PATH-45. INTEGRATION OF MULTI-REGION (EPI)GENOMICS WITH MULTIMODALITY ADVANCED IMAGING HIGHLIGHTS GLIOMA INTRATUMORAL HETEROGENEITY
Bibliographic record
Abstract
Intratumoral (epi)genetic heterogeneity is recognized to be an important driver in glioma therapy resistance. A single biopsy is unlikely to represent the true spatial and temporal heterogeneity. Therefore, multiple sampling schemes guided by imaging are needed. We obtained 74 stereotactic image-guided biopsies preceding craniotomy in eight patients with glioma. Imaging included standard MRI, diffusion and perfusion weighted MRI, MR spectroscopy and PET FET and CHO. We performed multi-region genome-wide methylation profiling of all samples with DNA copy number profiles inferred from methylation array data. To assess variability in methylation profiles from Ceccarelli et al, Cell, 2016, we conducted supervised classification of each sample biopsy. Phylo(epi)genetic trees were constructed to investigate tumor evolutionary paths. Multimodality imaging data was used to predict (epi)genetic characteristics. Data was validated in two independent populations of respectively 32 multi-region samples in 5 patients and 80 single and multi-region initial and patient-matched recurrence samples in 19 patients. Six out of eight gliomas demonstrated spatial heterogeneity of epigenetic classification. In three IDH mutant gliomas of the G-CIMP high subtype (LGm2) regions were classified as G-CIMP low (LGm1) or Codel subtype (LGm3). In three IDH wild type gliomas of the Mesenchymal subtype (LGm5) regions were classified as Classic subtype (LGm4). This was validated in the second dataset in which one of six gliomas showed spatial and three of twenty gliomas showed temporal heterogeneity. Phyloepigenetic and phylogenetic trees showed high concordance. IDH status of the samples could be predicted using multimodality imaging. These findings provide valuable information concerning intratumoral heterogeneity in gliomas. Moreover, imaging was able to predict molecular characteristics, which could lead to multi-region sampling schemes that direct future therapy development.
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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 | 0.000 | 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) | 0.002 | 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".