Baseline antibody profiles predict toxicity in melanoma patients treated with immune checkpoint inhibitors
Abstract Background Immune checkpoint inhibitors (anti-CTLA-4, anti-PD-1, or the combination) enhance anti-tumor immune responses, yielding durable clinical benefit in several cancer types, including melanoma. However, a subset of patients experience immune-related adverse events (irAEs), which can...
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| Format: | Article |
| Language: | English |
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BMC
2018-04-01
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| Series: | Journal of Translational Medicine |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12967-018-1452-4 |
| _version_ | 1818939504843554816 |
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| author | Michael F. Gowen Keith M. Giles Danny Simpson Jeremy Tchack Hua Zhou Una Moran Zarmeena Dawood Anna C. Pavlick Shaohui Hu Melissa A. Wilson Hua Zhong Michelle Krogsgaard Tomas Kirchhoff Iman Osman |
| author_facet | Michael F. Gowen Keith M. Giles Danny Simpson Jeremy Tchack Hua Zhou Una Moran Zarmeena Dawood Anna C. Pavlick Shaohui Hu Melissa A. Wilson Hua Zhong Michelle Krogsgaard Tomas Kirchhoff Iman Osman |
| author_sort | Michael F. Gowen |
| collection | DOAJ |
| description | Abstract Background Immune checkpoint inhibitors (anti-CTLA-4, anti-PD-1, or the combination) enhance anti-tumor immune responses, yielding durable clinical benefit in several cancer types, including melanoma. However, a subset of patients experience immune-related adverse events (irAEs), which can be severe and result in treatment termination. To date, no biomarker exists that can predict development of irAEs. Methods We hypothesized that pre-treatment antibody profiles identify a subset of patients who possess a sub-clinical autoimmune phenotype that predisposes them to develop severe irAEs following immune system disinhibition. Using a HuProt human proteome array, we profiled baseline antibody levels in sera from melanoma patients treated with anti-CTLA-4, anti-PD-1, or the combination, and used support vector machine models to identify pre-treatment antibody signatures that predict irAE development. Results We identified distinct pre-treatment serum antibody profiles associated with severe irAEs for each therapy group. Support vector machine classifier models identified antibody signatures that could effectively discriminate between toxicity groups with > 90% accuracy, sensitivity, and specificity. Pathway analyses revealed significant enrichment of antibody targets associated with immunity/autoimmunity, including TNFα signaling, toll-like receptor signaling and microRNA biogenesis. Conclusions Our results provide the first evidence supporting a predisposition to develop severe irAEs upon immune system disinhibition, which requires further independent validation in a clinical trial setting. |
| first_indexed | 2024-12-20T06:24:48Z |
| format | Article |
| id | doaj.art-25bad45d849a486cbdacef0da4e5f3a3 |
| institution | Directory Open Access Journal |
| issn | 1479-5876 |
| language | English |
| last_indexed | 2024-12-20T06:24:48Z |
| publishDate | 2018-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj.art-25bad45d849a486cbdacef0da4e5f3a32022-12-21T19:50:19ZengBMCJournal of Translational Medicine1479-58762018-04-0116111210.1186/s12967-018-1452-4Baseline antibody profiles predict toxicity in melanoma patients treated with immune checkpoint inhibitorsMichael F. Gowen0Keith M. Giles1Danny Simpson2Jeremy Tchack3Hua Zhou4Una Moran5Zarmeena Dawood6Anna C. Pavlick7Shaohui Hu8Melissa A. Wilson9Hua Zhong10Michelle Krogsgaard11Tomas Kirchhoff12Iman Osman13The Ronald O. Perelman Department of Dermatology, New York University School of MedicineThe Ronald O. Perelman Department of Dermatology, New York University School of MedicineDivision of Epidemiology, New York University School of MedicineThe Ronald O. Perelman Department of Dermatology, New York University School of MedicineApplied Bioinformatics Core, New York University School of MedicineThe Ronald O. Perelman Department of Dermatology, New York University School of MedicineThe Ronald O. Perelman Department of Dermatology, New York University School of MedicineDivision of Hematology & Oncology, Perlmutter Cancer Center, New York University School of MedicineCDI LaboratoriesDivision of Hematology & Oncology, Perlmutter Cancer Center, New York University School of MedicineDepartment of Population Health, New York University School of MedicineDepartment of Pathology, New York University School of MedicineDivision of Epidemiology, New York University School of MedicineThe Ronald O. Perelman Department of Dermatology, New York University School of MedicineAbstract Background Immune checkpoint inhibitors (anti-CTLA-4, anti-PD-1, or the combination) enhance anti-tumor immune responses, yielding durable clinical benefit in several cancer types, including melanoma. However, a subset of patients experience immune-related adverse events (irAEs), which can be severe and result in treatment termination. To date, no biomarker exists that can predict development of irAEs. Methods We hypothesized that pre-treatment antibody profiles identify a subset of patients who possess a sub-clinical autoimmune phenotype that predisposes them to develop severe irAEs following immune system disinhibition. Using a HuProt human proteome array, we profiled baseline antibody levels in sera from melanoma patients treated with anti-CTLA-4, anti-PD-1, or the combination, and used support vector machine models to identify pre-treatment antibody signatures that predict irAE development. Results We identified distinct pre-treatment serum antibody profiles associated with severe irAEs for each therapy group. Support vector machine classifier models identified antibody signatures that could effectively discriminate between toxicity groups with > 90% accuracy, sensitivity, and specificity. Pathway analyses revealed significant enrichment of antibody targets associated with immunity/autoimmunity, including TNFα signaling, toll-like receptor signaling and microRNA biogenesis. Conclusions Our results provide the first evidence supporting a predisposition to develop severe irAEs upon immune system disinhibition, which requires further independent validation in a clinical trial setting.http://link.springer.com/article/10.1186/s12967-018-1452-4MelanomaImmunotherapyAntibodiesToxicityBiomarker |
| spellingShingle | Michael F. Gowen Keith M. Giles Danny Simpson Jeremy Tchack Hua Zhou Una Moran Zarmeena Dawood Anna C. Pavlick Shaohui Hu Melissa A. Wilson Hua Zhong Michelle Krogsgaard Tomas Kirchhoff Iman Osman Baseline antibody profiles predict toxicity in melanoma patients treated with immune checkpoint inhibitors Journal of Translational Medicine Melanoma Immunotherapy Antibodies Toxicity Biomarker |
| title | Baseline antibody profiles predict toxicity in melanoma patients treated with immune checkpoint inhibitors |
| title_full | Baseline antibody profiles predict toxicity in melanoma patients treated with immune checkpoint inhibitors |
| title_fullStr | Baseline antibody profiles predict toxicity in melanoma patients treated with immune checkpoint inhibitors |
| title_full_unstemmed | Baseline antibody profiles predict toxicity in melanoma patients treated with immune checkpoint inhibitors |
| title_short | Baseline antibody profiles predict toxicity in melanoma patients treated with immune checkpoint inhibitors |
| title_sort | baseline antibody profiles predict toxicity in melanoma patients treated with immune checkpoint inhibitors |
| topic | Melanoma Immunotherapy Antibodies Toxicity Biomarker |
| url | http://link.springer.com/article/10.1186/s12967-018-1452-4 |
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