pVACtools: a computational toolkit to identify and visualize

cancer neoantigens

pVACtools: toolkit for tumor neoantigen characterization

Jasreet Hundal1+

, Susanna Kiwala1+

, Joshua McMichael1, Christopher A. Miller

1,2,4, Huiming

Xia1, Alexander T. Wollam

1, Connor J. Liu

1, Sidi Zhao

1 ,Yang-Yang Feng

1, Aaron P. Graubert

1,

Amber Z. Wollam1, Jonas Neichin

1, Megan Neveau

1, Jason Walker

1, William E Gillanders

4,5,

Elaine R. Mardis3, Obi L. Griffith

1,2,4,6,*, Malachi Griffith

1,2,4,6,*

Affiliations

(1) McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO,

USA

(2) Division of Oncology, Department of Medicine, Washington University School of Medicine,

St. Louis, MO, USA

(3) Institute for Genomic Medicine, Nationwide Children’s Hospital, 575 Children’s Crossroad,

Columbus OH, USA

(4) Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA

(5) Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA

(6) Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA

+ These authors contributed equally to this work

* Corresponding authors. obigriffith@wustl.edu, mgriffit@wustl.edu

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Abstract

Identification of neoantigens is a critical step in predicting response to checkpoint

blockade therapy and design of personalized cancer vaccines. This is a cross-

disciplinary challenge, involving genomics, proteomics, immunology, and computational

approaches. We have built a computational framework called pVACtools that, when

paired with a well-established genomics pipeline, produces an end-to-end solution for

neoantigen characterization. pVACtools supports identification of altered peptides from

different mechanisms including point mutations, in-frame or frameshift insertions and

deletions, and gene fusions. Prediction of peptide:MHC binding is accomplished by

supporting an ensemble of MHC Class I and II binding algorithms, within a framework

designed to facilitate the incorporation of additional algorithms. Prioritization of predicted

peptides occurs by integrating diverse data including mutant allele expression, peptide

binding affinities, and determination whether a mutation is clonal or subclonal.

Interactive visualization via a web interface allows clinical users to efficiently generate,

review, and interpret results, selecting candidate peptides for individual patient vaccine

designs. Additional modules support design choices needed for competing vaccine

delivery approaches. One such module optimizes peptide ordering to minimize

junctional epitopes in DNA-vector vaccines. Downstream analysis commands for

synthetic long peptide vaccines are available to assess candidates for factors that

influence peptide synthesis. All of the aforementioned steps are executed via a modular

workflow consisting of tools for neoantigen prediction from somatic alterations

(pVACseq and pVACfuse), prioritization, and selection using a graphical web-based

interface (pVACviz), and design of DNA vector-based vaccines (pVACvector) and

synthetic long peptide vaccines. pVACtools is available at pvactools.org.

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Introduction

The increasing use of cancer immunotherapies has spurred interest in identifying and

characterizing predicted neoantigens encoded by a tumor’s genome. The facility and

precision of computational tools for predicting neoantigens has become increasingly

important(1), and several resources aiding in these efforts have been published(2–4).

Typically, these tools start with a list of somatic variants (in Variant Call Format (VCF) or

other formats such as Browser Extensible Data (BED)) with annotated protein changes,

and predict the strongest MHC binding peptides (8-11-mer for class I MHC and 13-25-

mer for class II) using one or more prediction algorithms(5–7). The predicted

neoantigens are then filtered and ranked based on defined metrics including

sequencing read coverage, variant allele fraction (VAF), gene expression, and

differential binding compared to the wildtype peptide (agretopicity index score(8)).

However, of the prediction tools overviewed in Supp. Table S1 (Vaxrank(3), MuPeXI(2),

CloudNeo(4), FRED2(9), Epi-Seq(8), and ProTECT(10)) most lack key functionality,

including predicting neoantigens from gene fusions, aiding optimized vaccine design for

DNA cassette vaccines, and explicitly incorporating nearby germline or somatic

alterations into the candidate neoantigens(11). In addition, none of these tools offer an

intuitive graphical user interface for visualizing and efficiently selecting the most

promising candidates— a key feature for facilitating involvement of clinicians and other

researchers in the process of neoantigen prediction and evaluation.

To address these limitations, we created a comprehensive and extensible toolkit for

computational identification, selection, prioritization, and visualization of neoantigens—

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‘pVACtools’, that facilitates each of the major components of neoantigen identification

and prioritization. This computational framework can be used to identify neoantigens

from a variety of somatic alterations, including gene fusions and insertion/deletion

frameshift variants, both of which potentially create strong immunogenic

neoantigens(12–14). pVACtools can facilitate both MHC class I and II predictions and

provides an interactive display of predicted neoantigens for review by the end user.

Materials and Methods

TCGA data pre-processing

Aligned (build GRCh38) tumor and normal BAMs from BWA(15) (version 0.7.12-r1039)

as well as somatic variant calls from VarScan2(16,17) (in VCF format) were

downloaded from the Genomic Data Commons (GDC, https://gdc.cancer.gov/). Since

the GDC does not provide germline variant calls for TCGA data, we used GATK’s(18)

HaplotypeCaller to perform germline variant calling using default parameters. These

calls were refined using VariantRecalibrator in accordance with GATK Best

Practices(19). Somatic and germline missense variant calls from each sample were

then combined using GATK’s CombineVariants, and the variants were subsequently

phased using GATK’s ReadBackedPhasing algorithm.

Phased Somatic VCF files were annotated with RNA depth and expression information

using VAtools (http://vatools.org). We restricted our analysis to only consider ‘PASS’

variants in these VCFs as these are higher confidence than the raw set, and the

variants were annotated using the “--pick” option in VEP (Ensembl version 88).

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Existing in silico HLA typing information was obtained from The Cancer Immunome

Atlas (TCIA) database(20).

