Drug sensitivity prediction constitutes one of the main challenges in personalized medicine. Critically, the sensitivity of cancer cells to treatment depends on an unknown subset of a large number of biological features. Here, we compare standard, data-driven feature selection approaches to feature selection driven by prior knowledge of drug targets, target pathways, and gene expression signatures. We asses these methodologies on Genomics of Drug Sensitivity in Cancer (GDSC) dataset, evaluating 2484 unique models. For 23 drugs, better predictive performance is achieved when the features are selected according to prior knowledge of drug targets and pathways. The best correlation of observed and predicted response using the test set is achieved for Linifanib (r = 0.75). Extending the drug-dependent features with gene expression signatures yields the most predictive models for 60 drugs, with the best performing example of Dabrafenib. For many compounds, even a very small subset of drug-related features is highly predictive of drug sensitivity. Small feature sets selected using prior knowledge are more predictive for drugs targeting specific genes and pathways, while models with wider feature sets perform better for drugs affecting general cellular mechanisms. Appropriate feature selection strategies facilitate the development of interpretable models that are indicative for therapy design. The emergence of large scale high-throughput screening studies6 have allowed researchers to develop computational models for drug response prediction from molecular profiles of human cancer cell lines or drug properties8. Although the inconsistencies and limitations of cell line data have been raised and extensively studied12, these resources remain a vital tool for development of such models.The ability to predict a response of a specific cancer type to a therapy is one of the main goals in precision medicine. Considering molecular features of cancer cells is crucial for mitigating heterogeneity and for tailoring the therapy to specific patientsArguably, the desired quality of computational models of drug response is not only their predictive performance, but also interpretability. To evaluate candidate drug efficacy on a specific patient’s tumor, many approaches apply black-box algorithms with a set of highly dimensional features as input. In clinical practice, the capability of extracting such high-volume data from patient’s material is limited. Thus, there is a growing need of proper identification of concise, limited subset of features, or biomarkers, that are most informative of drug response. Therefore, strong emphasis should be put on feature selection approaches for drug sensitivity prediction. Despite its paramount importance, no systematic assessment of feature selection strategies in the task of drug response prediction was so far performed.18. Multitask learning was proposed to improve drug sensitivity prediction by pooling information learned for different drugs19. Finally, a number of kernel-based multi-view and multi-task models were introduced for drug sensitivity22. Although these approaches show very good predictive performance, they suffer from low interpretability. As a remedy, a multi-task learning approach based on a Bayesian model for collaborative filtering was proposed23, which allows for identifying general interactions between features of the drugs with features of the cell lines. For example, it gives insights in the form of ”activation of pathway Y will confer sensitivity to any drug targeting protein X”. This approach, however, does not directly address the crucial need of identifying biomarkers for specific drugs.The problem of drug response prediction has been approached by a wide spectrum of linear and non-linear machine learning algorithms, including regularized linear regression, k-nearest neighbors (KNN), support vector machines and random forests24. Generally, the problem of identifying the optimal subset of features is intractable25. Data-driven feature selection thus proceeds either as a heuristic search over the space of feature combinations, or is embedded directly in the learning algorithm by imposing sparsity of parameters associated with the features via regularization. Although these methods can achieve good predictive performance and deal with the curse of high data dimensionality17, feature importance estimates and selection might not always be accurate and stable, especially in vastly high-dimensional data and in the presence of correlation between features26. Stability selection was proposed to mitigate this problem when regularized regression is applied27, but it still comes without the guarantee to choose the most biologically relevant predictive features.For that aim, data-driven, automatic techniques of feature selection were applied14. Accordingly, genome-wide gene expression is the most common choice in the case of models utilizing single data type28. Other studies reported that in some cases gene expression alone might not be sufficient, especially in a cancer- or drug-specific setting30. Importantly, expanding the feature space related only to cancer cell lines’ biology with drug-related properties was shown to improve predictive performance30. The predictive drug-specific features may be related to their chemical properties, such as compound structure30, their known primary targets or pathway activation22. Recently, multiple methods based on deep learning have emerged, showing promising results in the application to drug sensitivity prediction31. The published neural network architectures range from common stacks of fully connected layers32 to more sophisticated architectures involving residual and convolutional networks35. Furthermore, methods employing autoencoders37 and variational autoencoders38 have been proposed. Due to their complicated, non-linear structure, neural networks may suffer from the lack of interpretablity, including difficulties in assessment of feature importance. However, methods from the growing field of explainable artificial intelligence can help to mitigate this problem39.Drug prediction approaches largely differ with respect to the type of features that they model. Among the molecular data feature types which characterize the cancer cell lines, gene expression was assessed as the most informative, with remaining types such as mutation or copy number data bringing limited predictive power25. It was however, never exploited in the task of drug response prediction. We assess this methodology in a systematic fashion for a broad spectrum of anti-cancer compounds, integrating multiple data types and comparing the results to the baseline models utilizing genome-wide gene expression data and data-driven feature selection techniques. On top of that, we evaluate gene expression signatures as the means of dimensionality reduction of the transcriptomics data and evaluate their predictive power in this context. This comprehensive analysis pin-points a set of drugs for which easily interpretable, informative, small sets of features can be identified.Here, we utilize the knowledge regarding drug targets and their mode of action to select plausible features describing the cancer cell lines. This drug-related prior knowledge is thus used to directly limit the initial feature space, rather than first expanding it and next using data-driven selection techniques to narrow it down. We argue that this approach for feature selection in combination with common regression techniques can provide a simple and highly interpretable model without losing the predictive performance characteristic for models starting from high-dimensional data. In fact, the direct utilization of prior knowledge is the number one strategy recommended for feature selection according to the classics in machine learningIn order to comprehensively evaluate different feature selection strategies, we devised the following workflow Fig. . We firsk, is shown. The median k values are 70 and 1155 for random forests and for stability selection, respectively. All foregoing values constitute a drastic decrease in comparison to the number of 17737 genome-wide input features.The median numbers of input features are 3 and 387 for only targets and pathway genes feature sets, respectively Fig. . The inpThe number of samples for each drug also slightly differs for only targets and pathway genes feature sets, since for some cell lines the coding variants or CNV information are not available Fig. . This reThe area under the dose-response curve  measures the overall drug efficacy, with lower values corresponding to stronger efficacy. The distribution of this metric varies significantly among compounds with different target pathways Fig. . The medSince root mean squared error (RMSE) measures the level of model error, and correlation measures the model agreement with the test set, both large (1-RMSE) and high correlation should coherently indicate a high model performance. The negative relation between (1 - RMSE) quantity and correlation, however, confirms the fact that raw RMSE is not a good metric for performance comparison between compounds Fig. ; MethodsBoth these facts support that relative root mean squared error  is a better performance measure than raw RMSE Fig. . Indeed,It is apparent from Fig. The considered set of drugs is diversified in terms of available data Fig. . The bigIn general, genome-wide feature set combined with elastic net (GW EN) emerges as the best model with the median correlation of 0.39 Fig. . HoweverThe standard, genome-wide model achieves the best performance for over half of considered drugs Fig. . HoweverNext, we investigate the general tendencies concerning which feature selection is particularly better suited for modeling drugs targeting specific pathways. To this end, we compare the overall performance of biologically driven feature selection as one group to the baseline of genome-wide features and the genome-wide features with automatic selection as another Fig. , for difWe next inspect in detail the results for distinct drugs coming from DNA replication and RTK signaling pathways, respectively Fig. . Among tk most predictive features in each model and record the frequencies of particular data classes among them. Results confirm the fact that gene expression is the most predictive feature type, although