Most glioblastoma studies incorporate the layer of tumor molecular subtype based on the four-subtype classification system proposed in 2010. Nevertheless, there is no universally recognized and convenient tool for glioblastoma molecular subtyping, and each study applies a different set of markers and/or approaches that cause inconsistencies in data comparability and reproducibility between studies. Thus, this study aimed to create an applicable user-friendly tool for glioblastoma classification, with high accuracy, while using a significantly smaller number of variables. The study incorporated a TCGA microarray, sequencing datasets, and an independent cohort of 56 glioblastomas (LUHS cohort). The models were constructed by applying the Agilent G4502 dataset, and they were tested using the Affymetrix HG-U133a and Illumina Hiseq cohorts, as well as the LUHS cases. Two classification models were constructed by applying a logistic regression classification algorithm, based on the mRNA levels of twenty selected genes. The classifiers were translated to a RT-qPCR assay and validated in an independent cohort of 56 glioblastomas. The classification accuracy of the 20-gene and 5-gene classifiers varied between 90.7–91% and 85.9–87.7%, respectively. With this work, we propose a cost-efficient three-class  tool for glioblastoma molecular classification based on the mRNA analysis of only 5–20 genes, and we provide the basic information for classification performance starting from the wet-lab stage. We hope that the proposed classification tool will enable data comparability between different research groups. IDH mutation and MGMT methylation status [The molecular classification of glioblastoma has received increasing scientific attention over the past 15 years . Since gn status , the vasDespite the fact that many glioblastoma molecular classification models were suggested since the Verhaak classification system was proposed ,22,23,24https://github.com/GiSteps/GBM-Molecular-Classifier (accessed on 1 December 2022).The GBM classification tool is designed based on machine learning and the data visualization toolkit “Orange”. The subtyping model, data normalization, and the file architecture instructions, as well as general usage instructions, are available to download at: Signature–gene selection for GBM subtyping was based on preselected signatures proposed elsewhere ,6,11,26.n = 151; CL n = 140; and PN n = 128). We tested two classification algorithms: logistic regression with LASSO (L1) and support vector machine (SVM) for classifier development to select the most suitable one. Both algorithms revealed highly comparable results applying a 10-fold cross-validation design. We reduced the number of genes to suggest the minimal number of genes that allows tumor classification with an acceptable accuracy. We found that a minimum of five genes is required to receive an overlap with the original classification of at least 85%. First, we selected the first top five ranked genes according to the ANOVA scoring method  samples, while the RNA sequencing dataset consisted of 122  cases . We applied both classification models (20-gene and 5-gene) for testing. As was suspected, the classification accuracy of data generated by different array platforms or even methods was slightly lower, but it was, nevertheless, sufficiently high to be used as a tool. The accuracy of the 20-gene classifier of the Affymetrix HT dataset reached 90.7%, while the Illumina HiSeq dataset revealed 91% of overall accuracy. Five five-gene classifiers revealed 85.9% classification accuracy when testing the Affymetrix HT dataset and 87.7% accuracy when testing the Illumina HiSeq cases (see The classification models were tested on two public datasets generated by a gene expression array (Affymetrix HT) and by RNA sequencing (Illumina HiSeq). The gene expression microarray dataset consisted of 419 (MES ases see .Receiver operating characteristic (ROC) curve analysis revealed perfect test results and the ability to discriminate between subtypes (testing subtype versus other two subtypes). Comparable results between both classifiers using an expression array and sequencing data were obtained  is shown in the heatmap see .p < 0.001, ANOVA). BCAS1, GPR17, ERBB3, PDGFRA, SNAP91, DNM3, KLRC3, and PFN2 gene expressions were significantly increased in PN compared to MES and CL cases. MET, FCGR2B, DAB2, and PTPRC levels were significantly increased in MES cases compared with the other two subtypes, while EGFR, SPRY2, VAV3, CDH4, NR2E1, and NES expressions were significantly increased in CL cases. CHI3L1 and CSPG5 expression was significantly decreased in PN and MES subtypes, respectively  cohort. The molecular and demographical characteristics of patients are shown in ACTB, GAPDH, and YWHAZ) CT values (ΔCT). Data of each analyzed gene were normalized (centered) based on the cohort average and x-x−  was included. Then LUHS cohort was tested by applying both 20-gene and five-gene classification models. The calculated molecular subtype matched in 52 specimens out of 56 (92.86%) when comparing the 20-gene and five-gene models’ outputs. The highest match was in the PN group, where 18 out of 19 (94.7%) specimens were identically assigned. MES and CL groups demonstrated a slightly lower match—17 out of 20 (85%) were identically assigned in both cases . The proportions of each cohort are shown in Next, we performed a subtype proportion comparison after classifying the LUHS cohort by both models. A Z-score test was used to compare subtype proportions between cohorts. Analysis revealed no difference between LUHS cohort subtypes, calculated applying 20-gene or five-gene models, compared with the Affymetrix HG and Illumina HiSeq datasets classified according to the Verhaak system , were also upregulated in PN samples of the LUHS cohort that was classified by applying the 20-gene classifier  , as well as PN, MES and CL survival means of 669, 484, and 503 days, respectively   . The logectively A. Neithe= 0.657) B,C. The Most glioblastoma studies incorporate a layer of tumor molecular subtyping based on the classification system instituted by Verhaak et al., 2010 . OriginaThe overall accuracy of the proposed glioblastoma subtyping models (five-gene and 20-gene) varied between ~86% and 91%. The original Verhaak classifier was developed by applying silhouette width filtering when selecting only the “core samples”, which then were used for model construction . MadurgaThe current study, in addition to a list of the genes and the classification model, also provides a user-friendly tool with all instructions for glioblastoma cohort subtype identification. Moreover, the current study provides technical information on optimized and validated qPCR conditions that enable data reproducibility and comparability between the researchers if they follow the described protocol. Different target sequences selected for amplification of the same gene may result in different expression levels. Thus, here we are providing designed and tested primers and optimized qPCR conditions to maximize the reproducibility of the data. Thus, we hope that the proposed glioblastoma classification model with basic information for target analysis in a wet-lab stage will encourage researchers and physicians to use the suggested system more frequently in the future.IDH status, survival data, and Verhaak subtype were collected from the UCSC Xena [n = 539), the Agilent 244K custom gene expression G4502A microarray platform (n = 585), and the RNA sequencing platform Illumina HiSeq 2000 RNA Sequencing (n = 172). The cases for the analysis were selected on the basis of four criteria: the subtype of a sample according to Verhaak was specified; the sample type was a primary tumor (recurrent or secondary tumors were eliminated); the case had information about all targets selected for the analysis; and the tumor was not treated prior to resection. After data filtering, based on the above-listed criteria, 505 cases from the Affymetrix HG-U133a, 505 cases from the Agilent G4502, and 162 cases from the Illumina HiSeq datasets were selected for further steps, respectively. Cases that were assigned to the neural subtype were removed from the datasets. The final gene expression microarray dataset consisted of 419 cases, and the RNA sequencing dataset consisted of 122 cases. Because the development of models incorporated different datasets, the data were normalized by applying a standard score: The Cancer Genome Atlas (TCGA) coordination center data  were useCSC Xena  and the CSC Xena  data porCSC Xena . The anaA total of 56 human glioblastoma specimens diagnosed according to the guidelines of WHO, classification 2016, 4th edition (update 3), were used for gene expression analysis. The surgical resections of GBM tumors were performed at the Department of Neurosurgery, Hospital of Lithuanian University of Health Sciences, from September 2017 to June 2021. Tumorous tissues of glioblastoma patients were collected, stored, and analyzed following written informed consent after approval by the Kaunas region Ethics Committee for Biomedical Research (permission code: P2-9/2003). The study was performed following the Lithuanian regulations, alongside the principles of the Helsinki and Taipei Declarations. All tissue samples after surgical resection were snap-frozen and stored in liquid nitrogen. Clinical data  as well as survival data, were collected for each patient. The overall patient survival rates were calculated from the date of tumor resection to the date of patient death or database closure (15 May 2022). None of the patients had received chemotherapy or radiotherapy prior to surgery. The demographic and molecular characteristics of patients are shown in ACTB, GAPDH, and YWHAZ). The final values were used as GBM tumor specimens were homogenized by applying cryogenic grinding with liquid nitrogen, and total RNA from pulverized tissue was extracted using the TRIzol reagent . In total, 2 µg of total RNA was used for cDNA synthesis by applying the High-Capacity cDNA Reverse Transcription Kit . Selected targets’ mRNA expression was investigated by applying quantitative RT-PCR SYBR Green I assay, in three replicates, on a 7500 Fast Real-time PCR detection system . The PCR reaction consisted of 3 μL  of cDNA, 6 µL of 2x Power SYBR™ Green PCR Master Mix , 0.29–0.67 µM of primer, and nuclease-free water. For detailed information on primer sequences, amplicons, primer amounts per reaction, as well as PCR cycling conditions, see t-test. A chi-square test was used for categorical data analysis. Survival analysis was performed by applying the Kaplan–Meier curve method, and a log-rank test was used to compare the difference in survival curves across groups. To show the reliability of the survival estimate, the confidence interval (CI), with a 95% confidence level, was presented. The level of significance was p < 0.05. Data visualization, target selection, model construction, and testing were performed using the machine learning and data visualization toolkit “Orange” . IBM SPSS Statistics  and GraphPad Prism  software were used for statistical analysis and data visualization.Differences across two independent groups were analyzed by applying a


