Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma

By Willy Hugo, J. Zaretsky, Lu Sun, Chunying Song, B. H. Moreno, S. Hu-Lieskovan, B. Berent-Maoz, Jia Pang, B. Chmielowski, Grace Cherry and 8 more. Cell, 2016

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SUMMARY PD-1 immune checkpoint blockade provides significant clinical benefits for melanoma patients. We analyzed the somatic mutanomes and transcriptomes of pretreatment melanoma biopsies to identify factors that may influence innate sensitivity or resistance to anti-PD-1 therapy. We find that, while overall high mutational loads associate with improved survival both in responding and non-responding patients, responding tumors are specifically enriched for mutations in the DNA repair gene BRCA2. Innately resistant tumors display a transcriptional signature (referred to as the IPRES or Innate anti-PD-1 Resistance) indicating concurrent upexpression of genes involved in the regulation of mesenchymal transition, cell adhesion, ECM remodeling, angiogenesis and wound-healing. Notably, MAPK-targeted therapy (MAPKi) induces similar signatures in melanoma, suggesting that a non-genomic form of MAPKi resistance mediates cross-resistance to anti-PD-1 therapy. Validation of the IPRES in other independent tumor cohorts defines a transcriptomic subset across distinct types of advanced cancer. These findings suggest that attenuating the biological processes that underlie IPRES may improve anti-PD1 response in melanoma and other cancer types.

Subfields: Cancer Research, Molecular Biology, Oncology

View Paper: https://www.semanticscholar.org/paper/1a29a0b1c2066bbf94be80552b2d46cfbfff00bb

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Citations in the corpus

Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab (2017, methodology): “Right: Long-termOS associates with clustered groups of patients from the entire cohort and from the combined Hugo et al. (2016) and Van Allen et al. (2015) cohorts.”
A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade (2018, positive): “, 2017), and signatures generated by analyzing previous melanoma ICI cohorts (Hugo et al., 2016; Riaz et al., 2017).”
Defining T cell states associated with response to checkpoint immunotherapy in melanoma (2018, neutral): “, 2016); and signatures of mesenchymal transition, wound healing, and angiogenesis (Hugo et al., 2016).”
Tumor Microenvironment Characterization in Gastric Cancer Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures (2019, methodology): “Genomic and clinical data sets with immune-checkpoint blockade Five genomic and transcriptomic data sets from patients with metastatic urothelial cancer (13) treatedwith ananti–PD-L1 agent (atezolizumab), patients with metastatic melanoma (43) treated with anti–PD-1 (pembrolizumab), patients with ad”
Single-cell analysis reveals fibroblast clusters linked to immunotherapy resistance in cancer. (2020, methodology): “As we did not have access to data from breast cancer treated by immunotherapy, we took advantage of publicly available data from patients with metastatic melanoma treated with anti–PD-1 (pembrolizumab) therapy (33), which has revolutionized melanoma treatment.”
Pan-cancer single-cell landscape of tumor-infiltrating T cells (2021, positive): “The Tex cell connection was reproduced in another dataset (43), showing that the responder group was enriched with more Tex Trm hi tumors than that of the nonresponders (Fisher’s exact test, P = 0.”
Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data (2021, positive): “We focused our analysis on 1,894 T cells from the metastatic melanoma tumor microenvironment 27 and collected 70 melanoma bulk patients with known immunotherapy response information from two studies 28, 29 .”