Neoantigen prediction

The VEP-annotated VCF files were then analyzed with pVACseq using all eight Class I

prediction algorithms for neoantigen peptide lengths 8-11. The current MHC Class I

algorithms supported by pVACseq are NetMHCpan(21), NetMHC(7,21),

NetMHCcons(22), PickPocket(23), SMM(24), SMMPMBEC(25), MHCflurry(26), and

MHCnuggets(27). The four MHC Class II algorithms that are supported are

NetMHCIIpan(28), SMMalign(29), NNalign(30), and MHCnuggets(31). For the

demonstration analysis, we limited our prediction to only MHC Class I alleles due to

availability of HLA typing information from TCIA, though binding predictions for Class II

alleles can also be generated using pVACtools.

Ranking of Neoantigens

To help prioritize neoantigens, a rank is assigned to all neoantigens that pass initial

filters where each of the following four criteria are assigned a rank-ordered value (where

the best is assigned rank = 1):

B = Rank of binding affinity

F = Rank of Fold Change between MT and WT alleles

M = Rank of mutant allele expression, calculated as (rank of Gene expression * rank of

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Mutant allele RNA Variant allele fraction)

D = Rank of DNA Variant allele fraction

A final ranking is based on a score obtained from combining these values:

B+F+(M*2)+(D/2).

Whereas agretopicity is considered in ranking, we do not recommend filtering

candidates solely based on mutant versus wildtype binding value (default fold change

value is 0). A previous study on the binding properties of known neoantigen candidates

showed that most changes reside in T cell receptor contact regions and not in anchor

residues(32). To help end users make an informed decision while selecting candidates,

we also provide the position of the variant in the final report. We are not comfortable

relying on hard rules about “anchor positions” (e.g. position 2 and C-terminal) because

these may vary by MHC allele and the peptide length. As we gain more empirical results

and validation data, we hope that future versions of pVACtools may include more

options for prioritizing based on these factors.

The rank is not meant to be a definitive metric of peptide suitability for vaccines, but was

designed to be a useful first step in the peptide selection process.

Implementation

pVACtools is written in Python3. The individual tools are implemented as separate

command line entry points that can be run using the `pvacseq`, `pvacfuse`,

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`pvacvector`, `pvacapi`, and `pvacviz` commands for each respective tool.

pVACapi is required to run pVACviz so both the `pvacapi` and `pvacviz` commands

need to be executed in separate terminals.

The code test suite is implemented using the Python unittest framework and GitHub

integration tests are run using travis-ci (travis-ci.org). Code changes are integrated

using GitHub pull requests (https://github.com/griffithlab/pVACtools/pulls). Feature

additions, user requests, and bug reports are managed using the GitHub issue tracking

(https://github.com/griffithlab/pVACtools/issues). User documentation is written using

the reStructuredText markup language and the Sphinx documentation framework

(sphinx-doc.org). Documentation is hosted on Read the Docs (readthedocs.org) and

can be viewed at pvactools.org.

The pymp-pypi package was used to add support for parallel processing. The number

of processes is controlled by the --n-threads parameter.

pVACseq

For pVACseq, the pyvcf package is first used for parsing the input VCF file and

extracting information about the supported missense, inframe insertion, inframe

deletion, and frameshift variants into TSV format.

This output is then used to determine the wildtype peptide sequence by extracting a

region around the somatic variant according to the --peptide-sequence-length

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specified by the user. The variant’s amino acid change is incorporated in this peptide

sequence to determine the mutant peptide sequence. For frameshift variants, the new

downstream protein sequence calculated by VEP is reported from the variant position

onward. The number of downstream amino acids to include is controlled by the --

downstream-sequence-length parameter. If a phased VCF with proximal variants

is provided, proximal missense variants that are in phase with the somatic variant of

interest are incorporated into the mutant and wildtype peptide sequences, as

appropriate. The mutant and wildtype sequences are stored in a FASTA file. The

FASTA file is then submitted to the individual prediction algorithms for binding affinity

predictions. For algorithms included in IEDB, we either use the IEDB API or a

standalone installation, if an installation path is provided by the user (--iedb-

install-directory). The mhcflurry and mhcnuggets packages are used to run

the MHCflurry(26) and MHCnuggets(27) prediction algorithms, respectively.

The predicted mutant antigens are then parsed into a TSV report format, and, for each

mutant antigen, the closest wildtype antigen is determined and reported. Predictions for

each mutant antigen/neoantigen from multiple algorithms are aggregated into the “best”

(lowest) and median binding scores. The resulting TSV is processed through multiple

filtering steps (as described below in results: Comparison of filtering criteria): (1) Binding

filter: this filter selects the strongest binding candidates based on the mutant binding

score and the fold change (WT score / MT score). Depending on the --top-score-

metric parameter setting, this filter is either applied to the median score across all

chosen prediction algorithms (default) or the best (lowest) score amongst the chosen

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prediction algorithms. (2) Coverage filter: this filter accepts VAF and coverage

information from the tumor DNA, tumor RNA, and normal DNA if these values are

available in the input VCF. (3) Transcript-support-level (TSL) filter: this filter evaluates

each transcript’s support level if this information was provided by VEP in the VCF. (4)

Top-score filter: the filter picks the top mutant peptide for each variant using the binding

affinity as the determining factor. This filter is implemented to only select the best

candidate from amongst multiple candidates that could result from a single variant due

to different peptide lengths, variant registers, transcript sequences, and HLA alleles.

