Cited by 474Cancer Genomics and DiagnosticsGene expression and cancer classificationBioinformatics and Genomic Networks

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Pattern discovery and cancer gene identification in integrated cancer genomic data

Open access

Mo, Qianxing, Wang, Sijian, Seshan, Venkatraman E., Olshen, Adam B., Schultz, Nikolaus, Sander, Chris, Powers, R. Scott, Ladanyi, Marc, Shen, Ronglai·2013·Proceedings of the National Academy of Sciences

DOI 10.1073/pnas.1208949110PMC PMC3600490

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Abstract

Large-scale integrated cancer genome characterization efforts including the cancer genome atlas and the cancer cell line encyclopedia have created unprecedented opportunities to study cancer biology in the context of knowing the entire catalog of genetic alterations. A clinically important challenge is to discover cancer subtypes and their molecular drivers in a comprehensive genetic context. Curtis et al. [ Nature (2012) 486(7403):346–352] has recently shown that integrative clustering of copy number and gene expression in 2,000 breast tumors reveals novel subgroups beyond the classic expression subtypes that show distinct clinical outcomes. To extend the scope of integrative analysis for the inclusion of somatic mutation data by massively parallel sequencing, we propose a framework for joint modeling of discrete and continuous variables that arise from integrated genomic, epigenomic, and transcriptomic profiling. The core idea is motivated by the hypothesis that diverse molecular phenotypes can be predicted by a set of orthogonal latent variables that represent distinct molecular drivers, and thus can reveal tumor subgroups of biological and clinical importance. Using the cancer cell line encyclopedia dataset, we demonstrate our method can accurately group cell lines by their cell-of-origin for several cancer types, and precisely pinpoint their known and potential cancer driver genes. Our integrative analysis also demonstrates the power for revealing subgroups that are not lineage-dependent, but consist of different cancer types driven by a common genetic alteration. Application of the cancer genome atlas colorectal cancer data reveals distinct integrated tumor subtypes, suggesting different genetic pathways in colon cancer progression.

Integrity signals

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“• K - m e a n s , a p r i o r i a l g o r i t h m , J o i n t N M F [ 91 ] , [ 92 ] , i C l u s t e r [ 94 ] , i C l u s t e r + [ 95 ] , s M B P L S [ 101 ] , S N P L S [ 102 ] , C N A m e t [ 23 ] , e t c .”
Method
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“For the CNAs, we condensed the genomic segments as described in the literature [33].”
Method
Synthetic augmentation of cancer cell line multi-omic datasets using unsupervised deep learning
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Systematic genomic identification of colorectal cancer genes delineating advanced from early clinical stage and metastasis
Lee, H., Flaherty, P., & Ji, H. P. (2013) · BMC Medical Genomics · doi:10.1186/1755-8794-6-54 · Cited by 42
“We also demonstrated that the predictive power was relatively improved using our integrative analysis approach (Table 2) as several other studies have demonstrated previously [24-29].”
BackgroundResult
Deep Subspace Mutual Learning for cancer subtypes prediction
Yang, B., Xin, T., Pang, S., Wang, M., & Wang, Y.-J. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab625 · Cited by 40
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Self-citation
Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence
Park, Y., Heider, D., & Hauschild, A.-C. (2021) · Cancers · doi:10.3390/cancers13133148 · Cited by 46
“Two of the most vibrantly evolving areas are systems biology and machine learning which tackle different tasks such as understanding the cancer pathways [9], finding crucial genes in pathways [22,53], or predicting functions of unidentified or understudied genes [54].”
Background
JSNMF enables effective and accurate integrative analysis of single-cell multiomics data
Ma, Y., Sun, Z., Zeng, P., Zhang, W., & Lin, Z. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac105 · Cited by 40
Computational Analysis of Phosphoproteomics Data in Multi‐Omics Cancer Studies
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Integrating Multi-Omics with environmental data for precision health: A novel analytic framework and case study on prenatal mercury induced childhood fatty liver disease
Goodrich, J. A., Wang, H., Jia, Q., Stratakis, N., Zhao, Y., Maître, L., Bustamante, M., Vafeiadi, M., Aung, M. T., Andrušaitytė, S., et al. (2024) · Environment International · doi:10.1016/j.envint.2024.108930 · Cited by 41
“…consensus clustering; Gabasova et al., 2017; Jovanovski and Kocarev, 2019; Kirk et al., 2012; Lock et al., 2013; Luo et al., 2017; Min et al., 2018; Mo et al., 2013; Shen et al., 2009) and network and Bayesian network analysis (e.g., XMWAS; Friedman, 2003; Heckerman, 1998; Liu et al., 2012; Uppal…”
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Labory, J., Fierville, M., Ait-El-Mkadem, S., Bannwarth, S., Paquis‐Flucklinger, V., & Bottini, S. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.590842 · Cited by 41
“, 2014), iClusterPlus (Mo et al., 2013), or Pathway Recognition Algorithm using Data Integration on Genomic Models (PARADIGM) (Vaske et al.”
