A machine learning model for ranking candidate HLA class I neoantigens based on known neoepitopes from multiple human tumor types-Reference-Cited by-同舟云学术

A machine learning model for ranking candidate HLA class I neoantigens based on known neoepitopes from multiple human tumor types

Published:2021-05 Issue:5 Volume:2 Page:563-574
ISSN:2662-1347
Container-title:Nature Cancer
language:en
Short-container-title:Nat Cancer

Author:

Gartner Jared J.^ORCID,Parkhurst Maria R.,Gros Alena,Tran Eric,Jafferji Mohammad S.,Copeland Amy,Hanada Ken-Ichi^ORCID,Zacharakis Nikolaos,Lalani Almin^ORCID,Krishna Sri,Sachs Abraham,Prickett Todd D.,Li Yong F.,Florentin Maria^ORCID,Kivitz Scott,Chatmon Samuel C.^ORCID,Rosenberg Steven A.^ORCID,Robbins Paul F.

Publisher

Springer Science and Business Media LLC

Subject

Cancer Research,Oncology

Link

http://www.nature.com/articles/s43018-021-00197-6.pdf

Reference56 articles.

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2. Zhou, J., Dudley, M. E., Rosenberg, S. A. & Robbins, P. F. Persistence of multiple tumor-specific T-cell clones is associated with complete tumor regression in a melanoma patient receiving adoptive cell transfer therapy. J. Immunother. 28, 53–62 (2005).

3. Robbins, P. F. et al. Mining exomic sequencing data to identify mutated antigens recognized by adoptively transferred tumor-reactive T cells. Nat. Med. 19, 747–752 (2013).

4. Lu, Y. C. et al. Mutated PPP1R3B is recognized by T cells used to treat a melanoma patient who experienced a durable complete tumor regression. J. Immunol. 190, 6034–6042 (2013).

5. Lu, Y. C. et al. Efficient identification of mutated cancer antigens recognized by T cells associated with durable tumor regressions. Clin. Cancer Res. 20, 3401–3410 (2014).

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