Tissue-specific identification of multi-omics features for pan-cancer drug response prediction

Zhao, Zhi; Wang, Shixiong; Zucknick, Manuela; Aittokallio, Tero

doi:10.1016/j.isci.2022.104767

Cited by 6 publications

(7 citation statements)

References 64 publications

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“…A possible extension of CSEA is to use Fisher’s exact test or hypergeometric test for unranked list of features. EnrichIntersect also enables an interactive means to visualize identified associations based on, for example, the multilevel task grouping method ( Han and Zhang, 2015 ), mix-lasso ( Zhao et al , 2022 ) or any other similar method most suitable for particular application scenarios with multitask and multilevel data, as illustrated in Figure 1 . It refines the use of the networkD3 package to allow the visualization of relationships between a large number of features and multiple tasks corresponding to multiple sample groups or levels.…”

Section: Discussionmentioning

confidence: 99%

“…When there are heterogeneous sample groups or levels in each task, we are interested in identifying associated features with each task in each group or level. For example, Han and Zhang (2015) proposed the multilevel task grouping method and Zhao et al (2022) proposed mix-lasso for identifying multitask multilevel features. Let us suppose there are m learning tasks, e.g.…”

Section: Methodsmentioning

confidence: 99%

“…( B ) A Sankey diagram illustrating how a set of 56 genes is associated with two drugs (RG-108 and JQ-1), corresponding to three cancer types (colorectal, melanoma and multiple myeloma). The plot shows the results from a mix-lasso analysis ( Zhao et al , 2022 ) to identify genomic features that are associated with the sensitivity of cell lines from these cancer types to RG-108 or JQ-1 as measured by cell viability assays…”

Section: Methodsmentioning

confidence: 99%

“…For wider applications, there is a need to implement computational tools that enable standalone enrichment analysis for any feature list of interest, based on user-defined custom sets. For instance, Zhao et al (2022) analyzed tissue-specific drugs as drug classes enriched among a ranked drug list corresponding to a given cancer tissue. To enable more flexible enrichment analyses among any ranked features and user-defined custom set, we introduce the R ( R Core Team, 2021 ) package EnrichIntersect for custom set enrichment analysis (CSEA).…”

Section: Introductionmentioning

confidence: 99%

“…However, when there are many intermediate latent variables, such as genomic variables as covariates, our EnrichIntersect package makes it easier to visualize their relationships at multiple levels and with respect to multiple tasks using R package networkD3 ( Allaire et al , 2017 ). For example, Zhao et al (2022) developed cancer tissue-specific variable selection for large-scale drug screens, where a Sankey diagram, which is implemented in the EnrichIntersect package, was used to visualize the relationships between multiple genomic features and several drugs of interest (i.e. multiple tasks), corresponding to several cancer tissue types (i.e.…”

Section: Introductionmentioning

confidence: 99%

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EnrichIntersect: an R package for custom set enrichment analysis and interactive visualization of intersecting sets

Zhao

Zucknick

Aittokallio

2022

Bioinformatics Advances

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Enrichment analysis has been widely used to study whether pre-defined sets of genes or other molecular features are over-represented in a ranked list associated with a disease or other phenotype. However, computational tools that perform enrichment analysis and visualization are usually limited to pre-defined sets available from public databases. To make such analyses more flexible, we introduce an R package, EnrichIntersect, that enables enrichment analyses among any ranked features and user-defined custom sets. For interactive visualization of multiple covariates, such as genes or other features, which are associated with multiple phenotypes and multiple sample groups, such as drug responses in various cancer types, EnrichIntersect illustrates all associations at a glance, hence explicitly indicating intersecting covariates between multiple phenotypic variables and between multiple sample groups. Availability The EnrichIntersect R package is available at https://CRAN.R-project.org/package=EnrichIntersect via an open-source MIT license. A package installation process is described on CRAN at https://cran.r-project.org/. A user-manual description of features and function calls can be found from the vignette of our package on CRAN.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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EnrichIntersect: an R package for custom set enrichment analysis and interactive visualization of intersecting sets

Zhao

Zucknick

Aittokallio

2022

Bioinformatics Advances

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Debiased inference for heterogeneous subpopulations in a high-dimensional logistic regression model

