Heterogeneity is a common finding within tumours. We evaluated the imaging features of tumours based on the decomposition of tumoural dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data to identify their prognostic value for breast cancer survival and to explore their biological importance.n = 14), such as texture, histogram distribution and morphological features, were extracted to determine their associations with recurrence-free survival (RFS) in patients in the training cohort (n = 61) from The Cancer Imaging Archive (TCIA). The prognostic value of the features was evaluated in an independent dataset of 173 patients (i.e. the reproducibility cohort) from the TCIA I-SPY 1 TRIAL dataset. Radiogenomic analysis was performed in an additional cohort, the radiogenomic cohort (n = 87), using DCE-MRI from TCGA-BRCA and corresponding gene expression data from The Cancer Genome Atlas (TCGA). The MRI tumour area was decomposed by convex analysis of mixtures (CAM), resulting in 3 components that represent plasma input, fast-flow kinetics and slow-flow kinetics. The prognostic MRI features were associated with the gene expression module in which the pathway was analysed. Furthermore, a multigene signature for each prognostic imaging feature was built, and the prognostic value for RFS and overall survival (OS) was confirmed in an additional cohort from TCGA.Imaging features . The maximum probability feature from the fast-flow kinetics subregion was also significantly associated with RFS and OS in the reproducibility cohort. Additionally, this feature had a high correlation with the gene expression module (r = 0.59), and the pathway analysis showed that Ras signalling, a breast cancer-related pathway, was significantly enriched . Gene signatures (n = 43) associated with the maximum probability feature were assessed for associations with RFS (p = 0.035) and OS (p = 0.027) in an independent dataset containing 1010 gene expression samples. Among the 43 gene signatures, Ras signalling was also significantly enriched.Three image features  were independently correlated with RFS (Dynamic pattern deconvolution revealed that tumour heterogeneity was associated with poor survival and cancer-related pathways in breast cancer. Breast cancer is the most common malignancy in women. Neoadjuvant chemotherapy (NAC) is commonly used to treat patients with large and locally advanced breast tumours with the aim of decreasing tumour size, thereby minimizing micro-metastatic disease. In patients who achieved a pathologic complete response (pCR) after NAC, both the overall survival (OS) and recurrence-free survival (RFS) rates were favourable . HoweverDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a technology that has the ability to monitor tumour morphological and physiological characteristics by measuring the enhancement velocity of the contrast material in a noninvasive way. Various studies have been performed to quantitatively evaluate DCE-MRI phenotypes through radiomic/radiogenomic analyses for their association with genomic features –4, breasTumour heterogeneity not only among different tumours but also within individual tumours is common in breast cancer. A study has revealed that spatially separated regions within a single tumour exhibit distinct gene expression signatures of good and poor prognoses . RegardiThe purpose of this study is to evaluate intratumoural heterogeneity based on decomposed DCE-MR images and to evaluate imaging features inside these heterogeneous regions for the determination of breast cancer prognosis. The gene signatures that are associated with the prognostic imaging features are also identified. These gene expression signatures are further examined on an independent dataset to identify their association with RFS or OS.The imaging dataset was collected from a publicly available dataset at The Cancer Imaging Archive (TCIA) , whereasThe first dataset (i.e. breast MRI-NACT pilot in the TCIA), which was termed the training cohort, initially included the preoperative DCE-MRI and RFS data of 64 breast cancer patients; however, no gene expression data were available for these patients. Among them, 3 patients with incomplete dynamic series were removed, resulting in 61 patients for inclusion in the training cohort.We included an additional cohort, the reproducibility cohort, which initially included 222 breast cancer patients (from the I-SPY 1 TRIAL in the TCIA database) with available DCE-MRI and corresponding RFS and OS data . We exclAn independent dataset, termed the Radiogenomic cohort, initially included 137 patients with available DCE-MRI data from TCGA-BRCA and the corresponding gene expression data from the TCGA dataset. To reduce variation among the imaging protocols, we retained 101 patients who were evaluated with a GE 1.5-T Medical Systems imaging unit . After that, we excluded 1 patient who had no available gene expression data, 7 patients who had no available clinical information and 6 who had incomplete imaging data. Thereafter, the final dataset included 87 patients for analysis.The fourth dataset, termed the TCGA cohort, included the data of 1010 patients collected from the TCGA database, all of whom had RNA sequencing data available for tumour samples along with RFS and OS data but without imaging data.As shown in Fig. The prognostic features from the training cohort were first established and validated in the reproducibility cohort by associating tumour MRI features and the survival of breast cancer patients. In the Radiogenomic cohort, imaging features from the entire tumour and CAM-based tumour subregions were extracted from both the entire tumour and CAM-based tumour subregions to evaluate the association with gene expression modules. Pathway analysis was performed in the gene modules that had a high correlation with prognostic imaging features that were explored in the training cohort. A linear regression model was established to identify gene signatures that were related to prognostic imaging features. These signatures were further assessed in an independent dataset (the TCGA cohort) with available gene expression and survival data (i.e. OS and RFS). Details of these cohorts have been published elsewhere , 22.k-space sampling.DCE-MR images collected for the training cohort were acquired using a 1.5-T scanning system . Breast MRI examinations were performed with patients placed in the prone position. T1-weighted, fat-suppressed MR images were acquired using the following parameters: repetition time (TR) = 8 ms, echo time (TE) = 4.2 ms, matrix = 256 × 192 × 60, flip angle = 20°, field of view = 180–220 mm, in-plane resolution = 0.7–0.9 mm and slice thickness = 2–2.4 mm. A bolus of 0.1 mmol/kg gadobutrol was intravenously injected using an MRI-compatible power injector. The early and late postcontrast images were obtained 2.5 min and 7.5 min after the contrast material injection, respectively, using standard For the reproducibility cohort, MRI was performed on a 1.5-T scanner using a dedicated breast radiofrequency coil. A contrast-enhanced, T1-weighted series was acquired in the sagittal orientation. A three-dimensional, fat-suppressed, gradient echo sequence was acquired with TR ≤ 20 ms, TE = 4.5 ms, flip angle ≤ 45°, field of view = 160–180 mm, minimum matrix 256 × 192, 64 slices, slice thickness ≤ 2.5 mm and in-plane spatial resolution ≤ 1 mm. The precontrast sequence was acquired, followed by early-phase and delayed-phase sequences at 2.5 min and 7.5 min after the contrast material injection, respectively.For the Radiogenomic cohort, DCE-MRI data were collected from the TCGA-BRCA dataset, which includes data contributed by four institutions, including the Memorial Sloan Kettering Cancer Center, the Mayo Clinic, the University of Pittsburgh Medical Center and Roswell Park Cancer Institute. The imaging protocols included one precontrast image and three to five postcontrast images obtained using a T1-weighted, 3-dimensional (3D) spoiled gradient echo sequence with a gadolinium-based contrast agent. The typical in-plane resolution was from 0.53 to 0.85 mm, the typical spacing between slices was from 2 to 3 mm and the flip angle was 10°.i as x(i), and the time-series curve in a heterogeneous tumour tissue can be modelled as the linear combination of the time-series curves aj(t) from each type of tissue, weighted by the tissue type proportions Kj(i) at that pixel. Assuming that every tissue type has a similar dynamic enhancement pattern, the signal decomposition problem can be addressed using the following equation:After the manual annotation of the centre location of the suspicious breast tumour, image segmentation was performed on each series using a fuzzy C-means (FCM) algorithm . After taj is a nonnegative vector of the time-series dynamic signal aj(t) over time, and J is the number of mixed tissue types reflecting distinct kinetic patterns. This method first applies affinity propagation clustering [xm}, and the mixture model was fitted by an expectation-maximization method. More specifically, CAM was performed to identify the tissue-specific pixel clusters spatially located at the corners of the clustered pixel time-series scatter simplex via a minimum error margin convex hull for data fitting:where ustering  of voxeli is determined to belong to a specific tissue type if its value of proportions Kj(i) is nontrivial (i.e. larger than 1e− 2). Therefore, a pixel was referred to as a mixture of several different tissue types if there were various nontrivial values of the tissue type proportions for this pixel. The number of underlying vascular compartments was detected using the minimum description length (MDL) of the model. In our previous studies, we performed convex analysis of mixtures (CAM) on tumour images to decompose the tumours into three compartments corresponding to plasma input, fast-flow kinetics and slow-flow kinetics. Using these criteria, most of the cases showed an optimal number of three subregions [Thereafter, the time-series dynamic signal for each pixel was decomposed into several tissue types with certain proportions. An image pixel bregions , 17. To Based on the tumour subregions generated by CAM, we extracted features inside these regions on the precontrast series, on the image subtractions between the postcontrast image series (i.e. the early postcontrast (approximately 2.5 min) and the late postcontrast (approximately 7.5 min)) and on the precontrast series, which were termed S-0, S-1 and S-2, respectively. The histogram-based features included the skewness, kurtosis and median value of the tumour images. Haralick features that measure the textural heterogeneity based on the grey-level co-occurrence matrix (GLCM) were calculated, including the energy, maximum probability and correlation. Both the histogram features and Haralick features were obtained on the image series of S-0 and S-1. The morphological features of volume and compactness were also evaluated on S-0. We omitted performing CAM on imaging data from the training cohort because most of the imaging series of these patients only had two postcontrast series, which would have resulted in inaccurately decomposed subregions. All image processing and feature extraction processes were performed in MATLAB .We evaluated the prognostic value of image features in the training cohort by individually establishing their associations with the patients’ RFS. Furthermore, a multivariate Cox regression model using all of these features was established to evaluate which features were independently associated with RFS. The prognostic value of the image features was confirmed using an additional, independent dataset using available DCE-MRI data and survival data of RFS and OS.To establish relationships between the tumour image phenotype and the corresponding gene expression, we extracted the identical image features from the Radiogenomic