Pathway signatures derived from on-treatment tumor specimens predict response to anti-PD1 blockade in metastatic melanoma

Kuang Du, Shiyou Wei, Zhi Wei (), Dennie T. Frederick, Benchun Miao, Tabea Moll, Tian Tian, Eric Sugarman, Dmitry I. Gabrilovich, Ryan J. Sullivan, Lunxu Liu, Keith T. Flaherty, Genevieve M. Boland (), Meenhard Herlyn () and Gao Zhang ()
Additional contact information
Kuang Du: New Jersey Institute of Technology
Shiyou Wei: Sichuan University
Zhi Wei: New Jersey Institute of Technology
Dennie T. Frederick: Massachusetts General Hospital Cancer Center
Benchun Miao: Massachusetts General Hospital Cancer Center
Tabea Moll: Massachusetts General Hospital
Tian Tian: New Jersey Institute of Technology
Eric Sugarman: Philadelphia College of Osteopathic Medicine
Dmitry I. Gabrilovich: Cancer Immunology, AstraZeneca
Ryan J. Sullivan: Massachusetts General Hospital Cancer Center
Lunxu Liu: Sichuan University
Keith T. Flaherty: Massachusetts General Hospital Cancer Center
Genevieve M. Boland: Massachusetts General Hospital
Meenhard Herlyn: Molecular and Cellular Oncogenesis Program and Melanoma Research Center, The Wistar Institute
Gao Zhang: Duke University School of Medicine

Nature Communications, 2021, vol. 12, issue 1, 1-16

Abstract: Abstract 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.

Date: 2021
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DOI: 10.1038/s41467-021-26299-4

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