BRANEnet, a novel multi-omics integration framework for multilayer heterogeneous networks. BRANEnet is an expressive, scalable, and versatile method to learn node embeddings, leveraging random walk information within a matrix factorization framework. Our goal is to efficiently integrate multi-omics data to study different regulatory aspects of multilayered processes that occur in organisms. We evaluate our framework using multi-omics data of Saccharomyces cerevisiae, a well-studied yeast model organism.Gene expression is regulated at different molecular levels, including chromatin accessibility, transcription, RNA maturation, and transport. These regulatory mechanisms have strong connections with cellular metabolism. In order to study the cellular system and its functioning, omics data at each molecular level can be generated and efficiently integrated. Here, we propose BRANEnet on transcriptomics (RNA-seq) and targeted metabolomics (NMR) data for wild-type yeast strain during a heat-shock time course of 0, 20, and 120 min. Our framework learns features for differentially expressed bio-molecules showing heat stress response. We demonstrate the applicability of the learned features for targeted omics inference tasks: transcription factor (TF)-target prediction, integrated omics network (ION) inference, and module identification. The performance of BRANEnet is compared to existing network integration methods. Our model outperforms baseline methods by achieving high prediction scores for a variety of downstream tasks.We test The online version contains supplementary material available at 10.1186/s12859-022-04955-w. Gene expression in eukaryotes is regulated at different molecular levels, including chromatin accessibility, transcription, RNA maturation, and transport. These regulatory processes are affected by specific bio-molecules potentially in separate cellular compartments with interconnected steps . One of To this end, networks are widely used to represent individual bio-molecules (nodes) and their biological relationships (edges). These relationships may reflect gene co-expression/regulation, protein–protein interactions (PPIs) or information about production or consumption of metabolites . The majOhmNet [Multi-Net [DeepWalk [Node2Vec [deepNF [BRANE-Exp [MOSS [BRANEnet, a novel multi-omics integration framework for multilayer heterogeneous networks. In particular, we embed graph-based information from multi-omics data into a lower-dimensional space. We leverage a properly chosen random walk-based Positive Pointwise Mutual Information (PPMI) matrix, that is able to capture relevant context around each node of interest. We introduce network integration with multilayered heterogeneous graph embeddings, that perform matrix factorization by approximating the spectrum of this PPMI matrix. We apply BRANEnet over a multi-omics dataset for heat-shock response in the well-known yeast model Saccharomyces cerevisiae [BRANEnet’s effectiveness by comparing its performance to existing network integration approaches.In the related literature, a variety of methods have been proposed for computing network embeddings either based on random walks , 19, matOhmNet  is a hieulti-Net  is an exDeepWalk  and Node [deepNF , a netwoRANE-Exp  leverageRANE-Exp , 26. It xp [MOSS  performsxp [MOSS . In suchrevisiae  and learTo test our framework, we have used recently published multi-omics yeast datasets acquired in the study of . These dcs tools . These cmentclass2pt{minimGenes, metabolites, TFs are hereafter referred to as bio-molecules. More generally, genes are regulated by TFs. Therefore, we separate TFs (genes coding for TFs) and non-TFs (genes not coding for TFs) from transcriptomics data. Now, to obtain differentially expressed bio-molecules, we first take the average of control samples in the four biological replicates. Then, we compute the log2 of Fold Change (log2FC) for each bio-molecule in eight test samples (four in t20 and four in t120) by taking its ratio against the average of four control samples (t0). For each test sample, we select non-TFs if log2FC is higher than 2 (over-expressed) or lower than  non-TFs . TherefoIntra-omics networks are constructed using the same type of bio-molecules, for example, gene-gene co-expression, or metabolite-metabolite correlation networks. These networks are built on data obtained from multi-omics experiments, for instance: genomics, epigenetics, transcriptomics, proteomics, and metabolomics. We obtain differentially co-expressed bio-molecules by computing the Pairwise Pearson correlation coefficient (ument}ρ)  of log2FInter-omics networks link bio-molecules of different types. They are constructed using biological a priori information showing presence of TF binding sites or H4K12ac epigenetic marks in the promoter of gene, biochemical reactions within genes and metabolites. This information can be acquired from various bioinformatics databases such as SGD , YEASTRABRANEnet is presented. The model takes two sets of networks, intra- and inter-omics X and Y respectively. It first builds a multilayer network G using both sets which are represented by N is a set of all bio-molecules. Then, we compute a random walk-based PPMI matrix d-dimensional embedding vectors In Fig. X is a set of x intra-omics networks represented as Y is a set of G of |N| nodes (bio-molecules) and |E| edges (interactions) is built using sets X and Y. The network is represented by its supra-adjacency matrix Y. The final output of G. v in G, a random walk traverses the multilayer graph, moving across neighborhood nodes of v chosen uniformly at random. This process repeats for a predefined number of walks per node. Nevertheless, for large networks, simulating random walks is computationally expensive. In particular, the space and time complexity for DeepWalk is N|)), respectively. Here, |N| denotes the number of nodes; l is the walk length; and T denotes the window size [Skip-Gram model  and matDeepWalk . In partd models , 19 in Nd models , 36. The|+k|N|)) , where | samples . In ordesing SVD , given bBRANEnet can be used for various downstream tasks, for instance TF-target prediction, ION inference, identification of biomarkers (e.g. heat stress responsive genes/TFs), identification of biologically related clusters, and visualization. Their details are shown below.The embeddings learned from i and j of size d, given by average: weighted L2: BRANEnet for TF-target prediction is compared with baseline methods.To predict TF-targets, we adapt the traditional link prediction task  to TF-taerations , 26 (i) rization . The perrization . The peri and j is defined as:k and the Matthews Correlation Coefficient (MCC). Precision@k gives us the fraction of true edges among the inferred top k ones. To compute Precision@k, we sort edges based on the pairwise similarity score, then take the top k ones. Then, we compute Precision  and embedding dimension (d). For deepNF, we choose to keep the default model architecture configuration proposed by the authors [We compare the performance of link prediction and ION inference using the embeddings learned by e OhmNet , MultiNeMultiNet , deepNF  [deepNF , BRANE-EANE-Exp,  and MOSSp, [MOSS  as our b authors .BRANEnet applied to the yeast multi-omics dataset [We present the results of  dataset . We have dataset . Gene-me dataset . EmbeddiBRANEnet with parameter d, we learn node embeddings using each baseline method. The edge features are computed using the operators mentioned in the “BRANEnet compared to baselines models are summarized in Fig. BRANEnet is average  than all the other methods. Overall from Fig. BRANEnet outperforms the baseline methods for both operators . With the exception of deepNF, all the other algorithms constitute mostly extensions of Skip-Gram-like GRL models  to multilayer graphs. For single layer graphs, the computational advantages of the spectral SVD algorithm over stochastic gradient training have been well-studied [Skip-Gram’s training procedure, BRANEnet does not require layer-wise observations of each node and its respective neighborhood. Hence, it is easily trainable. Previous studies have demonstrated that factorizing the PPMI random walk transition matrix using SVD achieves better performance on downstream tasks compared to Skip-Gram derived models [Skip-Gram-like network embedding models for multilayer graphs [We have performed TF-target prediction as mentioned in the “-studied , 50. Fird models . Referrir graphs , 22, 24.BRANEnet. To validate the performance of ION inference, we reconstruct the gold-standard (GS) using the learned embeddings. The performance is measured by computing Precision@k and MCC (Mathews Correlation Coefficient). We choose to study the top 500 edges of the inferred ION. We compare the performance of our model to the performance of the baseline methods used. The results are shown in Fig. k show that BRANEnet scores, up to top 320 edges, are higher than deepNF. BRANEnet outperforms MOSS, OhmNet, MultiNet, deepNF, and BRANE-Exp (Fig. BRANEnet’s performance for different thresholds (OhmNet, MultiNet, deepNF, MOSS and BRANE-Exp. As shown in Fig. BRANEnet was gradually improved with increasing threshold, and began to drop quite sharply at 0.6. For Precision@k metrics, deepNF is the second best performing model whereas for MCC@threshold, MOSS is the second best performing method. Moreover, we have also observed that, to obtain a well-trained model, methods like deepNF require large training data involving tuning of numerous parameters. This may cause overfitting/underfitting problems. The best performance of such baseline models can be achieved by extensive hyper-parameter tuning. As an advantage, BRANEnet comes with very few parameters. In the case of MOSS, the method is different from BRANEnet, pertaining to computation of random walk based PPMI matrix. This difference could make BRANEnet perform better than MOSS.We infer an ION using the embeddings learned by Exp Fig. a. The reBRANEnet with BRANEnet integrated tool, we are able to recover information from 11 different heat shock response studies. The references of these articles are given in Table The network inferred by mentclass2pt{minimmentclass2pt{minimp value lower than 0.05. Their enrichment results are shown in Fig. To identify modules from the inferred ION, we perform community detection using the Clauset–Newman–Moore greedy modularity maximization algorithm . We seleBRANEnet with learned integrated embeddings. We used grid-search to assess the uncertainty in the model outputs that is attributed to different values of the window size T and dimension d. We choose average and weighted L2 