Decoding the patterns of clonal dynamics in breast cancer metastasis using single-cell sequencing in patient-derived xenograft models
Bibliographic record
Abstract
Introduction: Selection and evolution of tumour cells occurs during cancer progression and metastasis. Understanding the mechanism of clonal dynamics and evolution is an important key to develop new therapeutic strategies for cancer metastasis. This thesis summarizes the development of breast cancer patient-derived xenograft (PDX) metastasis models and measurement of clonal dynamics during metastasis by identifying genomic changes in single cells from primary tumours and metastases. Methods: Tumour cells from untreated primary breast cancer patients were used to develop PDX tumours in immunodeficient mice. Tumours were removed when they reached maximum allowed endpoint size (1,000mm³) during a survival surgery and mice were monitored for metastasis. Immunohistochemical (IHC) staining was performed with 10 markers to characterize tumours. Single cell whole-genome sequencing (scWGS) was used to analyse primary and metastatic tumour cells and copy number alterations (CNAs) were identified which allow us to cluster cells and identify clones. Phylogenetic analysis was performed to identify clonal relationship between primary and metastatic tumour cells. Results: Nine different triple-negative breast cancer PDX lines were tested and 5 developed metastases (SA919, SA535, SA1142, SA605, SA609). We observed that protein marker expression was similar between primary tumour and metastases. Metastatic sites were reproducible over multiple passages in both SA919 and SA535. We also observed that metastatic potential increased with passage number in SA919 while 4 different passages of SA535 showed similar metastases development. From single cell analysis, we observed that the ability to metastasize of primary tumour increases with passage number due to the evolution of clonal population in SA919 and metastatic potential is a property distributed across CNA-defined clones in both SA919 and SA535. We also observed that metastasis to specific anatomical site was not associated with genomic clones and CNA induced genotype and LOH are potential factors that can affect metastatic potential of clones. Conclusion: We established breast cancer metastasis mouse model using patient-derived tissues and were able to capture different patterns of metastases in several PDXs. From the two transplant systems studied in detail, we observed metastatic potential was distributed across many genomic clones and CNAs have potential impact on metastatic potential of tumour cells.
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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) | 0.000 | 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) | 0.002 | 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".