@@ -9,12 +9,183 @@ ottrpal::set_knitr_image_path()
99
1010This chapter will cover:
1111
12- - Running pVACtools
12+ - Starting an interactive Docker session
13+ - Running pVACseq
14+ - Running pVACfuse
1315- Understanding pVACtools outputs
1416
15- ## Running pVACtools
17+ ## Starting Docker
1618
17- This section will explain how to run pVACtools either using Docker.
19+ In your Terminal execute the following command:
20+
21+ ``` {r, engine = 'bash', eval = FALSE}
22+ mkdir pVACtools_outputs
23+
24+ docker run \
25+ -v HCC1395_inputs:/HCC1395_inputs \
26+ -v pVACtools_outputs:/pVACtools_outputs \
27+ -it griffithlab/pvactools:4.0.0 \
28+ /bin/bash
29+ ```
30+
31+ This will pull the 4.0.0 version of the griffithlab/pvactools Docker image and
32+ start an interactive session of that Docker image. The `-v
33+ HCC1395_inputs:/HCC1395_inputs` part of the command will mount the
34+ ` HCC1395_inputs ` folder at ` /HCC1395_inputs ` inside of the Docker container
35+ so that you will have access to the input data from inside the Docker
36+ container. The ` -v pVACtools_outputs:/pVACtools_outputs ` part of the command
37+ will mount the ` pVACtools_outputs ` folder you just created. We will write the
38+ outputs from pVACseq and pVACfuse to that folder so that you will have access
39+ to it once you exit the Docker image.
40+
41+ ## Running pVACseq
42+
43+ The pVACseq pipeline is run using the ` pvacseq run ` command.
44+
45+
46+ ### Required Parameters
47+
48+ The ` pvacseq run ` command takes a number of required parameters in the
49+ following order:
50+
51+ - ` vcf_file ` : A VEP-annotated single- or multi-sample VCF containing genotype,
52+ transcript, Wildtype protein sequence, and Frameshift protein sequence
53+ information.
54+ - ` sample_name ` : The name of the tumor sample being processed. When processing
55+ a multi-sample VCF the sample name must be a sample ID in the input VCF #CHROM
56+ header line. Only variants that are called (genotype/GT 0/1 or 1/1) in that
57+ sample will be processed.
58+ - ` allele(s) ` : The name of the HLA allele to use for epitope prediction. Multiple
59+ alleles can be specified using a comma-separated list. These should be the
60+ HLA alleles of your patient. You might have clinical typing information for
61+ your patient. If not, you will need to computational predict the patient's
62+ HLA type using software such as OptiType.
63+ - ` prediction_algorithms ` : The epitope prediction algorithms to use. Multiple
64+ prediction algorithms can be specified, separated by spaces. Use ` all ` to
65+ run all available prediction algorithms.
66+ - ` output_dir ` : The directory for writing all result files.
67+
68+ ### Optional Parameters
69+
70+ The ` pvacseq run ` command offers quite a few optional arguments to fine-tune
71+ your run. Here are a list of parameters we generally recommend:
72+
73+ - ` --phased-proximal-variants-vcf ` : This is an additional VCF file that
74+ includes both somatic and germline variants with phasing information. This
75+ file is used to identify variants near a somatic variant of interest and
76+ in-phase that would, as a result, change the predicted protein sequence
77+ around the somatic variant of interest and, thus, change the predicted
78+ neoantigens. Please note that pVACseq is currently only able to incorporate
79+ proximal missense variants so users should still manually investigate their
80+ candidates for other types of nearby variants (e.g. inframe and frameshift
81+ indels)
82+ - ` --normal-sample-name ` : When using a tumor-normal input VCF, this parameter
83+ is used to identify the normal sample in the VCF in order to parse
84+ coverage metrics for the normal sample.
85+ - ` --iedb-install-directory ` : For speed and reliability, we generally recommend
86+ that users use a standalone installation of the IEDB software. The pVACtools
87+ Docker containers already come with this software pre-installed in the
88+ ` /opt/iedb ` directory.
89+ - ` --allele-specific-binding-thresholds ` : When filtering and tiering
90+ neoantigen candidates, one main criteria is the predicted peptide-MHC
91+ binding affinity. By default, pVACseq uses a cutoff of <500 nmol IC50.
92+ However, for some HLA alleles, other cutoffs are more appropriate depending
93+ on the distribution of binding affinities across peptides. Setting
94+ this flag enables allele-specific binding cutoffs as recommended by
95+ [ IEDB] ( https://help.iedb.org/hc/en-us/articles/114094152371-What-thresholds-cut-offs-should-I-use-for-MHC-class-I-and-II-binding-predictions ) .
96+ - ` --allele-specific-anchors ` : When considering a neoantigen candidate, only a
97+ subset of peptide positions are presented to the T cell receptor
98+ for recognition, while others are responsible for anchoring to the MHC, making
99+ these positional considerations critical for predicting T cell responses.
