Diagnostic and Prognostic Biomarkers of Adrenal Cortical Carcinoma
Bibliographic record
Abstract
The diagnosis of low-grade adrenal cortical carcinoma (ACC) confined to the adrenal gland can be challenging. Although there are diagnostic and prognostic molecular tests for ACC, they remain largely unutilized. We examined the diagnostic and prognostic value of altered reticulin framework and the immunoprofile of biomarkers including IGF-2, proteins involved in cell proliferation and mitotic spindle regulation (Ki67, p53, BUB1B, HURP, NEK2), DNA damage repair (PBK, γ-H2AX), telomere regulation (DAX, ATRX), wnt-signaling pathway (beta-catenin) and PI3K signaling pathway (PTEN, phospho-mTOR) in a tissue microarray of 50 adenomas and 43 carcinomas that were characterized for angioinvasion as defined by strict criteria, Weiss score, and mitotic rate-based tumor grade. IGF-2 and proteins involved in cell proliferation and mitotic spindle regulation (Ki67, p53, BUB1B, HURP, NEK2), DNA damage proteins (PBK, γ-H2AX), regulators of telomeres (DAXX, ATRX), and beta-catenin revealed characteristic expression profiles enabling the distinction of carcinomas from adenomas. Not all biomarkers were informative in all carcinomas. IGF-2 was the most useful biomarker of malignancy irrespective of tumor grade and cytomorphologic features, as juxtanuclear Golgi-pattern IGF-2 reactivity optimized for high specificity was identified in up to 80% of carcinomas and in no adenomas. Loss rather than qualitative alterations of the reticulin framework yielded statistical difference between carcinoma and adenoma. Angioinvasion defined as tumor cells invading through a vessel wall and intravascular tumor cells admixed with thrombus proved to be the best prognostic parameter, predicting adverse outcome in the entire cohort as well as within low-grade ACCs. Low mitotic tumor grade, Weiss score, global loss of DAXX expression, and high phospho-mTOR expression correlated with disease-free survival, but Weiss score and biomarkers failed to predict adverse outcome in low-grade disease. Our results underscore the importance of careful morphologic assessment coupled with ancillary diagnostic and prognostic biomarkers of ACC.
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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) | 0.000 | 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 | 0.000 | 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".