Support vector machine classifier for estrogen receptor positive and negative early-onset breast cancer
Support vector machine classifier for estrogen receptor positive and negative early-onset breast cancer
Two major breast cancer sub-types are defined by the expression of estrogen receptors on tumour cells. Cancers with large numbers of receptors are termed estrogen receptor positive and those with few are estrogen receptor negative. Using genome-wide single nucleotide polymorphism genotype data for a sample of early-onset breast cancer patients we developed a Support Vector Machine (SVM) classifier from 200 germline variants associated with estrogen receptor status (p<0.0005). Using a linear kernel Support Vector Machine, we achieved classification accuracy exceeding 93%. The model indicates that polygenic variation in more than 100 genes is likely to underlie the estrogen receptor phenotype in early-onset breast cancer. Functional classification of the genes involved identifies enrichment of functions linked to the immune system, which is consistent with the current understanding of the biological role of estrogen receptors in breast cancer.
Age of Onset, Biomarkers, Tumor/genetics, Breast Neoplasms/diagnosis, Female, Gene Expression Profiling, Humans, Molecular Sequence Annotation, Polymorphism, Single Nucleotide, ROC Curve, Receptors, Estrogen/genetics, Support Vector Machine
e68606
Upstill-Goddard, Rosanna
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Eccles, Diana
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Ennis, Sarah
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Rafiq, Sajjad
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Tapper, William
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Fliege, Joerg
54978787-a271-4f70-8494-3c701c893d98
Collins, Andrew
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19 July 2013
Upstill-Goddard, Rosanna
8257134d-5d86-4b71-9efb-7c86d6c41117
Eccles, Diana
5b59bc73-11c9-4cf0-a9d5-7a8e523eee23
Ennis, Sarah
7b57f188-9d91-4beb-b217-09856146f1e9
Rafiq, Sajjad
54722709-929f-4faa-b4d9-863d4d563056
Tapper, William
9d5ddc92-a8dd-4c78-ac67-c5867b62724c
Fliege, Joerg
54978787-a271-4f70-8494-3c701c893d98
Collins, Andrew
dfaf2088-2c1c-44b3-a347-c18b66a2082d
Upstill-Goddard, Rosanna, Eccles, Diana, Ennis, Sarah, Rafiq, Sajjad, Tapper, William, Fliege, Joerg and Collins, Andrew
(2013)
Support vector machine classifier for estrogen receptor positive and negative early-onset breast cancer.
PLoS ONE, 8 (7), .
(doi:10.1371/journal.pone.0068606).
Abstract
Two major breast cancer sub-types are defined by the expression of estrogen receptors on tumour cells. Cancers with large numbers of receptors are termed estrogen receptor positive and those with few are estrogen receptor negative. Using genome-wide single nucleotide polymorphism genotype data for a sample of early-onset breast cancer patients we developed a Support Vector Machine (SVM) classifier from 200 germline variants associated with estrogen receptor status (p<0.0005). Using a linear kernel Support Vector Machine, we achieved classification accuracy exceeding 93%. The model indicates that polygenic variation in more than 100 genes is likely to underlie the estrogen receptor phenotype in early-onset breast cancer. Functional classification of the genes involved identifies enrichment of functions linked to the immune system, which is consistent with the current understanding of the biological role of estrogen receptors in breast cancer.
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Published date: 19 July 2013
Keywords:
Age of Onset, Biomarkers, Tumor/genetics, Breast Neoplasms/diagnosis, Female, Gene Expression Profiling, Humans, Molecular Sequence Annotation, Polymorphism, Single Nucleotide, ROC Curve, Receptors, Estrogen/genetics, Support Vector Machine
Organisations:
Cancer Sciences, Operational Research
Identifiers
Local EPrints ID: 354858
URI: http://eprints.soton.ac.uk/id/eprint/354858
ISSN: 1932-6203
PURE UUID: 79ddd993-4e8c-4cf7-8d87-441aedeece21
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Date deposited: 29 Jul 2013 13:23
Last modified: 15 Mar 2024 03:30
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Contributors
Author:
Rosanna Upstill-Goddard
Author:
Sajjad Rafiq
Author:
Andrew Collins
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