Lysine Methyltransferase 9 (KMT9) Is an Actionable Target in Muscle-Invasive Bladder Cancer
Bibliographic record
Abstract
Novel treatment modalities are imperative for the challenging management of muscle-invasive and metastatic BC to improve patient survival rates. The recently identified KMT9, an obligate heterodimer composed of KMT9α and KMT9β, regulates the growth of various types of tumors such as prostate, lung, and colon cancer. While the overexpression of KMT9α was previously observed to be associated with aggressive basal-like MIBC in an analysis of patients’ tissue samples, a potential functional role of KMT9 in this type of cancer has not been investigated to date. In this study, we show that KMT9 regulates proliferation, migration, and invasion of various MIBC cell lines with different genetic mutations. KMT9α depletion results in the differential expression of genes regulating the cell cycle, cell adhesion, and migration. Differentially expressed genes include oncogenes such as EGFR and AKT1 as well as mediators of cell adhesion or migration such as DAG1 and ITGA6. Reduced cell proliferation upon KMT9α depletion is also observed in Pten/Trp53 knockout bladder tumor organoids, which cannot be rescued with an enzymatically inactive KMT9α mutant. In accordance with the idea that the catalytic activity of KMT9 is required for the control of cellular processes in MIBC, a recently developed small-molecule inhibitor of KMT9 (KMI169) also impairs cancer cell proliferation. Since KMT9α depletion also restricts the growth of xenografts in mice, our data suggest that KMT9 is an actionable novel therapeutic target for the treatment of MIBC.
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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".