mutation (coding variant) and tissue type are also important, especially for drugs designed to target specific cancer type with a particular mutation. In contrast, copy number variants seem not to incorporate much useful information. The relative effect of gene expression data increases with number of considered most predictive features, but this is expected given that this category is the most frequent of all available data types overall. Finally, the high frequency of gene expression signatures among the top predictive features implies that the signatures can act as good representatives of genome-wide information.In order to assess, which feature types are most informative of drug response, we consider such models with biologically driven feature space, which use all five available data types Fig. . To makeWe further focus the analysis on ten drugs of most interest Fig. , based o41, the skin tissue feature is the third most predictive one for the best OT + S RF model.From all analyzed drugs, Dabrafenib emerges as the compound which is the easiest to model. The highest correlation of 0.75 is achieved by the model combining only targets features with gene expression signatures and random forest (OT + S RF), and performance of other approaches is only slightly worse Fig. , which l44. Contrary to the Dabrafenib’s example, Linifanib is one of the rare examples where good modeling results are achievable despite low standard deviation of the AUC distribution  is accomplished by using only 7 features related to the drug’s targets , which significantly outperforms the genome-wide models  uses features related to genes present in drug’s target pathway (218 features), and the most important feature is expression of FLT3. The accurate prediction done by PG RF model for the single outlying, responsive sample  and access to more information, compared to the models with biologically driven features .Both Jang Our results bring important conclusions about feature selection strategies for drug sensitivity prediction. In general, the baseline genome-wide set of features or data-driven feature selection yields higher median predictive performance than biologically driven features. There are, however, multiple individual drugs, for which the feature selection driven by biological knowledge gives the best results, including models for the drugs with the top two performance scores. Moreover, feature selection driven by prior knowledge drastically reduces the number of features. At the same time, if the drop of performance in comparison with genome-wide models occurs, it is often only slight.In addition, the presented analysis illuminates the mechanisms behind the sensitivity of different cancer cell lines to different types of drugs, suggesting which types of features should be used to model different classes of drugs. Drugs that are generally toxic or target general cellular mechanisms such as DNA replication or metabolism affect a relatively large proportion of cancer cell lines and thus have a wide response distribution. These compounds tend to be better modeled using genome-wide features, indicating that their effect on the cancer cells depends on a large spectrum of different cellular features. Conversely, for drugs targeting specific pathways, sensitivity distribution tends to be narrow, with most cells not responding at all and only a few interesting outliers of sensitive cells. For these compounds, high-level drug properties such as direct targets or target pathways allow to build highly predictive models with small numbers of interpretable features, such as Dabrafenib, Linifanib or Quizartinib. In particular, highly predictive models with an extremely low number of input features can be obtained, as in the cases of Linifanib, Afatinib, and GNF-2. Overall, this analysis shows the importance of using adequate feature selection strategies for each individual drug.3 database. A total of 251 compounds were included in the analysis. Each was assigned one of 24 classes of target pathways, defined by the GDSC.The analyzed dataset was acquired from the Genomics of Drug Sensitivity in Cancer (GDSC)The total set of samples consisted of 983 cancer cell lines originated from 13 tissue sites. The available data types for describing the cell lines included: gene expression (17737 features), coding variants (310 features), copy number variants  and tissue type (13 features). Coding variants and copy number variants were represented as binary calls determining the presence or absence of a variant in a given gene or segment, respectively. We have dummy encoded the tissue types resulting in 13 distinct binary features for every cell line. All biological input data were acquired directly from the GDSC resource.GDSC provides two types of metrics representing the drug efficacy: half maximal inhibitory concentration (IC50) and area under the dose-response curve (AUC). Since in our analysis we did not observe significant differences in predictive performance when using one metric in favor of the other, we picked AUC as our single target variable.46. See Supplementary Methods for descriptions of the algorithms and implementation details.We employed two common machine learning algorithms in order to predict the AUC values: elastic net linear regression and random forest regression. We implemented both methods using Python3 scikit-learn 0.19.2 libraryWith a total of 18485 biological features that can be used to describe the cancer cell lines, the analyzed dataset is very high-dimensional. In contrast, the number of samples is in the order of hundreds, which poses the danger of overfitting. This might especially be the case when considering all available genome-wide information regardless of the drug being modeled. Here, we investigate different feature selection methods to mitigate this problem. These approaches can be divided into two groups: biologically driven and automatic, data-driven selection methods.47 database, assigning targets for 88 matched compounds. For each drug target, we included features representing the target gene’s expression, coding variant and copy number variation. In the case of copy number data, a given genetic feature was incorporated if the corresponding segment included at least one of the drug target genes. We only considered drugs with explicit gene targets annotation in GDSC or DrugBank and for which at least one feature in addition to the tissue type was available in the data. These conditions were met for 184 compounds. Applying two regression algorithms for each drug resulted in 368 separate models.In the most restricted feature space, we included only predictors corresponding to the direct targets of the drugs, as well as tissue type. Drug targets information was derived directly from GDSC. As an additional resource, we used DrugBank49 database (version 66 accessed on October 2018). For each compound, first its target set was derived, followed by finding all pathways which included at least one of the given targets. The total set of considered genes was then computed as the union of all members of the found pathways. Lastly, corresponding gene expressions, coding variants, copy number variants and tissue types were extracted to create the final feature set. The drug targets and pathway information was available for 186 drugs, producing 372 models.In this approach, we included features related to genes that belonged to the same signaling pathway as the set of target genes. Pathways information was derived from ReactomeS with i genes, we calculated two scores. The first characterizes the coherent expression and the second estimates the activation level of S. Given a gene expression matrix for S in n samples  for each sample is calculated by first z-scoring the gene expression values across samples, followed by averaging the resulting z-scores across genes. Here, we calculated the signature scores using the cancer cell line expression data provided by GDSC. We set the threshold for a significantly coherent activation of S to Gene expression signatures can explain the activation level of complex biological phenomena in the investigated cell lines. Here, we refer to a gene signature as a set of genes related to a certain known biological phenomenon that can be deduced from cancer gene expression data . For the second technique, feature importance estimates derived directly from random forest were used. These features were then used for random forest regression models (GW SEL RF). For more detailed description of both techniques, see Supplementary Methods.In addition to feature pre-selection based on drug properties and biological relevance, we also evaluated automated feature selection algorithms in application to genome-wide expression data. We used two techniques, based on linear and non-linear methods. First, stability selection, which uses lasso regression on multiple bootstrap samples in order to choose robust featuresMean Squared Error (MSE) as a scoring metric for best hyperparameters search. Although MSE is suitable for evaluation of different models within one compound, it is not reliable when comparing results across diverse drugs because of differences in corresponding AUC distributions. Furthermore, when a given target variable distribution has little variation, one can achieve a reasonably low MSE just by predicting the mean of a target variable. In order to avoid this problem and identify the models which performed well, we used Relative Root Mean Squared Error (RelRMSE), which is normalized in such a way that the score of 1 corresponds to a dummy model which always predicts the mean of target variable in the training data. RelRMSE is defined as the fraction of the dummy model’s RMSE on the test data and the analyzed model’s RMSE on the test data:During cross-validation tuning, we used The use of RelRMSE allowed us to distinguish drugs for which predictive algorithms could not outperform the dummy model, meaning that for those compounds no actual learning occurred.In order to make further assessments and comparisons between compounds, we used Pearson correlation coefficient with the response AUC in the test set as a performance metric. As stated in a previous section, the recorded results for each method were averaged over five modeling procedures that were performed with different data splits.Supplementary Information.


