Abstract B012: Application of shallow whole genome sequencing to identify therapeutic opportunities in p53abn endometrial cancers
Bibliographic record
Abstract
Abstract Background: Shallow whole genome sequencing (sWGS) can be used to detect copy number (CN) aberrations. In high-grade serous ovarian (HGSOC) it has been used to identify CN signatures to direct therapy, recognizing two signatures associated with homologous recombination deficiency (HRD). p53abn endometrial carcinomas (EC) represent the most lethal subtype of EC with molecular similarities to HGSOC. Recent data have shown improved outcomes in patients with p53abn EC when adjuvant chemotherapy is used in addition to radiation, however, overall prognosis is still poor and therapeutic advances are urgently needed. Our aim was to apply sWGS with targeted sequencing to assess CN signatures in a cohort of p53abn ECs to identify additional prognostic or predictive stratification. Methods: sWGS was performed on formalin fixed paraffin embedded (FFPE) tissue from 215 p53abn ECs and 188 samples had targeted panel sequencing for 275 cancer related genes. CN variations, common mutations and CN signatures were derived, and associations to clinicopathologic and outcomes data were assessed. Results: 187 p53abn ECs had sufficient quality DNA to generate CN signatures (including 96 serous, 39 endometrioid, 33 carcinosarcoma, 12 clear cell and 7 other), with 5 distinct CN signatures identified. Signature 5 was associated with HRD due to CN loss of BRCA1 and 2 and was found in 35 (19%) of patients. Signatures 3 and 4 were associated with whole genome duplications, with mutations in PIK3CA defining Signature 3. 28% of cases had CCNE1 amplification, and this was enriched with carcinosarcoma histotype (p= 0.0019). 34% of patients had either HER2 gene amplification or HER2 overexpression by immunohistochemistry (IHC), which was seen across all histotypes, and was associated with worse outcomes (p=0.017). The most common mutations beyond TP53 were PIK3CA (32%), PPP2R1A (29%), PIK3R1 (18%), FBXW7 (16%) and PTEN (16%). There was no significant difference in outcomes observed between the CN signatures (35% of patients had recurred and 22% had died from disease at 3 years in total cohort), nor in the distribution of histotypes across signatures. After adjusting for stage, there was no difference in outcomes between histotypes. sWGS in combination with selective sequencing and IHC identified targetable pathways in 84% of patients with p53abn EC (HRD-PARP inhibitors, HER2-anti HER2 therapy, CCNE1-Wee1 inhibitor, PPP2R1A/FBXW7-PKMYT1 inhibitor). Conclusions: sWGS is a relatively inexpensive tool that can be performed on standard FFPE material and identified therapeutic opportunities in 84% of p53abn EC patients. Outcomes were uniformly poor in this cohort of p53abn ECs highlighting the need for improved treatment options beyond surgery and conventional chemoradiation. Further research is needed to determine the efficacy of treatments targeting these identified pathways within p53abn ECs. Citation Format: Amy Jamieson, Juliana Sobral de Barros, Dawn R. Cochrane, Maxwell Douglas, Sameer Shankar, Samuel Leung, Branden Lynch, Janine Senz, Blake Gilks, David G. Huntsman, Jessica N. McAlpine. Application of shallow whole genome sequencing to identify therapeutic opportunities in p53abn endometrial cancers [abstract]. In: Proceedings of the AACR Special Conference on Endometrial Cancer: Transforming Care through Science; 2023 Nov 16-18; Boston, Massachusetts. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(5_Suppl):Abstract nr B012.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".