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AACR GENIE Data Guide
About this Document
Version of Data
Data Access
Terms of Access
Introduction to AACR GENIE
Human Subjects Protection and Privacy
Summary of Data by Center
Genomic Profiling at Each Center
Pipeline for Annotating Mutations and Filtering Putative Germline SNPs
Description of Data Files
Clinical Data
Abbreviations and Acronym Glossary

About this Document
This document provides an overview of the first public release of American Association for
Cancer Research (AACR) GENIE data.

Version of Data
AACR GENIE Project Data: Version 1.0

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Data Access
AACR GENIE Data is currently available via two mechanisms:
●
●

Sage Synapse Platform: http://synapse.org/genie
cBioPortal for Cancer Genomics: http://www.cbioportal.org/genie/

Terms of Access
All users of the AACR Project GENIE data must agree to the following terms of use; failure to
abide by any term herein will result in revocation of access.
●
●

Users will not attempt to identify or contact individual participants from whom these data
were collected by any means.
Users will not redistribute the data without express written permission from the AACR
Project GENIE Coordinating Center (send email to: info@aacrgenie.org).

When publishing or presenting work using or referencing the AACR Project GENIE dataset
please include the following attributions:
●
●

Please cite: The AACR Project GENIE Consortium. AACR Project GENIE: Powering
Precision Medicine Through An International Consortium, in preparation.
The authors would like to acknowledge the American Association for Cancer Research
and its financial and material support in the development of the AACR Project GENIE
registry, as well as members of the consortium for their commitment to data sharing.
Interpretations are the responsibility of study authors.

Posters and presentations should include the AACR Project GENIE logo.

Introduction to AACR GENIE
The AACR Project Genomics, Evidence, Neoplasia, Information, Exchange (GENIE) is a multiphase, multi-year, international data-sharing project that aims to catalyze precision cancer
medicine. The GENIE platform will integrate and link clinical-grade cancer genomic data with
clinical outcome data for tens of thousands of cancer patients treated at multiple international
institutions. The project fulfills an unmet need in oncology by providing the statistical power
necessary to improve clinical decision-making, to identify novel therapeutic targets, to
understand of patient response to therapy, and to design new biomarker-driven clinical trials.

2

The project will also serve as a prototype for aggregating, harmonizing, and sharing clinicalgrade, next-generation sequencing (NGS) data obtained during routine medical practice.
The data within GENIE is being shared with the global research community. The database
currently contains CLIA-/ISO-certified genomic data obtained during the course of routine
practice at multiple international institutions (Table 1), and will continue to grow as more patients
are treated at additional participating centers.
Table 1: AACR GENIE Contributing Centers.
Center Abbreviation

Center Name

DFCI

Dana-Farber Cancer Institute, USA

GRCC

Institut Gustave Roussy, France

JHU

Johns Hopkins Sidney Kimmel Comprehensive Cancer Center, USA

MDA

MD Anderson Cancer Center, USA

MSK

Memorial Sloan Kettering Cancer Center, USA

NKI

Netherlands Cancer Institute, The Netherlands

UHN

Princess Margaret Cancer Centre, University Health Network, Canada

VICC

Vanderbilt-Ingram Cancer Center, USA

Human Subjects Protection and Privacy
Protection of patient privacy is paramount, and the AACR GENIE Project therefore requires that
each participating center share data in a manner consistent with patient consent and centerspecific Institutional Review Board (IRB) policies. The exact approach varies by center, but
largely falls into one of three categories: IRB-approved patient-consent to sharing of deidentified data, captured at time of molecular testing; IRB waivers and; and IRB approvals of
GENIE-specific research proposals. Additionally, all data has been de-identified via the HIPAA
Safe Harbor Method. Full details regarding the HIPAA Safe Harbor Method are available online
at: https://www.hhs.gov/hipaa/for-professionals/privacy/special-topics/de-identification/.

3

Summary of Data by Center
The first data release includes genomic and clinical data from eight cancer centers. Tables 2-3
summarize genomic data provided by each of the eight centers, followed by descriptive
paragraphs describing genomic profiling at each of the eight centers.

