Predicting cancer drug TARGETS - TreAtment Response Generalized Elastic-neT Signatures

Abstract We are now in an era of molecular medicine, where specific DNA alterations can be used to identify patients who will respond to specific drugs. However, there are only a handful of clinically used predictive biomarkers in oncology. Herein, we describe an approach utilizing in vitro DNA and...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Nicholas R. Rydzewski, Erik Peterson, Joshua M. Lang, Menggang Yu, S. Laura Chang, Martin Sjöström, Hamza Bakhtiar, Gefei Song, Kyle T. Helzer, Matthew L. Bootsma, William S. Chen, Raunak M. Shrestha, Meng Zhang, David A. Quigley, Rahul Aggarwal, Eric J. Small, Daniel R. Wahl, Felix Y. Feng, Shuang G. Zhao
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Genetics QH426-470
Acceso en línea:	https://doaj.org/article/683460200e2546bd8518fb156fad7efa
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:683460200e2546bd8518fb156fad7efa
record_format	dspace
spelling	oai:doaj.org-article:683460200e2546bd8518fb156fad7efa2021-12-02T15:15:24ZPredicting cancer drug TARGETS - TreAtment Response Generalized Elastic-neT Signatures10.1038/s41525-021-00239-z2056-7944https://doaj.org/article/683460200e2546bd8518fb156fad7efa2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41525-021-00239-zhttps://doaj.org/toc/2056-7944Abstract We are now in an era of molecular medicine, where specific DNA alterations can be used to identify patients who will respond to specific drugs. However, there are only a handful of clinically used predictive biomarkers in oncology. Herein, we describe an approach utilizing in vitro DNA and RNA sequencing and drug response data to create TreAtment Response Generalized Elastic-neT Signatures (TARGETS). We trained TARGETS drug response models using Elastic-Net regression in the publicly available Genomics of Drug Sensitivity in Cancer (GDSC) database. Models were then validated on additional in-vitro data from the Cancer Cell Line Encyclopedia (CCLE), and on clinical samples from The Cancer Genome Atlas (TCGA) and Stand Up to Cancer/Prostate Cancer Foundation West Coast Prostate Cancer Dream Team (WCDT). First, we demonstrated that all TARGETS models successfully predicted treatment response in the separate in-vitro CCLE treatment response dataset. Next, we evaluated all FDA-approved biomarker-based cancer drug indications in TCGA and demonstrated that TARGETS predictions were concordant with established clinical indications. Finally, we performed independent clinical validation in the WCDT and found that the TARGETS AR signaling inhibitors (ARSI) signature successfully predicted clinical treatment response in metastatic castration-resistant prostate cancer with a statistically significant interaction between the TARGETS score and PSA response (p = 0.0252). TARGETS represents a pan-cancer, platform-independent approach to predict response to oncologic therapies and could be used as a tool to better select patients for existing therapies as well as identify new indications for testing in prospective clinical trials.Nicholas R. RydzewskiErik PetersonJoshua M. LangMenggang YuS. Laura ChangMartin SjöströmHamza BakhtiarGefei SongKyle T. HelzerMatthew L. BootsmaWilliam S. ChenRaunak M. ShresthaMeng ZhangDavid A. QuigleyRahul AggarwalEric J. SmallDaniel R. WahlFelix Y. FengShuang G. ZhaoNature PortfolioarticleMedicineRGeneticsQH426-470ENnpj Genomic Medicine, Vol 6, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Genetics QH426-470
spellingShingle	Medicine R Genetics QH426-470 Nicholas R. Rydzewski Erik Peterson Joshua M. Lang Menggang Yu S. Laura Chang Martin Sjöström Hamza Bakhtiar Gefei Song Kyle T. Helzer Matthew L. Bootsma William S. Chen Raunak M. Shrestha Meng Zhang David A. Quigley Rahul Aggarwal Eric J. Small Daniel R. Wahl Felix Y. Feng Shuang G. Zhao Predicting cancer drug TARGETS - TreAtment Response Generalized Elastic-neT Signatures
