Novel Agents and Immunotherapies for Primary Diffuse Large B-Cell Lymphoma of the Central Nervous System: Innovating for Impact in a Disease With Unmet Needs
Bibliographic record
Abstract
Primary diffuse large B-cell lymphoma of the central nervous system (CNS-DLBCL), the most prevalent subtype of primary CNS lymphoma (PCNSL), is a highly aggressive extranodal non-Hodgkin lymphoma (NHL) that arises in the brain, spinal cord, leptomeninges, and orbits. Systemic methotrexate-based chemoimmunotherapy regimens, followed by consolidative autologous stem cell transplantation (ASCT), are the standard treatments for newly diagnosed PCNSL. A considerable number of patients with PCNSL, however, are medically frail and possess multiple comorbidities, which render them unsuitable for these intensive treatments. Furthermore, a substantial proportion of those who undergo treatment still face the challenge of disease recurrence. There is no universally accepted treatment for relapsed disease, particularly for patients who are unable to tolerate systemic chemotherapy, and participation in clinical trials is encouraged. There is a notable treatment gap in PCNSL, underscoring an urgent need to investigate novel agents and immunotherapies that could potentially offer superior tolerability and efficacy profiles. Emerging therapies could play a pivotal role in expanding the therapeutic landscape and addressing the limitations inherent in current treatments. This review examines the oncogenesis of PCNSL, highlighting its reliance on chronic active B-cell receptor (BCR) and nuclear factor-kappa B (NF-κB) signaling pathways, mechanisms of immune evasion, and characteristics of its immunosuppressive tumor microenvironment (TME), which have facilitated the exploration of methotrexate-free targeted therapies for this disease.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".