Table 2: Genomic Data Characterization by Center.
DFCI

GRCC

JHU

MDA

MSK

NKI

UHN

VICC

Specimen
Types

Formalin-fixed,
paraffin-embedded
(FFPE) v. Fresh
Frozen (Fresh Froz)

FFPE

Fresh
Froz

FFPE

FFPE

FFPE

FFPE

FFPE

FFPE

Specimen
Tumor
Cellularity

Tumor Cellularity
Cutoff

>20%

>10%

>10%

>20%

>10%

>10%

>10%

>20%

Assay
Type

Hybridization Capture
v. PCR

Capture

PCR

PCR

PCR

Capture

PCR

PCR

Capture

Coverage

Hotspot Regions

x

x

x

x

x

Coding Exons

x

x

x

Introns (selected)

x

x

x

Promoters (selected)
Platform

Illumina

x
x

Ion Torrent

x

x

x

x

x

x

x

Tumor
Only

Tumor
Only

Tumor
Only

Tumor
Only

TumorNormal

Tumor
Only

TumorNormal

Tumor
Only

Calling
Strategy

Unmatched (Tumoronly) v. Matched
(Tumor-Normal)

Alteration
Types

Single Nucleotide
Variants (SNV)

x

x

x

x

x

x

x

x

Small Indels

x

x

x

x

x

x

x

x

Gene-Level CNA

x

[1]

x

Intragenic CNA
Structural Variants

x

x
[1]

x

x

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[1] Structural variants or copy number events are identified and reported, but have not been transferred to
GENIE.

Table 3: Gene Panels Submitted by Each Center.
Panel File

Panel Type
(PCR/Capture)

All Exons v.
Hotspot Regions

# of Genes

DFCI-ONCOPANEL-1.txt

Custom

All Exons

275

DFCI-ONCOPANEL-2.txt

Custom

All Exons

300

MSK-IMPACT341.txt

Custom

All Exons

341

MSK-IMPACT410.txt

Custom

All Exons

410

GRCC-CHP2.txt

Ion AmpliSeq Cancer Hotspot
Panel v2

Hotspot Regions

50

GRCC-MOSC3.txt

Ion AmpliSeq Cancer Hotspot
Panel v2

Hotspot Regions

74

JHU-50GP-V1.txt

Ion AmpliSeq Cancer Hotspot
Panel v2

Hotspot Regions

50

MDA-46-V1.txt

Custom, based on Ion AmpliSeq
Cancer Hotspot Panel v1

Hotspot Regions

46

NKI-TSACP.txt

TruSeq Amplicon Cancer Panel

Hotspot Regions

48

UHN-48-V1.txt

TruSeq Amplicon Cancer Panel

Hotspot Regions

48

VICC-01-T5a.txt

Foundation Medicine

All Exons

322

VICC-01-T7.txt

Foundation Medicine

All Exons

429

(all files are prepended as:
data_gene_panel_XXX)

Genomic Profiling at Each Center
Dana-Farber Cancer Institute (DFCI)