is d and slightly decreased with T. From the parameter sensitivity analysis for both tasks, we see that our model has lower variance in the results with respect to different parameter settings. Therefore for the new datasets, we recommend users to consider the default parameter settings (To examine the added value of integration, we have learned node embeddings by considering only one layer of information, i.e. transcriptomics. First, we consider only gene expression data and learn node embeddings. Secondly, we add the a priori knowledge to the transcriptomics data and learn node features. We compare the ION performance of using only one layer of information with the integrated embeddings acquired from multiple layers. Figure entclass1pt{minimaBRANEnet relies on a GRL technique to learn embeddings that can capture relevant features from complex networks built from single omics data. Considering the biological context and need for omics integration, methods like BRANEnet enable us to study the cross-talk between genes, transcription factors, and metabolites at transcriptional and metabolic levels of regulation.The recent wide application of high-throughput experimental techniques has provided complex high-dimensional heterogeneous data. In turn, the wide availability of these omics data has driven the need to develop methods that can efficiently analyse and integrate this data. We have presented an integrative analysis of multilayer heterogeneous networks for learning low-dimensional features for bio-molecules. BRANEnet is especially appealing mainly because of four reasons. First, our approach integrates experimental data with biological a priori knowledge which facilitates the inference of inter-omics relationships. Second, it can generate meaningful embeddings by preserving the inter- and intra-omics interactions. Third, its objective function is independent of the downstream tasks, thereby it is adaptable to various omics data inference tasks. And fourth, it has a low number of parameters to tune. For the predicted bio-markers, experimental as well as in silico investigation can help in identifying their functions and biological significance [BRANEnet on multiple omics datasets including epigenetics, proteomics, and metagenomics. We would also like to explore promising directions of multilayer network embedding that can support fast approximations of higher-order random walks transition matrices. BRANEnet is a versatile tool that combines data-driven information and biological a priori knowledge and provides an effective and scalable network integration framework with diverse downstream integrative omics applications. We consider BRANEnet to be a valuable method of biological network integration for experimental follow-up, as well as a baseline candidate for further development of the graph-based multi-omics data integration field.Besides, we have compared the performance of our approach for various downstream tasks with existing network integration methods. To summarize the empirical analysis, the performance of ificance . As a fuAdditional file 1. The list of all DE bio-molecules with their FC and SGD annotation.


Identification of cancer sub-types is a pivotal step for developing personalized treatment. Specifically, sub-typing based on changes in RNA splicing has been motivated by several recent studies. We thus develop CHESSBOARD, an unsupervised algorithm tailored for RNA splicing data that captures “tiles” in the data, defined by a subset of unique splicing changes in a subset of patients. CHESSBOARD allows for a flexible number of tiles, accounts for uncertainty of splicing quantification, and is able to model missing values as additional signals. We first apply CHESSBOARD to synthetic data to assess its domain specific modeling advantages, followed by analysis of several leukemia datasets. We show detected tiles are reproducible in independent studies, investigate their possible regulatory drivers and probe their relation to known AML mutations. Finally, we demonstrate the potential clinical utility of CHESSBOARD by supplementing mutation based diagnostic assays with discovered splicing profiles to improve drug response correlation. RNA splicing variations could help identify cancer subtypes, but this task is computationally challenging. Here, the authors develop CHESSBOARD, a Bayesian tile finding algorithm for splicing data which identifies patterns in the form of tiles and can discover leukemia subgroups associated with therapeutic response. Unsupervised analysis typically involves identifying latent substructures in the data which can be used to learn more about disease etiology, such as cancer subtypes5. One approach to quantify changes in the transcriptome is at the level of alternative splicing (AS). AS is the process by which different segments of pre-mRNA can be removed while others are joined, or spliced, together to form mature mRNA. AS is regulated by intricate interactions between hundreds of RNA binding proteins (RBPs) and is thus highly susceptible to disease-causing disruption, especially in cancer8. Given the strong association between splice variants and disease, we propose an unsupervised learning algorithm for identifying substructures in a matrix of RNA splicing measurements from cancer patients.Analysis of RNA sequencing (RNA-seq) data from large patient cohorts is commonly used to reveal transcriptomic variations that are associated with complex disease. Within the framework of machine learning, such analysis can be framed as either supervised or unsupervised learning tasks. In a supervised setting, the objective is usually to identify transcriptomic variations that act as predictive markers for disease state or are strongly correlated with clinically significant variables9. Instead, several works have pointed to splicing aberrations which can severely perturb the function of regulatory proteins involved in apoptosis and cancer suppression11. Second, many cancers have been shown to be heterogeneous, with patients exhibiting high variability in splicing measurements. While some of this variability is likely due to confounders such as batch effects, recent studies have shown that this variability can result from mutations which seldom appear in a large fraction of the patients12. Rather, they are observed in small subsets of patients with both cis acting effects due to mutations at splice sites and trans acting effects due to mutations in splicing regulatory machinery13. These observations motivate the derivation of a dedicated method for identifying “tile” substructures in the RNA splicing data matrix, i.e., subsets of patients that exhibit distinct splicing changes in a subset of genes.The focus on identifying substructures in RNA splicing cancer data is motivated by several additional observations. First, in cancers such as acute myeloid leukemia (AML), the mutation burden is particularly low such that analyzing genetic mutations alone is insufficient for explaining disruption of key oncogenic pathways14, SALMON15, Kalisto16) or relative isoform usage . In contrast, methods such as MAJIQ19, SUPPA220, rMATS21, quantify splicing of events, or local splicing variations (LSVs), such as cassette exons. These local splice variations measure splicing as the ratio of RNA segment  inclusion  for isoforms that contain the segment vs. isoforms that do not. While quantifying whole isoforms is clearly appealing, we focus here on event based splicing quantification. Some advantages of using AS events include the fact they do not require a-priori assumptions about the underlying isoforms, can handle un-annotated (de novo) isoforms which is crucial in cancer analysis, and can be directly validated with orthogonal methods .Splicing variations derived from RNA-seq are commonly defined at the level of whole transcripts or at the level of AS “events”. Transcript based approaches rely on estimating the abundance of whole isoforms  or columns (patients) are unlikely. Instead, only a small subset of LSVs may be perturbed in a subset of samples.Despite the above advantages, there are several modeling challenges associated with unsupervised tile identification using event based quantifications. First, splicing measurements are inherently different from gene expression measurements which are modeled as Gaussian (TPM) or negative binomial (read counts) distributions in many previous works27. However, in RNA-seq data, the missingness rate is inversely proportional to the sequencing depth where higher read coverage results in a lower probability of missingness. Furthermore, splicing quantifications, unlike expression measurements, cannot be meaningfully imputed since a missing Ψ quantification can be denoted by a 0 or 1 representing alternate junction usage. Thus, naive imputation  can lead to unlikely intermediate values. This necessitates an alternate model in which values are MNAR (missing not at random). Under this model, the missingness rate depends on observations in the data matrix and external factors such as coverage. In cancer data specifically, values can also be systematically missing due to genetic mutations which could result in a specific junction not being observed  and should be modeled as a secondary signal.Another important challenge we address here, which has implications beyond the analysis of RNA splicing data, is the modeling of missing values. Genomics data often contains missing values that result from technical limitations in sequencing technologies and are assumed to be missing completely at random (MCAR). Under this model, the missingness rate does not depend on observed or unobserved values and can be imputed or ignoredHere we address the above modeling challenges, by developing CHESSBOARD . CHESSBOARD is a Bayesian tile finding algorithm tailored for splicing data with missing values and includes a suite of data processing and visualization tools Fig. . The inpWe first apply CHESSBOARD to synthetic datasets to show it outperforms several baseline methods and validate the effectiveness of our modeling approach. Next, we show that CHESSBOARD recovers tiles characterized by splicing aberrations which are reproducible in multiple AML patient cohorts. Finally, we show that tiles we discover are correlated with drug responses, pointing to the translational potential of our findings. We also develop GAMBIT , a web-based visualization tool which allows users to visually explore the discovered tile structures and Bayesian output. Both CHESSBOARD and GAMBIT are available as open source tools to facilitate reproducible workflow and analysis.X contains the number of junction spanning reads xij mapped to the representative  splice junction of LSV j in sample i and the total number of reads mapped to the LSV, denoted ηij  for having a missing value. This mixture distribution over observed and missing values captures the background. In specific patient subsets however, additional