Tissue based biomarkers in non-clear cell RCC: Correlative analysis from the ASPEN clinical trial
Bibliographic record
Abstract
Biomarkers are needed in patients with non-clear cell renal cell carcinomas (NC-RCC), particularly papillary renal cell carcinoma, in order to inform on initial treatment selection and identify potentially novel targets for therapy. We enrolled 108 patients in ASPEN, an international randomized open-label phase 2 trial of patients with metastatic papillary, chromophobe, or unclassified NC-RCC treated with the mTOR inhibitor everolimus (n=57) or the vascular endothelial growth factor (VEGF) receptor inhibitor sunitinib (n=51), stratified by MSKCC risk and histology. The primary endpoint was overall survival (OS) and secondary efficacy endpoints for this exploratory biomarker analysis were radiographic progression-free survival (rPFS) defined by intention-to-treat using the RECIST 1.1 criteria and radiographic response rates. Tissue biomarkers (n=78) of mTOR pathway activation (phospho-S6 and -Akt, c-kit) and VEGF pathway activation (HIF-1α, c-MET) were prospectively explored in tumor tissue by immunohistochemistry prior to treatment and associated with clinical outcomes. We found that S6 activation was more common in poor risk NC-RCC tumors and S6/Akt activation was associated with worse PFS and OS outcomes with both everolimus and sunitinib, while c-kit was commonly expressed in chromophobe tumors and associated with improved outcomes with both agents. C-MET was commonly expressed in papillary tumors and was associated with lower rates of radiographic response but did not predict PFS for either agent. In multivariable analysis, both pAkt and c-kit were statistically significant prognostic biomarkers of OS. No predictive biomarkers of treatment response were identified for clinical outcomes. Most biomarker subgroups had improved outcomes with sunitinib as compared to everolimus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".