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DFCI uses a custom, hybridization-based capture panel (OncoPanel) to detect single nucleotide
variants, small indels, copy number alterations, and structural variants from tumor-only
sequencing data. Two versions of the panel have been submitted to GENIE: version 1
containing 275 genes, and version 2 containing 300 genes. Specimens are reviewed by a
pathologist to ensure tumor cellularity of at least 20%. Tumors are sequenced to an average
unique depth of coverage of approximately 200x for version 1 and 350x for version 2. Reads are
aligned using BWA, flagged for duplicate read pairs using Picard Tools, and locally realigned
using GATK. Sequence mutations are called using MuTect for SNVs and GATK
SomaticIndelDetector for small indels. Putative germline variants are filtered out using a panel
of historical normals or if present in ESP at a frequency >= .1%, unless the variant is also
present in COSMIC. Copy number alterations are called using a custom pipeline and reported
for fold-change >1. Structural rearrangements are called using BreaKmer. Testing is performed
for all patients across all solid tumor types.
Institut Gustave Roussy (GRCC)
Gustave Roussy Cancer Centre submitted data includes somatic variants (single nucleotide
variants and small indels) identified with Cancer Hotspot Panel v2 from tumor-only sequence
data. Two versions of the panel have been used: CHP2 covering hotspots in 50 genes, and
MOSC3 covering hotspots in 74 genes. Tumors are sequenced to an average unique depth of
coverage of >500X. The sequencing data were analyzed with the Torrent Suite Variant Caller
4.2 software and reported somatic variants were compared with the reference genome hg19.
The variants were called if >5 reads supported the variant and/or total base depth >50 and/or
variant allele frequency >1% was observed. All the variants identified were visually controlled on
.bam files using Alamut v2.4.2 software (Interactive Biosoftware). All the germline variants found
in 1000 Genomes Project or ESP (Exome Sequencing Project database) with frequency >0.1%
were removed. All somatic mutations were annotated, sorted, and interpreted by an expert
molecular biologist according to available databases (COSMIC, TCGA) and medical literature.
The submitted data set was obtained from selected patients that were included in the
MOSCATO-01 trial (MOlecular Screening for CAncer Treatment Optimisation). This trial
collected on-purpose tumour samples (from the primary or from a metastatic site) that are
immediately fresh-frozen, and subsequently analysed for targeted gene panel sequencing.
Tumour cellularity was assessed by a senior pathologist on a haematoxylin and eosin slide from
the same biopsy core to ensure tumor cellularity of at least 10%.
University of Texas MD Anderson Cancer Center MD Anderson Cancer Center (MDA)
MD Anderson Cancer Center submitted data in the current data set includes sequence variants
(small indels and point mutations) identified using an amplicon-based targeted hotspot tumoronly assay. Two different amplicon pools and pipeline versions are included: a 46-gene assay
(MDA-46) corresponding to customized version of AmpliSeq Cancer Hotspot Panel, v1 (Life
Technologies), and a 50-gene assay (MDA-50) corresponding to the AmpliSeq Hotspot Panel
v2. DNA was extracted from unstained sections of tissue paired with an stained section that
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was used to ensure adequate tumor cellularity (human assessment > 20%) and marking of the
tumor region of interest (macrodissection). Sequencing was performed on an Ion Torrent PGM
using 318 chip, 260 flows. Tumors were sequenced to a minimum depth of coverage (per
amplicon) of approximately 250X. Bioinformatics pipeline for MDA-46 was executed using
TorrentSuite 2.0.1 signal processing, basecalling, alignment and variant calling. For MDA-50,
TorrentSuite 3.6 was used. Initial calls were made by Torrent Variant Caller (TVC) using lowstringency somatic parameters.
All called variants were parsed into a custom annotation & reporting system, OncoSeek, with a
back-end SQL Server database using a convergent data model for all sequencing platforms
used by the laboratory. Calls were reviewed with initial low stringency to help ensure that low
effective tumor cellularity samples do not get reported as false negative samples. Nominal
variant filters (5% variant allelic frequency minimum, 25 variant coverage minimum) can then be
applied dynamically. Clinical sequencing reports were generated using OncoSeek to transform
genomic representations into HGVS nomenclature. To create VCF files for this project,
unfiltered low stringency VCF files were computationally cross checked against a regular
expressions based variant extract from clinical reports. Only cases where all extracted variants
from the clinical report were deterministically mappable to the unfiltered VCF file and
corresponding genomic coordinates were marked for inclusion in this dataset. This filters a
small number of cases where complex indels may not have originally been called correctly at
the VCF level. Testing is performed for patients with advanced metastatic cancer across all
solid tumor types.
Memorial Sloan Kettering Cancer Center (MSK)
MSK uses a custom, hybridization-based capture panel (MSK-IMPACT) to detect single
nucleotide variants, small indels, copy number alterations, and structural variants from matched
tumor-normal sequence data. Two versions of the panel have been submitted to GENIE:
version 1 containing 341 genes, and version 2 containing 410 genes. Specimens are reviewed
by a pathologist to ensure tumor cellularity of at least 10%. Tumors are sequenced to an
average unique depth of coverage of approximately 750X. Reads are aligned using BWA,
flagged for duplicate read pairs using GATK, and locally realigned using ABRA. Sequence
mutations are called using MuTect and reported for >5% allele frequency (novel variants) or
>2% allele frequency (recurrent hotspots). Copy number alterations are called using a custom
pipeline and reported for fold-change >2. Structural rearrangements are called using Delly. All
somatic mutations are reported without regard to biological function. Testing is performed for
patients with advanced metastatic cancer across all solid tumor types.
Johns Hopkins Sidney Kimmel Comprehensive Cancer Center (JHU)
Johns Hopkins submitted genomic data from the Ion AmpliSeq Cancer Hotspot Panel v2, which
detects mutations in cancer hotspots from tumor-only analysis. Data from a single panel
(JHU_50GP_V1) covering frequently mutated regions in 50 genes was submitted to GENIE.

