Advances in high-throughput sequencing have elucidated the complexity of the genomic and epigenomic landscapes in malignancies and have led to the identification of recurrent alterations that can be targeted for therapy. These accomplishments have understandably shifted the treatment of cancer towards a more personalized approach. In the past, our prediction of how well a novel compound would achieve safety and efficacy in clinical trials has relied on testing cancer cell lines that were propagated from samples that were collected decades ago. These cell lines lacked the genetic and phenotypic heterogeneities that are evident in patient samples. They were devoid of complex clonal dynamics and interactions with extracellular matrix, as well as stromal and immune cells present in human tumors. This fundamental concern has been addressed by the development of patient-derived cancer models with defined genomic origins, which could more faithfully recapitulate the complexity of human tumors and be biologically stable for the time of the assays. Patient-derived cancer models have advantages that are now transforming the fields of cancer drug discovery and personalized medicine, particularly in target discovery, biomarker testing, tumor-stromal interactions, heterogeneity between primary and metastatic sites, clonal evolution, drug activity screening, assessment of combination and immunotherapies, and therapy resistance. Despite the promising benefits of patient-derived cancer models, we must continue to refine these platforms and define the utility and limitations of these models in order to ensure the greatest impact on improving human health.
The scope of this research topic will cover the use of patient-derived cancer models, including patient-derived tumor xenografts (PDXs) in murine, zebrafish and chicken hosts, humanized PDXs, orthotopic models, induced pluripotent stem (iPS) cells, tissue-derived organoids, stem cell-derived organoids, tumor slices, spheroids, and conditionally reprogrammed cells (CRCs). The goal of this research topic is to provide a resource of articles that describes advances over existing technologies, demonstrates an increase in the successful “take rate”, or renders a model more “humanized”. Studies on the utility of specific models or comparisons of two or more models to predict patient response to therapies, and studies focused on the critical evaluation of intratumoral heterogeneity, tumor-stromal interactions, clonal evolution or therapy resistance are encouraged. We welcome the submission of original articles, perspective articles, reviews, theory, opinion, protocols and methods, technology reports, and case reports.
Advances in high-throughput sequencing have elucidated the complexity of the genomic and epigenomic landscapes in malignancies and have led to the identification of recurrent alterations that can be targeted for therapy. These accomplishments have understandably shifted the treatment of cancer towards a more personalized approach. In the past, our prediction of how well a novel compound would achieve safety and efficacy in clinical trials has relied on testing cancer cell lines that were propagated from samples that were collected decades ago. These cell lines lacked the genetic and phenotypic heterogeneities that are evident in patient samples. They were devoid of complex clonal dynamics and interactions with extracellular matrix, as well as stromal and immune cells present in human tumors. This fundamental concern has been addressed by the development of patient-derived cancer models with defined genomic origins, which could more faithfully recapitulate the complexity of human tumors and be biologically stable for the time of the assays. Patient-derived cancer models have advantages that are now transforming the fields of cancer drug discovery and personalized medicine, particularly in target discovery, biomarker testing, tumor-stromal interactions, heterogeneity between primary and metastatic sites, clonal evolution, drug activity screening, assessment of combination and immunotherapies, and therapy resistance. Despite the promising benefits of patient-derived cancer models, we must continue to refine these platforms and define the utility and limitations of these models in order to ensure the greatest impact on improving human health.
The scope of this research topic will cover the use of patient-derived cancer models, including patient-derived tumor xenografts (PDXs) in murine, zebrafish and chicken hosts, humanized PDXs, orthotopic models, induced pluripotent stem (iPS) cells, tissue-derived organoids, stem cell-derived organoids, tumor slices, spheroids, and conditionally reprogrammed cells (CRCs). The goal of this research topic is to provide a resource of articles that describes advances over existing technologies, demonstrates an increase in the successful “take rate”, or renders a model more “humanized”. Studies on the utility of specific models or comparisons of two or more models to predict patient response to therapies, and studies focused on the critical evaluation of intratumoral heterogeneity, tumor-stromal interactions, clonal evolution or therapy resistance are encouraged. We welcome the submission of original articles, perspective articles, reviews, theory, opinion, protocols and methods, technology reports, and case reports.