variation or signal in underlying Ψ  or an elevated missingness rate (captured by a separate θj1) may be observed. Thus, the first part of the CHESSBOARD pipeline is to filter out non-informative splicing events which can be captured well by the background distribution. This is achieved using a parametric bootstrap Kolmogorov-Smirnov test (Supplementary Note i belonging to tile k has its (unknown) group indicator variable set ci = k and every LSVs j belonging to this tile has a matching  indicator variable rjk = 1. A specific CHESSBOARD model is represented by a learned tile configuration and distribution parameters for all the background and signal groups. Under this Bayesian formulation, every such model can be assigned a posterior probability, and the CHESSBOARD algorithm uses an efficient blocked Gibbs sampling procedure to sample from the posterior distribution over possible models given the observed data matrix X. See Methods for a detailed description of the CHESSBOARD model.To address the challenges of analyzing heterogeneous AS datasets, CHESSBOARD directly models properties of the data that arise from biological and technical processes. Briefly, the model’s input data matrix Beta) and generate reads for each Ψ at various coverage levels from a binomial distribution  and read counts from each Ψ at varying levels of coverage as before. The results shown in Fig. CHESBOARD’s model further alleviates the effect of coverage dependent uncertainty in heterogeneous data by sharing information across samples. Specifically, CHESSBOARD uses empirical Bayesian shrinkage to learn group specific priors, taking advantage of samples with higher coverage assigned to the same cluster to improve estimates for samples with lower coverage (Methods). To demonstrate the advantages of CHESSBOARD’s modeling approach we generated Ψ values from entclass1pt{minimaNext, we turned to assess CHESSBOARD’s missing values modeling. To account for missing values, CHESSBOARD uses a MNAR model where missing values are treated as a secondary signal when the missingness rate of an LSV is much higher than expected under the null missingness rate associated with sequencing limitations. We first replace each unquantifiable splicing event with a missingness indicator. We then estimate priors for the missingness rates using an Empirical Bayes procedure (Methods). During CHESSBOARDs model fit, we obtain posterior estimates for both the background and signal missingness rate, where the latter can account for other factors such as unobserved values due to mutations (Methods). We show that the MNAR missing value model is effective in identifying tiles containing missing value signals. For comparison, we implemented an alternative version of CHESSBOARD that uses the MCAR model assumption where missing values are integrated out. Both CHESSBOARD and CHESSBOARD-MCAR were then applied to a simulated homogeneous data matrix in which the read counts for each LSV (row) were drawn from a background distribution with parameters estimated from beatAML and values were missing at a fixed dropout rate of 10%. To these we added a single tile of varying size with a missingness rate of 60%. We then assessed the algorithm’s ability to recover this tile by information gain which measures the purity of the clusters  prior (Methods), we first evaluated the behavior of this feature under various parameters (Supplementary Note τpr) and recall (τrc) statistic adapted from the recovery and relevance score25   (change in Ψ) of LSVs belonging to the tile between the 2 groups in each dataset are highly correlated (r = 0.779) suggesting that the splicing perturbations captured by the tile are similar in both datasets  and U2AF2 (1.54%), both of which have known roles in promoting expression of antiapoptotic isoforms of oncogenes in several hematopoietic maglicanies30. Another candidate splicing regulator which appeared to be differentially expressed and spliced between the signal and background groups is HNRNPC (2.44%), which has been implicated in AML in a recent study31. Next, we analyzed eCLIP data for each RBP to test whether there was evidence for direct RBP binding around the tile’s differentially spliced junctions. We observed high binding rates for SRSF1 and U2AF2 (>4% of tile junctions). However, the binding rate was lower compared to spliceosome components including AQR, SF3B4, PRPF8 and EFTUD2 which are known to bind spuriously to constitutive splice sites. Surprisingly, almost no binding was observed for HNRNPC. For SRSF1 specifically, there was also significant enrichment of CLIP binding to junctions that were also DS suggesting direct splicing regulation by SRSF1 . Intersecting the tile’s differentially spliced junctions with those observed as differentially spliced in ENCODE’s RBP knockdown experiments implicated 17 RBPs, all of which were either differentially expressed or spliced between the signal and background patient groups  to identify similar pathways in the tile gene sets. Since GSEA requires ranking genes within a group we developed a probabilistic ranking method based on the CHESSBOARD model which account for both splicing changes and missingness rates. Specifically, we score each LSV based on the likelihood gain achieved in the learned tile configuration compared to an inverted tile configuration and then used the score for the highest scoring LSV in each gene as input to GSEA , pathways where other splicing perturbations exist in a different subset of patients. Similarly, LSVs not present in the initial pre-filtered data matrix may provide additional support for such tiles. To address this scenario we developed a recursive clustering solution that naturally fits into CHESSBOARD’s probabilistic model and serves as a scalable means to probe the entire transcriptome , NPM1 (p < 0.001), and CEBPA (p = 0.025) in patients assigned to the tile cluster (Supplementary Note 36. We observed a fourth association of the cluster with mutations in NRAS (p = 0.025), but mutations in this gene were actually depleted in the tile samples. Continued recursive tile discovery showed a sharp decrease in the likelihood ratio  corresponding to a different subset of the patients (ARI = 0.0066 between recursive and base case sample clusters) . Details about this measurement are discussed in Supplementary Note FLT3 mutation is highest for Gilteritinib and FLT3-ITD is highest for Sunitinib/Sorafenib which are known mutation-drug associations.Since the mutational status in many of these AML associated genes is used by clinicians to decide on drug administration we correlated the samples belonging to each tile with drug response measured by area under the FLT3-ITD and NPM1 for FAB classification AML genes38 as well as RUNX1 and SF3B139. To assess usefulness of combining splicing changes and mutations to predict drug response, we prioritize FLT3-ITD and Sorafenib and NPM1 and Venetoclax due to reliable mutation calls and their prominent role in AML clinical diagnosis. Specifically, we developed a simple decision tree  to confirm that the effect size differences between the groups is biologically meaningful. Accordingly, using FLT3-ITD alone had median(ΔAUC) = 64.36 and Log3FC = 2.09, while the combined classification had a median(ΔAUC) = 76.29 and Log3FC = 2.73  = 23.34 and Log3FC = 0.86, while the combined classification had a median(ΔAUC) = 57.91 and Log3FC = 2.65 (p = 0.048).Next, given the possible functional consequences of splicing changes between the identified tiles in many AML associated genes, we hypothesized that our splicing based patient grouping could improve clinical decisions that are based on mutation analysis alone. Notably, the added value of splicing changes to AML classification has been shown recently for ree Fig. c combini.8% Fig. d. We alsFLT3-ITD mutation and functions as a tyrosine kinase inhibitor with high specificity for FLT3. At a molecular level, FLT3-ITD result in constitutive activation of receptors that lead to downstream activation of PI3K/AKT/mTOR, Ras/Raf/MEK/ERK, and JAK/STAT pathways. This activation in turn results in enhanced proliferation and reduced apoptosis of the myeloblasts, which contribute to leukemogenesis40. Inline with this known mechanism, we observed multiple LSVs in FLT3 that were differentially spliced between the patients’ subgroups. We therefore analyzed several specific splicing events in FLT3 to determine if there was enrichment of splice isoforms in the signal that may lead to reduced transcript viability and thus higher sensitivity. Indeed, for FLT3 we identified two differential splicing events involving skipping of exon 4b  and exon 17b  that are highly correlated (r = 0.858) and have a higher skipping rate in the background. Notably, exon 4b has not been previously reported (de novo exon and junctions) and both events have not been previously reported with respect to AML to the best of our knowledge. Skipping both of these exons results in the functional canonical isoform of FLT3 which was correlated with an increase in expression of FLT3  with constitutive FLT3 canonical isoform expression, defined as inclusion of both events being > 0.9. In contrast, the patients that were FLT3-ITD- and in the signal group showed enrichment for intermediate canonical isoform levels .Taken together, the above analysis suggests that there is over-expression of ile Fig. d. IndeedEZH2. The change in inclusion levels was not particularly large, yet there was over a 15% increased missingness rate of two EZH2 LSV in the background cluster  consisting of TARGET pediatric AML and beatAML samples  data , a markedly different type of leukemia characterized by proliferation of lymphoid blasts in the bone marrow . Under CHESSBOARD’s formulation, each sample has an unobserved label {c1, c2, …, cn} which assigns it to a patients’ group or type k is defined by a vector rk ∈ {0, 1}m where m is the dimension of the vector. The assignment rjk = 0 indicates LSV j is not part of the unique pattern of group k such that observed inclusion levels for this LSV in samples that do not belong to group k follow some (learned) background Ψ distribution. In contrast, rjk = 1 indicates an abnormal splicing signal in LSV j across all samples belonging to group k. We thus formulate the generative process for each observed Ψ entry of the data matrix asi in LSV j follows a Binomial distribution with a Beta mixture prior over the level of inclusion Ψij. This Beta-Binomial model naturally handles uncertainty in Ψ estimates since observations with low read counts will have higher variance. If observation xij is assigned to the signal in group k as denoted by ci = k, rjk = 1, its likelihood is evaluated using the signal prior distribution Beta. Likewise, the observation is evaluated using the background prior distribution Beta when rjk = 0. Notice that we reparameterize the Beta in terms of mean and variance using μj = αj/(αj + βj) and κ is inversely proportional