TY - JOUR
T1 - Key Parameters of Tumor Epitope Immunogenicity Revealed Through a Consortium Approach Improve Neoantigen Prediction
AU - The Tumor Neoantigen Selection Alliance
AU - Wells, Daniel K.
AU - van Buuren, Marit M.
AU - Dang, Kristen K.
AU - Hubbard-Lucey, Vanessa M.
AU - Sheehan, Kathleen C.F.
AU - Campbell, Katie M.
AU - Lamb, Andrew
AU - Ward, Jeffrey P.
AU - Sidney, John
AU - Blazquez, Ana B.
AU - Rech, Andrew J.
AU - Zaretsky, Jesse M.
AU - Comin-Anduix, Begonya
AU - Ng, Alphonsus H.C.
AU - Chour, William
AU - Yu, Thomas V.
AU - Rizvi, Hira
AU - Chen, Jia M.
AU - Manning, Patrice
AU - Steiner, Gabriela M.
AU - Doan, Xengie C.
AU - Khan, Aly A.
AU - Lugade, Amit
AU - Lazic, Ana M.Mijalkovic
AU - Frentzen, Angela A.Elizabeth
AU - Tadmor, Arbel D.
AU - Sasson, Ariella S.
AU - Rao, Arjun A.
AU - Pei, Baikang
AU - Schrörs, Barbara
AU - Berent-Maoz, Beata
AU - Carreno, Beatriz M.
AU - Song, Bin
AU - Peters, Bjoern
AU - Li, Bo
AU - Higgs, Brandon W.
AU - Stevenson, Brian J.
AU - Iseli, Christian
AU - Miller, Christopher A.
AU - Morehouse, Christopher A.
AU - Melief, Cornelis J.M.
AU - Puig-Saus, Cristina
AU - van Beek, Daphne
AU - Balli, David
AU - Gfeller, David
AU - Haussler, David
AU - Jäger, Dirk
AU - Cortes, Eduardo
AU - Artyomov, Maxim N.
AU - Schreiber, Robert D.
N1 - Publisher Copyright:
© 2020 Elsevier Inc.
PY - 2020/10/29
Y1 - 2020/10/29
N2 - Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community. Genomic tumor sequencing data with matched measurements of tumor epitope immunogenicity allows for insights into the governing parameters of epitope immunogenicity and generation of models for effective neoantigen prediction.
AB - Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear. Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community. Genomic tumor sequencing data with matched measurements of tumor epitope immunogenicity allows for insights into the governing parameters of epitope immunogenicity and generation of models for effective neoantigen prediction.
KW - TESLA
KW - epitope
KW - immunogenicity
KW - immunogenomics
KW - immunotherapy
KW - neoantigen
UR - http://www.scopus.com/inward/record.url?scp=85094120480&partnerID=8YFLogxK
U2 - 10.1016/j.cell.2020.09.015
DO - 10.1016/j.cell.2020.09.015
M3 - Article
C2 - 33038342
AN - SCOPUS:85094120480
SN - 0092-8674
VL - 183
SP - 818-834.e13
JO - Cell
JF - Cell
IS - 3
ER -