Alternative splicing (AS) plays critical roles in generating protein diversity and complexity. Dysregulation of AS underlies the initiation and progression of tumors. Machine learning approaches have emerged as efficient tools to identify promising biomarkers. It is meaningful to explore pivotal AS events (ASEs) to deepen understanding and improve prognostic assessments of lung adenocarcinoma (LUAD) via machine learning algorithms.RNA sequencing data and AS data were extracted from The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database. Using several machine learning methods, we identified 24 pairs of LUAD-related ASEs implicated in splicing switches and a random forest-based classifiers for identifying lymph node metastasis (LNM) consisting of 12 ASEs. Furthermore, we identified key prognosis-related ASEs and established a 16-ASE-based prognostic model to predict overall survival for LUAD patients using Cox regression model, random survival forest analysis, and forward selection model. Bioinformatics analyses were also applied to identify underlying mechanisms and associated upstream splicing factors (SFs).Each pair of ASEs was spliced from the same parent gene, and exhibited perfect inverse intrapair correlation (correlation coefficient = − 1). The 12-ASE-based classifier showed robust ability to evaluate LNM status of LUAD patients with the area under the receiver operating characteristic (ROC) curve (AUC) more than 0.7 in fivefold cross-validation. The prognostic model performed well at 1, 3, 5, and 10 years in both the training cohort and internal test cohort. Univariate and multivariate Cox regression indicated the prognostic model could be used as an independent prognostic factor for patients with LUAD. Further analysis revealed correlations between the prognostic model and American Joint Committee on Cancer stage, T stage, N stage, and living status. The splicing network constructed of survival-related SFs and ASEs depicts regulatory relationships between them.In summary, our study provides insight into LUAD researches and managements based on these AS biomarkers. Lung cancer is the most common and deadliest cancer worldwide, in which non-small cell lung cancer (NSCLC) accounts for 85% of all cases , 2. NSCLFindings of The Human Genome Project indicated a truth that the number of human protein-coding genes  is far less than the previous estimation from the diversity of human proteome . Further studies revealed this proteomic diversity may be attributed to post-transcriptional processing in the RNA level. Recent estimates indicated that nearly 95% of human genes are involved in alternative splicing (AS), where a pre-mRNA can be spliced into several mRNA isoforms with different functions . Apart fMachine learning is a discipline in computer science based on algorithms that parse data, learn from data, and make predictions or decisions on a wide variety of complex issues. The development of machine learning technology and its wide application in biomedical studies provide researchers with powerful tools to find the most informative detection markers from large, highly complex datasets . In thishttps://portal.gdc.cancer.gov/). AS data of LUAD were downloaded from TCGA SpliceSeq (https://bioinformatics.mdanderson.org/TCGASpliceSeq) [The mRNA data in fragments per kilobase per million mapped reads format and patients’ clinical information of LUAD were retrieved from The Cancer Genome Atlas (TCGA) database (liceSeq) . The perThe name of each ASE consists of three parts: gene symbol, ID number designated in TCGA SpliceSeq database, and splicing type. For example, the name “CHEK-19309-AP” indicates the parent gene of this event is CHEK, its ID number in TCGA SpliceSeq database is 19309, and its splicing type is AP.Random forests is an ensemble learning technique that makes a prediction based on constructing multiple unpruned decision trees, each of which is constructed on several bootstrap samples of the training set data using a subset of randomly picked variables . The treIn the formula, X is an input vector and ψ(t) represents the independent trees in random forests and each tree elects the most popular class for X via a unit vote. Then the decisions made by all the trees were aggregated and the class of X is determined based on the principle of majority voting. This supervised non-parametric machine learning method can help researchers acquire key information from massive complicated data and resist both overfitting and underfitting .Boruta algorithm is a random-forest based feature selection method. This algorithm estimates the importance of features and captures important features in the dataset . ThroughRSF is an extension of the original random forest technique which can be used for survival data . Based oh, b, and t refer to the terminal node, survival tree, and time, respectively. dl,h represents the number of death, Il,h represents patients at risk, and tl,h represents distinct time events. The same CHF is assigned to all cases in h. Then an ensemble CHF is computed for the survival forest with B trees for a given d-dimensional case xi:In this formula, lated as :\documenSurv and var.select functions in this package we preliminary screened out survival-related ASEs.Through the above methods, RSF adapts traditional random forest algorithm and can handle problems associated with survival. And these procedures in the present study are carried by randomForestSRC R package. Using Cox regression model is a model simultaneously analyzing the effects of several variables on survival. Based on the condition of the proportional hazard, this model assumes the hazard functions for different individuals are proportional and covariates’ effects on individuals are constant. Cox regression model can be formulated as:h0(t) is the baseline hazard function, t is a time variable, and βi is a coefficient vector weighing the contribution of feature Xi.In this formula, We used a forward selection model to select prognosis-related genes from survival-related genes. This selection was achieved by rbsurv R package in the following procedures : (1) TheSD reflects the information entropy of a feature. The greater the SD, the more informative the feature. To filter out less informative ASE and to decrease the computation of subsequent analyses, we analyzed SDs of all the ASEs in the dataset and excluded ASEs with SD < 0.1. Besides, ASEs whose mean PSI ≤ 0.05 were also excluded.ovun.sample function of ROSE R package. Thus, we generated augmented data with a balanced class distribution. Second, we applied Boruta algorithm to select ASEs which were important for distinguishing between normal and LUAD samples. Third, to further explore ASEs implicated in splicing switch, we separately analyzed the correlations of LUAD-related ASEs derived from the same gene.First, to circumvent the problem caused by severely imbalanced data  in the learning process, we balanced the proportions of normal and LUAD samples by oversampling normal samples using the Applying Boruta algorithm, we selected ASEs correlated with outcome variables (LNM or not). Using these ASEs, we performed nested five-fold cross-validation based on the random forest model. The cross-validation sequentially reduced the number of ASEs  and this process repeated five rounds. The mean cross-validation error was calculated and the classifier with the minimum error rate was chosen. The classifier’s classification capacity was evaluated by cross-validation.P < 0.05 was considered statistically significant.Cox regression is the traditional method for survival analyses with an understandable output, while RSF provides more insight into the relative importance of model covariates . Based ohttp://193.206.120.249/splicing_tissue.html) [P < 0.01 was considered to be significant. Spearman correlation analysis was conducted to evaluate the correlations between survival-related SFs and ASEs. The criterion of selecting correlated variables was P < 0.01 and |coefficient| > 0.2. Finally, their correlations were visualized via Cytoscape.A total of 390 SFs were retrieved from SpliceAid 2 database . R package Boruta, randomForest, RandomforestSRC, caret, survival, rbsurv, pROC, timeROC, pheatmap, and ggplot2 were used in this study for analyzing data or drawing purposes. All codes are available on GitHub  at The flow chart presenting the overall analysis process of the current study is shown in Fig. A total of 43,948 ASEs corresponding to 10,367 genes were included in this study, of which ES had the highest proportion, which was 27.2%, and the lowest was contributed by ME, which had only 0.9%. One single gene could have seven splicing types at most   between the high-risk group and low-risk group in the test set  Fig. d. The AU01) Fig. e. Sampleset Fig. i. ROC anset Fig. j.Table 1P < 0.01), T stage (P < 0.05), N stage (P < 0.05), and vital status (P < 0.001). The correlations between risk score and AJCC stage, T stage, N stage, M stage, vital status, smoking history, gender, and age were shown in Fig. Further analysis of the risk score model indicated correlations between the 16-ASE prognostic model and clinical variables including American Joint Committee on Cancer (AJCC) stage  on OS of LUAD patients. Spearman test was used to identify correlations between survival-related SFs and ASEs, and a correlation network was established using Cytoscape software .Additional file 4: Table S4. Information of samples included in the construction of prognostic model.Additional file 5: Table S5. Feature importances and selection results presented by Boruta algorithm for the splicing events differentiating normal and tumor tissues.Additional file 6: Table S6. Feature importances and selection results presented by Boruta algorithm for the classifier recognizing lymph node metastasis (LNM).Additional file 7: Table S7. Unvariate Cox regression results.Additional file 8: Table S8. Survival-related alternative splicing events selected by random survival forest model.Additional file 9: Table S9. Alternative splicing events and their coefficient in the prognostic model.


