Induction of Apoptosis Using Inhibitors of Lysophosphatidic Acid Acyltransferase-β and Anti-CD20 Monoclonal Antibodies for Treatment of Human Non-Hodgkin's Lymphomas
Bibliographic record
Abstract
PURPOSE: Lysophosphatidic acid acyltransferase-beta (LPAAT-beta) is a transmembrane enzyme critical for the biosynthesis of phosphoglycerides whose product, phosphatidic acid, plays a key role in raf and AKT/mTor-mediated signal transduction. EXPERIMENTAL DESIGN: LPAAT-beta may be a novel target for anticancer therapy, and, thus, we examined the effects of a series of inhibitors of LPAAT-beta on multiple human non-Hodgkin's lymphoma cell lines in vitro and in vivo. RESULTS: We showed that five LPAAT-beta inhibitors at doses of 500 nmol/L routinely inhibited growth in a panel of human lymphoma cell lines in vitro by >90%, as measured by [3H]thymidine incorporation. Apoptotic effects of the LPAAT-beta inhibitors were evaluated either alone or in combination with the anti-CD20 antibody, Rituximab. The LPAAT-beta inhibitors induced caspase-mediated apoptosis at 50 to 100 nmol/L in up to 90% of non-Hodgkin's lymphoma cells. The combination of Rituximab and an LPAAT-beta inhibitor resulted in a 2-fold increase in apoptosis compared with either agent alone. To assess the combination of Rituximab and a LPAAT-beta inhibitor in vivo, groups of athymic mice bearing s.c. human Ramos lymphoma xenografts were treated with the LPAAT-beta inhibitor CT-32228 i.p. (75 mg/kg) daily for 5 d/wk x 4 weeks (total 20 doses), Rituximab i.p. (10 mg/kg) weekly x 4 weeks (4 doses total), or CT-32228 plus Rituximab combined. Treatment with either CT-32228 or Rituximab alone showed an approximate 50% xenograft growth delay; however, complete responses were only observed when the two agents were delivered together. CONCLUSIONS: These data suggest that Rituximab, combined with a LPAAT-beta inhibitor, may provide enhanced therapeutic effects through apoptotic mechanisms.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".