Abstract 2182: The evolution of high grade serous ovarian cancer under the pressure of non-cytotoxic and cytotoxic treatment
Bibliographic record
Abstract
Abstract Introduction: High-grade serous ovarian cancer (HGSOC) is driven by loss of TP53 and genome instability . Despite the recent success of PARP inhibitors, advanced BRCA mutant and homologous recombination repair deficient (HRD) HGSOC, chemotherapy remains the first line treatment. Little is known about how chemotherapy exposure alters tumor heterogeneity and subsequent response to targeted therapies. Certain mutations may survive the exposure of chemotherapy better than others. The question of whether targeted therapies should be given before or after response to chemotherapy also remains unanswered. We use the principles of natural selection to investigate how HGSOC evolves over time and selection operates on clones in the context of cytotoxic/non-cytotoxic combination therapies, and what changes in genomes/transcriptomes at the single cell level drive tumor progression. Methods: Cell lines ID8 Trp53 −/−; Brca1 −/− and WT were used to represent defective/proficient homologous recombination were given intraperitoneally to C57B6 mice to develop HGSOC models. Transfected Luciferase expression in the cells used to monitor tumor response to olaparib (Ola)+/- Bevacizumab (BEV-various doses) +/- Atezolizumab (ATz) combinations by bioluminescence imaging (BLI). Ascitic fluid and mouse organs were harvested for the evidence of seeding and identification of biomarkers. In addition, 10 treatment naïve HGSOC PDX were developed in immunodeficient mice and treated with either cisplatin or olaparib. Single-cell whole-genome sequencing (scWGS) was performed using direct library preparation (DLP+). Hierarchical clustering and Sitka are used to identify the clonal structure of each condition following treatment. Phylogenetic tree was computed using copy number data. Results: In the BRCA WT HRp study group, the greatest response was seen in the triplet Ola+BEV+ATz combinations and interestingly no significant differences were observed using a lower dose of BEV. Furthermore, from scWGS data of PDX passages we captured initial clonal heterogeneity leading to emergent clones. We found evolving copy number changes on chromosome 19, 8, 3 and loss of heterozygosity of TP53. Conclusion: The triplet combination of low-dose-intensity bevacizumab with other non-cytotoxic drugs was an effective regimen for BRCA WT syngeneic mouse tumors. In HGSOC PDX, we are able to capture diversification between HGSOC PDX passages-specifically after drug exposures. Citation Format: Farhia Kabeer, Goldman Lam, Naila Adam, Maxwell Douglas, Amal El-Naggar, Forouh Kalantari, Mengke Han, Vinci Au, Michael Van Vliet, Cindy Shen, Sean Beaty, Daniel Lai, Andy Mungall, Richard Moore, Sam Aparicio, Andrew Roth, David Huntsman, Yvette Drew. The evolution of high grade serous ovarian cancer under the pressure of non-cytotoxic and cytotoxic treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2182.
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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 | 0.000 |
| 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) | 0.002 | 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".