Anti-tumor T cells recognize tumor somatic mutations, translated as single amino acid substitutions, as "neoantigens" (upon processing and presentation in the context of MHC molecules). From NGS somatic variant calling data, we can attempt to reconstruct peptide sequences of such neoantigens, and thus produce personalised cancer vaccines.
- Introduction - Biology - Approach - Prediction methods - Ranking tools - Links - More papers
- Installation
- Usage
- Processing test samples
- pVACseq
- pVACfuse - Ideas
- NeoepitopePred
- OpenVax tools
The main paradigm behind the development of cancer vaccines rests on the assumption that if the immune system is stimulated to recognize neoantigens, it may be possible to elicit the selective destruction of tumor cells. Vaccines incorporate these neoantigen peptides with the aim of enhancing the immune system’s anti-tumor activity by selectively increasing the frequency of specific CD8+ T cells, and hence expanding the immune system’s ability to recognize and destroy cancerous cells. This process is dependent on the ability of these peptides to bind and be presented by HLA class I molecules, a critical step to inducing an immune response and activating CD8+ T cells.
As we move from vaccines targeting ‘shared’ tumor antigens to a more ‘personalized’ medicine approach, in silico strategies are needed to first identify, then determine which somatic alterations provide the optimal neoantigens for the vaccine design. Ideally, an optimal strategy would intake mutation calls from tumor/normal DNA sequencing data, identify the neoantigens in the context of the patient’s HLA alleles, and parse out a list of optimal peptides for downstream testing.
Several in silico epitope binding prediction methods have been developed: 11, 12, 13, 14, 15. These methods employ various computational approaches such as Artificial Neural Networks (ANN) and Support Vector Machines (SVM) and are trained on binding to different HLA class I alleles to effectively identify putative T cell epitopes. Allele-specific software like NetMHC perform better compared to pan-specific methods such as NetMHCpan in case of well-characterized alleles due to availability of large amounts of training data. However, pan-specific methods could be beneficial in cases where there is limited peptide binding data for training, for arbitrary HLA molecules, or when predicting epitopes for non-human species.
Class I predictors: SMM [111], SMMPMBEC [112], Pickpocket [113], NetMHC [114], NetMHCpan [87], NetMHCcons [180], MHCflurry [102], MHCnuggets [181], MHCSeqNet [103], EDGE [104]
Class II predictors: SMMAlign [111], NNAlign [182], ProPred [183], NetMHCII(2.3) and NetMHCIIpan(3.2) [116], TEPITOPE [184], TEPITOPEpan [185], RANKPEP [186], MultiRTA [187], OWA-PSSM [188]
- NetChop20S [89],
- NetChopCterm [89],
- ProteaSMM [89, 90],
- PAProC [179] (Class I),
- PepCleaveCD4 [91] (Class II)
There are also existing tools (IEDB, EpiBot, EpiToolKit) that compile the results generated from individual epitope prediction algorithms to improve the prediction accuracy with consensus methods or a unified final ranking. EpiToolKit also has the added functionality of incorporating sequencing variants in its Galaxy-like epitope prediction workflow (via its Polymorphic Epitope Prediction plugin). However, it does not incorporate sequence read coverage or gene expression information available, nor can it compare the binding affinity of the peptide in the normal sample (WT) versus the tumor (mutant). Another multi-step workflow Epi-Seq uses only raw RNA-Seq tumor sample reads for variant calling and predicting tumor-specific expressed epitopes.
pVACseq is a workflow that identifies and shortlist candidate neoantigen peptides from tumor mutational repertoire. It can use both DNA and RNA sequencing data. Predicted peptides can be used in a personalized vaccine after immunological screening. The tool offers the functionality to compare and differentiate the epitopes found in normal cells against the neoepitopes specifically present in tumor cells for use in personalized cancer vaccines.
vaxrank is another workflow that prioritizes epitopes after running them agains prediction software like NetMHC.
NeoepitopePred is an online-hosted workflow, can run from either single mutations or fusions.
