A Genomic Biomarker for Breast Cancer Development in High-Risk Women.
Bibliographic record
Abstract
Abstract BackgroundFamily history is an important factor contributing to a woman's risk of breast cancer development. This increased risk reflects the participation of inherited genetic components such as breast cancer susceptibility genes. However, many of the genetic components contributing to breast cancer remain unknown, and a number of women with a family history of breast cancer never develop breast cancer despite their high-risk status, while other high-risk woman go on to develop breast cancer. Thus, it is clear that we lack crucial pieces of information to help define a person's true risk of developing breast cancer.Methods and FindingsWe hypothesize that there are many undiscovered germline variations in genes that lead to altered gene expression patterns predictive of breast cancer development in high-risk women. We have developed a genomic model capable of predicting which high-risk women, both BRCA1/2 mutation carriers and BRCAX women, will actually develop breast cancer. Specifically, we use exon-level genome-wide expression profiling of peripheral blood mononuclear cells (PBMCs) to develop a model of disease risk (n=118 samples of either control or high-risk women). From this data, we generate a biomarker consisting of genes that most correlate to cancer development in women with strong family histories of breast cancer. Using an internal independent dataset, our biomarker can predict the high-risk women who will or will not develop cancer with over 85% accuracy. Further, we have validated this model on an independent external cohort (n=36) which was obtained and processed at sites unique to our training dataset. Our genomic biomarker can accurately predict breast cancer development in high-risk women with over 78% accuracy using this external dataset. Therefore, from our analyses, we can calculate with high accuracy an individual woman's true risk of developing breast cancer. This method provides a personalized approach to hereditary breast cancer risk assessment that is not currently available. This personalized risk assessment will aide clinicians in counseling their patients regarding specific management options based on a patient's individual risk of breast cancer. Lastly, these studies have also identified novel genes associated with breast cancer risk, which may provide a basis for targeted therapies that may help modify the risk of breast cancer development in high risk patients.ConclusionsTogether, these studies deliver both a non-invasive biomarker for hereditary breast cancer risk and a characterization of genes that contribute to breast cancer development. Overall, we expect these experiments to identify the genetic changes that underlie breast cancer predisposition, and assist clinicians and patients in determining the appropriate preventative measures based on their personal risk of developing breast cancer. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 4059.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".