Molecular determinants of response to PD-L1 blockade across tumor types

Romain Banchereau (), Ning Leng, Oliver Zill, Ethan Sokol, Gengbo Liu, Dean Pavlick, Sophia Maund, Li-Fen Liu, Edward Kadel, Nicole Baldwin, Suchit Jhunjhunwala, Dorothee Nickles, Zoe June Assaf, Daniel Bower, Namrata Patil, Mark McCleland, David Shames, Luciana Molinero, Mahrukh Huseni, Shomyseh Sanjabi, Craig Cummings, Ira Mellman, Sanjeev Mariathasan, Priti Hegde and Thomas Powles ()
Additional contact information
Romain Banchereau: Genentech
Ning Leng: Genentech
Oliver Zill: Genentech
Ethan Sokol: Foundation Medicine
Gengbo Liu: Genentech
Dean Pavlick: Foundation Medicine
Sophia Maund: Genentech
Li-Fen Liu: Genentech
Edward Kadel: Genentech
Nicole Baldwin: Baylor Institute for Immunology Research
Suchit Jhunjhunwala: Genentech
Dorothee Nickles: Genentech
Zoe June Assaf: Genentech
Daniel Bower: Genentech
Namrata Patil: Genentech
Mark McCleland: Genentech
David Shames: Genentech
Luciana Molinero: Genentech
Mahrukh Huseni: Genentech
Shomyseh Sanjabi: Genentech
Craig Cummings: Genentech
Ira Mellman: Genentech
Sanjeev Mariathasan: Genentech
Priti Hegde: Foundation Medicine
Thomas Powles: Queen Mary University of London

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: Abstract Immune checkpoint inhibitors targeting the PD-1/PD-L1 axis lead to durable clinical responses in subsets of cancer patients across multiple indications, including non-small cell lung cancer (NSCLC), urothelial carcinoma (UC) and renal cell carcinoma (RCC). Herein, we complement PD-L1 immunohistochemistry (IHC) and tumor mutation burden (TMB) with RNA-seq in 366 patients to identify unifying and indication-specific molecular profiles that can predict response to checkpoint blockade across these tumor types. Multiple machine learning approaches failed to identify a baseline transcriptional signature highly predictive of response across these indications. Signatures described previously for immune checkpoint inhibitors also failed to validate. At the pathway level, significant heterogeneity is observed between indications, in particular within the PD-L1+ tumors. mUC and NSCLC are molecularly aligned, with cell cycle and DNA damage repair genes associated with response in PD-L1- tumors. At the gene level, the CDK4/6 inhibitor CDKN2A is identified as a significant transcriptional correlate of response, highlighting the association of non-immune pathways to the outcome of checkpoint blockade. This cross-indication analysis reveals molecular heterogeneity between mUC, NSCLC and RCC tumors, suggesting that indication-specific molecular approaches should be prioritized to formulate treatment strategies.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.nature.com/articles/s41467-021-24112-w Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-24112-w

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-021-24112-w

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().