Cited by 3,448Cancer Immunotherapy and BiomarkersCutaneous Melanoma Detection and ManagementMelanoma and MAPK Pathways

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

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Hugo, Willy, Zaretsky, Jesse M., Sun, Lu, Song, Chunying, Moreno, Blanca Homet, Hu-Lieskovan, Siwen, Berent-Maoz, Beata, Pang, Jia, Chmielowski, Bartosz, Cherry, Grace, Seja, Elizabeth, Lomeli, Shirley, Kong, Xiangju, Kelley, Mark C., Sosman, Jeffrey A., Johnson, Douglas B., Ribas, Antoni, Lo, Roger S.·2016·Cell

DOI 10.1016/j.cell.2016.02.065PMC PMC4808437

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Abstract

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 overall high mutational loads associate with improved survival, and tumors from responding patients are 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 up-expression of genes involved in the regulation of mesenchymal transition, cell adhesion, extracellular matrix remodeling, angiogenesis, and wound healing. Notably, mitogen-activated protein kinase (MAPK)-targeted therapy (MAPK inhibitor) induces similar signatures in melanoma, suggesting that a non-genomic form of MAPK inhibitor 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-PD-1 response in melanoma and other cancer types.

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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nature Protocols · doi:10.1038/nprot.2008.211 · Cited by 35,890
PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer
Hartkopf, A. D., Taran, F., Wallwiener, M., Walter, C. B., Krämer, B., Grischke, E., & Brucker, S. Y. (2016) · Breast Care · doi:10.1159/000453569 · Cited by 30,989
GSVA: gene set variation analysis for microarray and RNA-Seq data
Hänzelmann, S., Castelo, R., & Guinney, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-7 · Cited by 15,440
Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn923 · Cited by 14,108
TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions.
Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., & Salzberg, S. L. (2013) · Genome Biology · doi:10.1186/gb-2013-14-4-r36 · Cited by 12,668
Safety, activity, and immune correlates of anti–PD-1 antibody in cancer
Topalian, S. L., Hodi, F. S., Brahmer, J. R., Gettinger, S. N., Smith, D. C., McDermott, D. F., Powderly, J. D., Carvajal, R. D., Sosman, J. A., Atkins, M. B., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1200690 · Cited by 12,628
Self-citation
Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks
Trapnell, C., Roberts, A., Goff, L. A., Pertea, G. M., Kim, D., Kelley, D. R., Pimentel, H., Salzberg, S. L., Rinn, J. L., & Pachter, L. S. (2012) · Nature Protocols · doi:10.1038/nprot.2012.016 · Cited by 12,145
PD-1 Blockade in Tumors with Mismatch-Repair Deficiency
Le, D. T., Uram, J. N., Wang, H., Bartlett, B. R., Kemberling, H., Eyring, A. D., Skora, A. D., Luber, B. S., Azad, N. S., Laheru, D., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1500596 · Cited by 9,377
Comprehensive molecular characterization of human colon and rectal cancer
The Cancer Genome Atlas Network (2012) · Nature · doi:10.1038/nature11252 · Cited by 8,499

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Trávníčková, J., Wojciechowska, S., Khamseh, A., Gautier, P. E., Brown, D. V., Lefevre, T., Brombin, A., Ewing, A., Capper, A., Spitzer, M., et al. (2019) · Cancer Research · doi:10.1158/0008-5472.can-19-0037 · Cited by 49
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Tao, H., Jin, C., Zhou, L., Deng, Z., Li, X., Dang, W., Fan, S., Li, B., Ye, F., Lu, J., et al. (2023) · Cancer Research · doi:10.1158/0008-5472.can-23-1082 · Cited by 44
Self-citation

Authors: Hugo, W., Zaretsky, J. M., Sun, L., Song, C., Moreno, B. H., Hu-Lieskovan, S., Berent-Maoz, B., Pang, J., Chmielowski, B., Cherry, G., Seja, E., Lomeli, S., Kong, X., Kelley, M. C., Sosman, J. A., Johnson, D. B., Ribas, A., & Lo, R. S.
Affiliations: University of California, Los Angeles (US)
Vanderbilt University (US)
Corresponding author: Lo, Roger S.
ORCID: Hugo, Willy
Zaretsky, Jesse M.
Sun, Lu
Moreno, Blanca Homet
Berent-Maoz, Beata
Pang, Jia
Chmielowski, Bartosz
Lomeli, Shirley
Sosman, Jeffrey A.
Johnson, Douglas B.
Ribas, Antoni
Lo, Roger S.
Venue: Cell
Publisher: Elsevier BV
Published: 2016-03
Volume: 165
Issue: 1
Pages: 35-44
ISSN: 0092-8674 (ISSN-L)
DOI: 10.1016/j.cell.2016.02.065
PMC: PMC4808437
OpenAlex: W2299181842
Cited by: 3,448
References: 46
Citations by year: 2026: 175 · 2025: 281 · 2024: 370 · 2023: 409 · 2022: 478 · 2021: 461 · 2020: 406 · 2019: 316 · 2018: 268 · 2017: 205 · 2016: 70
FWCI: 153.57
Type: journal article
Language: en
Update notices: erratum (2017-01-26)
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 388 d)
Locations: Cell (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
eScholarship (California Digital Library) (submittedVersion)
Funders: National Institutes of Health (UL1TR000124)
National Cancer Institute (P01CA168585, K12 CA 0906525, K12CA090625, R35CA197633, P30CA016042, R01CA176111)
National Center for Advancing Translational Sciences
Topics: Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Melanoma and MAPK Pathways
Keywords: metastatic melanoma, anti-PD-1 therapy, immune checkpoint blockade, treatment response prediction, BRCA2 mutation, non-specific immunity, mesenchymal transition, extracellular matrix remodeling, angiogenesis, MAPK inhibitor resistance, cross-resistance, tumor transcriptome
MeSH terms: Nivolumab, Antibodies, Monoclonal, Antineoplastic Agents, Humans, Melanoma, Neoplasm Metastasis, Drug Resistance, Neoplasm, MAP Kinase Signaling System, BRCA2 Protein, Transcriptome, Programmed Cell Death 1 Receptor, Antibodies, Monoclonal, Humanized

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

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