Abstract 885: A risk prediction tool for individuals with a family history of breast, ovarian, or pancreatic cancer: BRCAPANCPRO
Bibliographic record
Abstract
Abstract Introduction: Identifying families with an underlying inherited cancer predisposition is a major goal of cancer prevention efforts. Mendelian risk models have been developed to better predict the risk associated with a pathogenic variant of developing breast/ovarian cancer (with BRCAPRO), and the risk of developing pancreatic cancer (PANCPRO). Given that pathogenic variants involving BRCA2 and BRCA1 predispose to all three of these cancers, we developed a joint risk model to capture shared susceptibility. Methods: We expanded the existing framework for PANCPRO and BRCAPRO to model underlying risk of pancreatic, breast, and ovarian cancer. We then validated this model on three datasets each reflecting the target populations for these models, including patients referred to a high-risk genetics clinic, patients with multiple family members with pancreatic cancer who underwent genetic testing for BRCA1/2, as well as a prospective registry of pancreatic cancer families. The new model is designated BRCAPANCPRO. Results: BRCAPANCPRO yielded good discrimination for differentiating BRCA1 and BRCA2 carriers from non-carriers (AUC = 0.84, 95% CI: 0.78, 0.87) and was reasonably well-calibrated for predicting future risk of pancreatic cancer (observed-to-expected (O/E) ratio = 0.82 [0.69, 0.94]). In these families, BRCAPANCPRO outperformed both the prior BRCAPRO and PANCPRO models. Discussion: The BRCAPANCPRO model improves the identification of families with a pathogenic variant in BRCA2 or BRCA1, particularly for families where there is the co-occurrence of pancreatic cancer in addition to breast and/or ovarian cancer. The model also improves the prediction of pancreatic cancer among families with a pancreatic cancer family history, with or without breast and/or ovarian cancer. Citation Format: Amanda L. Blackford, Erica J. Childs, Nancy Porter, Gloria Petersen, Kari Rabe, Steven Gallinger, Ayelet Borgida, Sapna Syngal, Ann Schwartz, Michele Cote, Ralph Hruban, Michael Goggins, Alison P. Klein, Giovanni Parmigiani. A risk prediction tool for individuals with a family history of breast, ovarian, or pancreatic cancer: BRCAPANCPRO [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 885.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".