In the era of precision medicine,cancer researchers and oncologists are eagerly searching for more realistic,cost effective,and timely tumor models to aid drug development and precision oncology.Tumor models that can ...In the era of precision medicine,cancer researchers and oncologists are eagerly searching for more realistic,cost effective,and timely tumor models to aid drug development and precision oncology.Tumor models that can faithfully recapitulate the histological and molecular characteristics of various human tumors will be extremely valuable in increasing the successful rate of oncology drug development and discovering the most efficacious treatment regimen for cancer patients.Two‐dimensional(2D)cultured cancer cell lines,genetically engineered mouse tumor(GEMT)models,and patient‐derived tumor xenograft(PDTX)models have been widely used to investigate the biology of various types of cancers and test the efficacy of oncology drug candidates.However,due to either the failure to faithfully recapitulate the complexity of patient tumors in the case of 2D cultured cancer cells,or high cost and untimely for drug screening and testing in the case of GEMT and PDTX,new tumor models are urgently needed.The recently developed patient‐derived tumor organoids(PDTO)offer great potentials in uncovering novel biology of cancer development,accelerating the discovery of oncology drugs,and individualizing the treatment of cancers.In this review,we will summarize the recent progress in utilizing PDTO for oncology drug discovery.In addition,we will discuss the potentials and limitations of the current PDTO tumor models.展开更多
Precision oncology aims to offer the most appropriate treatments to cancer patients mainly based on their individual genetic information. Genomics has provided numerous valuable data on driver mutations and risk loci;...Precision oncology aims to offer the most appropriate treatments to cancer patients mainly based on their individual genetic information. Genomics has provided numerous valuable data on driver mutations and risk loci; however, it remains a formidable challenge to transform these data into therapeutic agents. Transcriptomics describes the multifarious expression patterns of both mRNAs and non-coding RNAs （ncRNAs）, which facilitates the deciphering of genomic codes. In this review, we take breast cancer as an example to demonstrate the applications of these rich RNA resources in precision medicine exploration. These include the use of mRNA profiles in triple-negative breast cancer （TNBC） subtyping to inform corresponding candidate targeted therapies; current advancements and achievements of high-throughput RNA interference （RNAi） screening technologies in breast cancer; and microRNAs as functional signatures for defining cell identities and regulating the biological activities of breast cancer cells. We summarize the benefits of transcriptomic analyses in breast cancer management and propose that unscrambling the core signaling networks of cancer may be an important task of multiple-omic data integration for precision oncology.展开更多
The disagreements in clinical data and therapy recommendations extracted from different sources/studies are a common finding in oncology research. Knowingly “biology is less reproducible than physics and mechanic eng...The disagreements in clinical data and therapy recommendations extracted from different sources/studies are a common finding in oncology research. Knowingly “biology is less reproducible than physics and mechanic engineering”, in order to overcome the disagreements and to find common grounds, we still rely on meta-analysis and systemic reviews for the highest level of evidence. To gather systemic review data base, a bibliographic search usually is conducted in the PubMed and in Cochrane Central Register of Controlled Trials databases to address a common clinical challenge. That said, frequently due to common conflicts between articles outcomes, an opinion of a third investigator is sought. Here in this article, we propose a rationale that could explain the differences in outcomes as a result of imperfect understanding of the current research database secondary to the unique biology of the tumor, rather than statistical interpretation on findings. We believe that the differences in findings merely are based on blinded inclusion criteria, and lack of accurate companion diagnostics to correlate the magnitude of response to each therapy. The objective of this article is to discuss a strategy to overcome such discordance by providing quantitative biological measures for genomic classification and correlation of tumor response to the selected targeted therapy. We further review such analysis in a case series of Her 2 positive breast cancer and conclude that translational research would be clinically relevant when customized to the biological findings.展开更多
文摘In the era of precision medicine,cancer researchers and oncologists are eagerly searching for more realistic,cost effective,and timely tumor models to aid drug development and precision oncology.Tumor models that can faithfully recapitulate the histological and molecular characteristics of various human tumors will be extremely valuable in increasing the successful rate of oncology drug development and discovering the most efficacious treatment regimen for cancer patients.Two‐dimensional(2D)cultured cancer cell lines,genetically engineered mouse tumor(GEMT)models,and patient‐derived tumor xenograft(PDTX)models have been widely used to investigate the biology of various types of cancers and test the efficacy of oncology drug candidates.However,due to either the failure to faithfully recapitulate the complexity of patient tumors in the case of 2D cultured cancer cells,or high cost and untimely for drug screening and testing in the case of GEMT and PDTX,new tumor models are urgently needed.The recently developed patient‐derived tumor organoids(PDTO)offer great potentials in uncovering novel biology of cancer development,accelerating the discovery of oncology drugs,and individualizing the treatment of cancers.In this review,we will summarize the recent progress in utilizing PDTO for oncology drug discovery.In addition,we will discuss the potentials and limitations of the current PDTO tumor models.
基金supported by the National Natural Science Foundation of China(Grant Nos.31230042,31671349,and31700712)
文摘Precision oncology aims to offer the most appropriate treatments to cancer patients mainly based on their individual genetic information. Genomics has provided numerous valuable data on driver mutations and risk loci; however, it remains a formidable challenge to transform these data into therapeutic agents. Transcriptomics describes the multifarious expression patterns of both mRNAs and non-coding RNAs （ncRNAs）, which facilitates the deciphering of genomic codes. In this review, we take breast cancer as an example to demonstrate the applications of these rich RNA resources in precision medicine exploration. These include the use of mRNA profiles in triple-negative breast cancer （TNBC） subtyping to inform corresponding candidate targeted therapies; current advancements and achievements of high-throughput RNA interference （RNAi） screening technologies in breast cancer; and microRNAs as functional signatures for defining cell identities and regulating the biological activities of breast cancer cells. We summarize the benefits of transcriptomic analyses in breast cancer management and propose that unscrambling the core signaling networks of cancer may be an important task of multiple-omic data integration for precision oncology.
文摘The disagreements in clinical data and therapy recommendations extracted from different sources/studies are a common finding in oncology research. Knowingly “biology is less reproducible than physics and mechanic engineering”, in order to overcome the disagreements and to find common grounds, we still rely on meta-analysis and systemic reviews for the highest level of evidence. To gather systemic review data base, a bibliographic search usually is conducted in the PubMed and in Cochrane Central Register of Controlled Trials databases to address a common clinical challenge. That said, frequently due to common conflicts between articles outcomes, an opinion of a third investigator is sought. Here in this article, we propose a rationale that could explain the differences in outcomes as a result of imperfect understanding of the current research database secondary to the unique biology of the tumor, rather than statistical interpretation on findings. We believe that the differences in findings merely are based on blinded inclusion criteria, and lack of accurate companion diagnostics to correlate the magnitude of response to each therapy. The objective of this article is to discuss a strategy to overcome such discordance by providing quantitative biological measures for genomic classification and correlation of tumor response to the selected targeted therapy. We further review such analysis in a case series of Her 2 positive breast cancer and conclude that translational research would be clinically relevant when customized to the biological findings.
