The two types of omics data were projected onto a unified space using SCCA, followed by data fusion to identify cancer subtypes. The subtypes we identified recapitulated subtypes previously recognized by other groups , but display more significant clinical associations. Especially in ovarian cancer, the four subtypes we identified were significantly associated with overall survival, while the taxonomy previously established by TCGA did not . The multi-omics classifiers we established can not only classify individual types of data but also demonstrated higher accuracies on the fused data. Compared with iCluster, SCCA-CC demonstrated its superiority by identifying subtypes of higher coherence, clinical relevance, and time efficiency. In conclusion, we developed an integrated bioinformatic framework SCCA-CC for cancer molecular subtyping. Using two case studies in breast and ovarian cancer, we demonstrated its effectiveness in identifying biologically meaningful and clinically relevant subtypes. SCCA-CC presented a unique advantage in its ability to classify both single-omics data and multi-omics data, which significantly extends the applicability to various data types, and making more efficient use of published omics resources.It is now clear that major malignancies are heterogeneous diseases associated with diverse molecular properties and clinical outcomes, posing a great challenge for more individualized therapy. In the last decade, cancer molecular subtyping studies were mostly based on transcriptomic profiles, ignoring heterogeneity at other (epi-)genetic levels of gene regulation. Integrating multiple types of (epi)genomic data generates a more comprehensive landscape of biological processes, providing an opportunity to better dissect cancer heterogeneity. Here, we propose sparse canonical correlation analysis for cancer classification (SCCA-CC), which projects each type of single-omics data onto a unified space for data fusion, followed by clustering and classification analysis. Without loss of generality, as case studies, we integrated two types of omics data, mRNA and miRNA profiles, for molecular classification of ovarian cancer ( It has been recognized that cancers are heterogeneous diseases comprising multiple subtypes with distinct molecular properties associated with discrepant clinical outcomes. In the last decade, tremendous efforts have been made in identifying cancer molecular subgroups . Unlike To address the challenge, several computational models have been proposed, which showed promising performance. For instance, non-negative matrix factor (NMF) can be used to project multi-omics data onto dimension-reduced space for integration based on non-negative matrix decomposition , 2012. HTo overcome the above-mentioned challenges, we propose to fuse different types of omics data for clustering and classification of tumor samples by canonical correlation analysis (CCA) , a class1 as well as single-omics datasets from other independent datasets. Furthermore, we demonstrated that SCCA-CC is superior to other popular methods such as iCluster in the coherence and clinical relevance of identified cancer subtypes, and the running time consumed.In this study, we propose to project single-omics data onto a unified space by SCCA for data fusion, followed by clustering analysis on the fused data to identify cancer subtypes . The tran = 285) (n = 133), OC179 , and Bagnoli  datasets (n = 216) and a miRNA dataset , of which 207 samples have both types of data.We collected mRNA and miRNA expression profiles for 462 ovarian cancer patients and 451 breast cancer patients from the TCGA database. Single-omics (mRNA or miRNA) datasets were collected from gene expression omnibus (GEO). More specifically, we downloaded one mRNA dataset  , and thrdatasets  in the odatasets , which iA =  ∈ RDa×n, B =  ∈ RDb×n, we can obtain the objective projection matrices Pa ∈ RDa×m and Pb ∈ RDb×m corresponding to A and B, respectively by SCCA to maximize the correlation coefficient ρ:Canonical correlation analysis was proposed in 1936, which was aimed to use fewer combinatorial variables to reflect the correlation between the original two variable groups . The meaFeature-level fusion meant the aggregation of features obtained from various methods of feature extraction. As the features were compressed and extracted to some extent compared with the raw data, the complexity is much lower, and the computation is much more efficient. Much more importantly, feature-level fusion is more tolerant to specific data types, enabling the effective fusion of various omics data. Sparse CCA was implemented using the “CCA” function in R package “PMA”, which performs sparse CCA using the penalized matrix decomposition. Lasso penalty was used to obtain the corresponding canonical vector to enforce sparsity by setting the parameters “typex” and “typez” to “standard”. The sparsity was determined by the penalties applied to the input matrix. The penalties were set to the default value of 0.3 in the analyses. The number of canonical vectors was determined by the lower number of dimensions of the preprocessed mRNA and miRNA data as mentioned in Methods by the parameter “K”. The other parameters were kept by default in the function.After projecting two types of omics data to the same space, where, α ∈  represents the fusion coefficient. In our case studies, we set an equal weight (fusion coefficient) for each type of omics data, and the fused data Z is used for the following consensus clustering analysis.To identify molecular subtypes, we performed unsupervised classification on the fused TCGA data in the unified space. To ensure robustness, we employed the widely adopted consensus clustering method , with 502). SigClust , suggesting that SCCA-CC recapitulated the four subtypes previously defined, i.e., proliferative, immunoreactive, differentiated, and mesenchymal , while the four TCGA subtypes did not , supporting our hypothesis that incorporating different types of omics data may identify more clinically relevant subtypes than single-omics approaches.In total, we obtained matched mRNA and miRNA expression profiles from 462 ovarian cancer samples in the TCGA cohort. To eliminate the impacts of magnitude scale and ensure the comparability of data, within each type of omics data we performed z-score normalization and filtered out genes or miRNAs with low between-sample variations . The preprocessed mRNA and miRNA data were subsequently projected onto a unified space using SCCA, followed by data fusion based on a weighted averaging strategy (α = 0.5) . Using tenchymal . NotablyTo further elucidate the OC subtypes, we performed differential gene expression analysis by comparing each subtype with the others and then identified subtype-specific biological functions based on GSEA . We confP < 0.001, Wilcoxon Signed–rank tests), and, remarkably, achieved even higher accuracies on the fused data . The results supported our hypothesis that SCCA-CC achieved higher classification performance when more information is incorporated.A unique advantage of a multi-omics classifier lies in its ability to handle both single-omics data and multi-omics data, making more efficient use of different types of data potentially . In our n = 279) and a miRNA dataset (n = 442) merged from GSE73581, GSE73582, and Bagnoli miRNA . More specifically, patients classified to OC2  had the worst overall survival, while those classified to OC3 (immunoreactive-like) had the best outcome, which was consistent with previous studies : 91.6% using the fused data, 83.0% using only the mRNA data, and 74.8% using only the miRNA data . Therefoli miRNA  datasets studies . These r studies .Breast carcinoma is the most common type of gynecological cancer, as it alone accounts for 24.2% of all new cancer incidences in women in 2018 . Over thP < 0.001, P < 0.001, hypergeometric tests; P < 0.001, McNemar–Bowker test). To further elucidate the biological properties associated with identified subtypes, we performed differential gene expression analysis by comparing each subtype with the others, followed by GSEA to detect subtype-specific biological functions. The GSEA results . We projected the preprocessed data onto a lower-dimensional space by SCCA for data fusion using the weighted averaging method (α = 0.5) subsequently. Based on consensus clustering of the fused data, we determined the optimal five breast cancer subtypes . Pairwis results  suggeste results . Since CP < 0.001, Wilcoxon Signed–rank tests), and they achieved even higher accuracies on the mRNA-miRNA fused data . Consistent with ovarian cancer, our results in breast cancer further demonstrated the improved classification performance of SCCA-CC on multi-omics data.For breast cancer, we also employed the five classification algorithms: RF, SVM, KNN, Min-Dis, and Bayesian classifier to build