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- Publisher Website: 10.1093/bib/bby026
- Scopus: eid_2-s2.0-85067539571
- PMID: 29659698
- WOS: WOS:000473756500002
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Article: Molecular subtyping of cancer: current status and moving toward clinical applications
Title | Molecular subtyping of cancer: current status and moving toward clinical applications |
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Authors | |
Keywords | Cancer Heterogeneity Subtyping Subtypes Challenges |
Issue Date | 2019 |
Publisher | Oxford University Press. The Journal's web site is located at http://bib.oxfordjournals.org/ |
Citation | Briefings in Bioinformatics, 2019, v. 20 n. 2, p. 572-584 How to Cite? |
Abstract | Cancer is a collection of genetic diseases, with large phenotypic differences and genetic heterogeneity between different types of cancers and even within the same cancer type. Recent advances in genome-wide profiling provide an opportunity to investigate global molecular changes during the development and progression of cancer. Meanwhile, numerous statistical and machine learning algorithms have been designed for the processing and interpretation of high-throughput molecular data. Molecular subtyping studies have allowed the allocation of cancer into homogeneous groups that are considered to harbor similar molecular and clinical characteristics. Furthermore, this has helped researchers to identify both actionable targets for drug design as well as biomarkers for response prediction. In this review, we introduce five frequently applied techniques for generating molecular data, which are microarray, RNA sequencing, quantitative polymerase chain reaction, NanoString and tissue microarray. Commonly used molecular data for cancer subtyping and clinical applications are discussed. Next, we summarize a workflow for molecular subtyping of cancer, including data preprocessing, cluster analysis, supervised classification and subtype characterizations. Finally, we identify and describe four major challenges in the molecular subtyping of cancer that may preclude clinical implementation. We suggest that standardized methods should be established to help identify intrinsic subgroup signatures and build robust classifiers that pave the way toward stratified treatment of cancer patients. |
Persistent Identifier | http://hdl.handle.net/10722/270132 |
ISSN | 2023 Impact Factor: 6.8 2023 SCImago Journal Rankings: 2.143 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhao, L | - |
dc.contributor.author | Lee, VHF | - |
dc.contributor.author | Ng, KP | - |
dc.contributor.author | Yan, H | - |
dc.contributor.author | Bijlsma, MF | - |
dc.date.accessioned | 2019-05-20T05:10:13Z | - |
dc.date.available | 2019-05-20T05:10:13Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Briefings in Bioinformatics, 2019, v. 20 n. 2, p. 572-584 | - |
dc.identifier.issn | 1467-5463 | - |
dc.identifier.uri | http://hdl.handle.net/10722/270132 | - |
dc.description.abstract | Cancer is a collection of genetic diseases, with large phenotypic differences and genetic heterogeneity between different types of cancers and even within the same cancer type. Recent advances in genome-wide profiling provide an opportunity to investigate global molecular changes during the development and progression of cancer. Meanwhile, numerous statistical and machine learning algorithms have been designed for the processing and interpretation of high-throughput molecular data. Molecular subtyping studies have allowed the allocation of cancer into homogeneous groups that are considered to harbor similar molecular and clinical characteristics. Furthermore, this has helped researchers to identify both actionable targets for drug design as well as biomarkers for response prediction. In this review, we introduce five frequently applied techniques for generating molecular data, which are microarray, RNA sequencing, quantitative polymerase chain reaction, NanoString and tissue microarray. Commonly used molecular data for cancer subtyping and clinical applications are discussed. Next, we summarize a workflow for molecular subtyping of cancer, including data preprocessing, cluster analysis, supervised classification and subtype characterizations. Finally, we identify and describe four major challenges in the molecular subtyping of cancer that may preclude clinical implementation. We suggest that standardized methods should be established to help identify intrinsic subgroup signatures and build robust classifiers that pave the way toward stratified treatment of cancer patients. | - |
dc.language | eng | - |
dc.publisher | Oxford University Press. The Journal's web site is located at http://bib.oxfordjournals.org/ | - |
dc.relation.ispartof | Briefings in Bioinformatics | - |
dc.subject | Cancer | - |
dc.subject | Heterogeneity | - |
dc.subject | Subtyping | - |
dc.subject | Subtypes | - |
dc.subject | Challenges | - |
dc.title | Molecular subtyping of cancer: current status and moving toward clinical applications | - |
dc.type | Article | - |
dc.identifier.email | Lee, VHF: vhflee@hku.hk | - |
dc.identifier.email | Ng, KP: michael.ng@hku.hk | - |
dc.identifier.authority | Lee, VHF=rp00264 | - |
dc.identifier.authority | Ng, KP=rp02578 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1093/bib/bby026 | - |
dc.identifier.pmid | 29659698 | - |
dc.identifier.scopus | eid_2-s2.0-85067539571 | - |
dc.identifier.hkuros | 297858 | - |
dc.identifier.volume | 20 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 572 | - |
dc.identifier.epage | 584 | - |
dc.identifier.isi | WOS:000473756500002 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 1467-5463 | - |