Multi-view based integrative analysis of gene expression data for identifying biomarkers

Background - In the NeoTRIPaPDL1 phase III trial, triple-negative breast cancer (TNBC) patients were randomized to receive nab-paclitaxel/carboplatin for 8 cycles (CT arm) with or without atezolizumab (CT/A arm). We previously reported that the majority of patients (65%) with PD-L1- at baseline converted to PD-L1+ (the majority with IC2/IC3) after the first cycle of treatment in the atezolizumab arm. Here we studied treatment associated changes in the molecular tumour features and immune microenvironment by PD-L1 expression. Methods – A total of 158 (56.4%) of patients enrolled in the NeoTRIPaPDL1 trial (76 and 82 from CT/A and CT arm, respectively) were included in this analysis by satisfying the following criteria: i) availability of a paired core biopsy at baseline and at day 1 of the second treatment cycle (D1C2); ii) evaluation of stromal TILs (sTILs) and staining for PD-L1 (Ventana SP142); iii) successful RNA-seq gene expression profiling. PD-L1 groups were defined as IC0/IC1 (<5% PD-L1low) vs IC2/IC3 (>=5% PD-L1high). Presence of different immune cell populations was quantified by gene expression profile deconvolution using ConsensusTME R package. Cancer hallmark gene set collection and custom signature activation status were estimated in each sample using singscore R package. Differences in score distribution were evaluated by 2-sided t-test. Results At baseline, both sTILs and immune cell signatures were upregulated in PD-L1high compared to PD-L1low tumours (p<0.001). No significant differences were found between the two treatment arms in the PD-L1high subpopulation. In the PD-L1low cases, both sTILs and immune related signatures were slightly downregulated in the CT/A arm (p<0.05), suggesting a modest unbalance among treatment arms with CT arm being slightly more inflamed than CT/A arm.At D1C2, PD-L1high tumours in the CT arm systematically had high sTILs (median=70%, range=30-90%), while PD-L1low tumours receiving CT/A had low sTILs in a significant proportion of cases (median=30%, range=0-90%; p<0.001). Similarly, at D1C2 several gene expression-estimated immune cell populations and immune-related signatures were upregulated in the CT arm compared to CT/A arm in PD-L1high tumours, with the weakest association observed for M2 macrophages (p=0.058). No tumour-related signatures were differentially expressed among the two treatment arms within groups with PD-L1high, suggesting that different treatment. modulate PD-L1 by engaging a different immune mileau instead of modulating tumor related features.Considering PD-L1low groups at D1C2, in the CT arm 21 tumors had >30% sTILs (n=21/69, 30.4%), while in the CT/A arm only 2 had sTILs >30% (2/27, 7.4%) (p<0.001). Analysis of gene expression data identified IFN-related signatures as the most upregulated in the CT compared to CT/A arm in PD-L1low cases at D1C2 (p<0.05). Conclusions Integrated dynamic analysis of PD-L1 expression and gene expression data highlighted significant treatment-specific changes of the immune landscape according to PD-L1 expression, when this biomarker is assessed during treatment. This indicates that the immune milieu associated with PD-L1 status is strongly dependent on the time of assessment in relationship to treatment received. Such observation may explain why in the NeoTRIP trial, baseline PD-L1 but not on-treatment PD-L1 was predictive of pCR in CT/A arm. In addition, our findings could have implications related to the use of PD-L1 as a predictive biomarker in pre-treated patients, especially when assessed early on during treatment. Citation Format: Maurizio Callari, Chiun-Sheng Huang, Daniel Egle, Begoña Bermejo, Claudio Zamagni, Matteo Dugo, Marc Thill, Antonio Anton, Marco Barreca, Stefania Russo, Eva Maria Ciruelos, Richard Greil, Stefania Zambelli, Balázs Gyorffy, Chanel Smart, Olivia Biasi, Pinuccia Valagussa, Giuseppe Viale, Luca Gianni, Giampaolo Bianchini. Immune milieu associated with PD-L1 status in TNBC is dependent on time of biomarker assessment and treatment received: A secondary analysis of the NeoTRIPaPDL1 trial [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P1-04-02.

BackgroundSerial Analysis of Gene Expression (SAGE) is a DNA sequencing-based method for large-scale gene expression profiling that provides an alternative to microarray analysis. Most analyses of SAGE data aimed at identifying co-expressed genes have been accomplished using various versions of clustering approaches that often result in a number of false positives.Principal FindingsHere we explore the use of seriation, a statistical approach for ordering sets of objects based on their similarity, for large-scale expression pattern discovery in SAGE data. For this specific task we implement a seriation heuristic we term ‘progressive construction of contigs’ that constructs local chains of related elements by sequentially rearranging margins of the correlation matrix. We apply the heuristic to the analysis of simulated and experimental SAGE data and compare our results to those obtained with a clustering algorithm developed specifically for SAGE data. We show using simulations that the performance of seriation compares favorably to that of the clustering algorithm on noisy SAGE data.ConclusionsWe explore the use of a seriation approach for visualization-based pattern discovery in SAGE data. Using both simulations and experimental data, we demonstrate that seriation is able to identify groups of co-expressed genes more accurately than a clustering algorithm developed specifically for SAGE data. Our results suggest that seriation is a useful method for the analysis of gene expression data whose applicability should be further pursued.

