MET status and treatment outcomes in papillary renal cell carcinoma (PRCC): Pooled analysis of historical data.
Bibliographic record
Abstract
e19321 Background: PRCC accounts for 10–15% of RCCs, but there are limited biomarker-based data to inform targeted treatment. Treatments for patients (pts) with advanced/metastatic PRCC include those approved for clear cell RCC such as sunitinib or everolimus, however their activity in PRCC can be limited. A subset of PRCCs are MET-driven, and it has been suggested that this may be a negative prognostic factor, although the role of MET activation in advanced/metastatic disease is unclear. We explored the prevalance of MET status in pts with advanced/metastatic PRCC treated with targeted therapies and impact on clinical outcomes. Methods: This large, international, retrospective, observational study included pts with previously treated, locally advanced/metastatic PRCC. MET status was determined retrospectively by NGS of archival tissue. MET-driven disease was defined as MET and/or HGF amplification, chromosome 7 gain (requiring > 30% tumor content) and/or MET kinase domain mutations. Study objectives included progression-free survival (PFS), time to treatment failure (TTF) and overall survival (OS) by MET status. Results: 305/308 pts (International Metastatic RCC Database Consortium, n = 72; The Asan Genito-Urinary Cancer Center, n = 40; Groupe Français d’Etude des Tumeurs Uro-Génitales, n = 196) received first-line (1L) treatment; sunitinib was most common (68%). 214 (69%) pts received 2L therapy; everolimus was most common (20%). Of 179 pts with valid NGS results, 38%, 49% and 13% had MET-driven, MET-independent (ind) tumors and chromosome 7 ploidy unevaluable, respectively. Baseline demographics were mostly balanced among groups. In sunitinib-treated pts, PFS and TTF were numerically longer in pts with MET-driven tumors vs pts with MET-ind tumors, but OS was similar (Table). In everolimus-treated pts, PFS and TTF were similar for both MET groups; however the sample size was small. Conclusions: MET alterations are frequent in advanced/metastatic PRCC and have potential to impact PFS and TTF, although our results show limited effect on OS. These data show that MET-driven tumours may not predict for poorer outcome vs MET-ind tumours and there is a need for suitable therapies for MET-driven PRCC. [Table: see text]
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".