We rigorously validated MODEL-P by stratifying patients in five independent datasets into these two survival groups and achieved significant survival difference, which is superior to current practice and other subtyping schemas. We believe the subtype-specific signatures would facilitate PDAC pathogenesis discovery, and MODEL-P can provide clinicians the prognoses information in the treatment decision-making to better gauge the benefits versus the risks.A major challenge for treating patients with pancreatic ductal adenocarcinoma (PDAC) is the unpredictability of their prognoses due to high heterogeneity. We present Multi-Omics DEep Learning for Prognosis-correlated subtyping (MODEL-P) to identify PDAC subtypes and to predict prognoses of new patients. MODEL-P was trained on autoencoder integrated multi-omics of 146 patients with PDAC together with their survival outcome. Using MODEL-P, we identified two PDAC subtypes with distinct survival outcomes (median survival 10.1 and 22.7 months, respectively, log rank p = 1 × 10  •We developed DL-based MODEL-P to identify prognosis-correlated PDAC subtypes•The identified subtypes related to DNA damage repair and immune response processes•MODEL-P stratified patients from independent datasets into distinct survival groups•MODEL-P could be used in clinics to aid treatment decision-making Biocomputational method; Cancer systems biology; Cancer Pancreatic cancer is the third leading cancer-related cause of death worldwide, with a 5-year survival rate of only 9% . PancreaTo improve patient care and provide more effective therapeutic plans, bioinformatics approaches have been developed to define PDAC subtypes from molecular perspectives enabling personalized diagnosis and treatment . HoweverIn this study we proposed a deep learning-based framework—Multi-Omics DEep Learning for Prognosis correlated subtyping (MODEL-P)—to create prognostic relevant PDAC subtypes that can then be used to stratify patients with PDAC into different survival risk groups. A similar multi-omics integration framework was proposed to identify liver cancer patient subtypes with distinct prognosis . In addiBefore the development of MODEL-P, we collected mRNA sequencing (mRNA-seq), microRNA sequencing, DNA methylation array, and clinical information  of 146 PK-means clustering was performed to identify prognosis-correlated PDAC subtypes. For the second aim, each patient from the single omics test sets was classified into one of the identified prognosis groups. To do so, we first deduced the subtype signatures in the original space for each data type separately and used those overlapping omics between the subtype signatures and the test set at hand as the test set-specific predictors. Afterward, Support Vector Machine (SVM) (Then MODEL-P  was devene (SVM)  classifip value of 1 × 10−6 .In the subtype identification phase carried out on the TCGA training set, the AE model generated 200 integrated features from the multi-omics data. After features selection, the 14 prognosis-correlated features were used to identify prognosis-correlated subtypes by performing k-means clustering. The number of optimized clusters was determined by the results of the silhouette width  and Cali1 × 10−6 A. The “mp values of 0.030, 0.031, 0.036, 0.007, and 0.029 for ICGC mRNA-seq, ICGC mRNA microarray, ICGC DNA methylation, GEO: GSE62452 mRNA microarray, and GEO: GSE62498 microRNA dataset, respectively.In the prognosis prediction phase, predictors of the MODEL-P subtypes were identified for each single omics data type and tested on the single omics external datasets to stratify patients into subgroups with distinct survival outcomes. As a result, 120 mRNA features, 120 microRNA features, and 90 DNA methylation features were selected as the subtype signatures . The prep value = 1 × 10−4), followed by microRNA data , and then DNA methylation . This indicates that the mRNA profiling is more informative than the other two omics features to define the PDAC prognosis subtypes . As this result is biased for the supervised approach, we next evaluated the prediction performance on the external datasets when the single omics features are chosen on the basis of the unsupervised subtypes. None of the predictors were able to stratify patients on prognosis . This suggests that one can benefit from (a learned) non-linear feature selection as was done by the AE.To compare the feature selection by our proposed AE approach with other popular feature extraction methods, we replaced the AE by either principal component analysis (PCA) or non-negative matrix factorization (NMF)  in our fTo further understand and improve MODEL-P for multi-omics-based prognosis-correlated subtype identification, we also considered the role of clinical factors, such as patient and tumor characteristics. Three analyses were performed on the TCGA training set for 146 non-metastatic patients who underwent surgical resection for PDAC. We included clinical information available in the TCGA training set that could potentially influence the prognosis: current tobacco usage, age, gender, alcohol usage, diabetes diagnosis, chronic pancreatitis, TNM classification of malignant tumors , neoplasm histologic grade, and residual tumor.p = 0.011). Men were more often associated with the “aggressive” subtype than women, which is also supported in the literature: men have significantly higher hazard ratios in both localized (T-stages 1/2) and extended (T-stages 3/4) PDAC  PDAC .p value = 0.004). For macroscopic residual tumor (R2) factor, no statistically significant hazard ratio was observed owing to the very small sample size of five  after pancreatic surgery had a significantly worse prognosis than patients with no residual tumor  (−5) independent of the postoperative residual tumor status.Third, we combined one of the factors each time together with the MODEL-P-identified subtypes in a multivariate Cox-PH model to assess their combined prognostic ability. The multivariate Cox-PH model showed that, within each subtype, patients with microscopic residual tumor (R1) had worse survival outcomes than those with no residual tumor  . Postope= 0.002) . None ofTo understand the biological processes and pathways related to our identified PDAC subtypes, a gene set enrichment analysis (GSEA)  was perfp value < 0.05. The top 12 reported biological processes GO terms identified from mRNA dataset are shown in The resulting pathways/GO terms were significantly enriched in the two subtypes but regulated to different extents. Twenty-seven KEGG pathways were identified from the mRNA dataset A with |nThe “moderate” subtype was mainly characterized by relatively up-regulated pathways related to immune response, including chemokine signaling , cell adTo further understand the genetic basis of the prognosis-correlated subtypes, we conducted two analyses on the single nucleotide variants (SNVs) data from the TCGA PAAD cohort containing 140 samples.First, we analyzed the association between SNVs and the identified MODEL-P subtypes on the TCGA dataset by Fisher's exact tests. The SNV data were available for 140 PDAC samples and 20,163 SNVs in total, which consist of 9,966 genes with five types of mutation  if available. Since the occurrence of the SNVs is extremely sparse , we excluded all these SNVs (with occurrence ≤1 sample) before testing to increase the statistical power. This left us with 41 SNVs in 30 genes that were subjected to a Fisher's exact test with an FDR cutoff of 0.05.−5, FDR = 0.002), which also has been reported in the TCGA study (p = 0.0011). With the multivariate Cox-PH test, we further observed that rs12193529 still had a significant impact on patient OS outcomes in addition to the MODEL-P subtypes .Only one significant association between the identified subtypes and the SNVs was found, being a KRAS missense mutation  as well as between tobacco use and the subtypes (p = 0.17).In both subtypes, the SBS15 signature  is the mUniquely to the “moderate” subtype, the polymerase epsilon-associated signatures 10b  and SBS1p = 1 × 10−6 based on three data modalities: mRNA, methylation, and microRNA expressions. Note that the number of subtypes was determined in a data-driven way by using the silhouette width and Calinski-Harabasz criterion. These two metrics suggested that, if the patients were split into two clusters, they had the most distinct survival outcomes between the clusters. To our knowledge, our study is the first attempt to integrate PDAC data with deep learning methods for patients' survival risk stratifications. The resulting subtypes (log rank p = 1 × 10−6) had much more distinct survival difference compared with the TNM stage (log rank p = 0.60) and tumor grade (log rank p = 0.44) on the TCGA training set. The AE model, which enabled the interaction of features with non-linear combination, outperformed the commonly used feature integration methods PCA and NMF . As for the contributions of the single omics data types to the multi-omics found clusters, mRNA was shown to be contributing more than microRNA and DNA methylation. Also, the clusters found by integrating all three omic data types have the most distinct survival outcomes (as compared with single or paired omics-based clusterings), indicating that all types of omics contributed to the prognosis differentiation from different aspects. In the future, we plan to include more types of data, and we are particularly interested in the whole slide images (WSIs). Studies  that is more feasible given the available resources and costs. Thus, we tested the two prognosis-correlated subtypes rigorously on external single-omics datasets. This was done by training the SVM prediction models based on the two identified subtypes and making subtype prediction on the independent five test sets. Patients in all test sets were successfully classified into two significantly distinct survival groups: the log rank The pathway analysis was conducted by comparing the mRNA expression profiles of the two identified subtypes. Our pathway studies showed that the “aggressive” and “moderate” subtypes had different regulated extents of the DNA damage repair pathways and immune response pathways, respectively, which is in line with previous studies. Since PDAC is known for the deficiency of the immune response, and the immune cell interactions with the tumor cells remarkably affect tumor progression , it makeOne KRAS missense mutation was found to be associated with OS as well as the MODEL-P subtypes. Moreover, the absence/presence of the mutation remained significant to predict OS in addition to the MODEL-P subtypes (in a multivariate Cox-PH regression). Moreover, we found that preoperative clinical risk factors did not add extra predictive value on top of identified prognosis-correlated subtypes. Not surprisingly, we did find that negative resection margin status (R0), in which no residual tumor is found, is associated with an improved survival outcome compared with R1 status. These results suggest that the subtypes identified by the three different types of omics data already cover the information contained in the clinical risk factors before the surgery. This is encouraging because it increases the clinical utility of our approach to support decision-making before surgery or other severe treatments, by profiling needle biopsy samples, for example. Furthermore, the mutational signature analysis showed that all patients with PDAC are closely associated with various types of DNA damage repair failures. Specifically, for patients in the “aggressive” subtype, the aging process-related repair defects and tobacco smoking play a major role. For the patients in the “moderate” subtype, the repair defects are related to clock-like and tobacco usage-related signatures as well as DNA polymerase epsilon mutations. Previous study  has alsoTaken together, our MODEL-P framework will facilitate PDAC subtype exploration and provide more effective support in personalized PDAC treatment to determine whether beneficial effects of the treatment or surgery outweighs the adverse events.To improve the robustness of the deep learning model, we need the addition of more samples that have better curation than the TCGA dataset, which contained patients who died of other reasons instead of PDAC , and the ICGC test sets contained patients who had