Method
Pan-cancer identification of clinically relevant genomic subtypes using outcome-weighted integrative clustering
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Self-citation
Network biology and artificial intelligence drive the understanding of the multidrug resistance phenotype in cancer
Bueschbell, B., Caniceiro, A. B., Suzano, P. M. S., Machuqueiro, M., Rosário‐Ferreira, N., & Moreira, I. S. (2022) · Drug Resistance Updates · doi:10.1016/j.drup.2022.100811 · Cited by 38
Heterogeneous data integration methods for patient similarity networks
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Integrative multi-omics analysis reveals novel idiopathic pulmonary fibrosis endotypes associated with disease progression
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“The scSNF method was compared to the alternative integrative clustering methods iCluster+ [31] and iClusterBayes [32].”
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Song, W., Wang, W., & Dai, D. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab398 · Cited by 36
Preferential Activation of the Hedgehog Pathway by Epigenetic Modulations in HPV Negative HNSCC Identified with Meta-Pathway Analysis
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“We applied the CoGAPS matrix factorization algorithm to simultaneously infer patterns associated with meta-pathway activity from a combined data matrix D containing gene expression data for nE genes and log transformed b values for DNA methylation of nM genes for the same set of m samples, analogous to [10,11].”
Systems Medicine: The Future of Medical Genomics, Healthcare, and Wellness
Saqi, M. A. S., Pellet, J., Balaur, I., Mazein, A., Ballereau, S. J., Meulder, B. D., & Auffray, C. (2015) · Methods in molecular biology · doi:10.1007/978-1-4939-3283-2_3 · Cited by 39
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Peng, C., Wang, J., Asante, I., Louie, S. G., Jin, R., Chatzi, L., Casey, G., Thomas, D. C., & Conti, D. V. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz667 · Cited by 35
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“Multi-view approaches can be roughly divided in three families, depending on whether they linearly combine the multiple loss functions in different views [14], [15], [16], or combine multiple graphs with an optimal combination of weights for different purposes [17], [18], [19], [20], or conform different view-specific…”
Background
Hierarchical Maximum Likelihood Clustering Approach
Sharma, A. K., Boroevich, K. A., Shigemizu, D., Kamatani, Y., Kubo, M., & Tsunoda, T. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2542212 · Cited by 34
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Uniform Projection for Multi-View Learning
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Shi, W. J., Zhuang, Y., Russell, P. H., Hobbs, B. D., Parker, M. M., Castaldi, P. J., Rudra, P., Vestal, B. E., Hersh, C. P., Saba, L. M., et al. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz226 · Cited by 34
On fusion methods for knowledge discovery from multi-omics datasets
Baldwin, E., Han, J., Luo, W., Zhou, J. J., An, L., Liu, J., Zhang, H. H., & Li, H. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.02.011 · Cited by 33
“iCluster has been used to identify known and novel subtypes of breast and lung cancers from concordant DNA copy number changes and gene expression [36] while iCluster+ has accurately grouped cell lines by their cell-of-origin for several cancer types using genomic, epigenomic and transcriptomic profiling [37].”
BackgroundMethod
Multi-view clustering for multi-omics data using unified embedding
Mitra, S., Saha, S., & Hasanuzzaman, M. (2020) · Scientific Reports · doi:10.1038/s41598-020-70229-1 · Cited by 32
“Mo et al. developed iClusterPlus 46 , the iCluster extension, which uses linear regression models to learn different properties of omics data.”
Method
Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer
Sharma, V., Singh, A. K., Chauhan, S., Sharma, P. K., Chaudhary, S., Sharma, A., Porwal, O., & Fuloria, N. K. (2023) · Current Drug Delivery · doi:10.2174/1567201821666230905090621 · Cited by 36
Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers
Karaman, E. D., & Işık, Z. (2023) · Medical Sciences · doi:10.3390/medsci11030044 · Cited by 31
“[44] performed a statistical integrative clustering analysis (iCluster+) using exome sequence, DNA copy number, promoter methylation, and mRNA expression data of TCGA colorectal carcinoma.”
