TY - JOUR
T1 - Pathway signatures derived from on-treatment tumor specimens predict response to anti-PD1 blockade in metastatic melanoma
AU - Du, Kuang
AU - Wei, Shiyou
AU - Wei, Zhi
AU - Frederick, Dennie T.
AU - Miao, Benchun
AU - Moll, Tabea
AU - Tian, Tian
AU - Sugarman, Eric
AU - Gabrilovich, Dmitry I.
AU - Sullivan, Ryan J.
AU - Liu, Lunxu
AU - Flaherty, Keith T.
AU - Boland, Genevieve M.
AU - Herlyn, Meenhard
AU - Zhang, Gao
N1 - Funding Information:
T.F. has/had served on the Board of Directors of Loxo Oncology, Clovis Oncology, Strata Oncology, Vivid Biosciences, Checkmate Pharmaceuticals, Kinnate Pharmaceuticals and Scorpion Therapeutics; Corporate Advisory Board of X4 Pharmaceuticals; Scientific Advisory Boards of PIC Therapeutics, Sanofi, Amgen, Asana, Adaptimmune, Aeglea, Shattuck Labs, Tolero, Apricity, Oncoceutics, Fog Pharma, Neon, Tvardi, xCures, Monopteros, Vibliome, and ALX Oncology; and as consultant to Lilly, Novartis, Genentech, BMS, Merck, Takeda, Verastem, Boston Biomedical, Pierre Fabre, Debiopharm; and received research funding from Novartis and Sanofi. R.J.S. has served as consultant and/or on Scientific Advisory Boards for Asana Biosciences, AstraZeneca, BMS, Eisai, Iovance, Merck, Novartis, Oncosec, Pfizer, Replimune and reports research funding from Merck outside the scope of this present work. The remaining authors declare no competing interests.
Funding Information:
We thank all former and current lab members for comments and helpful discussions; C. Chang, S. Majumdar, S. Bala, S. Widura, and T. Nguyen (Wistar Genomics Facility). The research was funded by NIH grants P01CA114046, P50CA174523, U54CA224070, DoD PRCRP grant CA150619-W81XWH-16-1-0119 to M.H.; the Dr. Miriam and Sheldon G. Adelson Medical Research Foundation to M.H. and K.T. F. The support for Shared Resources utilized in this study was provided by Cancer Center Support Grant (CCSG) CA010815 to The Wistar Institute. S.W. was funded by China Scholarship Council.
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12/1
Y1 - 2021/12/1
N2 - Both genomic and transcriptomic signatures have been developed to predict responses of metastatic melanoma to immune checkpoint blockade (ICB) therapies; however, most of these signatures are derived from pre-treatment biopsy samples. Here, we build pathway-based super signatures in pre-treatment (PASS-PRE) and on-treatment (PASS-ON) tumor specimens based on transcriptomic data and clinical information from a large dataset of metastatic melanoma treated with anti-PD1-based therapies as the training set. Both PASS-PRE and PASS-ON signatures are validated in three independent datasets of metastatic melanoma as the validation set, achieving area under the curve (AUC) values of 0.45–0.69 and 0.85–0.89, respectively. We also combine all test samples and obtain AUCs of 0.65 and 0.88 for PASS-PRE and PASS-ON signatures, respectively. When compared with existing signatures, the PASS-ON signature demonstrates more robust and superior predictive performance across all four datasets. Overall, we provide a framework for building pathway-based signatures that is highly and accurately predictive of response to anti-PD1 therapies based on on-treatment tumor specimens. This work would provide a rationale for applying pathway-based signatures derived from on-treatment tumor samples to predict patients’ therapeutic response to ICB therapies.
AB - Both genomic and transcriptomic signatures have been developed to predict responses of metastatic melanoma to immune checkpoint blockade (ICB) therapies; however, most of these signatures are derived from pre-treatment biopsy samples. Here, we build pathway-based super signatures in pre-treatment (PASS-PRE) and on-treatment (PASS-ON) tumor specimens based on transcriptomic data and clinical information from a large dataset of metastatic melanoma treated with anti-PD1-based therapies as the training set. Both PASS-PRE and PASS-ON signatures are validated in three independent datasets of metastatic melanoma as the validation set, achieving area under the curve (AUC) values of 0.45–0.69 and 0.85–0.89, respectively. We also combine all test samples and obtain AUCs of 0.65 and 0.88 for PASS-PRE and PASS-ON signatures, respectively. When compared with existing signatures, the PASS-ON signature demonstrates more robust and superior predictive performance across all four datasets. Overall, we provide a framework for building pathway-based signatures that is highly and accurately predictive of response to anti-PD1 therapies based on on-treatment tumor specimens. This work would provide a rationale for applying pathway-based signatures derived from on-treatment tumor samples to predict patients’ therapeutic response to ICB therapies.
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U2 - 10.1038/s41467-021-26299-4
DO - 10.1038/s41467-021-26299-4
M3 - Article
C2 - 34654806
AN - SCOPUS:85117449066
SN - 2041-1723
VL - 12
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 6023
ER -