cohort to those in the training cohort from the entire tumour and from the tumour subregions. Based on the corresponding gene expression data, gene module analysis was performed to identify a small number of representative genes that were associated with image features. We used a weighted gene co-expression network with block-wise module function to identify the gene expression modules . An eigeα and λ) were selected by applying a tenfold cross-validation to reduce potential model overfitting. The tumour genes that constituted the signature were investigated using the KEGG pathway enrichment analysis to confirm the previously identified pathways that were enriched in the entire tumour or tumour subregions.Inspired by the idea from previous studies that the prognostic value of the image features is evaluated by leveraging survival data in gene expression datasets , 26, 27,Gene signatures were identified by the radiogenomic link between the prognostic image features and gene expression data from the Radiogenomic cohort. We used tumour gene expression-based signatures for the image features, testing their prognostic value by assessing the associations with RFS and OS in independent cohorts from the TCGA cohort. Based on these gene signatures and the estimated parameters in the Radiogenomic cohort, a regression model was established, and the same threshold as that of the survival model in the training cohort was applied to stratify patients with different prognoses.p value in the training cohort to identify prognostic imaging features. The Harrell's concordance index (c-index) and the log-rank test were used to assess the prognostic performance. The hazard ratios (HRs) with 95% confidence intervals (CIs) were assessed to compare the OS and RFS rates between the stratified groups on Kaplan-Meier plots.The univariate and multivariate Cox proportional hazards models were both used to build survival models associated with OS and RFS. Kaplan-Meier analysis was used to estimate survival probability. We determined the optimal threshold value as the cutoff point with the smallest log-rank p values of less than 0.1 were considered to be statistically significant. The hypergeometric test was used to assess whether genes within a particular pathway were significantly overexpressed. All statistical analyses were performed in R .To control the false discovery rate (FDR) in multiple statistical testing, the Benjamini-Hochberg method was used in the univariate survival analysis. FDR-corrected p values < 0.05) associated with RFS (Table p = 0.0009) in RFS, and the optimal threshold was 0.096 . In addition, the image features of the median value of S-1 and tumour volume were independently associated with RFS with p values of 0.0036 and 0.0032, respectively.After removing features with high similarity to each other (i.e. Pearson correlation coefficient between two image features greater than 0.7), a multivariate Cox regression analysis was performed using seven features. More specifically, the image features of skewness, correlation and maximum probability in S-0, and kurtosis, skewness, median value and maximum probability in the postcontrast series were used and included in the multivariate Cox regression model. The results showed that the maximum probability obtained in the S-0 was the most significant feature that was independently correlated with RFS , which were also tested for significant association with RFS in the training cohort. Additionally, the feature of energy showed a significant association with RFS and OS with corrected p values of 0.042 and 0.09, respectively. As a comparison, this feature was significantly correlated with RFS, with a p value of 0.023, but was not significant after FDR correction (p = 0.054) in the training set of 61 patients.We performed a survival analysis using the same 14 MRI features in the reproducibility cohort to analyse any associations with RFS and OS Table . Image fn = 87) with the corresponding DCE-MRI and gene expression data. We removed genes expressed in only 20% of the patients and those with no expression values (n = 3759). We then deleted genes with low variance of expression across patients, and ultimately, the top 5000 genes with the largest variance were retained in the dataset. For the network construction, a pairwise correlation matrix was computed, and then an adjacency matrix was calculated by raising the correlation matrix to the power of five [Features that were identified to have prognostic implications were further examined by evaluating the associations with the gene modules in the Radiogenomic cohort . Among them, only tumour volume, which was also identified to have prognostic implications in the training cohort, remained relatively high related to the gene modulates Table .Table 4We also performed the same association analysis between gene expression modules and image phenotypes using features from the tumour subregions Table . Based on = 158). Additionally, the features in regions with fast-flow kinetics had a higher correlation with the gene expression module than that of the features from regions related to slow-flow kinetics. The correlation between image features and the other subregions, i.e. the plasma input and slow-flow kinetics regions, is shown in Additional file Specifically, the correlation between image features and fast-flow kinetics-related tumour subregions is shown in Fig. We examined the distributions of the image features from the entire tumour and from the decomposed tumour subregions. In low-risk patients, the maximum probability features obtained from the tumour subregions showed a lower level of variance and a lower interquartile range than those of features based on the entire tumour, and this trend of decreased variance in the subregions compared with that in the entire tumour was more obvious for high-risk patients Fig. . In othen = 158) using KOBAS 3.0 . Additionally, two significantly enriched pathways of apoptosis (p = 0.0165) and microRNAs in cancer (p = 0.0343) have been reported to be associated with breast cancer [A further KEGG enrichment analysis was performed on the gene module that was associated with prognostic image features tan, n = 58 using  pathway , 30, wast cancer , 32.Tabα and λ values of 0.2 and 0.0037, respectively. This model selected a subset of 38 gene signatures to estimate the tumour volume feature using elastic net (R2 = 0.8159) with parameters of α and λ of 0.65 and 0.156, respectively. Additionally, we identified 43 gene signatures for regressing the image maximum probability feature from the fast-flow kinetics subregion, using elastic net with an R2 of 0.8073. Finally, the regression model  with 57 gene signatures was built for predicting the maximum probability feature in the slow-flow kinetics-associated tumour subregion.In addition to the biological annotation for gene modules, we built a radiogenomic map to determine gene signatures by associating gene modules with prognostic image feature indicators. We selected 100 genes that were mostly correlated with the image features and then fed them into an elastic net regression model to regress the maximum probability feature. The parameters were selected through cross-validation with p < 0.1). The complete list of these 43 genes is shown in Additional file p values > 0.05) enriched pathways.We further examined the biological functions of these gene signatures related to the maximum probability feature, and the results of the KEGG pathway analyses are shown in Table We included 906 patients who had available OS data and 771 patients with available gene expression and survival data in the TCGA cohort. The identical gene signatures and parameters included in the previous regression model that was trained on the Radiogenomic cohort were applied using the elastic net model to regress the prognostic image features, for which both RFS and OS were associated. We evaluated the prognostic value of image features extracted from the entire tumour and from the tumour subregions by stratifying patients with different survival outcomes.p values of 0.190 and 0.200, respectively. The maximum probability feature identified by gene signatures showed a significant association with only OS (p = 0.033), whereas no significant association was found with RFS (p = 0.130)  Fig. a, b.Figp values of 0.027 and 0.035, respectively  with either RFS or OS (p = 0.210)  into 2 groups in terms of RFS and OS, with This study explored the prognostic tumour features from DCE-MRI to stratify patients into groups with different survival rates. Gene expression signatures were identified by establishing their correlations with the prognostic image features. The functional information of these features, both based on the entire tumour and CAM-generated subregions, was further investigated by evaluating their associations with gene expression modules, in which breast cancer-related pathways were identified. The prognostic value of these gene signatures was confirmed in an independent dataset, which indicated significant associations between the gene signatures and patient survival. The results demonstrated that imaging features derived from tumour subregions had more prognostic value than those derived from the entire tumour did.Previous studies  have assWe identified that large tumour volume was associated with poor RFS in the training and the reproducibility cohorts, which is consistent with the findings of a previous study . HoweverNotably, this texture feature derived from the precontrast image series showed better prognostic performance than that of the same feature derived from the postcontrast image series. Similar findings regarding imaging features associated with clinical biomarkers on DCE-MRI precontrast series are presented elsewhere , 37, finp < 0.1) in both of these gene sets. This pathway, which is a key regulator of tumour growth, metastasis [We performed pathway analysis on both gene modules and gene signatures that were related to prognostic image features. For both analyses, we identified the same Ras signalling pathway as being significantly enriched  in two gene sets, the agreement was low. This result may be partly explained by the heterogeneity of the gene signature data identified by the module analysis versus that identified by the regression model. Fourth, decomposition accuracy relies on the number of image series, and this method is difficult to perform in DCE-MR image series with little time-series data, i.e. less than three postcontrast series (e.g. DCE-MRI data in the training cohort). Therefore, we did not perform CAM in the training or the reproducibility datasets, which only had one or two postcontrast series.Despite some significant findings, several limitations should be addressed. First, our patient sample size was relatively small because only a limited number of breast MR images were available in the TCGA and TCIA databases. Further external studies should be performed to confirm the prognostic value of image features in our study. Second, DCE-MRI data were acquired from a multi-institution cohort with varied imaging parameters, introducing diversity among the images. Third, although we observed the same Ras signalling pathway that was significantly enriched  and the 43 genes in the signature for regressing the maximum probability feature from the fast-flow kinetics-related tumour subregion. Table S3. Pathway analysis of 38 identified genes in the signature for regressing the tumour volume feature. Table S4. Pathway analysis of 57 identified genes in the signature for regressing the maximum probability feature from the slow-flow kinetics-associated tumour subregion.Additional file 2: Figure S1. Image features from the plasma input subregion correlated with gene modules.Additional file 3: Figure S2. Image features from the slow-flow kinetics subregion correlated with gene modules.


