Managing leptomeningeal melanoma metastases in the era of immune and targeted therapy
Bibliographic record
Abstract
Melanoma frequently metastasizes to the brain, with CNS involvement being clinically evident in ∼30% of patients (as high as 75% at autopsy). In ∼5% cases melanoma cells also metastasize to the leptomeninges, the sub-arachnoid space and cerebrospinal fluid (CSF). Patients with leptomeningeal melanoma metastases (LMM) have the worst prognosis and are characterized by rapid disease progression (mean survival 8-10 weeks) and a death from neurological causes. The recent years have seen tremendous progress in the development of targeted and immune therapies for melanoma that has translated into an increased survival benefit. Despite these gains, the majority of patients fail therapy and there is a suspicion that the brain and the leptomeninges are a "sanctuary" sites for melanoma cells that escape both targeted therapy and immunologic therapies. Emerging evidence suggests that (1) Cancer cells migrating to the CNS may have unique molecular properties and (2) the CNS/leptomeningeal microenvironment represents a pro-survival niche that influences therapeutic response. In this Mini-Review, we will outline the clinical course of LMM development and will describe how the intracranial immune and cellular microenvironments offer both opportunities and challenges for the successful management of this disease. We will further discuss the latest data demonstrating the potential use of BRAF inhibitors and immune therapy in the management of LMM, and will review future potential therapeutic strategies for the management of this most devastating complication of advanced melanoma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".