to variance. This reparameterization enables the use of a Beta hyperprior over the mean of each mixture component to capture known biological behavior of AS. Specifically, normal splicing dynamics have a propensity toward high or low inclusion levels which can be modeled using the Jeffery prior α0 = 0.5, β0 = 0.5 and high concentration κ. Intermediate levels of inclusion modeled by a distribution with a long tail generally indicate aberrant splicing and can be modeled as a Beta distribution with α1 = β1 and low concentration. To control the number of tiles, we impose a L1 penalty with hyperparameter λ to induce sparsity in the number of groups for which LSV j is assigned to the signal distribution. Specifically, ∑krjk ~ Exp(λ).Consider a data matrix G0 is Bernoulli(δ)m and α0 is the concentration parameter. A larger value of α will result in the discovery of more sample groups. In taking the limit of k, the distribution of ci can be interpreted as a distribution of partitions of natural numbers which is usually formulated as the CRP or stick breaking process.In most biological contexts, the number of tiles is unknown a priori. This can be modeled as an infinite mixture of groups using a Dirichlet Process prior with Bernoulli base distribution27. Under this model, the missingness rate of a feature does not depend on any observed or unobserved values. However, this is generally only a valid assumption in scenarios when the data generating instrument malfunctions such as a defective microarray probe. The missingness rate of transcriptomic quantifications from RNA-seq is proportional to sequencing depth thus a model in which values are MNAR (missing not at random) will yield better estimates for the missingness rate. Under this model, the missingness rate of features depends on observations in the data matrix and external factors. In cancer data specifically, values can also be systematically missing due to genetic mutations resulting in no reads being mappable to the splice junction. This can occur when a mutation near a splice site reduces junction usage due to changes in splice factor binding or when the mutation introduces a PTC into an exon resulting in rapid degradation of the transcript due to NMD. Thus it becomes necessary to treat missing values as a secondary signal when the missingness rate of an LSV is much higher than expected under the null missingness rate associated with sequencing limitations. To handle missing values under the MNAR model and detect missing value signals, CHESSBOARD identifies unquantifiable LSVs with ηij < 10 and replaces xij with indicator ωij = 1. The indicator is modeled asθ0 is the background missingness rate that represents values that are missing due to techinical factors such as coverage and θ1 represents the signal missingness rate that is expected to be higher and represents values that are missing due to mutations. The priors can be estimated empirically. Specifically, we estimate the background priors by fitting the following Beta-Binomial regression model.In gene expression data, missing values typically arise due to low sequencing coverage which results in some transcripts lacking any observable reads even if they are expressed. However in splicing data, a lack of junction spanning reads mapped to a transcript that is expressed indicates inclusion of an alternative exon. Many algorithms handle missing values under the MCAR model of missingness by integrating missing values out of the modelχj = median is the median number of reads η that are mapped to LSV j across the n samples. υj is the number of samples with missing observations for LSV j. The fitted model returns MLE estimates for the coefficients β0 and β1 and the dispersion Φ. This trained model can then be used to estimate background priors αj0 = μjΦ and βj0 = (1 − μj)Φ by predicting μj from the median read depths for each LSV j in the cancer data to be analyzed. In this study the above model was fitted to whole blood samples from GTEX V8. Users can of course fit the model to more relevant healthy tissue samples for their specific cancer of interest. Generally, we expat that in healthy control samples the missingness rate will be inversely proportional to sequencing depth (MNAR) but these missing values would not represent signals caused by cancer. In a similar way, we use the same procedure over the training data (beatAML or TARGET) to get estimates for the matching signal prior αj1 and βj1.Here, ci is denoted byΨ and θ. This allows us to writeP(ci = k) which Intuitively captures the cluster’s proportion. The term Θ captures all other variables in the above likelihood not explicitly written again (for clarity). Since the integral over each rj(k+1) here is intractable, we follow Neal 2000 and sample vector rk+1)( from its prior distribution when attempting to open a new cluster. We choose the prior to be δj = P(rjk = 1) for each element for the vector j. Note that ω is not included in the notation above for clarity but is trivial to include. With Ψ and θ integrated out, we will only need to then explicitly sample r, α and β. The full posterior conditional of ci = k, sample a vector rj. with length k. Note that as k becomes large , computing this posterior becomes intractable since there are 2k binary vectors. Therefore, we approximate this posterior by sampling rj. in blocks of rja. . . rjb where b − a is the maximum blocksize.The entire joint likelihood of the CHESSBOARD model is given by:μj0 and μj1, we use a discrete approximation. We use possible values of μ on the interval p ∈  from 20 discrete bins. Thus we can evaluate the posterior of μ using a discrete categorical distribution defined as:κ0 and κ1 are hyperparameters that are inversely proportional to the variance of the prior distribution. We choose κ0 = 20 and κ1 = 10 to model low expected variance of the background and high expected variance of the signal.Finally, to sample rjk and ci, we apply the following posterior summary procedure. First, we obtain the pairwise matrix of probabilities that any two samples clustered together across all posterior samples (after burn-in and thinning). In other words, the probability that ci = cj for any two samples i and j. Determining the portion of the chain to discard can be evaluated using the Heidelberger-Welch diagnostic (Supplementary Note k being the median number of clusters across all posterior samples to obtain final clustering assignments. To obtain a point estimate for r, we obtain a matrix of the marginal probabilities that sample i in LSV j is assigned to the signal distribution. rjk = 1 if the mean of To obtain a point estimate for Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Reporting Summary


Tumor gene expression is predictive of patient prognosis in some cancers. However, RNA-seq and whole genome sequencing data contain not only reads from host tumor and normal tissue, but also reads from the tumor microbiome, which can be used to infer the microbial abundances in each tumor. Here, we show that tumor microbial abundances, alone or in combination with tumor gene expression, can predict cancer prognosis and drug response to some extent—microbial abundances are significantly less predictive of prognosis than gene expression, although similarly as predictive of drug response, but in mostly different cancer-drug combinations. Thus, it appears possible to leverage existing sequencing technology, or develop new protocols, to obtain more non-redundant information about prognosis and drug response from RNA-seq and whole genome sequencing experiments than could be obtained from tumor gene expression or genomic data alone. Computational approaches have been developed to estimate tumor microbial abundances from whole genomic and RNA-sequencing datasets. Here the authors report the predictive value of tumor microbial abundance, alone or in combination with gene expression data, for cancer prognosis and drug response. RNA-seq data is typically used to measure the expression of human genes, and there is a long history linking tumor gene expression to cancer outcomes8. Milanez-Almeida et al.9 recently showed that gene expression from TCGA RNA-seq data could predict overall survival (OS) or progression-free interval (PFI) better than classical clinical prognostic covariates—age at diagnosis, gender, and tumor stage. Importantly, Milanez-Almeida et al. used a data-driven machine learning (ML) based approach which selected features that were predictive of and correlated with prognosis, rather than approaches based on classical statistics or biological knowledge that chose features a priori.The Cancer Genome Atlas (TCGA), available from the NCI Genomic Data Commons (GDC)10 recently developed a computational workflow, using two orthogonal microbial detection pipelines, to estimate, decontaminate, normalize, and batch effect correct microbial abundances from human high-throughput sequencing data. They applied this workflow to create a comprehensive dataset of pan-cancer tumor microbial abundances derived from WGS or RNA-seq data for the entire TCGA cohort.Research into the human tumor microbiome has been rapidly expanding, and multiple laboratories have attempted to utilize existing technologies and data to identify microbes and quantify their abundance within human tumors compared to adjacent normal tissue. RNA-seq and WGS data not only contain human sequencing reads, but also reads from the local intratumor microbiome that are typically filtered out from the data when analyzing human gene expression or genomic alterations. Poore et al.Our central research questions then were, (1) does a data-driven ML approach reveal that tumor microbial abundances in TCGA data, quantified from these reads, are predictive of cancer prognosis or drug response, (2) what microbial genera are potentially predictive biomarkers of prognosis or drug response, (3) how do these models compare to equivalent models based on tumor gene expression data, and (4) does combining both microbial abundance and gene expression features produce models and select combinations of genes and microbial genera that are more predictive of prognosis or drug response than models from each individual data type? Here we use the processed microbial abundances directly from the Poore et al. dataset to build predictive models of prognosis and drug response for TCGA. We also use TCGA RNA-seq read counts to build equivalent predictive models for comparison.We show that in four cancer types, adrenocortical carcinoma, cervical squamous cell carcinoma, brain lower grade glioma, and subcutaneous skin melanoma, tumor microbial abundances are better predictors of prognosis than clinical covariates alone. However, we find that tumor gene expression is a more powerful predictor of prognosis, across a wider range of cancer types, than microbial abundances. Moreover, we find five cancer-drug pairs where tumor microbial abundances are more predictive of patient