It is generally acknowledged that RNA-seq data displays equidispersion and overdispersion characteristics; therefore, most RNA-seq analysis methods were developed based on a negative binomial model capable of capturing both equidispersed and overdispersed data. In this study, we reported that in addition to equidispersion and overdispersion, RNA-seq data also displays underdispersion characteristics that cannot be adequately captured by general RNA-seq analysis methods. Based on a double Poisson model capable of capturing all data characteristics, we developed a new RNA-seq analysis method (DREAMSeq). Comparison of DREAMSeq with five other frequently used RNA-seq analysis methods using simulated datasets showed that its performance was comparable to or exceeded that of other methods in terms of type I error rate, statistical power, receiver operating characteristics (ROC) curve, area under the ROC curve, precision-recall curve, and the ability to detect the number of differentially expressed genes, especially in situations involving underdispersion. These results were validated by quantitative real-time polymerase chain reaction using a real Foxtail dataset. Our findings demonstrated DREAMSeq as a reliable, robust, and powerful new method for RNA-seq data mining. The DREAMSeq R package is available at  With the development of next-generation sequencing technology, RNA sequencing (RNA-seq) has become a routine and powerful method for evaluating global dynamic changes in gene expression during certain biological processes. Compared with microarray technologies, RNA-seq technologies have several advantages, including a wider measurable range of expression levels, higher throughput, less noise, more information for detecting allele-specific expression, and a higher capability to detect novel promoters and alternative gene-splicing isoforms  sequencing the RNA samples to obtain raw reads, (2) filtering out low-quality reads, (3) mapping the high-quality reads to a reference genome or transcriptome, (4) summarizing the read counts for each gene, (5) detecting differentially expressed genes (DEGs), and (6) performing systems biology analysis   LuValle, , but canp-value between two p-values generated based on the DP and NB models chosen as the final p-value) in order to fit variable RNA-seq data. In order to evaluate the performance of DREAMSeq, we generated three simulated datasets using three real RNA-seq datasets. Because the DEGs can only be effectively identified when the sample size is ≥3 , which offers the advantage of flexibility in fitting a wide range of data exhibiting underdispersion and non-underdispersion using only two parameters Efron, . Based oijk and Xijk denote the read count and the true gene expression of gene i from sample j in treatment group k, where i = 1, …, I (the number of genes), j = 1, …, J , and k = 1, …, K , respectively.In this study, let Y represent the observed count and X the corresponding underlying gene expression (unknown) in an RNA-seq experiment. Let YWe assume that Y follows an NB model with two parameters: the mean, μ, and the dispersion, ϕ. The probability mass function (PMF) of the NB model is given as:The expected value is estimated as:We parameterize the variance of the NB model according to a previous study  can be calculated as:c being the normalizing constant nearly equal to 1. The constant c ensures that the density integrates to unity. The expected value and the variance of the DP model in reference to the exact density with andrespectively, where θ > 0 under RNA-seq data circumstances. The Poisson model is nested in the DP model for θ = 1, indicating that the DP model can fit equidispersed read-count data when θ = 1. Additionally, the DP model allows for both overdispersion (0 < θ < 1) and underdispersion (θ > 1) Efron, .ijk, μijk, is the product of Xijk and sjk:Here, we assume that the expectation of Yjk is the size factor corresponding to sample j in treatment group k, which can be estimated using various existing normalization methods, such as total counts, upper quartile   described by Love et al.  to estimn = KJ and substitute equation (10) with the following equation:We assume that the dispersions between two groups are the same under small sample sizes. Therefore, we denote i and th gene. We then estimate the variance of the ith gene, th gene can be estimated by:where μnd Huber  and Wu end Huber . For the−8 in DESeq) when th gene can be estimated by:Note that th gene:In RNA-seq experiments, there are typically tens of thousands of genes, but only a few replicates per treatment group, which describes the “large p and small n” phenomenon. It is quite difficult to estimate a reliable gene-specific dispersion with the MoMs described in such a scenario. To address this problem, we used maximum likelihood estimate (MLE) methods based on the initial dispersion estimator, andfNB(·) and fDP(·) are the PMF of the NB and DP models, respectively.respectively, where th gene by regressing the gene-wise dispersion estimators, i, of the normalized read counts. This is similar to the data-driven parameter estimation used by DESeq through the smooth function by modeling the observed mean-variance (or mean-dispersion) relationship for the genes in the read-count data , τ. The PMF of the LN model is given as:Shrinkage estimation can effectively improve statistical tests for differential gene expression in the case of a small number of samples , fDP(·), and fLN(·) are the PMF of the NB, DP, and LN models, respectively.respectively, where Bias in dispersion estimation has serious effects on the expected false-positive rates (FPRs) in small-sample situations  in the NB model and equation (8) in the DP model.where p-values. In order to obtain a more reliable list of DEGs, the p-values were adjusted by the Benjamini-Hochberg (BH) procedure   is an important cereal crop in northern China, and the whole-genome sequence of Foxtail millet (Yugu-1 cultivar) was published in 2012  according to manufacturer instructions. qRT-PCR was performed according to the standard protocol using a Bio-Rad CFX Connect real-time PCR system . Primers used are listed in Table When analyzing the Hammer, Arab, and Foxtail datasets, we found strong relationships between the variances and the means on the log-log scale for the read counts from different real datasets Figure . For conMost RNA-seq analysis methods were developed based on an NB model, which is able to capture both equidispersed and overdispersed data but not underdispersed data. In comparison, a DP model can capture all RNA-seq data Efron, . Using rWu et al.  reportedUsing the three simulated datasets, we first evaluated the type I error rates  of the three DREAMSeq methods  and five other widely used RNA-seq data-analysis methods  under the null hypothesis. We found that except for TSPM, all other methods were able to control type I error rates well in both non-underdispersion and underdispersion situations Figure . In compWe then evaluated the statistical powers  of different methods using the simulated datasets under the alternative hypothesis Figure . The resThe ROC curve was constructed using the TPR to FPR ratio for each method used for DE analysis. Theoretically, the method with the stronger statistical power at identifying DEGs should exhibit a ROC curve with a higher TPR relative to other methods at the same FPR level. Figure AUC is a relative measure of the quality of a DEG test, where a higher AUC indicates relatively better performance. To quantify the performances of different methods in detecting DEGs, AUCs of different methods were calculated. The result showed that the AUCs of DREAMSeq.DP and DREAMSeq.Mix were higher than those of DREAMSeq.NB, edgeR, DESeq, DESeq2, and NBPSeq in most of the situations, except slightly lower than DESeq2 when analyzing simHammer and simArab underdispersed data Figure . TogethePRC curve shows the precision for corresponding recall (TPR). Similar to the ROC curve, the PRC curve is also an important performance indicator used to evaluate different methods at identifying DEGs. Figure We also compared the identified DEG numbers of different methods, with the results showing that both DREAMSeq.DP and DREAMSeq.Mix generally detected a larger number of DEGs (except in the case of simHammer non-underdispersed data) than the other methods  when analyzing non-underdispersed or underdispersed data from three simulated datasets, respectively, Figure .Our comprehensive evaluations showed that edgeR, DESeq, DESeq2, and DREAMSeq.Mix generally performed better as analyzing different simulated RNA-seq datasets; therefore, these methods were chosen to test their abilities to detect DEGs, especially underdispersed DEGs, using a real Foxtail dataset. A total of 128 non-underdispersed and 17 underdispersed DEGs were identified by at least one of the four methods Figure –S5. OverWe then used qRT-PCR to validate whether the DEGs identified from the Foxtail dataset were “true” DEGs. Because DEGs identified by DESeq were also identified by edgeR, the unique DEGs identified by either edgeR, DESeq2, or DREAMSeq.Mix and the common DEGs identified simultaneously by any two methods were chosen for qRT-PCR analysis Figure . The resRNA-seq is an increasingly popular method used to analyze global changes in gene expression during certain biological processes. Identifying DEGs is a key step in mining RNA-seq data and important for downstream biological analyses, such as cluster analysis, PCA analysis, GO analysis, and Kyoto Encyclopedia of Genes and Genomes enrichment analysis. When analyzing RNA-seq data, most current methods focus on non-underdispersed data, with less attention given to underdispersed data. In this study, we observed that RNA-seq data also includes underdispersion characteristics. Additionally, Low et al.  found thUsing simulated datasets generated from three real RNA-seq experiments, we compared the performance of DREAMSeq.Mix at detecting DEGs with five other commonly used RNA-seq data-analysis methods. To provide a more comprehensive conclusion, we also added DREAMSeq.DP and DREAMSeq.NB methods, which were developed using only a DP model or an NB model, respectively, into the comparison. We found that DESeq, NBPSeq, and DREAMSeq.NB were often conservative, whereas TSPM, edgeR, and DESeq2 were more liberal in detecting DEGs. The poor performance of TSPM in our study might be due to the limited number of replicates in the RNA-seq datasets used  has rapidly become a powerful tool for analyzing gene-expression heterogeneity at the individual cell level and been widely applied to diverse fields of biological research, including stem cell differentiation, embryogenesis, and whole-tissue analysis  that combines both the DP and NB models to ensure its flexibility and robustness for RNA-seq data mining. Additionally, we used a “window scan” approach to estimate dispersion and enhance the reliability of DREAMSeq across a wider range of RNA-seq data. Using simulated datasets generated from three real RNA-seq datasets and an in-house-generated Foxtail dataset, we demonstrated the ability of DREAMSeq to reach a better balance between conservative and liberal tests as compared with other methods. Our findings demonstrated DREAMSeq as a reliable and robust RNA-seq data-analysis method that provides important improvements in the DE analysis of RNA-seq data, especially in underdispersion situations.http://tanglab.hebtu.edu.cn/tanglab/Home/DREAMSeq). This package also contains a real Foxtail dataset obtained by our own laboratory.DREAMSeq R package  is available publicly (WT and ZG designed the research; ZG wrote the DREAMSeq R package and performed all data analyses; ZZ performed Foxtail RNA-seq and qRT-PCR experiments; and WT and ZG wrote the 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.