The result of these filtering steps is reported in a filtered report TSV. The remaining

neoantigens are then annotated with cleavage site and stability predictions by NetChop

and NetMHCStabPan, respectively, and a relative rank (as described in the materials

and methods: Ranking of Neoantigens) is assigned. The rank-ordered final output is

reported in a condensed file. The pandas package is used for data management while

filtering and ranking the neoantigen candidates.

pVACvector

When running pVACvector with a pVACseq output file, the original input VCF must also

be provided (--input-vcf parameter). The VCF is used to extract a larger peptide

sequence around the target neoantigen (length determined by the --input-n-mer

parameter). Alternatively, a list of target peptide sequences can be provided in a FASTA

file. The set of peptide sequences are then combined in all possible pairs, and an

ordering of peptides for the vector is produced as follows:

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To determine the optimal order of peptide-spacer-peptide combinations, binding

predictions are made for all peptide registers overlapping the junction. A directed graph

is then constructed, with nodes defined as target peptides, and edges representing

junctions. The score of each edge is defined as the lowest binding score of its junctional

peptides (a conservative metric). Edges with scores below the threshold are removed,

and if heuristics indicate that a valid graph may exist, a simulated annealing procedure

is used to identify a path through the nodes that maximizes junction scores (preserving

the weakest overall predicted binding for junctional epitopes). If no valid ordering is

found, additional “spacer” amino acids are added to each junction, binding affinities are

re-calculated, and a new graph is constructed and tested, setting edge weights equal to

that of the best performing (highest binding score) peptide-junction-spacer combination.

The spacers used for pVACvector are set by the user with the --spacers parameter.

This parameter defaults to

None,AAY,HHHH,GGS,GPGPG,HHAA,AAL,HH,HHC,HHH,HHHD,HHL,HHHC, where

None is the placeholder for testing junctions without a spacer sequence. Spacers are

tested iteratively, starting with the first spacer in the list, and adding subsequent spacers

if no valid path is found. If the user wishes to specify a custom spacer such as furin

cleavage site (e.g. R-X-(R/K)-R) they can do so (e.g. --spacers RKKR).

If no result is found after testing with the full set of spacers, the ends of “problematic”

peptides, where all junctions contain at least one well-binding epitope, will be clipped by

removing one amino acid at a time, then repeating the above binding and graph-building

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process. This clipping may be repeated up to the number of times specified in the --

max-clip-length parameter.

It may be necessary to explore the parameter space when running pVACvector.

As binding predictions for some sites vary substantially across algorithms, the

most conservative settings may result in no valid paths, often due to one

"outlier" prediction. Carefully selecting which predictors to run may help

ameliorate this issue as well. In general, setting a higher binding threshold (e.g. 1000

nM) and using the median binding value (--top-score-metric median) will lead to

greater possibility of a design, while more conservative settings of 500 nM and

lowest/best binding value (--top-score-metric lowest) will give more confidence

that there are no junctional neoepitopes.

Our current recommendation is to run pVACvector several different ways, and

choose the path resulting from the most conservative set of parameters that produces a

vector design containing all candidate neoantigens.

pVACapi and pVACviz

pVACapi is implemented using the Python libraries Flask and Bokeh. The pVACviz

client is written in TypeScript using the Angular web application framework, the Clarity

UI component library, and the ngrx library for managing application state.

Data availability

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Data from 100 cases each of melanoma, hepatocellular carcinoma and lung squamous

cell carcinoma were obtained from TCGA and downloaded via the Genomics Data

Commons (GDC). This data can be accessed under dbGaP study accession

phs000178. Data for demonstration and analysis of fusion neoantigens was

downloaded from the Github repo for Integrate

(https://github.com/ChrisMaherLab/INTEGRATE-Vis/tree/master/example).

Software availability

The pVACtools codebase is hosted publicly on GitHub at

https://github.com/griffithlab/pVACtools and https://github.com/griffithlab/BGA-interface-

projects (pVACviz). User documentation is available at pvactools.org. This project is

licensed under the Non-Profit Open Software License version 3.0 (NPOSL-3.0,

https://opensource.org/licenses/NPOSL-3.0). pVACtools has been packaged and

uploaded to PyPI under the “pvactools” package name and can be installed on Linux

systems by running the `pip install pvactools` command. Installation requires a Python

3.5 environment which can be emulated by using Conda. Versioned Docker images are

available on DockerHub (https://hub.docker.com/r/griffithlab/pvactools/). Releases are

also made available on GitHub (https://github.com/griffithlab/pVACtools/releases).

Example pipeline for creation of pVACtools input files

pVACtools was designed to support a standard VCF variant file format and thus should

be compatible with many existing variant calling pipelines. However, as a reference, we

provide the following description of our current somatic and expression analysis pipeline

(manuscript in preparation) which has been implemented using docker, CWL(33), and

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Cromwell(34). The pipeline consists of workflows for alignment of exome DNA- and

RNA-Seq data, somatic and germline variant detection, RNA-Seq expression

estimation, and HLA typing.

This pipeline begins with raw patient tumor exome and cDNA capture(35) or RNAseq

data and leads to the production of annotated VCFs for neoantigen identification and

prioritization with pVACtools. Our pipeline consists of several modular components:

DNA alignment, HLA typing, germline and somatic variant detection, variant annotation,

and RNAseq analysis. More specifically, we use BWA-MEM(15) for aligning the

patient’s tumor and normal exome data. For germline variant calling, the output BAM

then undergoes merging (merging of separate instrument data) (Samtools(36) Merge),

query name sorting (Picard SortSam), duplicate marking (Picard MarkDuplicates),

position sorting (Picard SortSam), and base quality recalibration (GATK

BaseRecalibrator). GATK’s HaplotypeCaller(18) is used for germline variant calling and

the output variants were annotated using VEP(37) and filtered for coding sequence

variants.

For somatic variant calling, the pipeline is consistent with above except that the variant

calling step combines the output of four variant detection algorithms- Mutect2(38),

Strelka(39), Varscan(16,17) and Pindel(40). The combined variants were normalized

using GATK’s LeftAlignAndTrimVariants to left-align the indels and trim common bases.

