Unannotated proteins expand the MHC-I-restricted immunopeptidome in cancer

Jan 1, 2022·
T. ouspenskaia
,
T. law
,
K. r. clauser
,
S. klaeger
,
S. sarkizova
,
F. aguet
Dr. Bo Li
Dr. Bo Li
,
E. christian
,
B. a. knisbacher
,
P. m. le
,
C. r. hartigan
,
H. keshishian
,
A. apffel
,
G. oliveira
,
W. zhang
,
Y. t. chow
,
Z. ji
,
I. jungreis
,
S. a. shukla
,
P. bachireddy
,
M. kellis
,
G. getz
,
N. hacohen
,
D. b. keskin
,
S. a. carr
,
C. j. wu
,
A. regev
· 0 min read
Abstract
Tumor-associated epitopes presented on MHC-I that can activate the immune system against cancer cells are typically identified from annotated protein-coding regions of the genome, but whether peptides originating from novel or unannotated open reading frames (nuORFs) can contribute to antitumor immune responses remains unclear. Here we show that peptides originating from nuORFs detected by ribosome profiling of malignant and healthy samples can be displayed on MHC-I of cancer cells, acting as additional sources of cancer antigens. We constructed a high-confidence database of translated nuORFs across tissues (nuORFdb) and used it to detect 3,555 translated nuORFs from MHC-I immunopeptidome mass spectrometry analysis, including peptides that result from somatic mutations in nuORFs of cancer samples as well as tumor-specific nuORFs translated in melanoma, chronic lymphocytic leukemia and glioblastoma. NuORFs are an unexplored pool of MHC-I-presented, tumor-specific peptides with potential as immunotherapy targets.
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Nature Biotechnology
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Dr. Bo Li
Authors
Principal Scientist II
Dr. Bo Li is a Principal Scientist II at AI for Biology and Translation (AIBT), Genentech, Inc. His research focuses on three major topics: Science, Technology and Computational Methods. For Science, his team works on lung cancer and especially small cell lung cancer. For Technology, his team evaluates and adopts cutting-edge high-throughput data generation technologies, such as Cellanome and SBX sequencing. For Computational Methods, his team develops novel computational and deep learning tools for enabling insight discovery from high-throughput multi-modal data. Before joining in Genentech, he was an Assistant Professor of Medicine at Harvard Medical School and the director of Bioinformatics and Computational Biology at Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital. He received his Ph.D. in computer science from UW-Madison and completed two postdoctoral trainings with Dr. Lior Pachter at UC Berkeley and Dr. Aviv Regev at Broad Institute. He is best known for developing RSEM, an impactful RNA-seq transcript quantification software. RSEM is cited 22,602 times (Google Scholar) and adopted by several big consortia such as TCGA, ENCODE, GTEx and TOPMed.
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