HLA typing is needed to identify which HLA genes we need to check binding againt. Tools are
- Class I: Optitype [69], Polysolver [70]
- Class I and II: Athlates [70, 175], HLAreporter [176], HLAminer [176, 177], HLAscan [72, 178], HLA-VBSeq [72], PHLAT [71], seq2HLA [73], xHLA [74]
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MHCMotifViewer - diagrams of HLA allele motifs. Can be used to manually check epitope calls if have trust issues in predicion algorithms. The more letters match, the better:
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Novel tools to assist neoepitope targeting in personalized cancer immunotherapy
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Nice review: https://www.sevenbridges.com/neoantigen-discovery-using-ngs-data/
Refer to install_readme.sh for instructions.
nag -o OUTPUT_DIR \
BCBIO_DNA_RUN \
BCBIO_DNA_SAMPLE_NAME \
-R BCBIO_RNA_RUN \
-r BCBIO_RNA_SAMPLE_NAME \
[-j N]
# Example. Process NeverResponder on 4 cores:
nag -o nag_results \
/g/data3/gx8/projects/Saveliev_pVACtools/diploid/bcbio_hg38/final \
diploid_tumor \
-R /g/data/gx8/data/pVAC/hg38_wts_samples/final \
-r Unknown_B_RNA
-j 4
# Example. Run pVACfuse only (neoepitopes from fusions):
nag -o nag_results \
/g/data3/gx8/projects/Saveliev_pVACtools/diploid/bcbio_hg38/final \
diploid_tumor \
-R /g/data/gx8/data/pVAC/hg38_wts_samples/final \
-r Unknown_B_RNA
pVACfuse
# Example. Run pVACseq only on a sample with purity 40% (to make sure to get rid of subclonal mutations):
nag -o nag_results \
/g/data3/gx8/projects/Saveliev_pVACtools/diploid/bcbio_hg38/final \
diploid_tumor \
pVACseq \
--min-tvaf 40
Processing samples that have WTS and WGS bcbio runs against hg38. Command lines are in the spreadsheet's last column.
pVACfuse identifies and prioritizes neoantigens from a list of tumor mutations, and pre-calculated HLA types.
Here we are exploring applitation of the tool. In summary, we managed to run it from ensemble somatic variant calls generated by bcbio+umccrise, using HLA types predicted by Optitype in bcbio. The current pipeline is as follows.
# Run VEP to annotate the ensemble calls:
vep \
--input_file sample-somatic-ensemble-pon_hardfiltered.vcf.gz \
--format vcf \
--pick \
--output_file sample-somatic-ensemble-pon_hardfiltered.VEP.vcf \
--vcf --symbol --terms SO \
--plugin Downstream \
--plugin Wildtype \
--cache \
--dir_cache vep_data/GRCh37 \
--assembly GRCh37 \
--offline
# Subset the calls
bcftools view -s sample sample-somatic-ensemble-pon_hardfiltered.VEP.vcf > sample-somatic-ensemble-pon_hardfiltered.VEP.TUMOR.vcf
# Take HLA predictions out of bcbio-nextgen Opitype output (hg38 only)
# Run pVACseq:
pvacseq run \
sample-somatic-ensemble-pon_hardfiltered.VEP.TUMOR.vcf \
sample \
"HLA-A*02:01,HLA-A*26:01,HLA-B*35:02,HLA-B*18:01,HLA-C*04:01,HLA-C*05:01" \
NNalign NetMHCIIpan NetMHCcons SMM SMMPMBEC SMMalign \
pvacseq_results \
-e 9,10 \
-t \
--top-score-metric=lowest \
--iedb-install-directory /g/data3/gx8/projects/Saveliev_pVACtools \
--trna-vaf 10 \
--net-chop-method cterm \
--netmhc-stab \
--exclude-NAs \
--keep-tmp-files
For offline runs, we would omit --net-chop-method cterm and --netmhc-stab
Would keep -t to picking only the top epitope for a mutation, keeping in mind that we can go back to intermediate diploid_tumor.filtered.coverage.tsv, and refilter with pvacseq top_score_filter.
