Patient-derived xenografts of central nervous system metastasis reveal expansion of aggressive minor clones
Bibliographic record
Abstract
BACKGROUND: The dearth of relevant tumor models reflecting the heterogeneity of human central nervous system metastasis (CM) has hindered development of novel therapies. METHODS: We established 39 CM patient-derived xenograft (PDX) models representing the histological spectrum, and performed phenotypic and multi-omic characterization of PDXs and their original patient tumors. PDX clonal evolution was also reconstructed using allele-specific copy number and somatic variants. RESULTS: PDXs retained their metastatic potential, with flank-implanted PDXs forming spontaneous metastases in multiple organs, including brain, and CM subsequent to intracardiac injection. PDXs also retained the histological and molecular profiles of the original patient tumors, including retention of genomic aberrations and signaling pathways. Novel modes of clonal evolution involving rapid expansion by a minor clone were identified in 2 PDXs, including CM13, which was highly aggressive in vivo forming multiple spontaneous metastases, including to brain. These PDXs had little molecular resemblance to the patient donor tumor, including reversion to a copy number neutral genome, no shared nonsynonymous mutations, and no correlation by gene expression. CONCLUSIONS: We generated a diverse and novel repertoire of PDXs that provides a new set of tools to enhance our knowledge of CM biology and improve preclinical testing. Furthermore, our study suggests that minor clone succession may confer tumor aggressiveness and potentiate brain metastasis.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".