Abstract
Background: The clinical sequencing of cancer genomes to personalize therapy is becoming routine across the world. However, concerns over patient re-identification from these data lead to questions about how tightly access should be controlled. It is not thought to be possible to re-identify patients from somatic variant data. However, somatic variant detection pipelines can mistakenly identify germline variants as somatic ones, a process called "germline leakage". The rate of germline leakage across different somatic variant detection pipelines is not well-understood, and it is uncertain whether or not somatic variant calls should be considered re-identifiable. To fill this gap, we quantified germline leakage across 259 sets of whole-genome somatic single nucleotide variant (SNVs) predictions made by 21 teams as part of the ICGC-TCGA DREAM Somatic Mutation Calling Challenge. Results: The median somatic SNV prediction set contained 4325 somatic SNVs and leaked one germline polymorphism. The level of germline leakage was inversely correlated with somatic SNV prediction accuracy and positively correlated with the amount of infiltrating normal cells. The specific germline variants leaked differed by tumour and algorithm. To aid in quantitation and correction of leakage, we created a tool, called GermlineFilter, for use in public-facing somatic SNV databases. Conclusions: The potential for patient re-identification from leaked germline variants in somatic SNV predictions has led to divergent open data access policies, based on different assessments of the risks. Indeed, a single, well-publicized re-identification event could reshape public perceptions of the values of genomic data sharing. We find that modern somatic SNV prediction pipelines have low germline-leakage rates, which can be further reduced, especially for cloud-sharing, using pre-filtering software.
| Original language | English (US) |
|---|---|
| Article number | 28 |
| Journal | BMC Bioinformatics |
| Volume | 19 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 31 2018 |
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Keywords
- Cancer genomics
- Germline contamination
- Germline leakage
- Mutation calling
- Next-generation sequencing
- Patient identifiability
- Single nucleotide variant
- SNV
ASJC Scopus subject areas
- Structural Biology
- Biochemistry
- Molecular Biology
- Computer Science Applications
- Applied Mathematics
Cite this
Germline contamination and leakage in whole genome somatic single nucleotide variant detection. / Sendorek, Dorota H.; Caloian, Cristian; Ellrott, Kyle; Bare, J. Christopher; Yamaguchi, Takafumi N.; Ewing, Adam D.; Houlahan, Kathleen E.; Norman, Thea C.; Margolin, Adam; Stuart, Joshua M.; Boutros, Paul C.
In: BMC Bioinformatics, Vol. 19, No. 1, 28, 31.01.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Germline contamination and leakage in whole genome somatic single nucleotide variant detection
AU - Sendorek, Dorota H.
AU - Caloian, Cristian
AU - Ellrott, Kyle
AU - Bare, J. Christopher
AU - Yamaguchi, Takafumi N.
AU - Ewing, Adam D.
AU - Houlahan, Kathleen E.
AU - Norman, Thea C.
AU - Margolin, Adam
AU - Stuart, Joshua M.
AU - Boutros, Paul C.
PY - 2018/1/31
Y1 - 2018/1/31
N2 - Background: The clinical sequencing of cancer genomes to personalize therapy is becoming routine across the world. However, concerns over patient re-identification from these data lead to questions about how tightly access should be controlled. It is not thought to be possible to re-identify patients from somatic variant data. However, somatic variant detection pipelines can mistakenly identify germline variants as somatic ones, a process called "germline leakage". The rate of germline leakage across different somatic variant detection pipelines is not well-understood, and it is uncertain whether or not somatic variant calls should be considered re-identifiable. To fill this gap, we quantified germline leakage across 259 sets of whole-genome somatic single nucleotide variant (SNVs) predictions made by 21 teams as part of the ICGC-TCGA DREAM Somatic Mutation Calling Challenge. Results: The median somatic SNV prediction set contained 4325 somatic SNVs and leaked one germline polymorphism. The level of germline leakage was inversely correlated with somatic SNV prediction accuracy and positively correlated with the amount of infiltrating normal cells. The specific germline variants leaked differed by tumour and algorithm. To aid in quantitation and correction of leakage, we created a tool, called GermlineFilter, for use in public-facing somatic SNV databases. Conclusions: The potential for patient re-identification from leaked germline variants in somatic SNV predictions has led to divergent open data access policies, based on different assessments of the risks. Indeed, a single, well-publicized re-identification event could reshape public perceptions of the values of genomic data sharing. We find that modern somatic SNV prediction pipelines have low germline-leakage rates, which can be further reduced, especially for cloud-sharing, using pre-filtering software.
AB - Background: The clinical sequencing of cancer genomes to personalize therapy is becoming routine across the world. However, concerns over patient re-identification from these data lead to questions about how tightly access should be controlled. It is not thought to be possible to re-identify patients from somatic variant data. However, somatic variant detection pipelines can mistakenly identify germline variants as somatic ones, a process called "germline leakage". The rate of germline leakage across different somatic variant detection pipelines is not well-understood, and it is uncertain whether or not somatic variant calls should be considered re-identifiable. To fill this gap, we quantified germline leakage across 259 sets of whole-genome somatic single nucleotide variant (SNVs) predictions made by 21 teams as part of the ICGC-TCGA DREAM Somatic Mutation Calling Challenge. Results: The median somatic SNV prediction set contained 4325 somatic SNVs and leaked one germline polymorphism. The level of germline leakage was inversely correlated with somatic SNV prediction accuracy and positively correlated with the amount of infiltrating normal cells. The specific germline variants leaked differed by tumour and algorithm. To aid in quantitation and correction of leakage, we created a tool, called GermlineFilter, for use in public-facing somatic SNV databases. Conclusions: The potential for patient re-identification from leaked germline variants in somatic SNV predictions has led to divergent open data access policies, based on different assessments of the risks. Indeed, a single, well-publicized re-identification event could reshape public perceptions of the values of genomic data sharing. We find that modern somatic SNV prediction pipelines have low germline-leakage rates, which can be further reduced, especially for cloud-sharing, using pre-filtering software.
KW - Cancer genomics
KW - Germline contamination
KW - Germline leakage
KW - Mutation calling
KW - Next-generation sequencing
KW - Patient identifiability
KW - Single nucleotide variant
KW - SNV
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UR - http://www.scopus.com/inward/citedby.url?scp=85041452329&partnerID=8YFLogxK
U2 - 10.1186/s12859-018-2046-0
DO - 10.1186/s12859-018-2046-0
M3 - Article
C2 - 29385983
AN - SCOPUS:85041452329
VL - 19
JO - BMC Bioinformatics
JF - BMC Bioinformatics
SN - 1471-2105
IS - 1
M1 - 28
ER -