Cathepsin S Alterations Induce a Tumor-Promoting Immune Microenvironment in Follicular Lymphoma
Bibliographic record
Abstract
Tumor cells orchestrate their microenvironment. Here, we provide biochemical, structural, functional, and clinical evidence that Cathepsin S ( CTSS ) alterations induce a tumor-promoting immune microenvironment in follicular lymphoma (FL). We found CTSS mutations at Y132 in 6% of FL (19/305). Another 13% (37/286) had CTSS amplification, which was associated with higher CTSS expression. CTSS Y132 mutations lead to accelerated autocatalytic conversion from an enzymatically inactive profrom to active CTSS and increased substrate cleavage, including CD74, which regulates major histocompatibility complex class II (MHC class II)-restricted antigen presentation. Lymphoma cells with hyperactive CTSS more efficiently activated antigen-specific CD4 + T cells in vitro . Tumors with hyperactive CTSS showed increased CD4 + T cell infiltration and proinflammatory cytokine perturbation in a mouse model and in human FLs. In mice, this CTSS-induced immune microenvironment promoted tumor growth. Clinically, patients with CTSS-hyperactive FL had better treatment outcomes with standard immunochemotherapies, indicating that these immunosuppressive regimens target both the lymphoma cells and the tumor-promoting immune microenvironment.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".