Metabolic Lipids in Melanoma Enable Rapid Determination of Actionable BRAF-V600E Mutation with Picosecond Infrared Laser Mass Spectrometry in 10 s
Bibliographic record
Abstract
Rapid molecular profiling of biological tissues with picosecond infrared laser mass spectrometry (PIRL-MS) has enabled the detection of clinically important histologic types and molecular subtypes of human cancers in as little as 10 s of data collection and analysis time. Utilizing an engineered cell line model of actionable BRAF-V600E mutation, we observed statistically significant differences in 10 s PIRL-MS molecular profiles between BRAF-V600E and BRAF-wt cells. Multivariate statistical analyses revealed a list of mass-to-charge ( m / z ) values most significantly responsible for the identification of BRAF-V600E mutation status in this engineered cell line that provided a highly controlled testbed for this observation. These metabolites predicted BRAF-V600E expression in human melanoma cell lines with greater than 98% accuracy. Through chromatography and tandem mass spectrometry analysis of cell line extracts, a 30-member “metabolite array” was characterized for determination of BRAF-V600E expression levels in subcutaneous melanoma xenografts with an average sensitivity and specificity of 95.6% with 10 s PIRL-MS analysis. This proof-of-principle work warrants a future large-scale study to identify a metabolite array for 10 s determination of actionable BRAF-V600E mutation in human tissue to guide patient care.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".