Method
Cancer driver gene discovery through an integrative genomics approach in a non-parametric Bayesian framework
Hai, Y., Wei, Q., Zhong, X., Yang, H., & Li, B. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw662 · Cited by 30
Integrating Epigenomics into the Understanding of Biomedical Insight
Han, Y., & He, X. (2016) · Bioinformatics and Biology Insights · doi:10.4137/bbi.s38427 · Cited by 32
A hierarchical clustering and data fusion approach for disease subtype discovery
Pfeifer, B., & Schimek, M. G. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103636 · Cited by 31
Combined Som-Portrayal of Gene Expression and DNA Methylation Landscapes Disentangles Modes of Epigenetic Regulation in Glioblastoma
Hopp, L., Loeffler‐Wirth, H., Galle, J., & Binder, H. (2018) · Epigenomics · doi:10.2217/epi-2017-0140 · Cited by 30
Simultaneous discovery of cancer subtypes and subtype features by molecular data integration
Van, T. L., van Leeuwen, M., Fierro, A. C., Maeyer, D. D., den Eynden, J. V., Verbeke, L. P. C., Raedt, L. D., Kathleen Marchal, D., & Nijssen, S. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw434 · Cited by 29
A survey on data integration for multi-omics sample clustering
Lovino, M., Randazzo, V., Ciravegna, G., Barbiero, P., Ficarra, E., & Cirrincione, G. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.11.094 · Cited by 31
Invention of 3Mint for feature grouping and scoring in multi-omics
Yazıcı, M. Ü., Marron, J. S., Bakır-Güngör, B., Zou, F., & Yousef, M. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1093326 · Cited by 29
“Joint Non-negative Matrix Factorization (NMF) (Zhang et al., 2012), iCluster+ (Mo et al., 2013), Multiple Dataset Integration (MDI) (Kirk et al., 2012) and JIVE (Lock et al., 2013) have been proposed as unsupervised methods; and supervised approaches such as Network-based integration of multi-omics…”
Method
Generalized integrative principal component analysis for multi-type data with block-wise missing structure
Zhu, H., Li, G., & Lock, E. F. (2018) · Biostatistics · doi:10.1093/biostatistics/kxy052 · Cited by 29
“The iCluster+ approach [Mo et al., 2013], which enhanced iCluster, provides a feasible approach to the clustering of multi-source data with both discrete and continuous values.”
Method
Integrative clustering methods for multi‐omics data
Zhang, X., Zhou, Z., Xu, H., & Liu, C. (2021) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1553 · Cited by 31
A General Joint Matrix Factorization Framework for Data Integration and Its Systematic Algorithmic Exploration
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“For example, iCluster [16] and its successor iCluster+ [17] assume that data originates from a low-dimension representation and represent it by a common low-dimension data matrix Z.”
BackgroundMethod
PAMOGK: a pathway graph kernel-based multiomics approach for patient clustering
Tepeli, Y. I., Ünal, A. B., Akdemir, F. M., & Taştan, Ö. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa655 · Cited by 33
Statistical contributions to bioinformatics: Design, modelling, structure learning and integration
Morris, J. S., & Baladandayuthapani, V. (2017) · Statistical Modelling · doi:10.1177/1471082x17698255 · Cited by 30
“Mo et al. (2013) and Shen et al. (2013) proposed a clustering model ‘iCluster’, which uses a joint latent variable model to cluster samples into tumour subtypes.”
