Abstract B028: Functional precision medicine in challenging breast cancer subtypes: Developing preclinical models for tailored therapy in complex and rare histologies
Bibliographic record
Abstract
Abstract Functional precision medicine (FPM) integrates patient-derived preclinical models, molecular profiling, and drug sensitivity testing to advance personalized cancer treatments. This approach uses live patient tumor samples to conduct real-time functional assays, exposing them to a range of therapeutic agents to determine which treatments may be most effective. FPM enables a more tailored and potentially effective treatment strategy, particularly for complex cancers such as invasive breast carcinoma, which encompasses a spectrum of molecularly and clinically diverse diseases. Some subtypes, such as triple-negative breast cancer, metastatic or recurrent disease, and rare histologies, pose substantial treatment challenges. To address these complexities, we previously developed a resource of 37 hard-to-treat breast cancer patient-derived xenografts (PDXs) (Savage et al. 2020 - PMID: 32546838), demonstrating that PDXs preserve the molecular heterogeneity and multi-drug chemoresistance of the primary tumors. Expanding on this work, we have developed 35 new PDX models, including four derived from metaplastic breast cancers (MpBC), a rare and aggressive subtype characterized by minimal response to standard chemotherapy. We evaluated the molecular landscape of PDX and patient tumors, leveraging comprehensive omics data from this dataset. For each model, we evaluated potential vulnerabilities and the pattern of genomic alteration and expression profiles. Spatial transcriptomics of MpBC cases in our cohort was also evaluated (Visium - 10X technology) and we observed that the distinct transcriptomic populations overlap with the spatially defined histological features. To make our data accessible to the scientific community, we build a repository of rare breast cancer models, complete with detailed clinical, molecular, and pathological annotations, in addition to drug sensitivity profiles that may predict patient responses. By combining live biobanking and advanced profiling techniques, we identified tumor vulnerabilities, enabling the prioritization of drugs targeting these specific weaknesses. This multi-faceted approach represents a promising path to improving patient outcomes in difficult-to-treat breast cancer subtypes, helping to tailor preclinical testing and develop more effective therapies. Citation Format: Hellen Kuasne, Anne Marie Fortier, Sandrine Busque, Paul Savage, Constanza Martinez Ramirez, Anie Monast, Nebras Koudieh, Atilla Omeroglu, Lara Richer, Jamil Asselah, Nathaniel Bouganim, Francine Tremblay, Ari Nareg Meguerditchian, Sarkis Meterissian, Morag Park. Functional precision medicine in challenging breast cancer subtypes: Developing preclinical models for tailored therapy in complex and rare histologies [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr B028.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".