作者
Michal Marek Hoppe,Patrick Jaynes,Fan Shuangyi,Yanfen Peng,Shruti Sridhar,Phuong Mai Hoang,Xin Liu,Sanjay de Mel,Limei Poon,Esther Hian Li Chan,Joanne Lee,Choon Kiat Ong,Tiffany Tang,Soon Thye Lim,Chandramouli Nagarajan,Nicholas Francis Grigoropoulos,Soo Yong Tan,Susan Swee-Shan Hue,Sheng-Tsung Chang,Shih-Sung Chuang,Shaoying Li,Joseph D. Khoury,Hyungwon Choi,Carl Harris Iii,Alessia Bottos,Laura J Gay,Hendrik F P Runge,Ilias Moutsopoulos,Irina Mohorianu,Daniel J. Hodson,Pedro Farinha,Anja Mottok,David W. Scott,Jason J Pitt,Jinmiao Chen,Gayatri Kumar,Kasthuri Kannan,Wee Joo Chng,Yen Lin Chee,Siok-Bian Ng,Claudio Tripodo,Anand D. Jeyasekharan
摘要
Cancers often overexpress multiple clinically relevant oncogenes, but it is not known if combinations of oncogenes in cellular subpopulations within a cancer influence clinical outcomes. Using quantitative multispectral imaging of the prognostically relevant oncogenes MYC, BCL2, and BCL6 in diffuse large B-cell lymphoma (DLBCL), we show that the percentage of cells with a unique combination MYC+BCL2+BCL6- (M+2+6-) consistently predicts survival across four independent cohorts (n = 449), an effect not observed with other combinations including M+2+6+. We show that the M+2+6- percentage can be mathematically derived from quantitative measurements of the individual oncogenes and correlates with survival in IHC (n = 316) and gene expression (n = 2,521) datasets. Comparative bulk/single-cell transcriptomic analyses of DLBCL samples and MYC/BCL2/BCL6-transformed primary B cells identify molecular features, including cyclin D2 and PI3K/AKT as candidate regulators of M+2+6- unfavorable biology. Similar analyses evaluating oncogenic combinations at single-cell resolution in other cancers may facilitate an understanding of cancer evolution and therapy resistance.Using single-cell-resolved multiplexed imaging, we show that selected subpopulations of cells expressing specific combinations of oncogenes influence clinical outcomes in lymphoma. We describe a probabilistic metric for the estimation of cellular oncogenic coexpression from IHC or bulk transcriptomes, with possible implications for prognostication and therapeutic target discovery in cancer.