N = 1,258) and blood transcriptional profiling  were performed on MS patients from DEFINE/CONFIRM. ALCs were categorized as: SPL, < 500 cells/µL for ≥6 months; moderate prolonged lymphopenia (MPL), < 800 cells/µL for ≥6 months, excluding SPL; mildly reduced lymphocytes, < 910 cells/µL at any point, excluding SPL and MPL; no lymphopenia, ≥910 cells/µL. Genome-wide association, HLA, and cross-sectional gene expression studies were performed. No common variants, HLA alleles, or expression profiles clinically useful for predicting SPL or MPL were identified. There was no overlap between genetic peaks and genetic loci known to be associated with WBC. Gene expression profiles were not associated with lymphopenia status. A classification model including gene expression features was not more predictive of lymphopenia status than standard covariates. There were no genetic predictors of SPL (or MPL) secondary to DMF treatment. Our results support ALC monitoring during DMF treatment as the most effective way to identify patients at risk of SPL.In delayed-release dimethyl fumarate (DMF)-treated patients, absolute lymphocyte count (ALC) often declines in the first year and stabilizes thereafter; early declines have been associated with development of severe prolonged lymphopenia (SPL). Prolonged moderate or severe lymphopenia is a known risk factor for progressive multifocal leukoencephalopathy (PML); DMF-associated PML is very rare. It is unknown whether genetic predictors of SPL secondary to DMF treatment exist. We aimed to identify genetic predictors of reduced white blood cell (WBC) counts in DMF-treated multiple sclerosis (MS) patients. Genotyping ( Delayed-release dimethyl fumarate  for ≥6 months; MPL, ALC 0.5 – <0.8 × 109/L (corresponding to CTCAE grade 2) for ≥6 months; mildly reduced lymphocytes, (grade 1) ALC 0.8 – <0.91 × 109/L ; and no lymphopenia, ALC always >LLN.Patients with RRMS were divided into four phenotypic groups for analysis based on the following ALC criteria: SPL, ALC <0.5 × 10p = 5 × 10−6] and with SPL or MPL . The randomized controlled trial design of the study had a treatment washout period prior to the start of the trial and therefore covariates such as previous treatments were not adjusted for. Analyses were conducted in PLINK v1.9.A GWAS was performed on ∼1 million DNA variants using logistic regression analysis of SPL versus no lymphopenia, and SPL or MPL versus no lymphopenia . The analyses were run two ways: by adjusting for two significant ancestry principal components; and adjusting for the top two principal components and baseline ALC. We adjusted for baseline ALC because in this dataset it was associated with severe prolonged lymphopenia [odds ratio (OR) 5.2, 2 tests of association on genotypes to estimate a genomic inflation factor (λGC) based on the median χ2 for genotyped variants, adjusted for ancestry. P values were plotted in a quantile-quantile plot and in a Manhattan plot. The genome-wide significance threshold (p < 5e−8) was based on the standard Bonferroni correction for a GWAS. Power was estimated using Power for Genetic Analysis (v2) to plot the detectable OR versus variant allele frequency. The GWAS had 80% power to detect an OR of 4.0 in the SPL versus no lymphopenia cohort, and 80% power to detect an OR of 1.8 in the SPL or MPL versus no lymphopenia cohort  alleles  with HIBAG 1.8.3 . AnalyseWhole blood in PAXgene tubes was collected with informed consent from all patients in DEFINE/CONFIRM at baseline and at various time points during the study. RNA samples were prepared using the PreAnalytiX PAXgene 96 blood kit according to the manufacturer’s protocol at Expression Analysis Inc. . Following RNA extraction and QC, genome-wide gene expression data was generated using the Affymetrix GeneChip Human Genome U133 Plus 2.0 Array . The array contained 54,675 probes targeting most annotated genes in the human genome. Raw expression data was subject to rigorous QC and normalization. All array files (CEL) were normalized using guanine cytosine robust multiarray analysis to remove array processing-related signals. To remove observed effects related to upstream sample processing, expression data was normalized for RNA processing batch, initial yield of RNA from samples, final yield of labeled RNA, RNA integration number , and RNA degradation 5′-3′ slope. While adjusting for these effects, we also controlled for treatment arm and time point.limma package  and developed lymphopenia . Because package  availablglmnet package  and another including gene expression features to the covariates (full model) were built. For both models, logistic regression was used to predict class labels and performance metrics were estimated using 10-fold cross-validation. For the full model, feature selection within each fold of cross-validation was performed using an internally 5-fold cross-validated elastic-net algorithm implemented in the  package  availabl−4. We also excluded variants with a differential call rate. For sample QC, we dropped samples with heterozygosity ≥6 standard deviations, samples with cryptic relatedness, and samples that were ancestry outliers .Standard GWAS QC methods were applied in this study. For variant QC, we applied an Illumina Gen Train score threshold of ≥0.7, a genotyping rate of ≥0.98, a minor allele frequency of >0.01, a genotyping rate of ≥0.99, and a Hardy–Weinberg equilibrium threshold of 1 × 10p < 5e−8) in the analysis of SPL versus no lymphopenia or in the analysis of SPL or MPL . This SNP is located between HLA-DQB1 and HLA-DQA2. The greatest association for the SPL or MPL versus no lymphopenia analysis was at rs382259  . Of these 16–24% . There we 16–24% .Of 1,460 patients with documented lymphopenia status, 1,133 also had gene expression data Table 3.An exploratory classification model (full model) including gene expression features was not more predictive (mean accuracy 68%) of lymphopenia status than the base model including the clinical covariates age, sex, ALC, and disease duration alone , as well as a secondary signal for ulcerative colitis chr6: 32,280,470–32,282,252  with SPL or SPL or MPL in this exploratory analysis of DNA samples collected in the DEFINE/CONFIRM pivotal phase 3 clinical trials. Overall, we saw no overlap between nonsignificant peak GWAS regions observed in this study Table 4,www.biogenclinicaldatarequest.com). Publicly available datasets were analyzed in this study. This data can be found here: GSE55851, GSE29312, GSE29332, GSE17718, GSE6034, GSE38537, GSE33615, GSE57259, GSE19080.DEFINE and CONFIRM were registered with ClinicalTrials.gov . Requests for data supporting this manuscript should be submitted to the Biogen Clinical Data Request Portal (DS, MP, PB participated in the interpretation of the study results and in the drafting and critical revision of the manuscript. CS, HM, and NA participated in the drafting and critical revision of the manuscript. KA-R participated in the drafting and revision of the manuscript. All authors approved the final version of the manuscript.DS, NA, HM, CS, MP, and PB are employees of and hold stock/stock options in Biogen. KA-R is an employee of and holds stock/stock options in Envision Pharma Group. The study was funded/supported by Biogen. Biogen was involved in the study design, data collection, data analysis, and preparation of the manuscript.


Triple-negative breast cancers (TNBC) lack estrogen and progesterone receptors and HER2 amplification, and are resistant to therapies that target these receptors. Tumors from TNBC patients are heterogeneous based on genetic variations, tumor histology, and clinical outcomes. We used high throughput genomic data for TNBC patients (n = 137) from TCGA to characterize inter-tumor heterogeneity. Similarity network fusion (SNF)-based integrative clustering combining gene expression, miRNA expression, and copy number variation, revealed three distinct patient clusters. Integrating multiple types of data resulted in more distinct clusters than analyses with a single datatype. Whereas most TNBCs are classified by PAM50 as basal subtype, one of the clusters was enriched in the non-basal PAM50 subtypes, exhibited more aggressive clinical features and had a distinctive signature of oncogenic mutations, miRNAs and expressed genes. Our analyses provide a new classification scheme for TNBC based on multiple omics datasets and provide insight into molecular features that underlie TNBC heterogeneity. Breast tumors have been categorized clinically based on histological analyses of the levels of two hormonal receptors, estrogen (ER) and progesterone (PR), and human epidermal growth factor receptor 2 (HER2) amplification4. This classification system has been proven to have predictive power about a patient’s prognosis and is routinely used to make decisions about therapy. Perou et al. analyzed the inter-tumor heterogeneity in breast cancer using microarrays and developed a classification system that defined intrinsic subtypes 5. New breast tumors can now be assigned to one of these classes based on the expression pattern of 50 informative genes termed PAM50 (ref.5). These breast cancer subtypes have different molecular characteristics and clinical outcomes6. Luminal A has the best clinical outcome. Comparing luminal A and luminal B tumors, luminal B tumors have higher expression of cell cycle and cell proliferation genes, lower expression of luminal genes such as PR, higher mutational burden and more copy number changes. HER2-enriched tumors have the highest mutational burden of all subtypes and display high expression levels of HER2-regulated and cell proliferation genes. Basal-like tumors highly express cytokeratins that are usually present in the basal cells of normal breast, and cell proliferation genes. Another intrinsic subtype called claudin-low was subsequently identified7. The claudin-low subtype is associated with signatures related to mammary stem cells and epithelial-to-mesenchymal transition. In a large-scale study (~500 breast cancer patients) by The Cancer Genome Atlas (TCGA), the authors sought to identify breast cancer subtypes based on a combination of genomic, mutational, and epigenetic signatures. This study revealed four groups that showed high concordance with the four intrinsic PAM50 subtypes8.Breast cancer is heterogeneous in nature, as breast tumors can arise from different cells of origin, and can present with distinct mutational signatures, biological and clinical phenotypes, and survival outcomes6. Triple negative breast cancer (TNBC) is clinically defined based on the absence of the estrogen and progesterone receptor, and HER2 overexpression. TNBC constitutes approximately 16% of breast cancer cases9. It is a particularly proliferative and aggressive subtype of breast cancer, associated with large size, high tumor grade, high mitotic rate, and metastasis10. Because of the lack of druggable targets, TNBC are treated with conventional chemotherapy, and only approximately 50% of patients respond11. The 5-year survival rate is only 14%12, much worse than for other breast cancer subtypes. TNBC are more prevalent in young women and in women of African or Hispanic descent13.The majority of breast cancers (80–85%) are positive for hormonal receptors and/or HER2 amplification, and therefore respond to therapies that target these markers16. In contrast to other types of breast cancer in which several genes are mutated at high frequency, only two genes (TP53 and PIK3CA) are mutated in >10% of TNBC patients17. Thus, there are many different oncogenic drivers of TNBC. TNBC also demonstrates histological heterogeneity as nine different histologic subtypes have been identified based on immunohistochemistry (IHC), with invasive ductal carcinoma being the most prevalent type16.TNBC demonstrates genomic heterogeneity, as there is a substantial variation in mutational burden, copy number alterations, and genomic rearrangements across TNBC patients21. Most TNBCs are classified as basal-like subtype based on PAM50 gene expression