already received chemotherapy and radiotherapy before surgery. In addition, since the training and test sets were from public databases, there may be a discrepancy of preprocessing carried out by the data generators, which may decrease the accuracy of prognosis prediction. For example, in the GEO datasets, the mRNA array dataset was provided with each probeset summarized by their mean expression values, whereas in the TCGA training set, each gene of mRNA-seq was summarized by the maximum values. Also, the microRNA data from the GEO database was derived from the NanoString technique, which was different from the TCGA dataset. Another limitation of this study is that the TCGA training set mainly contained patients in TNM stage II, whereas most of the pancreatic cancer tumors in clinics have already progressed into more advanced stages. We will enhance MODEL-P with ongoing multi-omics data collected from prospective studies in our institute to include more advanced tumor stages.y.li.1@erasmusmc.nl)Further information and requests for resources should be directed to and will be fulfilled by the lead contact, Yunlei Li , we downloaded three types of data, including mRNA sequencing of 59 samples, mRNA microarray of 64 samples and DNA methylation array of 57 samples. For each type of data, the samples were derived from primary tumor tissues of patients with PDAC.For the ICGC-AU mRNA-seq dataset, the data has been normalized with Transcripts per Million (TPM) by the data generators. We then converted the Ensemble gene IDs to official HGNC gene symbols using R package biomaRt . EnsemblFor the ICGC PACA-AU mRNA microarray dataset, the data has been normalized by Robust Multiarray Average (RMA) by the data generators. The Illumina probesets were mapped to official HGNC gene symbols using R package illuminaHumanv4.db . ProbeseFor the ICGC PACA-AU DNA methylation array dataset, the CpG islands were annotated with the transcription start sites (TSS) of genes within distance of 1500 base pairs with the official HGNC gene symbols using R package FDb.InfiniumMethylation.hg19, based on which the methylation beta values were averaged . Genes wAfter data preprocessing, the features overlapping with those in the TCGA training set were kept.GSE62452 and GEO: GSE62498  transformation was applied.For the GEO: GSE62498 microRNA dataset, the data was derived from Nanostring nCounter Platform. The microRNAs were already normalized per feature by the geometric mean. Log2(x+1) transformation was performed before further processing.For the GEO: Following preprocessing, the features overlapping with those in the TCGA training set were kept for further process.An AE model  was consThe three types of normalized multi-omics data of 146 PDAC samples were concatenated and served as the input of the AE model. As an unsupervised neural network, AE consists of an encoding part to compress the features and a decoding part to reconstruct the encoded information. In this way, the model enables to capture important information and removes the noise in the original data source.TensorFlow1.15  model was built to assess the correlations between the transformed features and patients’ overall survival (OS) outcomes. Fourteen transformed features were found to be significantly correlated , based on which K-means clustering was performed to identify PDAC prognosis-correlated subtypes. We set the potential number of clusters from 2 to 5, and searched for the optimal number of clusters using two evaluation metrics: silhouette width, which estimated the distance between clusters  with identified subtypes were ranked based on their ANOVA F-scores. The potential numbers of subtype signatures were set to be 5 to 200 features with a stepwise of 5, within which the number of signatures that gave the best average accuracy based on the 3-fold CV on the training set were used as the subtype signatures of each data type.scikit-learn (To predict patient prognosis in the external test sets, the subtype signatures present in each test set were first normalized in the same two-step manner in the training and the test set. ANOVA-F test was applied on this normalized training set to select test set-specific predictors. For each test set, a SVM model was built based on its specific predictors in the TCGA training set. The best combination of hyperparameters for SVM models were determined by grid search with 3-fold CV on the TCGA training set and then used for model construction on the entire training set. This section was implemented using python it-learn .survival and survminer were used, also for plotting the Kaplan-Meier survival curves (We evaluated the performance of MODEL-P for prognosis-correlated subtype identification in the TCGA training set and prediction in five external test sets by the log rank tests. The log-rank test assesses the significance of the survival difference between different groups, in our case the prognosis-correlated subtypes. Log rank p values were calculated from the Cox-PH model based on the actual OS and identified subtype labels. R package l curves .


Combining machine learning-based modeling and whole transcriptome data with prior variable selection through protein-protein interaction network analysis by a diffusion kernel successfully predicted KRAS dependency in the KRASwt subgroup and in all investigated cancer cell lines. In contrast, modeling by RAS activating events (RAE) or previously published RAS RNA-signatures did not provide reliable results, highlighting the heterogeneous distribution of RAE in KRASwt cell lines and the importance of methodological references for expression signature modeling. Furthermore, we show that predictors of KRASwt models contain non-substitutable information signals, indicating a KRAS dependency phenotype in the KRASwt subgroup. Our data suggest that KRAS dependent cancers harboring KRAS wild type status could be targeted by directed therapeutic approaches. RNA-based machine learning models could help in identifying responsive and non-responsive tumors.Recent progress in clinical development of KRAS inhibitors has raised interest in predicting the tumor dependency on frequently mutated RAS-pathway oncogenes. However, even without such activating mutations, RAS proteins represent core components in signal integration of several membrane-bound kinases. This raises the question of applications of specific inhibitors independent from the mutational status. Here, we examined CRISPR/RNAi data from over 700 cancer cell lines and identified a subset of cell lines without KRAS gain-of-function mutations (KRAS Frequently, gain-of-function mutations or copy number alterations (CNA) at different levels of the signaling cascade lead to overactivity and thereby increased cell growth, migration, and invasion5. Due to its high relevance in cancer per se, intensive research has been conducted aiming to develop targeted therapies. In recent years, several drugs entered clinical application focusing on inhibitors that are directed against membrane receptors preventing constitutively activated signal transduction8. EGFR represents one of the most well-known examples9. However, therapy is limited by various resistance mechanisms in the receptor itself or other RAS/RAF pathway elements12. Common mechanisms with high clinical relevance include mutations in downstream RAS GTPases10, which represent important nodes in signal integration from cell membrane to nucleus13. Here, somatic point mutations lead to constitutive activation downstream of membrane receptors, thereby hindering therapeutic success10. Despite intensive research, drug binding pockets could not be identified in these proteins for a long time, making the development of direct inhibitory pharmacotherapy difficult. MEK inhibitors have become a first option to overcome this mechanism of resistance by inhibiting downstream mitogen-activated protein kinase kinases. However, until now, clinical efficacy has only been demonstrated for specific applications such as NRAS/BRAF mutated melanoma16. More recently, mutation-specific and panKRAS inhibitors have been developed that inhibit KRAS activity directly or indirectly20. The mutation-specific inhibitors exploit structural changes in the KRAS protein that result from oncogenic point mutations so that cells expressing wild-type protein are less affected, which is expected to reduce toxicity of the therapy. However, so far specific inhibitors could only be designed for a few KRAS mutations such as G12C. This led to the development of the panKRAS inhibitors, which downregulate KRAS activity by binding to SOS1 and thus enable an application independent of the mutation status. PanKRAS inhibitors such as BI1701963 are currently being tested in clinical trials including combinations with MEK inhibitors21. Furthermore, following the results of a phase II trial, Sotorasib became the first drug of mutation-specific inhibitors to receive preliminary approval for patients with therapy-refractory NSCLC22.The RAS signaling pathway is a key driver of carcinogenesis in many different tumor entities23. The selection of predictors depends on the respective tumor entity and its characteristics. For example, activating EGFR and ERBB2 mutations are considered positive predictive markers for therapy with tyrosine kinase inhibitors against receptors of the EGFR-family in non-small cell lung cancer (EGFR) and colorectal cancer (ERBB2)24. Response to ERBB2-antibody Trastuzumab correlates with ERBB2 expression and copy number status in breast cancer25. Furthermore, EGFR resistance mutations such as T790M are important in anti-EGFR therapy in non-small-cell lung cancer as well as activating KRAS mutations in colorectal cancer26. Besides activating mutations, expression-based RAS signatures may also improve therapy response prediction including treatment with KRAS inhibitors28. This could be particularly important in tumors with more complex activation mechanisms by yet unknown RAS-activating events (RAE).The emergence of new therapies with small molecule RAS inhibitors also increases the relevance of identifying responsive and resistant tumors as accurately as possible. Depending on the drug and tumor entity, different markers are currently used as predictors. Those include mutations, CNA and gene expression32. We identified a subgroup of KRAS dependent cell lines harboring wild type status in KRAS  and the Score-Project (CRISPR). Again, cell lines classified as KRAS dependent in the initial Achilles CRISPR screen exhibited a significantly higher dependency in both data , the overall composition of the dependent cell lines demonstrated a heterogeneous distribution . We chose AUC (GDSC) or Active Area (CCLE) for drug sensitivity quantification because validity of extrapolated IC50 values is limited for cell lines that were only partially responsive or unresponsive within the experimentally tested inhibitor concentrations, as stated by GDSC35. To this end, the AUC has been successfully used in a variety of other publications38. As expected, we found consistent associations to MEK inhibitors across multiple compounds and datasets . These include mutations, overexpression and CNA in upstream genes as well as amplification of KRAS itself41. Using differential expression analysis in KRASwt cancer cell lines between KRAS dependent and independent subsets we were able to detect several of already known pathway activating regulators. These included ERBB2 and KRAS  to various receptor tyrosine kinase (RTK) signaling pathways such as EGFR, FGFR, MET, NTRK, and IGFR . This simple approach allowed us to assign at least one RAE to 67% of the KRAS dependent cell lines either by co-dependency or amplification , which underlines the need for improved variable selection and integrative modeling.Therefore, we used the genome wide CRISPR data from the Achilles Project for the detection of RAE, as they reflect direct information about the survival potential of the gene. Consistent with the expression data, genes of the pathways mentioned above were associated with higher dependency in the KRAS dependent subgroup. In addition, we found other RTK pathway associations such as those to KIT and PI3K Fig. . In the ion Fig. . For theion Fig. . Howeverion Fig. . To furtwt cancers by known RAE. We therefore searched for suitable alternative