Abstract IA030: A population-based, whole genome sequencing approach to mapping the genetic basis of mesenchymal malignancies
Bibliographic record
Abstract
Abstract Cancer genetics has to date focused on epithelial malignancies, identifying multiple histotype-specific pathways underlying cancer susceptibility. Sarcomas are rare malignancies predominantly derived from embryonic mesoderm. To identify novel pathways specific to mesenchymal cancers, whole genome germline sequencing was performed on 1,644 sporadic cases, and 3,205 matched healthy elderly controls. Using an extreme phenotype design, a combined rare variant burden and ontologic analysis identified two new sarcoma-specific pathways involved in mitotic and telomere functions. Variants in centrosome genes are linked to malignant peripheral nerve sheath and gastrointestinal stromal tumors, while heritable defects in the shelterin complex link susceptibility to sarcoma, melanoma and thyroid cancers. These studies indicate a specific role for heritable defects in mitotic and telomere biology in risk of sarcomas. Citation Format: David M. Thomas. A population-based, whole genome sequencing approach to mapping the genetic basis of mesenchymal malignancies [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr IA030.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".