or primary tumor excision. Samples were procured and the study was conducted under the approval of the review boards of the institutions . Clinical and pathologic data were entered and maintained in our prospective database. All patients provided informed consent.3 pieces in antibiotic-containing medium; 2 other pieces were coated in Matrigel (BD Biosciences) and implanted subcutaneously in 4- to 6-week-old female NOD (nonobese diabetic)/SCID (severe combined immunodeficient) mice, as previously described [Tumor material not required for histopathologic analysis was collected and placed in medium 199 supplemented with 200 U/mL penicillin, 200 μg/mL streptomycin, and 100 μg/mL levofloxacin. Each sample was cut into 25- to 30-mmescribed , 25. At escribed . All aniWe generated RNA-seq profiles from 646 liver metastatic and primary CRC PDXs   account for at least the 20% of the sample total raw counts: indeed, these samples cannot be considered well-sequenced at the used sequencing depth. Moreover, for each patient, we kept a unique sample only, if needed choosing the one with the highest number of raw counts. Preprocessing of PDX batches included removing technical replicas, keeping only the samples with the highest number of raw counts. At the end of the preprocessing pipeline, we obtained 620 primary tumoral samples in the TCGA dataset and 606 samples in the PDX dataset, with a common set of 15,084 genes.For both TCGA and PDX datasets, we computed the CPM (counts per million) expression values  from thePublic single-cell RNA-seq data were downloaded from the NCBI Gene Expression Omnibus (GEO) database under the accession code GSE132465 . These dWe generated scRNA-seq and bulk profiles for an in-house collection of 5 CRC patient-derived organoids . The PDO single-cell profiles were generated by 10xGenomics, obtaining 15,766 single-cell profiles across all 5 organoid samples. To remove the potential doublets, we used the DoubletFinder tool . Then, wThe pseudo-bulks of GSE132465  and PDO Z-score value for each gene in each sample: these Z-score normalized profiles are then compared with the five CRIS class templates  to assign the sample to the class whose centroid is at the minimum significant distance from the sample normalized profile [https://github.com/cisella/multiCRIS, We employed, as a reference, the original CRIS classifier based on the Nearest Template Prediction algorithm (NTP) , which r profile . The thr profile , 11, 35.For single-cell data, all genes with at least one read per gene in a sample were included, for a total of 95% of the CRIS gene signatures. For the implementation of single-sample classifiers on the CRIS classification task, we used only confident NTP single-label assignments as state-of-the-art references for the training and testing phases of each alternative method; specifically, this meant using 562 confidently classified TCGA samples and 550 confidently classified PDX samples.We implemented and compared the performances of several state-of-the-art single-label classifiers to assign the most prominent CRIS class (primary class) to each sample individually: Random Forest (RF) , ExtremeUsing NTP single-label assignments as target references and CRIS gene signature as feature space, we trained all the assessed classifiers with a tenfold stratified cross-validation on 70% of the TCGA confident samples (BH.FDR < 0.2); the remaining 30% was instead kept aside for a first testing. We included 393 TCGA samples in the training set and 169 TCGA samples in the testing set, while all 550 PDX samples were used entirely as an independent testing set to further evaluate the performances of all the classifiers on completely independent data of a different type (liver metastasis PDXs instead of primary human tumors). During the training phase, we also performed hyperparameter tuning to optimize the performances of the classifiers by finding the best hyperparameter values, then used to train each model one last time on the entire training set.All the details about the mentioned models, data splitting, hyperparameter tuning, and training Supplementary and testing phases are described in To move towards multi-label classification, we made use of algorithm adaptation strategies ; this kiTo assess the performances of our multi-label single-sample classifiers, we used multi-label calls coming from our multiCRIS approach. In multiCRIS, a sample can be assigned to one or more “secondary” classes for which the membership is lower than for the primary class but still significant (BH.FDR < 0.2). To define reliable secondary classes as reference targets, the NTP multiCRIS calls of each sample were further compared to class-specific thresholds. These thresholds were computed