The SMAC mimetic LCL-161 displays antitumor activity in preclinical models of rituximab-resistant B-cell lymphoma
Bibliographic record
Abstract
Clinical observations suggest the existence of shared resistance pathways between rituximab and chemotherapy agents. To explore the mechanisms of rituximab resistance, our group created rituximab-resistant cell lines (RRCLs), which display altered expression of several inhibitor of apoptosis (IAP) family proteins. Here, we provide evidence to support pharmacologically targeting IAPs in lymphoma with LCL-161, a small molecule mimetic of the second mitochondria-derived activator of caspases (SMAC). The antitumor effect of LCL-161 was determined using luminescent adenosine triphosphate assays, flow cytometry, SCID mouse xenografts, and ex vivo patient biopsy sample studies. In vitro exposure to LCL-161 also resulted in a dose-dependent decrease in IAP levels, along with synergistic enhancement of the antitumor effect of cytotoxic chemotherapy, in rituximab-sensitive cell lines and RRCLs. In addition, LCL-161 increased the cytotoxic effect of the proteasome inhibitor carfilzomib in ex vivo lymphoma patient samples. The combination of LCL-161 with the chemotherapy regimen rituximab, gemcitabine, and vinorelbine (RGV) improved in vivo survival compared with RGV alone in severe combined immunodeficient mice implanted with RRCLs but not in animals implanted with rituximab-sensitive cell lines. In summary, LCL-161 exhibits synergistic antitumor activity in both in vitro and in vivo models of resistant lymphoma. Our data support further preclinical investigation of LCL-161 as a novel antilymphoma agent.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".