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Pathologist inspection of an H&E section ensured adequate tumor cellularity (approximately
10% or greater). DNA was extracted from the macro-dissected FFPE tumor region of interest.
Tumors are sequenced to an average unique read depth of coverage of greater than 500X. For
alignment the TMAP aligner developed by Life Technology for the Ion Torrent sequencing
platform is used to align to hg19/GRCh37 using the manufacturer’s suggested settings. Tumor
variants are called with a variety of tools. Samtools mpileup is run on the aligned .bam file and
then processed with custom perl scripts (via a naive variant caller) to identify SNV and INS/DEL.
Specimen variant filters have a total read depth filter of >= 100, a variant allele coverage of >=
10, variant allele frequency for substitutions >= 0.05, variant allele frequency for small (less than
50 base pair) insertions or deletions >= 0.05, and "strand bias" of total reads and of variant
alleles are both less than 2-fold when comparing forward and reverse reads. Additionally,
variants seen in greater than 20% of a set of non-neoplastic control tissues (>3 of 16 samples)
with the same filter criteria are excluded. Finally, variants documented as “common” in dbSNP
and not known to COSMIC are excluded. The cohort includes both primary and metastatic
lesions and some repeated sampling of the same patient.
Princess Margaret Cancer Centre, University Health Network (UHN)
Princess Margaret Cancer Centre uses the TruSeq Amplicon Cancer Panel (TSACP, Illumina)
to detect single nucleotide variants and small indels from matched tumor-normal sequencing
data. Specimens are reviewed by a pathologist to ensure tumor cellularity of at least 20%.
Tumors are sequenced to an average unique depth of coverage of approximately 500x and
normal blood samples to 100x. Reads are aligned to hg19 using BWA mem version 0.7.12
followed by local realignment and BQSR using GATK v3.3.0. Somatic sequence mutations are
called using MuTect (v1.1.5) for SNVs and Varscan (v2.3.8) using both normal and tumor data.
Default settings for this version of VarScan result in a minimum variant frequency of 20%. Data
are filtered to ensure there are no variants included with frequency of 3% or more in the normal
sample. SNV results are filtered to keep only those with tumor variant allele frequency of at least
10%. Testing is performed for patients with advanced disease from select tumor types.
Vanderbilt-Ingram Cancer Center (VICC)

VICC uses Illumina hybridization-based capture panels from Foundation Medicine to detect
single nucleotide variants, small indels, copy number alterations and structural variants from
tumor-only sequencing data. Two gene panels were used: Panel 1 (T5a bait set), covering 326
genes and; and Panel 2 (T7 bait set), covering 434 genes. DNA was extracted from unstained
FFPE sections, and H&E stained sections were used to ensure nucleated cellularity ≥ 80% and
tumor cellularity ≥ 20%, with use of macro-dissection to enrich samples with ≤20% tumor
content. A pool of 5’-biotinylated DNA 120bp oligonucleotides were designed as baits with 60 bp
overlap in targeted exon regions and 20bp overlap in targeted introns with a minimum of 3 baits
per target and 1 bait per SNP target. The goal was a depth of sequencing between 750x and
1000x. Mapping to the reference genome was accomplished using BWA, local alignment
optimizations with GATK, and PCR duplicate read removal and sequence metric collection with

