12540 Epigenetic Subtyping Of Adrenocortical Carcinoma In Archival Material And Implications For Disease Evolution
Bibliographic record
Abstract
Abstract Disclosure: D. Mohan: None. A.M. Lerario: None. M.Q. Almeida: None. B.B. Mendonca: None. A. Latronico: None. M.C. Fragoso: None. G.D. Hammer: None. Adrenocortical carcinoma (ACC) is a rare cancer of the adrenal cortex, to date cured only by surgery. Pangenomic studies demonstrate that ACC is comprised of three molecular subtypes with distinct clinical outcomes. CIMP-high ACC, the most aggressive molecular subtype, is defined by abnormal epigenetic patterning including inappropriate DNA methylation. CIMP-high status of the primary tumor accounts for 30% of all ACC, but 70% of metastatic relapses. It remains unclear if ACC transitions molecular subtypes, e.g. acquires CIMP-high status, during metastatic dissemination. We and others have demonstrated that G0S2 methylation is a sensitive and specific single-locus biomarker capturing CIMP-high ACC. However, our prior studies utilized frozen tissues, often only available at specialized centers. Here, we measured G0S2 methylation in an expanded cohort of n=73 formalin-fixed paraffin-embedded (FFPE) primary, recurrent, and metastatic ACC samples. We show that G0S2 methylation status is stable in archival FFPE material, concordant with measurement in frozen specimen (Pearson r=0.94, n=28). We identify that, within individual patients, the G0S2 methylation status of the primary is often preserved in the metastasis or recurrence. These data suggest that ACC subtype switching is not required for metastatic evolution. In cases of advanced ACC with accessible lesions amenable to biopsy, epigenetic subtypes can be diagnosed prior to oncologic resection, enabling subtype-specific therapeutic interventions in the neoadjuvant setting. Presentation: 6/1/2024
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".