for each class as the 5th percentile of the primary class NTP scores on the entire TCGA and PDX sets separately  between 36 and 60 months in the considered datasets. Because of this, all samples with disease-free survival greater than 36 months have been considered as disease-free at the 36-month endpoint. To assess the statistical significance of the difference between two compared survival distributions, we used the log-rank test, a non-parametric hypothesis test that can be used also when some observations are censored.p-value threshold of 0.05 to discriminate statistically significant annotations from not significant ones. Also, to enrich our evaluation of each clinical correlate, we computed odds ratio and effect sizes, which respectively make comparisons with the overall scenario or with the complementary cases, as detailed in For Fisher tests, we used a To improve stratification and capture the biological traits of CRC according to the CRIS classification, we reasoned that its Nearest Template Prediction (NTP) algorithm can be employed not only to assign the single most prominent class, but also to evaluate the assignment of each sample to all CRIS classes, as well as the false discovery rate of each assignment . Thus, wMultiCRIS was first applied to the 620-sample RNA-seq dataset from The Cancer Genome Atlas (TCGA) , to confTherefore, each tumor may be assigned either to a single class (48% of the assigned samples), to whom it displays the only significantly low distance, or to multiple classes Fig. b, of whiNotably, for all CRIS subtypes, the number of secondary assignments was grossly equivalent to primary assignments  and bulk profiles in an in-house collection of 5 CRC organoids derived from PDXs. These data allowed direct comparisons of single-cell and bulk transcriptional profiles. As a third option, pseudo-bulk profiles were obtained by aggregating all single-cell profiles derived from one sample. Notably, while the profiles from individual cells captured on average 1116 transcripts with at least 5 supporting reads  , focusin2465 , foAltogether, these results show that the heterogeneity of the CRIS transcriptional profile is rooted at the single-cell level and that the phenotypes of individual cells sum up to define the CRIS classification of the bulk tumor. Therefore, the evidence of multiCRIS tumors can be mainly explained either by a mosaic composition of different cell populations with specific functional characteristics, or by a small portion of hybrid cells with mixed phenotype.Z-score calculated on batches of samples, without allowing single-sample classification [MultiCRIS paves the way for complex biological and clinical readouts; however, it is influenced by its NTP implementation, which relies on centroid distance and gene-level fication . To overfication . The confication , Supportfication , Neural fication , and Extfication . These sn = 562) and of the cohort of patient-derived xenograft . TCGA data were divided in training and testing sets, keeping the same CRIS class proportions of the entire dataset. Each classifier was trained considering only the expression values of the CRIS genes as feature space and using the NTP primary class as target reference [To evaluate each algorithm and identify the one most suitable to predict CRIS class memberships in a clinically applicable single-sample classifier, we took advantage of the collection of primary CRC samples from the TCGA project ), and threshold-based measures , as to evaluate the performances of the considered algorithms and compare them with the ones of the original NTP approach  Additio. AlthougFurthermore, in the single-label scenario, the machine learning methods were compared with the TSP approach, a first attempt of a single-sample CRIS classifier, that had originally shown quite limited concordance with NTP classification . Its subAlso, on the PDX set, LSVM reaches the most convincing results. The graphs in Additional file Thus, based on performance evaluations, we identified LSVM as the best single-label classifier in predicting the primary CRIS class of each single CRC sample. However, all the three trained algorithms are capable of computing memberships to all the 5 CRIS classes by means of algorithm adaptation techniques , which pFollowing an algorithm adaptation strategy, we developed multi-label adapted (mla) single-sample CRIS classifiers. Specifically, each mla algorithm inherits the primary class assignment from its single-label version but can associate any heterogeneous sample with one or more additional secondary classes.To evaluate mla classifiers, we used both metrics analogous to the single-label ones, but adapted for the multi-label context  and specific multi-label