Copy number and transcriptome alterations associated with metastatic lesion response to treatment in colorectal cancer
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Therapeutic resistance is the main cause of death in metastatic colorectal cancer. To investigate genomic plasticity, most specifically of metastatic lesions, associated with response to first-line systemic therapy, we collected longitudinal liver metastatic samples and characterized the copy number aberration (CNA) landscape and its effect on the transcriptome. METHODS: Liver metastatic biopsies were collected prior to treatment (pre, n = 97) and when clinical imaging demonstrated therapeutic resistance (post, n = 43). CNAs were inferred from whole exome sequencing and were correlated with both the status of the lesion and overall patient progression-free survival (PFS). We used RNA sequencing data from the same sample set to validate aberrations as well as independent datasets to prioritize candidate genes. RESULTS: We identified a significantly increased frequency gain of a unique CN, in liver metastatic lesions after first-line treatment, on chr18p11.32 harboring 10 genes, including TYMS, which has not been reported in primary tumors (GISTIC method and test of equal proportions, FDR-adjusted p = 0.0023). CNA lesion profiles exhibiting different treatment responses were compared and we detected focal genomic divergences in post-treatment resistant lesions but not in responder lesions (two-tailed Fisher's Exact test, unadjusted p ≤ 0.005). The importance of examining metastatic lesions is highlighted by the fact that 15 out of 18 independently validated CNA regions found to be associated with PFS in this study were only identified in the metastatic lesions and not in the primary tumors. CONCLUSION: This investigation of genomic-phenotype associations in a large colorectal cancer liver metastases cohort identified novel molecular features associated with treatment response, supporting the clinical importance of collecting metastatic samples in a defined clinical setting.
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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