Modeling biological and genetic diversity in upper tract urothelial carcinoma with patient derived xenografts.

Clicks: 262
ID: 104731
2020
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #192 of 485 articles by views in Nature communications

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 485 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
Treatment paradigms for patients with upper tract urothelial carcinoma (UTUC) are typically extrapolated from studies of bladder cancer despite their distinct clinical and molecular characteristics. The advancement of UTUC research is hampered by the lack of disease-specific models. Here, we report the establishment of patient derived xenograft (PDX) and cell line models that reflect the genomic and biological heterogeneity of the human disease. Models demonstrate high genomic concordance with the corresponding patient tumors, with invasive tumors more likely to successfully engraft. Treatment of PDX models with chemotherapy recapitulates responses observed in patients. Analysis of a HER2 S310F-mutant PDX suggests that an antibody drug conjugate targeting HER2 would have superior efficacy versus selective HER2 kinase inhibitors. In sum, the biological and phenotypic concordance between patient and PDXs suggest that these models could facilitate studies of intrinsic and acquired resistance and the development of personalized medicine strategies for UTUC patients.
Reference Key
kim2020modelingnature Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kim, Kwanghee;Hu, Wenhuo;Audenet, François;Almassi, Nima;Hanrahan, Aphrothiti J;Murray, Katie;Bagrodia, Aditya;Wong, Nathan;Clinton, Timothy N;Dason, Shawn;Mohan, Vishnu;Jebiwott, Sylvia;Nagar, Karan;Gao, Jianjiong;Penson, Alex;Hughes, Chris;Gordon, Benjamin;Chen, Ziyu;Dong, Yiyu;Watson, Philip A;Alvim, Ricardo;Elzein, Arijh;Gao, Sizhi P;Cocco, Emiliano;Santin, Alessandro D;Ostrovnaya, Irina;Hsieh, James J;Sagi, Irit;Pietzak, Eugene J;Hakimi, A Ari;Rosenberg, Jonathan E;Iyer, Gopa;Vargas, Herbert A;Scaltriti, Maurizio;Al-Ahmadie, Hikmat;Solit, David B;Coleman, Jonathan A;
Journal Nature communications
Year 2020
DOI
10.1038/s41467-020-15885-7
URL
Keywords

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.