P-value in the Cox model than other methods on 10 cancer data from Jiang Dataset and Novel Dataset. We have drawn different survival curves for different cancers and found that some genes play a key role in cancer. For breast cancer, we find out that HSPA2A, RNASE1, CLIC6, and IFITM1 are highly expressed in some specific groups. For lung cancer, we ensure that C4BPA, SESN3, and IRS1 are highly expressed in some specific groups. The code and all supporting data files are available from https://github.com/guofei-tju/Uncovering-Cancer-Subtypes-via-LASSO.Recently, cancer has been characterized as a heterogeneous disease composed of many different subtypes. Early diagnosis of cancer subtypes is an important study of cancer research, which can be of tremendous help to patients after treatment. In this paper, we first extract a novel dataset, which contains gene expression, miRNA expression, and isoform expression of five cancers from The Cancer Genome Atlas (TCGA). Next, to avoid the effect of noise existing in 60, 483 genes, we select a small number of genes by using LASSO that employs gene expression and survival time of patients. Then, we construct one similarity kernel for each expression data by using Chebyshev distance. And also, We used SKF to fused the three similarity matrix composed of gene, Iso, and miRNA, and finally clustered the fused similarity matrix with spectral clustering. In the experimental results, our method has better  Numerous studies have shown that cancer is a heterogeneous disease  from The Cancer Genome Atlas (TCGA). It's worth noting that each cancer has three expression data, including gene expression, isoform expression, and miRNA expression. Second, we employed LASSO .X ∈ Rn×m, where n is the number of patients and m is the number of expression factors. Patient survival time is defined as T ∈ Rn×1. We choose the gene expression with the coefficient more than zero as the selected gene features.We represent patient data as p and q, with standard coordinates pi and qi, is defined as Equation (2):We make use of Chebyshev distance :The expression data are denoted as x is an element of E, x′ is corresponding elements of E after standardization, E and S is standard deviation of E. Here, we denote normalized expression data as E′.where E′, we construct similarity kernel K ∈ Rn×n for patients. Here, the similarity between two patients is defined as Equation (4):Based on the processed expression data Ki,j is the similarity between i-th patient and j-th patient, ei and ej are two vectors of i-th row and i-th row of E′.where Finally, we get three similarity kernels for a special cancer, including similarity kernel K* ∈ ℝn×n.We construct three similarity kernels for patients in the above section. Then, we use similarity kernel fusion (SKF) to combine these kernels into one kernel P ∈ ℝn×n and S ∈ ℝn×n for each similarity kernel, where P is a normalized kernel and S is a sparse kernel that eliminates weak similarity, as Equations (5) and (6):First, we construct two kernels P satisfies where S satisfies Ni is a set of top k nearest neighbors of i-th patient including itself.where Second, we uncover more information by using multiple iterations  is the status of l-th kernel after t iterations, α is a coefficient and satisfies α ∈ , and r = 1, 2, 3) represents the initial status of Pr.where t + 1 iterations, the overall kernel can be computed as Equation (8):After Through SKF, we have obtained the fusion kernel containing multi-angle information, and the invention of spectral clustering is to cluster through the kernel. So, We employ spectral clustering on integrated similarity kernel to divide all patients into multiple clusters. In order to ensure that the difference between each pair of classes should be as large as possible, also the similarity within one class should be as large as possible, this problem is a relaxation of the NCut problem  to represent the cluster result :We define a matrix D is the degree matrix of K*, L is the Laplacian matrix of K*, where Here, our proposed method can be shown in Algorithm 1.In this section, we analyze the performance of our method on the dataset in several ways. First, we introduce an evaluation criteria and a verification method that are used to evaluate the significant performance of cancer subtypes prediction. Second, we analyze the performance of SKF on the Jiang's dataset. Third, we analyze the performance of LASSO on the Jiang's dataset. Fourth, we compare our method with other methods. Fifth, we apply five novel data sets to evaluate our new method. Finally, we plot survival curves and heat maps for some cancers.P-value of Cox regression model and survival curve to evaluate the performance of our method, while the lower P-value indicates higher performance significance. Here, we use 0.05 as a standard for evaluating the performance of clustering results. The actual significance of P-value is the difference in survival rates among cancer subtypes. In addition, survival curve is the change of survival rate with survival time. We can find from the survival curve that different cancer subtypes have different survival odds. We can focus on cancer subtypes with high mortality.In this paper, we use the K in SKF. We chose K = 30 and α = 0.9 through experiments. Because the parameter space is very large, we mainly adjust K by fixing α first, and then fix K to adjust α to get an a local The optimal value.In this section, we compare our approach on the use of SKF with the same model on the use of SNF, UMKL, the average kernel fusion or the direct use of single kernel on the Jiang's dataset. There are two important parameters α and On the Jiang's dataset, we separately record the results of using SKF and using a single kernel, as shown in In SKF, the choice of kernel is a very important factor. In most cases, we will choose Euclidean distance as the kernel generation formula, but considering that the dimensionality of biological data is generally large, using Euclidean distance will not have a good effect, we choose Chebyshev distance to construct the kernel. Specifically, it can be seen from the X axis is the number of clusters and Y axis is the value of −log10. Red, green, yellow, and purple represent the results of using SKF, SNF, UMKL, and average kernel, respectively. And the horizontal line represents the p-value of 0.05. We can see that there is a better performance on Stomach, Lung, and Colon by using SKF. The use of SKF for the kernel fusion on Breast is very similar to that of SNF. It is not as good as SNF on Kidney, but similar to the results of other kernel fusions. Therefore, it can be found that the use of SKF for kernel fusion has an effect on most datasets.We compare the results using SKF with the results of SNF, UMKL, and the average kernel fusion, as shown in 10, the red line represents the data obtained after selection, the blue line represents the data obtained before selection, and the horizontal line represents the P-value of 0.05. We can find that the selection of expression data has a certain influence on the P-value on Stomach, Kidney, while the P-value is greatly improved on Lung, Breast, Colon. Therefore, it can be found that the use of LASSO for selection of expression data has an effect on most datasets.We observe that the original gene expression data has high dimension. Therefore, we use LASSO to identify the high-efficiency gene expression data. In We compare our approach with the method of Jiang et al. , as showIn the above section, our method has outstanding performance on Jiang Dataset. To further evaluate this model, we extract five novel datasets from the TCGA website and apply our method to these novel datasets. The detailed results are shown in P-value. In this section, We list the survival curves of five cancers on the novel dataset, as shown in From above, we have better measured the performance of clustering results on We analyze the importance of essential genes on Lung and Breast datasets. The association between clustering results and expression data are shown in For breast cancer, we select five essential genes, such as CTSA, HSPA2, RNASE1, CLIC6, IFITM1. We analyze the box plot of essential gene expression data in five categories, as shown in For lung cancer, we select five essential genes, such as PYGB, C4BPA, SESN3, MMP10, IRS1. We analyze the box plot of essential gene expression data in seven categories, as shown in In this paper, we extract five novel datasets  from the TCGA website. We find that our method not only works well on the Jiang's dataset, but also performs well on our newly extracted five datasets. In addition, we obtain some important genes that are related to a special cancer.In the future, we will try to employ more kinds of expression data to further uncover cancer subtype because cancer is a multi-factors disease (Guo F. et al., All datasets presented in this study are included in the article/supplementary material.FG, LJ, and SL conceived and designed the experiments. SL and LJ performed the experiments and analyzed the data. SL, NG, and FG wrote the paper. FG, NG, and JT supervised the experiments and reviewed the manuscript. All authors have participated in study discussion and manuscript preparation. 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.


