Abstract 1435: Phenotype:Genotype correlations of p53 mutation carriers: The 20-year Toronto experience
Bibliographic record
Abstract
Abstract Introduction: Li-Fraumeni syndrome (LFS) is a rare autosomal dominant disorder associated with germline mutations of the p53 gene. LFS is associated with early age of onset of breast cancer in women, childhood sarcomas and other malignancies. Previous studies have suggested potential correlations between p53 mutation type and clinical cancer phenotypes. For example, missense mutations within the DNA binding loop were found to be associated with higher prevalence of brain tumors and breast cancer whereas missense mutations outside the DNA binding loops were associated with higher prevalence of adrenocortical carcinoma. Through our Cancer Genetics Program (CGP), we have ascertained and conducted p53 mutation analysis on LFS patients/families from 1992 to the present. This represents one of the largest collections in the world. Here we set out to collate and characterize potentially novel genotype:phenotype associations from this database. Methods: TissueMetrix (Artificial Intelligence in Medicine (AIM), Toronto) was used to develop the CGP database. Clincal-pathologic data including family pedigrees, age of tumor onset, tumor type (confirmed with pathology reports where available were collected. From 1992-1998, Sanger sequencing of p53 encompassing the DNA binding domain (exons 5-8) was performed. Since 1998, the entire coding region and additional 50-100 bases spanning intron/exon splice sites have been sequenced and since 2007, MLPA testing has been performed to look for allelic deletion/duplication. Results: p53 mutations have been identified in 110 individuals, of whom 61 (55%) are less than 18years of age. Missense mutations the most common sequence alteration (83/110= 75%). The most commonly observed mutation was the dominant-negative Arg248Gln substitution (n=12). Monoallelic complete or partial gene deletion was observed in 10 cases, several of whom exhibited a non-cancer, developmental delay phenotype. The most common cancer were brain tumors (n=21), of which 10 were choroid plexus carcinomas. ADCC, breast cancer and sarcomas were commonly observed; non-classical LFS tumors, including neuroblastoma, papillary carcinoma of the thyroid and Wilms tumor are also seen. Conclusion: Our program, being situated in a pediatric tertiary centre has an inherent ascertainment bias which likely explains the high frequency of young p53 mutation carriers, as well as a disproportionate prevalence of childhood cancers. Nevertheless, unique correlations emerge from this rich resource including that of p53 deletion and hypotonia/developmental delay, as well as de novo and novel non-coding mutations. These correlations provide the foundation on which to develop practical genetic screening and clinical surveillance approaches to at-risk children and young adults. 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 1435. doi:1538-7445.AM2012-1435
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".