Expression-based subtypes define pathologic response to neoadjuvant immune-checkpoint inhibitors in muscle-invasive bladder cancer.
Expression-based subtypes define pathologic response to neoadjuvant immune-checkpoint inhibitors in muscle-invasive bladder cancer.
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DOI:
10.1038/s41467-023-37568-9
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发表时间:
2023-04-27
影响因子:
16.6
通讯作者:
Meeks, Joshua J.
中科院分区:
文献类型:
--
作者:
Robertson, A. Gordon;Meghani, Khyati;Cooley, Lauren Folgosa;McLaughlin, Kimberly A.;Fall, Leigh Ann;Yu, Yanni;Castro, Mauro A. A.;Groeneveld, Clarice S.;de Reynies, Aurelien;Nazarov, Vadim I.;Tsvetkov, Vasily O.;Choy, Bonnie;Raggi, Daniele;Marandino, Laura;Montorsi, Francesco;Powles, Thomas;Necchi, Andrea;Meeks, Joshua J.
Checkpoint immunotherapy (CPI) has increased survival for some patients with advanced-stage bladder cancer (BCa). However, most patients do not respond. Here, we characterized the tumor and immune microenvironment in pre- and post-treatment tumors from the PURE01 neoadjuvant pembrolizumab immunotherapy trial, using a consolidative approach that combined transcriptional and genetic profiling with digital spatial profiling. We identify five distinctive genetic and transcriptomic programs and validate these in an independent neoadjuvant CPI trial to identify the features of response or resistance to CPI. By modeling the regulatory network, we identify the histone demethylase KDM5B as a repressor of tumor immune signaling pathways in one resistant subtype (S1, Luminal-excluded) and demonstrate that inhibition of KDM5B enhances immunogenicity in FGFR3-mutated BCa cells. Our study identifies signatures associated with response to CPI that can be used to molecularly stratify patients and suggests therapeutic alternatives for subtypes with poor response to neoadjuvant immunotherapy. The response to checkpoint immunotherapy within bladder cancer patients is highly variable. Here, the authors use RNA-seq, ATAC-seq and digital spatial profiling of pre- and post-treatment samples from the PURE01 trial to identify subtypes associated with treatment response.
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影响因子:
5.8
作者:
Gu, Zuguang;Eils, Roland;Schlesner, Matthias
通讯作者:
Schlesner, Matthias
影响因子:
16.6
作者:
Chen Z;Zhou L;Liu L;Hou Y;Xiong M;Yang Y;Hu J;Chen K
通讯作者:
Chen K
影响因子:
37.3
作者:
Hayami S;Yoshimatsu M;Veerakumarasivam A;Unoki M;Iwai Y;Tsunoda T;Field HI;Kelly JD;Neal DE;Yamaue H;Ponder BA;Nakamura Y;Hamamoto R
通讯作者:
Hamamoto R
影响因子:
14.9
作者:
Gillespie M;Jassal B;Stephan R;Milacic M;Rothfels K;Senff-Ribeiro A;Griss J;Sevilla C;Matthews L;Gong C;Deng C;Varusai T;Ragueneau E;Haider Y;May B;Shamovsky V;Weiser J;Brunson T;Sanati N;Beckman L;Shao X;Fabregat A;Sidiropoulos K;Murillo J;Viteri G;Cook J;Shorser S;Bader G;Demir E;Sander C;Haw R;Wu G;Stein L;Hermjakob H;D'Eustachio P
通讯作者:
D'Eustachio P
影响因子:
4.6
作者:
Guo, Charles C.;Bondaruk, Jolanta;Czerniak, Bogdan
通讯作者:
Czerniak, Bogdan