Colorectal cancer (CRC) tumors can be partitioned into four biologically distinct consensus molecular subtypes (CMS1-4) using gene expression. Evidence is accumulating that tumors in different subtypes are likely to respond differently to treatments. However, to date, there is no clinical diagnostic test for CMS subtyping. In this study, we used novel methodology in a multi-cohort training domain  to develop the ColoType scores and classifier to predict CMS1-4 based on expression of 40 genes. In three validation cohorts  representing three distinct gene-expression measurement technologies, ColoType predicted gold-standard CMS subtypes with accuracies 0.90, 0.91, 0.88, respectively. To accommodate for potential intratumoral heterogeneity and tumors of mixed subtypes, ColoType was designed to report continuous scores measuring the prevalence of each of CMS1–4 in a tumor, in addition to specifying the most prevalent subtype. For analysis of clinical specimens, ColoType was also implemented with targeted RNA-sequencing (Illumina AmpliSeq). In a series of formalin-fixed, paraffin-embedded CRC samples (n = 49), ColoType by targeted RNA-sequencing agreed with subtypes predicted by two independent methods with accuracies 0.92, 0.82, respectively. With further validation, ColoType by targeted RNA-sequencing, may enable clinical application of CMS subtyping with widely-available and cost-effective technology. To resolve potential disparities between these systems, the Colorectal Cancer Subtyping Consortium (CRCSC) published the consensus molecular subtypes (CMS), which partitioned CRC tumors into CMS1, CMS2, CMS3, CMS4 and a small set of unclassified tumors7. Since publication of the CMS subtypes, numerous studies have explored how patients in different subtypes exhibit differential sensitivities to commonly used drugs15, contributing evidence to the clinical utility of CMS.Colorectal cancer (CRC) is the fourth most common cancer in the U.S. and the second leading cause of cancer deaths. Attempts to reduce CRC deaths have been complicated by the molecular heterogeneity of the disease leading to differential responses to therapies. Over the past decade, numerous gene expression-based subtype systems were developed to better characterize CRC7. The CMScaller application also uses whole-genome data and expression values of over 500 genes to report the CMS subtypes for tumor samples and preclinical models16. To analyze formalin-fixed, paraffin-embedded (FFPE) tissue samples collected in clinical studies, researchers have developed in-house classifiers using NanoString nCounter system17, microarrays13, or immunohistochemistry18. However, to date, there is no commercially available assay that meets the quality-control requirements of a clinical diagnostic test.Progress towards clinical application of the CMS subtypes has been limited by the complexity of the CMS subtyping method. CRCSC created the CMSclassifier computer application for CMS subtyping using expression of hundreds of genes from whole-genome dataIn this study, with the ultimate goal of producing a clinical assay for CMS subtyping, we developed the ColoType scores and classifier for CMS subtyping based on expression of 40 genes, implemented by whole-genome analysis or by targeted RNA-sequencing with an FFPE tissue source.Genome-wide gene expression data from multiple cohorts generated using multiple platforms (two microarray platforms and RNA-sequencing) were collated for development and validation of ColoType.Cohort A Cohort A  consensus molecular subtype classifications for each sample in Cohort A were also obtained from Synapse Project 2623706, and summarized herein Table . For theCohort B Cohort B (n = 688) consisted of samples from the PETACC-3 clinical trial21 for which mRNA was hybridized to an Almac custom Affymetrix microarray platform. Gene expression and clinical data were obtained from Synapse Project 2623706. The cohort was randomly divided into training (n = 342) and validation (n = 346) sets balanced for CRCSC CMS subtype (Table 22. Raw RNA-sequencing (RNA-seq) data (BAM files) were obtained from Genomic Data Commons (https://gdc.cancer.gov). Following alignment to the Ensembl v90 genome build, gene expression was measured as the log base 2 of transcripts per million (TPM) derived from count values using the feature counts algorithm24. This realignment enabled better comparison between COAD expression data and whole-genome and targeted RNA-seq data for the Marshfield cohort. Patient-level clinical data and CMS classifications for COAD were obtained from Synapse Project 2623706. To support the development of ColoType, COAD was randomly divided into training (n = 189) and validation sets (n = 193) balanced for CRCSC CMS subtypes (Table Cohort C: Cohort C was formed from TCGA-COAD (n = 382)ning n = 89 and vaTo enable coordinated derivation in these multiple cohorts we restricted expression data to array features annotated to a unique ENTREZID, and further restricted to ENTREZIDs assayed in all three cohorts. For each such ENTREZID, we restricted expression to the array feature with maximal interquartile range in the cohort’s training set, among all features representing the ENTREZID. In the end, for a set of ENTREZIDs assayed in all three cohorts, there was a unique expression value for each sample.25. To enable more general application, we used a so-called pseudo-reference-sample scaling factor26, as applied in the DESeq R package.To enable comparisons between samples, raw RNA-seq data are typically normalized by dividing read counts by a sample-specific normalization factor. In whole-genome normalization methods, such as RPKM or TPM, the normalization factor involves the total reads for the sample and the gene length, inappropriate for targeted RNA-sequencing. For targeted RNA-sequencing, data from a reference sample can be usedP of samples, and raw counts for each gene in a set G and sample in P, expression values by size factor normalization are computed as follows.size factor for gene i is the geometric mean of the raw counts for gene i for all samples in P;The scaled count value for a gene i and sample j is the raw count number divided by the size factor for gene i;The size factor for sample j is the median of the scaled counts in sample j over all genes in G;The size factor normalized expression value for gene i in sample j is then the scaled counts for gene i divided by the size factor for sample j.The Given a set Size factor normalization was applied to whole-genome sequencing data as well as targeted RNA-seq data to enable comparison of subtyping methods in data obtained by multiple sequencing methods. Modeling with size factor normalized data in this study used log2 transformed size factor normalized expression values as gene expression values.Data on a cohort of stage 2 colon cancer patients was obtained from Marshfield Clinic, Marshfield, Wisconsin (Table Tumor tissue was obtained from tumor-rich regions of unstained slides (1 or 2 per patient) and deparaffinized by Xylene wash. mRNA was then extracted with RecoverAll Total Nucleic Acid Isolation Kit.FFPE RNA was prepared for sequencing using two independent library construction methods: RNA-seq using ribosomal RNA depletion, and AmpliSeq for Illumina Custom RNA targeted panel. RNA was quantitated by Qubit using the RNA HS Assay (Thermo Fisher Scientific Inc.) and assessed for integrity on the Bioanalyzer 2100 (Agilent Technologies Inc.). 