signatures19. Further classification schemes focused more specifically on TNBC have also been developed. Lehmann et al.18 and Burstein et al.20 identified six and four TNBC subtypes, respectively, using microarray analysis. The six subtypes defined by Lehmann et al. have unique gene expression signatures and respond differently to chemotherapy. Some of the gene expression signatures associated with two  of the six subtypes likely reflect the presence of cells from the tumor microenvironment, rather than the cancer cells themselves21. To address this, Lehmann et al. recently published a revised classification containing four TNBC subtypes , basal-like 2 (BL2), mesenchymal (M), and luminal androgen receptor (LAR)) using RNA-seq data from TCGA21. The survival outcome of patients with the BL1 subtype was found to be significantly better than the survival for patients from the other three subtypes combined.Understanding the molecular basis of TNBC heterogeneity will be important for the development of precision therapy that targets specific molecular markers present in some TNBCs . Previous studies have defined subtypes of TNBC based on gene expression22. Integrative clustering methods24 combining gene expression data with other data types such miRNAs and CNVs have been performed to identify novel subtypes for breast26 and other tumors27. Integrative approaches have been shown to be more robust in comparison to methods using biomarkers of one type only23. To date, methods that integrate different datatypes have not been applied to understand TNBC heterogeneity.Inter-tumor heterogeneity for breast cancers, including TNBC, has been measured mainly by clustering tumors based on gene expression data from microarray or RNA sequencing methods. The availability of data from other sources  has provided new avenues to understand tumor heterogeneity. Recently, three TNBC subtypes based on DNA methylation sequencing have been proposed and shown to have different clinical coursesGiven the clinical importance of TNBC and the clear need for classification systems that capture the inter-tumor heterogeneity of TNBC in a comprehensive manner, we performed integrative clustering combining information from multiple types of omics data  collected from TNBC tumors. This led to the identification of three TNBC clusters with distinct molecular and clinical features. This new classification scheme may have implications for the treatment of TNBC.et al. based on low expression levels of ER, PR, and HER221. In addition to the cancer cells, a tumor sample may contain cells from the tumor microenvironment (TME), such as fibroblasts, endothelial cells, and immune cells. The presence of these cells may confound the signals originating from cancer cells. Sequencing data provided by TCGA were collected from only those cancer tissues with >60% cancer cells (>60% tumor purity) as determined by counting the tumor nuclei28. Recently, more refined computational methods using sequencing data  have been used to estimate tumor purity by taking into account the contribution of non-cancer cells29. Aran et al. provided consensus purity estimations (CPE) based on a combination of four methods—gene expression of non-cancer cells, somatic CNVs, immune-related methylation pattern, and immunohistochemical analysis. In an independent analysis, tumor purity scores ) for the same tumors were determined based on an analysis of copy number alterations31. For this study, we only included those TNBC primary tumors for which the tumor purity scores were estimated to be >60% by either Aran et al. or Li et al. 33. Consensus clustering was performed separately using gene expression (RNA-seq), miRNA expression (miRNA-seq), and copy number variants (CNV) (SNP 6.0 array) data provided by TCGA CGA Fig. .Figure 134 were applied that analyzed the degree of cluster compactness  and cluster separation . Out of two distance metrics, the Euclidean metric performed the best based on top scores in all the three validation tests   did not show much agreement as indicated by very low (NMF-gene/NMF-miRNA and NMF-miRNA/NMF-CNV) and negative (NMF-gene/NMF-CNV) adjusted rand index scoresns) Fig. . To gainns) Fig. . Thus, d19. The clustering indicates heterogeneity within the basal-like subtype as these tumors did not cluster together, but rather were split into separate clusters. One exception among single-data clusters was cluster 4 of the NMF-gene classification  compared to those generated by single-data types . The higher fraction of edges connecting the nodes within the clusters for the fused network indicates better separation (lower fraction of inter-cluster edges) compared to single-data types. The separations among SNF clusters 1–3 were maintained when DNA methylation data was incorporated with gene, miRNA, and CNV data (see Methods) to generate the fused network  upon combining gene, miRNA, and CNV data Fig. . The thrThe clusters generated by an integration of the three datatypes did not correspond to the clusters generated with any single datatype using NMF clustering methods. SNF Cluster 2 had the most overlap with NMF-gene cluster 4 (71%), NMF-miRNA cluster 4 (68%), and NMF-CNV cluster 1 (71%)  that was enriched in HER2 and luminal PAM50 subtypes, with the rest of the clusters containing a high fraction of tumors with a basal-like subtype.The SNF clusters that we generated did not align with the PAM50 intrinsic subtypes defined for all breast cancers Fig.  or the sNBC Fig. . Of the NBC Fig. . ClusterNBC Fig. , clusterNBC Fig.  and Lehm38. Thirty two percent of cluster 2 patients had tumors of higher pathologic stage (stages III and IV). In comparison, only 9% and 15% of cluster 1 and cluster 3 patient tumors, respectively, were of stage III and stage IV   had poorer disease-free survival (p = 0.000024) compared to the remaining breast cancer patient cohort from TCGA   with larger tumor size (tumor score of T3 and T4 in TNM staging system), compared to clusters 1 (11%) or 3 (4%)  Fig. . Higher st) Fig. . In addi98) Fig. . In cont36 . These scores  based on their normalized mutual information (NMI) scores39 and 50% of cluster 2 tumors are classified as LAR subtype , which in turn promotes cell proliferation43. miR-206 is downregulated in many cancers and has been shown to target K-Ras, annexin a2, and cell cycle genes45. Oncogenic miRNA miR-9 (produced from the mir-9-3 locus) was upregulated in cluster 346. miR-9 targets E-cadherin and thereby promotes cell migration and invasion. As with genes, the expression of cluster 2-specific miRNAs (up_2 and down_2) did not vary depending on PAM50 and Lehmann subtypes  are important drivers of carcinogenesis and can maintain the fitness of cancer cells49.In addition to CNVs, activating and inactivating mutations can also serve as oncogenic drivers. Mutational analysis showed that tumor protein p53 (TP53) and titin (TTN) were commonly mutated in tumors in all three clusters with frequencies ranging from 63–78% and 23–27%, respectively  that would assign a new TNBC patient to one of the SNF clusters based on the patient’s mRNA, miRNA, and CNV data. The workflow for training and testing the classifier is shown in Fig. 50. Gene expression-based PAM50 intrinsic classification of breast cancer5 includes basal-like and luminal A/B subtypes, and two models have been proposed to explain the cellular origin of these tumor subtypes50. In the cell of origin model, luminal tumors originate from committed late progenitors of luminal cells, while basal tumors originate from early progenitors of luminal cells. The alternative genetic mutation model postulates that luminal and basal-like tumors result from different genetic insults acting upon similar populations of luminal progenitors. With this model, TNBC tumors result from additional changes that lead to a loss of expression of ER and PR in tumors without amplification of HER2. Much less is known about the origin of HER2-enriched PAM50 subtype. The tumors in this subtype are heterogeneous with respect to the expression of hormonal receptors (ER and PR) and only 70% of these tumors are classified as HER2+, suggesting a different cell of origin of these tumors50.The breast epithelial layer consists of an inner layer of luminal cells and an outer layer of basal or myoepithelial cellsUsing an integrative approach to clustering, we discovered that we could classify TNBC tumors into three distinct tumor clusters using a combination of gene, miRNA, and CNV data. Comparison of our integrative clusters with the PAM50 subtypes revealed that integrative clusters contained a mixture of tumors from multiple PAM50 subtypes. Cluster 1 and cluster 3 had a similar composition of tumors based on PAM50 classification. Tumors from both clusters were 80–90% basal-like, which is the subtype often associated with triple negative breast cancer, and contained a small amount of luminal A (4–8%), with normal making up the rest Fig. . In cont21. Each of the integrative clusters contains tumors that had been assigned to multiple different Lehmann subtypes , suggesting agreement of integrative clusters with Lehmann subtypes in terms of clinical characteristics of the TNBCs. Cluster 3 of the integrative clustering was most strongly enriched (>60%) in the mesenchymal Lehmann subtype, with basal-like 1 (27%) the next most abundant. Comparing all of the classifications, we observe that three out of four Lehmann subtypes  mostly consisted of TNBCs that were assigned as basal-like PAM50 subtype, while the majority of LAR tumors were HER2-enriched and luminal PAM50 types. Therefore, clusters 1 and 3 were similar in being mostly comprised of tumors classified as basal-like by PAM50 and as either basal-like 1 or mesenchymal by Lehmann. Cluster 2 was enriched for basal-like 2 and luminal androgen receptor by Lehmann classification, making it distinct from clusters 1 and 3, a finding also supported by its low levels of basal-like tumors by PAM50. By generating cluster 2, our integrative clustering found similarities between tumors called basal, HER2-enriched or luminal A by PAM50, or basal-like 2, luminal androgen receptor or mesenchymal by Lehmann classification, that were not detected by previous clustering approaches. Our findings also suggest that there are global distinctions identified by the features we considered that lead to two distinct and separable groups (cluster 1 and cluster 3) among tumors that are assigned basal-like by PAM50 and basal-like 1 or mesenchymal by Lehmann.We also compared the three clusters generated by our integrative analysis with the four TNBC subtypes of the gene expression-based Lehmann classificationpes Fig. . IntegramiR-10b. Clusters 1 and 3 were similar to each other in terms of being mostly basal PAM50-subtype, similar disease-free progression and tumor size, similar numbers of copy number variants, and a similar set of oncogenic mutations with frequent mutations in p53 and titin. Tumors in clusters 1 and 3 were most strongly differentiated by downregulation of genes associated with the nervous system and synaptic transmission in the tumors in cluster 1 and downregulation of genes associated with the complement pathway and the humoral immune response in cluster 3. The variable expression of neuronal genes in TNBC clusters 1–3 is surprising, but neuronal-specific markers have been reported to be expressed in breast cancer cell lines and primary tumor tissues, possibly due to an abnormal trans-differentiation process51.Each integrative cluster had a distinct profile with regard to genes, microRNAs and CNVs. Cluster 2 was the most distinct and its gene expression signature included genes in the RhoA-PKN1-androgen receptor pathway. PIKC3 was more frequently mutated in the tumors in this cluster, while copy number variants and gene amplification events were relatively rare. Tumors in this cluster also contained higher levels of the oncogenic microRNA 52 and treatment options are mostly limited to chemotherapy (neoadjuvant and adjuvant) and surgery. Furthermore, the heterogeneous