Targeting the Ubiquitin–Proteasome System Using the UBA1 Inhibitor TAK-243 is a Potential Therapeutic Strategy for Small-Cell Lung Cancer
Bibliographic record
Abstract
PURPOSE: Small cell lung cancer (SCLC) is an aggressive disease with an overall 5-year survival rate of less than 10%. Treatment for SCLC with cisplatin/etoposide chemotherapy (C/E) ± radiotherapy has changed modestly over several decades. The ubiquitin-proteasome system is an underexplored therapeutic target for SCLC. We preclinically evaluated TAK-243, a first-in-class small molecule E1 inhibitor against UBA1. EXPERIMENTAL DESIGN: We assessed TAK-243 in 26 SCLC cell-lines as monotherapy and combined with C/E, the PARP-inhibitor, olaparib, and with radiation using cell viability assays. We interrogated TAK-243 response with gene expression to identify candidate biomarkers. We evaluated TAK-243 alone and in combination with olaparib or radiotherapy with SCLC patient-derived xenografts (PDX). RESULTS: Most SCLC cell lines were sensitive to TAK-243 monotherapy (EC50 median 15.8 nmol/L; range 10.2 nmol/L-367.3 nmol/L). TAK-243 sensitivity was associated with gene-sets involving the cell cycle, DNA and chromatin organization, and DNA damage repair, while resistance associated with cellular respiration, translation, and neurodevelopment. These associations were also observed in SCLC PDXs. TAK-243 synergized with C/E and olaparib in vitro across sensitive and resistant SCLC cell lines. Considerable TAK-243-olaparib synergy was observed in an SCLC PDX resistant to both drugs individually. TAK-243 radiosensitization was also observed in an SCLC PDX. CONCLUSIONS: TAK-243 displays efficacy in SCLC preclinical models. Enrichment of gene sets is associated with TAK-243 sensitivity and resistance. TAK-243 exhibits synergy when combined with genotoxic therapies in cell lines and PDXs. TAK-243 is a potential therapeutic strategy to improve SCLC patient outcomes, both as a single agent and in combination with existing therapies.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".