Reprofiling Metastatic Samples for Chromosome 9p and 14q Aberrations as a Strategy to Overcome Tumor Heterogeneity in Clear-cell Renal Cell Carcinoma
Bibliographic record
Abstract
Losses of chromosomes 9p and 14q are associated with worse outcomes in patients affected by clear-cell renal cell carcinoma (RCC) and are helpful for prognostic risk stratification. Both chromosomal loci harbor several hot-spot molecular pathways suitable for targeted therapeutic interventions. Intratumor heterogeneity may foster tumor adaptation and therapeutic failure. We sought to investigate the presence of losses of the hot spots of chromosomal loci 9p and 14q in primary clear-cell RCC and matched metastatic tissues. CD10 and CD13 were performed on 7 cases of clear-cell RCC with hematogenous tissue metastases. Cytogenetic fluorescence in situ hybridization analysis was performed on primary and matched metastatic tissues using specific probes mapping the 9p and the 14q loci. The loss of chromosome 9p was observed in 85% of both primary clear-cell RCCs and in matched metastases; 14% showed discordance between primary and matched metastases showing gains. The loss of chromosome 14q was observed in 58% of both primary and matched metastases. Only 3/7 (42%) did show an equal status of loss of chromosome 14q. Heterogeneity of the cytogenetic status between metastatic and primary clear-cell RCCs is observed for the loss of chromosome 14q rather than chromosome 9p. The impact of chromosome 14q cytogenetic status, harboring the HIF1 gene, a major driver for the angiogenenic switch, may drive the efficacy of targeted inhibitors, whereas the loss of chromosome 9p, harboring other hot-spot genes, seems to be related to the metastatic behavior per se, without cytogenetic modulation. Reprofiling the metastatic tissue, as compared with the primary tumor, in patients affected by metastatic RCC could be a novel approach to overcome resistance to VEGF(Rs)-targeting agents.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".