nature of TNBC limits the efficacy of standard chemotherapeutic agents because the response to these treatments varies greatly among patients. A few targeted therapies for TNBC have been proposed, including mTOR inhibitors, FGFR inhibitors, and EGFR-directed antibodies such as cetuximab52. Our results include the discovery that the androgen receptor is expressed in tumors in cluster 2. This finding suggests there may be an opportunity to treat this group of patients with anti-androgens53. Also, we identified two oncogenic miRNAs, miR-10b and miR-9, that are specifically upregulated in clusters 2 and 3, respectively. Silencing of miR-10b using an antagomir oligonucleotide54 and silencing of miR-9 using a miRNA sponge46 have been shown to inhibit metastasis in mouse models of breast cancer.TNBC patients do not respond to hormone-based or anti-HER2 therapiesCollectively, our results indicate that a new classification system for TNBC that addresses its inherent heterogeneity can be defined by an integrative clustering methodology that incorporates multiple different data types. With this approach, we identified specific molecular signatures for different TNBC clusters. The distinctiveness of the integrative clusters we identified in terms of their genetic and epigenetic properties, oncogenic drivers and clinical features suggests that this new cluster assignment could provide another perspective on TNBC heterogeneity.et al.21. RNA-Seq and miRNA-Seq data were downloaded as raw counts. The CNV data from Affymetrix SNP 6.0 array was processed by GDC into output files containing segment mean values , which were used for this work. DNA methylation levels  from Illumina Infinium Human Methylation 450 arrays (if available) were also obtained. (More information on GDC output files and pipelines can be found on the GDC website: https://docs.gdc.cancer.gov/.) Clinical metadata  were obtained from The Cancer Genome Atlas (TCGA) hosted by the Genomic Data Commons (GDC). Patients (n = 180) were identified as triple negative breast cancer patients based on the analysis performed by Lehmann et al.55 and Li et al.30. Patients were kept in the study if they had a consensus purity estimate (CPE)  or Clonal Heterogeneity Analysis Tool (CHAT)  purity measurement of 60% or higher. The purity scores are presented in Supplementary Table Purity estimates for the 180 TNBC tumors were obtained from Aran 2 transformed by DESeq256. The segment mean values for CNV events were processed and converted to a CNV region-by-patient matrix using the CNTools bioconductor package57. Standard deviation (sd) filters  were used to select the genes, miRNAs, and CNVs that showed maximum variation across the patients and, therefore, were indicative of TNBC inter-tumor heterogeneity. Applying these criteria led to the selection of 2587 genes, 369 miRNAs and 5000 CNVs. We applied consensus clustering using non-negative matrix factorization (NMF) as the underlying algorithm. NMF clustering was performed using the NMF bioconductor package58 with Euclidean and Kullback-Leibler divergence as distance measures. The cluster number was selected based on the values of cophentic correlation coefficient59, which is a metric for the stability of clusters58. We evaluated the cluster solutions from NMF methods by following internal validation metrics34: RMSSTD, r-squared and SD validity index.The workflow for single data type clustering is presented in Fig. 36 for the 134 patients for which all the data were available from TCGA. No preselection of genes, miRNAs, or CNVs was required for SNF method. The three data types were aggregated by SNFtool R package to generate a fused patient network and a fused patient similarity matrix using the following parameters: K = 13, alpha = 0.5, and t = 2036. Spectral clustering was performed on the fused patient similarity matrix using the function built into the SNF package. Cluster number was chosen using the supplied function, estimateNumberOfClustersGivenGraph. The networks were visualized in Cytoscape60 by generating a k-nearest neighbors graph from the values of the patient similarity matrix. Normalized mutual information (NMI) scores of the features  were obtained as the output from SNFtools package using its rankFeaturesByNMI function. For the fused patient network generated using gene, miRNA, CNV, and DNA methylation data, only the 87 of 134 patients with associated methylation data were used.Integrative clustering using gene, miRNA, and CNV data was performed using the similarity network fusion (SNF) method61 and Reactome62 databases of cellular pathways) of genes was performed using g:Cocoa module of g:Profiler63. The statistical significance of the results after multiple testing correction was computed using the built-in g:SCS method. Only the results (enriched pathways) with p values <0.05 were considered significant. The gene targets for miRNAs were obtained from miRTarBase v6.064 and miRTex65. The genes present in the CNV regions were extracted using genomicRanges bioconductor package66. SNF cluster-specific data for genomic alterations (mutations and genes amplified or deleted by CNVs) were obtained from cBioPortal for Cancer Genomics68: http://www.cbioportal.org.Gene ontology  and pathway analysis (based on KEGG56. Only the significant (adjusted p < 0.05) genes and miRNAs with absolute log2fold change >1 were selected. In order to find cluster-specific genes, we compared the log2fold-change values of the genes across the three pairwise differential expression analyses. Genes were considered specifically upregulated (downregulated) in one cluster if they were upregulated (downregulated) in that cluster compared to each of the other two clusters.Differential gene and miRNA expression between the SNF cluster pairs  was performed using DESeq2 bioconductor package69. Initial predictors were selected by taking the top-ranked NMI features from the SNF network analysis (top 1% for genes and CNVs and top 5% for miRNA). A 70/30 split was applied to the data (n = 134) to create a training set (n = 94) and a test set (n = 40). Samples were selected for the training set by the partition function built into the caret software package. Predictor selection was performed on the training set by applying a Kruskal-Wallis test across the top NMI features (FDR < 0.01). Predictors that passed the significance test  were used for the classifiers. Three classifiers (supplied by the carat package) were trained: elastic net logistic regression (glmnet function), random forest (randomForest), and a linear kernel support vector machine (lmSVM). Leave-one-out cross validation (LOOCV) was used to further validate and tune model parameters and the resulting classifiers were evaluated using accuracy and average F1 scores  obtained from the carat package (Supplementary Table Classifiers were created using the caret package in RStatistical tests employed for each analysis are provided in the text and legends. For all analyses, we used adjusted p < 0.05 to determine statistical significance. p values are adjusted for multiple hypothesis testing. Tests are two-sided.https://gdc.cancer.gov/.All the datasets used in the study are publicly available from NCI genomic data commons: The software packages used in this study are listed in Supplementary Table Supplementary InformationTable S1Table S2Table S3Table S4Table S5Table S6Table S7Table S8


The present data profiles a large scale transcriptome changes in seed tissues (embryo and endosperm) during maturation in dormant and non-dormant genotypes of hexaploid wheat. Seed dormancy is an adaptive trait that has a significant influence on the incidence of preharvest sprouting, which is referred to as the germination of grains on the spike prior to harvest, in wheat. Given that preharvest sprouting causes a substantial yield and quality losses, elucidation of the molecular features that regulate seed dormancy has a paramount significance in the development of preharvest sprouting resistant wheat cultivars. The data presented here was produced from total RNA/mRNA samples isolated from developing seeds of dormant and non-dormant wheat genotypes using the Affymetrix GeneChip Wheat Genome Array. The raw and normalized formats of these data are available in Gene Expression Ominbus (GEO), NCBI's gene expression data repository, with accession number GSE83077. Reproducibility of the transcriptomic data from the independent replicates of each sample was confirmed by scatter plot expression analysis with the squared Pearson correlation coefficient (R2) . The raw and normalized formats of these data are available in Gene Expression Ominbus (GEO), NCBI's gene expression data repository (ent (R2) , Fig. 4.22.1Plant of the dormant wheat genotype AC Domain and the non-dormant genotype RL4452 2.2Total RNA was extracted from both embryo and endosperm tissues. The total RNA from the embryos was isolated using RNeasy Plant Mini Kit  while the total RNA from endosperm tissue was isolated as described previously 2.3The total RNA or mRNA samples were used for cDNA synthesis. After purification, biotinylated cRNA samples were prepared using the GeneChip IVT Labelling Kit and the GeneChip Sample Cleanup Module (Affymetrix). Assessment of the quality of the labelled cRNAs was undertaken using an Agilent 2000 Bioanalyzer. Subsequent to fragmentation, labelled cRNA samples were hybridized for 16 hr at 45 °C on GeneChip Wheat Genome Array. Washing and staining of the GeneChips were performed in the Affymetrix Fluidics Station 450. Afterwards, the GeneChips were scanned using an Affymetrix Scanner 3000.2.4http://harvest.ucr.edu/) 2) (Using the Affymetrix Microarray Suite (MAS5) statistical algorithm, the number of probesets with ‘present’ detection was determined. Subsequently, the raw data was normalized using Robust Multi-array Average (RMA) methodology. HarvEST WheatChip (edu/) 2) . As a reedu/) 2) , Fig. 2 edu/) 2) , Fig. 4


Glioma is the most common type of primary brain tumor, accounting for 40% of malignant brain tumors. Although a single gene may not be a marker, an expression profiling and multivariate analyses for cancer immunotherapy must estimate survival of patients. In this study, we conducted expression profiling of immunotherapy-related genes, including those in Th1/2 helper T and regulatory T cells, and stimulatory and inhibitory checkpoint molecules associated with survival prediction in 571 patients with malignant and aggressive form of gliomas, glioblastoma multiforme (GBM). Expression profiling and Random forests analysis of 21 immunosuppressive genes and Kaplan-Meier analysis in 158 patients in the training data set suggested that CD276, also known as B7-H3, could be a single gene marker candidate. Furthermore, prognosis prediction formulas, composed of Th2 cell-related GATA transcription factor 3 (GATA3) and immunosuppressive galactose-specific lectin 3 (LGALS3), based on 67 immunotherapy-related genes showed poor survival with high scores in training data set, which was also validated in another 413 patients in the test data set. The CD276 expression helped distinguish survival curves in the test data set. In addition, inhibitory checkpoint genes, including T cell immunoreceptor with Ig and ITIM domains, V-set domain containing T cell activation inhibitor 1, T-cell immunoglobulin and mucin-domain containing 3, and tumor necrosis factor receptor superfamily 14, showed potential as secondary marker candidates. These results suggest that CD276 expression and the gene signature composed of GATA3 and LGALS3 are effective for prognosis in GBM and will help us understanding target pathways for immunotherapy in GBM. Glioma is the most common type of primary brain tumor accounting for 40% of all malignant brain tumors . The WorRecent trends of studies on central nervous system tumors have focused on cancer immunotherapy, which target immune checkpoint molecules on the surfaces of tumors, antigen-presenting cells (APCs), and T cells . The monThe study on glioma reported PD-L1 expression in the