A novel computational pipeline supported with in vivo vaccination models for identification and validation of neoantigens
K. Hiranuka1*, K. Takahashi1, D. Tang1, N. Watanabe1, T. Yamada1, Y. Mishima1, N. Fujinami2, M. Shimomura2, T. Suzuki2,3, T. Nakatsura2, N. Nakamura1*6389

1 BrightPath Biotherapeutics Co., Ltd., Kawasaki, Japan, 2 Exploratory Oncology Research & Clinical Trial Center,
National Cancer Center, Kashiwa, Japan, 3 Division of Pharmacology, School of Medicine, Teikyo University, Tokyo, Japan

Results

Variant call, tumor purity estimation and HLA LOH detection

We generated NGS data(WES & RNA-Seq) from normal/tumor
samples of 36 HCC, 26 mCRC, and 11 LC patient and analyzed thedata using our computational pipeline. We observed a wide range
in the number of somatic variants and estimated tumor purity(Fig.3). We performed HLA typing on DNA and RNA and estimatedthe allele frequencies (AFs) of HLA alleles (Fig.4). The
independently estimated AFs using DNA and RNA had a strong
correlation (Spearman’s rho = 0.8636) (Fig.5). We used the AFs andtumor purity estimations to call putative loss of heterozygosity(LOH) events in HLA genes (Fig.6).

Methods

SCORE

B7

where z is a linear predictor and TPM_SUMvar is a sum ofTPM(Transcription Per Million) of transcript isoforms in a mutant allele.

1000

Number of spots

B35
responder
non-responder

10050

Number of somatic variants
Fig. 5

Fig. 680%70%60%

HLA Typing(class I & II)

Tumor DNAExpression
Quantification

Somatic Call

Variant Annotation

Variant Annotation

Germline
Variants

SomaticVariants

Non-allelicExpression

VAF filter

Our presentation model was evaluated on an independent
immunopeptidome data (Abelin et al., 2017) and random peptides
at 1:100 ratio. It was found highly discriminating between naturally
presented ligands and random peptides (Fig.7). SCOREadj showed abetter performance over SCORE (Fig.8), indicating the importanceof mRNA expression for presentation prediction.

HLA LOH
Detection

Neoantigen
Candidate List
Personalized Protein
Sequence Database

Mutant AlleleExpression

Self Match Filter

Class I Epitope
Ranking

Fig. 8Fig. 7To interrogate in vivo immune responses, we immunized our top-ranked
neoepitopes into HLA-transgenic mice and performed ELISPOT (Fig.2).
Based on the results, we further constructed a neural network model thatdefines immunogenicity score to improve our prediction (see “Results”
section for details)
Fig. 2

Presentation prediction model

RNA VAFCalculation

PR AUC=0.7578

SCORE

NGS Analysis

VAF filter

Local Haplotype Phasing

Neoantigen Prediction

Tumor RNA

Tumor Purity
Estimation

Germline Callnomt

126-2-08

wt mt

126-1-21

wt mt

126-1-26

wt mt

PR AUC=0.6533

wt

126-1-31

mt

wt

Positeive

29

40%
30%
20%
10%

B35

110-113221-2

2-3

SCORE

Negative55

3-4

Positive peptides(17,197)

Random peptides(1,719,700)

RecallNote that, however, in the following Tg-mouse experiment where
the source of epitopes was the synthesized peptides, we ranked theneoepitopes based on SCORE instead of SCOREadj due toirrelevance of mRNA expression in patients.

73.31

2nd169 + 169

79.15
V2 model’s performanceon Test dataset

IMMUNE_SCOREIMMUNE_SCOREConclusion
 We developed neoantigen prediction pipeline andanalyzed clinical samples from over 100 patients.
 HLA LOH frequently occurs in tumor, and thus arobust detection algorithm was implemented.
 We validated predicted neoepitopes with HLAtransgenic mice to see in vivo immune response.
 We showed the feasibility of improving prediction
algorithms using HLA-transgenic mice data and aneural network model.
 Accurate prediction enables us to decrease thenumber of vaccine antigens to be immunized per
patient, leading to a decrease in the complexity inquality control and manufacturing cost.

Acknowledgement

1

The super-computing resource was provided by Human Genome Center (theUniv. of Tokyo).

