Comparison of tumor mutational burden (TMB) in <i>PBRM1/BAP1</i>-based subsets of advanced renal cell carcinoma (aRCC).
Bibliographic record
Abstract
634 Background: Using IHC, Joseph et al (J Urol 2016) propose that 40.1%, 48.6%, 8.7% and 1.8% of patients (pts) can be characterized as PBRM1+BAP1+, PRBM1-BAP1+, PBRM1+BAP1- and PBRM1-BAP1-, respectively. We sought to confirm consistency of the frequency of genomic alterations (GAs) and IHC data and to compare TMB across subsets. Methods: DNA was extracted from 40 microns of FFPE sections from pts with aRCC. Comprehensive genomic profiling (CGP) was performed on hybridization-captured, adaptor ligation based libraries to a mean coverage depth of 688X for up to 315 cancer-related genes plus 37 introns from 14 genes frequently rearranged in cancer. TMB was determined on 1.2 million Mb of sequenced DNA; results are reported in subsets segregated by presence or absence of PBRM1 and BAP1 alteration. Results: 648 consecutive pts (459:189 M:F) with clear cell RCC (ccRCC) were assessed with a median age of 58, and 368 consecutive pts (254:114 M:F) with non-clear cell RCC (nccRCC) were assessed with a median age of 57. Mutations in BAP1 and PBRM1 were found more frequently in ccRCC vs nccRCC (P < 0.05 for both). In pts with ccRCC, average TMB was highest in pts with co-occurring PBRM1 and BAP1 GAs (4.87 muts/Mb), and lowest in pts lacking both GAs (2.77 muts/Mb) (P < 0.05). TMB was similar across PBRM1/BAP1-based subsets amongst pts with nccRCC. Conclusions: As anticipated, the frequency of PBRM1/BAP1-mutated subsets by CGP is inversely related to the frequency of subsets with PBRM1/BAP1 loss by IHC from previous reports. In addition to these confirmatory findings, this large series identifies that pts with dual PBRM1/ BAP1 GAs (associated with the worst prognosis) had the highest TMB. [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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".