patients’ characteristics and the data were deposited in both Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA), however, it was hard to calculate the prognoses of the patients in mixed stages II-IV, primary, recurrence, and progressive gliomas . On the https://cancergenome.nih.gov/) were used. Training data set and test data set comprised 158 samples derived from Glioblastoma Multiforme   and 413 samples derived from Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma, 2016 (https://tcga-data.nci.nih.gov/docs/publications/lgggbm_2015/) available for survival analysis and expression profiling, respectively [RNA-Seq data and clinical information in glioblastoma multiforme (GBM) obtained from The Cancer Genome Atlas (TCGA)  .Random survival forests analysis was performed to determine variable importance factors distinguishing expression of genes with survival times, which was estimated by randomly permutating its values and recalculating the predictive accuracy of the model that were expressed as the log rank test statistics using the randomForestSRC package in R .Correlations between gene expression and survival times were evaluated by multivariate analyses. Clinical characteristics were used as an additional variable to execute multivariate analyses. The statistical data was determined by the Cox proportional hazards regression model using JMP (SAS Institute Inc.) .Correlation among variables were analyzed using the glasso package in R ,20.Statistical analysis was performed using R software, Bioconductor, and JMP (SAS Institute Inc.) . P < 0.0The aim of this study was the discovery of novel prognosis markers and gene signatures from a viewpoint of cancer immunotherapy, especially inhibitory checkpoints, based on the expression data and clinical information in two independent GBM data set. Therefore, we arranged for the gene set related to cancer immunotherapy including type 1 T helper cells, type 2 T helper cells, regulatory T cells, stimulatory checkpoint, and inhibitory checkpoint . This stFirst, we examined whether the genes encoding inhibitory checkpoint molecules could serve as prognosis markers . The exp2(x+1). Random survival forests analysis returned variable importance of each gene in the training data set -related genes, thereby, focusing on the immunosuppressive genes to identify prognosis markers . Since tdata set . Especiadata set . Immunosdata set . Cox prodata set . Based odata set : Z1 = 0.1 by 4.11. The subgroup with Z1 ≥ 4.11 showed poor prognosis for OS    and dise 0.0001) . In immu 0.0001) . On the  0.0001) . Besides 0.0001) . On the  0.0001) . These r2 = 0.601 × IL2RB—0.616 × GATA3–0.508 × TMIGD2–0.929 × LTA—0.873 × LGALS3–0.416 × TGFB2 + 0.394 × IL12RB2 + 0.566 × TGFB3.Similarly, we also analyzed the immunotherapy pathway-related genes using log scale-expression values in the test data set . Random 2 = 0.15. The subgroup with Z2 ≥ 0.15 showed poor prognosis for OS   . For imm 0.0001) . On the  0.0001) . Besides 0.0001) . On the  0.0001) . These r1 derived from the training data set and Z2 derived from test data set are effective to estimate prognosis in the test data set and training data set, respectively. However, the subgroup with Z1 ≥ -0.064 (median score) showed no difference in OS  in the test data set (2 ≥ -11.118 (median score) also showed no differences in OS  in the test data set  and Z4 ≥ 0.102 , showed poor prognosis for OS in each data set  is an immune checkpoint molecule that belongs to the CD28 family, which plays pivotal roles in T-cell suppression in glioma . The higIn summary, we have demonstrated that a single gene CD276 (B7-H3) and the gene signature composed of GATA3 and LGALS3 would be promising marker candidates for prognoses in GBM. Interestingly, a combination of the expression levels of GATA3 and LGALS3 enables prognosis prediction in GBM, but each gene individually is not a single marker. In addition, indices of each gene in the prediction formulas have distinct eigenvalues based on the data set, which should be further analyzed in future. However, the aim of this study, a detection for diagnosis and/or prognosis marker candidates for GBM, namely, CD276, GATA3, and LGALS3, would have been achieved successfully by using of their expression data, clinical information, and multivariable analyses. Besides, we also found the second candidate of GBM diagnosis/prognosis markers, including TIGIT, HAVCR2, PDCD1, TIGIT, and TNFRSF14. These genes were associated with patients’ survival and genetically interacted within a complex network hub, suggesting a possibility of simple diagnosis in GBM. Especially, it is of great importance that higher expression of the genes related to Th2 cells and stimulatory checkpoint molecules and lower expression of the Th1-related genes resulted in worse prognoses in the two independent GBM data sets. In addition, higher expression of the Treg-related genes also tended to show poor prognosis. These results could provide promising marker candidates for cancer immunotherapies, especially involving the inhibitory checkpoint, and would also make way for understanding and developing target therapies and pathways in GBM.S1 FigA-B) Construction of the training data set and the test data set for GBM used in this study. (A) Training data set (N = 158). (B) Test data set (N = 413). Both data set are derived from The Cancer Genome Atlas (TCGA) and are independent of each other. The training data was used for initial analysis, and furthermore, the test data set was used for validations of results from the training data set. (C-D) Overall survival (OS) distributions of the total samples in the training data set and test data set of GBM. (C) Training data set. (D) Test data set.((TIF)Click here for additional data file.S2 FigA) Coefficient value. (B) Hazard ratio.((TIF)Click here for additional data file.S3 FigA) Coefficient values. (B) Hazard ratios.((TIF)Click here for additional data file.S4 FigA) CD163. (B) FOXP3. (C) GATA3. (D) IL18R1. (E) TGFB3. (F) TGFB1. (G) TNFRSF18. (H) TNFSF14. (I) TNFSF4. (J) HHLA2. (K) STAT1. (L) TBX21. High and low indicate subgroups with over and under the threshold. OS, overall survival. HR, hazard ratio. Subgroups were divided by the median expression of genes.((TIF)Click here for additional data file.S5 FigA) Coefficient values. (B) Hazard ratios.((TIF)Click here for additional data file.S6 FigA) CSF2. (B) IL12RB2. (C) IL13. (D) IL2RB. (E) IL3. (F) IL4. (G) IL5. (H) IL6. (I) IL9. (J) TBX21. (K) TNF. (L) TNFRSF18. (M) TNFRSF4. (N) CD3D. (O) CD3E. (P) CD3G. (Q) GATA3. (R) LTA. (S) STAT1. (T) STAT4. (U) TGFB1. High and low indicate subgroups with over and under the threshold. OS, overall survival. HR, hazard ratio. Subgroups were divided by the median expression of genes.Numbers in the parentheses indicate the threshold of gene expression. ((PDF)Click here for additional data file.S7 FigA) Kaplan-Meier survival analysis using the Z1 score (= -0.064) in the test data set. (B) Kaplan-Meier survival analysis using the Z2 score (= -11.181) in the training data set. OS, overall survival. HR, hazard ratio. Subgroups were divided by the median scores of Z1 and Z2.((TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.


Glioblastoma multiforme (GBM), a deadly cancer, is the most lethal and common malignant brain tumor, and the leading cause of death in adult brain tumors. While genomic data continues to rocket, clinical application and translation to patient care are lagging behind. Big data now deposited in the TCGA network offers a window to generate novel clinical hypotheses. We hypothesized that a TCGA-derived gene-classifier can be applied across different gene profiling platforms and population groups. This gene-classifier validated three robust GBM-subtypes across six different platforms, among Caucasian, Korean and Chinese populations: Three Caucasian-predominant TCGA-cohorts , and three Asian-cohorts . To understand subtype-relevance in patient therapy, we investigated retrospective TCGA patient clinical sets. Subtype-specific patient survival outcome was similarly poor and reflected the net result of a mixture of treatment regimens with/without surgical resection. As a proof-of-concept, in subtype-specific patient-derived orthotopic xenograft (PDOX) mice, Classical-subtype demonstrated no survival difference comparing radiation-therapy versus temozolomide monotherapies. Though preliminary, a PDOX model of Proneural/Neural-subtype demonstrated significantly improved survival with temozolomide compared to radiation-therapy. A larger scale study using this gene-classifier may be useful in clinical outcome prediction and patient selection for trials based on subtyping. The median survival of GBM patients is less than 16 months despite a multitude of therapies11. Standard of care is radiation therapy and temozolomide, which gives the best 2-year overall survival of about 25%11. While genomic data continues to rocket, clinical application and translation to patient care are lagging behind. Big data now deposited in The Cancer Genome Atlas (TCGA) network offers a window to generate novel clinical hypotheses. One overarching theme is how genomics can be applied to derive clinically relevant information to improve therapy for patients. We hypothesized that a TCGA-derived gene-classifier can be applied across different gene profiling platforms and population groups. Using an enhanced gene expression data set from Caucasian-predominant TCGA-cohorts, we derived a subtype gene-classifier and validated our subtype-prediction model on six different platforms over a decade . This provides institutions profiling patient tumors on various platforms with a model to predict GBM-subtypes on different patient populations . In a therapeutic context, patient survival curves reflect a net result of a mixture of different treatment regimens, with or without surgical resection of varying extent. If we are able to dissect the knowledge of a particular GBM-subtype which is more temozolomide-responsive or radiotherapy-responsive, this will provide a core treatment stem for testing novel agents in combination, targeting at a specific GBM-subtype. As a proof-of-concept, we postulated that survival differed by subtypes in context of GBM-relevant therapies, and any difference in treatment response will be clearer if we study the individual effects with monotherapy arms of the two most common treatment modalities in GBM, radiation therapy versus temozolomide, in a panel of patient-derived orthotopic xenograft mice (PDOX) without any surgical resection of tumor (lessening tumor burden) or a mixture of therapies confounding the survival data. Our findings from this study, if done on a larger scale, may be useful in clinical outcome prediction and patient selection for trials based on subtyping. Our gene-classifier was validated on six different gene profiling platforms and relevant among populations of Caucasian, Korean and Chinese patients.Glioblastoma (GBM), a deadly brain cancer, is the most lethal and common malignant brain tumor and the leading cause of death in adult brain tumors. Despite the advances in genomics and molecular classification1, several studies14 have reported refinement of the classification into three GBM-subtypes. In agreement with these studies14, we validated three robust GBM-subtypes  but individually on six different platforms: TCGA-cohorts deposited over a decade , and three additional Asian-cohorts  by consensus clustering15. To curate outlying samples, we performed silhouette width analysis16 to include only the core samples that were most representative of each of the three clusters , we showed that a 500-gene-set was sufficient to recapitulate three GBM-subtypes  and three Asian-cohorts  and less clear clusters with k = 3. In contrast, the 500 gene-classifier formed three clear clusters , comparing the GBM-subtype nomenclature defined by Verhaak’s 840-gene-set and our 500 gene-classifier  of GBM tumors had original subtype nomenclature reclassified by 500 gene-classifier . The clustering