drug response than clinical covariates alone. These five pairs include docetaxel treatment for breast invasive carcinoma and sarcoma, and several treatments for stomach adenocarcinoma. We find that tumor microbial abundances are similarly as predictive of drug response as gene expression, but in mostly different cancer-drug combinations.An overview of the analytical workflow is presented in Fig. 11 algorithm, which jointly selects the most predictive subset of features via cross-validation (CV) while simultaneously being able to control for prognostic clinical covariates. In our models, we included and controlled for the clinical covariates age at diagnosis, gender, and tumor stage. For comparison, we also built standard Cox regression models based on the clinical covariates alone. We evaluated the predictive performance of our models using Harrell’s concordance index (C-index), which is a metric of survival model predictive accuracy. Each model analysis generated 100 model instances and C-index scores from randomly shuffled train-test CV splits on the data. We found 33 OS and PFI models for 21 tumor types that had a mean C-index score ≥0.6 and significantly outperformed their corresponding clinical covariate-only models )13, which is an extension of the area under the receiver-operating characteristic curve (AUROC) for continuous outcomes and can provide a more detailed resolution picture of predictive performance throughout the test outcome time range compared to the C-index score. Although 33 of our OS and PFI gene expression models had a statistically significant C-index score improvement compared to clinical covariates alone, only 22 of these models showed an improvement in AUCC/D(t), where the improvement in mean AUCC/D(t) over the entire test time range after diagnosis was ≥ 0.025 , where the improvement in mean AUCC/D(t) over the entire test time range after diagnosis was ≥0.025  or partial response (PR) were labeled as responders and those with stable disease (SD) or progressive disease (PD) as non-responders. Thirty TCGA cancer-drug combinations met our minimum dataset size thresholds  logistic regression (LGR) with elastic net16 (L1 + L2) penalties and embedded feature selection, and 3) logistic regression with an L2 penalty and limma17  or edgeR19 (for RNA-seq count datasets) differential abundance/expression feature scoring and wrapper selection methods (see Methods for details). All three ML methods unconditionally included the clinical covariates—age at diagnosis, gender, and tumor stage – in the model (bypassing feature selection) while selecting the most predictive subset of microbial abundance or gene expression features. For comparison, we built standard linear SVM or LGR models using the clinical covariates alone. We evaluated the predictive performance of drug response models using AUROC. Each analysis generated 100 model instances, AUROC, and area under the precision-recall curve (AUPRC) scores from randomly shuffled train-test CV splits on the data.We next asked whether tumor microbial abundances from pre-treatment biopsies could predict drug response better than the clinical covariates age at diagnosis, gender, and tumor stage alone. TCGA drug response clinical data were obtained from Ding et al.21.We found five microbial abundance cancer-drug combinations that had a mean AUROC score ≥0.6 and performed better than clinical covariates alone in at least two out of three ML methods Fig. . Three oC/D(t) metric showed a clear improvement in prognostic predictive power for these models, where the improvement in mean AUCC/D(t) over the entire time range after diagnosis was ≥0.025 compared to their respective single data type models. We also found five combined data type drug response models which performed significantly better than clinical covariates alone, although none of these models reached statistical significance when compared to their respective single data type models in terms of improvement in AUROC score, but one of these models, for BLCA cisplatin, did show an improvement in AUROC ≥0.025 compared to its corresponding single data type models  OS  cisplatin and oxaliplatin, had p-values <0.08 .Some of the TCGA drug response cohorts used in our study were of limited size and this could have an impact on the robustness of our analysis . We plotted how these scores were affected by the hyperparameters that controlled feature selection. Our decision to conservatively limit the feature selection search space in our drug response models to a maximum of 400 best scoring features, to reduce model complexity and the possibility of overfitting, appeared sufficient, as scores for our significant models reached a maximum or leveled off well within this search range Fig. . In the 22 that the tumor microbiome is tumor type-specific. The predictive genera we found span all non-eukaryotic domains of life, in total encompassing 365 bacterial, 17 archaeal, and 46 viral genera  fully within the inner nested CV is essential to avoid overfitting and to produce unbiased results, and that hyperparameter tuning should ideally also be performed in nested fashion. Finally, they found that performing an adequate number of CV folds was important to reduce bias. Our analyses have followed their observations and recommendations, employing them at every level of model development and evaluation, including additional techniques not reviewed in their work (see Methods for full details).There are also some limitations to our current study. As we described previously, some TCGA drug response cohorts were of limited size or had relatively few responder or non-responder cases within these cohorts and this could have an impact on the interpretability of the results. Vabalas et al.10 First, the study was retrospective, using existing data from the TCGA. As such, it did not involve any specific protocols to capture microbial reads or to control for contamination. Second, decontamination of such retrospective data is a highly involved and dataset-specific process, which they made great effort to validate. Poore et al. conclude from this validation that the retrospective study of TCGA was successful, and that similar retrospective studies would be valuable. A third point, which they touch on briefly, is that the protocols that were used have limitations with respect to capturing microbial reads and cannot distinguish if the source of microbial reads is intracellular or extracellular, or alive or dead when the sample was taken. Poore et al. suggest, correctly we believe, that additional protocols need to be developed for prospective studies.There are further limitations to this study inherited from limitations in the data, originally raised by Poore et al.p-value = 0.0014). Firmicutes reversed this trend, being more often positively predictive  and rectum (READ) adenocarcinomas) also using harmonized and normalized GDC TCGA data, they used WGS and whole exome sequencing (WXS) data instead of WGS and RNA-seq, they also used different methods for extraction and decontamination of microbial reads, and finally they also applied classical univariate statistics on their entire data to infer correlation with overall survival (OS). While we believe the use of harmonized GDC TCGA data is superior to legacy TCGA, Poore et al. applied robust computational methods to remove technical variation from legacy TCGA data and validated that their approach was effective. We also applied additional filters of TCGA samples to further remove technical variation. We also believe that, in general, applying classical univariate statistics on the entire data to find correlations has the potential to overfit the specific dataset and it does not consider the multivariate nature of high-dimensional biological data like intratumor microbial abundances. A data-centric, multivariate, and regularized ML approach focused on fitting models on training data and evaluating on unseen test data has potential to generalize better and discover whether features are potentially predictive of and correlated with the response variable, such as survival outcomes or drug response.Our prognosis analysis results were different than two recent reportsHelicobacter was a quantified microbial abundance feature in the Poore et al. dataset although notably, even though it is well established that patients infected with H. pylori have an increased risk of developing gastric cancer28, Helicobacter was not identified as a predictive feature of drug response in our STAD models. This finding is in line with recent research indicating reduced microbial diversity, decreased abundance of H. pylori, and enrichment of other mostly commensal bacterial genera in gastric carcinoma29. Instead, in STAD we found that known opportunistic bacteria Cedecea and Sphingobacterium were both strongly negatively predictive of leucovorin response, Sphingobacterium was strongly negatively predictive of cisplatin response, and the opportunistic bacteria Rouxiella was strongly negative predictive of oxaliplatin response. Cedecea and Sphingobacterium have been implicated in bacteremia in immunocompromised individuals in rare cases, including cancer33. As dysbiosis is frequent in stomach cancer35, and considering the mechanism of action of leucovorin, it may be of interest to study whether organisms from these two genera may sequester or prevent the bacterial production of folinic acid36.Looking at our drug response model results, in STAD, tumor microbial abundances were predictive of response to three different drugs: cisplatin, leucovorin, and oxaliplatin. The genus 37 has recently received considerable attention. In BRCA, we found that the genus containing Epstein-Barr virus (EBV) was negatively associated with response to docetaxel, which is concordant with previous findings that EBV is associated with chemoresistance to docetaxel in gastric cancer38. Interestingly, Cyanobacteria were predictive features in several cancers in our study and we identified a genus of Cyanobacteria as predictive of response to docetaxel in BRCA. Notably, the presence of Cyanobacteria in BRCA was recently confirmed by Nejman et al.22 by 16S-rRNA sequencing. While the genus we identified, Raphidiopsis, a planktonic Cyanobacteria that produces toxins harmful to human health and found in freshwater, is possibly a taxonomic identification error in the original microbial abundance estimates, our findings may point to a related genus under the recently discovered clade Melainabacteria of Cyanobacteria39, which is present in humans. Though Melainabacteria are difficult to culture, we believe that confirmation of the relationship between BRCA to response to docetaxel and Melainabacteria should be tested, and a first step would be to confirm our computationally derived findings in a dedicated 16S-rRNA analysis.We found three microbial genera whose abundances were strongly associated with breast cancer response to docetaxel. Indeed, the