Genomic features of mediastinal germ cell tumors: a narrative review
Bibliographic record
Abstract
Background and Objective: Germ cell tumors (GCTs) are uncommon malignancies generally originating from gonads. However, about 5% of GCTs arise outside the gonad (extragonadal), of which 80% develop from the mediastinum. While the prognosis of seminomas is not affected by the gonadal or extragonadal primary location, the prognosis of nonseminoma primary mediastinal GCTs (NS-PMGCTs) is poor, compared to its gonadal counterpart with an estimated 5-year overall survival of about 50%. The current treatments are sub-optimal to increase the cure rate of these rare GCTs. Therefore, molecular insights into these tumors would be valuable to develop novel therapies. The main objective of this review is to describe and dissect the genomic features associated with primary mediastinal GCTs (PMGCTs), highlighting the more frequent genomic alterations and their correlation with clinical outcomes. Methods: We conducted a narrative review of the English literature available in PubMed and Google Scholar between 1982 and 2021, including meta-analyses, systematic reviews, case series and case reports regarding the genomic and clinical features of PMGCTs. We analyzed the available data to describe the molecular characteristics of PMGCTs compared to testicular GCTs (TGCTs), highlighting the most relevant biological and prognostic factors. Key Content and Findings: mutations, and a distinct genomic landscape characterize this rare disease. Conclusions: Although some studies have unveiled recurrent molecular alterations in PMGCTs, few are particularly suitable for targeted therapy. Due to the rarity of PMGCTs, data sharing and the creation of an international consortium would be helpful to have a better understanding of the molecular drivers of these tumors.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| 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) | 0.002 | 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".