Abstract IA02: Novel strategies for early cancer detection and prevention: The Li-Fraumeni syndrome story
Bibliographic record
Abstract
Abstract More than 85% of patients with Li-Fraumeni syndrome harbor germline TP53 mutations. The spectrum of mutations and the heterogeneity of tumor presentation (age of onset and type) within and between families is remarkable and indicates that modifiers must play an important role in defining the phenotype of each patient and each family. We recently reported the feasibility and utility of a clinical surveillance protocol for early detection of tumors in TP53 mutation carriers (Villani et al., Lancet Oncol 2016). Results from deep sequencing of the genome and epigenome of germline and tumor tissues of TP53 mutation carriers provide exciting new insight into the role of modifiers on both the functional activity of p53 in the germline as well as their influence on tumor onset, tumor spectrum, and clinical outcome/response to therapy. Data will be presented to support the creation of molecular algorithms that may be used in a precise manner to predict cancer onset, as well as to better define the molecular landscape of cancers that arise in TP53 mutation carriers. It is anticipated that the combined use of this germline and somatic landscape data can more effectively refine and guide the creation of personalized surveillance and chemoprevention strategies for these patients. Citation Format: David Malkin, Nicholas Light, Valli Subrasi, Benjamin Brew, Sangeetha Paramathas, Arash Nabbi, Sergei Prykhozhij, Trevor Pugh, Jason Berman, Anna Goldenberg, Adam Shlien. Novel strategies for early cancer detection and prevention: The Li-Fraumeni syndrome story [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr IA02.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".