Whole-blood RNA transcript-based models can predict clinical response in two large independent clinical studies of patients with advanced melanoma treated with the checkpoint inhibitor, tremelimumab

Autor: Nina Bhardwaj, William Oh, Alan M. Christenfeld, Karl Wassmann, John M. Kirkwood, Philip Friedlander, Chrisann Kyi, David E. Fisher
Rok vydání: 2017
Předmět:
Male
0301 basic medicine
Neuroblastoma RAS viral oncogene homolog
Oncology
Cancer Research
Skin Neoplasms
Phases of clinical research
Predictive
Bioinformatics
0302 clinical medicine
CDKN2A
Immunology and Allergy
Gene Regulatory Networks
Melanoma
Aged
80 and over
education.field_of_study
Area under the curve
Antibodies
Monoclonal
Middle Aged
lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Gene Expression Regulation
Neoplastic
Treatment Outcome
030220 oncology & carcinogenesis
Molecular Medicine
Female
Research Article
medicine.drug
Adult
medicine.medical_specialty
Immunology
Population
Antineoplastic Agents
Antibodies
Monoclonal
Humanized
lcsh:RC254-282
Young Adult
03 medical and health sciences
Internal medicine
Biomarkers
Tumor
medicine
Humans
RNA
Messenger
education
Aged
Pharmacology
business.industry
Biomarker
Confidence interval
030104 developmental biology
CTLA-4
business
Tremelimumab
Zdroj: Journal for Immunotherapy of Cancer
Journal for ImmunoTherapy of Cancer, Vol 5, Iss 1, Pp 1-9 (2017)
ISSN: 2051-1426
DOI: 10.1186/s40425-017-0272-z
Popis: Background Tremelimumab is an antibody that blocks CTLA-4 and demonstrates clinical efficacy in a subset of advanced melanoma patients. An unmet clinical need exists for blood-based response-predictive gene signatures to facilitate clinically effective and cost-efficient use of such immunotherapeutic interventions. Methods Peripheral blood samples were collected in PAXgene® tubes from 210 treatment-naïve melanoma patients receiving tremelimumab in a worldwide, multicenter phase III study (discovery dataset). A central panel of radiologists determined objective response using RECIST criteria. Gene expression for 169 mRNA transcripts was measured using quantitative PCR. A 15-gene pre-treatment response-predictive classifier model was identified. An independent population (N = 150) of refractory melanoma patients receiving tremelimumab after chemotherapy enrolled in a worldwide phase II study (validation dataset). The classifier model, using the same genes, coefficients and constants for objective response and one-year survival after treatment, was applied to the validation dataset. Results A 15-gene pre-treatment classifier model (containing ADAM17, CDK2, CDKN2A, DPP4, ERBB2, HLA-DRA, ICOS, ITGA4, LARGE, MYC, NAB2, NRAS, RHOC, TGFB1, and TIMP1) achieved an area under the curve (AUC) of 0.86 (95% confidence interval 0.81 to 0.91, p
Databáze: OpenAIRE
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