Vt(41) was used to split multi-allelic variants. Several filters such as gnomAD allele

frequency (maximum population allele frequency), percentage of mapq0 reads, as well

as pass-only variants are applied prior to annotation of the VCF using VEP. We used a

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combination of custom and standard plugins for VEP annotation (parameters: --format

VCF --plugin Downstream --plugin Wildtype --symbol --term SO --transcript_version --tsl

--coding_only --flag_pick --hgvs). Variant coverageis assessed using bam-readcount

(https://github.com/genome/bam-readcount) for both the tumor and normal DNA exome

data, and this information is also annotated into the VCF output using VAtools

(http://vatools.org).

Our pipeline also generates a phased-VCF file by combining both the somatic and

germline variants and running the sorted combined variants through GATK

ReadBackedPhasing.

For RNAseq data, the pipeline first trims adapter sequences using flexbar(42) and

aligns the patient’s tumor RNAseq data using HISAT2(43). Two different methods,

Stringtie(44) and Kallisto(45), are employed for evaluating both the transcript and gene

expression values. Additionally, coverage support for variants in RNAseq data is

determined with bam-readcount. This information is added to the VCF using VAtools

and serves as an input for neoantigen prioritization with pVACtools.

Optionally, our pipeline can also run HLA-typing in silico using OptiType(46) when

clinical HLA typing is not available.

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We aim to support various versions of these pipelines (via the use of standardized file

formats), and are also actively developing an end-to-end public CWL workflow starting

from sequence reads to pVACseq output

(https://github.com/genome/analysis-

workflows/blob/master/definitions/pipelines/immuno.cwl).

Results

The pVACtools workflow (Fig. 1) is divided into modular components that can be run

independently. The main tools in the workflow are: (a) pVACseq: a significantly

enhanced and reengineered version of our previous tool(47) for identifying and

prioritizing neoantigens from a variety of tumor-specific alterations, (b) pVACfuse: a tool

for detecting neoantigens resulting from gene fusions, (c) pVACviz: a graphical user

interface web client for process management, visualization, and selection of results from

pVACseq, (d) pVACvector: a tool for optimizing design of neoantigens and nucleotide

spacers in a DNA vector that prevents high-affinity junctional neoantigens, and (e)

pVACapi: an OpenAPI HTTP REST interface to the pVACtools suite.

pVACseq

pVACseq(47) has been re-implemented in Python3 and extended to include many new

features since our initial report of its release. pVACseq no longer requires a custom

input format for variants, and now uses a standard VCF file annotated with VEP(37). In

our own neoantigen identification pipeline, this VCF is obtained by merging results from

multiple somatic variant callers and RNA expression tools (as described in the materials

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and methods section above: Example pipeline for creation of pVACtools input files).

Information that is not natively available in the VCF output from somatic variant callers

(such as coverage and variant allele fractions for RNA and DNA, as well as gene and

transcript expression values) can be added to the VCF using VAtools

(http://vatools.org), a suite of accessory routines that we created to accompany

pVACtools. pVACseq queries these features directly from the VCF, enabling

prioritization and filtering of neoantigen candidates based on sequence coverage and

expression information. In addition, pVACseq now makes use of phasing information

taking into account variants proximal to somatic variants of interest. Since proximal

variants can change the peptide sequence and affect neoantigen binding predictions,

this is important for ensuring that the selected neoantigens correctly represent the

individual’s genome(11). We have also expanded the supported variant types for

neoantigen predictions to include in-frame indels and frameshift variants. These

capabilities expand the potential number of targetable neoantigens several-fold in many

tumors(12,14)(Supplementary Fig. S2).

To prioritize neoantigens, pVACseq now offers support for eight different MHC Class I

antigen prediction algorithms and four MHC Class II prediction algorithms. The tool

does this in part by leveraging the Immune Epitope Database (IEDB)(48) and their suite

of six different MHC class I prediction algorithms, as well as three MHC Class II

algorithms (as described in the materials and methods: Neoantigen prediction).

pVACseq supports local installation of these tools for high-throughput users, access

through a docker container (https://hub.docker.com/r/griffithlab/pvactools), and ready-to-

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go access via the IEDB RESTful web interface. In addition, pVACseq now contains an

extensible framework for supporting new neoantigen prediction algorithms that has

been used to add support for two new non-IEDB algorithms—MHCflurry(26) and

MHCnuggets(27). By creating a framework that integrates many tools, we allow for (a) a

broader ensemble approach than IEDB, and (b) a system that other users can leverage

to develop improved ensemble ranking, or to integrate proprietary or not-yet-public

prediction software. This framework enables non-informatics-expert users to predict

neoantigens from sequence variant data sets.

Once neoantigens have been predicted, binding affinity values from the selected

prediction algorithms are aggregated into a median binding score and a report file is

generated. Finally, a rank is calculated that can be used to prioritize the predicted

neoantigens. The rank takes into account gene expression, sequence read coverage,

binding affinity predictions, and agretopicity (as described in the materials and methods:

Ranking of Neoantigens). The pVACseq rank allows users to pick the exemplar peptide

for each variant from a large number of lengths, registers, alternative transcripts, and

HLA alleles. In addition to applying strict binding affinity cutoffs, the pipeline also offers

support for MHC allele-specific cutoffs(49). The allele-specific binding filter incorporates

allele-specific thresholds from IEDB for the 38 most common HLA-A and HLA-B alleles,

representative of the nine major supertypes, instead of the 500 nM cutoff. Since these

allele-specific thresholds only include cutoffs for HLA-A and HLA-B alleles, we

recommend evaluating binding predictions for HLA-C allele separately with a less

stringent cutoff (such as 1,000 nM) and merging results when picking final candidates.

