Targeted and Shallow Whole-Genome Sequencing Identifies Therapeutic Opportunities in p53abn Endometrial Cancers
Bibliographic record
Abstract
PURPOSE: Shallow whole-genome sequencing (sWGS) can detect copy-number (CN) aberrations. In high-grade serous ovarian cancer (HGSOC) sWGS identified CN signatures such as homologous recombination deficiency (HRD) to direct therapy. We applied sWGS with targeted sequencing to p53abn endometrial cancers to identify additional prognostic stratification and therapeutic opportunities. EXPERIMENTAL DESIGN: sWGS and targeted panel sequencing was performed on formalin-fixed, paraffin-embedded p53abn endometrial cancers. CN alterations, mutational data and CN signatures were derived, and associations to clinicopathologic and outcomes data were assessed. RESULTS: In 187 p53abn endometrial cancers, 5 distinct CN signatures were identified. Signature 5 was associated with BRCA1/2 CN loss with features similar to HGSOC HRD signature. Twenty-two percent of potential HRD cases were identified, 35 patients with signature 5, and 8 patients with BRCA1/2 somatic mutations. Signatures 3 and 4 were associated with a high ploidy state, and CCNE1, ERBB2, and MYC amplifications, with mutations in PIK3CA enriched in signature 3. We observed improved overall survival (OS) for patients with signature 2 and worse OS for signatures 1 and 3. Twenty-eight percent of patients had CCNE1 amplification and this subset was enriched with carcinosarcoma histotype. Thirty-four percent of patients, across all histotypes, had ERBB2 amplification and/or HER2 overexpression on IHC, which was associated with worse outcomes. Mutations in PPP2R1A (29%) and FBXW7 (16%) were among the top 5 most common mutations. CONCLUSIONS: sWGS and targeted sequencing identified therapeutic opportunities in 75% of patients with p53abn endometrial cancer. Further research is needed to determine the efficacy of treatments targeting these identified pathways within p53abn endometrial cancers.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".