the classifiers. Using the labels obtained from the consensus clustering as the reference, we randomly selected 200 breast cancer samples from the TCGA cohort to construct classifiers based on the mRNA-miRNA fused data and evaluated the performance on other 251 samples. We repeated the tests 100 times and compared the results of all types of omics data for each classifier. Similar to the ovarian cancer case study, we also found that all the classifiers demonstrated higher accuracies on the mRNA data than on the miRNA data (n = 216), a miRNA dataset (n = 210), of which 207 samples have both types of data, were used for validations . More interestingly, a higher significance of prognosis was observed using the predicted subtypes based on the fused data . Regardless of single-omics or multi-omics classifications, the predicted Class 4 (Her2+ like) subtype always displayed the worst overall survival, while the Class 2  subtype showed more favorable clinical outcome. These results about clinical associations were consistent with previous studies, demonstrating the general applicability of the multi-omics classifier . Together, our case study suggested that SCCA-CC was able to identify subtypes that are more clinically relevant, and again supported our hypothesis that incorporating different types of omics data may capture more comprehensive intrinsic characteristics of breast cancer than a single data type.In this case study, SVM demonstrated the best performance and relatively low volatility: 94.8% using the fused data, 88.74% using only the mRNA data, and 80.4% using only the miRNA data. Therefore, we trained a multi-omics classifier based on SVM using all the 451 TCGA samples and evaluated the general applicability of the classifier to other independent datasets. The GSE22220 series, including a mRNA dataset  Silhouette score evaluating the cluster coherence. A higher Silhouette score indicates that samples are more similar within subtypes; and (iii) The algorithm running time evaluating computational complexity. Using varying numbers of genes preselected based on MAD, we performed subtyping analysis using SCCA-CC and iCluster, respectively. As a result, we found SCCA-CC outperformed iCluster based on the three different clustering performance measures in almost all the different scenarios  cenarios . The algcenarios .P = 0.031, Univariate Cox regression in P = 0.0387, and log–rank test in To further compare SCCA-CC with other cancer taxonomies and clinical risk factors, we performed Cox regression analyses in ovarian cancer (TCGA dataset) and breast cancer (GSE22220), respectively. For both cancer types, we first employed iCluster to identify the subtypes with default parameters and evaluated the associations between the identified subtypes with the reference subtyping results based on TCGA or PAM50 , 6. In oSparse CCA provides sets of variables with sparse loadings, which is consistent with the belief that only a small number of genes are expressed under specific conditions . Previoun = 1260) were calculated between these variables using their original expression data. Interestingly, we found apparent correlation (negative or positive) between the expression levels of mRNAs and miRNAs, suggesting their potential interactions  were calculated between these variables using their original expression data. As a result, we also found strong correlation (negative or positive) between the expression levels of mRNAs and miRNAs (Similarly, in BRCA we checked the first canonical variate pair of genes and miRNAs dataset and found 204 non-zero mRNA variables and 57 non-zero miRNA variables. Pairwise correlation coefficients (d miRNAs . A backgd miRNAs . We furtd miRNAs , suggestd miRNAs . Gene CKd miRNAs .Together, these results provide compelling evidence that the sparse CCA selected biologically relevant genes and miRNAs, which explains their strong expression correlation enabling multi-omic data fusion in the projected space.Cancer molecular heterogeneity hampers the selection of patients for more optimized clinical management and the design of targeted agents. During the last decade, tremendous efforts have been made to dissecting the inter-tumor heterogeneity in an overwhelming number of studies based on unsupervised classification of high-throughput omics profiles. These studies gained novel insights into cancer biology with important clinical implications, which laid a solid foundation for precision medicine. However, most of these studies were based on single-omics data, especially transcriptomic data, which ignored other genetic and epigenetic levels of gene regulation, and resulting in only partial understanding of cancer heterogeneity. Recent studies have seen a growing interest in integrating multiple types of omics data for more comprehensive cancer subtyping, but few existing methods can classify both single-omics and multi-omics data. In this study, we developed SCCA-CC, a robust and efficient framework for cancer subtyping and classifications based on data fusion using sparse CCA followed by unsupervised classification. Using two case studies on multiple independent cohorts, we demonstrated that SCCA-CC was able to identify biologically meaningful and clinically more relevant taxonomies.Conventional CCA may suffered from the high dimensionality of genomic data where the number of observations greatly exceeds the number of samples, leading to high risk of potential collinearity and unstable estimates . PCA is For both ovarian and breast cancers, the subtypes identified by SCCA-CC recapitulated the taxonomies previously established, as suggested by the pairwise statistical tests and GSEA. However, SCCA-CC derived subtypes showed a more significant association with clinical outcomes. Notably, ovarian cancer subtypes identified by SCCA-CC were significantly associated with overall survival in the TCGA cohort, while the four subtypes previously defined by TCGA did not show any significant clinical association. On independent mRNA, miRNA, and fused datasets, SCCA-CC demonstrated consistent clinical associations in both our ovarian and breast cancer studies. These results demonstrated that SCCA-CC is able to detect the biologically coherent subgroups in different types of single-omics data, and incorporating multiple types of omics data can further improve the prediction performance.More importantly, a number of published studies with only miRNA expression profiles cannot be classified using existing subtyping systems based on mRNA expression signatures. SCCA-CC presented a unique advantage in its ability to classify both single-omics data and multi-omics data, which significantly extends the general applicability to make efficient use of public resources. More specifically, we constructed multi-omics classifiers using the fused data with the consensus clustering labels as the reference and evaluated the robustness of the classification performance by iterative testing. The validity of multi-omics classifiers was verified by the observed significant prognostic power on both the mRNA dataset and the miRNA dataset. Notably, it was the first time ever that the miRNA datasets could be classified since the previous classification assays only take mRNA data as input.In a benchmark study against iCluster, SCCA-CC also demonstrated its superiority in the higher coherence and clinical relevance of identified cancer subtypes, and lower computational complexity. Furthermore, the strength of SCCA-CC also lies in the biological interpretability. The non-zero mRNAs and miRNAs selected by sparse CCA had strong correlation in their expression levels, which can be explained by their intense physical interactions. These results provide compelling evidence that the sparse CCA selected biologically relevant genes and miRNAs.Despite the demonstrated usefulness, the major limitation of SCCA-CC lies in the limited types of omics data we used in the study. Only mRNA and miRNA data were fused for classification, likely missing heterogeneity occurring at other omics levels. Thus, our future work will focus on integrating more types of omics data to dissect the heterogeneity more comprehensively. Considering the differences in the dimensionalities and data scales between various types of omics data, how to properly preprocess the data for effective data fusion remains a significant challenge.The original contributions presented in the study are included in the article/XW contributed to study concept and design. LQ, WW, and TW contributed to data collection, analysis, and interpretation. XW contributed to critical revision of the manuscript for important intellectual content. LZ and LH provided important advice and assistance for manuscript drafting. XW supervised the study. All authors read and approved the final manuscript.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.


