TY - JOUR
T1 - Deep learning using tumor HLA peptide mass spectrometry datasets improves neoantigen identification
AU - Bulik-Sullivan, Brendan
AU - Busby, Jennifer
AU - Palmer, Christine D.
AU - Davis, Matthew J.
AU - Murphy, Tyler
AU - Clark, Andrew
AU - Busby, Michele
AU - Duke, Fujiko
AU - Yang, Aaron
AU - Young, Lauren
AU - Ojo, Noelle C.
AU - Caldwell, Kamilah
AU - Abhyankar, Jesse
AU - Boucher, Thomas
AU - Hart, Meghan G.
AU - Makarov, Vladimir
AU - De Montpreville, Vincent Thomas
AU - Mercier, Olaf
AU - Chan, Timothy A.
AU - Scagliotti, Giorgio
AU - Bironzo, Paolo
AU - Novello, Silvia
AU - Karachaliou, Niki
AU - Rosell, Rafael
AU - Anderson, Ian
AU - Gabrail, Nashat
AU - Hrom, John
AU - Limvarapuss, Chainarong
AU - Choquette, Karin
AU - Spira, Alexander
AU - Rousseau, Raphael
AU - Voong, Cynthia
AU - Rizvi, Naiyer A.
AU - Fadel, Elie
AU - Frattini, Mark
AU - Jooss, Karin
AU - Skoberne, Mojca
AU - Francis, Joshua
AU - Yelensky, Roman
N1 - Funding Information:
We would like to thank C.J. Couter for her assistance with general laboratory tasks and establishment of the in vitro stimulation assays. T.A.C. acknowledges funding in part through the NIH/NCI Cancer Center Support Grant P30 CA008748, Pershing Square Sohn Cancer Research grant, the PaineWebber Chair, Stand Up 2 Cancer, NIH R01 CA205426, NIH R35 CA232097, and the STARR Cancer Consortium. V.T.D.M., O.M., G.S., P.B., S.N., N.K., R. Rosell, I.A., N.G., J.H., C.L., K. Choquette, A.S., E.F. and M.F. received research funding support for this study from Gritstone Oncology, Inc.
Publisher Copyright:
© 2019, Nature Publishing Group. All rights reserved.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Neoantigens, which are expressed on tumor cells, are one of the main targets of an effective antitumor T-cell response. Cancer immunotherapies to target neoantigens are of growing interest and are in early human trials, but methods to identify neoantigens either require invasive or difficult-to-obtain clinical specimens, require the screening of hundreds to thousands of synthetic peptides or tandem minigenes, or are only relevant to specific human leukocyte antigen (HLA) alleles. We apply deep learning to a large (N = 74 patients) HLA peptide and genomic dataset from various human tumors to create a computational model of antigen presentation for neoantigen prediction. We show that our model, named EDGE, increases the positive predictive value of HLA antigen prediction by up to ninefold. We apply EDGE to enable identification of neoantigens and neoantigen-reactive T cells using routine clinical specimens and small numbers of synthetic peptides for most common HLA alleles. EDGE could enable an improved ability to develop neoantigen-targeted immunotherapies for cancer patients.
AB - Neoantigens, which are expressed on tumor cells, are one of the main targets of an effective antitumor T-cell response. Cancer immunotherapies to target neoantigens are of growing interest and are in early human trials, but methods to identify neoantigens either require invasive or difficult-to-obtain clinical specimens, require the screening of hundreds to thousands of synthetic peptides or tandem minigenes, or are only relevant to specific human leukocyte antigen (HLA) alleles. We apply deep learning to a large (N = 74 patients) HLA peptide and genomic dataset from various human tumors to create a computational model of antigen presentation for neoantigen prediction. We show that our model, named EDGE, increases the positive predictive value of HLA antigen prediction by up to ninefold. We apply EDGE to enable identification of neoantigens and neoantigen-reactive T cells using routine clinical specimens and small numbers of synthetic peptides for most common HLA alleles. EDGE could enable an improved ability to develop neoantigen-targeted immunotherapies for cancer patients.
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U2 - 10.1038/nbt.4313
DO - 10.1038/nbt.4313
M3 - Article
C2 - 30556813
AN - SCOPUS:85059650557
SN - 1087-0156
VL - 37
SP - 55
EP - 71
JO - Nature biotechnology
JF - Nature biotechnology
IS - 1
ER -