measures . All these metrics compare the results of mla algorithms with target assignments obtained from the MultiCRIS approach, introduced in this work.Among mla algorithms, LSVM still reached the best overall performance when considering class precisions and recalls, revealing to be the most robust approach also in the multi-label context  and multi-label adapted  LSVM on the TCGA testing and PDX sets, respectively, are reported in Additional file Both the single-label and multi-label LSVM classification results were evaluated for molecular and clinical correlates.We first evaluated the prognostic value of LSVM-based models, on the TCGA dataset considering different scenarios: only the samples assigned to the class as primary class, only the samples assigned to the class as secondary class, or all the tumors classified to the CRIS class regardless of whether it is a primary or secondary assignment. In all such cases, any comparison using the Fisher test  is alwayResponse to anti-EGFR treatment was evaluated in the PDX cohort. We confirmed associations of CRIS-C and sensitivity to Cetuximab, for which single-label LSVM (odds ratio (O.R.) = 3.281, confidence interval (CI) = 1.66–6.73) and multi-label LSVM, including also secondary assignments in the CRIS-C cohort, displayed similar performances  , while assessing each patient individually in a clinical usage setting.All these evidences confirmed the reliability of the predictions obtained with our LSVM single-sample models, especially the mla LSVM, henceforth referred to as MLThe global transcriptome profile of bulk tumors provides and aggregates portraits of several cell types composing the whole tumor ecosystem, including cancer cells, vessels, fibroblasts, and immune cells. These data provide invaluable information to discern the biology of different tumor types and improve patient stratification for clinical practice. However, such data stem from different tissues, and in current biomedical research, it is essential to dissect the contribution of each cell type to tailor the most suited therapeutic strategy.In this perspective, the CRIS classifier was designed to subdivide CRC into five subtypes specifically based on the intrinsic epithelial cancer cell transcriptome , 11. ThiIn this work, we show that multiple CRIS subtypes can coexist in the same tumor and that this is a quite common occurrence in CRC. To investigate this aspect, we implemented a multi-label CRIS NTP classifier to enable statistically significant assignment of each tumor sample to one or more classes. With multi-label classification, the majority of TCGA CRC cases received multiple assignments. The observation that key biological and molecular features were associated with CRIS classes, even when a secondary assignment was attributed to the sample, indicates that a fuzzy classification reliably reflects the complexity of the tumor biology. Multiple assignments were driven by two main causes: (i) concomitant presence of cell subpopulations with distinct CRIS phenotypes, or (ii) a homogeneous cell population carrying a hybrid CRIS phenotype. To discern the relative contribution of each of the abovementioned drivers of heterogeneity, we took advantage of matched single-cell and bulk RNA-seq profiles to compare the fuzzy assignment of the tumor bulk with single-cell multi-label CRIS assignments. The observed concordance of classification between organoid bulk and single-cell RNA-seq profiles confirmed the adequacy of multiCRIS also for single-cell classification. It should be noted that the robustness of multiCRIS derives from its use of lists of signature genes, without any quantitative parameters assigned to them. This approach has proven robust to cross-platform analyses, which typically involve dropout of signature genes ; further2CRIS. Indeed, the here designed multi-label adaptation procedure allows enriching the original single-label assignments of the model with the addition of secondary classes. ML2CRIS correctly assigns the CRIS classes based on the original single-label NTP algorithm in 91.7% of TCGA test cases, with an average precision of 92.6% in estimating the multi-label characterization of each sample. Besides these encouraging performances, primary and secondary CRIS assignments of ML2CRIS confirmed that the molecular traits characterizing each of the five subtypes  were maintained also in secondary assignments. When the single-label LSVM-based classifier was applied, CRIS-C cases were significantly associated with Cetuximab sensitivity . In the multi-label scenario of ML2CRIS, when secondary CRIS-C cases were added to primary ones, the odds ratio increased (3.359), although with reduced significance . However, when secondary