UPDATE:

• v3.4 allows you to include descriptions into the annotated files with -d parameter.
• When annotating genomic change, HGVSg, three additional columns will be added. ONCOKB_HUGO_SYMBOL, ONCOKB_PROTEIN_CHANGE and ONCOKB_CONSEQUENCE
• See Columns added section for more details

oncokb-annotator

API token required, please see OncoKB™ API section for more information

Status

Install dependencies

For python 3

pip install -r requirements/common.txt -r requirements/pip3.txt

For python 2.7

pip install -r requirements/common.txt -r requirements/pip2.7.txt

Usage

Example input files are under data. An example script is here: example.sh

MAF

Annotates variants in MAF(https://docs.gdc.cancer.gov/Data/File_Formats/MAF_Format/) with OncoKB™ annotation. Supports both python2 and python3.
Get more details on the command line using python MafAnnotator.py -h.

We recommend processing VCF files by vcf2maf before using the MafAnnotator here.

Atypical Alteration

You can still use MAF format to annotate atypical alterations, such as MSI-H, TMB-H, EGFR vIII. Please see more examples HERE.

Copy Number Alteration

Use GISTIC data format

We use GISTIC 2.0 format by default. For more information, please see https://docs.cbioportal.org/5.1-data-loading/data-loading/file-formats#discrete-copy-number-data, please see examples HERE.
Columns Locus ID and Cytoband are not required.

Individual CNA

You can also list copy number alteration individually by specifying -f individual, please see examples HERE.

Get more details on the command line using python CnaAnnotator.py -h.

Fusion

OncoKB™ offers to annotate functional fusions. The fusion format for intragenic deletion is GENE-intragenic or GENE-GENE. For other fusions, please use GENEA-GENEB or GENEA-GENEB Fusion.

Get more details on the command line using python FusionAnnotator.py -h.

Structural Variant

OncoKB™ offers to annotate structural variant. The types supported are DELETION, TRANSLOCATION, DUPLICATION, INSERTION, INVERSION, FUSION, UNKNOWN. All other types will be converted to UNKNOWN.

All structural variants with two different gene partners, they will be considered as functional fusions.

Get more details on the command line using python StructuralVariantAnnotator.py -h.

Clinical Data (Combine MAF+CNA+Fusion)

You can combine all annotation on sample/patient level using the clinical data annotator.

Get more details on the command line using python ClinicalDataAnnotator.py -h.

Annotate with HGVSp_Short, HGVSp, HGVSg or Genomic Change

OncoKB™ MafAnnotator supports annotating the alteration with HGVSp, HGVSp_Short, HGVSg or Genomic Change format. Please specify the query type with -q parameter. The acceptable values are HGVSp_Short, HGVSp, HGVSg and Genomic_Change(case-insensitive). Please see data/example.sh for examples.
If you do not specify query type, the MafAnnotator will try to figure out the query type based on the headers.

For HGVSp_Short

The annotator takes alteration from the column HGVSp_Short or Alteration

For HGVSp

The annotator takes alteration from the column HGVSp or Alteration

For HGVSg

The annotator takes alteration from the column HGVSg or Alteration

For Genomic_Change

The annotator takes genomic change from columns Chromosome, Start_Position, End_Position, Reference_Allele, Tumor_Seq_Allele1(Optional) and Tumor_Seq_Allele2.
Typically Tumor_Seq_Allele1 is the reference allele, Tumor_Seq_Allele2 is the variant allele. This is why Tumor_Seq_Allele1 is optional.
The annotator uses both if the value is different from Reference_Allele. Tumor_Seq_Allele2 has higher priority than Tumor_Seq_Allele1.

Annotation with Genomic_Change is relatively slow. We need to annotate the variant first with GenomeNexus(https://www.genomenexus.org/) then get annotation one by one. There is a plan to improve this method. If you are annotating a lot of data, please prioritize using other query type if applicable.

Annotate with different reference genomes (GRCh37, GRCh38)

OncoKB™ MafAnnotator supports annotating the alteration with reference genome GRCh37 and GRCh38.