modeling strategies. Regarding the identification of RAS- and KRAS-dependent cancers, several approaches have been taken including RNA-based expression signatures28. To characterize whether these known signatures represent an improvement in prediction, we created machine learning models and examined their predictive performance (see methods). Overall, the resulting predictive performance was not satisfactory. Models based on the signature published by Loboda et al. achieved a correlation of 0.15 , those based on the signature by Singh et al. 0.18 . However, it should be noted that we were not able to assign all gene identifiers in the given signatures, leading to a loss of four genes (2.7%) in the signature by Loboda et al. and 14 (2.7%) in the signatures from Singh et al. , Diseases of signal transduction by growth factor receptors and second messengers (p = 2.4 * 10−15) as well as several pathways which involve tyrosine kinase signaling. Again, this highlights the heterogeneity of RAE and further potential approaches for combination therapies in the KRASwt subgroup.Next, we characterized the predictors of KRAS dependency in KRAS wild type models. Among the 12,000 models of CRISPR/RNAi data using gene sets selected by the diffusion kernel and hyperparameter optimization, the frequency of non-zero model coefficients was quantified for each predictor. In total, 1964 genes were used at least once in a model. Most frequently used predictors were NRAS and KRAS expression, followed by GNG11, HRAS, and RBX1 Fig. . KRAS exwt group exhibit a distinctive phenotype or activation state, which might be represented by expression data. However, RNA expression data are highly structured and intercorrelated48, so that genes selected by our literature-based approach might be replaced without losing predictive power of the models. Consequently, a specific phenotype or gene regulation in the context of KRAS dependency would be less likely. On the other hand, a decrease in prediction power would mean a loss of information that could not be compensated by other predictors and context-specific regulation would be more likely. To investigate this, we selected the models with best performing hyperparameters and extracted all predictors with non-zero coefficients, resulting in lists of 293 (CRISPR) and 184 (RNAi) genes. Then, separately for each gene list and dependency dataset we excluded the lists from the superset of all proteins in the interaction network and repeated modeling with these selections. In fact, we found a lower correlation for both datasets compared to the models using the total number of network genes. Performance in models of Achilles CRISPR dependency data dropped about 20%  and for DRIVE RNAi data about 29% . The loss of predictive power suggests that a non-compensable loss of information has taken place. This speaks for a regulation of genes selected by our interaction network-based in context of KRAS dependency, indicating a KRAS dependency phenotype in the KRASwt subgroup.The results indicate that KRAS dependent cancer cell lines in the KRASwt subgroup we also applied our selective modeling strategy to the entire set of cancer cell lines. Initial reference models using Lasso regression and all predictors available in the RNA expression dataset achieved correlations (Pearson’s r) of 0.55 (CRISPR) and 0.52 (RNAi) between observed and predicted KRAS dependency (CRISPR) , but not for the RNAi data, where models performed weaker (Pearson’s r: 0.51). Despite the good results, consistent with recently published literature models based on KRAS mutation status outperformed RNA-based models 47. Again a closer analysis of the models revealed specific challenges. For both types of predictors, we observed that absolute errors increase significantly toward the outsides of the distribution  Fig. . Prior vion Fig. . As expeThe RAS signaling pathway possesses a central position in the oncogenesis of several tumor entities via numerous mechanisms. Recent progress in clinical development of KRAS inhibitors has raised interest in predicting the tumor dependency on frequently mutated RAS-pathway oncogenes. However, even without such activating mutations, RAS proteins represent core components in signal integration of several membrane-bound kinases. This raises the question of applications of the inhibitors independent from the mutational status.17, these pan-KRAS inhibitors do not require a specific mutation and could be used in the therapy of KRAS wild type malignancies20. In addition, since the subgroup exhibits a significantly increased sensitivity to MEK inhibitors, a combined therapy, as currently being tested in clinical trials for KRAS mutated tumors, may also offer further advantages for some patients21.In this work we identified a KRAS dependent subset of KRAS wild type cell lines. Consistently, across six different datasets from CRISPR, RNAi, and chemosensitivity experiments, we found evidence for increased RAS activity in the elaborated subgroup. Our results suggest that patients without activating KRAS mutations may also benefit from targeted therapies against KRAS. Here, compounds such as the SOS1-KRAS interaction inhibitors BI-3406 or BI-1701963 could be of particular interest. In contrast to mutation-specific inhibitorswt tumors. The high number of KRAS-dependent cell lines within the KRASwt subgroup raises expectations for an equally high proportion of patients with increased response to therapy. In the future, further clinical research efforts are needed. In this regard our study should serve for deeper understanding of mechanisms in panKRAS inhibition and associated current clinical trials.Our results provide evidence for the presence of a KRAS-dependent subgroup in KRASwt subgroup. Moreover, RAEs were heterogeneously distributed, and presence of RAE did not necessarily follow a KRAS dependency and vice versa. This observation is corroborated by results from clinical trial data, as the presence of an activating KRAS G12C mutation predicted response to Sotorasib in only 32% of patients17. We interpreted this as further evidence for the complexity of signal transduction as previously described49. To address this complexity, we investigated modeling solutions using whole transcriptome RNA sequencing data as predictors. Recently, difficulties in RNA-based modeling of gene dependencies of the tumor drivers KRAS, NRAS, and HRAS have been described47.Identification of responsive and resistant tumors will be an essential task for an optimal therapy of the identified subgroup. We demonstrated that RAEs such as KRAS amplifications or EGFR co-dependencies are not reliable predictors of KRAS dependency in the KRASwt cell lines when compared to models using all available RNA predictors, RAE-based models or those of previously published expression signatures. Although not the main focus of the depicted study, we also achieved improvements in performance by our approach in the complete cell line set but could not outperform KRAS mutation status.In our analyses we tested modeling using Lasso regression with varying sets of predictors with a focus on variables selected by a literature-based protein-protein interaction network and subsequent gene centrality quantification through a diffusion kernel. This systematic approach with an initial variable reduction to 100–1000 genes significantly improved predictions of KRAS dependency in KRAS28. However, in our study the reported performance dropped significantly. This suggests that predictions of expression-based models are only valid for applications with high similarity to the training data and tend to overfitting. Generalizations of specific gene signatures without methodological and algorithmic reference seem to be limited, which underlines the need for standardized procedures for the prediction of therapy response to KRAS inhibitors in tumor patients.The importance of variable selection for modeling is emphasized by inferior predictions of models based on genes of previous RAS expression signatures. In the respective initial publication these signatures could provide robust predictions for training and test datawt subgroup, using predictors selected by the diffusion kernel, indicate the advantages of this strategy. Future efforts with the objective to establish diagnostically applicable models may possibly benefit from similar approaches as well.Even with high agreement between the training set data and those of the planned application, steps to reduce overfitting caused by artifacts in the training data are highly relevant to ensure external validity. In this regard, besides the use of statistical methods suitable for high-dimensional datasets collecting additional information about the predictors to select variables with context specific biological relevance can be important before modeling. Literature-based protein-protein interaction networks as used in this work represent one option for this purpose since they enable the integration of preexisting scientific knowledge to identify key regulatory genes in the context of KRAS signaling. These genes possibly represent more robust predictors, which reduce overfitting caused by dataset-specific artifacts. Due to the lack of additional data, we were not able to validate this hypothesis. Nevertheless, the significantly improved internal performance of KRAS dependency models in the KRASIn summary, our results suggest that a subset of patients without oncogenic KRAS mutations may benefit from targeted therapy with KRAS inhibitors. In the long run, we assume that machine learning models based on high-dimensional RNA expression data could help with therapeutic decisions. As already mentioned before, a crucial factor for clinical applicability of the proposed models will be a highly standardized test methodology. This includes all steps of the analysis including sample preparation, sequencing and bioinformatic analysis as well as the choice of a suitable parameter for therapy response quantification. The realization of such solutions appears to be possible and reasonable as costs for quantifying gene expression continue to fall.50. GDSC drug data (Release 8.2) were downloaded from the project’s website51. For all analyses, only cell lines originating from solid tumors were used.Cancer cell line data were obtained from the website of Dependency Map Consortium including Genomic, RNA-expression, CRISPR, RNAi and CCLE drug sensitivity data (Release 21Q1)wt/HRASmut, KRASwt/KRASmut, NRASwt/NRASmut). Initial analyses were performed in cell lines with wild type status and for each gene individually  were used.Overrepresentation analysis was performed using R package ReactomePA54, resulting in 43 different markers for RAE-based predictions  were used as dependent variables in gene dependency modeling. Different predictor types  were processed as follows. RAE were represented by Achilles CRISPR gene effect format and KRAS gene copy number. We restricted gene dependency RAE to oncogenes annotated in oncoKB database and receptor kinases/non-RTKs from HGNC56. Gene identifiers were assigned to each protein and duplicated interaction scores between the same genes were averaged. Interactions with a score lower than the 90th percentile were discarded. To focus the network on genes which may be involved in the context of KRAS signaling we restricted genes to those with a direct KRAS interaction (1st shell) and their respective interaction partners (2nd shell). The final network consisted of 7070 genes. Centrality (closeness) of each gene was determined by a diffusion kernel which captures the information flow within the network as previously described58. [Eq. β to a parameter which controls the degree of information flow. Similar to Lee et al.58 we tested several values of the β parameter  for variable selection. This resulted in seven different closeness estimates of the genes in our network.For our workflow based on whole transcriptome RNA-expression data (TPM) we either used the total number of available RNA predictors or subsets selected by our variable selection approach. For this purpose, we constructed a literature-based protein-protein network to identify significant predictors by gene centrality Fig. . The hyperaction st shell β parameter was tuned for the values mentioned above. Subsequently, the number