Tumor mutation burden testing: a survey of the International Quality Network for Pathology (IQN Path)
Bibliographic record
Abstract
While tumour mutation burden (TMB) is emerging as a possible biomarker for immune-checkpoint inhibitors (ICI), methods for testing have not been standardised as yet. In April 2019, the International Quality Network for Pathology (IQN Path) launched a survey to assess the current practice of TMB testing. Of the 127 laboratories that replied, 69 (54.3%) had already introduced TMB analysis for research purposes and/or clinical applications. Fifty laboratories (72.5%) used targeted sequencing, although a number of different panels were employed. Most laboratories tested formalin-fixed paraffin-embedded material (94.2%), while 18/69 (26%) tested also cell-free DNA. Fifty-five laboratories used both single nucleotide variants and indels for TMB calculation; 20 centers included only non-synonymous variants. In conclusion, the data from this survey indicate that multiple global laboratories were capable of rapidly introducing routine clinical TMB testing. However, the variability of testing methods raises concerns about the reproducibility of results among centers.
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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 | 0.059 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".