Background
Using association signal annotations to boost similarity network fusion
Ruan, P., Wang, Y., Shen, R., & Wang, S. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz124 · Cited by 28
Self-citation
Statistical genomics in rare cancer
Aghababazadeh, F. A., Mo, Q., & Fridley, B. L. (2019) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2019.08.021 · Cited by 26
Self-citation
Systems Biology and Kidney Disease
Schaub, J. A., Hamidi, H., Subramanian, L., & Kretzler, M. (2020) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.09990819 · Cited by 31
Patient-Specific Multi-Omics Models and the Application in Personalized Combination Therapy
John, A. J., Qin, B., Kalari, K. R., Wang, L., & Yu, J. (2020) · Future Oncology · doi:10.2217/fon-2020-0119 · Cited by 27
Multi-dimensional data integration algorithm based on random walk with restart
Wen, Y., Song, X., Yan, B., Yang, X., Wu, L., Leng, D., He, S., & Bo, X. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04029-3 · Cited by 27
DeFusion: a denoised network regularization framework for multi-omics integration
Wang, W., Zhang, X., & Dai, D. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab057 · Cited by 27
Multiomics Data Integration in Time Series Experiments
Tarazona, S., Balzano‐Nogueira, L., & Conesa, A. (2018) · Comprehensive analytical chemistry · doi:10.1016/bs.coac.2018.06.005 · Cited by 29
Prediction of drug sensitivity based on multi-omics data using deep learning and similarity network fusion approaches
Liu, X., & Mei, X.-Y. (2023) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2023.1156372 · Cited by 30
“, 2018) (Mo et al., 2013), patient stratification (S Khakabimamaghani et al.”
Background
Integration of Tumor Genomic Data with Cell Lines Using Multi-dimensional Network Modules Improves Cancer Pharmacogenomics
Webber, J. T., Ranall, M. V., Kaushik, S., & Bandyopadhyay, S. (2018) · Cell Systems · doi:10.1016/j.cels.2018.10.001 · Cited by 26
SRARP and HSPB7 are epigenetically regulated gene pairs that function as tumor suppressors and predict clinical outcome in malignancies
Naderi, A. (2018) · Molecular Oncology · doi:10.1002/1878-0261.12195 · Cited by 24
Separating common (global and local) and distinct variation in multiple mixed types data sets
Song, Y., Westerhuis, J. A., & Smilde, A. K. (2019) · Journal of Chemometrics · doi:10.1002/cem.3197 · Cited by 24
“Both of these approaches have already been generalized to discrete data sets [7, 8].”
Background
Multimodal Learning for Multi-omics: A Survey
Tabakhi, S., Suvon, M. N. I., Ahadian, P., & Lu, H. (2022) · World Scientific Annual Review of Artificial Intelligence · doi:10.1142/s2811032322500047 · Cited by 27
Multi-omics profiling approach in food allergy
Devonshire, A. L., Gautam, Y., Johansson, E., & Baye, T. M. (2023) · World Allergy Organization Journal · doi:10.1016/j.waojou.2023.100777 · Cited by 27
Integrated ‘omics analysis reveals new drug-induced mitochondrial perturbations in human hepatocytes
Wolters, J. E. J., Breda, S. G. J. V., Grossmann, J., Fortes, C., Caiment, F., & Kleinjans, J. C. S. (2018) · Toxicology Letters · doi:10.1016/j.toxlet.2018.02.026 · Cited by 25
Multi-view spectral clustering with latent representation learning for applications on multi-omics cancer subtyping
Ge, S., Liu, J., Cheng, Y., Meng, X., & Wang, X. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac500 · Cited by 26
Growth-stimulatory activity of TIMP-2 is mediated through c-Src activation followed by activation of FAK, PI3-kinase/AKT, and ERK1/2 independent of MMP inhibition in lung adenocarcinoma cells
Kim, H. I., Lee, H., Kim, T. H., Lee, J., Lee, S., & Lee, S.-J. (2015) · Oncotarget · doi:10.18632/oncotarget.5466 · Cited by 24
“Furthermore, integrative clustering [36] of copy number, DNA methylation, and mRNA expression data found six clusters (Figure 7) [30].”
Background
CWGCNA: an R package to perform causal inference from the WGCNA framework
Liu, Y. (2024) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqae042 · Cited by 31
“For multi-omics or single-omic clustering, the performance of CWGCNA was compared with the public tool iClusterPlus ( 23 ,24 ).”
Method
Strategies for Integrated Analysis of Genetic, Epigenetic, and Gene Expression Variation in Cancer: Addressing the Challenges
Thingholm, L. B., Andersen, L. V. B., Makalic, E., Southey, M. C., Thomassen, M., & Hansen, L. L. (2016) · Frontiers in Genetics · doi:10.3389/fgene.2016.00002 · Cited by 23
“A study by Mo et al. further developed the iCluser tool to iCluster+ to allow inclusion of discrete variables from sequencing data (Mo et al., 2013).”
Method
Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data
Wang, M., & Allen, G. I. (2019) · PubMed · doi:10.48550/arxiv.1912.05449 · Cited by 23
“In this subsection, we evaluate the performance of iGecco and (adaptive) iGecco+ on mixed multi-view data by comparing it with hierarchical clustering, iClusterPlus (Mo et al., 2013) and Bayesian Consensus Clustering (Lock and Dunson, 2013).”