8

Picard and Samtools. A Bayesian methodology incorporating tissue-specific prior expectations
allowed for detection of novel somatic mutations at low MAF and increased sensitivity at
hotspots. Final single nucleotide variant (SNV) calls were made at MAF≥ 5% (MAF≥ 1% at
hotspots) with filtering for strand bias, read location bias and presence of two or more normal
controls. Indels were detected using the deBrujn approach of de novo local assembly within
each targeted exon and through direct read alignment and then filtered as described for SNVs.
Copy number alterations were detected utilizing a comparative genomic hybridization-like
method to obtain a log-ratio profile of the sample to estimate tumor purity and copy number.
Absolute copy number was assigned to segments based on Gibbs sampling. To detect gene
fusions, chimeric read pairs were clustered by genomic coordinates and clusters containing at
least 10 chimeric pairs were identified as rearrangement candidates. Rare tumors and
metastatic samples were prioritized for sequencing, but ultimately sequencing was at the
clinician’s discretion.
Netherlands Cancer Center (NKI), The Netherlands
NKI uses Illumina TruSeq Amplicon – Cancer Panel (TSACP) to detect known cancer hotspots
from tumor-only sequencing data. A single gene panel, NKI-TSACP covering known hotspots in
48 genes with 212 amplicons has been used. Specimens are reviewed by a pathologist to
ensure tumor cellularity of at least 10%. Tumors are sequenced to an average unique depth of
coverage of approximately 4000x. The sample plate and sample sheet are made using the
Illumina Experiment Manager software before running the sample on the MiSeq Sequencing
System (Illumina, SY-410-1003) and MiSeq Reporter (v2.5) is used for data analysis. Reads are
aligned using Banded Smith Waterman (v2.5.1.3), and samtools is used to further sort and
index the BAM files. Variant calling is performed via the Illumina somatic variant caller
(v3.5.2.1). Further detailed variant analysis (e.g. removal of known artifacts, known benign
SNPs and variants with read depth < 200 or VAF < 0.05 and manual classification) is performed
via Cartagenia BenchLab (https://cartagenia.com/). Testing is performed for all patients across
all solid tumor types.

Pipeline for Annotating Mutations and Filtering
Putative Germline SNPs
Contributing GENIE centers provided mutation data in Variant Call Format (VCF v4.x,
samtools.github.io/hts-specs) or Mutation Annotation Format (MAF v2.x,
wiki.nci.nih.gov/x/eJaPAQ) with additional fields for read counts supporting variant alleles,
reference alleles, and total depth. Some “MAF-like” text files with minimal required columns
(github.com/mskcc/vcf2maf/blob/v1.6.12/data/minimalist_test_maf.tsv) were also received from

9

the participating centers. These various input formats were converted into a complete tabseparated MAF v2.4 format, with a standardized set of additional columns
(github.com/mskcc/vcf2maf/blob/v1.6.12/docs/vep_maf_readme.txt) using either vcf2maf or
maf2maf v1.6.12 (github.com/mskcc/vcf2maf/tree/v1.6.12), wrappers around the Variant Effect
Predictor (VEP v86, gist.github.com/ckandoth/f265ea7c59a880e28b1e533a6e935697). The
vcf2maf “custom-enst” option overrode VEP’s canonical isoform for most genes, with Uniprot’s
canonical isoform (github.com/mskcc/vcf2maf/blob/v1.6.12/data/isoform_overrides_uniprot).
While the GENIE data available from Sage contains all mutation data, the following mutation
types are automatically filtered upon import into the cBioPortal (http://www.cbioportal.org/genie):
Silent, Intronic, 3’ UTR, 3’ Flank, 5’ UTR, 5’ Flank and Intergenic region (IGR).
Six of the eight GENIE participating centers performed tumor-only sequencing i.e. without also
sequencing a patient-matched control sample like blood, to isolate somatic events. These
centers minimized artifacts and germline events using pooled controls from unrelated
individuals, or using databases of known artifacts, common germline variants, and recurrent
somatic mutations. However, there remains a risk that such centers may inadvertently release
germline variants that can theoretically be used for patient re-identification. To minimize this
risk, the GENIE consortium developed a stringent germline filtering pipeline, and applied it
uniformly to all variants across all centers. This pipeline flags sufficiently recurrent artifacts and
germline
events
reported
by
the
Exome
Aggregation
Consortium
(ExAC,
http://exac.broadinstitute.org). Specifically, the non-TCGA subset VCF of ExAC 0.3.1 was used
after
excluding
known
somatic
events
in
https://github.com/mskcc/vcf2maf/blob/v1.6.12/data/known_somatic_sites.bed, based on:
●
●
●

Hotspots from Chang et al. minus some likely artifacts (dx.doi.org/10.1038/nbt.3391).
Somatic mutations associated with clonal hematopoietic expansion from Xie et al.
(dx.doi.org/10.1038/nm.3733).
Somatically mutable germline sites at MSH6:F1088, TP53:R290, TERT:E280,
ASXL1:G645_G646.