effect was less distinct using Wang’s 150-gene-set  in the TCGA training set  per model respectively in No Treatment, Treatment 1 (Temozolomide) and Treatment 2 (Radiation Therapy) groups , from the two largest TCGA cohorts  versus Mesenchymal GBM (p < 0.05). The patient survival pattern paralleled some similarity to treatment naïve PDOX mice which received no treatment, although as expected mice survival was remarkably shorter than patient survival. Most survival data from GBM patients22, including our Asian cohorts, reflected the outcome of a heterogenous mixture of treatment regimens and different extent of surgical resection of tumor bulk, which posed further challenges to meaningfully interpret patient data for subtype-specific responses to individual effects of temozolomide versus radiation therapy, in context of GBM-relevant therapy. Therefore, we sought post-treatment mouse survival data to provide some biological evidence for new clinical insights, investigating the individual effects of each single-agent therapy, for the two common treatment modalities in the standard of care in GBM.Recent emerging evidence supports the importance of patient selection based on subtypes in clinical trials and therapeutic drug development26 have been reported to be a different disease from adults.Here, we present clinically relevant biological data in a proof-of-concept study investigating TCGA-cohorts to derive a gene-classifier that can be applied across different gene profiling platforms and population groups. Our findings from this study, if done on a larger scale, may be useful in clinical outcome prediction and patient selection for trials based on subtyping. We validated three GBM-subtypes using our gene-classifier on six different platforms among Caucasian, Korean and Chinese populations. This will provide institutions profiling patient tumors on various platforms internationally, with a model to predict GBM-subtypes in adults. We did not find this gene-classifier useful in subtyping pediatric GBMs  to validate our gene-classifier. Limitations include variation of demographics among the Asian-cohorts vary and lack of large-scale multi-dimensional genomics resource. Here, we present a descriptive observation, since other correlates such as mutational status were not analyzed, populations were demographically distinct and the Asian-cohorts were much smaller than TCGA-cohorts. Among the cohorts surveyed, Caucasian-predominant TCGA training set was Proneural/Neural subtype predominant (41.2%). This subtype was less common in Asian Cohort 1 , more common in Asian Cohort 2  and Asian Cohort 3 . Classical subtype appeared to be the more predominant subtype in Asian Cohort 1 , less common in Asian Cohort 2  and Asian Cohort 3 . This subtype was less common among Caucasians in the TCGA training set (21.2%). Mesenchymal subtype appeared more consistent in proportion among all cohorts surveyed. We found another cohort of 88 Chinese GBM patients (RNA-seq), comprising of Mesenchymal subtype (41%), Classical subtype (36%), and Proneural/Neural subtype (23%)34.Nonetheless, it is difficult to meaningfully interpret Asian GBM-subtype distribution based on the small sample size of Asians in TCGA training set (n = 13). We therefore sought additional larger Asian-cohorts . We report a TCGA-derived subtype-prediction model which can be useful for institutions profiling patient tumors on various platforms among populations of Caucasian, Korean and Chinese patients to predict GBM-subtypes. This approach can be applied to other tumor types with big data deposited in TCGA, investigating TCGA-cohorts to understand correlation with Asian ethnicity. Larger scale studies might be useful in clinical outcome prediction and patient selection for trials based on subtyping for other major cancers. Our gene-classifier is validated among Caucasian and Asian populations and can be relevant in understanding the subtype patterns among ethnic populations if applied on a larger scale. Subtype patterns may be relevant for translating subtype-specific trials across international patient populations.To our knowledge, while there are studies comparing glioma types of different WHO grading among ethnic populations 24 involving this panel of PDOX from Asian Cohort 1, and fewer PDOX models on Proneural/Nerual and Mesenchymal subtypes. However, our Classical PDOX models were well-represented, and our sample size of 13 PDOX models from Asian Cohort 1 was appropriately-sized for the funding and manpower resources allocated for each of these PDOX projects in the PDOX modelling field. Creation of PDOX models is a lengthy and costly process. PDOX models are still the preferred cancer model over culture-based spheroids or slice cultures. In 2016, the US National Cancer Institute (NCI) has decided to stop screening most drugs using the NCI-60, its panel of 60 human cancer cell lines grown in culture, after more than 25 years of heavy use by worldwide researchers, and is developing PDOX models that better mimic the human counterpart. Specifically, a targeted approach of testing drugs with subtype-specific models, which we illustrate here, but with extended collaborations to various international groups studying GBMs, and each research group with a defined focus on different subtype-specific models, may be a cost- and time-effective approach to team science using PDOX models, without compromising the value of data we can derive from PDOX models. Our observations highlight a gap to be addressed in larger scale PDOX studies to prospectively investigate the differential treatment response for subtype-based GBM patient selection.In the clinical context, patients had mostly received a heterogenous mixture or combination treatment regimens with or without surgical resection of varying extent. Using large retrospective clinical sets from TCGA with assessable outcome data for good statistical power, subtype-specific patient survival outcome was similarly poor and reflected the net result of a mixture of treatment regimens with or without surgical resection of varying extent. As a proof-of-concept, in subtype-specific PDOX mice, Classical subtype demonstrated no survival difference comparing the individual effects of radiation therapy versus temozolomide monotherapies. We also observed that temozolomide did not confer significant survival benefit in two Mesenchymal subtype PDOX models, compared to untreated mice. Though preliminary, a PDOX model of Proneural/Neural subtype demonstrated significantly improved survival with temozolomide compared to radiation therapy. Our approach is clinically relevant. It compares the individual effects of temozolomide versus radiation therapy, the two most common treatment modalities in GBM, among PDOX models without surgical resection (lessening tumor burden) of these tumors in mice or a mixture of therapies confounding the interpretation of survival data. It allows the treatment response of a single tumor subtype from each patient, to be studied in three different settings of no treatment, temozolomide monotherapy and radiation monotherapy, to investigate if there exist temozolomide-responsive or radiotherapy-responsive subtypes. Our study was limited in design as the data was extracted from post-treatment mouse survival data pooled from various studies23 with 3 additional new patients from the same single institution. Asian Cohort 2 comprised of 52 adult GBM patients from Yonsei University Hospital, normalized gene expression values were provided for Verhaak’s1 840 genes and 500 genes in our gene-classifier . TCGA training set comprised of 475 patients with identifiable ethnicity (core samples), of which 430 were Caucasians, 13 Asians, and 32 African-Americans  Research Network with data available on three different platforms RNA-seq, Affymetrix HT-HG-U133A arrays and custom designed Agilent arrays. We downloaded open access TCGA Level-3 data on 4 Feb 2015 using the TCGA data portal. Level-3 data has been already normalized for downstream analyses. We have chosen genes that were common on all 3 platforms for the analyses. We used 548 GBMs and 10 normal samples (Affymetrix HT-HGU133A array) and 588 GBMs and 10 normal samples (Agilent array) and 168 GBMs and 5 normal samples (RNA-seq) to identify differentially expressed genes (DEGs) between GBMs and normal brain tissue samples. Figure Several filters were applied to identify most tumour specific relevant genes for clustering. We identified differentially expressed genes between GBMs and normal brain tissue samples in all three datasets separately using ANOVA comparisons. A cut-off of P < 0.05, Q < 0.05 and |FC| > 2 were used which identified 2723 differentially expressed genes in Affymetrix HT-HGU133A dataset and 3284 differentially expressed genes in Agilent array and 6753 differentially expressed genes in RNA-seq dataset. We overlapped three sets of differentially expressed genes and identified a final set of 1500 genes (Gene List 15. The distance metric was 1-(Pearson’s correlation coefficient) and the procedure was run over 1000 iterations and a subsampling ratio of 0.8 was used to cluster the training cohort of 548 GBM samples with 500 reliably-expressed gene set identified based on various filtering.Consensus hierarchical clustering is a resampling-based clustering algorithm that provides a way to represent the clustering consensus across multiple runs of a clustering algorithm and helps to assess the stability of the discovered clusters. We used hierarchical clustering with agglomerative average linkage, as our basis for consensus clustering, to detect robust clusters15 approach to identify subgroups based on the 500 reliably-expressed genes identified using the above filter. We used Pearson’s correlation as the distance metric and 0.8 as subsampling ratio using the training set of 548 GBM’s. Over 1000 iterations were performed to identify the stable clusters using the top 500 genes. Silhouette width analysis using (R-package: Silhouette) was performed to identify the core samples that were most representative of the clusters16. ANOVA comparisons between all three subgroups both pairwise and in combination were performed to identify subtype specific genes which were highly expressed in specific subtype.We used consensus average linkage hierarchical clusteringStatistical Analysis was performed using Partek Genomics Suite software, version 6.6 Copyright ©; 2016 Partek Inc., St. Louis, MO, USA. Kaplan-Meier Survival Analysis was performed using PASW Statistics 18, Release 18.0.0 .We used Ingenuity Pathway Analysis (IPA) to identify significant pathways  and human recombinant bFGF and EGF  (NBE condition). Acutely dissociated GBM cells were stereotactically  injected into the brains of immune-compromised NOD/SCID Il2rg−/− (NOG) mice within 12 hours after surgery . Mice with the reduction of the total body weight (>20%) were sacrificed, and brains were processed for paraffin or frozen section.Animal experiments were approved by the Institutional Review Board and conducted in accordance with the “National Institutes of Health Guide for the Care and Use of Laboratory Animals” (NIH publication). Tumors were classified as GBM based on WHO criteria after review by pathologists. Parts of the surgical samples were enzymatically dissociated into single cells, following the procedures previously reportedin vitro culture in the NBE condition . Treatments were started at the half of the median survival length of the orthotopic xenograft animal models. The reduction of the total body weight (>20%) was regarded as mortality. Whole brain 2 Gy X-irradiations were applied daily for 5 days  using a blood irradiator . Mouse bodies were shielded with a custom-made lead shield device. Temozolomide (65 mg/kg) was orally administrated daily.Orthotopic xenograft tumors were made as described above, using primarily cultured GBM cells that had short term Supplementary Figures and Tables4A4B4CGene List A_500 Gene ClassifierGene List B_1500 Gene-SetGene List C_Subtype Specific Genes