ProTECT—Prediction of T-Cell Epitopes for Cancer Therapy
Somatic mutations in cancers affecting protein coding genes can give rise to potentially therapeutic neoepitopes. These neoepitopes can guide Adoptive Cell Therapies and Peptide- and RNA-based Neoepitope Vaccines to selectively target tumor cells using autologous patient cytotoxic T-cells. Currently...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-11-01
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| Series: | Frontiers in Immunology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2020.483296/full |
| _version_ | 1818240297481535488 |
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| author | Arjun A. Rao Arjun A. Rao Arjun A. Rao Ada A. Madejska Ada A. Madejska Jacob Pfeil Jacob Pfeil Jacob Pfeil Benedict Paten Benedict Paten Benedict Paten Sofie R. Salama Sofie R. Salama Sofie R. Salama David Haussler David Haussler David Haussler David Haussler |
| author_facet | Arjun A. Rao Arjun A. Rao Arjun A. Rao Ada A. Madejska Ada A. Madejska Jacob Pfeil Jacob Pfeil Jacob Pfeil Benedict Paten Benedict Paten Benedict Paten Sofie R. Salama Sofie R. Salama Sofie R. Salama David Haussler David Haussler David Haussler David Haussler |
| author_sort | Arjun A. Rao |
| collection | DOAJ |
| description | Somatic mutations in cancers affecting protein coding genes can give rise to potentially therapeutic neoepitopes. These neoepitopes can guide Adoptive Cell Therapies and Peptide- and RNA-based Neoepitope Vaccines to selectively target tumor cells using autologous patient cytotoxic T-cells. Currently, researchers have to independently align their data, call somatic mutations and haplotype the patient’s HLA to use existing neoepitope prediction tools. We present ProTECT, a fully automated, reproducible, scalable, and efficient end-to-end analysis pipeline to identify and rank therapeutically relevant tumor neoepitopes in terms of potential immunogenicity starting directly from raw patient sequencing data, or from pre-processed data. The ProTECT pipeline encompasses alignment, HLA haplotyping, mutation calling (single nucleotide variants, short insertions and deletions, and gene fusions), peptide:MHC binding prediction, and ranking of final candidates. We demonstrate the scalability, efficiency, and utility of ProTECT on 326 samples from the TCGA Prostate Adenocarcinoma cohort, identifying recurrent potential neoepitopes from TMPRSS2-ERG fusions, and from SNVs in SPOP. We also compare ProTECT with results from published tools. ProTECT can be run on a standalone computer, a local cluster, or on a compute cloud using a Mesos backend. ProTECT is highly scalable and can process TCGA data in under 30 min per sample (on average) when run in large batches. ProTECT is freely available at https://www.github.com/BD2KGenomics/protect. |
| first_indexed | 2024-12-12T13:11:12Z |
| format | Article |
| id | doaj.art-be19859171104a01a0a1923e6cbf455e |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-12-12T13:11:12Z |
| publishDate | 2020-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-be19859171104a01a0a1923e6cbf455e2022-12-22T00:23:32ZengFrontiers Media S.A.Frontiers in Immunology1664-32242020-11-011110.3389/fimmu.2020.483296483296ProTECT—Prediction of T-Cell Epitopes for Cancer TherapyArjun A. Rao0Arjun A. Rao1Arjun A. Rao2Ada A. Madejska3Ada A. Madejska4Jacob Pfeil5Jacob Pfeil6Jacob Pfeil7Benedict Paten8Benedict Paten9Benedict Paten10Sofie R. Salama11Sofie R. Salama12Sofie R. Salama13David Haussler14David Haussler15David Haussler16David Haussler17Department of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, United StatesComputational Genomics Lab, University of California, Santa Cruz, Santa Cruz, CA, United StatesUC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, United StatesComputational Genomics Lab, University of California, Santa Cruz, Santa Cruz, CA, United StatesDepartment of Molecular, Cell, and Developmental Biology, University of California, Santa Cruz, Santa Cruz, Santa Cruz, CA, United StatesDepartment of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, United StatesComputational Genomics Lab, University of California, Santa Cruz, Santa Cruz, CA, United StatesUC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, United StatesDepartment of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, United StatesComputational Genomics Lab, University of California, Santa Cruz, Santa Cruz, CA, United StatesUC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, United StatesDepartment of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, United StatesUC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, United StatesHoward Hughes Medical Institute, University of California, Santa Cruz, Santa Cruz, CA, United StatesDepartment of Biomolecular Engineering, University of California, Santa Cruz, Santa Cruz, CA, United StatesComputational Genomics Lab, University of California, Santa Cruz, Santa Cruz, CA, United StatesUC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, United StatesHoward Hughes Medical Institute, University of California, Santa Cruz, Santa Cruz, CA, United StatesSomatic mutations in cancers affecting protein coding genes can give rise to potentially therapeutic neoepitopes. These neoepitopes can guide Adoptive Cell Therapies and Peptide- and RNA-based Neoepitope Vaccines to selectively target tumor cells using autologous patient cytotoxic T-cells. Currently, researchers have to independently align their data, call somatic mutations and haplotype the patient’s HLA to use existing neoepitope prediction tools. We present ProTECT, a fully automated, reproducible, scalable, and efficient end-to-end analysis pipeline to identify and rank therapeutically relevant tumor neoepitopes in terms of potential immunogenicity starting directly from raw patient sequencing data, or from pre-processed data. The ProTECT pipeline encompasses alignment, HLA haplotyping, mutation calling (single nucleotide variants, short insertions and deletions, and gene fusions), peptide:MHC binding prediction, and ranking of final candidates. We demonstrate the scalability, efficiency, and utility of ProTECT on 326 samples from the TCGA Prostate Adenocarcinoma cohort, identifying recurrent potential neoepitopes from TMPRSS2-ERG fusions, and from SNVs in SPOP. We also compare ProTECT with results from published tools. ProTECT can be run on a standalone computer, a local cluster, or on a compute cloud using a Mesos backend. ProTECT is highly scalable and can process TCGA data in under 30 min per sample (on average) when run in large batches. ProTECT is freely available at https://www.github.com/BD2KGenomics/protect.https://www.frontiersin.org/articles/10.3389/fimmu.2020.483296/fullcancerneoepitopeneoantigenautomated predictionvaccinecancer immunotherapy |
| spellingShingle | Arjun A. Rao Arjun A. Rao Arjun A. Rao Ada A. Madejska Ada A. Madejska Jacob Pfeil Jacob Pfeil Jacob Pfeil Benedict Paten Benedict Paten Benedict Paten Sofie R. Salama Sofie R. Salama Sofie R. Salama David Haussler David Haussler David Haussler David Haussler ProTECT—Prediction of T-Cell Epitopes for Cancer Therapy Frontiers in Immunology cancer neoepitope neoantigen automated prediction vaccine cancer immunotherapy |
| title | ProTECT—Prediction of T-Cell Epitopes for Cancer Therapy |
| title_full | ProTECT—Prediction of T-Cell Epitopes for Cancer Therapy |
| title_fullStr | ProTECT—Prediction of T-Cell Epitopes for Cancer Therapy |
| title_full_unstemmed | ProTECT—Prediction of T-Cell Epitopes for Cancer Therapy |
| title_short | ProTECT—Prediction of T-Cell Epitopes for Cancer Therapy |
| title_sort | protect prediction of t cell epitopes for cancer therapy |
| topic | cancer neoepitope neoantigen automated prediction vaccine cancer immunotherapy |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2020.483296/full |
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