of selected genes by centrality was tested for 100, 500, and 1000 genes. This approach resulted in a total of 60 different predictor sets for modeling. Independent of the hyperparameter optimization, we also tested modeling either with all unfiltered genes of the network (6967 genes) or with the total number of RNA predictors available . For all predictor sets gene expressions with missing values were discarded before the final selection step.Hyperparameter optimization of the variable selection by the diffusion kernel was performed for the following variables. The minimum level of gene expression was tested for the percentiles P25, P50, P75, and P95. wt subgroup after variable selection through the diffusion kernel. Lasso and Elastic Net regression minimize the following quantities [Eqs. n is the number of observations, p the number of different predictors, β0 the y-intercept, βj the coefficient of the respective predictor, yi the values of the independent variable, and xji the values of the predictors.Despite our variable selection workflow, the number of predictors for RNA-based models was still high. To prevent overfitting, we consistently used Lasso regression for all models and predictor types. In addition, we also tested Elastic Net and Random Forest regression in KRAS dependency models of the KRASies Eqs. –3:2\docuFor every set of independent variables 100 models were trained, each using 80% of the cell lines randomly selected by R base sample function. The remaining 20% served as an independent test set. Test set predictions were averaged for each predictor set and correlated with the experimental data Fig. .59. The package glmnet was used for Lasso regression and Elastic Net60. Random Forest regression was performed with randomForest package61. Differential expression analyses were carried out with DESeq262 and network analysis was generated with igraph63. RAWGraphs 2.0 was used for additional graphics64. Standard statistical methods are mentioned in figures and legends.All statistical analyses were performed in R programming languageFurther information on research design is available in the Supplementary InformationReporting SummarySupplementary Data 1-6


Single-cell gene expression distributions measured by single-cell RNA-sequencing (scRNA-seq) often display complex differences between samples. These differences are biologically meaningful but cannot be identified using standard methods for differential expression.Here, we derive and implement a flexible and fast differential distribution testing procedure based on the 2-Wasserstein distance. Our method is able to detect any type of difference in distribution between conditions. To interpret distributional differences, we decompose the 2-Wasserstein distance into terms that capture the relative contribution of changes in mean, variance and shape to the overall difference. Finally, we derive mathematical generalizations that allow our method to be used in a broad range of disciplines other than scRNA-seq or bioinformatics.waddR, which is freely available at https://github.com/goncalves-lab/waddR, along with documentation and examples.Our methods are implemented in the R/Bioconductor package Bioinformatics online. A typical task in genomic analyses is to identify genes whose expression varies across biological conditions. When comparing the single-cell expression distribution of a gene between two samples measured with single-cell RNA-sequencing (scRNA-seq), complex differences can often be observed between two distributions , includiDESeq2  based on the 2-Wasserstein distance , that isAF (with mean Aμ and standard deviation Aσ) and BF (with mean Bμ and standard deviation Bσ) that model the distributions for conditions A and B, respectively, the squared 2-Wasserstein distance d, referred to as the ‘2-Wasserstein distance’ for convenience throughout the text, is given by  given by d:=d rrection . To addrrrection  S1.waddR approaches, we assume a pre-processed  scRNA-seq data matrix consisting of expression values for multiple genes over multiple cells.We distinguish two different scenarios in the context of DD testing for discrete scRNA-seq data: in the first setting A, gene expression data are only available from one replicate per condition (e.g. when comparing two cell types within a sample), while in the second scenario B, there are expression data from multiple replicates per condition . As the starting point for our waddR SP testing procedure to the non-zero expression values only. This approach yields two P-values, P-values P-value In the setting in which expression data is only available for one replicate per condition, the aim is to test whether two expression distributions of a gene are significantly different. To incorporate the special role of zero expression in scRNA-seq data, i.e. the point mass at zero, we divide the procedure into two parts and propose the following two-stage approach: (i) test for differential proportions of zero expression (DPZ) between the two conditions using a Bayesian logistic regression model, taking account of the cellular detection rate in order to correct for differences in total counts per cell  test, a multi-dimensional generalization of the classical Fisher’s exact proportion test, yielding a respective R replicates and two conditions A and B, we calculate the 2-Wasserstein distances P-value For non-zero expression testing we employ the following procedure based on 2-Wasserstein distances, which can in principle be applied to both paired and unpaired settings. Based on P-values P.zero and P.non-zero can be combined into an overall P-value P.comb for DD using the classical Fisher method.As for variant A, the waddR is potentially useful in a diverse range of practical applications beyond scRNA-seq, i.e. whenever the aim is to test whether there are significant differences between two distributions. Examples include methylation array experiments .3.2.1BASiCS (DEsingle (BASiCS) or differences in the proportion of zeros (DEsingle). Our method tests instead for any change in the full distribution, thus capturing the highly complex expression patterns frequently observed in scRNA-seq datasets  assumptions or modelling, thus requiring less computation time. While for non-zero differential expression, scDD provides a single category of DD as an output (categorization), waddR provides information on differences via the decomposition of the 2-Wasserstein distance test statistic. For example, it is possible to combine results from different replicates with our method, by averaging the corresponding fractions of location, size and shape components for each replicate as discussed before. This is not directly possible with the categorical output of scDD.SigEMD  We created simulated scRNA-seq data based on the simulation procedures in G = 1000 genes are generated as differentially distributed and equally divided into the following four different categories of DDs (i.e. 25 genes per category):In each scenario, 100 of the traditional differential expression (DE): two unimodal distributions with different means,differential proportions of cells within each component (DP): two bimodal distributions with the same modes, but with different proportions of cells of each mode,differential modality (DM): one unimodal distribution and one bimodal distribution with one mode being identical to that of the univariate distribution,both differential modality and different component means (DB): one unimodal distribution and one bimodal distribution with no common modes.scDD] and the other half (i.e. 450 genes) from the same negative binomial model with two components [corresponds to the equivalent proportion (EP) category in scDD]. Finally, we consider three different degrees of DDs  by varying the fold changes between the modes for the DP, DM and DB settings from 2 over 4 to 6, while fixing standard deviations of the fold changes to 2, see scDD package manual for details. For each method, 1000 permutations are used to obtain the respective testing results.We generated the remaining 900 genes as non-DD genes by creating one half (i.e. 450 genes) from the same unimodal distribution [corresponds to the equivalent expression (EE) category in 3.2.3waddR and the reference methods by considering sensitivity, specificity, precision, accuracy and the receiver operating characteristic (ROC) curves with corresponding area under the curve (AUC) values . In particular, waddR outperforms scDD or performs similarly well in the majority of the considered cases. In some cases, SigEMD performs a little better than waddR and scDD. Overall, waddR yields good detection powers (sensitivity) with respect to the four different scDD categories for simulated DD genes, especially when there is a medium or strong degree of DD and a reasonably large number of cells . The benefit of waddR becomes more obvious when looking at the running times when using 10000 permutations, which is typically a more reasonable number of permutations to obtain reliable results. C = 50 with a weak degree of DD. While an increase in the number of employed permutations increases the running time of scDD drastically  and that of SigEMD considerably (by approximately a day), the increase for waddR is very moderately . Further, running times are also expected to increase with the number of genes. As typical scRNA-seq datasets comprise much more than 1000 genes as considered in our simulation studies, computational running times may get even more important, with waddR providing the fastest of all considered implementations.The main advantage of ing time  and 2. Iparately . IntereswaddR is equivalent or outperforms the reference methods scDD and SigEMD with respect to common performance criteria. The most obvious benefit of waddR is a reduced computation time, even though, as scDD and SigEMD, it is based on a permutation testing procedure. waddR also benefits from its conceptual simplicity, as it comes without sophisticated modelling (scDD) or the need to solve optimization problems (SigEMD) that may be computationally intense.In summary, in our simulation studies of variant A, waddR variant B for multiple replicates, we apply our method to the real Fluidigm C1 platform-based scRNA-seq dataset of To validate the performance of waddR shows adequate ROC curves  and FDR curves , with a reasonable balance between sensitivity and specificity (Supplementary Table S7). Moreover, waddR exhibits a good control of type I errors . The results are consistent across different considered normalization methods . Moreover, the performance of waddR variant B meaningfully increases with the number of replicates. This is to be expected, as the powers of the involved testing procedures, in particular the Wilcoxon rank sum test, rise with increasing number of replicates . Along with the benefit of drastically reduced computation times, waddR variant B furthermore outperforms or is competitive to the scDD and SigEMD reference approaches, which are not explicitly designed to handle replicates, with respect to all the evaluation criteria mentioned before .In this benchmarking study, 3.4.1waddR variant B in a real-data case study, we apply it to a subset of the scRNA-seq dataset in A) versus NK cells in blood (condition B). After normalizing and filtering out those genes that show zero expression in all cells across both conditions and across all replicates, this dataset comprises log-normalized expression distributions for 21138 genes  is applied to this pre-processed data. Our procedure gives three different P-values: a P-value P-value P-value P-values are then adjusted for multiple testing using the method of edgeR method offers a single adjusted P-value For the single-cell-based analysis, the 3.4.2edgeR detected 3987 differentially expressed genes [Benjamini-Hochberg (BH) adjusted P-value waddR  normalised using most common methods, such as those implemented in the Seurat (scran (waddR.We implemented our method as a package for the statistical environment R and distribute it within the Bioconductor project. As input for scRNA-seq analysis, it expects a table of pre-filtered and normalised count data. As filtering and normalisation are important steps that can have a profound impact in a scRNA-seq workflow (btab226_Supplementary_DataClick here for additional data file.