MethodInfluential
Multi-omic integration by machine learning (MIMaL)
Dickinson, Q., Aufschnaiter, A., Ott, M., & Meyer, J. G. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac631 · Cited by 23
Transkingdom Network Analysis (TkNA): a systems framework for inferring causal factors underlying host–microbiota and other multi-omic interactions
Newman, N. K., Macovsky, M. S., Rodrigues, R. R., Bruce, A. M., Pederson, J. W., Padiadpu, J., Shan, J., Williams, J., Patil, S. S., Dzutsev, A., et al. (2024) · Nature Protocols · doi:10.1038/s41596-024-00960-w · Cited by 25
“Additional R packages (e.g., MixOmics 28 , MOFA+ 29,30 and iClusterPlus 31,32 ) also use sophisticated statistical methods to combine multiple -omics data measured from the same patients.”
Real-world clinical multi-omics analyses reveal bifurcation of ER-independent and ER-dependent drug resistance to CDK4/6 inhibitors
Kan, Z., Wen, J., Bonato, V., Webster, J., Yang, W., Ivanov, V., Kim, K. H., Roh, W., Liu, C., Mu, X. J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-55914-x · Cited by 29
Trans-omics biomarker model improves prognostic prediction accuracy for early-stage lung adenocarcinoma
Dong, X., Zhang, R., He, J., Lai, L., Alolga, R. N., Shen, S., Zhu, Y., You, D., Lin, L., Chen, C., et al. (2019) · Aging · doi:10.18632/aging.102189 · Cited by 21
“iCluster plus is a significant enhancement of the iCluster method, which integrates diverse data types and performs well in recognition of high- and lowmortality risk patterns [33].”
Method
Genomic analysis of head and neck cancer cases from two high incidence regions
Perdomo, S. P., Anantharaman, D., Foll, M., Abedi‐Ardekani, B., Durand, G., Rosa, L. A. R., Holmila, R. J., Calvez‐Kelm, F. L., Tajara, E. H., Wünsch‐Filho, V., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0191701 · Cited by 21
Integrative Analyses of Cancer Data: A Review from a Statistical Perspective
Wei, Y. (2015) · Cancer Informatics · doi:10.4137/cin.s17303 · Cited by 21
Computational approaches leveraging integrated connections of multi-omic data toward clinical applications
Demirel, H. C., Arici, M. K., & Tunçbağ, N. (2021) · Molecular Omics · doi:10.1039/d1mo00158b · Cited by 29
Integrative network analysis for survival-associated gene-gene interactions across multiple genomic profiles in ovarian cancer
Jeong, H.-H., Leem, S., Wee, K., & Sohn, K.-A. (2015) · Journal of Ovarian Research · doi:10.1186/s13048-015-0171-1 · Cited by 21
“Many computational attempts have been made to identify molecular abnormalities that affect clinical outcomes and therapeutic targets, by integrating multiple genomic profiles and clinical data [1–15].”
Background
Dissecting cancer heterogeneity with a probabilistic genotype-phenotype model
Cho, D.-Y., & Przytycka, T. M. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt577 · Cited by 20
“Thus unlike pattern-discovery methods, such as iCLUSTER (26,27), which introduces hidden variables for subtype membership to associate genotypic variations with gene expression, our approach models the actual relation between putative genetic causes and expression phenotype.”
Method
Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content
Kling, T., Johansson, P., Sánchez, J., Marinescu, V. D., Jörnsten, R. J., & Nelander, S. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv413 · Cited by 21
“Another increasingly important challenge is to integrate molecular data from several different cancers to identify common vulnerabilities that can be exploited therapeutically (3,4).”
A review on multi-omics integration for aiding study design of large scale TCGA cancer datasets
Han, E., Kwon, H., & Jung, I. (2025) · BMC Genomics · doi:10.1186/s12864-025-11925-y · Cited by 22
“…imputing missing values offer considerable benefits within MOSD, allowing deeper insight into latent structures that span multiple data types. iClusterPlus iClusterPlus [49] is an advanced version of iCluster [50] that integrates multi-omics data using a probabilistic joint latent-variable model.”