Recently, the possibility of tumour classification based on genetic data has been investigated. However, genetic datasets are difficult to handle because of their massive size and complexity of manipulation. In the present study, we examined the diagnostic performance of machine learning applications using imaging-based classifications of oral squamous cell carcinoma (OSCC) gene sets.RNA sequencing data from SCC tissues from various sites, including oral, non-oral head and neck, oesophageal, and cervical regions, were downloaded from The Cancer Genome Atlas (TCGA). The feature genes were extracted through a convolutional neural network (CNN) and machine learning, and the performance of each analysis was compared.The ability of the machine learning analysis to classify OSCC tumours was excellent. However, the tool exhibited poorer performance in discriminating histopathologically dissimilar cancers derived from the same type of tissue than in differentiating cancers of the same histopathologic type with different tissue origins, revealing that the differential gene expression pattern is a more important factor than the histopathologic features for differentiating cancer types.KCNA10, FOSL2, and PRDM16, and extracted leader genes from pairwise comparisons were FGF20, DLC1, and ZNF705D.The CNN-based diagnostic model and the visualisation methods using RNA sequencing data were useful for correctly categorising OSCC. The analysis showed differentially expressed genes in multiwise comparisons of various types of SCCs, such as The online version contains supplementary material available at 10.1186/s12903-021-01642-9. Traditionally, the choice of treatment modality for cancer primarily depends on the histopathologic diagnosis, and a definite diagnosis plays a major role in choosing a treatment strategy and determining the prognosis of the disease , 2. ThusUnderstanding the genetic heterogeneity of cancers can provide clues for increasing diagnostic accuracy and developing efficient biomarkers as well as improving treatment efficacy –11. A maOSCC, the most common cancer of the oral cavity, derives from the oral mucosa (which is lined by stratified squamous epithelium) and shares morphologic findings and histochemical features with SCC of other sites or tissues. All SCCs are diagnosed as the same type of disease due to similar histologic findings, but the gene expression of each SCC has not been compared. Therefore, it is not well understood whether the genetic features of SCCs of various tissues or organs are similar. Knowledge of the genetic features distinguishing OSCC from SCCs of other tissues might provide clues for differential diagnosis and the development of treatment paradigms. In the present study, we examined the diagnostic performance of machine learning applications using CNN image classification for OSCC samples. In addition, we investigated the differences in genetic features between OSCC and other types of SCC, which may provide a basic rationale for potential diagnostic models and the development of targeted therapy.http://cancergenome.nih.gov/). The platforms used, experiment types, and numbers of samples analysed for mRNA expression for each cancer are shown in Additional file RNA sequencing data from samples of OSCC, non-oral head and neck SCC (HNSCC), oesophageal SCC, oesophageal adenocarcinoma, and cervical SCC were downloaded from the TCGA database , and noise data were filtered out by using a variance threshold of 1. Then, the data were embedded into 1-D images.The differentiation step used the CNN model to process the 1-D images output from the preprocessing step. The training and testing were performed using a 10-fold cross validation method.The class activation map method was used to visualise what the CNN had learned and heatmap images representing the contribution of each gene were produced from the image input.Since each pixel corresponds to one gene, feature extraction methods take the most important pixels with the most intensity within the image and categorise the corresponding represented genes into a list of dominant genes.A CNN model , 22, whiBecause we needed to extract features learned through CNN analysis, we employed the class activation map (CAM) method to visualise the learned features from the CNN output. The CAM method helps understand what regions of an input image influence the CNN’s output prediction. The technique relies on the generation of a heatmap for visualisation, which highlights pixels of the image that trigger the model to associate the image with a particular class. In this experiment, since each pixel represented a corresponding gene, the CAM method could visualise which gene was dominant for each type of cancer.To evaluate the performance of the tool in OSCC diagnosis, its performance capabilities, such as test recall, test precision, and test accuracy, were compared using the dataset containing OSCC, non-oral HNSCC, cervical SCC, and oesophageal SCC samples in a multiwise fashion. After repeated training using the dataset, the tool showed ~ 82% test precision and test accuracy in diagnosing SCCs of various origins Fig. . The finTo clearly determine diagnostic accuracy in differentiating OSCC from SCC of other tissues or organs, a confusion matrix was generated, as shown in Fig. To further clarify the importance of the origin of cancer cells, the set of differentially expressed genes between oesophageal squamous cell carcinoma (ESCC) and oesophageal adenocarcinoma was compared to that between OSCC and ESCC. Although both OSCC and ESCC share very similar histopathologic features, the machine learning tool differentiated them easily, with only ~ 2% error when discriminating ESCC from OSCC, whereas 38% of ESCCs were misdiagnosed as oesophageal adenocarcinoma Fig. A, B, impTo identify valuable genes for diagnosing OSCC, the dominant genes that could discriminate OSCC from other SCCs were extracted through the class activation map feature extraction method in a multiwise manner. In addition, data on the characteristic genes of OSCC were extracted by individually comparing OSCC with other types of SCC in a pairwise manner: comparison 1) between OSCC and non-oral HNSCC, (2) OSCC and cervical SCC, and (3) OSCC and ESCC. Then, the shared genes from each comparison were selected  that were not found in our results [PRDM16 and DLC1 have been studied in various types of cancers, and the significance of these genes in OSCC has not been examined [KCNA10 and ZNF705D in the pathogenesis of cancer, including OSCC, have not been investigated, implying the novel insights gained from this study and new avenues for the diagnosis of and new treatment strategies for OSCC.In addition, a previous study using the TCGA and Gene Expression Omnibus (GEO) databases reported 6 primitive biomarkers of OSCC ( results . This diexamined –26. FurtIn summary, we implemented a computation approach to develop novel biomarkers for OSCC based upon site-specific genetic variations that occur during the process of cancer progression. Further validation of these biomarkers in both experimental and clinical settings will strengthen the power of this computation approach in improving the accuracy of diagnosis and the assessment of treatment outcome and prognosis, in addition to its aid in development of optimized therapeutic strategies.Additional file 1. Numbers and types of tumor samples used in this study.


Multiple studies have shown the utility of transcriptome-wide RNA-seq profiles as features for machine learning-based prediction of response to chemotherapy in cancer. While tumor transcriptome profiles are publicly available for thousands of tumors for many cancer types, a relatively modest number of tumor profiles are clinically annotated for response to chemotherapy. The paucity of labeled examples and the high dimension of the feature data limit performance for predicting therapeutic response using fully-supervised classification methods. Recently, multiple studies have established the utility of a deep neural network approach, the variational autoencoder (VAE), for generating meaningful latent features from original data. Here, we report the first study of a semi-supervised approach using VAE-encoded tumor transcriptome features and regularized gradient boosted decision trees (XGBoost) to predict chemotherapy drug response for five cancer types: colon, pancreatic, bladder, breast, and sarcoma.We found: (1) VAE-encoding of the tumor transcriptome preserves the cancer type identity of the tumor, suggesting preservation of biologically relevant information; and (2) as a feature-set for supervised classification to predict response-to-chemotherapy, the unsupervised VAE encoding of the tumor’s gene expression profile leads to better area under the receiver operating characteristic curve and area under the precision-recall curve classification performance than the original gene expression profile or the PCA principal components or the ICA components of the gene expression profile, in four out of five cancer types that we tested.Given high-dimensional “omics” data, the VAE is a powerful tool for obtaining a nonlinear low-dimensional embedding; it yields features that retain biological patterns that distinguish between different types of cancer and that enable more accurate tumor transcriptome-based prediction of response to chemotherapy than would be possible using the original data or their principal components.The online version contains supplementary material available at 10.1186/s12859-021-04339-6. Although chemotherapy is a mainstay of treatment for aggressive cancers, many agents have serious side effects . WhetherTumorigenesis is driven by alterations in the somatic genome and epigenome in cancer cells ; howeversemi-supervised approach—reveal multivariate patterns that could ultimately improve predictive accuracy. Semi-supervised approaches that fuse unsupervised data reduction methods for low-dimensional embedding with supervised methods (such as decision trees) for prediction have proved beneficial in problems where large unlabeled datasets are available; for example, a principal components analysis (PCA)-XGBoost method has been previously used in finance [For typical cancers, most available tumor transcriptomes are not labeled for chemotherapeutic response; the ratio of such unlabeled to labeled tumor datasets in the Cancer Genome Atlas  is in t finance , and an  finance .Multiple studies –23 have We first asked to what extent VAE-encoding tumor transcriptomes would preserve characteristics that are associated with distinct cancer types. To that end, we trained a pan-cancer transcriptome VAE and used it to encode over 11k tumor transcriptomes from 33 cancer types. By comparing two-dimensional embeddings of the original tumor transcriptomes with embeddings of the VAE-encoded transcriptomes, we found “” sectionIn the main part of this work, we focused “” sectiont-distributed