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We also offer cleavage position predictions via optional processing through

NetChop(50) as well as stability predictions made by NetMHCstabPan(51).

pVACfuse

Previous studies show that the novel protein sequences produced by known driver gene

fusions frequently produce neoantigen candidates(52). pVACfuse provides support for

predicting neoantigens from such gene fusions. One possible input to pVACfuse is

annotated fusion file from AGFusion(53), thus enabling support for a variety of fusion

callers such as STAR-Fusion(54), FusionCatcher(55), JAFFA(56) and TopHat-

Fusion(57). pVACfuse also supports annotated BEDPE format from any fusion caller

which can, for example, be generated using INTEGRATE-Neo(52). These variants are

then assessed for presence of fusion neoantigens using predictions from any of the

pVACseq-supported binding prediction algorithms.

pVACviz and pVACapi

Implementing cancer vaccines in a clinical setting requires multidisciplinary teams,

which may not include informatics experts. To support this growing community of users,

we developed pVACviz, which is a browser-based user interface that assists in

launching, managing, reviewing, and visualizing the results of pVACtools processes.

Instead of interacting with the tools via terminal/shell commands, the pVACviz client

provides a modern web-based user experience. Users complete a pVACseq (Fig. 2)

process setup form that provides helpful documentation and suggests valid values for

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inputs. The client also provides views showing ongoing processes, their logs, and

interim data files to aid in managing and troubleshooting. After a process has

completed, users may examine the results as a filtered data table or as a scatterplot

visualization—allowing them to curate results and save them as a CSV file for further

analysis. Extensive documentation of the visualization interface can be found in the

online documentation (https://pvactools.readthedocs.io/en/latest/pvacviz.html).

To support informatics groups that want to incorporate or build upon the pVACtools

features, we developed pVACapi, which provides an HTTP REST interface to the

pVACtools suite. Currently, it provides the API that pVACviz uses to interact with the

pVACtools suite. Advanced users could develop their own user interfaces or use the

API to control multiple pVACtools installations remotely over an HTTP network.

pVACvector

Once a list of neoantigen candidates has been prioritized and selected, the pVACvector

utility can be used to aid in the construction of DNA-based cancer vaccines. The input is

either the output file from pVACseq or a FASTA file containing peptide sequences.

pVACvector returns a neoantigen sequence ordering that minimizes the effects of

junctional peptides (which may create novel antigens) between the sequences (Fig. 3).

This is accomplished by using the core pVACseq module to predict the binding scores

for each junctional peptide and by modifying junctions with spacer(58,59) amino acid

sequences, or by trimming the ends of the peptides in order to reduce reactivity. The

final vaccine ordering is achieved through a simulated annealing procedure(60) that

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returns a near-optimal solution, when one exists (details can be found in the materials

and methods: Implementation).

As many prediction algorithms are CPU-intensive, pVACseq, pVACfuse, and

pVACvector also support using multiple cores to improve runtime. Using this feature,

calls to IEDB and other prediction algorithms are made in parallel over a user-defined

number of processes (details can be found in the materials and methods:

Implementation).

pVACtools has been used to predict and prioritize neoantigens for several

immunotherapy studies(61–63) and cancer vaccine clinical trials (e.g. NCT02348320,

NCT03121677, NCT03122106, NCT03092453 and NCT03532217). We also have a

large external user community that has been actively evaluating and using these

packages for their neoantigen analysis and has aided in subsequent refinement of

pVACtools through extensive feedback. The original ‘pvacseq’ package has been

downloaded over 41,000 times from PyPI, the ‘pvactools’ package has been

downloaded over 18,000 times, and the ‘pvactools’ docker image has been pulled over

30,000 times.

Analysis of TCGA data using pVACtools

To demonstrate the utility and performance of the pVACtools package, we downloaded

exome sequencing and RNA-Seq data from The Cancer Genome Atlas (TCGA)(19)

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from 100 cases each of melanoma, hepatocellular carcinoma, and lung squamous cell

carcinoma and used patient-specific MHC Class I alleles (Supplemental Fig. S1) to

determine neoantigen candidates for each patient. There were a total of 64,422 VEP-

annotated variants reported across 300 samples, with an average of 214 variants per

sample. Of these, 61,486 were single nucleotide variants (SNVs), 479 were inframe

insertions and deletions, and 2,465 were frameshift mutations (Supplemental Fig. S2).

We used this annotated list of variants as input to the pVACseq component of

pVACtools to predict neoantigenic peptides. pVACseq reported 14,599,993 unfiltered

neoantigen candidates. The original version of pVACseq(20) reported 10,284,467

neoantigens. This demonstrated that, by extending support for additional variant types

as well as prediction algorithms (due to support for additional alleles), we produced 42%

more raw candidate neoantigens.

After applying our default median binding affinity cutoff of 500 nM across all eight MHC

Class I prediction algorithms, there were 96,235 predicted strong binding neoantigens,

derived from 34,552 somatic variants (32,788 missense SNVs, 1,603 frameshift

variants, and 131 in-frame indels). This set of strong binders was further reduced by

filtering out mutant peptides with median predicted binding affinities (across all

prediction algorithms) greater than that of the corresponding wildtype peptide (i.e.

mutant/wildtype binding affinity fold change > 1), resulting in 70,628 neoantigens from

28,588 variants (26,880 SNVs, 1,583 frameshift, and 125 in-frame indels).

This set was subsequently filtered by evaluation of exome sequencing data coverage

and our recommended defaults as follows: by applying the default criteria of variant

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allele fraction (VAF) cutoff of > 25% in tumors and < 2% in normal samples, with at least

10X tumor coverage and at least 5X normal coverage, 10,730 neoantigens from 4,891

associated variants (4,826 SNVs, 56 frameshift, and 9 in-frame indels) were obtained,

with an average of 36 neoantigens predicted per case. Since RNAseq data also were

available, the filtering criteria included RNA-based coverage filters (tumor RNA VAF >

25% and tumor RNA coverage > 10X) as well as a gene expression filter (FPKM > 1).

