An Update on the Systemic Therapy of Malignant Salivary Gland Cancers: Role of Chemotherapy and Molecular Targeted Agents
Bibliographic record
Abstract
Salivary gland cancers are a rare malignancy accounting for less than 1% of all cancers and 3-6% of cancers of the head and neck region. The classification of salivary gland tumors is traditionally based on morphology and the different subtypes exhibit various clinical behaviors. The low grade and biologically indolent cell types include the adenoid cystic, acinic cell and adenocarcinoma while the salivary duct, squamous and mucoepidermoid are more active and high grade. The initial management of salivary gland malignancies is to assess resectability and possible adjuvant radiation therapy. Those with locoregional recurrence or metastatic disease are treated with systemic therapy. Numerous studies with small sample sizes have assessed the activity of different cytotoxic agents. Both single agent and combination chemotherapy have been used for the treatment of this disease. For these agents, the response rates are generally modest with objective response rates ranging from 15-50%. Duration of response is typically cited in the range of 6-9 months. Clinicopathological data have demonstrated correlations between poor clinical outcomes and the expression of molecular markers such as mutated p53 protein and vascular endothelial growth factor (VEGF) in salivary gland cancers. Recent studies have also evaluated the epidermal growth factor receptor family including erbB1/EGFR and erbB2/HER2 as potential therapeutic targets. While the prognostic significance of EGFR overexpression has not been well defined, overexpression of the HER2 oncoprotein has been associated with biological aggressiveness and poor prognosis in most series. Given the suboptimal response rates, duration of response, and toxicity of conventional chemotherapy, a better understanding of the biology of salivary gland malignancies will lead to improved prognostication and treatment. With the emergence of molecular targeted therapy, these tumors become an optimal candidate for trials of investigational drugs and established drugs for new indications.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".