Understanding the transcriptome has become an essential step towards the full interpretation of the biological function of a cell, a tissue or even an organ. Many tools are available for either processing, analysing transcriptome data, or visualizing analysis results. However, most existing tools are limited to data from a single sequencing platform and only several of them could handle more than one analysis module, which are far from enough to meet the requirements of users, especially those without advanced programming skills. Hence, we still lack an open-source toolkit that enables both bioinformatician and non-bioinformatician users to process and analyze the large transcriptome data from different sequencing platforms and visualize the results.https://github.com/dongxuemin666/RNA-combine.We present a Linux-based toolkit, RNA-combine, to automatically perform the quality assessment, downstream analysis of the transcriptome data generated from different sequencing platforms, including bulk RNA-seq (Illumina platform), single cell RNA-seq (10x Genomics) and Iso-Seq (PacBio) and visualization of the results. Besides, this toolkit is implemented with at least 10 analysis modules more than other toolkits examined in this study. Source codes of RNA-combine are available on GitHub: Our results suggest that RNA-combine is a reliable tool for transcriptome data processing and result interpretation for both bioinformaticians and non-bioinformaticians. In thisHere, we present a Linux-based command-line toolkit, RNA-combine, for the comprehensive analysis of bulk RNA-seq, scRNA-seq as well as Iso-Seq data, performing analyses for multiple modules . This toolkit represents the assembly of a wide range of routine and customized transcriptome analysis workflows and is free from the abovementioned issues and thus is friendly and easily implemented for users.Our RNA-combine consists of 16 modules and could process transcriptome data  from three sequencing platforms . All analysis modules are scripted in the form of bash command lines, which can be easily customized and launched by users.The usage of each module is provided in the user manual and the scheme of our toolkit is shown in Fig. The workflow of bulk RNA-seq data analysis starts with raw sequencing file preprocessing, including removing rRNA sequences using Sortmerna , removinRPKM (Reads Per Kilobase of transcript per Million mapped reads)/FPKM (Fragments Per Kilobase of exon model per Million mapped fragments) normalized data. In the module of splicing site detection, three methods with different strategies are adopted: DEXseq [In the variant calling module, two methods are introduced: the one is conducted by GATK , which h: DEXseq  based on: DEXseq  based on: DEXseq  based on: DEXseq , which m: DEXseq , which c: DEXseq  to the fCellranger  is used Currently, the library construction used for scRNA-seq often causes doublet artifacts , which may generate biased results. To overcome this limitation, we apply Scrublet  in the tWe use one of the most well-established pipeline-PacBio’s IsoSeq V3  to proceFor omics (e.g. transcriptome) analysis, one of the most labor-intensive and time-consuming processes is the formatting of input files for different tools. In our toolkit, we develop a layer of interface that could automatically format inputs for user-specified tools, guaranteeing that these tools could be linked smoothly. Under this environment, bioinformaticians and non-bioinformaticians could quickly link different tools to a reliable processing workflow without extra commands. More importantly, the users just need to input the workflow with raw transcriptome sequencing data and reference genomes. Our toolkit will start from initial preprocessing and load the modules of interest. For instance, in the analysis of bulk RNA-seq data, one can set up a workflow starting from raw sequencing data preprocessing, to the calculation of expression level of each gene or transcript, identification of differentially expressed gene or transcript, detection of alternative splicing event and differential splicing site, construction of co-expression network and sample-specific network, variant calling , and biological pathway annotation.Because each module of transcriptomic analysis involves thousands of genes and multiple dimensions , the data presentation with graphical visualization is in need to facilitate the result interpretation. However, this has been neglected in most of the previous tools. To address this issue, we develop a visualization plugin under the environment of R and Python, which realizes the visualization of the results for each module and can generate qualified graphs for publication.During the processing of large datasets (e.g. transcriptomes of multiple samples), spurious interruption might occur due to unexpected factors . This abolishes the entire workflow and users have to spend a lot efforts to manually check all the processes. To minimize this effect as much as possible, we develop a Backtracking Executor plugin in our toolkit. It automatically records the interrupted processes (i.e. error logs) in a user-specified directory. Based on these log files, it is easy for users to resume data processing from the interrupted points. As a consequence, the plugin enables users to keep tracking the processes and thus greatly reducing the efforts spent on debugging.MUC1, which has been approved by FDA as a diagnostic marker to monitor clinical course of patients with breast tumor during treatment [MUC1 in either tumor or normal tissues were shown in Fig. In this section, we focused on the assessments of reliability and automation of RNA-combine using several published transcriptome data. We first tested the workflow of the bulk RNA-seq analysis with a transcriptome dataset from three normal breast tissues and three breast tumor tissues . Startinreatment , was sigNKG7 for natural killer cells and T cells [PPBP for Megakaryocytes [CD79A for B cells [Next, using the pipeline of scRNA-seq data analysis in RNA-combine we analyzed a scRNA-seq dataset of 3k PBMCs  from a healthy donor generated from 10x Genomics (https://support.10xgenomics.com). The gene-barcode matrix was used for doublet detection, cell clustering, cell type annotation and cell connectivity analysis. The number of 35 candidates were detected as potential doublets among 2700 cells Fig. a and thu T cells , PPBP foryocytes , CD79A f B cells    to test Overall, these results suggested that our toolkit worked smoothly and automatically for real transcriptome data from different sequencing platforms and it could process the data with raw sequencing fastq files and reference genomes as inputs.We compared the features of RNA-combine with four toolkits  publishRegarding the analysis module, our toolkit covers all the analysis modules implemented in RNASeqR, RASflow, scGEAToolbox, and most modules in NASQAR , and it also includes nine extra modules  that are absent in the abovementioned software. One major difference between RNA-combine and other toolkits is that it implements the modules of raw data preprocessing, which enables users to use raw fastq files as input directly. Another difference is the development of the input formatting interface, which automatically formats inputs for different analysis modules, therefore enabling end-to-end analysis from raw sequencing files to direct graphic visualization of results. This is important because input formatting is both time- and labor-consuming, especially for users without advanced programming skills. RNA-combine is a Linux-based toolkit, enabling users to parallel jobs, thus speeding up the analysis processes. Collectively, RNA-combine is more comprehensive on data analysis and more user-friendly.We conclude that RNA-combine presented in this study is a user-friendly, reliable toolkit for the comprehensive analysis of transcriptome data generated from different sequencing platforms. It enables users to set up a customized workflow to analyze data from multiple platforms and generate analysis reports and result visualization. Importantly, this toolkit empowers researchers without advanced bioinformatics skills to analyze their data by working with human-readable configuration files. We will continue to provide user supports and feature enhancements in future releases.Project name: RNA-combinehttps://github.com/dongxuemin666/RNA-combineProject home page: The software and usage guideline are available online at Operating system(s): LinuxProgramming language: Python, R, ShellOther requirements: R 3.6, Python 3.7License: MIT, licenses for dependent packages, tools and methods are shown in Additional file Any restrictions to use by non-academics: license neededAdditional file 1. Estimated running time for each module, organization of all modules and functions, licenses for dependent packages of RNA-combine.


