Apoptosis signaling molecules as treatment targets in head and neck squamous cell carcinoma
Bibliographic record
Abstract
OBJECTIVES: To evaluate BCL-2 family signaling molecules in head and neck squamous cell carcinoma (HNSCC) and examine the ability of therapeutic agents with variable mechanisms of action to induce apoptosis in HNSCC cells. METHODS: messenger ribonculeic acid (mRNA) expression of BAK, BAX, B-cell lymphoma (Bcl-2), BCL2 Like 1 (BCL2L1), and MCL1 were measured in The Cancer Genome Atlas (TCGA) head and neck cancer dataset, as well as in a dataset from a cohort at Montefiore Medical Center (MMC). Protein expression was similarly evaluated in a panel of HNSCC cell lines (HN30, HN31, HN5, MDA686LN, UMSCC47). Cell viability and Annexin V assays were used to assess the efficacy and apoptotic potential of a variety of agents (ABT-263 [navitoclax], A-1210477, and bortezomib. RESULTS: Expression of BAK, BAX, BCL2L1, and MCL1 were each significantly higher than expression of BCL2 in the TCGA and MMC datasets. Protein expression demonstrated the same pattern of expression when examined in HNSCC cell lines. Treatment with combined ABT-263 (navitoclax)/A-1210477 or with bortezomib demonstrated apoptosis responses that approached or exceeded treatment with staurospaurine control. CONCLUSION: HNSCC cells rely on inhibition of apoptosis via BCL-xL and MCL-1 overexpression, and induction of apoptosis remains a potential therapeutic option as long as strategies overcome redundant anti-apoptotic signals. LEVEL OF EVIDENCE: NA Laryngoscope, 130:2643-2649, 2020.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".