17q22–24 Chromosomal Losses and Alterations of Protein Kinase A Subunit Expression and Activity in Adrenocorticotropin-Independent Macronodular Adrenal Hyperplasia
Bibliographic record
Abstract
CONTEXT: Primary adrenocortical hyperplasias leading to Cushing syndrome include primary pigmented nodular adrenocortical disease and ACTH-independent macronodular adrenal hyperplasia (AIMAH). Inactivating mutations of the 17q22-24-located PRKAR1A gene, coding for the type 1A regulatory subunit of protein kinase A (PKA), cause primary pigmented nodular adrenocortical disease and the multiple endocrine neoplasia syndrome Carney complex. PRKAR1A mutations and 17q22-24 chromosomal losses have been found in sporadic adrenal tumors and are associated with aberrant PKA signaling. OBJECTIVE: The objective of the study was to examine whether somatic 17q22-24 changes, PRKAR1A mutations, and/or PKA abnormalities are present in AIMAH. PATIENTS: We studied fourteen patients with Cushing syndrome due to AIMAH. METHODS: Fluorescent in situ hybridization with a PRKAR1A-specific probe was used for investigating chromosome 17 allelic losses. The PRKAR1A gene was sequenced in all samples, and tissue was studied for PKA activity, cAMP responsiveness, and PKA subunit expression. RESULTS: We found 17q22-24 allelic losses in 73% of the samples. There were no PRKAR1A-coding sequence mutations. The RIIbeta PKA subunit was overexpressed by mRNA, whereas the RIalpha, RIbeta, RIIalpha, and Calpha PKA subunits were underexpressed. These findings were confirmed by immunohistochemistry. Total PKA activity and free PKA activity were higher in AIMAH than normal adrenal glands, consistent with the up-regulation of the RIIbeta PKA subunit. CONCLUSIONS: PRKAR1A mutations are not found in AIMAH. Somatic losses of the 17q22-24 region and PKA subunit and enzymatic activity changes show that PKA signaling is altered in AIMAH in a way that is similar to that of other adrenal tumors with 17q losses or PRKAR1A mutations.
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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 | 0.001 | 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".