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Report
Genomic and bioinformatic profiling of mutational neoepitopes
reveals new rules to predict anticancer immunogenicity
Fei Duan1*, Jorge Duitama2*, Sahar Al Seesi2*, Cory M. Ayres3, Steven A. Corcelli3, Arpita P. Pawashe1, Tatiana Blanchard1,
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4
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David McMahon , John Sidney , Alessandro Sette , Brian M. Baker , Ion I. Mandoiu , and Pramod K. Srivastava
1Department
of Immunology and Carole and Ray Neag
Comprehensive Cancer Center, University of Connecticut
School of Medicine
2Department
of Computer Science and Engineering, University
of Connecticut
3Department of Chemistry and Biochemistry and Harper
Abstract
Cancer Research Institute, University of Notre Dame
4LaJolla Institute of Allergy and Immunology
* Authors contributed equally to this work
Bioinformatics pipeline and summary statistics
The mutational repertoire of cancers creates the neoepitopes that make
cancers immunogenic. Here, we introduce two novel tools that identify,
with relatively high accuracy, the small proportion of neoepitopes
(among the hundreds of potential neoepitopes) that protect the host
through an antitumor T cell response. The two tools consist of (a) the
numerical difference in NetMHC scores between the mutated
sequences and their unmutated counterparts, termed the differential
agretopic index (DAI), and (b) the conformational stability of the MHC I–
peptide interaction. Mechanistically, these tools identify neoepitopes
that are mutated to create new anchor residues for MHC binding, and
render the overall peptide more rigid. Surprisingly, the protective
neoepitopes identified here elicit CD8-dependent immunity, even
though their affinity for Kd is orders of magnitude lower than the 500nM threshold considered reasonable for such interactions.
Ld
Landscape of protective tumor immunity elicited by tumor specific epitopes
(A-B) Tumor-protective activity of the mutated epitopes with top NetMHC (A) or top DAI (B) scores for CMS5 and Meth A. Area under the curve (AUC) for each individual tumor growth curve
was calculated and normalized by setting the naive group to a value of 100, shown by a horizontal red line (C) Examples of tumor growth curves in untreated mice (naive) and mice immunized
with indicated mutant peptides from CMS5 or their WT counterparts. Stau1.2 is a representative neoepitope that does not elicit protective immunity, whereas Atxn10.1 and Alkbh6.2 are
representative neoepitopes that do. Each line shows the kinetics of tumor growth in a single mouse. The experiments were performed three times.
Structural stability as a correlate with immunogenicity
(A) Mutations within neoepitopes lead to structural alterations across the peptide backbone, as illustrated with structural snapshots from the simulations of the mutant and WT Tnpo3.1 epitope
bound to H-2Kd. (B) Summary of structural differences for highly DAI ranked nonamers. Differences were quantified by superimposing average peptide conformations from the molecular
dynamics simulations and computing RMSDs for all common atoms. Green and red bars indicate epitopes that led to either positive or negative immunological responses, respectively. The yellow
bar shows the results for control calculations for an immunogenic HBV core epitope. (C) The numbers in the legend give the mean RMSF for the all amino acids of each peptide; those at the right
give the value for only the C-terminal α carbon. Mutated amino acids are indicated by lower case in the x-axis. (D) Effects of mutations on the conformational stability of all nonamers, calculated
as the difference between the mean RMSF of the mutant and the WT peptide. (E) Fluctuations at the peptide C-terminal ends and immunogenicity. The dashed vertical line shows the mean value
for all mutant nonamers. The yellow bar shows the C-terminal stability of the HBV core epitope control.
Conclusions and ongoing work
We applied our method to multiple independent tumors. The pipeline, including the DAI
algorithm, was first derived empirically on the data from Meth A tumor cell line, and then
tested on the CMS5 cell line. Antitumor activity predicted from the DAI algorithm was
significantly stronger in CMS5 than in Meth A. This variation is most likely a reflection of the
immuno-suppressive mechanisms unique to the Meth A tumor and thus unrelated to the
merits of the DAI algorithm per se. The DAI algorithm has since been tested in yet another
mouse tumor, the B16 melanoma, and data on T cell responses in this line as well, are
consistent with significant superiority of DAI over NetMHC alone.
Future and Ongoing Work:
(1) Extending the analysis to MHC II-restricted epitopes of CD4 T cells
(2) Currently developing a human pipeline to predict epitopes from paired tumor/normal
exomes and tumor RNA-Seq
References
• F. Duan, J. Duitama, S. Al Seesi, C. Ayres, S. Corcelli, A. Pawashe,
T. Blanchard, D. McMahon, J. Sidney, A. Sette, B. Baker, I.I.
Mandoiu, and P.K. Srivastava, Genomic and bioinformatic
profiling of mutational neo-epitopes reveals new rules to
predict anti-cancer immunogenicity, Journal of Experimental
Medicine 211, pp. 2231-2248, 2014
• J. Duitama, P.K. Srivastava, and I.I. Mandoiu, Towards Accurate
Detection and Genotyping of Expressed Variants from Whole
Transcriptome Sequencing Data, BMC Genomics 13(Suppl
2):S6, 2012

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