description	Abstract We are now in an era of molecular medicine, where specific DNA alterations can be used to identify patients who will respond to specific drugs. However, there are only a handful of clinically used predictive biomarkers in oncology. Herein, we describe an approach utilizing in vitro DNA and RNA sequencing and drug response data to create TreAtment Response Generalized Elastic-neT Signatures (TARGETS). We trained TARGETS drug response models using Elastic-Net regression in the publicly available Genomics of Drug Sensitivity in Cancer (GDSC) database. Models were then validated on additional in-vitro data from the Cancer Cell Line Encyclopedia (CCLE), and on clinical samples from The Cancer Genome Atlas (TCGA) and Stand Up to Cancer/Prostate Cancer Foundation West Coast Prostate Cancer Dream Team (WCDT). First, we demonstrated that all TARGETS models successfully predicted treatment response in the separate in-vitro CCLE treatment response dataset. Next, we evaluated all FDA-approved biomarker-based cancer drug indications in TCGA and demonstrated that TARGETS predictions were concordant with established clinical indications. Finally, we performed independent clinical validation in the WCDT and found that the TARGETS AR signaling inhibitors (ARSI) signature successfully predicted clinical treatment response in metastatic castration-resistant prostate cancer with a statistically significant interaction between the TARGETS score and PSA response (p = 0.0252). TARGETS represents a pan-cancer, platform-independent approach to predict response to oncologic therapies and could be used as a tool to better select patients for existing therapies as well as identify new indications for testing in prospective clinical trials.
format	article
author	Nicholas R. Rydzewski Erik Peterson Joshua M. Lang Menggang Yu S. Laura Chang Martin Sjöström Hamza Bakhtiar Gefei Song Kyle T. Helzer Matthew L. Bootsma William S. Chen Raunak M. Shrestha Meng Zhang David A. Quigley Rahul Aggarwal Eric J. Small Daniel R. Wahl Felix Y. Feng Shuang G. Zhao
author_facet	Nicholas R. Rydzewski Erik Peterson Joshua M. Lang Menggang Yu S. Laura Chang Martin Sjöström Hamza Bakhtiar Gefei Song Kyle T. Helzer Matthew L. Bootsma William S. Chen Raunak M. Shrestha Meng Zhang David A. Quigley Rahul Aggarwal Eric J. Small Daniel R. Wahl Felix Y. Feng Shuang G. Zhao
author_sort	Nicholas R. Rydzewski
title	Predicting cancer drug TARGETS - TreAtment Response Generalized Elastic-neT Signatures
title_short	Predicting cancer drug TARGETS - TreAtment Response Generalized Elastic-neT Signatures
title_full	Predicting cancer drug TARGETS - TreAtment Response Generalized Elastic-neT Signatures
title_fullStr	Predicting cancer drug TARGETS - TreAtment Response Generalized Elastic-neT Signatures
title_full_unstemmed	Predicting cancer drug TARGETS - TreAtment Response Generalized Elastic-neT Signatures
title_sort	predicting cancer drug targets - treatment response generalized elastic-net signatures
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/683460200e2546bd8518fb156fad7efa
work_keys_str_mv	AT nicholasrrydzewski predictingcancerdrugtargetstreatmentresponsegeneralizedelasticnetsignatures AT erikpeterson predictingcancerdrugtargetstreatmentresponsegeneralizedelasticnetsignatures AT joshuamlang predictingcancerdrugtargetstreatmentresponsegeneralizedelasticnetsignatures AT menggangyu predictingcancerdrugtargetstreatmentresponsegeneralizedelasticnetsignatures AT slaurachang predictingcancerdrugtargetstreatmentresponsegeneralizedelasticnetsignatures AT martinsjostrom predictingcancerdrugtargetstreatmentresponsegeneralizedelasticnetsignatures AT hamzabakhtiar predictingcancerdrugtargetstreatmentresponsegeneralizedelasticnetsignatures AT gefeisong predictingcancerdrugtargetstreatmentresponsegeneralizedelasticnetsignatures AT kylethelzer predictingcancerdrugtargetstreatmentresponsegeneralizedelasticnetsignatures AT matthewlbootsma predictingcancerdrugtargetstreatmentresponsegeneralizedelasticnetsignatures AT williamschen predictingcancerdrugtargetstreatmentresponsegeneralizedelasticnetsignatures AT raunakmshrestha predictingcancerdrugtargetstreatmentresponsegeneralizedelasticnetsignatures AT mengzhang predictingcancerdrugtargetstreatmentresponsegeneralizedelasticnetsignatures AT davidaquigley predictingcancerdrugtargetstreatmentresponsegeneralizedelasticnetsignatures AT rahulaggarwal predictingcancerdrugtargetstreatmentresponsegeneralizedelasticnetsignatures AT ericjsmall predictingcancerdrugtargetstreatmentresponsegeneralizedelasticnetsignatures AT danielrwahl predictingcancerdrugtargetstreatmentresponsegeneralizedelasticnetsignatures AT felixyfeng predictingcancerdrugtargetstreatmentresponsegeneralizedelasticnetsignatures AT shuanggzhao predictingcancerdrugtargetstreatmentresponsegeneralizedelasticnetsignatures
_version_	1718387511224434688

Predicting cancer drug TARGETS - TreAtment Response Generalized Elastic-neT Signatures

Ejemplares similares