We investigate the diagnostic classification aspect by applying statistical tests and dimension reduction techniques for feature selection and probabilistic modeling for the cancer type classification, and we also study the effect of sequencing depth.We experiment with a variety of statistical methods that use different feature selection and feature extraction methods as well as probabilistic classifiers for diagnostic decision making. We test the (moderated) t-tests and the Fisher’s exact test for feature selection, principal component analysis (PCA) as well as iterative supervised PCA (ISPCA) for feature generation, and GLMnet and logistic regression methods with sparsity promoting priors for classification. Probabilistic programming language Stan is used to implement Bayesian inference for the probabilistic models.We compare overlaps of differentially methylated genomic regions as chosen by different feature selection methods, and evaluate probabilistic classifiers by evaluating the area under the receiver operating characteristic scores on discovery and validation cohorts. While we observe that many methods perform equally well as, and occasionally considerably better than, GLMnet that was originally proposed for cfMeDIP-seq based cancer classification, we also observed that performance of different methods vary across sequencing depths, cancer types and study cohorts. Overall, methods that seem robust and promising include Fisher’s exact test and ISPCA for feature selection as well as a simple logistic regression model with the number of hyper and hypo-methylated regions as features.The online version contains supplementary material available at 10.1186/s12859-022-04651-9. In recent years the interest in utilizing circulating cell-free DNA (cfDNA) for cancer diagnostics has grown, enhanced by the development of next-generation sequencing (NGS) technologies. Cell-free DNA refers to DNA fragments that are not associated with cells and is considered to originate from cell apoptosis and necrosis , 2. In tctDNA is believed to represent the tumor burden and to carry the same genomic and epigenetic properties as the tumor of origin . TherefoEnterobacteria phageCell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) is a protocol for measuring the methylation status of cell-free DNA . cfMeDIPmaterial . Comparematerial . While bmaterial .Along with the cfMeDIP-seq protocol, statistical methods for finding differentially methylated regions (DMRs) and machine learning methods for classification of the cancer types were proposed in . In brieThere are also other works reporting usage of cfMeDIP-seq or MeDIP-seq measurements of cfDNA for cancer classification –15. SimiThe results in  showed tThe aim of this study was to design and test various feature selection and probabilistic classification methods and compare them to the methods presented in  on cfMeDThe cfMeDIP-seq data set used in this work was received from the authors of  by requeWe followed the workflow of the feature selection, model training and evaluation of the models presented in  but someWe trained the classifiers with the training data, using the found features. This resulted in Our modifications and additions to the workflow of  include The read count data was subsampled to simulate a lower sequencing depth than in the original data. The total read count in the discovery cohort data set, calculated from the preprocessed read count data, varies between 10,659,729 and 67,228,099 before extracting the 505,027 genomic windows of interest. The thinning was carried out by randomly sampling the desired number of reads without replacement from all genomic windows. All reads had equal probability of being picked, and the probability of getting a read from a genomic window was proportional to the read count of the window in question. The subsampling was performed for all samples separately. This way we generated three subsampled versions of the data set with Depending on the classification model, the count data can be used as it is or transformed. We used logarithmic transformations as proposed in 1\documen used in  is obtaiBefore fitting probabilistic classification methods, the count data was normalized based on the total read count in the 505,027 genomic windows. This was done by dividing the read counts of these genomic windows with the sum of the read counts and multiplying with the mean of the read count sums over the discovery cohort. The read count normalization accounts for possible differences in sequencing depth between samples. In the case of the subsampled data, where the total read counts per sample were equal, this step should have affected only little. However, this is not the case with the non-thinned data where the total read counts varied greatly. Furthermore, the features were standardized to have zero mean and standard deviation of 1, so that the same prior mean and scale can be used for all features during the probabilistic classification step. The scaling factors were calculated based on the training data of each data split. Validation cohort was normalized and scaled accordingly, using normalizing and scaling factors of the training data.To generate results with the same methods as in , we usedFisher’s exact test was performed to the count data as an alternative method to the moderated t-statistic based DMR finding. For each genomic window, a contingency table with one-vs-one comparison setting was formed of the training data and p-value was calculated with fisher.test function from the R package stats . GenomicWe utilized the 2100-dimensionalIterative supervised principal component analysis (ISPCA)  is a metFor our case with eight classes, both binary and multiclass approaches are possible. In the multiclass setting, features maximizing the score for one class versus other classes are searched for each class separately, and the feature with maximum score is picked. That is, the new component separates one of the classes from the others the best. With the multiclass approach ISPCA needs to be run only once per data split. For the binary approach, the multiclass labels have to be transformed into one-vs-rest binary labels before running ISPCA. ISPCA is then run for each class separately.As finding too many supervised components might lead to overfitting, the ISPCA method includes a permutation test approach to calculate the p-value of there being relevant information left in We used the ISPCA implemention from R package dimreduce , namely N is the number of observations, GLMnet classifiers were trained and evaluated as described in  by utilio. As in , the parN is the number of samples. A linear model can then be connected to parameter i. After estimating the model coefficients, the class of a new sample can be predicted by calculating In logistic regression model, each of the elements in the target vector D is the number of features and the modified local scale parameter c controls the magnitude of the largest coefficients and is given an inverse-Gamma priors were set to 4 and 2 respectively, following the default values of the regularized horseshoe prior implementation in R package brms [D is the number of features, n is the number of training samples, The regularized horseshoe prior  is a tecage brms . The gloposed in  is10\docThe logistic regression model with horseshoe prior was fitted with different kinds of features: moderated t-statistic DMRs with the original data transformation, the moderated t-statistic DMRs with the modified data transformation, Fisher’s exact test DMRs, PCA coordinates and ISPCA coordinates. The original data transformation was applied before model fitting on the corresponding moderated t-statistic DMRs and Fisher’s exact test DMRs, while the modified data transformation was applied on the corresponding moderated statistic DMRs. Before logistic regression we normalized the features for total read counts and standardized each feature to have zero mean and variance of 1.The model was implemented with the R version of the probabilistic programming language Stan . For theith test/validation sample kth parameter sample. See description of the MCMC sampling method below.The predictions for test and validation data sets were made by calculating the posterior predictive probabilities. For the We also formulated a simpler logistic regression model for binary classification, where the model includes only an intercept term and two features. These two features are the numbers of hypermethylated and hypomethylated DMRs with nonzero read counts. The DMRs can be found either with the moderated t-statistic or Fisher’s exact test. The two features were normalized based on total read counts. and scaled to have zero mean and standard deviation of 0.5. The model intercept and coefficients were given Cauchy priors with scale parameters 10 and 2.5, respectively, as recommended in . The modThe classification of the test and validation sets are done in the same way as for the logistic regression model with RHS prior.Stan uses MCMC sampling to retrieve samples from the posterior distribution, specifically the no-U-turn sampler (NUTS) algorithm, which is a variant of the Hamiltonian Monte Carlo algorithm . The parThe evaluation of the classification methods was performed in similar manner as in  and the To see whether the results produced with the data set from  generaliWe tested three different methods to find differentially methylated regions that can be utilized as features in the classification: the moderated t-test method used in , moderatComparing Fig. In most cases a large fraction of the DMRs was shared with all of the three methods. The overlap between the Fisher’s exact test and t-test with the new data transformation was often quite high, as was the number of DMRs unique to the original t-test method. The numbers of DMRs unique to either Fisher’s exact test or the t-test with the new data transformation were often low in comparison. The overlaps between the original t-test method and the two other methods separately were quite modest compared to the overlap between Fisher’s exact test and t-test with new transformation. Altogether, it seems that a large part of the DMRs was shared between all of the methods, which suggests that DMRs can indeed be identified reliably from cfMeDIP-seq data. On the other hand, there were also DMRs not shared by all three methods. These DMRs may partly explain the differences in the performances of the classifiers utilizing these DMR sets.The lists of DMRs for each of the three DMR finding methods are available in Additional files In , DMRs idThe results of the comparison in Fig. Figure A shows the first six principal components from the standard PCA, two at a time. The input data to the analysis was the DMR set for the AML class, and based on the plot, the AML class could be separated well from the other classes using PC1. The training and test set samples from AML class seemed to mix to some extend, which indicates that the principal component generalized well to the test samples. The other plotted components seemed not to separate AML class from the others.Figure 4We generated similar plots for the binary and multiclass ISPCA approaches, presented in Fig. With the two other subsampled data versions, with total read counts To justify the choice of picking 300 DMRs per one-vs-one class comparison as features for the classifiers, we performed a comparison of GLMnet classifiers with different DMR numbers. The tested DMR numbers were 100, 300 and 400. The AUROC and AUPRC values for each class are presented in Additional file After finding the model features, the different classification models were fitted using the training data sets of the discovery cohort. The data was partitioned to training and test data sets 100 times, and each of the trained models was evaluated with corresponding test set. This resulted in 100 AUROC values per each of the eight classes, from which we can calculate median AUROC values which are presented in Additional file To better compare each of the methods to the original approach, we computed the differences between median AUROCs Fig. . The difContinuing the comparisons with the LR models that use dimension reduction features, the logistic regression model with RHS prior that used PCA components as features worked approximately equally well as the original GLMnet method for all subsampled data versions. But perhaps the most promising methods in this comparison were the logistic regression models with RHS priors that used ISPCA components as features. Both the models using multiclass ISPCA and binary ISPCA components got higher median AUROC values than the original GLMnet method for most of the classes in Fig. 106 Fig. C, the moAdditionally, median AUROC and AUPRC values over the data splits are presented in Additional file The models trained with the discovery cohort training sets were used to predict the class of samples in validation data set. The validation data was also thinned in the same manner as the discovery cohort data. The predictions were made with the models for each of the 100 data splits, and AUROC values were calculated for each set of predictions.The AUROC medians over the 100 values were calculated and they can be found from Additional file We also calculated the median AUROC differences between the original GLMnet method and the new methods Fig. . The logAll in all, compared to the discovery cohort results, there seemed to be a lot more variation in the AUROC differences for the validation data set. When the total read count was As in , we alsoAgain, the corresponding median AUROC and AUPRC values are presented in Additional file Until now, the results we have presented have been for the thinned data set. To assess how well the classifiers presented in this work perform with the original non-thinned data set, we ran the feature selection methods and classifier training to the data without thinning it first. We calculated the median AUROC values, the median AUROC differences between the original GLMnet method and the other classifiers for both the discovery and validation cohorts and produced ROC curves of the mean predictions for the validation cohort. The results are presented in Additional file The median AUROC values over the 100 data splits are presented in Additional file In Additional file Additional file From the validation cohort ROC curves in Additional file The conclusions described above are supported with median AUROC and AUPRC values for discovery cohort in Additional file We repeated the analysis with our workflow  using the intracranial tumors data set from , to see Our results are in line with the classifier performances reported in . The AURBased on the AUROC barplots in Additional file There seems to be a lot of variation in how well the compared methods performed with different classes, thinning versions and also between discovery and validation cohorts. For example, the LR model with RHS prior using binary and multiclass ISPCA features seemed to perform better than the original method when looking at the discovery cohort results for the lowest sequencing depth. But looking at the validation cohort performance, these two approaches had considerably weaker median AUROC values than the original GLMnet method. One possible cause to the differences between the three subsampled data versions is that the subsampling is done by taking random subsets of the original data, and that could cause differences even if the probabilities of obtaining reads from each of the genomic window were the same for all three thinning versions. The differences between the discovery and validation cohort data sets are visible with the non-thinned data set too, and this indicates that there are perhaps differences between the two cohorts, making it harder for the classifiers to perform well. However, the GLMnet-based methods seemed to all cope quite well with the validation cohort, especially if the data is non-thinned or the data has not been thinned very much. It could also be, that not all DMR finding methods and classifiers are suited to all classes. It could be considered, that different classifiers would be used with different classes to obtain optimal performance.The overlaps between the DMRs found with the three different approaches showed that there are indeed differences between the methods, even if many of the DMRs were shared between all methods. We noticed, that the Fisher’s exact test and the moderated t-test with new data transformation often shared DMRs which were not found by the original moderated t-test method, while the moderated t-test found DMRs that were not found by other methods. The numbers of unique DMRs to Fisher’s exact test or the moderated t-test with the new data transformation were often low in comparison. The DMRs that were not found by all methods could be a source of differences in the classification results.The dimension reduction techniques, PCA and two versions of ISPCA, showed varying performance. While logistic regression models using ISPCA components did not perform well with the validation data cohort, their AUROC values were promising for the discovery cohorts when total read count was Both logistic regression with RHS prior and GLMnet methods implement the logistic regression model and enable sparsity of the feature coefficients. The difference between these two models is the prior enabling sparsity and how the model is fitted. In our approach we use the regularized horseshoe prior and GLMnet utilizes elastic net regularization. On the model fitting side, we utilized probabilistic programming language Stan to obtain posterior samples of the model, while GLMnet model is fitted with cyclical coordinate descent approach . The GLMBased on the promising results in TCR analysis application , we expeThe results for the independent intracranial tumors data set seemed to support our findings with the non-thinned data set, indicating that these results could generalize to other data sets. However, the differences between methods were greater with the intracranial tumors data set than with the one from .We performed a method comparison to investigate if we the classification accuracy of the classifier based on cfMeDIP-seq data could be improved in a case where the sequencing depth of a cfMeDIP-seq experiments is low. To simulate lower sequencing depths, we thinned a cfMeDIP-seq data set by randomly subsampling the reads of each of the samples. We obtained three data sets with total read counts of Based on the comparisons between the performances of the classification methods, there seemed to be no one method that would consistently perform better with all thinning versions, all classes and for both discovery and validation cohorts. But there were cases, where the different feature selection and classifying methods seemed to give advantage when the data had been thinned. Such methods include the Fisher’s exact test, binary and multiclass ISPCA for DMR finding and feature selection and logistic regression model with DMR count variables.Additional file 1. Supplementary figures and tables.Additional file 2. Lists of found DMRs for moderated t-test.Additional file 3. Lists of found DMRs for moderated t-test with new data transformation.Additional file 4. Lists of found DMRs for Fisher's exact test.