The annotator will get the reference genome from MAF file column NCBI_Build or Reference_Genome.
If there is no reference genome specified in the file, we will use the default reference genome through -r parameter.

You can specify the default reference genome using -r parameter (This is only applicable to MafAnnotator.py).
The acceptable values are GRCh37, GRCh38 (case in-sensitive).

If both values are not specified, the annotator will use OncoKB™ default reference genome which is GRCh37.

Levels of Evidence

Introducing Simplified OncoKB™ Levels of Evidence:

• New Level 2, defined as “Standard care biomarker recommended by the NCCN or other expert panels predictive of response to an FDA-approved drug in this indication” (formerly Level 2A).
• Unified Level 3B, defined as “Standard care or investigational biomarker predictive of response to an FDA-approved or investigational drug in another indication” (combination of previous Levels 2B and 3B).

We have implemented these changes for 2 reasons:

• To be consistent with the Joint Consensus Recommendation by AMP, ASCO and CAP and the ESMO Scale for Clinical Actionability of molecular Targets (ESCAT)
• To reflect the clinical data that demonstrates patients with investigational predictive biomarkers for a specific tumor type based on compelling clinical evidence (currently Level 3A) are more likely to experience clinical benefit compared to patients with predictive biomarkers that are considered standard care in a different tumor type (previously Level 2B, now combined into Level 3B).

OncoKB™ API

When you run MafAnnotator.py, FusionAnnotator.py and CnaAnnotator.py, you need a token before accessing the OncoKB™ data via its web API. Please visit OncoKB™ Data Access Page for more information about how to register an account and get an OncoKB™ API token.
With the token listed under OncoKB™ Account Settings Page, you could use it in the following format.