100+ Conventionally, the 1st, 2nd, n-1 and n position in a neoantigen candidates
101+ were considered anchors while recent studies [ @Xia2023 ] have shown that
102+ these positions will depend on the HLA allele. Setting this flag will use
103+ allele-specific anchor locations.
104+ - ` --run-reference-proteome-similarity ` : One consideration when selecting
105+ neoantigen candidates, is that the neoantigen should not occur natively in
106+ the patient's proteome. When this flag is set, pVACseq will search for each
107+ neoantigen candidate in the reference proteome and report any hits found.
108+ By default this is done using BLASTp but we recommend using a proteome FASTA
109+ file via the ` --peptide-fasta ` parameter to speed up this step.
110+ - ` --pass-only ` : By default, all variants that were called in the tumor sample
111+ are considered by pVACseq. This flag will lead pVACseq to skip variants that
112+ have a FILTER applied in the VCF to, e.g., exclude variants that were marked
113+ as low quality by the variant caller.
114+ - ` --percentile-threshold ` : When considering the peptide-MHC binding affinity
115+ for filtering and prioritizing neoantigen candidates, by default only the
116+ IC50 value is being used. Setting this parameter will additional also filter
117+ on the predicted percentile. We recommend a value of 0.01 (1%) for this
118+ threshold.
119+
120+ Additionally there are a number of parameters that might be useful depending
121+ on your specific analysis needs:
122+
123+ - ` --class-i-epitope-length ` and ` --class-ii-epitope-length ` : By default 8,
124+ 9, 10, 11 and 12, 13, 14, 15, 16, 17, 18 are set for these parameters,
125+ respecitively but different lengths might be desired.
126+ - ` --tumor-purity ` : This parameter is used to bin variants into clonal and
127+ sub-clonal. This parameter might need to be adjusted based on the tumor
128+ purity of your data.
129+ - ` --problematic-amino-acids ` : Some vaccine manufacturers will consider certain amino
130+ acids in the neoantigen candidates difficult to manufacture. For example, a
131+ Cysteine is commonly considered problematic as it makes the peptide
132+ unstable. This parameter allows users to set their own rules as to which
133+ peptides are considered problematic and peptides meeting those rules will be marked in the
134+ pVACseq results and deprioritized.
135+ - ` --threads ` : This argument will allow pVACseq to run in multi-processing
136+ mode.
137+ - ` --keep-tmp-files ` : Setting this flag will save intermediate files created by pVACseq.
138+ - ` --downstream-sequence-length ` : For frameshift variants, the downstream
139+ sequence can potentially be very long, which can be computationally
140+ expensive. This parameter limits how many amino acids of the downstream
141+ sequence are included in the prediction.
142+
143+ ### pVACseq Command
144+
145+ Given the considerations outlined above, let's run pVACseq on our sample data.
146+
147+ From the
148+ ` optitype_normal_result.tsv ` we know that the patient's class I alleles are HLA-A\* 29:02, HLA-B\* 45:01,
149+ HLA-B\* 82:02, and HLA-C\* 06:02. We also have clinical typing information that confirms
150+ these class I alleles as well as identified DQA1\* 03:03, DQB1\* 03:02, and DRB1\* 04:05 as the
151+ patient's class II alleles.
152+
153+ To identify the tumor and normal sample names we will grep the VCF file for
154+ the CHROM header:
155+
156+ ``` {r, engine = 'bash', eval = FALSE}
157+ zgrep CHROM /HCC1395_inputs/annotated.expression.vcf.gz
158+ ```
159+
160+ This shows that the tumor sample is named ` HCC1395_TUMOR_DNA ` and the normal sample is named ` HCC1395_NORMAL_DNA ` .
161+
162+ For our test run, please execute the ` pvacseq run ` command below. The
163+ prediction run might take a while but pVACseq will output progress messages as
164+ it processeses through the pipeline.
165+
166+ ``` {r, engine = 'bash', eval = FALSE}
167+ pvacseq run \
168+ /HCC1395_inputs/annotated.expression.vcf.gz \
169+ HCC1395_TUMOR_DNA \
170+ HLA-A*29:02,HLA-B*45:01,HLA-B*82:02,HLA-C*06:02,DQA1*03:03,DQB1*03:02,DRB1*04:05 \
171+ all \
172+ /pVACtools_outputs/pvacseq_predictions \
173+ --normal-sample-name HCC1395_NORMAL_DNA \
174+ --phased-proximal-variants-vcf /HCC1395_inputs/phased.vcf.gz \
175+ --iedb-install-directory /opt/iedb \
176+ --pass-only \
177+ --allele-specific-binding-thresholds \
178+ --percentile-threshold 0.01 \
179+ --allele-specific-anchors \
180+ --run-reference-proteome-similarity \
181+ --peptide-fasta /HCC1395_inputs/Homo_sapiens.GRCh38.pep.all.fa.gz \
182+ --problematic-amino-acids C \
183+ --downstream-sequence-length 100 \
184+ --n-threads 8 \
185+ --keep-tmp-files
186+ ```
187+
188+ ## Running pVACfuse
18189
19190## Understanding pVACtools outputs
20191
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