Recent research has focused on incorporating biological function and pathway information into the analysis of gene expression data, partly as a means of compensating for insufficient experimental replications, low signal to noise, lack of reproducibility and/or multiple testing confounds. A Bayesian approach seems to be ideal for incorporating functional information into gene expression data analysis. In this study, we tested the feasibility of using literature derived gene relationships in a Bayesian model to analyze gene expression data. Prior distributions were constructed based on gene associations derived from the biomedical literature using Latent Semantic Indexing (LSI). The LSI model was built using more than 1 million Medline abstracts corresponding to 22,000 human and mouse genes. A key advantage of LSI is that both explicit and implicit gene relationships can be derived from the literature. Gene neighborhoods were determined using latent Gaussian Markov random fields and logistic transformation of the latent variables. We tested the procedure on a microarray dataset for interferon-stimulated genes in mouse embryonic fibroblasts. By integrating functional information from literature, Bayesian approach identified relevant genes that previously did not meet the 0.05 significance level. In comparison to a standard mixture model, spatial mixture model has more power for identifying direct and indirect interferon regulated genes. The spatial model enhanced the ranks of some genes which are known to be affected by interferon treatment, such as Nmi (NMI N-myc and STAT interactor) and ifi35 (interferon-induced protein 35). It also identified some genes that previously were ignored because of the marginal p-values, such as dpysl2, map2k1, msn, Psck5, and Il6st. Interestingly, these genes appear to be indirectly related to interferon treatment. In summary, we show that our procedure increases statistical power and produces more biologically meaningful gene lists. These results suggest that Bayesian methods which incorporate functional information from the literature may improve analysis of gene expression data.

BackgroundMicroarrays have become extremely useful for analysing genetic phenomena, but establishing a relation between microarray analysis results (typically a list of genes) and their biological significance is often difficult. Currently, the standard approach is to map a posteriori the results onto gene networks in order to elucidate the functions perturbed at the level of pathways. However, integrating a priori knowledge of the gene networks could help in the statistical analysis of gene expression data and in their biological interpretation.ResultsWe propose a method to integrate a priori the knowledge of a gene network in the analysis of gene expression data. The approach is based on the spectral decomposition of gene expression profiles with respect to the eigenfunctions of the graph, resulting in an attenuation of the high-frequency components of the expression profiles with respect to the topology of the graph. We show how to derive unsupervised and supervised classification algorithms of expression profiles, resulting in classifiers with biological relevance. We illustrate the method with the analysis of a set of expression profiles from irradiated and non-irradiated yeast strains.ConclusionIncluding a priori knowledge of a gene network for the analysis of gene expression data leads to good classification performance and improved interpretability of the results.

Powerful methods for integrated analysis of multiple biological data sets must be developed to maximize researchers’ ability to interpret them and acquire meaningful knowledge. Projection onto the most interesting statistical evidence (PROMISE) is a powerful statistical procedure that has been recently developed to incorporate a biological paradigm for the relationships among endpoint variables into an integrated analysis of microarray gene expression data with multiple biological and clinical endpoints. Here, PROMISE is adapted to perform an integrated analysis of pharmacokinetic, clinical, and genome-wide genotype data that incorporates a clinically relevant biological paradigm for pharmacokinetic and clinical response data. An efficient permutation-testing algorithm is introduced so that statistical calculations are computationally feasible in this higher-dimension setting. The new method is applied to a pediatric leukemia data set. The results clearly indicate that PROMISE can be a very powerful statistical tool for identifying genomic features that exhibit a pattern of association with multiple endpoint variables which is concordant with a practically useful biological paradigm.

Diabetes is a worldwide health issue affecting millions of people. Machine learning methods have shown promising results in improving diabetes prediction, particularly through the analysis of gene expression data. While gene expression data can provide valuable insights, challenges arise from the fact that the number of patients in expression datasets is usually limited, and the data from different datasets with different gene expressions cannot be easily combined. This work proposes a novel approach to address these challenges by integrating multiple gene expression datasets and domain-specific knowledge using knowledge graphs, a unique tool for biomedical data integration, and to learn uniform patient representations for subjects contained in different incompatible datasets. Different strategies and KG embedding methods are explored to generate vector representations, serving as inputs for a classifier. Extensive experiments demonstrate the efficacy of our approach, revealing weighted F1-score improvements in diabetes prediction up to 13% when integrating multiple gene expression datasets and domain-specific knowledge about protein functions and interactions.