python ${FILE_NAME.py} -i ${INPUT_FILE} -o ${OUTPUT_FILE} -b ${ONCOKB_API_TOKEN}

Columns added

MafAnnotator/CnaAnnotator/StructuralVariantAnnotator/FusionAnnotator

Column	Conditions	Possible Values	Description
ANNOTATED		True, False	Whether the variant is annotated by OncoKB successfully.
ONCOKB_HUGO_SYMBOL	Only added when annotating genomic change or HGVSg		When annotating genomic change, we obtained gene hugo symbol from GenomeNexus. This can be cross-referenced with your own gene name.
ONCOKB_PROTEIN_CHANGE	Only added when annotating genomic change or HGVSg		When annotating genomic change, we obtained alteration protein change from GenomeNexus. This can be cross-referenced with your own protein change.
ONCOKB_CONSEQUENCE	Only added when annotating genomic change or HGVSg		When annotating genomic change, we obtained alteration consequence from GenomeNexus. This can be cross-referenced with your own consequence/Variant Class.
GENE_IN_ONCOKB		True, False	Whether the gene has been curated by the OncoKB Team.
VARIANT_IN_ONCOKB		True, False	Whether the variant has been curated by the OncoKB Team. Note: when a variant does not exist, it may still have annotations.
MUTATION_EFFECT		Gain-of-function, Likely Gain-of-function, Loss-of-function, Likely Loss-of-function, Switch-of-function, Likely Switch-of-function, Neutral, Likely Neutral, Inconclusive, Unknown	The biological effect of a mutation/alteration on the protein function that gives rise to changes in the biological properties of cells expressing the mutant/altered protein compared to cells expressing the wildtype protein.
MUTATION_EFFECT_CITATIONS		PMID, Abstract, Website link	All citations related to the biological effect.
ONCOGENIC		Oncogenic, Likely Oncogenic, Likely Neutral, Inconclusive, Unknown, Resistance	In OncoKB™, “oncogenic” is defined as “referring to the ability to induce or cause cancer” as described in the second edition of The Biology of Cancer by Robert Weinberg (2014).
LEVEL_*		Therapeutic implications	The leveled therapeutic implications.
HIGHEST_LEVEL		LEVEL_1, LEVEL_2, LEVEL_3A, LEVEL_3B, LEVEL_4, LEVEL_R1, LEVEL_R2	The highest level of evidence for therapeutic implications. Order: LEVEL_R1 > LEVEL_1 > LEVEL_2 > LEVEL_3A > LEVEL_3B > LEVEL_4 > LEVEL_R2
HIGHEST_SENSITIVE_LEVEL		LEVEL_1, LEVEL_2, LEVEL_3A, LEVEL_3B, LEVEL_4	The highest sensitive level of evidence for therapeutic implications. Order: LEVEL_1 > LEVEL_2 > LEVEL_3A > LEVEL_3B > LEVEL_4
HIGHEST_RESISTANCE_LEVEL		LEVEL_R1, LEVEL_R2	The highest resistance level of evidence for therapeutic implications. Order: LEVEL_R1 > LEVEL_R2
TX_CITATIONS		PMID, Abstract, Website link	All citations related to therapeutic implications.
LEVEL_Dx*		Tumor type the level of evidence is assigned to	The leveled diagnostic implications.
HIGHEST_DX_LEVEL		LEVEL_Dx1, LEVEL_Dx2, LEVEL_Dx3	The highest level of evidence for diagnostic implications.
DX_CITATIONS		PMID, Abstract, Website link	All citations related to diagnostic implications.
LEVEL_Px*		Tumor type the level of evidence is assigned to	The leveled prognostic implications.
HIGHEST_PX_LEVEL		LEVEL_Px1, LEVEL_Px2, LEVEL_Px3	The highest level of evidence for prognostic implications.
PX_CITATIONS		PMID, Abstract, Website link	All citations related to prognostic implications.
GENE_SUMMARY	Only when parameter -d is specified		Brief overview of the gene and its role in cancer
VARIANT_SUMMARY	Only when parameter -d is specified		Variant summary describes the variant oncogenicity, last review if it is VUS
TUMOR_TYPE_SUMMARY	Only when parameter -d is specified		Tumor type summary describes the therapeutic implication that applies to the indication
DIAGNOSTIC_SUMMARY	Only when parameter -d is specified		Diagnostic summary that applies to the indication, for hematologic malignancies only
PROGNOSTIC_SUMMARY	Only when parameter -d is specified		Prognostic summary that applies to the indication, for hematologic malignancies only
MUTATION_EFFECT_DESCRIPTION	Only when parameter -d is specified		The mutation effect description provides a brief overview of the biological and oncogenic effect of the VPS and includes appropriate references to peer-reviewed literature.

ClinicalDataAnnotator

Please see description above for columns LEVEL_*, HIGHEST_LEVEL, HIGHEST_SENSITIVE_LEVEL, HIGHEST_RESISTANCE_LEVEL, LEVEL_Dx*, HIGHEST_DX_LEVEL, LEVEL_Px*, HIGHEST_PX_LEVEL.
Beside these columsn, the following columns will also be added.

Column	Description
ONCOGENIC_MUTATIONS	The list of mutations that are Oncogenic or Likely Oncogenic.
#ONCOGENIC_MUTATIONS	Number of oncogenic mutations.
RESISTANCE_MUTATIONS	The list of resistance mutations.
#RESISTANCE_MUTATIONS	Number of resistance mutations.
#MUTATIONS_WITH_SENSITIVE_THERAPEUTIC_IMPLICATIONS	Number of mutations in the sample with sensitive therapeutic implications.
#MUTATIONS_WITH_RESISTANCE_THERAPEUTIC_IMPLICATIONS	Number of mutations in the sample with resistance therapeutic implications.
#MUTATIONS_WITH_DIAGNOSTIC_IMPLICATIONS	Number of mutations in the sample with diagnostic implications.
#MUTATIONS_WITH_PROGNOSTIC_IMPLICATIONS	Number of mutations in the sample with prognostic implications.
#MUTATIONS	Number of mutations in the sample.

Questions?

The best way is to email [email protected], so all our team members can help.