Abstract P3-08-09: Development and molecular characterization of hard-to-treat breast cancer pre-clinical models to enhance precision medicine
Bibliographic record
Abstract
Abstract Invasive breast carcinoma is a combination of heterogeneous diseases with distinct molecular and clinical features. Some subsets of breast cancer present major clinical challenges, including triple-negative, metastatic/recurrent disease and rare breast histologies. Previously, we developed a unique resource of 37 hard-to-treat breast cancer patient-derived xenografts (PDX). This set included mainly triple negative breast cancer (TNBC) patients that presented poor response to neoadjuvant chemotherapies (Savage et al. 2020 - PMID: 32546838). PDXs accurately reproduce the molecular heterogeneity of the primary tumors and show that multi-drug chemoresistance was retained upon xenotransplantation. Here, we present the characterization of PDX 3-dimensional cultures organoids (8) and PDX derived epithelial cell lines (11). Using single-cell RNAseq we showed that an organoid cultured for several passages (P8) maintained the heterogeneity of the matched PDX. Although in different proportions, all cancer cell populations found in the PDX were retained in matched organoids supporting that organoids are suitable models that recapitulates the tumor heterogeneity and are therefore a suitable model for drug screening. Among our new PDXs models (30), we have developed four PDXs from rare metaplastic breast cancers (MpBC), an aggressive subtype of breast cancer that present the poorest response to standard of care chemotherapy. We also developed one male breast cancer PDX with matched organoid. Omic analysis on our pre-clinical models and patient primary tumor and metastasis will inform development of therapeutic opportunities. This molecular information will guide selection of compounds that will be validated using our high throughput organoid drug screening pipeline. This will allow rapid screens of thousands of approved drugs, enhancing drug repurposing with potential for rapid clinical translation. The combined use of 3D tumor organoids and PDXs, is an important opportunity poised to transform identification of new therapeutic options for hard-to-treat lethal breast cancers. Citation Format: Hellen Kuasne, Anne-Marie fortier, Sandrine Busque, Simon Mathien, Paul Savage, Constanza Martinez Ramirez, Anie Monast, Margarita Soleinova, Atilla Omeroglu, Jamil Asselah, Nathaniel Bouganim, Sarkis Meterissian, Mark Basik, Morag Park. Development and molecular characterization of hard-to-treat breast cancer pre-clinical models to enhance precision medicine [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-08-09.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".