80–100 ng of RNA was used as input for the library construction for both approaches. Quantifiable mRNA was obtained for 49 samples.24, and gene expression values computed by size factor normalization, log2 transformed.For whole-genome analysis, libraries were constructed using the NEBNext Ultra II Directional Library Prep kit with NEBNext rRNA Depletion module (New England Biolabs)  following manufacturers recommendations. Libraries were quality assessed on the Bioanalyzer DNA 7,500 chip and quantified by qPCR using the Illumina Kapa Library Quantification Kit (Roche Diagnostics). The libraries were normalized, pooled in equal molar amounts, and sequenced on 6 lanes of a NextSeq v2.5 High Output flowcell using paired 75 bp reads. All libraries generated > 27 million raw reads. FASTQ files were aligned to the Ensembl v90 genome build, and gene counts computed using the feature counts algorithm24 and gene expression values computed as log2 of size factor normalized values, as for whole-genome sequencing data.ColoType panel genes served as targets for a custom AmpliSeq RNA panel. Libraries were constructed using the AmpliSeq for Illumina custom RNA panel reference guide (Illumina Inc.) following the standard workflow. Libraries were quality assessed on the Bioanalyzer DNA 7,500 chip and quantified by qPCR using the Illumina Kapa Library Quantification Kit. The libraries were normalized, pooled in equal molar amounts, and sequenced on 1 lane of a MiSeq v2 (300 cycle) kit using paired 150 bp dual indexed reads for an average output of 203,000 raw reads per library. FASTQ files were aligned to the Ensembl v90 genome build and gene counts computed using the feature counts algorithm27. This topic is discussed below and in greater detail in Supplementary Methods The cornerstone of a multigene expression-based clinical diagnostic test is the precise and reproducible quantification of species of mRNA. The multigene risk score (MRS) classification method was designed to classify samples by making efficient use of a small number of precisely measured mRNA species, perhaps including use of a reference set of data. Gene expression values are applied in an MRS classification system using a so-called risk score model of the gene, introduced in the EarlyR prognostic signatureg in a patient cohort P is defined from the distribution of the gene’s expression values in P using the statistical method of mixture modeling30. In its simplest form, a mixture model of a gene with bimodal expression in P partitions the expression values into the two modes, a high component and a low component. In this case, a risk score for g would be near 0 for most samples in one mode, say the low component, and near 1 for most samples in the high component, and rising steeply from 0 to 1 near the threshold between the low and high components. A risk score is created using bootstrap resampling and differs from a binary variable by representing potential uncertainty in the value of the threshold. This methodology can also be applied to genes that are not bimodal but whose expression values have a somewhat irregular distribution, presenting several possible boundary points between high and low components. In this case, a risk score model of the gene could be defined around any boundary between the possible high and low components. Depending on the desired application, a risk score may be alternatively defined to be near 1 for the low component instead of the high component. In either case, the graph of a risk score is S-shaped or the mirror image of such.A risk score for a gene X in a cohort P, there are typically genes that are significantly differentially expressed between the samples in and out of X. A risk score of a differentially expressed gene is a representation of the probability that a sample is in X based on the gene’s expression values. As such, a risk score is a single-gene predictive score for X.In this study, risk scores were used to build scores predictive for subtypes. For a well-defined subtype P and Q that contain similar patient populations, the distributions of the expression values of a gene g in P and Q would be comparable. Similarly, we define risk scores in P and Q to be equivalent if they are models of the same gene and have comparable distributions , p(v) is the number of samples in both X and V, divided by the number of samples in V. Using a mathematical smoothing method, we can require that the MRS score is non-decreasing and a smooth curve.We first identify genes g and new sample x, we find the sample y in the training set whose expression value of g is closest to that of x, and define the risk score value of x to be that of y in the training set. The MRS score value for a new sample can be defined from means of risk score values by the same method.An MRS score can be extended to other cohorts, e.g., a validation set, using data from the training set as a reference set of lookup tables. For example, to find the value of a risk score for a gene In this study, we defined an MRS score for each of the CMS subtypes. Using these, we classified a sample into the subtype whose MRS score value was above a classification threshold  method. The CMScaller R package16  was applied with the CMS template. CRCassigner subtyping was accomplished by applying the partitioning around medoids (PAM) methodology to the published table of 786 gene centroids6.Multiple subtyping systems were generated for comparison with ColoType. The CMSclassifier R packagehttps://www.r-project.org) version 3.6.2 and Bioconductor packages (https://bioconductor.org). The package mclust29 was used for fitting mixture models.All statistical analyses were performed using R (plotROC R package was used for these computations31.The ability of a continuous score to predict membership in a subtype was assessed with the receiver operator characteristic (ROC) curve which plots sensitivity (y-axis) by 1—specificity (x-axis) for all values of the score. The ROC curve is a test of how the accuracy of classification varies with values of the continuous score. The area under the ROC curve (AUC) gives a numerical measure of a score’s predictive significance. The quality of the prediction is better than random if AUC is > 0.5 and improves as AUC increases up to 1. The caret R package32.ColoType defined a discrete classification that predicted the four subtypes of the CRCSC CMS classification. The quality of the prediction was assessed using the overall accuracy, along with the sensitivity, specificity and Cohen’s Kappa statistic. These statistics were computed using the confusionMatrix function of the The Marshfield cohort was created with the approval of the Marshfield Clinic Institutional Review Board (protocol number CAR60214). The IRB approved a waiver of informed consent because the research presented no more than minimal risk to study participants and the waiver of informed consent would not adversely affect the rights or welfare of participants. The protocol for this project was also approved by the IRB committees of the University of Notre Dame and Indiana University School of Medicine. All research was performed in accordance with relevant guidelines and regulations.A, rB, rC) so that each member of the triplet was a risk score of the same gene in Cohort A, B, C training sets, respectively, and the three risk scores were pairwise equivalent (“Methods”). We ranked all such triplets  by a consensus measure of significance in the three training cohorts as follows.For each of CRCSC CMS1–4 we developed a continuous score that predicted membership in the subtype following the MRS score methodology (“Methods”). Here, we describe the process for CMS1; the process was identical for the other subtypes. The first step was to identify the panel of genes and their risk scores from which the predictive score was derived. To help reduce the potential bias due to a particular assay technology or tissue source we evaluated a gene’s predictive significance simultaneously in the training sets of Cohort A, B, and C, as they represent distinct analytical platforms. This was organized using triplets of risk scores , and ranked all such risk scores in Cohort A training set by p-values of the linear models. We also ranked the other risk scores in the triplets by significance in the training sets of Cohort B and Cohort C, respectively. Finally, for each triplet, we computed the mean of the ranks of the three component risk scores, and ranked all of the triplets by these mean values, lowest to highest. This resulted in a ranked list of genes and triplets of the genes’ risk scores in the three training sets.Candidates for the score to predict CMS1 in each training set were generated by MRS methodology using varying numbers of genes and evaluated by ROC analysis. Specifically, from the highest ranked 5, 7, 10, 15 and 20 triplets, and the risk scores for Cohort A training set from these