Kim,

Lee,

Park

2023

Sci Rep

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Due to the prevalence of complex data, data heterogeneity is often observed in contemporary scientific studies and various applications. Motivated by studies on cancer cell lines, we consider the analysis of heterogeneous subpopulations with binary responses and high-dimensional covariates. In many practical scenarios, it is common to use a single regression model for the entire data set. To do this effectively, it is critical to quantify the heterogeneity of the effect of covariates across subpopulations through appropriate statistical inference. However, the high dimensionality and discrete nature of the data can lead to challenges in inference. Therefore, we propose a novel statistical inference method for a high-dimensional logistic regression model that accounts for heterogeneous subpopulations. Our primary goal is to investigate heterogeneity across subpopulations by testing the equivalence of the effect of a covariate and the significance of the overall effects of a covariate. To achieve overall sparsity of the coefficients and their fusions across subpopulations, we employ a fused group Lasso penalization method. In addition, we develop a statistical inference method that incorporates bias correction of the proposed penalized method. To address computational issues due to the nonlinear log-likelihood and the fused Lasso penalty, we propose a computationally efficient and fast algorithm by adapting the ideas of the proximal gradient method and the alternating direction method of multipliers (ADMM) to our settings. Furthermore, we develop non-asymptotic analyses for the proposed fused group Lasso and prove that the debiased test statistics admit chi-squared approximations even in the presence of high-dimensional variables. In simulations, the proposed test outperforms existing methods. The practical effectiveness of the proposed method is demonstrated by analyzing data from the Cancer Cell Line Encyclopedia (CCLE).

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Incorporating knowledge of disease-defining hub genes and regulatory network into a machine learning-based model for predicting treatment response in lupus nephritis after the first renal flare

Lee

Tsai²,

Chen³

et al. 2023

J Transl Med

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Background Identifying candidates responsive to treatment is important in lupus nephritis (LN) at the renal flare (RF) because an effective treatment can lower the risk of progression to end-stage kidney disease. However, machine learning (ML)-based models that address this issue are lacking. Methods Transcriptomic profiles based on DNA microarray data were extracted from the GSE32591 and GSE112943 datasets. Comprehensive bioinformatics analyses were performed to identify disease-defining genes (DDGs). Peripheral blood samples (GSE81622, GSE99967, and GSE72326) were used to evaluate the effect of DDGs. Single-sample gene set enrichment analysis (ssGSEA) scores of the DDGs were calculated and correlated with specific immunology genes listed in the nCounter panel. GSE60681 and GSE69438 were used to examine the ability of the DDGs to discriminate LN from other renal diseases. K-means clustering was used to obtain the separate gene sets. The clustering results were extended to data derived using the nCounter technique. The least absolute shrinkage and selection operator (LASSO) algorithm was used to identify genes with high predictive value for treatment response after the first RF in each cluster. LASSO models with tenfold validation were built in GSE200306 and assessed by receiver operating characteristic (ROC) analysis with area under curve (AUC). The models were validated by using an independent dataset (GSE113342). Results Forty-five hub genes specific to LN were identified. Eight optimal disease-defining clusters (DDCs) were identified in this study. Th1 and Th2 cell differentiation pathway was significantly enriched in DDC-6. LCK in DDC-6, whose expression positively correlated with various subsets of T cell infiltrations, was found to be differentially expressed between responders and non-responders and was ranked high in regulatory network analysis. Based on DDC-6, the prediction model had the best performance (AUC: 0.75; 95% confidence interval: 0.44–1 in the testing set) and high precision (0.83), recall (0.71), and F1 score (0.77) in the validation dataset. Conclusions Our study demonstrates that incorporating knowledge of biological phenotypes into the ML model is feasible for evaluating treatment response after the first RF in LN. This knowledge-based incorporation improves the model's transparency and performance. In addition, LCK may serve as a biomarker for T-cell infiltration and a therapeutic target in LN.

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Tissue-specific identification of multi-omics features for pan-cancer drug response prediction

Cited by 6 publications

References 64 publications

EnrichIntersect: an R package for custom set enrichment analysis and interactive visualization of intersecting sets

EnrichIntersect: an R package for custom set enrichment analysis and interactive visualization of intersecting sets

Debiased inference for heterogeneous subpopulations in a high-dimensional logistic regression model

Incorporating knowledge of disease-defining hub genes and regulatory network into a machine learning-based model for predicting treatment response in lupus nephritis after the first renal flare

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