Advanced Adrenocortical Carcinoma (ACC): a Review with Focus on Second-Line Therapies
Bibliographic record
Abstract
Advanced adrenocortical cancer (ACC) is a rare, highly aggressive malignancy, which typically has a poor prognosis. In advanced ACC, the overall trend is toward a short PFS interval following first-line systemic therapy, highlighting a clear need for improved second-/third-line treatment strategies. We conducted a review of the literature and relevant scientific guidelines related to systemic therapy for advanced ACC. Public indexes including PubMed/MEDLINE were searched. Treatment selection in the second-line setting is based on small phase 2 trials, case reports, and pre-clinical evidence. The best data available for initial second-line therapy selection supports the use of gemcitabine and capecitabine (G + C) or streptozotocin (S), both with or without mitotane. G + C is becoming increasingly recommended based on phase 2 clinical trial data in patients of good PS, due to the inferred superior PFS and OS from non-comparative trials. Alternatively, streptozotocin was better tolerated than EDP + M in the FIRM-ACT study and remains an option when warranted. Beyond this, further treatment approaches should be tailored to individual patient characteristics, utilizing a mixture of systemic therapies, local therapies, and enrolment in clinical trials where available. Additionally, the role of molecular stratification, predictive biomarkers, and immune checkpoint inhibitors in specific individuals, such as Lynch syndrome, is evolving and may become increasingly utilized in clinical practice. Advanced ACC necessitates a multidisciplinary approach and is best managed in a specialist center. Although there is no one definitive second-line treatment strategy, there are some favorable approaches, which require further validation in larger clinical trials.
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".