involvement of the tumor microbiome in breast cancer (BRCA)Lactococcus to be positively associated with response to docetaxel. Lactococcus contains species that can sometimes cause opportunistic infections in humans, as Lactococcus are similar to Streptococcus and formerly belonged to that genus. The result that this genus was positively associated with response in our model initially appeared counterintuitive, although while the use of therapeutic bacteria as antitumor agents has not been an extensively studied field, there have been some limited findings in the literature that suggest the use of bacteriotherapy as anticancer agents40. Historically, the intentional use of the toxins of various Streptococcus species showing significant antitumor activity in SARC has been documented43. One possible testable explanation for some microbes being strongly positive predictive of docetaxel response in our model is that they might produce some extracellular products or toxins that could work as an adjuvant to the chemotherapy.Interestingly, in sarcoma (SARC), among the most predictive microbial features, we found the genus 44.In summary, while these findings and others reported in this study are computationally derived associations, we believe that they can serve as leads for further experimental studies of the role of microbial species in modulating patient survival and drug response, potentially by metabolizing drug levels in the tumor microenvironment as suggested above, or by altering the immune response, either by changing the levels of specific immunometabolites or by having the tumors present specific bacterial antigens10 . Specifically, the “Kraken-TCGA-Voom-SNM-Plate-Center-Filtering-Data.csv” microbial abundance data file and adjoining “Metadata-TCGA-Kraken-17625-Samples.csv” metadata file were used as the starting input for further data processing.Normalized and batch effect corrected microbial abundance data for 32 TCGA tumor types were downloaded from the online data repository referenced in Poore et al.We first filtered the data for primary tumor samples . Poore et al. generated microbial abundances from all the available WGS and RNA-seq data in legacy TCGA , which frequently contained replicate WGS and RNA-seq data for the same case and sample type. It was common in legacy TCGA to increase WGS sequencing coverage by performing an additional sequencing run from the same sample and these secondary runs typically had a much lower number of reads and coverage compared to their corresponding primary sequencing runs. When comparing the normalized and batch effect corrected read counts between these WGS runs, we found that microbial abundance data which came from lower coverage secondary runs could be substantially different from abundances derived from the larger primary sequencing runs. Therefore, we excluded microbial abundance data which came from secondary runs. In addition, legacy TCGA commonly contained data for the same samples analyzed using different computational pipeline versions. We excluded replicate microbial abundance data from older TCGA analysis pipeline versions if a replicate from a newer version existed. After the above filters, the Poore et al. data went from 17,625 samples and 10,183 unique cases to 12,111 samples and 9812 unique cases .45 were obtained from UCSC Xena. Cases which had both missing overall survival (OS) and progressive-free interval (PFI) outcome data were excluded from survival modeling.TCGA gender, age at diagnosis, and tumor stage demographic and clinical data and as well as primary tumor RNA-seq read count data for the 32 TCGA tumor types included in our study were obtained from the NCI Genomic Data Commons (GDC Data Release v29.0) using the R Bioconductor package GenomicDataCommons. TCGA GENCODE v22 gene annotations were obtained from the GDC data portal and Ensembl Gene v98 using the R package rtracklayer and R Bioconductor packages AnnotationHub and ensembldb. The downloaded GDC primary tumor cohort with RNA-seq read count data comprised of 9735 samples from 9680 unique cases. There were 68 cases at the GDC which had missing age of diagnosis but existing values in the Poore et al. data and we chose not to exclude these data and used the Poore et al. age of diagnosis values for these cases. TCGA curated survival phenotypic data14 Our drug response models used the following binary classification targets: complete response (CR) and partial response (PR) were labeled as responders and stable disease (SD) and progressive disease (PD) as non-responders. All TCGA samples with drug response phenotypic data were from pre-treatment biopsies. Due to the limited cancer-drug combination cohort sizes in TCGA, we modeled each drug individually, even if a patient received multiple drugs concurrently. If the same drug was given at multiple timepoints to a patient, we only considered their first drug response. We considered cancer-drug combinations that contained a minimum of 18 cases and at least 4 cases per response binary class, except for STAD oxaliplatin, where we allowed a minimum of 14 cases so that the gene expression dataset could be included. In total, we analyzed 30 cancer-drug combinations which had paired microbial abundance and gene expression data that met the above thresholds. Combined feature microbial abundance and gene expression datasets were created by joining data from each individual dataset which had matching TCGA sample UUIDs. For some TCGA cases, data existed from multiple different aliquots per sample or multiple technical runs per aliquot, therefore in these cases all combinations were joined at the sample UUID level. Cross-validation sampling probability weights as well as model and scoring sample weights were applied to account and adjust for any imbalance caused by the process. Supplementary Data TCGA curated drug response clinical data were compiled from Ding et al.46 and scikit-survival libraries49. Custom extensions to scikit-learn and scikit-survival were developed to add methods and functionalities required by this project. Survival models were built using Coxnet— regularized Cox regression with elastic net penalties11. Coxnet models controlled for gender, age at diagnosis, and tumor stage clinical prognostic covariates by including them as unpenalized features in the model . Drug response classification models were built using three different ML methods: (1) a variant of the linear support vector machine recursive feature elimination (SVM-RFE) algorithm15 that we developed with a number of additional features and better performance than the scikit-learn built-in version, (2) logistic regression (LGR) with elastic net16 (L1 + L2) penalties and embedded feature selection, and (3) LGR with an L2 penalty and limma17  or edgeR19 (for RNA-seq count datasets) differential abundance/expression feature scoring inside a k-best wrapper feature selection method around the learning algorithm. Limma differential abundance analysis was run inside the ML pipeline with default parameters except for fitting an intensity-dependent trend to the prior variances and running a robust empirical Bayes procedure (eBayes function parameters trend = TRUE and robust = TRUE). edgeR differential expression analysis was run inside the ML pipeline with default parameters except for enabling robust estimation of the negative binomial dispersion  and robust estimation of the prior quasi-likelihood (QL) dispersion (glmQLFit function robust = TRUE). Both limma and edgeR methods scored and ranked features by differential abundance/expression p-value.Machine learning (ML) models were built using the scikit-learnAll three drug response ML methods unconditionally included the same three clinical covariates in the model as in the prognosis models by having them bypass feature selection in the ML pipeline, though in drug response models, clinical covariates were modeled as L2 penalized features. In SVM-RFE, clinical covariate features bypassed recursive feature elimination but were always included at each RFE recursive feature elimination model fitting step as well as final model refitting. To the best of our knowledge, no available comprehensive ML library in python or R currently provides an elastic net LGR algorithm with the functionality to specify features that can bypass embedded feature selection and be modeled with an L2 penalty . In order to develop this functionality for our study, our elastic net LGR model pipeline was designed as a two-level LGR, (1) an elastic net LGR and embedded feature selection on only microbial abundance or gene expression features with clinical covariates bypassing this step, followed by (2) an L2 penalized LGR on features selected by the elastic net LGR step and the clinical covariates. We know this design does not likely produce the exact same model settings and results of a single-level elastic net LGR algorithm with the functionality we needed, if such an implementation it existed, though we tested every drug response model through an ML pipeline with elastic net LGR and no clinical feature selection bypass and found that model predictive performance, feature coefficients and signs, and feature importance rankings were similar to our two-level ML pipeline setup.Gender was one-hot encoded and tumor stage ordinal encoded by major stage. In the final cohort included in our prognosis and drug response models, 3363 out of 9708 tumor microbial abundance cases (34.64%) and 3244 out of 9484 gene expression cases (34.21%) had tumor stage “not reported” or BRCA stage “X”. Since missing tumor stage metadata is so prevalent in TCGA, we took the approach of including these in our study and modeled missing tumor stage with as neutral an ordinal encoding as possible. Looking at the distribution of reported major tumor stages in our cohort, we determined that encoding missing data as an ordinal between tumor stage II and III was as close to the middle of the distribution of stages in TCGA as we could possibly achieve with ordinal encoding.All prognosis and drug response models included the previously described feature selection as well as normalization and transformation steps integrated into the ML modeling pipeline using an extended version of the scikit-learn Pipeline framework. Each cancer, data type, and survival or drug response target type combination was modeled individually using a nested cross-validation (CV) strategy to perform model selection and evaluation on held-out test data. Training data splits always underwent feature selection, normalization, and transformation through the ML pipeline independently from held-out test or validation data splits before learning. Models built using gene expression read count data included edgeR low count filtering, weighted trimmed mean of M-values (TMM) normalization, and log counts per million (CPM) transformation steps within the ML pipeline. These were developed and integrated into our scikit-learn-based framework via R and rpy2. All models also included standardization of features within the ML pipeline just before learning. During prediction, held-out test or validation data were feature selected, normalized, and transformed through the ML pipeline using the parameters learned from the training data at each pipeline step before model prediction and scoring. Hyperparameter search and optimization of all model pipeline steps was performed in nested fashion within the inner nested CV. All cross-validation iterators kept replicate sample data per case grouped together such that data would only reside in either the train or test split during each CV iteration.