Chemogenomic profiling of breast cancer patient-derived xenografts reveals targetable vulnerabilities for difficult-to-treat tumors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Subsets of breast tumors present major clinical challenges, including triple-negative, metastatic/recurrent disease and rare histologies. Here, we developed 37 patient-derived xenografts (PDX) from these difficult-to-treat cancers to interrogate their molecular composition and functional biology. Whole-genome and transcriptome sequencing and reverse-phase protein arrays revealed that PDXs conserve the molecular landscape of their corresponding patient tumors. Metastatic potential varied between PDXs, where low-penetrance lung micrometastases were most common, though a subset of models displayed high rates of dissemination in organotropic or diffuse patterns consistent with what was observed clinically. Chemosensitivity profiling was performed in vivo with standard-of-care agents, where multi-drug chemoresistance was retained upon xenotransplantation. Consolidating chemogenomic data identified actionable features in the majority of PDXs, and marked regressions were observed in a subset that was evaluated in vivo. Together, this clinically-annotated PDX library with comprehensive molecular and phenotypic profiling serves as a resource for preclinical studies on difficult-to-treat breast tumors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it