To condense the results even further, only the top ranked neoantigen was selected per

variant (‘top-score filter’) across all alleles, lengths, and registers (position of amino acid

mutation within peptide sequence), resulting in 4,891 total neoantigens with an average

of 16 neoantigens per case. This list was then processed with pVACvector to determine

the optimum arrangement of the predicted high quality neoantigens for a DNA-vector

based vaccine design (Fig. 3).

Correlations and bias among binding prediction tools

Since we offer support for as many as eight different class I binding prediction tools, we

assessed agreement in binding affinity predictions (IC50) between these algorithms

from a random subset of 100,000 neoantigen peptides from our TCGA analysis,

described above (Fig. 4). The highest correlation was observed between the two

algorithms that are based on a stabilization matrix method (SMM)— SMM and

SMMPMBEC. The next best correlation was observed between NetMHC and

MHCflurry, possibly due to both being allele-specific predictors employing neural

network-based models. Overall the correlation between prediction algorithms is low

(mean correlation of 0.388 and range of 0.18-0.89 between all pairwise comparisons of

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algorithms).

We also evaluated if there were any biases among the algorithms to predict strong

binding epitopes (i.e. binding affinity < = 500 nM) by plotting the number of predicted

peptides based on their respective strong-predicting algorithms (Fig. 5 and

Supplemental Fig. S3). We found that MHCnuggets (v2.2) predicted the highest number

of strong-binding candidates alone (Fig. 5A). Of the total number of strong binding

candidates predicted, 64.7% of these candidates were predicted by a single algorithm

(any one of the eight algorithms), 35.2% were predicted as strong-binders by two to

seven algorithms, and only 1.8% of the strong-binding candidates were predicted as

strong binders by the combination of all eight algorithms (Supplemental Fig. S3). In fact,

as shown in Figure 6, even if one (or more) algorithms predict a peptide to be a strong

binder, often another algorithm will disagree by a large margin, in some cases predicting

that same peptide as a very weak binder. This remarkable lack of agreement

underscores the potential value of an ensemble approach that considers multiple

algorithms.

Next we determined if the number of human HLA alleles supported by these eight

algorithms differed. As shown (Supplemental Fig. S4), MHCnuggets supports the

highest number of human HLA alleles.

Comparison of filtering criteria

Since pVACtools offers a multitude of ways to filter a list of predicted neoantigens, we

evaluated and compared the effect of each filter for selecting high quality neoantigen

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candidates. There were 14,599,993 unfiltered neoantigen candidates predicted by

pVACseq resulting from 64,422 VEP-annotated variants reported across 300 samples.

We first compared the effect of running pVACtools with the commonly used standard

binding score cutoff of <= 500 nM (parameters: -b 500) to the newly added allele-

specific score filter (parameters: -a). These cutoffs were, by default, applied to the

“median” binding score of all prediction algorithms. In total, 96,235 neoantigens

(average 320.8 per patient) were predicted using the 500 nM binding score cutoff

compared to 94,068 neoantigens (average 313.6 per patient) using allele-specific filters.

About 79% of neoantigens were shared between the two sets.

We also further narrowed these sets to include only those predictions where the default

“median” predicted binding affinities (<= 500 nM) were lower than each corresponding

median wildtype peptide affinity (parameters: -b 500 -c 1) (i.e. a binding affinity

mutant/wildtype ratio or differential agretopicity value indicating that the mutant version

of the peptide is a stronger binder) (21). Using the agretopicity value filter, 70,628

neoantigens (average 235.43 per patient) were predicted versus the previously reported

96,235 neoantigens without this filter.

We also evaluated the effect of the agretopicity value filter when applied to the set of

peptides filtered on “lowest” binding score of <= 500 nM (parameters: -b 500 -c 1 -m

lowest). This filter limits peptides to those where at least one of the algorithms predicts a

strong binder, instead of calculating a median score and requiring that to meet the 500

nM threshold (Figure 6). Using the lowest binding score filter resulted in an 11-fold

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increase in the number of candidates predicted (827,423 candidates, average 2,758.08

per patient). This approach may be useful for finding candidates in tumors with low

mutation burden (but may also presumably lead to a higher rate of false positives).

Using the median (default) binding affinity filtering criteria, we next applied coverage

and expression-based filters. First, we filtered using the recommended defaults, i.e.

greater than 5X normal DNA coverage, less than 2% normal VAF, greater than 10X

tumor RNA and DNA coverage and greater than 25% tumor RNA and DNA VAF, along

with FPKM > 1 for transcript level expression (parameters: --normal-cov 5 --tdna-cov 10

--trna-cov 10 --normal-vaf 0.02 --tdna-vaf 0.25 --trna-vaf 0.25 --expn-val 1). A total of

10,730 neoantigens were shortlisted across all samples with an average of 35

neoantigens per case. We then compared this set with slightly more stringent criteria

using tumor DNA and RNA VAF of 40% (parameters: --tdna-vaf 0.40 --trna-vaf 0.40).

This shortened our list of predicted neoantigens to 4,073 candidates with an average of

13 candidates per patient.

Demonstration of neoantigen analysis using pVACfuse

To demonstrate the potential of neoantigens resulting from gene fusions, we analyzed

TCGA prostate cancer RNA-seq data from 302 patients. This dataset was previously

used as a demonstration set for the fusion neoantigen prediction supported by

INTEGRATE-Neo(22). We wanted to assess the difference (if any) in neoantigen

candidates reported by INTEGRATE-Neo using the one MHC Class I prediction

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algorithm it supports (NetMHC) versus an ensemble of eight Class I prediction

algorithms supported in pVACfuse.