Classification of tumors into subtypes can inform personalized approaches to treatment including the choice of targeted therapies. The two most common lung cancer histological subtypes, lung adenocarcinoma and lung squamous cell carcinoma, have been previously divided into transcriptional subtypes using microarray data, and corresponding signatures were subsequently used to classify RNA-seq data. Cross-platform unsupervised classification facilitates the identification of robust transcriptional subtypes by combining vast amounts of publicly available microarray and RNA-seq data. However, cross-platform classification is challenging because of intrinsic differences in data generated using the two gene expression profiling technologies. In this report, we show that robust gene expression subtypes can be identified in integrated data representing over 3500 normal and tumor lung samples profiled using two widely used platforms, Affymetrix HG-U133 Plus 2.0 Array and Illumina HiSeq RNA sequencing. We tested and analyzed consensus clustering for 384 combinations of data processing methods. The agreement between subtypes identified in single-platform and cross-platform normalized data was then evaluated using a variety of statistics. Results show that unsupervised learning can be achieved with combined microarray and RNA-seq data using selected preprocessing, cross-platform normalization, and unsupervised feature selection methods. Our analysis confirmed three lung adenocarcinoma transcriptional subtypes, but only two consistent subtypes in squamous cell carcinoma, as opposed to four subtypes previously identified. Further analysis showed that tumor subtypes were associated with distinct patterns of genomic alterations in genes coding for therapeutic targets. Importantly, by integrating quantitative proteomics data, we were able to identify tumor subtype biomarkers that effectively classify samples on the basis of both gene and protein expression. This study provides the basis for further integrative data analysis across gene and protein expression profiling platforms. Lung cancer is a heterogeneous disease and the classification of tumors using histological and molecular features can inform personalized approaches to treatment, in particular the choice of targeted therapies4. The increasing use of biomarkers and targeted therapies against receptor tyrosine kinases, angiogenic factors and inhibitory immune checkpoint proteins has indeed resulted in improved patient outcomes8. Lung cancer is generally divided into small cell lung cancer and non-small cell lung cancer (NSCLC) which comprises lung adenocarcinoma (LUAD), squamous cell carcinoma (LUSC) and large cell lung cancer9.Lung cancer is the leading cause of cancer mortality  and although multiple treatment options are available, the five-year survival rate remains low and there is an unmet need for better therapies11. Gene expression signatures were further applied to RNA-seq data by The Cancer Genome Atlas (TCGA) and successfully classified tumors into corresponding subtypes13. The natural extension of these analyses, namely subtype discovery in combined data from the two platforms, presents challenges because of intrinsic differences in data generated using different gene expression profiling technologies, although previous studies showed that dedicated normalization methods enabled cross-platform pattern discovery and classification15, and comparative differential expression analyses also showed good agreement between the two gene expression profiling platforms16.The two most common NSCLC histological subtypes (LUAD and LUSC) have been classified into molecular subtypes associated with clinically relevant characteristics including prognosis and survival, oncogenic drivers, and response to targeted therapies. Transcriptional subtypes (three in LUAD and four in LUSC) were initially identified by clustering gene expression microarray data from three LUAD  and five LUSC  discovery cohortsIn this study, we explored cross-platform subtype discovery in LUAD and LUSC using public gene expression data from over 3500 normal and tumor lung samples. We tested 384 combinations of preprocessing, cross-platform normalization, and unsupervised feature selection methods. The results were evaluated based on the agreement between subtypes identified in single-platform and cross-platform normalized data using various statistics including clustering comparison measures. We show that unsupervised learning can be achieved with combined microarray and RNA-seq data. We further show that tumor subtype biomarkers can be identified in integrated gene expression and quantitative proteomics data.25. We therefore evaluated various single and cross-platform normalization and unsupervised feature selection methods to identify an optimal combination of data processing methods for cross-platform classification of lung cancer subtypes as detailed in Fig. Our main objective was to identify robust expression subtypes in combined microarray and RNA-seq data for the two most common lung cancer histological subtypes (LUAD and LUSC). We collected a total of 2079 lung microarrays  and 1673 lung RNA-seq samples . Although cross-platform analysis of a large number of samples can facilitate expression subtype analyses, clustering may nevertheless be sensitive to the presence of experimental (platform and batch) effects, outliers  as well as to data processing procedures including normalization and feature selection26 analyses were subsequently performed on expression data from a total of 384 data processing combinations, and resulting clusters were evaluated using different statistics. Cluster purity27 and clustering comparison measures28 were used to evaluate agreement between clusters identified using single-platform and combined data, while entropy27, a measure of association29 and randomness31 were used to evaluate the tendency of data to cluster by platform rather than by subtype. Lastly, we used min (O/E) , the minimum ratio of observed  relative to expected (number of samples divided by number of clusters), to identify data containing spurious clusters.After preprocessing single-platform data and performing cross-platform normalization, we proceeded to a first round of iterative ensemble classification 14 and quantile normalization33, performed rather poorly showing entropy below 0.1 . Based on these results, TDM and quantile normalization were excluded from further analyses, leaving 192 data processing combinations for each class.First, the analysis of platform entropy revealed that two cross-platform normalization methods, feature-specific quantile normalization (FSQN)35 combined with the above filters. This analysis supported the same result showing three LUAD subtypes and two LUSC subtypes  > 0.1 to eliminate results containing spurious clusters. These filters had little effect on the proportion of single-platform processing or unsupervised feature selection methods in the remaining combinations; however, the vast majority of clustering results remaining after filtering were associated with three LUAD subtypes and two LUSC subtypes  measures related to platform entropy and platform-cluster association: entropy, Cramér’s V; (3) platform randomness within clusters: number of runs divided by sample size, rank version of von Neumann's ratio and (4) min(O/E) for minimizing the presence of spurious clusters. The top-ranking data processing combination was: microarray suite 5.0 (mas5)36 for microarray data, edgeR reads per kilobase per million (rpkm)37 for RNA-seq, FSQN15 for cross-platform normalization, and median absolute deviation (mad) for unsupervised feature selection (Supplementary Table Remaining data processing combinations retained in both LUAD and LUSC (78 combinations) were then ranked using various statisticsUsing clusters resulting from the top-ranking data processing combination as class labels, all lung samples  were submitted to a final round of supervised classification, which in our experience improves the classification of some of the samples that are more difficult to classify using unsupervised methods alone. The heatmap in Fig. 13. Since the publication of these studies by TCGA, numerous additional patients have been enrolled and all data have been reanalyzed and harmonized to a newer version of the human genome (GRCH38)38 by the Genomics Data Commons (GDC)39.To take advantage of our robust cross-platform tumor subtype classification, we proceeded to a reanalysis of LUAD and LUSC genomic alterations and patient outcomes and compared findings to those from the original studies40. Lung adenocarcinoma subtypes 1–3 correspond to the proximal-proliferative, proximal-inflammatory and terminal respiratory unit subtypes, respectively, whereas LUSC-1 regroups the basal, primitive and secretory subtypes and LUSC-2 corresponds to the classical subtype. Analysis of available survival data  revealed significant (p < 1e−10) differences between subtypes , whereas LUAD-3 contained none. Interestingly, LUAD subtypes 1–2 also contained KDR (a.k.a VEGFR) amplification, which was not reported in the original TCGA LUAD study13. Epithelia growth factor receptor (EGFR) was most frequently amplified in four subtypes . We further evaluated the percentage of samples carrying mutations using averages from MuTect50, VarScan 251, Somatic Sniper52 and MuSE53 in lung cancer therapeutic targets 55. Our goal was to select a small set of biomarkers for subtype classification, capable of classifying LUAD tumors using either gene expression or quantitative proteomics data. To illustrate the robustness of the biomarkers, we combined gene expression data with proteomics data without any special cross-platform normalization other than scaling the mean and variance using microarray data as reference. Labels for CPTAC samples (205 classified samples with both RNA-seq and proteomics data) were assigned using classification of RNA-seq samples as described above. The top-10 features were selected for each one-against-one (OAO) class comparison using the log2 fold change (log2FC) and the overlap of locally adaptive kernel densities56. Figure Because of the importance of protein assays in the clinic11. Transcriptional subtypes can further be integrated with other data such as somatic mutations and DNA methylation into multi-omics subtypes13. Previous studies have also shown that classification of NSCLC into histological subtypes can be achieved using relatively simple methods, using both microarray and RNA-seq data, for example a nearest class centroid approach using