Malignant cells from pleural fluids in malignant mesothelioma patients reveal novel mutations
Bibliographic record
Abstract
OBJECTIVES: Malignant mesothelioma (MM) is an asbestos related tumour affecting cells of serosal cavities. More than 70% of MM patients develop pleural effusions which contain tumour cells, representing a readily accessible source of malignant cells for genetic analysis. Although common somatic mutations and losses have been identified in solid MM tumours, the characterization of tumour cells within pleural effusions could provide novel insights but is little studied. MATERIALS AND METHODS: DNA and RNA were extracted from cells from short term cultures of 27 human MM pleural effusion samples. Whole exome and transcriptome sequencing was performed using the Ion Torrent platform. Somatic mutations were identified using VarScan2 and SomaticSniper. Copy number alterations were identified using ExomeCNV in R. Significant copy number alterations were identified across all samples using GISTIC2.0. The association between tumour intrinsic properties and survival was analyzed using the Cox proportional hazards regression model. RESULTS: We identified BAP1, CDKN2A and NF2 alterations in the cells from MM pleural effusions at a higher frequency than what is typically seen in MM tumours from surgical series. The median mutation rate was 1.09 mutations/Mb. TRAF7 and LATS2 alterations were also identified at a high frequency (66% and 59% respectively). Novel regions of interest were identified, including alterations in FGFR3, and the regions 19p13.3, 8p23.1 and 1p36.32. CONCLUSION: Short term cultures of tumour cells from MM pleural effusions offer an accessible alternative to surgical tumour biopsies in the study of MM genomics and reveal novel mutations of interest. Pleural effusion tumour cells provide an opportunity for the monitoring of tumour dynamics, treatment response and the clonal evolution of MM tumours.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".