Integrative molecular and clinical modeling of clinical outcomes to PD-1 blockade in metastatic melanoma patients
By David Liu, B. Schilling, Derek Liu, A. Sucker, E. Livingstone, Livnat Jerby-Arnon, L. Zimmer, R. Gutzmer, I. Satzger, C. Loquai and 26 more. Nature Medicine, 2019
824 citations recorded by the source corpus.
Immune-checkpoint blockade (ICB) has demonstrated efficacy in many tumor types, but predictors of responsiveness to anti-PD1 ICB are incompletely characterized. In this study, we analyzed a clinically annotated cohort of patients with melanoma (n = 144) treated with anti-PD1 ICB, with whole-exome and whole-transcriptome sequencing of pre-treatment tumors. We found that tumor mutational burden as a predictor of response was confounded by melanoma subtype, whereas multiple novel genomic and transcriptomic features predicted selective response, including features associated with MHC-I and MHC-II antigen presentation. Furthermore, previous anti-CTLA4 ICB exposure was associated with different predictors of response compared to tumors that were naive to ICB, suggesting selective immune effects of previous exposure to anti-CTLA4 ICB. Finally, we developed parsimonious models integrating clinical, genomic and transcriptomic features to predict intrinsic resistance to anti-PD1 ICB in individual tumors, with validation in smaller independent cohorts limited by the availability of comprehensive data. Broadly, we present a framework to discover predictive features and build models of ICB therapeutic response. Analysis of fully clinically annotated and sequenced melanoma tumor samples collected before anti-PD1 treatment suggests that determinants of response differ on the basis of previous anti-CTLA4 therapy, and that tumor mutational burden may not be a strong predictor of response across melanoma subtypes.
Subfields: Cancer Research, Oncology
View Paper: https://www.semanticscholar.org/paper/4608c72da6aba70ac1ada4886075268a15b891f2
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Citations in the corpus
- Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine (2024, background): “Liu et al. developed a logistic regression-based classifier trained on genomic, transcriptomic, and clinical data from treatment-naïve patients to predict resistance to PD-1 inhibitors in advanced melanoma patients [58].”
- Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition (2021, positive): “Test cohort 2 was obtained from a recently published cohort from University Hospital Essen of melanoma samples (Liu et al., 2019) (n = 121), and similarly, a significantly better performance was observed for the multivariable model (AUC = 0.”
- Tumor-infiltrating lymphocytes in the immunotherapy era (2020, background): “57,58,64 This is supported by a high-dimensional single-cell RNA analysis of melanoma tumors treated with checkpoint inhibition, which identified TCF7+CD8+ T cells in particular to be associated with better tumor regression and overall response in checkpoint-treated patients.60 Taken together, these”
- Acquired Resistance to Immune Checkpoint Inhibitors (2020, background): “PD-L1 expression, tumor mutational burden, tumor infiltrating lymphocytes (TILs) or related gene expression signatures have been explored as potential predictors and various other markers are currently under investigation (Chen et al., 2016; Cristescu et al., 2018; Garon et al., 2015; Hugo et al., 2”