BackgroundLower-grade gliomas (LGGs) is characteristic with great difference in prognosis. Due to limited prognostic biomarkers, it is urgent to identify more molecular markers to provide a more objective and accurate tumor classification system for LGGs.MethodsIn the current study, we performed an integrated analysis of gene expression data and genome-wide methylation data to determine novel prognostic genes and methylation sites in LGGs.ResultsTo determine genes that differentially expressed between 44 short-term survivors (<2 years) and 48 long-term survivors (≥2 years), we searched LGGs TCGA RNA-seq dataset and identified 106 differentially expressed genes. SERPINA5 and TIMP1 were selected for further study. Kaplan–Meier plots showed that SERPINA5 and TIMP1 expression were significantly correlated with overall survival (OS) and relapse-free survival (RFS) in TCGA LGGs patients. We next validated the correlation between the candidate genes expression and clinical outcome in CGGA LGGs patients. Multivariate analysis showed that TIMP1 mRNA expression had a significant prognostic value independent of other variables (HR = 4.825, 95% CI = 1.370–17.000, P = 0.014). Then, differential methylation sites were identified from differentially candidate gene expression groups, and all four methylation sites were significantly negatively correlated with gene expression (spearman r < − 0.5, P < 0.0001). Moreover, hyper-methylation of four methylation sites indicated better OS (P < 0.05), and three of them also shown statistical significantly association with better RFS, except for SERPINA5 cg15509705 (P = 0.0762).ConclusionTaken together, these findings indicated that the gene expression and methylation of SERPINA5 and TIMP1 may serve as prognostic predictors in LGGs and may help to precise the current histology-based tumors classification system and to provide better stratification for future clinical trials.

Fuzzy C-Means (FCM) algorithm has been widely used in cluster analysis of gene expression data. It can converge rapidly and provide more information regarding relationships within the data thanks to the usage of fuzzy sets to represent the degrees of cluster membership for every data point. However, FCM has a shortcoming in that it requires a priori specification of cluster number and cluster validation. Most cluster validity indices are based on the data themselves and may not be applicable for gene expression data. In this paper, we propose a Bayesian method using Gene Ontology (GO) annotations for gene expression cluster validation. We show that our method outperforms popular validity indices on gene expression datasets.

Non-small cell lung cancer (NSCLC) is the most prevalent histological cancer subtype worldwide1. As the majority of patients present with invasive, metastatic disease2, it is vital to understand the basis for lung cancer progression. Hmga2 is highly expressed in metastatic lung adenocarcinoma where it contributes to cancer progression and metastasis3-6. Here we show that Hmga2 promotes lung cancer progression by operating as a competing endogenous RNA (ceRNA)7-11 for the let-7 microRNA (miRNA) family. Hmga2 can promote the transformation of lung cancer cells independent of protein-coding function but dependent upon the presence of let-7 sites; this occurs without changes in the levels of let-7 isoforms, suggesting that Hmga2 affects let-7 activity by altering miRNA targeting. These effects are further observed in vivo, where Hmga2 ceRNA activity drives lung cancer growth, invasion and dissemination. Integrated analysis of miRNA target prediction algorithms and metastatic lung cancer gene expression data reveals the TGF-β co-receptor Tgfbr312 as a putative target of Hmga2 ceRNA function. Tgfbr3 expression is regulated by the Hmga2 ceRNA via differential recruitment to Argonaute-2 (Ago2), and TGF-β signalling driven by Tgfbr3 is largely necessary for Hmga2 to promote lung cancer progression. Finally, analysis of NSCLC patient gene expression data reveals that HMGA2 and TGFBR3 are co-ordinately regulated in NSCLC patient material, a vital corollary to ceRNA function. Taken together, these results suggest that Hmga2 promotes lung carcinogenesis as both a protein-coding gene and a non-coding RNA; such dual-function regulation of gene expression networks reflects a novel means by which oncogenes promote disease progression.

Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of gene expression data. However, more complex analysis for classification of sample observations, or discovery of feature genes requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though the methods are discussed in the context of expression microarrays, they can also be applied for the analysis of RNA sequencing and quantitative proteomics datasets. We discuss the types of missing values, and the methods and approaches usually employed in their imputation. We also discuss methods of data normalization, feature selection, and feature extraction. Lastly, methods of classification and class discovery along with their evaluation parameters are described in detail. We believe that this detailed review will help the users to select appropriate methods for preprocessing and analysis of their data based on the expected outcome.

