Unraveling the genetics of cancer using whole-exome sequencing
Bibliographic record
Abstract
Hereditary cancer studies have shed light on the understanding of cancer genetics.Inherited components are particularly important in breast cancer and ovarian cancer.Whole-exome sequencing (WES) that targets the protein-coding region of the genome has emerged as a powerful method to reveal genetic alterations in cancer.This thesis work focuses on uncovering the genetic basis underlying familial breast cancer and ovarian cancer.Using WES as the primary investigative tool, deleterious germ-line mutations in SMARCA4 were identified in all the small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) families that we studied.Genomic analysis revealed that SCCOHT is universally characterized by the complete loss of SMARCA4.By performing WES analysis on French-Canadian, BRCA1 and BRCA2-negative breast cancer families Contribution of The AuthorsChapter 2. Genomic Characterization Revealed SMARCA4 Alterations as the Major Genetic Cause of Malignant Small Cell Carcinoma of the Ovary, Hypercalcemic Type: Jian Carrot-Zhang performed the bioinformatics analysis and contributed to the novel findings.Leora Witkowski collected samples and performed the Sanger sequencing analysis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".