Therapeutic Perspectives in the Systemic Treatment of Kaposi’s Sarcoma
Bibliographic record
Abstract
In patients with Kaposi's sarcoma (KS), the therapeutic goal is to achieve a durable remission in the size and number of skin and visceral lesions. Although most patients show tumor regression in response to standard systemic chemotherapy regimens, alternative systemic treatments are needed for patients who develop refractory KS. Anti-angiogenic therapies represent attractive therapeutic targets in this context, due to the central role of angiogenesis in KS pathogenesis. Pomalidomide, which exhibits such anti-angiogenic activity through inhibition of VEGF, currently constitutes the most promising agent of this class and has been recently approved by the FDA. In addition, immune checkpoint blockade also represents an interesting alternative therapeutic approach through the restoration of immunity against HHV-8, the causative agent of KS, and improvement of tumor control. Although small series of cases treated successfully with these drugs have been reported, there is no marketing approval for anti-immune checkpoint antibodies for KS to date. In the present review, we will discuss potential therapeutic options for patients with recurrent or refractory KS, including systemic chemotherapies, immune checkpoint inhibitors, anti-herpesvirus agents, and anti-angiogenic drugs. Well-conducted clinical trials in this population are urgently needed to correctly address the efficacy of targeted agents and immunomodulators, while monitoring for adverse effects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".