stochastic neighbor embedding (t-SNE) to the expression levels of the the top-20% highest-variance genes , pancreatic adenocarcinoma (PAAD), bladder carcinoma (BLCA), sarcoma (SARC), and breast invasive carcinoma (BRCA); Table The ratio of responding tumors to progressive disease tumors  ranged from a low of 0.77 for pancreatic cancer to a high of 8.61 for breast cancer.t-test) classification performance than the features generated by the L2 VAEs, t-test) classification performance than the features generated by the binary cross-entropy VAEs and thus, for all subsequent analyses, we used VAEs trained with L1 loss.Having obtained 2,606 transcriptomes of tumors of five cancer types (with 806 of the tumors labeled by response), we next sought to determine which type of VAE reconstruction loss function—L1, L2, or binary cross entropy—would yield transcriptome encodings that are most amenable to accurate XGBoost-based prediction chemotherapy response. On the 2,606 tumor transcriptomes, we trained three sets of cancer type-specific VAEs “” sectionunlabeled tumor transcriptomes (for the top 20% most variable genes) for a single cancer type, into a low-dimensional space .Training and testing a supervised classifier for predicting chemotherapy response.Because some cancer types benefited from a deeper VAE network architecture than others for effective encoding, we used three different VAE architectures for learning features for predicting chemotherapy response in the context of three subsets of cancer types  or expression levels of individual genes (fully-supervised). We found , in both semi-supervised and fully supervised analyses, and thus we chose XGBoost as the classification algorithm for subsequent analyses.To select the supervised classification algorithm for step (3) above, we used an empirical approach, comparing the AUROC performance of XGBoost, kernel SVM, and To address the primary question of to what extent a VAE-based, semi-supervised (VAE-XGBoost) approach could advance the state-of-the-art for transcriptome-based prediction of chemotherapy response, we sought to compare VAE-XGBoost’s performance to that of three alternative approaches: (1) a semi-supervised approach using a regular auto-encoder (AE) with the same architecture as the VAE; (2) a fully supervised approach directly using the transcriptome data; and (3) a semi-supervised approach based on a traditional dimensional reduction technique .t-SNE analysis and the VAE-3 architecture for BLCA and SARC (Table t-test) classification performance. Thus, we used the VAE for neural network-based unsupervised embeddings, for subsequent analyses.To address alternative approach 1) , we compared the performance of VAE-XGBoost to that of a model consisting of a regular auto-encoder combined with XGBoost  (“fully supervised”), we empirically compared the performance of the VAE-XGBoost method to a fully supervised model in which we applied XGBoost directly to the tumor expression levels of the top 20% most variable genes 13,584 genes) as feature data. For five out of five cancer types , in terms of test-set AUROC, the VAE-XGBoost approach outperformed the fully-supervised XGBoost approach , we empirically compared VAE-XGBoost to models in which PCA or ICA components were used as XGBoost features . We aimed to empirically study prediction performance of these models for each of the five cancer types separately, using the set of cancer type-specific labeled tumors . For four out of five cancer types , in terms of AUROC the semi-supervised VAE-XGBoost method significantly outperformed the semi-supervised PCA-XGBoost method , in terms of AUPRC the semi-supervised VAE-XGBoost method significantly outperformed the semi-supervised PCA-XGBoost method . Specifically, we investigated whether the strong performance of PCA-XGBoost on COAD is attributable to differences in the distributions of XGBoost feature importance scores of the PCA features versus VAE latent-space features. We found that the distribution of feature importance scores (as a function of rank) was more sharply peaked at lowest-ranked features in the VAE than in the PCA Fig. , suggestAs far as we are aware, this work is the first report of a multi-cancer investigation of the potential for a VAE-based, semi-supervised approach for predicting in vivo chemotherapy response from the tumor transcriptome. Across the five cancer types that we studied, the ratio of responding tumors to progressive disease tumors ranged from a low of 0.77 for pancreatic cancer to a high of 8.61 for breast cancer, reflecting a broad range of resistances to standard-of-care chemotherapy. Our results clearly demonstrate the utility of the VAE for compressing high-dimensional data to a continuous, low-dimensional latent space while retaining features that are essential for distinguishing different cancer types and for predicting response to chemotherapy. Nevertheless, three limitations of this work bear noting.The first limitation concerns the type(s) of tumor “omics” data from which features are derived for the predictive model. While in this work we focused on tumor transcriptome data which can be measured with high precision over a wide dynamic range of transcript abundances by RNA-seq, we note that TCGA datasets of tumor somatic mutations and copy number alteration events are also available . Given tt-SNE analysis.Second, for decision tree-based response-to-chemotherapy prediction, the performance of VAE-encoded transcriptome features is somewhat sensitive to the type of normalization used for the gene expression levels (data not shown). We explored various published normalization methods for the RNA-seq data including standardization of log counts and using FPKM; we ultimately chose min-max-normalized  methods  that cou methods , we usedThe third limitation relates to the VAE architecture. While it is promising that a single deep VAE architecture  yielded features that outperformed PCA and the original RNA-seq feature data for two different cancer types (breast and pancreatic), for the other three cancer types, it was necessary to use shallower (two-layer) VAE architectures with bigger latent space dimensions . Of the five cancer types that we studied, colon cancer and sarcoma had the lowest proportions of labeled samples . One possible explanation for the colon cancer-specific superior performance of PCA features over VAE features for predicting response to chemotherapy may be due to the fact that while (for COAD) feature importance for the VAE features is sharply peaked for the first few features and falls off fairly rapidly with feature rank, the PCA features have a significantly flatter distribution of relative feature importance Fig. . Follow-For four of the five cancer types that we studied, the semi-supervised VAE-XGBoost approach significantly outperformed a semi-supervised PCA-XGBoost approach for tumor transcriptome-based prediction of response to chemotherapy, reaching a top AUROC of 0.738 for pancreatic adenocarcinoma. For three of the five cancer types that we studied, the semi-supervised VAE-XGBoost approach significantly outperformed a semi-supervised ICA-XGBoost approach for tumor transcriptome-based prediction of response to chemotherapy. For BLCA and SARC, the semi-supervised VAE-XGBoost and ICA-XGBoost models’ performances were statistically indistinguishable. For five out of five cancer types, the semi-supervised VAE-XGBoost approach significantly outperformed a fully-supervised approach consisting of XGBoost applied to the expression levels of the top 20% most variably expressed genes. Given high-dimensional “omics” data, the VAE is a powerful tool for obtaining a nonlinear low-dimensional embedding; it yields features that retain biological patterns that distinguish between different types of cancer and that enable more accurate tumor transcriptome-based prediction of response to chemotherapy than would be possible using the original data or their principal components.We carried out all data processing and machine-learning tasks on a Dell XPS 8700 workstation equipped with Nvidia Titan RTX GPU and running the Ubuntu GNU/Linux operating system version 16.04. All of the analysis code that we implemented was executed in Python version 3.5.5 except that we used R version 3.3.3 for statistical analysis of AUROC and AUPRC values “” sectionFrom the Xena data portal , we obtads, FPKM ) expressmentclass2pt{minimds, FPKM  with defoss see “” sectiont-SNE embedding components of the tumors using the function sklearn.manifold.TSNE from the python software package scikit-learn version 0.19.1 with parameters We computed random variable (r.v.). Below, we mathematically define the VAE.An autoencoder is a type of model that combines “encoder” and “decoder” neural networks to learn a low-dimensional continuous data encoding from which the input signal can be approximately reconstructed . A key am denote the number of genes for which transcript abundances are used to represent the tumor transcriptome. After min-max transformation of the tumor transcriptome measurements  the following regularization function:The reconstruction loss measures bias error, whose minimization must be balanced against the simultaneous goal of controlling variance error through regularization. In the VAE, regularization requires incentivizing  a tumor would respond to chemotherapy, we used XGBoost , an effiFor PCA, we used the function decomposition.PCA (with parameters For predicting whether or not (based on its transcriptome-drived feature-set: raw or VAE) a tumor would respond to chemotherapy, we used SVM . We usedFor predicting whether or not (based on its transcriptome-drived feature-set: raw or VAE) a tumor would respond to chemotherapy, we used KNN , an implrs Table  by exhaut-test). For the L1 versus L2 analysis , we used the function metrics.roc_auc_score from the python software package scikit-learn version 0.19.1 with parameter average=“weighted”. We logit-transformed AUROC values before testing . We carried out 30 replications of five-fold cross-validation; within each replication, across the five folds, we obtained prediction scores for each tumor from the fold in which the tumor was in the test set, enabling us to compute an overall AUPRC within each replication. For each training data set, we have done 30 replications of five-fold cross-validation by altering the random seed used for assign split of data during cross-validation. We have conducted the same procedure for five different cancer types  as shown in the panel names of Additional file For computing the AUPRC, we used the function metrics.precision_recall_curve and metrics.auc from the python software package scikit-learn version 0.19.1. We logit-transformed AUPRC values before testing (using two-tailed Welch’s Additional file 1. This supplementary file contains Supplementary Figures S1, S2, S3, S4, S5, S6, S7, and S8, as well as Table S1 and Supplementary Note C.


