Transcriptome modeling and phenotypic assays for cancer precision medicine
Cancer precision medicine requires clinically actionable biomarkers for patient stratification and a better prediction of clinical outcome. Although thousands of cancer-enriched mutated genes have been reported by global sequencing projects, to date, only a few oncogenic mutations have been confirme...
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| Published in | Archives of pharmacal research Vol. 40; no. 8; pp. 906 - 914 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Seoul
Pharmaceutical Society of Korea
01.08.2017
대한약학회 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0253-6269 1976-3786 1976-3786 |
| DOI | 10.1007/s12272-017-0940-z |
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| Abstract | Cancer precision medicine requires clinically actionable biomarkers for patient stratification and a better prediction of clinical outcome. Although thousands of cancer-enriched mutated genes have been reported by global sequencing projects, to date, only a few oncogenic mutations have been confirmed as effective biomarkers in cancer therapies. The low frequency and varied profile (i.e., allele frequency, mutation position) of mutant genes among cancer types limit the utility of predictive biomarkers. The recent explosion of cancer transcriptome and phenotypic screening data provides another opportunity for finding transcript-level biomarkers and targets, thus overcoming the limitation of cancer mutation analyses. Technological developments enable the rapid and extensive discovery of potential target-biomarker combinations from large-scale transcriptome-level screening combined with physiologically relevant phenotypic assays. Here, we summarized recent progress as well as discussed the outlook of transcriptome-oriented data mining strategies and phenotypic assays for the identification of non-genetic biomarkers and targets in cancer drug discovery. |
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| AbstractList | Cancer precision medicine requires clinically actionable biomarkers for patient stratification and a better prediction of clinical outcome. Although thousands of cancer-enriched mutated genes have been reported by global sequencing projects, to date, only a few oncogenic mutations have been confirmed as effective biomarkers in cancer therapies. The low frequency and varied profile (i.e., allele frequency, mutation position) of mutant genes among cancer types limit the utility of predictive biomarkers. The recent explosion of cancer transcriptome and phenotypic screening data provides another opportunity for finding transcript-level biomarkers and targets, thus overcoming the limitation of cancer mutation analyses. Technological developments enable the rapid and extensive discovery of potential target-biomarker combinations from large-scale transcriptome-level screening combined with physiologically relevant phenotypic assays. Here, we summarized recent progress as well as discussed the outlook of transcriptome-oriented data mining strategies and phenotypic assays for the identification of non-genetic biomarkers and targets in cancer drug discovery.Cancer precision medicine requires clinically actionable biomarkers for patient stratification and a better prediction of clinical outcome. Although thousands of cancer-enriched mutated genes have been reported by global sequencing projects, to date, only a few oncogenic mutations have been confirmed as effective biomarkers in cancer therapies. The low frequency and varied profile (i.e., allele frequency, mutation position) of mutant genes among cancer types limit the utility of predictive biomarkers. The recent explosion of cancer transcriptome and phenotypic screening data provides another opportunity for finding transcript-level biomarkers and targets, thus overcoming the limitation of cancer mutation analyses. Technological developments enable the rapid and extensive discovery of potential target-biomarker combinations from large-scale transcriptome-level screening combined with physiologically relevant phenotypic assays. Here, we summarized recent progress as well as discussed the outlook of transcriptome-oriented data mining strategies and phenotypic assays for the identification of non-genetic biomarkers and targets in cancer drug discovery. Cancer precision medicine requires clinically actionable biomarkers for patient stratification and a better prediction of clinical outcome. Although thousands of cancer-enriched mutated genes have been reported by global sequencing projects, to date, only a few oncogenic mutations have been confirmed as effective biomarkers in cancer therapies. The low frequency and varied profile (i.e., allele frequency, mutation position) of mutant genes among cancer types limit the utility of predictive biomarkers. The recent explosion of cancer transcriptome and phenotypic screening data provides another opportunity for finding transcript-level biomarkers and targets, thus overcoming the limitation of cancer mutation analyses. Technological developments enable the rapid and extensive discovery of potential target-biomarker combinations from large-scale transcriptome-level screening combined with physiologically relevant phenotypic assays. Here, we summarized recent progress as well as discussed the outlook of transcriptome-oriented data mining strategies and phenotypic assays for the identification of non-genetic biomarkers and targets in cancer drug discovery. Cancer precision medicine requires clinicallyactionable biomarkers for patient stratification and a betterprediction of clinical outcome. Although thousands ofcancer-enriched mutated genes have been reported byglobal sequencing projects, to date, only a few oncogenicmutations have been confirmed as effective biomarkers incancer therapies. The low frequency and varied profile (i.e.,allele frequency, mutation position) of mutant genes amongcancer types limit the utility of predictive biomarkers. Therecent explosion of cancer transcriptome and phenotypicscreening data provides another opportunity for findingtranscript-level biomarkers and targets, thus overcomingthe limitation of cancer mutation analyses. Technologicaldevelopments enable the rapid and extensive discovery ofpotential target-biomarker combinations from large-scaletranscriptome-level screening combined with physiologicallyrelevant phenotypic assays. Here, we summarizedrecent progress as well as discussed the outlook of transcriptome-oriented data mining strategies and phenotypicassays for the identification of non-genetic biomarkers andtargets in cancer drug discovery. KCI Citation Count: 3 |
| Author | Jeong, Euna Yoon, Sukjoon Song, Mee Moon, Sung Ung |
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| CitedBy_id | crossref_primary_10_1016_j_alcohol_2018_05_004 crossref_primary_10_3389_fgene_2021_770857 crossref_primary_10_1016_j_bbcan_2023_189030 crossref_primary_10_3389_fonc_2020_00423 crossref_primary_10_1016_j_leukres_2017_11_008 |
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| Keywords | Biomarker Phenotypic screening Precision medicine Cancer transcriptome |
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