triplets, we generated MRS scores for Cohort A training sets. The AUC values in Cohort A training set for MRS scores generated by 5, 7, 10, 15 and 20 risk scores were 0.95, 0.96, 0.98, 0.98, 0.98, respectively. We similarly generated scores for Cohort B and Cohort C training sets from the same highest ranked 5, 7, 10, 15 and 20 triplets. The AUC values of the corresponding scores in the Cohort B training set were 0.88, 0.89, 0.91, 0.95, 0.97, and for the Cohort C training set they were 0.97, 0.97, 0.98, 0.98, 0.98. Inspecting these results, we selected the 10-gene panel as the smallest one with the AUC values above 0.90 in the three cohorts.ColoType CMS1-score, similarly, for the other subtypes. See Supplementary Table The above method was repeated to identify optimal scores for CMS2, CMS3 and CMS4, all of which used 10 genes. The resulting score for CMS1 was defined to be the The ColoType CMS1-score was extended to all samples in Cohort A using the score in the Cohort A training set as a reference set (“Methods”), and similarly for other subtypes and cohorts. The significance of the scores as predictors of the CRCSC CMS subtypes in the validation sets were assessed with ROC plots and AUC values  and CMScaller were generated from the log2 size factor normalized expression values in the Marshfield cohort (“Methods”). For further comparison of subtyping methods, we also computed ColoType subtypes for Marshfield cohort with these expression values using Cohort C with size factor normalized expression values as a reference set. Specifically, for each panel gene we identified a risk score in the Marshfield cohort equivalent to the risk score of the gene in Cohort C. Then, for CMS1, we computed the mean of the risk scores of the 10 panel genes, for each sample, and then obtained a ColoType CMS1-score value for each sample by reference to the ColoType CMS1-score in Cohort C (“Methods”). Continuous scores for other subtypes were computed likewise. ColoType subtype classifications were computed using these continuous score values and the same classification thresholds as for ColoType in Cohort C with size factor normalized expression data Table .Preservation of samples with formalin can degrade some species of mRNA, leading to degenerate expression values. Of note, expression of each of the 40 ColoType panel genes measured by whole-genome RNA-seq in the Marshfield cohort had distributions comparable to those in Cohort C. As a consequence, these 40 genes are appropriate candidates for an implementation of ColoType by targeted RNA-seq for FFPE samples.A custom library containing amplicons for the 40 ColoType panel genes was designed with AmpliSeq for Illumina custom RNA panel reference guide (Illumina Inc.) following the standard workflow.For each sample in the Marshfield cohort, count values were then obtained for the 40 ColoType panel genes by AmpliSeq , we computed the CRCassigner subtypes for Cohort A and compared these classifications with ColoType subtypes in the Cohort A training set (Supplementary Table Enterocyte-score) to predict the Enterocyte subtype using the two genes CA1 and CA2 (AUC = 0.96 in Cohort A training set). Turning to other subtypes, recall that Stem-like was predicted by CMS4-score alone with AUC 0.93 in Cohort A training set. However, 17% of CMS4 in this cohort were Enterocyte samples. To better isolate Stem-like, we defined Stem-like-score to be CMS4-score minus Enterocyte-score; i.e., the score for sample s is the CMS4-score(s)–Enterocyte-score(s). This Stem-like-score predicted the Stem-like subtype with AUC 0.94 in Cohort A training. TA was approximated by CMS2-score, however the CMS2 subtype in Cohort A training also included significant numbers of Enterocyte and Stem-like samples. Accordingly, we defined the TA-score to be CMS2-score minus Enterocyte-score minus CMS4-score, which predicted TA in Cohort A training with AUC 0.94. We defined Inflammatory-score to be CMS1-score and Goblet-like-score to be CMS3-score because altering with other scores did not increase the AUC values. The predictive significance of these five CRCassigner subtype scores were assessed in Cohort A validation set by ROC analysis , Kappa = 0.58. Apparently, a sample of mixed type exhibits features of both positive subtypes, and different classifiers may reasonably predict either subtype. Reporting the continuous score values for all subtypes is a more complete description of the tumor.Patient treatment for colorectal cancer today is largely determined by tumor stage. To enable better outcomes, patients need to be stratified in a manner that takes into account the molecular heterogeneity of the disease. The consensus molecular subtypes effectively stratify CRC tumors by tumor biology, however the lack of a commercial assay for subtyping has made clinical applications impossible. As a step towards filling this gap, we developed the 40-gene ColoType assay for CMS subtyping that can be implemented with targeted RNA-sequencing of FFPE tissue.We showed , it is possible to sequence many samples in one run, allowing for overall cost-effective sample analysis, with turn-around times that meet clinical expectations. The increasing prevalence of next-generation sequencing instrumentation in clinical pathology laboratories further supports the choice of this technology for a commercial clinical solution.Targeted RNA-seq is increasingly being used for clinical diagnostics40. Rather than classifying a tumor into a single subtype, it may be more accurate to measure the concentration of a particular cell type throughout a tumor. To this end, ColoType was designed to report a continuous measurement of the prevalence of each CMS subtype using mRNA harvested throughout the tumor sample. ColoType reports a single subtype that has greatest prevalence, when such exists, but we envision that ColoType will report continuous score values for all subtypes for completeness.Research following the derivation of the CMS subtypes suggested that classification of a tumor into a single subtype may be inappropriate due to intratumoral heterogeneity39. This is consistent with the observation that mixed types involving CMS4+ were most common in Cohort A. Also note that CMS4 subtypes do not appear to occur as precancerous lesions41. Additional research is required to analyze the degree to which tumors of mixed type have biological features characteristic of the component subtypes, and how they arise in the span of a patient’s disease.Significant intratumoral heterogeneity may be a source of samples with mixed types. One instance of heterogeneity is the observation that cells of a mesenchymal type (CMS4) are more often found at the invasive front than the core of the tumorClinical application of ColoType will require establishing analytic validity of a targeted RNA-seq implementation in a CLIA certified pathology laboratory. Additional studies are also needed to verify the clinical utility of ColoType. The ability of CMS subtyping to predict response to specific drugs has been reported, however subtypes were defined using in-house solutions in some of these studies. These results need to be calibrated to the ColoType scores for optimal clinical application. It is possible that the greatest predictive significance results from score thresholds other than the classification thresholds; in other words, the continuous scores may be more significantly predictive of drug response than the subtypes themselves.In summary, we developed the 40-gene ColoType signature for CMS subtyping CRC tumors using whole-genome assays or targeted RNA-seq of frozen or FFPE tissues. The resulting subtypes correlated highly with those reported by multiple independent subtype systems. Since some CRC tumors exhibit intratumoral heterogeneity, ColoType’s emphasis on continuous scores likely provides a more accurate description of tumor biology than classification into a single subtype. The planned analytic validation of the ColoType targeted RNA-seq assay will enable accurate and reproducible CMS subtype analysis for clinical applications.Supplementary Information.Supplementary Table 1.Supplementary Table 2.Supplementary Table 3.Supplementary Table 4.