Objective: We conducted this study to characterize somatic genomic alterations in circulating tumor DNA (ctDNA) from patients with ovarian cancer and compare GAs detected in ctDNA with tissue databases.Methods: Hybrid capture-next generation sequencing genomic profiling of 150 genes was performed on ctDNA from 138 patients with ovarian cancer with 1,500× sequencing depth. The GAs detected in ctDNA were compared with those in our ovarian cancer tissue database (N = 488) and the Cancer Genome Atlas (TCGA) database (N = 489).Results: 115 patients (83%) had at least 1 GA detected in ctDNA. The most frequently altered genes detected in ctDNA were TP53 (72%), KRAS (11%), LRP1B (10%), ZNF703 (9%) and NF1 (8%). Comparative analysis with our tissue database showed similar frequencies of GAs per gene, although PIK3CA and KRAS mutations were more frequent in tissue and ctDNA, respectively (p < 0.05). Gene amplification and rearrangement were more frequent in ctDNA samples. The mutation frequency of homologous recombination repair associated-genes, VEGF signal/angiogenesis pathways, RAS pathways, NOTCH pathways and MSI-H ratio was not statistically different either in ctDNA or in tissue database. However, the mutation frequency of AKT, PIK3CA, PTEN and STK11 in PI3K/AKT/mTOR pathway was significantly lower than that in tissue samples (p < 0.05).Conclusions: Our results suggest that genomic profiling of ctDNA could detect somatic GAs in a significant subset of patients with ovarian cancer. Hybrid capture-NGS based on liquid biopsy has the potential capability to serve as a substitute to tissue biopsy and further studies are warranted. BRCA1/2 mutations are the primary beneficiaries of PARP inhibitors, followed by homologous recombination repair (HRR) [BRCA1/2, dMMR/MSI-H and NTRK1/2/3, be performed prior to initiation of therapy [Ovarian cancer is the leading cause of cancer-related death among gynecological cancers and the standard treatment for primary disease is cytoreductive surgery of the tumor followed by adjuvant chemotherapy, platinum and taxane-combined treatment. However, clinical data suggest that more than half of ovarian cancer patients develop chemotherapy resistance and recurrent disease . TherefoCurrently, genomic detection based on tissue tumor specimens remains the standard for genetic testing. However due to some clinical risks and surgical complications, as well as the specificity of the mechanism of ovarian cancer metastasis, a considerable number of patients do not receive tissue biopsy. The metastasis of ovarian cancer is known to develop in two completely different pathways. Although the primary mechanism for metastasis of ovarian cancer has long been the passive dissemination of the tumor globule through the peritoneal fluid and ascites, hematogenous metastasis of circulating cancer cells has subsequently been found to be the preferred route to the omentum . Blood-bCancer Genome Atlas (TCGA) database.We conducted a retrospective study of hybrid capture-based NGS genomic profiling of 150 genes panel on ctDNA from patients with ovarian cancer. We compared somatic alterations detected in ctDNA with our tissue database and the Blood samples were obtained from 138 patients with ovarian cancer between January 2017 and January 2020, and ctDNA sequencing was performed on Illumina HiSeq sequencer  with a median unique exon coverage depth of 1,500× in a College of American Pathologists (CAP) and Clinical Laboratory Improvement Amendments (CLIA) certified laboratory  [g for 10 min to remove residual cells and debris. The buffy coat was then transferred to a new tube for genomic DNA (gDNA) extraction. The QIAamp Circulating Nucleic Acid Kit (Qiagen) was used to extract ctDNA from the plasma, respectively, following the standard protocols, and then fragmented to a size ranging from 200 bp to 400 bp using Covaris S2 SonoLAB (Covaris). DNA concentrations were determined by the Qubit dsDNA HS Assay Kit (LifeTechnologies).Briefly, 20 ml of peripheral whole blood was collected for genomic profiling of ctDNA. The blood was centrifuged in Streck tubes at 1,600 g for 20 min at room temperature to separate the plasma. Then, the plasma layer was carefully transferred to a new 1.5 ml Eppendorf tube, followed by room-temperature centrifugation at 16,000 The assay methodology and procedure were as previously described . BrieflyThe paired-end reads were mapped by BWA  MEM algop value <0.05 was considered statistically significant. The SPSS22.0 software  was carried out for statistical analysis.Categorical variables were described as number and proportions. Categorical relationships were examined by using Pearson’s chi-square test with the Yates continuity correction when applicable and Hybrid capture-based genomic profiling was performed on plasma samples collected from 138 patients with ovarian cancer. The baseline characteristics for the patients were described in The MSAF was determined as the maximum allele frequency for all the mutations detected per sample in blood. The MSAF greater than zero was used as the evidence of ctDNA in the blood and ctDNA was detected in 115 (83%) samples. The median MSAF across all cases was 0.026 , and among cases with evidence of ctDNA present, the average of reportable GA was 3.83 GA/case .TP53 (72%), KRAS (11%), LRP1B (10%), ZNF703 (9%) and NF1 (8%) (TP53 (72% vs. 81% vs. 63.8%), KRAS (11% vs. 9% vs. 0.41%), LRP1B (10% vs. 13% vs. 2.9%), ZNF703 (9% vs. 1% vs. 0%), NF1 (8% vs. 13% vs. 4%) and so on. The gene amplification detected in ctDNA samples was inconsistent with our tissue samples. The most commonly detected gene amplification in of blood was PTK2. The detected amplification rates of PDGFRA (3.5% vs. 0%), DDR2 (2.6% vs. 0.2%) and KIT (2.6% vs. 0.2%) were higher in blood than tissue (p < 0.05), while the amplification rate of MYC was lower in blood than tissue  . We furtNF1 (8%) . These iBRCA1/2 (HRR associated-genes) were observed in 6.1% of ctDNA cases and 14.1% of tissue cases. Somatic SNV alterations in other HRR associated-genes including ATM, CHEK1/2 and RAD50 were detected in 6.1%, 5.2% and 1.7% of patients in ctDNA compared to 4.9%, 2.0% and 1.2% of patients in the tissue database (AKT, PIK3CA, PTEN and STK11 in PI3K/AKT/mTOR pathway was significantly lower than that in tissue samples (p < 0.05, Potentially actionable GAs informing selection of matched targeted therapies and clinical trials or predicting lack of response to antibody therapies were identified. GAs of database . In addidatabase . And 13.BRCA1/2 were detected in 6.1% of cases.In this study, we have reported results of hybrid capture-based NGS of 138 ovarian cancer ctDNA samples. ctDNA was detected in 83% of samples, which is similar to the rate in ovarian cancer in other recent reports . Among cFurthermore, we compared common GAs detected in ctDNA with those detected in tissue samples from patients with ovarian cancer and with those from TCGA. Almost every gene mutation tested in our tissue database could be found in ctDNA data. And among the detected GAs, most genes had similar frequencies in both the ctDNA samples and the tissue samples, while different from TCGA. Tumor stage and sequencing depth are probably reasons for this difference. Overall, our findings had indicated that there was a certain concordance rate of genomic alterations in both tissue and ctDNA samples of ovarian cancer.Several studies had found that gene amplification could predict overall survival in ovarian cancer . The freBRCA1/2 mutations [BRCA1/2 in ctDNA samples was 6.1%, lower than in tissue samples. However, studies have found that fluid biopsy is almost 100% sensitive to stage IV ovarian tumors [BRCA1/2, other HR deficiency genes such as ATM, RAD50 and CHEK1/2 were also analyzed in our study. Most HRR genes had a higher frequency of GAs in ctDNA than tissue. Alterations of HRR deficiency genes detected by ctDNA could be regarded as a complement to reflect HRR deficiency.PARP inhibitors have recently been approved to treat the advanced ovarian cancer patients with n tumors . CombiniThe limited availability of tumor tissue in advanced ovarian cancer presents a major clinical challenge. In cases where biopsy is prohibited, blood-derived ctDNA may provide an alternative method for genomic analysis, and ctDNA testing may have additional advantages in identifying heterogeneous alterations not present at a single tumor site. However, further research, particularly in patients with matched tissue and ctDNA samples, is needed to validate the clinical significance of ctDNA.In conclusion, NGS-based ctDNA testing in ovarian cancer may provide a valuable alternative or complement to tissue testing, particularly in cases in which tissue biopsy is prohibitive or repeat genomic assessment in the setting of disease progression is indicated.