CRIS-C cases were removed from both groups, and CRIS-C primary cases were compared with non-CRIS-C (not even secondary), the odds ratio increased together with its significance .To overcome the need of NTP for a large sample series, machine learning-based single-sample classification methods  have been evaluated here. These methods can reflect sample inner complexity while overcoming the dataset-dependence issue of the multiCRIS NTP approach. This is essential towards a clinical application of such transcriptional classification of patients’ samples, single or in small batches, to assist in therapy decision in clinical practice. Furthermore, this method can also be successfully applied to data from clinical trials, which typically target a specific subgroup of cases that are likely not balanced in terms of CRIS class representation. Among all the tested classifiers, the Linear Support Vector Machine resulted as the best algorithm, both in single-label and in its multi-label adapted configuration, named ML2CRIS shows promising capabilities in predicting CRIS-C patients responsive to Cetuximab pharmacological treatment, with adjustable sensitivity and specificity depending on the assignments of CRIS-C subtype secondary samples.Therefore, MLThese results confirm that tumors harboring multiple CRIS phenotypes also express clinical features that reflect their multiple assignments. However, our study was limited to broad multi-label analysis. Dedicated studies with a higher number of multi-label samples may allow, in the future, to explore relations between different classes and how they affect the course of the disease.In summary, this work provides the biological and methodological basis for multi-label fuzzy classification of CRC. The results presented here confirm that capturing intratumor heterogeneity provides a more comprehensive picture of the biological and clinical features of human tumors. This information represents a milestone for deciphering tumor biology and for the development of novel therapeutic strategies, and it could be extended to other tumor types.Additional file 1: Table S1. Gene expression profile measured as reads count per genes of PDX collectionaccording to GeneCode version 27 quantified by featureCounts. Reads mapped to Mus Musculus genome were removed from the gene quantification.Additional file 2: Table S2. Assignment of 620 TCGA samples using the Nearest Template PredictionmultiCRIS classifier. In the results, we identified samples with single label class CRIS, multi-label classes CRIS, and not classified. For classassignmentby NTP, FDR < 0.2 was considered significant.Additional file 3: Table S3. Numbers and percentage of CRIS call assignments with different BH.FDR thresholds.Additional file 4: Table S4. NTP-based multiCRIS classification of the RNA-seq bulk profiles of TCGA samples. Summary table of results with the number of samples with single-label class CRIS, multi-label class CRIS, and not primary assignment, primary assignment and any assignment.Additional file 5: Table S5. Average of distance to the centroids of CRIS classes for primary and not primary assignments. The statistical significance has been computed by Student's T-test.Additional file 6: Table S6. Overlap between CRIS classes in NTP multilabel assignments. Analysis performed on TCGA dataset.Additional file 7: Table S7. Summary of the number of samples assigned to secondary classes with peculiar molecular features as MSI status, BRAF/KRAS mutation and EMT, WNT and paneth scoring. For dichotomous variables, hypergeometric p-value has been computed to assign statistical significance at the ratio Observed over the Expected. For continuous variable, Student's T-test was performed. NC, not assigned to reference class, i.e. not assigned to that class.Additional file 8: Figure S1. Distribution of genes detected in scRNA-seq data of patient derived organoidswith a coverage of 1 to 5 reads for transcripts. Figure S2. Distribution of Pearson correlation values obtained by analysing unmatchedand matchedCRC PDO bulk / PDO pseudo-bulk pairs. Figure S3. CRIS classification of CRC single sample data from GSE132465, pseudo-bulk, and at scRNA-seq resolution. B) represents the distribution of CRIS distances evaluated on each single cell, grey points. Black points represent distances for cells with significant assignment. Red dots, represent the class distances on pseudo-bulks. C) waterfall of CRIS distances for significant assigned cells. Supplementary Fig. 4. Distribution of the CRIS classes on testing samples of TCGA and PDX datasets, using LSVM single-labeland adapted to multi-label. The TCGA dataset has 169 samples, while the PDX dataset 550 samples. Figure S5. Forest plots for LSVM-based