–2. Alpha is the constant multiplier of the penalty terms in the Coxnet objective function. Optimal alpha and L1 ratio settings were determined via inner CV and a model with these settings was then refit on the entire outer CV train data split. Model performance was evaluated in both inner and outer CV on each held-out validation or test data split, respectively, by generating model test predicted risk scores and using these scores to directly calculate C-index scores. We also evaluated and compared model predictive performance for each test data split survival time period by calculating time-dependent cumulative/dynamic AUCs13.Survival models used a stratified and randomly shuffled outer CV with 75% train and 25% test split sizes that was repeated 100 times. The CV procedure stratified the splits on event status. Each training set from the outer CV was used to perform hyperparameter tuning and model selection by optimizing Harrell’s concordance index (C-index) over a stratified, randomly shuffled, 4-fold inner CV on the training set repeated 5 times. A few cancer datasets contained fewer than four uncensored cases which required reducing the number of inner CV folds for these models such that at least one case per fold was uncensored. The data derived from Poore et al. often included more than one sample per case, and an unequal number of samples between cases, therefore requiring either ML model sample weighting or CV random sampling per case depending on what is supported by the modeling and scoring methods used. The Coxnet implementation in scikit-survival does not currently support sample weighting, therefore our custom outer CV iterator randomly sampled one replicate sample per case during each iteration, using a sampling procedure with probability weights that balanced the probability that a replicate WGS- or RNA-seq-based sample was selected during each CV iteration. Model selection grid search was performed on the following hyperparameters: elastic net penalty L1 ratios 0.1, 0.3, 0.5, 0.7, 0.8, 0.9, 0.95, 0.99, and 1, and for each L1 ratio a default alpha path of 100 alphas using an alpha min ratio of 10–5 to 103, elastic net LGR L1 ratios of 0.1, 0.3, 0.5, 0.7, 0.8, 0.9, 0.95, 0.99 and 1, elastic net LGR C regularization parameter from a range of 10−2 to 103  or from 10−2 to 101 (gene expression and combined data type), and finally RFE, elastic net LGR, and limma and edgeR feature scorer k-best feature selection search range from 1 to 400 top scoring microbial abundance, gene expression, or combined data type features. SVM-RFE models performed a feature elimination procedure of the one worst feature per recursive step for microbial abundance models  and 5% of worst remaining features per recursive step until 1300 features were reached followed by the one worst feature per recursive step for gene expression  and combined data type models . Optimized hyperparameter settings were determined via inner CV and a model with the optimized settings was then refit on the entire outer CV train data split. Model performance was evaluated in both inner and outer CV on each held-out validation or test data split, respectively, by AUROC, average precision (AVPRE) or area under precision-recall curve (AUPRC), and balanced accuracy (BCR). AUROC was used to evaluate and select the best model and optimized hyperparameter settings from the grid search.Drug response models used a stratified, randomly shuffled, 4-fold outer CV that was repeated 25 times . Each training set from the outer CV was used to perform hyperparameter tuning and model selection by optimizing the area under the receiver-operating characteristic curve (AUROC) over a stratified, randomly shuffled, 3-fold inner CV repeated 5 times. Case replicate sample weights were provided to SVM-RFE and LGR learning algorithms and all model selection and evaluation scoring methods. Class weights were provided to SVM-RFE and LGR learning algorithms to adjust for any class imbalance. Model selection grid search was performed on the following hyperparameters: L2 penalized SVM and LGR C regularization parameter from a range of 10p-values generated from the signed-rank test across survival or drug response analyses from the same data type were adjusted for multiple testing using the Benjamini–Hochberg (BH) procedure to control the false discovery rate (FDR), and a threshold FDR ≤0.01 was used to determine statistical significance. To test whether a combined data type model was significantly better than its corresponding microbial abundance or gene expression model, a two-sided Dunn test was performed between all three groups of data type model scores. Each Dunn test raw p-value was adjusted for multiple testing using the Benjamini-Hochberg (BH) procedure to control the false discovery rate (FDR), and a threshold FDR ≤0.05 was used to determine statistical significance.Gender, age at diagnosis, and tumor stage clinical covariate-only survival models were built using standard unpenalized Cox regression. Clinical covariate-only drug response models were built using L2 penalized linear SVM or LGR. Models included standardization of features as part of the ML pipeline. Models were trained and tested using the same outer CV iterators and train/test data splits as their corresponding microbial abundance, gene expression, or combination data type models. To test whether a Coxnet, SVM-RFE, or LGR microbial abundance or gene expression model was significantly better than their corresponding Cox, linear SVM, or LGR clinical covariate-only model, respectively, a two-sided Wilcoxon signed-rank test was performed between the 100 pairs of C-index or AUROC scores between both models. All raw p-value was calculated from the fraction of permutation mean scores that were greater than or equal to the true mean score. A p-value ≤0.05 was used to determine statistical significance. The Freedman-Draconis rule was used in permutation test histogram plots to compute the bin width. Analysis of the effect of number of selected features on model performance was performed via the hyperparameter grid search and tuning that occurred in the nested inner CV during each model instance fitting, where scores for every combination of hyperparameter setting and inner CV train/validation fold were saved for all model instances and used for plotting.Permutation tests were performed by shuffling dataset class labels 1000 times and each time running the outer CV procedure on the permuted dataset, where for each CV iteration we fit a model instance and calculated an AUROC score, totaling 100,000 fits and scores for each model. Permutation mean AUROC scores were compared to the true mean AUROC score for the model and a one-sided empirical 50, which allows exact calculation of p-values. Coefficients were ignored when a genus was assigned a zero coefficient or absent from a model. Second, within each model, all coefficients, ignoring the results of the Wilcoxon test, were ranked by absolute magnitude. We then kept genera that were among the top 50 features in at least 20% of the models and for which the Holm-adjusted, two-sided Wilcoxon signed-rank test p-value was ≤0.01. Having a Coxnet feature coefficient equal to zero or feature being absent from an SVM-RFE or LGR model was not strong enough evidence that the genus has no effect, but rather that one or more features with stronger effect were chosen. Thus, we ignored genera with a zero coefficient or absent from a model when computing mean coefficient weight and Wilcoxon statistics on the means.For each analysis, 100 prognosis or drug response model instances were generated from the outer CV procedure. Each model instance selected a subset of features that performed best during CV and the model algorithm learned coefficients (or weights) for each feature. To select microbial genera for downstream investigation from the feature results across all these model instances, we proceeded as follows. First, we applied a two-sided Wilcoxon signed-rank test that the mean feature coefficient rank generated by the model is shifted away from zero, and thus that the genus is identifiably positively or negatively associated with survival or drug response. For all Wilcoxon tests, we used the package coinFor the drug response models, where three ML methods were tested, we noted the features selected by individual models and the median rank the feature attained in the instances in which it appeared, but further filtered the features to account for the consensus between ML models. We kept features selected in any two ML model methods that individually met our criteria for inclusion, ignoring features in ML models that did not meet these criteria. We then computed the Spearman correlation between the median ranks attained by the features.For each selected microbial feature, we tested whether it was a significantly univariate feature of survival or drug response. This is a strictly different question than whether the coefficient of a feature has consistent sign— sign may be consistent when used in combination with other features, but the feature may not be individually predictive. For drug response models, we divided individuals into responders and non-responders, and for survival data we divided individuals whose survival time was greater or less than the censored median, ignoring those who were lost to follow up before median time. For cases that had technical replicates, we randomly selected a single replicate. For each cancer-test type pair, we applied a two-sided Wilcoxon rank-sum test. We applied a Benjamini-Hochberg multiple hypothesis correction for each cancer-test type pair and report the false discovery rate in Supplementary Data We analyzed the distribution of features, selected by the rules described above, that had positive or negative signs for their mean coefficient. We used a two-sided binomial test to show that selected features had significantly more negative the positive mean coefficients. We used a two-sided Fisher’s exact test to determine if selected genera belonging to Firmicutes had a statistically significant difference in the breakdown between positive and negative mean coefficients than selected features as a whole.Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting SummaryPeer Review File