Figure - PMC

Skip to main content

An official website of the United States government

Here's how you know

Here's how you know

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

View full-text article in PMC

. 2023 Apr 6;55(5):807–819. doi: 10.1038/s41588-023-01355-5

Search in PMC
Search in PubMed
View in NLM Catalog
Add to search

© The Author(s) 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

PMC Copyright notice

Extended Data Fig. 9 — (a) Cross-correlation heatmap of the top response and resistance associated features in the SU2C-MARK cohort as assessed in TCGA LUAD and LUSC combined datasets (N = 1018)^35,45–50. Correlation cluster and response association colorbars based on designations in the SU2C-MARK cohort are plotted. Unsupervised hierarchical clustering re-identifies the previously recognized feature clusters corresponding to Wound Healing (C1), Immune Activation/Exhaustion (C2), and Neoantigens (C3). Nearly all features retain their original cluster designations (the relocation of the De-Differentiated TI-1 signature may relate to its association with high mutation burden as described earlier). (b) Contribution of SU2C-MARK predictors to clinically relevant biomarker subsets. The addition of features from the Wound Healing (C1) and Immune Activation/Exhaustion (C2) clusters meaningfully stratify traditionally favorable (for example, PDL1 high) and unfavorable (for example, PDL1 low) clinical subgroups (q = 0.06 and q = 0.16, respectively, Benjamini–Hochberg adjusted logrank test). TMB = Tumor Mutation Burden. (c) Association of top genomic predictors from SU2C-MARK cohort with Progression-Free Survival (PFS) for clinically relevant subgroups of NSCLC, namely high TMB ( > 10 mut/MB, top; favorable), high PD-L1 tumor proportion score (PDL1 TPS) corresponding to PDL1 TPS ≥ 50% (middle; favorable), and low PD-L1 expression (PDL1 TPS ≤ 1%, bottom; unfavorable). Signed FDR q-values based on Benjamini–Hochberg adjustment of logrank p-values are plotted for each feature (Methods).