The resulting VCF was used with vcf2maf’s “filter-vcf” option, to match each variant position and
allele to per-subpopulation allele counts. If a variant was seen more than 10 times in any of the
7 ExAC subpopulations, it was tagged as a “common_variant” (vcf2maf’s “max-filter-ac” option),
and subsequently removed. This >10 allele count (AC) cutoff was selected because it tagged no
more than 1% of the somatic calls across all MSK-IMPACT samples with patient-matched
controls.

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Description of Data Files
The following is a summary of all data files available in the release.
Table 4: GENIE Data Files.
File Name

Description

Details

data_mutations_extended.txt

Mutation data.

For a description of the MAF file
format, see:
https://wiki.nci.nih.gov/display/T
CGA/Mutation+Annotation+Form
at+(MAF)+Specification

Tab-delimited Mutation
Annotation Format (MAF).

data_CNA.txt

Discretized copy number data.
Tab-delimited: rows represent
genes, columns represent
individual samples.
Note: not all centers contributed
copy number data to GENIE.

data_fusions.txt

Structural variant data.
Tab-delimited: rows represent
individual structural variants
identified in samples, columns
represent variant details.
Note: not all centers contributed
structural rearrangement data to
GENIE.

-2: deep loss, possibly a
homozygous deletion
-1: single-copy loss
(heterozygous deletion)
0: diploid
1: low-level gain
2: high-level amplification.

HUGO_SYMBOL: HUGO gene
symbol.
CENTER: GENIE center.
TUMOR_SAMPLE_BARCODE:
GENIE Sample ID.
FUSION: A description of the
fusion, e.g., "TMPRSS2-ERG
fusion".
DNA_SUPPORT: Fusion
detected from DNA sequence
data, "yes" or "no".
RNA_SUPPORT: Fusion
detected from RNA sequence
data, "yes" or "no".
FRAME: "in-frame" or

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"frameshift".

genie_combined.bed

Combined BED file describing
genomic coordinates covered by
all platforms contributed to
GENIE.

genie_data_cna_hg19.seg

Segmented copy number data.
Tab-delimited: rows represent
copy number events within
samples, columns represent
genomic coordinates and
continuous copy number values.

For a description of the BED file
format, see:
https://genome.ucsc.edu/FAQ/F
AQformat#format1

Note: not all centers contributed
segmented copy number data to
GENIE.
data_clinical.txt

De-identified tier 1 clinical data.

See Clinical Data section below
for more details.

Tab-delimited: rows represent
samples, columns represent deidentified clinical attributes.

Clinical Data
A limited set of Tier 1 clinical data have been submitted by each center to provide clinical
context to the genomic results (Table 5). Additional clinical data elements, including staging,
treatments, and outcomes will be added in the future. When possible the clinical data are
collected at the institutions in a fashion that can be mapped to established oncology data
specifications, such as the North American Association of Central Cancer Registrars
(NAACCR).
Table 5: GENIE Tier 1 Clinical Data Fields.
Data Element

Example Values

Data Description

AGE_AT_SEQ_REPORT

Integer values, <18 or >89.

The age of the patient at the time that the
sequencing results were reported. Age is
masked for patients aged 90 years and
greater and for patients under 18 years.

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CENTER

DFCI
GRCC
JHU
MSK
NKI
UHN
MDA
VICC

The center submitting the clinical and
genomic data.

ETHNICITY

Non-Spanish/non-Hispanic
Spanish/Hispanic
Unknown

Indication of Spanish/Hispanic origin of the
patient; this data element maps to the
NAACCR v16, Element #190. Institutions
not collecting Spanish/Hispanic origin have
set this column to Unknown.

ONCOTREE_CODE

LUAD

The primary cancer diagnosis code based
on the OncoTree ontology
(http://cbioportal.org/oncotree).

PATIENT_ID

GENIE-JHU-1234

The unique, anonymized patient identifier
for the GENIE project. Conforms to the
following the convention: GENIECENTER-1234. The first component is the
string, "GENIE"; the second component is
the Center abbreviation. The third
component is an anonymized unique
identifier for the patient.

