The Science Behind The Test

OncoVue was developed from the analysis of 117 genetic markers in candidate genes likely to influence breast carcinogenesis in a large case-control study from 6 geographic regions in the United States. The criteria for selecting these candidate gene markers required that:

They have a demonstrated role in major tumorigenic pathways and that the variant alleles produce functional changes in the gene coding or regulatory regions

They produce non-synonymous amino acid changes with the potential to alter enzymatic activity in protein coding region variants

They have the potential to cause alterations in mRNA transcription rates or alter stability in regulatory sequence in promoter or 3’untranslated regions

They have published associations with increased or decreased risk of breast and/or other cancers

The minor allele frequency had to be common across major ethnic groups in the population. The minor allele frequency of the SNPs used in OncoVue range from 7% to 49% with mean and median allele frequencies of 30% and 32%, respectively. For reference, in the general female population of the U.S. the frequency of carriers of any BRCA mutation is estimated at 0.2%.1

Breast cancer is a complex disease. The associations between these genes and breast cancer risk was assessed by systematically applying a multivariate logistic regression (MLR) modeling process to a population of 5022 Caucasian women (1671 breast cancer cases and 3351 cancer-free controls) ranging in age from 30-69. The model was then validated in two independent populations. The first validation population consisted of 1193 Caucasian women (400 cases and 793 controls) and the second was an ethnically distinct population of 581 African American women (164 cases and 417 controls) (Table 1).

Table 1. Model Building and Validation Populations


Cases

Controls

Total

Model Building

1671

3351

5022

Validation 1

400

793

1193

Validation 2

164

417

581

Based on our recent demonstration of age-specific association of steroid hormone gene polymorphisms with breast cancer risk2, both age invariant and age interactive analyses were utilized. To develop OncoVue, the data on genetic polymorphisms was combined with recognized clinical risk information and analyzed using MLR modeling.3 The relative risks obtained from this analysis are converted to absolute risk estimates based upon competing causes of mortality and other age adjusted risks4 and then 95% confidence intervals are calculated to provide a range of risk estimates for each result.5 The OncoVue model contains three integrated components employing 22 SNPs along with the clinical risk questions used in the NCI Breast Cancer Risk Assessment Tool (Gail Model).6,7 The SNPs in OncoVue are found in genes involved in various biological pathways such as steroid hormone metabolism, DNA repair (including a BRCA1 interacting protein), cell cycle control, apoptosis, growth control and detoxification. A complete list of the OncoVue SNP genes and their pathways is shown in Table 2.

Table 2. OncoVue® Genes,
SNPs and Pathways

Gene

Gene Name

Base Change

Pathway

ACCA (IVS17)

Acetyl CoA carboxylase alpha

T?C

BRCA1 Interaction

ACCA (5’UTR)

Acetyl CoA carboxylase alpha

T?C

BRCA1 Interaction

ACCA (PIII)

Acetyl CoA carboxylase alpha

T?G

BRCA1 Interaction

COMT

Catechol - O- methyltransferase

A?G

Steroid Hormone Metabolism

CYP11B2

Cytochrome P450, subfamily XIB, polypeptide
2

T?C

Steroid Hormone Metabolism

CYP19

Cytochrome P450, family 19, subfamily A,
polypeptide 1

T?C

Steroid Hormone Metabolism

CYP1A1

Cytochrome P450, subfamily 1A, polypeptide
1

T?C

Steroid Hormone Metabolism

CYP1B1

Cytochrome P450, subfamily 1B; polypeptide
1

A?G

Steroid Hormone Metabolism

CYP1B1

Cytochrome P450, subfamily 1B; polypeptide
1

C?G

Steroid Hormone Metabolism

EPHX

Epoxide Hydrolase

T?C

Xenobiotic Metabolism

ERA

Estrogen Receptor alpha

T?C

Steroid Hormone Metabolism

FASL

Fas Ligand

C?T

Apoptosis

IGF2 (IVS)

Insulin-Like Growth Factor II

G?A

Growth Factor/hormone

INS

Insulin

C?T

Growth Factor/hormone

KLK10

Kallikrein 10

G?T

Cell Cycle

MSH6

MutS, E.Coli Homolog of 6

G?A

DNA Repair

RAD51L3

S. Cerevisiae, Homolog of D

G?A

DNA Repair

SOD2

Superoxide dismutase 2

T?C

Free Radical Scavenger

VDR

Vitamin D Receptor

T?G

Hormone Receptor

XPC

Xeroderma Pigmentosum, Complentation Group
C

C?T

DNA Repair

XPG

Xeroderma pigmentosum, Group G Correcting
protein

G?C

DNA Repair

XRCC2

X-ray Repair, Complementing Defective, in
Chinese Hamster, 2

G?A

DNA
Repair

Genotyping Accuracy

The OncoVue test examines 22 single nucleotide polymorphisms (SNPs) in 19 genes. Our CLIA laboratory's ability to accurately determine the genotype of each of the 22 SNPs in OncoVue was examined by analyzing genotypes determined by DNA sequencing and comparing them to genotypes determined by the OncoVue test. The reference standard was the genotype results obtained from bi-directional DNA sequencing of 27 individuals encompassing all known allelic variants. All genotypes obtained by OncoVue agreed with the DNA sequencing data. All genotype calls for each of the 22 SNPs in the 27 samples were 100% concordant including replicates and comparison data from one run to the next. Procedurally, each of the 27 individual samples was arrayed and assayed in duplicate on the same plate along with four positive controls and two empty template (blank) controls. The OncoVue genotyping test was performed two times daily on eight separate days. Genotyping results from each of the 22 SNPs is shown in Table 3.

Table 3. OncoVue® Genotyping Accuracy

SNP

Number of Times

Genotyped

Genotyping
Concordance

DNA

Sequencing
Concordance

ACCA (IVS17)

432

100%

100%

ACCA (5’UTR)

432

100%

100%

ACCA (PIII)

432

100%

100%

COMT

432

100%

100%

CYP11B2

432

100%

100%

CYP19

432

100%

100%

CYP1A1

432

100%

100%

CYP1B1

432

100%

100%

CYP1B1

432

100%

100%

EPHX

432

100%

100%

ERA

432

100%

100%

FASL

432

100%

100%

IGF2 (IVS)

432

100%

100%

INS

432

100%

100%

KLK10

432

100%

100%

MSH6

432

100%

100%

RAD51L3

432

100%

100%

SOD2

432

100%

100%

VDR

432

100%

100%

XPC

432

100%

100%

XPG

432

100%

100%

XRCC2

432

100%

100%