TY - JOUR
T1 - TimiGP
T2 - Inferring cell-cell interactions and prognostic associations in the tumor immune microenvironment through gene pairs
AU - Li, Chenyang
AU - Zhang, Baoyi
AU - Schaafsma, Evelien
AU - Reuben, Alexandre
AU - Wang, Linghua
AU - Turk, Mary Jo
AU - Zhang, Jianjun
AU - Cheng, Chao
N1 - Publisher Copyright:
© 2023 The Authors
PY - 2023/7/18
Y1 - 2023/7/18
N2 - Determining the prognostic association of different immune cell types in the tumor microenvironment is critical for understanding cancer biology and developing new therapeutic strategies. However, this is challenging in certain cancer types, where the abundance of different immune subsets is highly correlated. In this study, we develop a computational method named TimiGP to overcome this challenge. Based on bulk gene expression and survival data, TimiGP infers cell-cell interactions that reveal the association between immune cell relative abundance and prognosis. As demonstrated in metastatic melanoma, TimiGP prioritizes immune cells critical in prognosis based on the identified cell-cell interactions. Highly consistent results are obtained by TimiGP when applied to seven independent melanoma datasets and when different cell-type marker sets are used as inputs. Additionally, TimiGP can leverage single-cell RNA sequencing data to delineate the tumor immune microenvironment at high resolutions across a wide range of cancer types.
AB - Determining the prognostic association of different immune cell types in the tumor microenvironment is critical for understanding cancer biology and developing new therapeutic strategies. However, this is challenging in certain cancer types, where the abundance of different immune subsets is highly correlated. In this study, we develop a computational method named TimiGP to overcome this challenge. Based on bulk gene expression and survival data, TimiGP infers cell-cell interactions that reveal the association between immune cell relative abundance and prognosis. As demonstrated in metastatic melanoma, TimiGP prioritizes immune cells critical in prognosis based on the identified cell-cell interactions. Highly consistent results are obtained by TimiGP when applied to seven independent melanoma datasets and when different cell-type marker sets are used as inputs. Additionally, TimiGP can leverage single-cell RNA sequencing data to delineate the tumor immune microenvironment at high resolutions across a wide range of cancer types.
KW - cell-cell interaction network
KW - prognostic associations of immune cells
KW - prognostic model
KW - transcriptome-based method
KW - tumor immune microenvironment
UR - http://www.scopus.com/inward/record.url?scp=85165097168&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85165097168&partnerID=8YFLogxK
U2 - 10.1016/j.xcrm.2023.101121
DO - 10.1016/j.xcrm.2023.101121
M3 - Article
C2 - 37467716
AN - SCOPUS:85165097168
SN - 2666-3791
VL - 4
JO - Cell Reports Medicine
JF - Cell Reports Medicine
IS - 7
M1 - 101121
ER -