Transfer RNA (tRNA), a key component of the translation machinery, plays critical roles in stress conditions and various diseases. While knowledge regarding the importance of tRNA function is increasing, its biological roles are still not well understood. There is currently no comprehensive database or web server providing the expression landscape of tRNAs across a variety of human tissues and diseases. Here, we constructed a user-friendly and interactive database, DBtRend, which provides a profile of mature tRNA expression across various biological conditions by reanalyzing the small RNA or microRNA sequencing data from the Cancer Genome Atlas (TCGA) and NCBI’s Gene Expression Omnibus (GEO) in humans. Users can explore not only the expression values of mature individual tRNAs in the human genome, but also those of isodecoders and isoacceptors based on our specific pipelines. DBtRend provides the expressed patterns of tRNAs, the differentially expressed tRNAs in different biological conditions, and the information of samples or patients, tissue types, and molecular subtype of cancers. The database is expected to help researchers interested in functional discoveries of tRNAs. Transfer RNAs (tRNAs) are key components of the translation machinery that function as adaptors to deliver amino acids to the ribosome by matching mRNA codons with their corresponding amino acids. The abundance of tRNAs varies across biological conditions including tissue type , cell cyDepositing datasets in the Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information (NCBI) ensures the reproducibility of published studies and promotes their reuse. Large consortium projects, such as TCGA, have provided opportunities to systematically characterize molecular mechanisms. Several re-analysis studies on these data provided access to the integration of large datasets to simplify their use and supported functionalities for downstream analyses . For example, cBioPotal is an online resource for exploring, visualizing, and analyzing multidimensional cancer genomic datasets and provides rapid and intuitive access to molecular profiles and clinical properties from large consortium projects . Gene Exhttps://trend.pmrc.re.kr/ accessed on 10 June 2020), which provides and utilizes standardized tRNA expression profiles in different tissues, cell types, developmental stages, and other conditions, including cancer, from public datasets (TCGA and GEO)  . This rehttps://portal.gdc.cancer.gov/, accessed on 15 January 2020) approved by the dbGaP (dbGaP accession: phs000178.v11.p8). To collect the small RNA sequencing data from GEO , project IDs were first obtained by searching “Non-coding RNA profiling by high throughput sequencing” for each experiment type on two platforms: GPL10999  and GPL11154 (Illumina HiSeq 2000 [Homo sapiens]). We subsequently used the GEOmetadb R package [https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?view=software, accessed on 16 August 2020). The sample information was obtained from the website, and molecular subtype information of TCGA was obtained from TCGAbiolinks [We downloaded microRNA sequencing from the TCGA portal  from raw sequencing data (FASTQ files). To apply this method, BAM files from the TCGA data portal were converted into FASTQ using SAMtools [https://github.com/jinoklee/tReasure/blob/master/doc/Detailed-Mapping-Methods.pdf (accessed on 24 September 2021). In brief, we built two genomes for two mapping steps, an artificial human genome and a human mature tRNA genome, using predicted tRNA sequences. Human tRNA gene annotations (hg38) were obtained using tRNAscan-SE [Recently, we applied a novel quantification method that modified previous methods ,12 for tSAMtools . Raw FASSAMtools . TrimmedSAMtools . DetaileAscan-SE , which pAscan-SE . These tAscan-SE . DBtRendAll statistical tests were performed using R. To compare the expression profile patterns of tRNAs across biological conditions, we utilized projects that included at least two different conditions and biological replicates. We used the edgeR package to examine the differential expression of groups using the trimmed mean of M values (TMM) by normalized methods. tRNAs with an average log2 CPM < 1 were removed. Quasi-likelihood F-tests were used for testing, and the Benjamini-Hochberg (BH) procedure was applied for multiple corrections.Currently, the DBtRend is based on 183 projects, investigating 33 cancers from TCGA and 150 projects from GEO. As a result of quality control of the sequencing raw data, DBtRend provided 14,986 out of 15,170 samples, comprising 11,078 samples from TCGA and 3908 samples from GEO, sequenced using GPL11154 (Illumina HiSeq 2000: 3077 samples), GPL10999 , GPL9115 , and GPL15520 (Illumina MiSeq: 39 samples). For differentially expressed tRNA gene (DEtRNA) analysis, we selected 105 of 183 projects, comprising 21 projects from TCGA with at least 2 samples of matched normal and tumor samples, and 84 projects from GEO with 2 replicates per condition. These results are displayed on the ‘Explore DEtRNA’ page.The DBtRend was constructed based on the R programming language (version 4.0) using the ‘shiny’ web application framework package. The web interface is based on the package ‘semantic.dashboard’, HTML and CSS. We will continue to maintain a database for possible updates. Based on these datasets, DBtRend provide four main modules to retrieve various data: ‘Home’, ‘Explore Projects’, ‘Explore DEtRNAs’ and ‘Explore tRNAs’. A detailed tutorial concerning the exploration of the DBtRend is available on the ‘User Guide’ page.The sample distribution is shown on the ‘Home’ page. Four pie charts display the sample percentage of ‘project’, ‘source’, ‘cell line’, and ‘cell’ parts, respectively. A table with detailed project information is displayed on this page. The table shows ‘Project’, ‘Study.title’, ‘Source’, ‘Instrument’, ‘Sample.n’, and ‘Reference’. By clicking the name on the pie chart, the table is reconstructed to display the project of the matched samples.Users can perform a search by selecting a project from a pull-down menu. The query results are displayed as a table of sample information in the tab of ‘Sample Information’ and three heatmaps, visualizing the tRNAs expression profiles within the project in each corresponding tab . In the sample list, ‘Sample.id’, ‘Source’, ‘Project’ and ‘DE.Group’  are commonly provided for each project. There may be additional content depending on the project. Heatmaps also provide hierarchical clustering of samples based on the relative abundance of all tRNA genes. DBtRend provides two types of static and interactive heatmaps using the pheatmap R package and heatmaply R package. The names of the tRNAs can be displayed by positioning the cursor over each of the points in the interactive heatmap. Using the sub-tab ‘Download’, users can download normalized read counts as the matrix of CPM (counts per million) and the list of sample information . In addiFor the analysis of DEtRNAs within that project, users should search a project by clicking the pull-down menu and then selecting a comparison in the project from the pull-down menu shown below. To search for statistically significant results, users can select the threshold values of logFC and the adjusted p-value. The results are displayed in tables of statistical tests and a plot of the results of DEtRNAs. A results table with ‘GeneName’, ‘logFC’, ‘logCPM’, ‘F’, ‘PValue’, ‘FDR’ and ‘Sig’ is displayed in each corresponding tabs.Three plots will be displayed: a volcano plot for individual tRNAs, a bar plot for isodecoders, and a pyramid plot for isoacceptors in each corresponding table. By positioning the cursor over each of the points in the volcano plot, the names of the tRNAs are displayed .http://genome.ucsc.edu/, accessed on 5 September 2021), GtRNAdb , HGNC , and RNAcentral , on the right side. Users can find a specific reference page for the matched tRNAs by clicking on the link text. In addition, a boxplot was displayed to represent the expression pattern across 18 normal tissues, 24 tumor tissues, 21 immortal cell lines, 37 cancer cell lines, and groups in a selected project for the selected individual tRNAs, isodecoders, and isoacceptors, respectively. By positioning the cursor over each box plot, the summarized statistical values  will be shown  project in DBtRend, we typed ‘Alzheimer’ in the search box of the table directly on the ’Home’ page. The results were displayed in three GEO projects with blood or tissue sources. To explore tRNA gene expression, one project (PRJNA210613) was explored by selecting the pull-down on the Explore Project page. We selected the project, ‘PRJNA210613: Alternation of the microRNA network during the progression of Alzheimer’s disease’. The query results are presented below. The ‘Sample Information’ tab displayed six samples each from prefrontal cortex between the early and the late stage group of AD groups. Heatmaps for the expression pattern of tRNA genes were displayed by clicking the subsequent three tabs. Each heat-map-based hierarchical clustering of samples separated early and late stages of AD for DEtRNA expression within projects; then, we went to the ‘Explore DEtRNAs’ page. First, we searched for the project and then verified the comparison condition (such as early stage vs. late stage: 6 vs. 6′) in the following filter. DBtRend displayed DEtRNAs in the late-stage group compared with the early stage group. We identified 118 individual tRNAs , 72 isodecoders (29 upregulated and 43 downregulated isodecoders), and 14 isoacceptors (six upregulated and eight downregulated isoacceptors) in the late stage of AD compared with the early stage. From the results of this analysis, two interesting results were identified. First, the most discovered the differential expressed isodecoders were encoding cysteine, followed by arginine on bar plot in the ‘isodecoder’ Table These isodecoders could be proposed as novel critical factors related to AD. Second, the isodecoders and isoacceptors transferring arginine were up- or down-regulated between the conditions. This suggests that each tRNA encoding arginine with different anticodons has a different function. This needs to be further studied and suggested as a candidate marker for follow-up work.DBtRend is an interactive web-based database to explore and investigate tRNA expression under various biological conditions retrieved from the TCGA and GEO datasets. This data collection was larger than that of previous studies using TCGA data . We willWe developed a comprehensive database, DBtRend, for tRNA expression under various biological conditions. We provide the tRNA expression profile along with differential expression across biological conditions and among different groups of samples at individual tRNA, isodecoder, and isoacceptor levels. DBtRend will be useful for studying human biology and applied medical research, such as biomarker discovery.