models. Along y-axis, an identifier number for each test is reported. For each test, the odds ratioand its confidence interval are shown; the numeric label annoated on each odds ratio represents the p-value. Other details of the testsare listed below the corresponding plot.Additional file 9: Table S8a. Counts data of pseudo-bulk RNA-seq profiles of CRC organoids. Pseudo-bulk profiles have been computed by summing the gene counts of scRNA-seq profiles. Table S8b. Counts data of bulk RNA-seq profiles of CRC organoids.Additional file 10: Table S9. NTP-based multiCRIS classification in the bulk CRC organoids. NTP analysis has been performed on bulk PDO data identifying sample with Single-label CRIS classification, Multi-labels CRIS classification, and Not classification. For classassignmentby NTP, FDR < 0.2 was considered significant.Additional file 11: Table S10a. NTP-based multiCRIS classification on scRNA-seq profiles of CRC organoids. NTP analysis has been performed on scRNA-seq PDO data identifying cell with single-label class CRIS, Multi-label classes CRIS, and not classification. For classassignmentby NTP, FDR < 0.2 was considered significant. Table S10b. NTP-based multiCRIS classification in the CRC organoids. Summary table of NTP results on total single-cell datasets and at sample levels with percentages of cells assigned, not classified, single-label CRIS class assignment and multi-labels CRIS assignments.Additional file 12: Table S11. NTP-based multiCRIS classification on pseudo-bulk CRC patients. NTP analysis has been performed on CRC patients data identifying sample with Single-label CRIS classification, Multi-labels CRIS classification, and Not classification. For classassignmentby NTP, FDR < 0.2 was considered significant.Additional file 13: Table S12a. NTP-based multiCRIS classification on the single-cell RNA-seq profiles from CRC patient. NTP analysis has been performed on scRNA-seq CRC patient data identifying cell with Single-label class CRIS, Multi-labels classes CRIS, and Not classification. For classassignmentby NTP, FDR < 0.2 was considered significant. Table S12b. NTP-based multiCRIS classification on the single-cell RNA-seq profiles from CRC patient. Summary table of NTP results on total single-cells datasets and at sample levels with percentages of Assigned, Not Classified, Single-label CRIS class assignment and Multi-labels CRIS assignments.Additional file 14: Table S13. Single-label performance evaluations of the considered algorithms on testing data, comparing their results with the ones of the original NTP approach.Additional file 15: Table S14. Multi-label performance evaluations of the considered algorithm-adapted strategies on testing data, comparing their results with multi-label assignments obtained from the multiCRIS version of the NTP approach.Additional file 16: Table S15. Multi-label assignments of the algorithm-adapted LSVM on TCGA testing samples, compared with multi-label assignments from the multiCRIS version of the NTP approach.Additional file 17: Table S16. Multi-label assignments of the algorithm-adapted LSVM on PDX samples, compared with multi-label assignments from the multiCRIS version of the NTP approach.Additional file 18: Table S17. Genes selected based on feature importance.Additional file 19. Supplementary material of "Multi-label transcriptional classification of colorectal cancer reflects tumour cell population heterogeneity.


To predict CTLA4 expression levels and prognosis of clear cell renal cell carcinoma (ccRCC) by constructing a computed tomography‐based radiomics model and establishing a nomogram using clinicopathologic factors.n = 102) datasets. The support vector machine (SVM) was employed to establish the radiomics signature for predicting CTLA4 expression. Receiver operating characteristic curve (ROC), decision curve analysis (DCA), and precision‐recall curve were utilized to assess the predictive performance of the radiomics signature. Correlations between radiomics score (RS) and selected features were also evaluated. An RS‐based nomogram was constructed to predict prognosis.The clinicopathologic parameters and genomic data were extracted from 493 ccRCC cases of the Cancer Genome Atlas (TCGA)‐KIRC database. Univariate and multivariate Cox regression and Kaplan–Meier analysis were performed for prognosis analysis. Cibersortx was applied to evaluate the immune cell composition. Radiomic features were extracted from the TCGA/the Cancer Imaging Archive (TCIA) (p = 0.004). The radiomics model for the prediction of CTLA4 expression levels  was established using seven radiomic features. A significant elevation in infiltrating M2 macrophages was observed in the RS high group (p < 0.001). The predictive efficiencies