Expression Profile of BCL-2, BCL-XL, and MCL-1 Predicts Pharmacological Response to the BCL-2 Selective Antagonist Venetoclax in Multiple Myeloma Models
Bibliographic record
Abstract
BCL-2 family proteins dictate survival of human multiple myeloma cells, making them attractive drug targets. Indeed, multiple myeloma cells are sensitive to antagonists that selectively target prosurvival proteins such as BCL-2/BCL-XL (ABT-737 and ABT-263/navitoclax) or BCL-2 only (ABT-199/GDC-0199/venetoclax). Resistance to these three drugs is mediated by expression of MCL-1. However, given the selectivity profile of venetoclax it is unclear whether coexpression of BCL-XL also affects antitumor responses to venetoclax in multiple myeloma. In multiple myeloma cell lines (n = 21), BCL-2 is expressed but sensitivity to venetoclax correlated with high BCL-2 and low BCL-XL or MCL-1 expression. Multiple myeloma cells that coexpress BCL-2 and BCL-XL were resistant to venetoclax but sensitive to a BCL-XL-selective inhibitor (A-1155463). Multiple myeloma xenograft models that coexpressed BCL-XL or MCL-1 with BCL-2 were also resistant to venetoclax. Resistance to venetoclax was mitigated by cotreatment with bortezomib in xenografts that coexpressed BCL-2 and MCL-1 due to upregulation of NOXA, a proapoptotic factor that neutralizes MCL-1. In contrast, xenografts that expressed BCL-XL, MCL-1, and BCL-2 were more sensitive to the combination of bortezomib with a BCL-XL selective inhibitor (A-1331852) but not with venetoclax cotreatment when compared with monotherapies. IHC of multiple myeloma patient bone marrow biopsies and aspirates (n = 95) revealed high levels of BCL-2 and BCL-XL in 62% and 43% of evaluable samples, respectively, while 34% were characterized as BCL-2(High)/BCL-XL (Low) In addition to MCL-1, our data suggest that BCL-XL may also be a potential resistance factor to venetoclax monotherapy and in combination with bortezomib. Mol Cancer Ther; 15(5); 1132-44. ©2016 AACR.
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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".