0.9

0.8

0.7

R² = 0.0527

Copies of this e-Poster obtained through QR codes arefor personal use only and may not be reproduced
without written permission of the authors.
Disclosure
Hiranuka K.: Employee of BrightPath Biotherapeutics

0.6

0.5

97 + 102

4-Fig. 12Fig. 11SCORESCOREadj

Once a week, 3 times
Immunization (5 peptides mix (50 μg each) + polyI:CLC)
A peptide pool is a mixture of 5-6 peptides with similarbinding scores from the prediction algorithm
126-1-0137

The 199 of the validated HLA–A peptides were used Fig. 10
to construct an immunogenicity prediction model
IMMUNE_SCORE(Fig.10) where a peptide sequence was converted
Linearinto a numeric vector of physicochemical properties
Pooling
which was passed through neural network layers
and the final score was obtained as IMMUNE_SCORE.
Self-Attention
The model training was performed as followed: 1)
BiGRU
the peptide dataset was split into Train and Test
BiGRU
dataset at 8:2 ratio, 2) a model with initially chosen
hyper-parameters was trained by the Training
BiGRU
dataset in a 5-fold cross validation, 3) the averageEmbeddingF1 score of 5 cv models (Dev. F1 score) wascalculated, and 4) 2)&3) were repeated byIYNGTTLFF
progressively updating hyper-parameters.
After generating a large number of such models, each model wasevaluated on the Test dataset to calculate Test F1 score. Although thecorrelation with Dev. F1 score was weak (Fig.11), top10 models whose
average Dev. F1 score was 82.07 showed comparably high Test F1score (73.31 on average). Afterall, we selected the one with the best
Dev. F1 score as the first release model (V1 model).

Dev.
F1scorescore
dev. F1

Normal DNA

25

IMMUNE_SCORE

Quality Check

29

Immunogenicity prediction model based on Tg-mouse dataPrecision

NGSQuality Check

1st48

50%

HLARNA-Seq

Ave. Test F1 score over top 10 models

690%

0%

B7

Input data (Pos. + Neg.)

100%

0
A24

Leaning cycle

V1 model’s performanceon Test dataset

2500A2Whole Exome
Sequence

Fig. 13

A24

HLA Tg mouse results

S𝐶𝐶𝐶𝐶𝐶𝐶𝐸𝐸𝑎𝑎𝑎𝑎𝑎𝑎 = 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎(𝑇𝑇𝑇𝑇𝑇𝑇_𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆)・𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆

Fig. 4

Fig. 3%𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 = 𝑙𝑙𝑙𝑙𝑙𝑙(1 + 𝑒𝑒𝑧𝑧 )Fig. 9A02

Estimated tumor purity

We have developed a computational pipeline to identify neoantigens
from patients’ NGS data and rank candidates of HLA class I neoepitope(Fig.1). To prioritize neoantigen peptides expected to be presented on
patient’s HLA class I molecules, we constructed a logistic regression
model that takes the results of NetMHCpan-4.0, MHCflurry-1.4,
NetChop-3.1 of each peptide, optimizing the model’s parameter basedon the immunopeptidome data in SysteMHC Atlas database and randompeptides generated from human reference protein sequences. A linear
predictor was converted using a softplus function to define SCORE
between 0 and a positive value as well as SCOREadj as followed to reflect
the effect of mRNA expression on presentation prediction:

From 27 patient cases, 275 of HLA-A02:01-, A24:02-, B07:02-, orB35:01-predicted neoepitopes were tested in the HLA-transgenicmice. The rate of positively reacted epitopes increased in acorrelated manner to SCORE, achieving 47% (ROC AUC=0.6871)
overall and 82% if focusing on those with SCORE>4.0 (Fig.9). The
number of neoepitopes whose SCORE exceeds 4.0, however, isusually very low or zero per patient depending on the number ofsomatic variants. This brought up the question whether an
additional prediction model trained with these immunogenicity
data could improve the prediction performance.

To further evaluate our immunogenicity prediction model, we analyzed 15
additional LC patients to prioritize neoepitopes by IMMUNE_SCORE after
pre-filtered by SCORE >=2.5. The immunogenicity of 51 newly selected
neoepitopes were validated with HLA transgenic mice, resulting in 66.7%
overall positive rate(Fig. 12). Notably, those with IMMUNE_ SCORE >=0.75
showed 93.75% positive rate (15/16) although all but only one had SCORE <4.0, indicating high efficiency of the model on selecting immunogenic
neoepitopes. We augmented the training dataset with the rest of thepreviously validated peptides and the newly validated peptides to update
the model. Robust prediction improvement was observed, and the second
release model(V2 model) was selected (Fig.13). To repeat this learning cycle,
over 100 additional peptides are currently being tested in Tg-mice.

Test data

Personalized neoantigen-based therapies are being actively developed.
However, clinical reports have thus far showed that the number ofactionable neoantigens was not as high as originally expected. One
possible reason is due to a lack of appropriate prediction models,
especially for immunogenicity, which is largely hindered by a scarcity of
training data based on in vivo vaccination models.

Testing predicted neoantigens using HLA-Tg micehiranuka_k@brightpathbio.com,
nakamura_n@brightpathbio.com

Test data

BackgroundFig. 1*Email contact:

0.5

0.6

0.7

0.8

test F1
F1 score
Testscore

0.9

1

Immunogenic(34)

Non-immunogenic(17)