- Caudell J, Echevarria M, Yang G, Kim Y, Kirtane K, Kish J, Muzaffar J, Zahid M, Chung C, Enderling H. Phase 2 Prospective Trial of Personalized Radiation Therapy Fractionation in Human Papillomavirus Positive Oropharyngeal Cancer. Int J Radiat Oncol Biol Phys. 2026 Jul.125(4):1043-1050. Pubmedid: 41519405. Pmcid: PMC13131356.
- Tomaszewski MR, Fan S, Garcia A, Qi J, Kim Y, Gatenby RA, Schabath MB, Tap WD, Reinke DK, Makanji RJ, Reed DR, Gillies RJ. AI-Radiomics Can Improve Inclusion Criteria and Clinical Trial Performance. Tomography. 2022 Feb.8(1):341-355. Pubmedid: 35202193. Pmcid: PMC8880510.
- Kim Y, Kim D, Cao B, Carvajal R, Kim M. PDXGEM: patient-derived tumor xenograft-based gene expression model for predicting clinical response to anticancer therapy in cancer patients. BMC Bioinformatics. 2020 Jul.21(1):288. Pubmedid: 32631229. Pmcid: PMC7336455.
- Naghavi AO, Kim Y, Yang GQ, Ahmed KA, Caudell JJ. Alterations in genetic pathways following radiotherapy for head and neck cancer. Head Neck. 2020 Feb.42(2):312-320. Pubmedid: 31833149. Pmcid: PMC7771332.
- Chen YZ, Kim Y, Soliman H, Ying G, Lee JK. Single drug biomarker prediction for ER- breast cancer outcome from chemotherapy. Endocr Relat Cancer. 2018 Jun.25(6):595-605. Pubmedid: 29599124. Pmcid: PMC5920016.
- Kim Y, Dillon PM, Park T, Lee JK. CONCORD biomarker prediction for novel drug introduction to different cancer types. Oncotarget. 2018 Jan.9(1):1091-1106. Pubmedid: 29416679. Pmcid: PMC5787421.
- Ma Z, Kim Y, Hu F, Lee JK. Point success rate for patient therapeutic response prediction by continuous biomarker scores. Stat Methods Med Res. 2016 Aug.25(4):1638-1647. Pubmedid: 23839122. Pmcid: PMC7771551.
- Kim Y, Guntupalli SR, Lee SJ, Behbakht K, Theodorescu D, Lee JK, Diamond JR. Retrospective analysis of survival improvement by molecular biomarker-based personalized chemotherapy for recurrent ovarian cancer. PLoS One. 2014 Feb.9(2):e86532. Pubmedid: 24505259. Pmcid: PMC3914805.
- Lee JK, Coutant C, Kim YC, Qi Y, Theodorescu D, Symmans WF, Baggerly K, Rouzier R, Pusztai L. Prospective comparison of clinical and genomic multivariate predictors of response to neoadjuvant chemotherapy in breast cancer. Clin Cancer Res. 2010 Jan.16(2):711-718. Pubmedid: 20068086. Pmcid: PMC2807997.
Overview
PDXGEM is a statistical bioinformatics and pharmacognomics pipeline to build a multi-gene expression model for predicting responses of cancer patients to anti-cancer therapeutics on the basis of data on mRNA expression profiles and drug-sensitivity of Patient-Derived tumor Xenograft models (PDXs). Please refer to the following preprint for more details.
- Kim Y. et al. (2020) PDXGEM: patient-derived tumor xenograft based gene expression model for predicting clinical response to anticancer therapy in cancer patients, BMC Bioinformatics, 21, 288 (2020). https://doi.org/10.1186/s12859-020-03633-z
Please click ‘PDXGEM(Guest)‘ menu to use PDXGEM web software without log-in. If you signed in, select ‘Run PDXGEM‘ menu.
PDXGEM process diagram
In brief, PDXGEM consists of four subsequent analysis steps, 1) in vivo drug senstivity biomarker discovery, 2) Concordant Co-Expression Analysis, 3) Multi-gene drug response prediction model training step, and 4) testing the performance of the prediction model.
Currently available PDXGEMs by cancer types and anti-cancer drugs