PRIMARY_RACE

Asian
Black
Native American
Other
Unknown
White

The primary race recorded for the patient;
this data element maps to the NAACCR
v16, Element #160. For institutions
collecting more than one race category,
this race code is the primary race for the
patient. Institutions not collecting race
have set this field to Unknown..

SAMPLE_ID

GENIE-JHU-1234-9876

The unique, anonymized sample identifier
for the GENIE project. Conforms to the
following the convention: GENIECENTER-1234-9876. The first component
is the string, "GENIE"; the second
component is the Center abbreviation.
The third component is an anonymized,
unique patient identifier. The fourth
component is a unique identifier for the

13

sample that will distinguish between two or
more specimens from a single patient.
SAMPLE_TYPE

Primary
Metastasis
Unspecified

Sample type, e.g. Primary or Metastasis.

SEQ_ASSAY_ID

DFCI-ONCOPANEL-1
DFCI-ONCOPANEL-2
MSK-IMPACT341
MSK-IMPACT410

The institutional assay identifier for
genomic testing platform. Components are
separated by hyphens, with the first
component corresponding to the Center's
abbreviation. All specimens tested by the
same platform should have the same
identifier.

SEX

Female
Male

The patient’s sex code; this data element
maps to the NAACCR v16, Element #220.

CANCER_TYPE

Non-Small Cell Lung Cancer The primary cancer diagnosis “main type”,
based on the OncoTree ontology
(http://cbioportal.org/oncotree). For
example, the OncoTree code of LUAD
maps to: “Non-Small Cell Lung Cancer”.

CANCER_TYPE_DETAILED Lung Adenocarcinoma

The primary cancer diagnosis label, based
on the OncoTree ontology
(http://cbioportal.org/oncotree). For
example, the OncoTree code of LUAD
maps to the label: “Lung Adenocarcinoma
(LUAD)”

Cancer types are reported using the OncoTree ontology (http://oncotree.mskcc.org/oncotree/),
originally developed at Memorial Sloan Kettering. Version 1.0 of GENIE uses the OncoTree
specification from August 18, 2016, containing diagnosis codes for 524 tumor types from 32 tissues.
The centers participating in GENIE applied the OncoTree cancer types to the tested specimens in a
variety of methods depending on center-specific workflows. A brief description of how the cancer
type assignment process for each center is specified in Table 6.

Table 6: Center Strategies for OncoTree Assignment.
Center

OncoTree Cancer Type Assignments

14

DFCI

Molecular pathologists assigned diagnosis and mapped to OncoTree cancer type.

GRCC

OncoTree cancer types were mapped from ICD-O codes.

JHU

Molecular pathologists assigned diagnosis and mapped to OncoTree cancer type.

MSK

Molecular pathologists assigned diagnosis and mapped to OncoTree cancer type.

NKI

Molecular pathologists assigned diagnosis and mapped to OncoTree cancer type.

UHN

Original diagnosis from pathologist was mapped to OncoTree diagnosis by
medical oncologist and research manager.

MDA

OncoTree cancer types were mapped from ICD-O codes.

VICC

OncoTree cancer types were mapped from ICD-O codes. If no ICD-O code was
available, research manager mapped pathologist and/or medical oncologist
diagnosis to OncoTree cancer type.

Abbreviations and Acronym Glossary
Abbreviation

Full Term

AACR

American Association for Cancer Research

CNA

Copy number alterations

CNV

Copy number variants

DFCI

Dana-Farber Cancer Institute

FFPE

Formalin-fixed, paraffin-embedded

GENIE

Genomics, Evidence, Neoplasia, Information, Exchange

GRCC

Institut Gustave Roussy

HIPAA

Health Insurance Portability and Accountability Act

IRB

Institutional Review Board

JHU

Johns Hopkins Sidney Kimmel Comprehensive Cancer Center

15

MAF

Mutation annotation format

MDA

M.D. Anderson Cancer Center

MSK

Memorial Sloan Kettering Cancer Center

NAACCR

North American Association of Central Cancer Registries

NGS

Next-generation sequencing

NKI

Netherlands Cancer Institute

PCR

Polymerase chain reaction

PHI

Protected Health Information

SNP

Single-nucleotide polymorphism

SNV

Single-nucleotide variants

UHN

Princess Margaret Cancer Centre, University Health Network

VICC

Vanderbilt-Ingram Cancer Center

16



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