Using 1,619 gene fusions across 302 samples as input, pVACfuse reported 2,104

strong binding neoantigens (binding affinity <= 500nM) resulting from 739 gene fusions.

On average, there were about 7 neoantigens per sample resulting from an average of 2

fusions per case. This is an eightfold increase in the number of strong binding

neoantigens predicted by pVACfuse versus those reported by INTEGRATE-Neo alone,

which reported 261 neoantigens across 210 fusions.

Validation of pVACtools results

To assess the real-world validity of pVACtools results, we reanalyzed raw sequencing

data from published studies that had performed immunological validation of candidate

neoantigens (Supplemental Tables S2, S3). Analysis of a melanoma patient (pt3713)

(68) showed that pVACtools was able to recapitulate eight (80%) of the positively

validated peptides in the filtered list of neoantigens using our suggested filtering

methodology (median binding predicted IC50 < 500 nM, > 5x DNA & RNA coverage, >

10% DNA & RNA VAF, > 1 TPM gene expression). Two of the positively validated

peptides were filtered out due to low RNA depth & VAF values but had median binding

affinity predictions of 13.707 nM and 4.799 nM. We were also able to eliminate 68% of

negatively validated peptides using these filters. In an additional analysis of a chronic

lymphocytic leukemia patient (pt5002) (69), we were able to recapitulate two (100%) of

the positively validated peptides in our filtered list of neoantigens using our suggested

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filtering methodology (median binding predicted IC50 < 500 nM, > 5x DNA coverage, >

10% DNA VAF). No RNA data was available for this sample. We were also able to

eliminate 57% of negatively validated peptides using these filters.

Discussion

As reported from our demonstration analysis, a typical tumor has too many possible

neoantigen candidates to be practical for a vaccine. There is therefore a critical need for

a tool that takes in the input from a tumor sequencing analysis pipeline and reports a

filtered and prioritized list of neoantigens. pVACtools enables a streamlined, accurate,

and user-friendly analysis of neoantigenic peptides from NGS cancer datasets. This

suite offers a complete and easily configurable end-to-end analysis, starting from

somatic variants and gene fusions (pVACseq and pVACfuse respectively), through

filtering, prioritization, and visualization of candidates (pVACviz), and determining the

best arrangement of candidates for a DNA vector vaccine (pVACvector). By supporting

additional classes of variants as well as gene fusions, we predict an increased number

of predicted neoantigens which may be critical for low mutational burden tumors.

Finally, by extending support for multiple binding prediction algorithms, we allow for a

consensus approach. The need for this integrated approach is made abundantly clear

by the disagreement between the candidate neoantigens identified by these algorithms,

as observed in our demonstration analyses.

To support a wider range of variant events not easily representable in VCF format such

as alternatively spliced transcripts that may create tumor antigens(65), upcoming

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versions of pVACtools will add a new tool, pVACbind, which can be used to execute our

prediction pipeline from sequences contained in a FASTA input file. Another major

planned feature is the addition of reference proteome similarity assessment of predicted

peptides in order to exclude peptides that are not true neoantigens because they occur

in other parts of the proteome. Upcoming versions will also include the calculation of

various metrics for peptide manufacturability for use in synthetic long peptide-based

vaccines. In addition, we are planning on adding binding affinity percentile ranks to our

prediction outputs for those prediction algorithms that support it. This would also allow

us to support additional IEDB prediction algorithms, such as Combinatorial Library(66),

and Sturniolo(67). The IEDB software added support for various class II epitope lengths

starting with version 2.22. We plan on updating pVACtools in an upcoming version to

take advantage of this new functionality. Lastly, we are working on a common workflow

language (CWL) immunotherapy pipeline that will execute all steps from alignment,

somatic and germline variant calling, HLA-typing, and RNAseq analysis including fusion

calling, followed by running pVACseq and pVACfuse. This will support users that would

like a full end-to-end solution not supported by pVACtools itself.

The results from pVACtools analyses are already being used in dozens of cancer

immunology studies, including studying the relationship between tumor mutation burden

and neoantigen load to predict response to immunotherapies(63,70,71), and the design

of cancer vaccines in ongoing clinical trials (e.g. NCT02348320, NCT03121677,

NCT03122106, NCT03092453, and NCT03532217). Whether the predicted

neoantigens will induce T-cell responses remains an open question for the field(72–74).

Pipelines like pVACtools are meant to assist these groups to obtain a prioritized set of

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neoantigen candidates from amongst a myriad of possible epitope predictions. Pursuant

to that, they should perform additional functional validations according to the standards

in this field(73). Whereas it may not be possible to evaluate a de novo response to all

predicted candidate neoantigens prior to vaccination, we highly recommend adding a

similar validation step as part of the clinical workflow to determine those candidates that

have a pre-existing immune response. Validation data from the studies currently using

pVACtools will be incorporated into our toolkit as they become available, and we hope

that over time this will improve the prioritization process. We anticipate that pVACtools

will make such analyses more robust, reproducible, and facile as these efforts continue.

Acknowledgements

We thank the patients and their families for donation of their samples and participation

in clinical trials. We also thank our growing user community for testing the software and

providing useful input, critical bug reports, as well as suggestions for improvement and

new features. We are grateful to Drs. Robert Schreiber, Gavin Dunn and Beatriz

Carreno for their expertise and guidance on foundational work on cancer immunology

using neoantigens and suggestions on improving the software. Obi Griffith was

supported by the National Cancer Institute (NCI) of the National Institutes of Health

(NIH) under Award Numbers U01CA209936, U01CA231844 and U24CA237719.

