PATH-45. INTRAEPIGLIOM - RAPID SEQUENCING-BASED DIAGNOSIS OF BRAIN TUMORS
Bibliographic record
Abstract
Abstract The intraoperative diagnosis of brain tumors remains a clinical challenge despite recent technological advances. The current clinical practice differentiates non-surgical brain tumors from those preferably treated with cytoreductive surgery employing intraoperative frozen section diagnostics. A detailed molecular diagnosis required for this classification task within the timeframe of a routine neurosurgical procedure is currently unavailable. We have analyzed a clinical cohort of several brain tumor entities using Nanopore long-read sequencing on two Oxford Nanopore Technologies sequencing platforms (MinION, PromethION). Since currently available molecular cancer classifiers such as the DKFZ methylation profiling classifier cannot be readily adapted to real-time sequencing analysis, we implemented a novel algorithm (MethyLYZR) to predict the underlying cancer type. Publicly available Illumina Infinium array data were used to train the classifier to distinguish 91 brain tumor classes. For validation of classification accuracy, we conducted a comprehensive validation strategy. Both nanopore platforms could sequence more than 5,000 pre-selected CpG within less than 20 minutes for most of our samples. When combining an optimized library preparation protocol with the time used for sequencing the minimal number of CpGs needed for classification, we saw sample-to-answer times of less than 1 hour – in many cases within 45 minutes - from receiving a fresh biopsy to a robust cancer type prediction. Comparing actual and predicted diagnoses resulted in a favorable error rate, indicating potentially highly clinical validity. Our real-time based molecular diagnostic algorithm enables, in most cases, a reliable diagnostic call within the timeframe of a typical neuro-oncological surgery. MethyLYZR as a predictive tool may allow us to adjust the surgical strategy and deliver the prognosis to our patients right after surgery, thus allowing for as-of-yet unexplored opportunities for the intraoperative application of individualized therapeutic modalities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".