Systems engineering approaches to gene expression profiling

Cytoscape is a bioinformatic data analysis and visualization platform that is well-suited to the analysis of gene expression data. To facilitate the analysis of yeast microarray data using Cytoscape, we constructed an interaction network (interactome) using the curated interaction data available from the Saccharomyces Genome Database (www.yeastgenome.org) and the database of yeast transcription factors at YEASTRACT (www.yeastract.com). These data were formatted and imported into Cytoscape using semi-automated methods, including Linux-based scripts, that simplified the process while minimizing the introduction of processing errors. The methods described for the construction of this yeast interactome are generally applicable to the construction of any interactome. Using Cytoscape, we illustrate the use of this interactome through the analysis of expression data from a recent yeast diauxic shift experiment. We also report and briefly describe the complex associations among transcription factors that result in the regulation of thousands of genes through coordinated changes in expression of dozens of transcription factors. These cells are thus able to sensitively regulate cellular metabolism in response to changes in genetic or environmental conditions through relatively small changes in the expression of large numbers of genes, affecting the entire yeast metabolome.

ObjectiveThis study aimed to identify prognostic genes for diffuse large B-cell lymphoma (DLBCL), using bioinformatic methods.MethodsFive gene expression data sets were downloaded from the Gene Expression Omnibus database. Significance analysis of microarrays algorithm was used to identify differentially expressed genes (DEGs) from two data sets. Functional enrichment analysis was performed for the DEGs with the Database for Annotation, Visualization and Integration Discovery (DAVID). Survival analysis was performed with the Kaplan–Meier method using function survfit from package survival of R for the other three data sets. Cox univariate regression analysis was used to further screen out prognostic genes.ResultsThirty-one common DEGs were identified in the two data sets, mainly enriched in the regulation of lymphocyte activation, immune response, and interleukin-mediated signaling pathway. Combined with 47 DLBCL-related genes acquired by literature retrieval, a total of 78 potential prognostic genes were obtained. Cases from the other three data sets were used in hierarchical clustering, and the 78 genes could cluster them into several subtypes with significant differences in survival curves. Cox univariate regression analysis revealed 45, 33, and eleven prognostic genes in the three data sets, respectively. Five common prognostic genes were revealed, including LCP2, TNFRSF9, FUT8, IRF4, and TLE1, among which LCP2, FUT8, and TLE1 were novel prognostic genes.ConclusionFive prognostic genes of DLBCL were identified in this study. They could not only be used for molecular subtyping of DLBCL but also be potential targets for treatment.

The death of early embryo development will bring huge economic losses in animal husbandry, especially for dairy production of cattle. Thus, it has an extremely important value to study the process of early embryonic development. In our study, one dataset of gene expression was chosen from the public database of GEO, which was related to bovine preimplantation development. Significance and clustering analyses were used to study the genome expression patterns of each stage during bovine preimplantation development. The whole process was assigned to three main groups. The waves of significant gene expression in each stage indicated that both 8-cell and 16-cell stages played an important role during the process of bovine preimplantation development. Further functional analysis and pathway analysis revealed that some major factors and related pathways are required for distinctive developmental stages.

Aberrant methylation is a known cause of cancer initiation and/or progression. There are scant data on the genome-wide methylation pattern of nonfunctioning pancreatic neuroendocrine tumors (NFPanNETs) and sporadic and hereditary NFPanNETs. Thirty-three tissue samples were analyzed: they included samples from sporadic (n=9), von Hippel-Lindau (VHL)-related (n=10), and multiple endocrine neoplasia type 1 (MEN1)-related NFPanNETs (n=10) as well as normal islet cells (n=4) for comparison. Genome-wide CpG methylation profiling was performed with the Infinium MethylationEPIC BeadChip assay and was analyzed with R-based tools. In unsupervised hierarchical clustering, sporadic and MEN1-related NFPanNETs clustered together, and the VHL group was in a separate cluster. MEN1-related NFPanNETs had a higher rate of hypermethylated CpG sites in comparison with sporadic and VHL-related tumor groups. Differentially methylated region analysis confirmed the higher rate of hypermethylation in MEN1-related tumors. Moreover, in an integrated analysis of gene expression data for the same tumor samples, downregulated gene expression was found in most genes that were hypermethylated. In a CpG island methylator phenotype analysis, 3 genes were identified and confirmed to have downregulated gene expression: secreted frizzle-related protein 5 (SFRP5) in sporadic NFPanNETs and cell division cycle-associated 7-like (CDCA7L) and RNA binding motif 47 (RBM47) in MEN1-related NFPanNETs. MEN1 NFPanNETs have a higher rate of geno me-wide hypermethylation than other NFPanNET subtypes. The similarity between the pathways enriched in a methylation analysis of known genes involved in NFPanNET tumorigenesis suggests a key role for aberrant methylation in the pathogenesis of NFPanNETs.