TY - JOUR
T1 - From bench to bedside
T2 - Single-cell analysis for cancer immunotherapy
AU - Davis-Marcisak, Emily F.
AU - Deshpande, Atul
AU - Stein-O'Brien, Genevieve L.
AU - Ho, Won J.
AU - Laheru, Daniel
AU - Jaffee, Elizabeth M.
AU - Fertig, Elana J.
AU - Kagohara, Luciane T.
N1 - Funding Information:
We thank Janelle Montagne, Ben K. Johnson, Jackie Zimmerman, and Jacob Mitchell for feedback on the manuscript. Figures were created with BioRender.com. This work was supported by a Lustgarten Foundation Pancreatic Cancer Research grant (to E.M.J.), a Sol Goldman Pancreatic Cancer Research Center grant (to L.T.K.), the Emerson Collective Cancer Research Fund (to E.M.J.), an Allegheny Health Network (AHN) grant (to E.J.F.), U01CA212007 (to E.J.F.), U01CA253403 (to E.J.F.), the JHU Discovery Award (to E.J.F.), P30CA006973 and F31CA250135-01A1 (to E.F.D.M.), a Kavli NDI postdoctoral fellowship (to G.S.O.), and a JHU Provost postdoctoral fellowship (to G.S.O.). W.J.H. is a coinventor of patents with potential for receiving royalties from Rodeo Therapeutics/Amgen, is a consultant for Exelixis, and receives research funding from Sanofi. E.M.J. is a paid consultant for Adaptive Biotech, CSTONE, Achilles, DragonFly, and Genocea; receives funding from the Lustgarten Foundation and Bristol Myer Squibb; is the chief medical advisor for Lustgarten, and an SAB advisor to the Parker Institute for Cancer Immunotherapy (PICI) and for the C3 Cancer Institute. E.J.F. is a member of the Scientific Advisory Board of Vioscera Therapeutics/ResistanceBio. All other authors have nothing to disclose.
Funding Information:
W.J.H. is a coinventor of patents with potential for receiving royalties from Rodeo Therapeutics/Amgen, is a consultant for Exelixis, and receives research funding from Sanofi. E.M.J. is a paid consultant for Adaptive Biotech, CSTONE, Achilles, DragonFly, and Genocea; receives funding from the Lustgarten Foundation and Bristol Myer Squibb; is the chief medical advisor for Lustgarten, and an SAB advisor to the Parker Institute for Cancer Immunotherapy (PICI) and for the C3 Cancer Institute. E.J.F. is a member of the Scientific Advisory Board of Vioscera Therapeutics/ResistanceBio. All other authors have nothing to disclose.
Funding Information:
This work was supported by a Lustgarten Foundation Pancreatic Cancer Research grant (to E.M.J.), a Sol Goldman Pancreatic Cancer Research Center grant (to L.T.K.), the Emerson Collective Cancer Research Fund (to E.M.J.), an Allegheny Health Network (AHN) grant (to E.J.F.), U01CA212007 (to E.J.F.), U01CA253403 (to E.J.F.), the JHU Discovery Award (to E.J.F.), P30CA006973 and F31CA250135-01A1 (to E.F.D.M.), a Kavli NDI postdoctoral fellowship (to G.S.O.), and a JHU Provost postdoctoral fellowship (to G.S.O.).
Publisher Copyright:
© 2021 Elsevier Inc.
PY - 2021/8/9
Y1 - 2021/8/9
N2 - Single-cell technologies are emerging as powerful tools for cancer research. These technologies characterize the molecular state of each cell within a tumor, enabling new exploration of tumor heterogeneity, microenvironment cell-type composition, and cell state transitions that affect therapeutic response, particularly in the context of immunotherapy. Analyzing clinical samples has great promise for precision medicine but is technically challenging. Successfully identifying predictors of response requires well-coordinated, multi-disciplinary teams to ensure adequate sample processing for high-quality data generation and computational analysis for data interpretation. Here, we review current approaches to sample processing and computational analysis regarding their application to translational cancer immunotherapy research.
AB - Single-cell technologies are emerging as powerful tools for cancer research. These technologies characterize the molecular state of each cell within a tumor, enabling new exploration of tumor heterogeneity, microenvironment cell-type composition, and cell state transitions that affect therapeutic response, particularly in the context of immunotherapy. Analyzing clinical samples has great promise for precision medicine but is technically challenging. Successfully identifying predictors of response requires well-coordinated, multi-disciplinary teams to ensure adequate sample processing for high-quality data generation and computational analysis for data interpretation. Here, we review current approaches to sample processing and computational analysis regarding their application to translational cancer immunotherapy research.
KW - computational biology
KW - single-cell proteomics
KW - single-cell transcriptomics
KW - spatial proteomics
KW - spatial transcriptomics
KW - translational medicine
KW - tumor immunology
UR - http://www.scopus.com/inward/record.url?scp=85111883273&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85111883273&partnerID=8YFLogxK
U2 - 10.1016/j.ccell.2021.07.004
DO - 10.1016/j.ccell.2021.07.004
M3 - Review article
C2 - 34329587
AN - SCOPUS:85111883273
VL - 39
SP - 1062
EP - 1080
JO - Cancer Cell
JF - Cancer Cell
SN - 1535-6108
IS - 8
ER -