P088: What's trending: Comparing variant of uncertain significance (VUS) rates in cancer predisposition genes over time in Black and White individuals
Bibliographic record
Abstract
Methods: Through the Canada-wide CHARM Consortium (https://charmconsortium.ca), we have collected >100 plasma samples from >50 NF1 patients, including both pediatric and adult patients, and 30 healthy controls.Plasma samples underwent deep plasma whole genome sequencing (pWGS, 40x) and cellfree methylated DNA immunoprecipitation (cfMeDIP, 60M clusters) with a subset (n = 10) also undergoing targeted panel sequencing (2,000x, 201 genes).We also secured an external dataset of 85 plasma samples from healthy controls and NF1 patients resulting in a total cohort of healthy controls (n = 46), NF1 patients unaffected with pNF/MPNST (n = 11), NF1 patients with pNF (n = 40), and NF1 patients with MPNST (n = 51).Results: Targeted panel sequencing was performed to investigate if differences in somatic mutations between pNF and MPNST could be detected in plasma.A mean of 19 somatic alterations were detected per plasma sample which were validated using fragment size analysis to rule out germline and clonal hematopoiesis related variants.However, none of the pathogenic variants were in recurrent MPNST-associated genes (NF1, SUZ12, EED, TP53) and the variant allele frequencies were similar across clinical groups.As an alternative approach, we investigated several fragmentomic features (fragment length, fragment ratio, fragment coverage) using pWGS.Several comparisons were performed: 1) healthy controls vs NF1 patients, 2) cancer negative NF1 patients vs cancer positive NF1 patients, 3) patients with pNF and patients with MPNST, to investigate differences in fragmentation patterns.Across all fragmentomic features, cancer negative NF1 patients showed similar fragmentomic profiles compared to healthy controls while patients with pNF showed low levels of genome-wide aberration and MPNST patients exhibited high levels of aberration.Integrating these fragmentomic features using a machine learning classifier we observed robust classification between healthy controls and NF1 patients with MPNST.However, the more interesting observation was that patients with pNF exhibited values that ranged between healthy control and MPNST reminiscent of the clinical trajectory of malignant transformation in NF1 patients.Conclusion: Our approach demonstrates the clinical utility and potential of fragmentomic analysis in cfDNA as a non-invasive method to monitor the malignant transformation of pNF to MPNST in NF1 patients.Future directions will be to integrate cell-free methylation analysis and benchmark accuracy and sensitivity against current clinical practice and streamline for clinical implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 0.000 |
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 teacher head, 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".