Abstract
Background: The landscape of cancer-predisposing genes has been extensively investigated in the last 30 years with various methodologies ranging from candidate gene to genome-wide association studies. However, sequencing data are still poorly exploited in cancer predisposition studies due to the lack of statistical power when comparing millions of variants at once. Method: To overcome these power limitations, we propose a knowledge-based framework founded on the characteristics of known cancer-predisposing variants and genes. Under our framework, we took advantage of a combination of previously generated datasets of sequencing experiments to identify novel breast cancer-predisposing variants, comparing the normal genomes of 673 breast cancer patients of European origin against 27,173 controls matched by ethnicity. Results: We detected several expected variants on known breast cancer-predisposing genes, like BRCA1 and BRCA2, and 11 variants on genes associated with other cancer types, like RET and AKT1. Furthermore, we detected 183 variants that overlap with somatic mutations in cancer and 41 variants associated with 38 possible loss-of-function genes, including PIK3CB and KMT2C. Finally, we found a set of 19 variants that are potentially pathogenic, negatively correlate with age at onset, and have never been associated with breast cancer. Conclusions: In this study, we demonstrate the usefulness of a genomic-driven approach nested in a classic case-control study to prioritize cancer-predisposing variants. In addition, we provide a resource containing variants that may affect susceptibility to breast cancer.
| Original language | English |
|---|---|
| Article number | 63 |
| Journal | Breast Cancer Research |
| Volume | 19 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - May 31 2017 |
Fingerprint
Keywords
- Breast cancer
- Germline mutations
- Predisposition
- Somatic mutations
ASJC Scopus subject areas
- Oncology
- Cancer Research
Cite this
A knowledge-based framework for the discovery of cancer-predisposing variants using large-scale sequencing breast cancer data. / Melloni, Giorgio E.M.; Mazzarella, Luca; Bernard, Loris; Bodini, Margherita; Russo, Anna; Luzi, Lucilla; Pelicci, Pier Giuseppe; Riva, Laura.
In: Breast Cancer Research, Vol. 19, No. 1, 63, 31.05.2017.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A knowledge-based framework for the discovery of cancer-predisposing variants using large-scale sequencing breast cancer data
AU - Melloni, Giorgio E.M.
AU - Mazzarella, Luca
AU - Bernard, Loris
AU - Bodini, Margherita
AU - Russo, Anna
AU - Luzi, Lucilla
AU - Pelicci, Pier Giuseppe
AU - Riva, Laura
PY - 2017/5/31
Y1 - 2017/5/31
N2 - Background: The landscape of cancer-predisposing genes has been extensively investigated in the last 30 years with various methodologies ranging from candidate gene to genome-wide association studies. However, sequencing data are still poorly exploited in cancer predisposition studies due to the lack of statistical power when comparing millions of variants at once. Method: To overcome these power limitations, we propose a knowledge-based framework founded on the characteristics of known cancer-predisposing variants and genes. Under our framework, we took advantage of a combination of previously generated datasets of sequencing experiments to identify novel breast cancer-predisposing variants, comparing the normal genomes of 673 breast cancer patients of European origin against 27,173 controls matched by ethnicity. Results: We detected several expected variants on known breast cancer-predisposing genes, like BRCA1 and BRCA2, and 11 variants on genes associated with other cancer types, like RET and AKT1. Furthermore, we detected 183 variants that overlap with somatic mutations in cancer and 41 variants associated with 38 possible loss-of-function genes, including PIK3CB and KMT2C. Finally, we found a set of 19 variants that are potentially pathogenic, negatively correlate with age at onset, and have never been associated with breast cancer. Conclusions: In this study, we demonstrate the usefulness of a genomic-driven approach nested in a classic case-control study to prioritize cancer-predisposing variants. In addition, we provide a resource containing variants that may affect susceptibility to breast cancer.
AB - Background: The landscape of cancer-predisposing genes has been extensively investigated in the last 30 years with various methodologies ranging from candidate gene to genome-wide association studies. However, sequencing data are still poorly exploited in cancer predisposition studies due to the lack of statistical power when comparing millions of variants at once. Method: To overcome these power limitations, we propose a knowledge-based framework founded on the characteristics of known cancer-predisposing variants and genes. Under our framework, we took advantage of a combination of previously generated datasets of sequencing experiments to identify novel breast cancer-predisposing variants, comparing the normal genomes of 673 breast cancer patients of European origin against 27,173 controls matched by ethnicity. Results: We detected several expected variants on known breast cancer-predisposing genes, like BRCA1 and BRCA2, and 11 variants on genes associated with other cancer types, like RET and AKT1. Furthermore, we detected 183 variants that overlap with somatic mutations in cancer and 41 variants associated with 38 possible loss-of-function genes, including PIK3CB and KMT2C. Finally, we found a set of 19 variants that are potentially pathogenic, negatively correlate with age at onset, and have never been associated with breast cancer. Conclusions: In this study, we demonstrate the usefulness of a genomic-driven approach nested in a classic case-control study to prioritize cancer-predisposing variants. In addition, we provide a resource containing variants that may affect susceptibility to breast cancer.
KW - Breast cancer
KW - Germline mutations
KW - Predisposition
KW - Somatic mutations
UR - http://www.scopus.com/inward/record.url?scp=85020050259&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85020050259&partnerID=8YFLogxK
U2 - 10.1186/s13058-017-0854-1
DO - 10.1186/s13058-017-0854-1
M3 - Article
AN - SCOPUS:85020050259
VL - 19
JO - Breast Cancer Research
JF - Breast Cancer Research
SN - 1465-5411
IS - 1
M1 - 63
ER -