Abstract 38: Augmenting drug responses to PARP inhibitors in high grade serous cancer with inhibitors of the anti-apoptotic protein Bcl-XL
Bibliographic record
Abstract
Abstract While most patients with high grade serous ovarian cancer (HGSC) respond to platinum-based chemotherapy, the response is rarely durable and recurrence almost inevitable. A characteristic of HGSC is defective DNA repair. A class of drugs called PARP inhibitors (PARPi) exploit this vulnerability and have proven useful in delaying recurrence. However, resistance is inevitable. In models of HGSC a protein that frequently confers resistance is called Bcl-xL, a member of the Bcl-2 family of proteins that prevent apoptosis. If treating the patients with a PARPi makes cancer cells dependent on Bcl-xL then adding an inhibitor of Bcl-xL to their treatment might overcome resistance to Olaparib and cause the cancer cells to die. To test this, we have initiated companion studies for a clinical trial in which patients that have had a recurrence after receiving platinum-based therapy will first be treated with the PARPi Olaparib and then, an inhibitor of Bcl-xL, Navitoclax, will be added to their course of treatment. Navitoclax has a well-known on-target side effect of thrombocytopenia. We show that with continuous dosing of Navitoclax, platelet counts recover and are maintained. Our preliminary data from the first few patients enrolled in the trial suggest that the combination is well tolerated and for several participants there was a reduction in tumor burden. However, Bcl-xL is only one of the five known inhibitors of apoptosis. To identify which women will benefit most from adding Navitoclax to their treatment we need a biomarker(s). Our hypothesis is that patient derived organoids can be used as a pragmatic way to identify for patient cohorts which Bcl-2 protein inhibitor will synergize with a PARPi to optimize treatment. By combining novel methods for cell aggregation and hydrogel based synthetic ECM supports we can reproducibly generate HGSC patient-specific tumor organoids models in weeks with greater than 90% success. Organoids grown 384 well format are stained with novel non-toxic dyes enabling live cell painting of chemoresponses to drugs alone and in combination with drugs targeting anti-apoptotic proteins. Our data suggest that this approach captures the inherent heterogeneity of the disease, albeit local to the sampled site. We are employing deep learning AI algorithms to enable automated analyses of 3D confocal image stacks of organoids to infer drug responses that will be compared to patient responses in ongoing clinical trials. Citation Format: Alla Buzina, Wiebke Schormann, Lilian Gien, Helen MacKay, David W. Andrews. Augmenting drug responses to PARP inhibitors in high grade serous cancer with inhibitors of the anti-apoptotic protein Bcl-XL [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 38.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".