Malachi Griffith was supported by the National Human Genome Research Institute

(NHGRI) of the NIH under Award Number R00HG007940 and the V Foundation for

Cancer Research under Award Number V2018-007.

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Author contributions

JH and SK were involved in all aspects of this study including designing and developing

the methodology, and writing the manuscript, with help from CAM, ERM, OLG and MG.

JH analyzed and interpreted data with input from HX, CJL, SZ and Y-YF. SK wrote the

software code with help from ATW, APG, AZW, MN and JW. JM developed pVACviz

and pVACapi with assistance from APG and ATW. JN, MN, and CAM developed

pVACvector. WEG provided input about clinical needs for vaccine design to help

improve the tool’s features. OG and MG supervised the project and revised the paper.

All authors read and approved the final manuscript.

Competing interests

The authors declare no competing interests.

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Figure legends

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Figure 1: Overview of pVACtools workflow: The pVACtools workflow is highly

modularized and is divided into flexible components that can be run independently. The

main tools under the workflow include pVACseq for identifying and prioritizing

neoantigens from a variety of somatic alterations (red inset box); pVACfuse (green) for

detecting neoantigens resulting from gene fusions; pVACviz (blue) for process

management, visualization, and selection of results; and pVACvector (orange) for

optimizing design of neoantigens and nucleotide spacers in a DNA vector. All of these

tools interact via the pVACapi (purple), an OpenAPI HTTP REST interface to the

pVACtools suite.

Figure 2: pVACviz GUI client: pVACtools offers a browser-based graphical user

interface, pVACviz, which provides an intuitive means to launch pipeline processes,

monitor their execution, and analyze, export, or archive their results. To launch a

process, users navigate to the Start Page (A) and complete a form containing all of the

relevant inputs and settings for a pVACseq process. Each form field includes help text

and provides typeahead completion where applicable. For instance, the Alleles field

provides a typeahead dropdown menu that filters available alleles to only display those

alleles that match the text that the user has entered. Once a process is launched, a user

may monitor its progress on the Manage Page (B), which lists all running, stopped, and

completed processes. The Details Page (C) shows a process’ current log, attributes,

and any results files as well as provides controls for stopping, restarting, exporting, and

archiving the process. The results of pipeline processes may be analyzed on the

Visualize Page (D), which displays a customizable scatterplot of a file's rows. The X and

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Y axis may be set to any column in the result set, and filters may be applied to values in

any column. Additionally, points may be selected on the scatter plot or data grid (not

visible in this figure) for further analysis or export as CSV files.

Figure 3: An example pVACvector output showing the optimum arrangement of

candidate neoantigens for a DNA-vector based vaccine design. The figure depicts

a circularized DNA insert carrying 10 encoded neoantigenic peptide sequences to be

synthesized and encoded/cloned into a DNA plasmid. DNA sequences encoding each

peptide are ordered (with use of spacer sequences where needed) to ensure there are

no strong-binding junctional epitopes. Each neoantigenic peptide candidate is shown in

Blue, Green, Red, Orange, Purple, and Brown. In order to minimize junctional epitope

affinity, pVACvector adds spacer sequences where needed. These are depicted in

Black, along with the binding affinity value of the junctional epitope. Labels represent

the Gene Name and Amino Acid Change for each candidate.

Figure 4: Correlation between prediction algorithms: Spearman Correlation

between prediction values from all 8 class I prediction algorithms (MHCflurry,

MHCnuggets, NetMHC, NetMHCcons, NetMHCpan, PickPocket, SMM, and

SMMPMBEC) generated from a random subsample of 100,000 peptides.

Figure 5: Intersection of peptide sequences predicted by different algorithms

(MHCflurry, MHCnuggets, NetMHC, NetMHCcons, NetMHCpan, PickPocket, SMM,

and SMMPMBEC): The y-axis displays the number of overlapping unique neoantigenic

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peptides predicted for each combination of algorithm depicted on the x-axis. Each

collection of connected circles shows the set contained in an exclusive intersection (i.e.

the identity of each algorithm), while the light gray circles represent the algorithm(s) that

do not participate in this exclusive intersection. (A) Upset plot for the top 20 algorithm

combinations ranked by the number of peptides predicted to be a good binder (mutant

IC50 score < 500 nM). The combination of all eight algorithms (highlighted orange)

ranks 8th highest; (B) Upset plot for algorithm combinations where at least six

algorithms agree on predicting a peptide to be a good binder (MT IC50 score < 500 nM).

The combination of all eight algorithms (highlighted orange) ranks the highest.

Figure 6: Overall distribution of binding affinity scores (nM) for peptides where at

least one of the algorithms predicted a strong binder. Out of 14,599,993 peptides

predicted, 126,648 peptides were predicted to be strong binders by at least one of the

following algorithms: MHCflurry, MHCnuggets, NetMHC, NetMHCcons, NetMHCpan,

PickPocket, SMM, or SMMPMBEC. To define the set of peptides that were strong

binders according to at least one algorithm, HLA allele subtype-specific thresholds were

applied when available, otherwise the default cutoff binding affinity of 500 nM was used.

The peptides were further filtered using the default coverage-based filters (Normal

coverage > 5x and VAF < 2%, Tumor DNA coverage > 10x and VAF > 25%, Tumor

RNA coverage > 10x and VAF > 25%, Transcript expression > 1 fpkm). Peptides with

predicted MT IC50 scores lower than their respective cutoff scores are highlighted in

orange, while those higher are plotted in grey. The median MT IC50 scores of each

algorithm’s prediction (purple line) and the 500 nM binding affinity cutoff (red dotted line)

are indicated for reference.

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Published OnlineFirst January 6, 2020.Cancer Immunol Res   Jasreet Hundal, Susanna Kiwala, Joshua McMichael, et al.   cancer neoantigenspVACtools: a computational toolkit to identify and visualize

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