In the precision medicine of lung adenocarcinoma, the identification and prediction of tumor phenotypes for specific biomolecular events are still not studied in depth. Various earlier researches sheds light on the close correlation between genetic expression signatures and DNA copy number variations (CNVs), for which analysis of CNVs provides valuable information about molecular and phenotypic changes in tumorigenesis. In this study, we propose a comprehensive analysis combining genome-wide association analysis and an Elastic Net Regression predictive model, focus on predicting the levels of many gene expression signatures in lung adenocarcinoma, based upon DNA copy number features alone. Additionally, we predicted many other key phenotypes, including clinical features , gene mutations, and protein expressions. These Elastic Net prediction methods can also be applied to other gene sets, thereby facilitating their use as biomarkers in monitoring therapy. The enormous genetic heterogeneity caused by multiple types of DNA aberrations is a key factor in tumorigenesis. Thus, the capacity to analyze tumor inhomogeneity is essential to elucidating cancer etiopathogenesis and to more accurately defining patient subgroups in precision medicine . One limLung cancer is a kind of malignancies with highest morbidity and mortality in the world . About 81  was set at 0.01.For identifying the association between CNVs and gene expression signatures, we employed two different statistical approach  on gene The Elastic-Net regression method, a linear combination of L1/L2 regularization in the Ridge regression and LASSO (Least absolute shrinkage and selection operator) regression, was utilized for building the CNV-based tumor phenotype predictions . First, Since the variables involved in the prediction in this study are continuous data, we divided the sample into two-halves (using the top quartile and bottom three quartiles) to perform model predictions. For clinical characteristics and gene mutation status, which have binary outcomes, the model was built directly for their prediction. For gene mutation prediction, we selected the top 20 genes with the highest mutation frequencies in lung adenocarcinoma. Clinical features included pathological stage (stage I-II vs. stage III-IV) and TNM stage.SPSS software (version 23.0) or R software (version 4.0.3) were used for statistical analysis. Curve analysis of receiver operating characteristic (ROC) was utilized to evaluate the predictive performance of CNV-based Elastic Net regression models. The AUC, ranging from 0.5 (for an uninformative marker) to 1 (for a perfect predictive marker), was a measurement for how well patient survival can be predicted with the gene signatures.q values were conducted for both sets of statistical methods  information was used  to measuAdditionally, the capacity of CNV-based Elastic Net predictive models to foretell somatic mutation was tested. We first downloaded the mutation information of lung adenocarcinoma samples in TCGA database , from whFurthermore, we explored whether CNV-based Elastic Net predictive regression models can be used to forecast clinical features. Here, pathological stage and TNM stage were selected to divide the sample into two halves . After grouping the samples for model building and prediction, the AUC of the prediction results was found to be 0.52 . The modIn this study, we applied chromosomal segment-level CNVs downloaded from TCGA database to perform Elastic Net predictive model building, and the resulting models were used for the forecasting of mutations, protein expression, clinical features, and gene signature scores in lung adenocarcinoma.The ability to predict key tumor phenotypes is essential for elucidating the biocomplexity in multiple cancer types . The staNowadays, many similar studies are based on the expression levels of related genes to build the prediction models for molecular expression signatures, clinical indicators, disease progression and prognosis. However, our study starts from the perspective of DNA copy number data, intending to excavate the potential information contained in which, and predicting key tumor phenotypes, like mutation status or biomarker levels or complex expression phenotypes in a proposed copy number-driven tumor type, e.g., lung adenocarcinoma. In addition, instead of some simple methods such as Logistics regression and random forest used in some other conventional prediction model researches, the DNA CNVs-based prediction models in our study are built on the basis of Elastic Net regression. With the characteristics of both Ridge regression and LASSO regression, the Elastic Net algorithm can effectively achieve the screening of eigenvectors with group effect and compress the selected variables to avoid the model over-fitting, so as to maintain the simplicity and accuracy of the model at the same time.This modeling strategy might have clinical benefit and offer an orthogonal method to addressing significant features like TP53 status, especially considering the increasing value of gene exons and genetic mutations goes with the diagnosis and treatment of tumors . Our stuThe strengths of this present study are that the model has been validated in both the test and validation sets, and that some results have previously been shown to be associated with lung adenocarcinoma, such as GSEA_Median_MYC_amplified_chr8q24 and Pcorr_magnoid_PLOS.2012. Nevertheless, there are still many shortcomings in our study. For example, in the prediction of clinical features, we analyzed the pathological stage, TNM stage, and radiotherapy, but only the prediction outcome of the pathological stage in the validation set was > 0.5. No new sample sets with both CNV and expression profile data were found for further validation, so we used 70% TCGA data for testing, and another part of the data was used for validation.Overall, our study exhibited the capability to construct CNV-based Elastic Net predictors for multiple key tumor phenotypes in patients with lung adenocarcinoma. Although most studies have focused on the search for genetic drivers of tumorigenesis, our results had the important connotation that DNA information can be used to predict significant complex tumor phenotypes, which could have applications in the clinical settings.https://github.com/xyouli/DNA-based-predictors-of-non-genetic-cancer-phenotypes). All computational analyses are done using public R packages.All code supporting the current study is deposited in GitHub (The original contributions presented in the study are included in the article/The collection of human tissue samples or clinical data were not involved in this study. All data was downloaded from network open databases.YX designed the study, wrote the manuscript, and took responsibility for the integrity of the data and the accuracy of the data analysis. YX and CW performed the database analysis from Xena and GSEA. YX, WZhon, and JW carried out the CNV identification. YX, HS, and XX did the CNV-based signature prediction. CW, WZhon, JW, WZhou, and XZe revised the manuscript. LH and XW planned and supervised all these works. All authors have read and approved the final manuscript.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.


Evaluation of gene interaction models in cancer genomics is challenging, as the true distribution is uncertain. Previous analyses have benchmarked models using synthetic data or databases of experimentally verified interactions – approaches which are susceptible to misrepresentation and incompleteness, respectively. The objectives of this analysis are to (1) provide a real-world data-driven approach for comparing performance of genomic model inference algorithms, (2) compare the performance of LASSO, elastic net, best-subset selection, Five large This analysis explores a novel approach for comparisons of model selection approaches in real genomic data from 5 cancers. Our benchmarking datasets have been made publicly available for use in future research. Our findings support the use of  Regression models are frequently used in cancer genomics, where they provide insight into the interactions between genes. Sparse regression models were developed to allow modelling of a large set of variables with a small number of samples – a scenario encountered frequently in genomics. However, evaluation of genomic model structures remains challenging, due to uncertainty regarding the true system of interactions. Previous studies have compared methods with synthetic data, which may not reflect the challenges of real-world data. In this study, genomic datasets were identified which contained enough samples to provide reasonable estimates of the true structures – which were used as ‘gold-standards’. Sparse regression methods were tasked with estimating the true structure given a small proportion of the available samples, allowing for comparison against the gold standards.Our results show that the interaction strengths estimated by the 17 The tractability of penalised regression approaches in this setting has led to a large volume of research into their applications.79 Penalised regression offers robust predictions in high dimensional data and mechanistic insights through the estimated coefficient vector.7911 However, it is difficult to test the assumptions which penalised approaches require for valid model selection in real world datasets.13 Furthermore, standard model selection approaches such as cross-validation and the Bayesian information criterion may be unreliable for model selection in the high-dimensional setting.15High-dimensional regression problems are ubiquitous in modern oncological research, as datasets often contain fewer observations than variables.16 Penalised regression methods facilitate modelling in the high-dimensional setting through the addition of bias terms. The inverse covariance matrix, Here, notation is conventionally abused such that the  penalises the model by the Ridge regression The Least Absolute Selection and Shrinkage Operator (LASSO) penalty penalises the model by the Ridge regression partially alleviates instability under collinearity by constraining coefficient magnitude.19 An additional benefit of LASSO shrinkage is a tendency to shrink small coefficients to zero, leading to a ‘sparse’  This behaviour is highly useful in bioinformatics, where analytic tasks often require the selection of a small number of predictive variables given a large candidate set. However, the lasso model structure is subject to inconsistency under subsampling. The Elastic Net is a combines the sparsity of 21 Penalties of ridge regression, LASSO and elastic net affect large coefficients more than small