It does not include the coverage and expression filters. The full data is prepared and pVACseq is run within the NAG pepeline, refer to usage.
pVACfuse is a part of pVACtools package. It predicts neoantigens produced specifically by gene fusion events.
Here we are exploring how can we run the tool in house. The tool works with input generated by a fusion caller INTEGRATE. Unfortuantely, it's not documented, the code base is not supported, and it doesn't install in our HPCs. Out of all other fusion callers, the most relevant for us is pizzly, as it shows a high performance, is integrated in bcbio-nextgen, and can work with both GRCh37 and hg38. The downside is that it runs from pseudo-alignments and does not provide genomic coordinates. We managed to convert pizzly calls to genomic coordinates, and annotate it to generate a INTEGRATE-like output that is get accepted by pVACfuse. Basically all it needs is peptide sequences flanking a fusion breakpoint, and the breakpoint position in the peptide.
The current pipeline is as below, given that we have a standard pizzly output CCR180081_MH18T002P053_RNA{-flat-filtered.tsv,.json,.fusions.fasta}.
# annotate and convert pizzly to bedpe format
python pizzly_to_bedpe.py CCR180081_MH18T002P053_RNA
# produces CCR180081_MH18T002P053_RNA-flat-filtered.bedpe
# run pVACfuse
pvacfuse run \
CCR180081_MH18T002P053_RNA-flat-filtered.bedpe \
CCR180081_MH18T002P053_RNA-VACfuse \
"HLA-A*33:24,HLA-B*55:29,HLA-B*15:63,HLA-C*07:02,HLA-C*04:61" \
NNalign NetMHCIIpan NetMHCcons SMM SMMPMBEC SMMalign \
pvacfuse_out \
-e 8,9,10,11 \
--top-score-metric=lowest \
--keep-tmp-files
The data is prepared and pVACfuse is run within the NAG pepeline, refer to usage.
- From fusion gene calling paper:
- Each fusion transcript candidate aligned to the corresponding artificially fused genomic sequence by using the SIM4 program, and the alignment around the fusion point was manually inspected. Only those that aligned precisely, without a gap or overlap, were retained
- The transcripts that included human repetitive sequences were removed by using the repeatmasker program
- Should we treat cDNA chimeras (breakpoint before transcription) from transcript chimeras (breakpoint before transcription)? "Chimeric transcripts can be distinguished from artificial chimeras, which are created by accidental ligation of different cDNAs during the cloning procedure, by examining the sequence at the fusion point. The fusion point in the chimeras from true fusion genes will usually coincide with a canonical exon boundary because the genes are likely to break in an intron because introns are generally much longer than exons. In contrast, the fusion point for an artificial chimera will usually be within an exon of each gene because the fusion occurs between two cDNAs."
Another tool that can run both from mutations and fusions is NeoepitopePred.
Unfortunately, it requires to upload FASTQ or BAM data on the server, which might be a problem for us.
- Runs from fastq (calls optitype directly on fastq) or BAM (extacts HLA reads and unmapped reads, converts to fastq, then calls optitype)
- Can run EITHER from mutations, or fusions (like pVACtools)
- Need mutations in a specific format
- Uses NetMHCcons (consensus on a set of NetMHC methods).
OpenVax is a group at the Icahn School of Medicine at Mount Sinai that develop open source personalized cancer vaccine pipeline. Part of the tools are a predictor MHCFlurry, a wrapper vaxrank, and a NGS pipeline neoantigen-vaccine-pipeline. Exploring here.
Given a list of somatic mutations (VCF file), MuPeXI returns a table containing all mutated peptides (neo-peptides) of user-defined lengths, along with information relevant for identifying which of these neo-peptides are likely to serve as neo-epitopes. Works only with NetMHCpan.
- Doesn't support fusions
- Input: somatic mutations, a list of HLA types, optionally a gene expression profile.
- Output: tumor-specific peptides from SNPs and indels, and annotation: HLA binding and similarity to normal peptides. Sorted to a priority score which is intended to roughly predict immunogenicity.
- Only supports NetMHCpan