Apis cerana, to these varied habitats. A. cerana were collected from 14 locations in China. Their genomes were sequenced, and nucleotide polymorphisms were identified at more than 9 million sites. Both STRUCTURE and principal component analysis placed the bees into seven groups. Phylogenomic analysis groups the honeybees into many of the same clusters with high bootstrap values (91%–100%). Populations from Tibet and South Yunnan are sister taxa and together represent the earliest diverging lineage included in this study. We propose that the evolutionary origin of A. cerana in China was in the southern region of Yunnan Province and expanded from there into the southeastern regions and into the northeastern mountain regions. The Cold‐Temperate West Sichuan Plateau and Tropical Diannan populations were compared to identify genes under adaptive selection in these two habitats. Pathway enrichment analysis showing genes under selection, including the Hippo signaling pathway, GABAergic pathway, and trehalose‐phosphate synthase, indicates that most genes under selection pressure are involved in the process of signal transduction and energy metabolism. qRT‐PCR analysis reveals that one gene under selection, the AcVIAAT gene, involved in the GABAergic pathway, is responding to cold temperature stress. Through homologous recombination, we show that the AcVIAAT gene is able to replace the CNAG_01904 gene in the fungus Cryptococcus neoformans and that it makes the fungus less sensitive to conditions of oxidative stress and variations in temperature. Our results contribute to our understanding of the evolutionary origin of A. cerana in China and the molecular basis of environmental adaptation.The geographic and biological diversity of China has resulted in the differential adaptation of the eastern honeybee,  C. neoformans, we firstly clarified the gene involved in the pathway of transporting gamma‐aminobutyrate has contribution to tolerate the abiotic stress.We identified the Asian honeybee as seven genetic lineages and clarified their genetic relationship and then proposed that the origin of Chinese honeybee in the southern Asian geographic region including south Tibet, south Yunnan, and Himalaya–Hengduan Mountains. We found many genes in response to the abiotic stress were under selection for the genetic population in the regions of higher altitudes. Among these selective genes, based on homologous recombination in  In addition, we examined the genetic relationships associated with geographic proximity and propose a location for the evolutionary origin of A. cerana in China. We also compare two populations from very different physical and climatological regions to identify differences in genetic composition and gene expression to explore the molecular basis of adaptation to environmental stress.Currently, the number of genetic groups associated with morphological clusters of 22.1A. cerana were field or colony collected from a wide variety of habitats throughout their known range in China and preserved in 75% ethanol. Forager bees were collected by sweep netting and bees from managed or wild colonies were collected with tweezers. The geographic distribution of all samples is shown in Table Samples of 138 2.2http://hannonlab.cshl.edu/fastx_toolkit/). The resulting data are shown in Table A. cerana reference genome  of the SNPs was performed using the package EIGENSOFT  and genetic differentiation (FST). The distribution of πθ and Fst throughout the entire genome was presented under 10‐kb window size sliding in 1‐kb steps. The number of unique and common SNPs among groups was compiled and then visualized using R language with the UpSetR package.Population genetic structure and individual ancestry admixture were inferred by the maximum‐likelihood routine using the expectation–maximization algorithm in the program ADMIXTURE v1.2 . The bootstrap support was evaluated based on 1,000 replicates.A neighbor‐joining phylogenetic tree was constructed using the TreeBest  and the FST values. We selected windows with significantly low and high πθ ratios (the 5% left and right tails) and significantly high FST values (the 5% right tail) as indications of strong selective sweep along the genome. We employed XP‐EHH analysis as a second method to identify loci under selection .A comparative analysis comparing the WSichPl and Diannan populations was performed. To detect those regions showing significant selective sweep values, we examined the distribution of the 2.5A. cerana which had been subjected to treatments of 0°C and 25°C were obtained from a previous study  at 0°C and 25°C using the bowtie2, RSEM, and edgeR programs. The RNA‐seq segments were identified by matching with annotated genes of A. cerana using Bowtie2  >2. The results were visualized as a volcano plot by R language with ggplot2.The raw RNA‐seq data of 2.6A.cerana colonies were collected from the Daba mountains （location: N32°06′39.33″, E108°28′23.23″) and maintained in our apiary. Four groups of adult foraging bees (n = 30 bees/group) from the same hive were held in incubators for 2 hr at 0°C, 4°C, 10°C, and 25°C. Total brain RNAs from each bee were extracted using Trizol reagent and then reverse‐transcribed into cDNA to use as a PCR template using the Transcriptor First Strand cDNA Synthesis Kit (Roche). To amplify messages for the AcVIAAT gene, positive (5’'CGCATATCTGTTGTTACACTG'3) and negative strand (5'GACTTGAGGAAACCGAGGG'3) primers were designed for use in qRT‐PCR using the Primer premier 5.0 software. Primers for an actin gene  of A. cerana were used as a positive control. The qPCR amplification procedure was as follows: 95°C for 30 s, followed by 40 cycles of 95°C for 5 s and 60°C for 30 s. The transcript abundance was calculated relative to the gene encoding actin using the 2−ΔΔCt method. 25°C was used as the calibration temperature. Three biological replicates were conducted in this experiment. The t test was used to determine the significance of a difference among different temperature conditions.2.7AcVIAAT is homologous to the CNAG_01904 gene of the fungus Cryptococcus neoformans. To test whether AcVIAAT functions as a homolog of the CNAG_01904 gene under conditions of abiotic stress, we knocked out the CNAG_01904 gene in the C. neoformans H99 wild‐type strain using the split‐marker recombination transformation method previously described   initially revealed four clusters separated in PC1‐PC2 space and five clusters separated in PC1‐PC3 space  lies at an altitude of 3,000 meters at the junction of the southwest edge of the Qinghai–Tibet Plateau at the northern end of the Himalaya–Hengduan Mountains and the Sichuan northwest alpine canyon. The yearly average temperature is 8–9°C, and the annual precipitation is 753 mm with an average humidity of 63%. In contrast, the Dianna region in the Yunnan province is a semitropical region that lies at 500–800 meters above sea level and has an average temperature of 18–20°C with an annual precipitation of 1100–2,500 mm and an average of 82% humidity.πθ and FST statistics and shows regions of the genome differentially selected for the WSichPl and Diannan regions , lethal (2) essential for life (I(2)efl,APICC_00695), and glycerol kinase , stimulate transcriptional activity in response to changes in temperature efl gene is a heat‐shock protein known to respond to oxidative and temperature stress in some organisms efl can be induced in A. cerana by exposure to cold temperatures  thought to be an inhibitor of GABA uptake. Since GABA is known to be a free amino acid that helps to counteract biotic and abiotic stress in some organisms  were determined to be under selection pressure Figure . Two of 3.4AcVIAAT at various temperatures. Based on the analysis of these data, the expressed level of AcVIAAT in A. cerana increases significantly at 0°C compared to the 25°C control , and the CNAG_01904Δ::APICC_05210 strain (CNAG_01904Δ restored with the AcVIAAT) from A. cerana is shown in Figure Cryptococcus neoformans was grown in 30°C on YPD medium, there was a slight difference in the H99, CNAG_01904Δ and CNAG_01904Δ::APICC_05210 growth rates  when K = 6  form a clade with a 91% bootstrap value. The central C2 group where the HainanID, XunPn, ZheFuH, and JingMt populations form a clade with a 97% bootstrap value  and their relationships with the Hainan island population need to be examined and should yield interesting results.The possible origin of TPS, I(2)efl and GK genes play a role in cold stress, indicative of the adaption of A. cerana to abiotic stress in the cold plateau environments  in A. cerana involved in the GABA transport pathway are undergoing selection in the population from the western Sichuan highland. We show that AcVIAAT (vesicular inhibitory amino acid transporter) plays a role in the response to abiotic stress including cold and oxidative stress, based on the transcriptome analysis, qPCR, and knockout experiment. To our knowledge, this is the first time the potential function of a gene related to environmental adaption has been described for A. cerana.Our genomic comparison of the Western Sichuan highland population (WSichPI) from an alpine canyon at 3,000 m and the tropical Diannan population revealed 151 genes that are subject to selection. Some of the identified genes such as A. cerana.Genes undergoing positive selection related to hypoxia have been reported from a number of organisms including mammals, birds, and fish ; Investigation (lead); Writing‐original draft . Jun Zhou: Data curation ; Investigation . Huali Song: Methodology ; Validation ; Visualization . Yujuan Wu: Methodology . Lan Lan: Methodology ; Software ; Visualization (supporting). Xiangyou Tang: Formal analysis ; Software . Zhengang Ma: Formal analysis . Vossbrinck R. Charles: Validation ; Writing‐review & editing . Bettina Vossbrinck: Writing‐review & editing . Zeyang Zhou: Data curation ; Supervision . Jinshan Xu: Funding acquisition (lead); Supervision (lead); Writing‐original draft .Fig S1Click here for additional data file.Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.Table S4Click here for additional data file.Table S5Click here for additional data file.Table S6Click here for additional data file.Table S7Click here for additional data file.Table S8Click here for additional data file.