A p-Median approach for predicting drug response in tumour cells
Fersini, E., Messina, E., & Archetti, F. (2014) · BMC Bioinformatics · doi:10.1186/s12859-014-0353-7 · Cited by 20
“Most of the multi-source clustering approaches follow one of the following paradigms: (a) clustering each data source separately to then ad-hoc integrate the separate clustering solutions [26,27] or (b) combining all data sources to determine a single “joint” clustering [28,29] as Traditional p-Median and Probabilisti…”
Method
A multiobjective multi-view cluster ensemble technique: Application in patient subclassification
Mitra, S., & Saha, S. (2019) · PLoS ONE · doi:10.1371/journal.pone.0216904 · Cited by 20
PIntMF: Penalized Integrative Matrix Factorization method for multi-omics data
Pierre-Jean, M., Mauger, F., Deleuze, J.-F., & Le Floch, E. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btab786 · Cited by 20
“1 Simulations on independent datasets (non-correlated blocks) The performance of PIntMF to cluster samples and to select relevant variables was evaluated on simulated data described by Pierre-Jean et al. (2020). The framework of these simulations is composed of three blocks with three different types of distribution (…”
Method
Consensus clustering methodology to improve molecular stratification of non-small cell lung cancer
Manganaro, L., Bianco, S., Bironzo, P., Cipollini, F., Colombi, D., Corà, D., Corti, G., Doronzo, G., Errico, L., Falco, P., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-33954-x · Cited by 23
“Specifically, we employed K-Means clustering 72 , spectral clustering 73 , hierarchical clustering 74 , iClusterPlus 14 , and SNF 75 , the first three methods being of general applicability, whereas the last two have been specifically designed for genomic data analysis.”
The Prognostic Significance of Low-Frequency Somatic Mutations in Metastatic Cutaneous Melanoma
Zhao, X., Little, P., Hoyle, A. P., Pegna, G. J., Hayward, M. C., Ivanova, A., Parker, J. S., Marron, D. L., Soloway, M. G., Jo, H., et al. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2018.00584 · Cited by 20
“Rather, multiple genetic, and epigenetic aberrations within the cancer itself (42), the host (43), and the environment (44) may play an even larger role in established metastases as opposed to primary melanoma.”
Background
Accurate cancer phenotype prediction with AKLIMATE, a stacked kernel learner integrating multimodal genomic data and pathway knowledge
Uzunangelov, V., Wong, C. K., & Stuart, J. M. (2020) · bioRxiv · doi:10.1371/journal.pcbi.1008878 · Cited by 13
Detecting molecular subtypes from multi-omics datasets using SUMO
Sienkiewicz, K., Chen, J., Chatrath, A., Lawson, J. T., Sheffield, N. C., Zhang, L., & Ratan, A. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2021.100152 · Cited by 18
“More sophisticated methods (e.g., iCluster [Shen et al., 2009], iClusterPlus [Mo et al., 2013], and Bayesian consensus clustering [Lock and Dunson, 2013]) model the probabilistic distribution of each data type and infer subtypes by maximization of the likelihood of the observed data.”
Multimodal data integration in early-stage breast cancer
Llinas-Bertran, A., Butjosa-Espín, M., Barberi, V., & Seoane, J. A. (2025) · The Breast · doi:10.1016/j.breast.2025.103892 · Cited by 21
“Mo et al. (2013) leveraged iClusterPlus and recapitulated ERBB2 amplification as a driver of alterations in breast cancer cell lines [60].”
An information theoretic method to identify combinations of genomic alterations that promote glioblastoma
Melamed, R., Wang, J., Iavarone, A., & Rabadán, R. (2015) · Journal of Molecular Cell Biology · doi:10.1093/jmcb/mjv026 · Cited by 20
“Combinations of genes have been found to jointly predict cancer phenotype (Varadan and Anastassiou, 2006; Mo et al., 2013), but, to our knowledge, no unsupervised method exists for finding related genetic alterations.”
Background
Practical aspects of NGS-based pathways analysis for personalized cancer science and medicine
Kotelnikova, E. A., Pyatnitskiy, M. A., Paleeva, A. G., Kremenetskaya, O. S., & Vinogradov, D. V. (2016) · Oncotarget · doi:10.18632/oncotarget.9370 · Cited by 19
“While several algorithms have been developed such as GSAA [183], iCluster+ [184] and GSOA [185], most of them are designed to deal with a cohort of samples and require large training sets.”
