Abstract 2114: Horvath clock as a predictor of cancer risk in LFS patients
Bibliographic record
Abstract
Abstract Li-Fraumeni syndrome (LFS) is an autosomal dominantly inherited cancer predisposition syndrome associated with germline mutations of the TP53 tumor suppressor gene. TP53 mutation carriers are susceptible to a wide range of cancers that occur at strikingly earlier age of onset than their sporadic counterparts. The lifetime cancer risk in TP53 mutation carriers is estimated to be ~73% for males and approaching 100% in females. Although improved survival outcomes have been demonstrated for carriers undergoing intense clinical surveillance, there is continued interest in identifying new environmental, genetic, and epigenetic risk factors that could improve our ability to predict disease onset and outcome. A number of studies have demonstrated age-associated DNA methylation (DNAm) changes at specific CG dinucleotides and that these changes can be combined into epigenetic age predictors to estimate chronological age. Deviation of chronological and predicted age have been associated with age-associated illnesses such as metabolic disease and cancer. For a given chronological age, older epigenetic age is presumed to indicate poorer health. An epigenetic profile defining the DNA methylation age (DNAm age) of an individual has been suggested to be a biomarker of aging, and thus possibly providing a tool for assessment of health and mortality. Our goal was to test whether DNAm age could be a possible predictor of cancer risk in LFS patients. We applied the DNA age calculator (http://dnamage.genetics.ucla.edu/) (Horvath 2013) to DNA methylation profiles derived from lymphocytes extracted from 157 LFS patients' blood samples using the Illumina HumanMethylation450 BeadChip. While a correlation of DNAm age and actual age was observed in both ‘normal' and LFS patients, the latter showed significant deviations (differences between DNAm age and chronological age). Moreover, the extent of deviations seems selectively associated with two distinct age groups (0-5 years and 20-50 years). Remarkably, this bimodal DNAm age profile shows striking resemblance to the epidemiologically characterized age dependency of LFS cancers (Amadou, 2018). Individuals with germline mutant or germline wild-type TP53 and no cancer showed no epigenetic age acceleration whereas individuals who were carriers of mutant TP53 who developed cancer showed accelerated epigenetic aging. Our preliminary results suggest that DNAm age is a dynamic, real-time correlate of patient-specific cancer risk in LFS. Further, the age dependent deviations suggest that the cancer risk profiles derived from Horvath signatures are dynamic and reflect the changes in cancer risk throughout an individual's lifetime, and could be used as a predictor of cancer onset in TP53 mutation carriers. Citation Format: Malgorzata Pienkowska, Nardin Samuel, Sanaa Choufani, Vallijah Subasri, Nish Patel, Rosanna Weksberg, Ran Kafri, David Malkin. Horvath clock as a predictor of cancer risk in LFS patients [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 2114.
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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 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".