Abstract 693: Development of a protein-based functional assay for the determination of BRCA1 carrier status
Bibliographic record
Abstract
Abstract While most breast cancers are sporadic, 5-10% are hereditary and attributable to the presence of a mutation in the breast cancer associated gene 1 (BRCA1). The BRCA1 gene encodes a tumour suppressor with several well described roles in maintaining genomic integrity, such as cell cycle checkpoint control and DNA repair. BRCA1 is also known to play a role in the differentiation of breast epithelial cells. Given the high risk of cancer development associated with BRCA1 mutation carriers, it is important that they be identified early and accurately. Previous work in our lab developed a novel functional assay to predict BRCA1 status based on gene expression profiles. That study analyzed EBV-transformed lymphocyte cell lines (LCLs) of BRCA1 mutation carriers (BRCA1+/−) and controls (BRCA1+/+) and found that lymphocytes from BRCA1 carriers could be distinguished with high fidelity (∼90% accuracy) from individuals with two wild type copies of the gene. Interestingly, it was found that many of the genes used to distinguish BRCA1 mutation carriers were also markers of differentiation in blood cells. We are now using this information to develop a flow cytometric protein-based biomarker assay. To do this, our transcriptome microarray data was analyzed to identify genes with strong edictability of BRCA1 status, including genes with known roles in lymphocyte differentiation such as TBX21 and CXCR3. These genes were then used to model the assay in-silico using a training set of data comprised of 53 LCLs. The model was designed by selecting genes both up- and down-regulated in BRCA1 carriers, placing them in pairs and looking at the ratio of their expression values. This model was subsequently validated using a test data set comprising 16 LCLs. Overall accuracy of the model was 94%, correctly predicting 54/59 LCLs. Based on in-silico modelling, we chose six antibodies to develop the assay. To identify other candidate proteins, LCL lysates were analyzed by mass spectrometry (MS). The results identified several other candidate proteins for assay development including IgG1, IgD, BCR and MX1. Interestingly, as was the case in our transcriptome analysis, these MS identified proteins also suggested that BRCA1 mutation carriers displayed defects in cellular differentiation. This was indicated by the higher expression of surface markers in responding to less differentiated B-lymphocytes in comparison with controls. Ultimately, the assay will be applied to fresh patient blood samples with the expectation that it will become an assay that can be implemented in a clinical setting to provide accurate identification of BRCA1 mutation carriers and aid in disease management. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 693. doi:1538-7445.AM2012-693
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".