Method
Tendon and multiomics: advantages, advances, and opportunities
Sarmiento, P., & Little, D. (2021) · npj Regenerative Medicine · doi:10.1038/s41536-021-00168-6 · Cited by 19
Subtype identification from heterogeneous TCGA datasets on a genomic scale by multi-view clustering with enhanced consensus
Cai, M., & Li, L. (2017) · BMC Medical Genomics · doi:10.1186/s12920-017-0306-x · Cited by 19
“For example, [1] proposes a framework for joint modeling of discrete and continuous variables that arise from integrated genomic, epigenomic, and transcriptomic profiling which is applied on distinct integrated tumor subtypes discovery.”
Background
Integrative omics analysis. A study based on Plasmodium falciparum mRNA and protein data
Tomescu, O. A., Mattanovich, D., & Thallinger, G. (2014) · BMC Systems Biology · doi:10.1186/1752-0509-8-s2-s4 · Cited by 18
“Mo and colleagues [5] describe integrative clustering of genomic, epigenomic and transcriptomic profiling.”
Background
MOSClip: multi-omic and survival pathway analysis for the identification of survival associated gene and modules
Martini, P. G. V., Chiogna, M., Calura, E., & Romualdi, C. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz324 · Cited by 18
“However, these are focused mainly for two-class comparison and for identification of cancer subtypes (not necessarily different prognoses) (15–21).”
A Novel Method for Cancer Subtyping and Risk Prediction Using Consensus Factor Analysis
Tran, D., Nguyen, H., Le, U. Q., Bebis, G. N., Luu, H. N., & Nguyen, T. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.01052 · Cited by 18
“Methods in the second category (statistical modeling) include BCC (34), MDI (35), iClusterBayes (36), iClusterPlus (37), and iCluster (38, 39).”
Clinical Genomics: Challenges and Opportunities
Vijay, P., McIntyre, A. B. R., Mason, C. E., Greenfield, J. P., & Li, S. (2016) · Critical Reviews in Eukaryotic Gene Expression · doi:10.1615/critreveukaryotgeneexpr.2016015724 · Cited by 18
Systems Biology Approaches Toward Understanding Primary Mitochondrial Diseases
Maldonado, E. M., Taha, F. A., Rahman, J., & Rahman, S. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00019 · Cited by 19
Representation Learning for the Clustering of Multi-Omics Data
Viaud, G., Mayilvahanan, P., & Cournède, P.-H. (2021) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2021.3060340 · Cited by 17
“…Factorization [6] decomposes non-negative input source matrices into non-negative loadings and factors, the latter being common across sources, while iCluster [7] and its extension iCluster+ [8] also decomposes source matrices into loadings and factors but without the need for non-negativity.”
Background
Transforming primary human hepatocytes into hepatocellular carcinoma with genetically defined factors
Jiang, Z., Cheng, L., Wu, Z., Zhou, L., Wang, H., Hong, Q., Wu, Q., Long, Y., Huang, Y., Xu, G., et al. (2022) · EMBO Reports · doi:10.15252/embr.202154275 · Cited by 17
“…formed by integrating various molecular platforms of iHCC, we utilized iCluster, which formulates the problem of subgroup discovery as a joint multivariate regression of multiple data types with reference to a set of common latent variables that represent the underlying tumor subtypes (Mo et”
Statistical Methods for Integrative Clustering of Multi-omics Data
Chalise, P., Kwon, D., Fridley, B. L., & Mo, Q. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-2986-4_5 · Cited by 18
Self-citation
Deciphering gastric inflammation-induced tumorigenesis through multi-omics data and AI methods
Zhang, Q., Yang, M., Zhang, P., Wu, B., Wei, X., & Li, S. (2023) · Cancer Biology and Medicine · doi:10.20892/j.issn.2095-3941.2023.0129 · Cited by 17
“Mo et al. 53 have proposed a flexible matrix decomposition structure that uses the EM algorithm to analyze the regularized clustering structures among structural data and the intrinsic connections in multi-level data.”
BackgroundMethod
iCluF: an unsupervised iterative cluster-fusion method for patient stratification using multiomics data
Shakyawar, S. K., Sajja, B. R., Patel, J. C., & Guda, C. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae015 · Cited by 17
“The omics cohorts of large numbers of patients have improved our understanding of the molecular basis of cancer diagnosis, prognosis, and subtyping (Gao and Church 2005, Mo et al. 2013, Menyh � art and Gy } orffy 2021, He et al. 2023, Li et al. 2023).”