differentially expressed genes and Pearson correlation as a similarity measure57, or a two gene (KRT5 and AGR2) expression ratio which classified LUAD and LUSC samples with relatively high accuracy58. However, to our knowledge, systematic evaluation of data processing methods for unsupervised classification of LUAD and LUSC transcriptional subtypes across gene expression profiling platforms has not been performed previously. In this study, we used an unsupervised approach combining 384 data processing methods to analyze public gene expression data . This analysis provided insights into the optimal combination of data processing methods for cross-platform clustering, and enabled the identification of robust LUAD and LUSC expression subtypes in combined microarray and RNA-seq data.Lung adenocarcinoma and LUSC have previously been classified into transcriptional subtypes associated with important characteristics such as response to targeted therapies27, here it was used to evaluate the maximum agreement between single and cross-platform data. Measures of randomness enabled the evaluation of the tendency of samples to regroup by platform within clusters. In addition, the use of min(O/E) allowed effective filtering of small, spurious clusters. For unsupervised feature selection methods, the simplest and fastest method, namely median absolute deviation (mad), performed well and ranked above more complex and computationally intensive methods. The approach of binning genes by expression level also helped to avoid enrichment of features sets with low-expressed genes.Combinations of data processing methods were evaluated using single and cross-platform consensus clustering, and various statistics including cross-platform purity, clustering comparison measures, platform entropy, as well as randomness and min(O/E). Whereas cluster purity is generally used to evaluate the ability of a clustering method to recover known classes15 performed better than other methods tested in this study. This method normalizes RNA-seq data in a feature-specific manner, using microarray data as a reference (quantiles for each gene). This method performs best with larger numbers of samples, as was the case in our study. As discussed in Franks et al.15, its superior performance can be attributed to the fact that FSQN preserves “distribution information about the center and spread of each individual gene”. Interestingly, ComBat21, a method originally developed to adjust microarray data for batch effects, also performed relatively well for removing platform effects. The latter method estimates model parameters by pooling information across genes and experimental conditions. The resulting empirical Bayes estimates are used to adjust the data for unwanted sources of experimental variation. The other two methods tested for cross-platform normalization, namely quantile normalization33 and TDM14, did not perform well as evidenced by low entropy, meaning that unsupervised learning with data normalized using these methods would identify primarily platform-specific clusters. Quantile normalization ranks features using expression levels, and assigns to each feature the average value of other features with the same rank in other samples. This method can be used with a single matrix, or with a target and a reference matrix. Training distribution matching uses a similar approach, whereas target distributions are adjusted to match certain properties of a reference distribution  and expression data in a target sample is mapped into a range from the minimum to the maximum of the reference data. Altogether, this analysis showed that feature-specific methods such as FSQN perform better for cross-platform normalization, especially for unsupervised learning which is more sensitive than supervised approaches to experimental biases including platform effects.For cross-platform normalization, FSQN37 and DeSeq2 fpkm59) performed well as input for classification after cross-platform normalization. Although RNA-seq counts normalized using gene or transcript length are generally used to compare gene expression within samples, here we show that such units are very compatible with both supervised and unsupervised classification approaches, and integrate better with microarray data for cross-platform normalization. This can be explained by the fact that methods used to summarize microarray data (mas5 or RMA) are averages across probes, and there is no direct link between gene length and expression level. RNA-seq data normalized using effective gene length are thus more similar to, and integrate better with microarray data for cross-platform normalization and machine learning tasks such as feature selection and classification.After the filtering and ranking of data processing methods, we found that RNA-seq data normalized using effective gene length  may have some utility in terms of prognostics/diagnostics, but our results show that they are grouped into a single class by our data-driven, unsupervised cross-platform tumor subtype identification methodology. This is a strength in our approach, that only the most robust clusters, identified across platforms, are retained as candidate tumor subtypes. Robust classification schemes are more likely to successfully transfer to real-world applications by minimizing reliance on features that are variable due to batch or platform effects.The three LUAD subtypes identified in our analysis were highly concordant with those identified in a previous studyEach tumor subtype was analyzed separately for focal amplifications and somatic mutations in genes coding for targets of clinical-stage or approved lung cancer therapeutics, and patterns presented here show that patients within each subtype may benefit from a particular subset of targeted therapeutics. In addition, to demonstrate the robustness of transcriptional subtypes identified in this study, we showed that biomarkers accurately separating the different tumor subtypes and normal tissues can be selected and validated with mass spectrometry-based quantitative proteomics data, even though the number of proteins quantified is limited as compared with gene expression data, and generally biased towards highly expressed genes. With further validation, the identified proteins may be used as biomarkers to classify tumors using classical protein-based methods such as immunohistochemistry.Cross-platform classification of microarray and RNA-seq data is challenging because of intrinsic differences and biases in data distribution between the two platforms. A careful selection of data processing and machine learning methods enabled cross-platform classification of lung tumor expression subtypes. Our study confirmed three LUAD expression subtypes, but only two subtypes in LUSC as opposed to four subtypes previously identified. Such classification provides insights into clinical management and drug development for LUAD and LUSC, in particular with respect to identifying subtype-specific targets for antibody-based therapeutics. This study provides the basis for further integrative analysis of microarray, RNA-seq and quantitative proteomics data, and for the classification of tumors into expression subtypes.61 and 1673 lung samples profiled using RNA-seq  were obtained from the Sequence Read Archive (SRA)62  and GDC39 (projects TCGA and CPTAC-3). All data were processed using GDC reference files : a custom chip definition  file was created for microarray data using the methods of Dai et al.63, and GTEx samples were re-aligned to GRCh38.d1.vd1 using GDC mRNA analysis pipeline (STAR two-pass)64. Microarray data were combined into one expression set and processed using R library affy65. RNA-seq data were combined into a single count matrix and processed using edgeR37, DESeq266 and limma (voom)67  to that of microarray data and then merged and normalized the data from the two platforms using R libraries FSQN15, TDM14, sva32 and preprocessCore68  were obtained from GEO69, Rdimtools70 and an in-house package for singular value decomposition entropy implemented using Rcpp71  using NbClust35. Agreement between clusters identified using single-platform and combined data were evaluated using R package aricode72 as well as a custom R function to evaluate purity (maximum agreement between single and cross-platform data). The tendency of cross-platform data to cluster by platform rather than by cancer subtype was evaluated using NMF package73 (function entropy) and DescTools package74 . Spurious clusters were evaluated using min(O/E), the size of the smallest cluster over sample size divided by number of clusters.Normalized data (single-platform and cross-platform) were submitted to unsupervised feature selection with eight different methods using R libraries stats71 Table . For eac56 to include rounds of repeated random sampling  moderated t-test77, the overlapping coefficient of locally adaptive kernel density estimates79, and the weights of support vectors (WSV)80. Locally adaptive kernel densities and overlapping coefficients were computed using an in-house R package implemented using Rcpp71. The WSV were computed using the e1071 R package81. Classification was achieved using three algorithms implemented in the RWeka package82: k-nearest neighbors83, random forests84 and support vectors machines85.Samples were submitted to five rounds of a Monte Carlo iterative ensemble classification algorithm, modified from41 for RNA-seq data, and from GEOmetadb86 for microarray data. Survival analysis was performed using the R package survival87 using default parameters. Masked copy number segments were obtained from GDC and processed using GISTIC 2.048. The search for focal amplifications was restricted to peaks covering less than 3 Mb as recommended in Krijgsman et al.88. Somatic calls from MuTect50, VarScan 251, Somatic Sniper52 and MuSE53 were obtained from the GDC, and lung tumor RNA-seq data were analyzed using VarDict89. These data were analyzed using the R packages maftools90 and VariantAnnotation91.Survival data were obtained from the TCGA pan-cancer clinical resource36 and RNA-seq data normalized using DESEq292 and log2 protein expression data from CPTAC55. Proteomics and RNA-seq data were scaled (mean and variance) using microarray data as reference. Features were selected to maximize the log2FC and to minimize the overlap of locally adaptive kernel densities56 with a total of 10 features for each OAO class comparison.To select biomarkers for LUAD, we used microarray data processed using mas5Supplementary Figures.Supplementary Legend.Supplementary Table S1.