Abstract B48: Germline mutations in BRCA1 and BRCA2 as predictive biomarkers of therapy response
Bibliographic record
Abstract
Abstract The results of in vitro chemosensitivity studies have shown some consistency with response to chemotherapy in patients, indicating that this approach may be used to identify patients that are resistant to conventional chemotherapy regimens. However, this approach is not routinely used in an adjuvant or neoadjuvant setting as a treatment strategy. Treatment selection for breast and ovarian cancer is currently based on pathological classification of the tumour, without assessment of in vitro chemosensitivity. BRCA1 and BRCA2 germline mutations are considered to be both prognostic cancer biomarkers and predictive biomarkers of breast and ovarian cancer susceptibility; however, their utilization as predictive biomarkers of therapy response has not been investigated. Expression levels of BRCA1 modulate chemosensitivity in breast cancer cell lines, and epigenetic regulation of BRCA1 leading to decreased BRCA1 expression is often observed in sporadic breast cancer. We have hypothesized that cells with mutations in BRCA1 or BRCA2 may be differentially sensitive to DNA damaging agents used in cancer therapy due to decreased levels of BRCA1 or BRCA2 gene expression in these cells. Using lymphocytes isolated from individuals with germline mutations in BRCA1 and BRCA2, we will present results showing the in vitro response to ionizing radiation and chemosensitivity to Paclitaxel and Doxorubicin, two chemotherapeutic agents commonly used in the adjuvant systemic treatment of breast cancer. Citation Information: Cancer Res 2009;69(23 Suppl):B48.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.010 | 0.002 |
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".