BackgroundMethod
Block-Constraint Robust Principal Component Analysis and its Application to Integrated Analysis of TCGA Data
Liu, J., Gao, Y.-L., Zheng, C.-H., Xu, Y. D., & Yu, J. (2016) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2016.2574923 · Cited by 17
“improve identification performance of the aberrations of tumor genes as numerous other studies have shown this by using genomic data [9], [23], [24].”
Background
Stepwise iterative maximum likelihood clustering approach
Sharma, A. K., Shigemizu, D., Boroevich, K. A., López, Y., Kamatani, Y., Kubo, M., & Tsunoda, T. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1184-5 · Cited by 17
“In the most recent multi-omics data analysis tools, like iCluster, and iClusterPlus [1], the underlying clustering method used was also k -means.”
Method
Bipartite graph-based approach for clustering of cell lines by gene expression–drug response associations
Chi, C., Ye, Y., Chen, B., & Huang, H. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab143 · Cited by 17
Novel cancer subtyping method based on patient-specific gene regulatory network
Nakazawa, M. A., Tamada, Y., Tanaka, Y., Ikeguchi, M., Higashihara, K., & Okuno, Y. (2021) · Scientific Reports · doi:10.1038/s41598-021-02394-w · Cited by 17
Fast tree aggregation for consensus hierarchical clustering
Hulot, A., Chiquet, J., Jaffrézic, F., & Rigaill, G. J. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-3453-6 · Cited by 17
“…consisting in a preprocessing of the original data set before concatenation into a single data set ready for some standard clustering analysis [12, 13]; ii ) regulatory integrative clustering, which are based on pathways [14]; iii ) clustering of clusters, i.e., methods that take clustering…”
Method
From multi-omics to predictive biomarker: AI in tumor microenvironment
Hai, L., Jiang, Z., Zhang, H., & Sun, Y. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1514977 · Cited by 21
Integrative analysis of multi-omics and imaging data with incorporation of biological information via structural Bayesian factor analysis
Bao, J., Chang, C., Zhang, Q., Saykin, A. J., Shen, L., Long, Q., & Initiative, F. T. A. D. N. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad073 · Cited by 16
“Later, Mo et al. [24] extended the iCluster to the iCluster + framework by allowing the simultaneous integration of continuous and discrete datasets.”
BackgroundMethod

Authors: Mo, Q., Wang, S., Seshan, V. E., Olshen, A. B., Schultz, N., Sander, C., Powers, R. S., Ladanyi, M., & Shen, R.
Affiliations: Baylor College of Medicine (US)
University of Wisconsin–Madison (US)
Department of Epidemiology and Biostatistics,
University of California, San Francisco (US)
Computational Biology Program, and
Cancer Genome Center, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11797
Memorial Sloan Kettering Cancer Center (US)
ORCID: Mo, Qianxing
Wang, Sijian
Seshan, Venkatraman E.
Olshen, Adam B.
Schultz, Nikolaus
Sander, Chris
Ladanyi, Marc
Shen, Ronglai
Venue: Proceedings of the National Academy of Sciences
Publisher: National Academy of Sciences
Published: 2013-02-21
Volume: 110
Issue: 11
Pages: 4245-4250
ISSN: 0027-8424 (ISSN-L)
1091-6490
DOI: 10.1073/pnas.1208949110
PMC: PMC3600490
OpenAlex: W2023461603
Cited by: 474
References: 36
Citations by year: 2026: 29 · 2025: 33 · 2024: 34 · 2023: 54 · 2022: 40 · 2021: 74 · 2020: 40 · 2019: 48 · 2018: 27 · 2017: 27 · 2016: 25 · 2015: 23 · 2014: 13 · 2013: 7
FWCI: 7.98
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: Proceedings of the National Academy of Sciences (publishedVersion)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
eScholarship (California Digital Library) (submittedVersion)
Funders: National Cancer Institute (U24CA143840, CA143840, U01CA168409, P30CA008748)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Keywords: tumor subtypes, cancer driver genes, integrative clustering, somatic mutations, copy number alterations, gene expression, Cancer Cell Line Encyclopedia, TCGA, colorectal cancer, breast cancer, cancer subtyping, cell of origin
MeSH terms: Breast Neoplasms, Female, Gene Expression Regulation, Humans, Male, Colorectal Neoplasms, Genomics, Databases, Genetic, Genes, Neoplasm, Epigenomics

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Pattern discovery and cancer gene identification in integrated cancer genomic data

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