Mcl-1 Is a Novel Therapeutic Target for Human Sarcoma
Bibliographic record
Abstract
PURPOSE: Little is known about the role that Mcl-1, an antiapoptotic Bcl-2 family member, plays in solid tumor biology and susceptibility to anticancer therapy. We observed that the Mcl-1 protein is widely expressed in human sarcoma cell lines of different histological origin (n = 7). Because the expression of antiapoptotic Bcl-2 family proteins can significantly contribute to the chemoresistance of human malignancies, we used an antisense strategy to address this issue in sarcoma. EXPERIMENTAL DESIGN: SCID mice (n = 6/group) received s.c. injections of SW872 liposarcoma cells. After development of palpable tumors, mice were treated by s.c.-implanted miniosmotic pumps prefilled with saline or antisense or universal control oligonucleotides (20 mg/kg/day for 2 weeks). On days 2, 6, and 10, mice were treated with low-dose cyclophosphamide (35 mg/kg i.p) or saline control. During the experiments, tumor weight was assessed twice weekly by caliper measurements. On day 14, animals were sacrificed. Tumors were weighed and fixed in formalin for immunohistochemistry and terminal deoxynucleotidyl transferase-mediated dUTP nick end-labeling analysis. RESULTS: Mcl-1 antisense oligonucleotides specifically reduced Mcl-1 protein expression but produced no reduction in tumor weight compared with saline-treated control animals. Cyclophosphamide monotreatment caused only modest tumor weight reduction compared with saline control. However, use of Mcl-1 antisense oligonucleotides combined with cyclophosphamide clearly enhanced tumor cell apoptosis and significantly reduced tumor weight by more than two-thirds compared with respective control treatments. CONCLUSION: A combination of Mcl-1 antisense oligonucleotides with low-dose cyclophosphamide provides a synergistic antitumor effect and might qualify as a promising strategy to overcome chemoresistance in human sarcoma.
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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) | 0.002 | 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".