of the RS‐based nomogram measured by AUC were 0.826 at 12 months, 0.805 at 36 months, and 0.76 at 60 months.CTLA4 was significantly overexpressed in ccRCC tissues and was related to lower overall survival. A higher CTLA4 expression was independently linked to the poor prognosis (HR = 1.458, 95% CI 1.13–1.881, CTLA4 mRNA expression status in ccRCC could be predicted noninvasively using a radiomics model based on nephrographic phase contrast‐enhanced CT images. The nomogram established by combining RS and clinicopathologic factors could predict overall survival for ccRCC patients. Our findings may help stratify prognosis of ccRCC patients and identify those who may respond best to ICI‐based treatments. CTLA4expression could be predicted non‐invasively using radiomics model. Overall survival of ccRCC could be predicted by a nomogram combined with radiomics score and clinicopathological factors. Cases with overall survival <30 days (n = 17), no genomic data (n = 11), unknown TNM stage and grade (n = 8), unknown race (n = 7), and bilateral tumor (n = 1) were excluded. Finally, a set of 493 cases was obtained according to these criteria. Detailed clinicopathologic information and genomic data were obtained. Gene expression data from ccRCC tumor samples and normal tissues were collected from the TCGA and GTEx . After log2 transformation, the Toil pipelinehttps://xenabrowser.net/datapages/). The expression levels of CTLA4 were compared between normal tissues and tumor. The Kaplan–Meier method was carried out to plot survival curves. The log‐rank test was employed to analyze differences in outcomes between groups. Univariate and multivariate Cox regression using the “survival” and “forest plot” package in R was performed to identify independent risk elements for overall survival.Data of 537 ccRCC patients in total were collected from the TCGA database (2.2n = 237) were obtained from the TCIA database (http://www.cancerimagingarchive.net/). The exclusion criteria used to select images for further analysis were specified as follows: (1) non‐contrast scans, (2) postoperative scans, and (3) poor‐quality scans. Additionally, imaging data without corresponding matched clinical and genomic data in the TCGA‐KIRC cohort were also excluded, and the final integrated cohort comprising 102 ccRCC cases.The imaging data of all ccRCC patients  was utilized for radiomic feature extraction. The categories of radiomic features consisted of 18 first‐order statistical features, 14 shape‐based features (2D and 3D), 16 gray‐level run‐length matrix (GLRLM) features, 24 gray‐level cooccurrence matrix (GLCM) features, 14 gray‐level dependence matrix (GLDM) features, 16 gray level size zone matrix (GLSZM) features, and 5 neighborhood gray‐tone difference matrix (NGTDM) features.An open‐source PyRadionics software version 2.1 (n = 91) were considered stable and selected for further analysis. The radiomics workflow is displayed in Figure By using the R package “irr,” the intraclass correlation coefficient (ICC) was applied to evaluate the stability of the features,2.5The prediction model was trained on the initial feature set and weight was assigned to each feature. Next, features with the lowest absolute weights were recursively removed from the current feature set until the required number of features was reached. To minimize overfitting, the most valuable features from the primary feature set were selected via recursive feature elimination (RFE), a method for reducing the number of features used in a classification algorithm. Also, several machine learning methods, including random forest, logistic regression, deep neural network, and support vector machine (SVM), have been applied to select features.The radiomics signature model was validated and evaluated using 10‐fold internal cross‐validation on both the training set and the validation set. First, the dataset was randomly divided into 10 groups of roughly equal size; then one of the folds was selected to be the holdout set and fit the model on the remaining nine‐folds; this process was repeated nine times using a different set each time as the holdout one. The receiver operating characteristic curve (ROC) and the area under the ROC (AUC) were calculated to assess the overall performance using the R package “pROC”.2.6The RS‐based nomogram was constructed with “rms” R package2.7To gain insight into the molecular mechanism associated with CTLA4 expression differences, the Gene Set Enrichment Analysis (GSEA) was carried out using the “cluster Profiler” R package2.8https://cibersortx.stanford.edu/) was used. The association between CTLA4 expression and immune cell infiltration in ccRCC was analyzed using the “limma” R package.To estimate the relative