The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Pattern discovery and cancer gene identification in integrated cancer genomic data
Open accessAbstract
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.
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“Along with the introduction of high-throughput technologies and the availability of multi-omics data derived from a number of samples, a large set of multi-omics data integrative tools and methods have been reported for the discovery of biomarkers.”
From Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer (2024) · cites this paper as [98]
In the citing paper
“These methods can be divided into categories, such as Bayesian integration (e.g., Pathway Recognition Algorithm using Data Integration on Genomic Models (PARADIGM) [97], iClusterPlus [98], Patient-specific data fusion (PSDF) [99]), network and pathway integration (e.g., Similarity network fusion (SNF) [100], PaintOmics [101], MultiGSEA [102], multi-omics gene-set analysis (MOGSA) [103], Cytoscape”
From Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer (2024) · cites this paper as [98]
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“Integrative clustering with iCluster—The iCluster clustering algorithm formulates the problem of subgroup discovery as a joint multivariate regression of multiple data types with reference to a set of common latent variables, which represent the underlying 28 tumor subtypes (Mo et al., 2013; Shen et al., 2009, 2012).”
MethodSelf-citationComprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma
Ally, A., Balasundaram, M., Carlsen, R., Chuah, E., Clarke, A., Dhalla, N., Holt, R. A., Jones, S. J. M., Lee, D., Ma, Y., et al. (2017) · Cell · doi:10.1016/j.cell.2017.05.046 · Cited by 2,490
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Multi-omics Data Integration, Interpretation, and Its Application
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MethodResultInfluential
Metadata
- 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
- 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
- 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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