coefficients, biassing coefficient estimates. ‘Best subset selection’ provides a theoretical solution to this issue through the selection of the optimal model attainable with :The LASSO approach has ‘oracle’ properties under some conditions, meaning that predictions are nearly as good as if the true set of predictor variables were known.Thus, for some  This combination of simplicity and unbiasedness has been described as a ‘holy grail’ of sparse modelling. However, models suffer from inconsistency. Furthermore, issues such as non-convexity and NP-hardness complicate best-subset model selection.23 Recent developments such as mixed integer optimisation have facilitated best subset model learning. Combinations of Best subset selection be may be approximated through 2427 or semi-synthetic2830 datasets to assess variable selection performance. Real data analyses have focussed primarily on the models’ predictive capacity.3133 Accurate predictions may not guarantee correct model structure, especially in the highly collinear conditions commonly encountered in genomics. The representativeness of synthe-tic datasets is both uncertain and untestable. Further-more, results of these studies have been discordant, suggesting dependence on the benchmark datasets and validation techniques.10The true generating distribution for observational biological data is typically uncertain, complicating validation of estimated coefficient vectors. Consequently, many model assessments have employed synthetic and KEGG contain experimentally verified interactions, which may be used to externally validate genomic model structure. This approach has been used in previous analyses37 and is limited by the uncertain completeness of such databases. Furthermore, the activity profile of interactions between a given set of genes may change with experimental conditions and unobserved confounders.39 Consequently, the set of active predictors for a specific dataset may not align exactly with a static database. Finally, effect sizes may not be comparable between documented interactions, precluding the assessment of model coefficients by this method. Data-partitioning facilitates model validation without ground truth data, by assessing model generalisability to unseen observations. As training and validation observations are sampled from the same data, their distribution is asymptotically identical. However, the distribution may be difficult to estimate when 15Genomic databases such as REACTOMEGiven the limitations of currently available methods for assessment of variable selection performance in genomic data, an urgent need exists for a novel approach.The primary objectives of this study were to:Provide a real-world data-driven approach for comparing performance of high dimensional model inference algorithms in cancer genomics for both prediction and variable selection. We evaluate models by simulating Compare the performance of penalised linear regression methods for prediction and variable selection.Compare algorithmic selection by internal cross-validation to preselection according to performance in external test datasets under our validation approach.These objectives are realised by subsampling real  with the GEOquery library. Local institutional review board approval and informed participant consent were documented in each data publication.4246Five cancer genomics datasets were extracted from Gene Expression Omnibus contains serum miRNA expression profiles for 4113 individuals; 1280 with breast cancer, 54 with benign breast disease, 63 with non-benign breast disease, 451 with various other cancers and 2836 non-cancer controls. Participants with breast cancer were recruited through admissions and referrals to the National Cancer Centre Hospital Japan between 2008 and 2014. Exclusion criteria were (1) administration of medication prior to serum sampling and (2) advanced cancer in other organs. Controls were recruited from (1) National Cancer Centre Biobank, Yokohama Minoru clinic and the Toray Industries staff. Samples from individuals with non-benign breast diseases and other cancers were extracted from the National Cancer Centre Biobank. miRNA expression was measured with was collected on the Toray Industries 3D-Gene Human miRNA Oligo Chip microarray.GSE73002 contains serum miRNA expression profiles for lung cancer patients. About 1566 pre-operative and 180 post-operative samples are available, in addition to 2178 samples from patients without cancer, collected from the National Cancer Centre Japan and the Yokohama Minoru Clinic. Exclusion criteria were (1) miRNA expression quality check failure, (2) history of other malignancy, (3) missing clinical information, (4) pre-collection therapy and (5) over 180 days had passed between collection and surgery. miRNA expression was measured with was collected on the Toray Industries 3D-Gene Human miRNA Oligo Chip microarray.GSE137140 contains full length single-cell RNAseq data from 5902 cells extracted from 18 patients with stage I to IV squamous cell carcinoma (SCC) of the oral cavity at the Massachusetts Eye and Ear Infirmary. Tissue samples were extracted from surgical biopsies of the primary tumour or lymph node. Sequencing was performed on the Illumina Nextseq 500 platform and transcript-per-million values reported.GSE103322 contains single-cell RNAseq data from 22 ascites samples in 11 patients with high-grade serous ovarian cancer at Brigham and Women’s Hospital and the Dana-Farber Cancer Institute. About 9609 CD45+ depleted samples, profiled with 10× were included in this analysis. Sequencing was performed on the Illumina NextSeq 500 platform and transcript-per-million values reported.GSE146026 contains 6341 single-cell RNAseq profiles from patients with isocitrate dehydrogenase mutant astrocytoma at Massachusetts General Hospital. Tumour tissue was collected from surgical resections and malignancy confirmed under frozen section. Following disaggregation, profiling was performed by Smart-seq2. Sequencing was performed on the Illumina NextSeq 500 and transcript-per-million values reported.GSE8956748:Where datasets had Here Here 50 The same cross-validation folds were employed for each penalisation method in a given experiment. Predictive performance and variable selection performance were assessed using the remaining test observations. Experiments were repeated for 100 different training samples, for each of 5 datasets and for both cross-validation routines, yielding 1000 experiments with which to compare penalisation methods for each sample size.In each experiment, 500 design variables and a response were randomly selected from the available gene expression variables in 1 of the 5 datasets. A small proportion of the observations rameters . Regular10 As the true coefficient vector, Model assessment metrics and notation followed previous comparative analyses. measured 3 metrics of predictive performance – proportion of variance explained (PVE), relative risk (RR) and relative test error (RTE).Thus, :Higher PVE indicates superior fit, and PVE is limited by the signal to noise ratio (SNR) such that Optimal relative risk is 0 and nullity is 1.Relative risk (RR) was employed as an performance metric in Bertsimas’ analysis.Relative test error (RTE) compares error to the noise variance: we measured the cosine similarity of Following calls for model coefficient similarity assessment,t-tests:Active (non-zero) variable selection performance was also estimated under  and predictors were classified according to a cutoff Significance was adjusted for multiple comparisons using false-discovery-rate (FDR) controlt-test over all 3000 experiments.To evaluate our model validation approach, we deployed it as a penalty preselection method, comparing it to traditional selection by minimisation of the internal cross-validation error. For each experiment, for each of 3 comparison metrics , a penalisation method was ‘preselected’ according to performance in experiments of equivalent sample size in the other 4 datasets. In each relevant experiment, penalisation methods’ performances were ranked and the method with the lowest rank aggregate performance was selected. The test performance of this method was compared to that of the penalisation method which yielded the lowest mean squared error on internal cross-validation. Overall performance of preselected penalties was compared to internal cross-validation selected penalties using a 2-sided paired Experiments represented a broad range of signal:noise ratios , with high SNR in experiments sampled from GSE73002 , intermediate SNR in GSE137140  and low SNRs in GSE103322 , GSE146026  and GSE89567 . The number of significant coefficients in each experiment was typically small  and followed a right-skewed distribution . This is consistent with the scale-free property of genomic networks, in which a small number of genes have many interactions.P < 2e-16. PVE:SNR curves .Predictive performance metrics are provided in R curves  demonstrVariable selection performance metrics are provided in −16), F1 score (P = 3.3 × 10−6) and coefficient similarity (−16) when compared to selection by internal cross-validation. In many cases the same penalisation method was selected under preselection and internal validation, leading to equivalent performance. Although aggregated improvements under preselection were statistically significant, internal validation outperformed in some experiments  lacked the graduation required to compare penalisation methods at the n = 25 level. This limitation was particularly important in the setting of active variables estimated according to a sharp significance cutoff. The coefficient similarity metric proved useful in this regard, as it was continuous and independent of any significance cutoff. However, coefficient similarity provides little insight into on model complexity, a central aspect of genomic network inference. Indeed, although Real-world genomic datasets were employed in this analysis. Accordingly, our results are expected to be more representative of actual experimental modelling conditions. Data-driven model assessment was facilitated by the large number of observations available in these datasets. However, our results may not generalise to datasets with incomparably distributed signal or noise. Logistic and Cox regression tasks present addition challenges such as class imbalance and censoring, which are beyond the scope of this analysis.Click here for additional data file.Supplemental material, sj-txt-1-cix-10.1177_11769351211056298 for Sparse Regression in Cancer Genomics: Comparing Variable Selection and Predictions in Real World Data by Robert J O’Shea, Sophia Tsoka, Gary JR Cook and Vicky Goh in Cancer Informatics