immune cell infiltration levels of 22 cell types, a matrix of gene expression data from ccRCC samples were uploaded and the Cibersortx software  and low (n = 175) CTLA4 expression groups using the CTLA4 expression level of 0.4200767 as an optimized cutoff. The demographic and clinical features of the patients are shown in Table TCGA‐KIRC cohorts were used to investigate CTLA4 expression in adjacent non‐tumor tissues and ccRCC tissues. The results suggested that the CTLA4 mRNA expression level in ccRCC tissues was notably higher than that in normal tissues Figure . The optp < 0.05). In addition to this, no other significant differences were found between the two groups. The clinicopathologic characteristics of the analysis cohort were visualized by heatmaps according to CTLA4 status  cells compared to the CTLA4 low expression group . After the establishment of the radiomics CTLA4 prediction model via the SVM algorithm, seven features were finally selected to construct the radiomics signature for CTLA4 assessment through the REF algorithm. The selected features were GLCM‐cluster shade, SHAPE‐maximum 2D diameter slice, GLSZM‐size zone non‐uniformity, SHAPE‐least axis length, GLSZM‐gray level nonuniformity, GLCM‐sum squares, and GLSZM‐small area emphasis, and their importance is shown in Figure p = 0.079). Importantly, the DCA graphically indicated the clinical applicability of the radiomics signature.As shown in Figure p value <0.05  according to RS status are presented in Figure The RS calculated by radiomics signature represented the predicted probability of CTLA4 mRNA expression levels. The optimal cutoff point of RS was determined as 0.658 using the R package “survminer”.5 Figure . The cli3.5Based on the variables independently associated with prognosis from the multivariate Cox regression analysis, an RS‐based nomogram was developed to predict overall survival probability at 12, 36, and 60 months Figure . The AUC4Over the past 20 years, the treatment paradigm for ccRCC has significantly revolutionized with the emergence of targeted agents and immunotherapeutic drugs. A previous study illuminated that ICI can target and block PD‐1 or CTLA4 to restore tumor‐specific T‐cell immunity.CTLA4 is an immune checkpoint molecule and a transmembrane protein expressed by regulatory T cells and activated T cells, which delivers inhibitory signals to T‐cell activation. Sharing a significant sequence identity with a homologous receptor CD28, CTLA4 has a higher affinity for the costimulatory ligands CD80 and CD86 on antigen‐presenting cells.Accumulating evidence indicates that CTLA4 is a valuable biomarker for ccRCC. However, there is no available method to noninvasively assess the CTLA4 mRNA expression levels. According to a previous study, higher serum CTLA4 level was correlated with melanoma response and clinical benefit from ipilimumab therapy, but the work is greatly limited due to a very small sample size.n = 102) and advanced/metastatic ccRCC cases (n = 37) recruited in this study were relatively small. Moreover, it remains unclear whether the established radiomics signature and the RS‐based nomogram can be applied to advanced/metastatic ccRCC with adequate predictive value. Therefore, there is an urgent need to further explore a larger number of ccRCC patients with different disease stages .Despite these novel findings, this study has some limitations that deserve comment. First, the conclusion of this study was based on the data obtained from public databases, which was inevitably limited by the quality of data and images. Second, the number of total ccRCC cases ; data curation ; formal analysis ; writing – original draft . Zhijia Jin: Data curation ; formal analysis . Jun Dai: Data curation ; formal analysis . Haofei Wang: Visualization . Jianqi Sun: Visualization . Danfeng Xu: Conceptualization ; writing – review and editing .This study was supported by the Medical and Technology Intercrossing Research Foundation of Shanghai Jiaotong University (YG2016QN65) and the Guangci Excellent Youth Program of Ruijin Hospital (GCQN‐2018‐B15).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.https://portal.gdc.cancer.gov/) and TCIA database (http://www.cancerimagingarchive.net/). TCGA and TCIA belong to public databases. The patients involved in the database have obtained ethical approval. Users can download relevant data free for research and publication of relevant articles. Our study is based on open‐source data, so there are no ethical issues and other conflict of interests.All data were collected from the TCGA database (Figure S1.Click here for additional data file.Supplemental Table S1.Click here for additional data file.Supplemental Table S2.Click here for additional data file.