Therapeutic Targeting of TFE3/IRS-1/PI3K/mTOR Axis in Translocation Renal Cell Carcinoma
Bibliographic record
Abstract
Abstract Purpose: Translocation renal cell carcinoma (tRCC) represents a rare subtype of kidney cancer associated with various TFE3, TFEB, or MITF gene fusions that are not responsive to standard treatments for RCC. Therefore, the identification of new therapeutic targets represents an unmet need for this disease. Experimental Design: We have established and characterized a tRCC patient-derived xenograft, RP-R07, as a novel preclinical model for drug development by using next-generation sequencing and bioinformatics analysis. We then assessed the therapeutic potential of inhibiting the identified pathway using in vitro and in vivo models. Results: The presence of a SFPQ-TFE3 fusion [t(X;1) (p11.2; p34)] with chromosomal break-points was identified by RNA-seq and validated by RT-PCR. TFE3 chromatin immunoprecipitation followed by deep sequencing analysis indicated a strong enrichment for the PI3K/AKT/mTOR pathway. Consistently, miRNA microarray analysis also identified PI3K/AKT/mTOR as a highly enriched pathway in RP-R07. Upregulation of PI3/AKT/mTOR pathway in additional TFE3–tRCC models was confirmed by significantly higher expression of phospho-S6 (P < 0.0001) and phospho-4EBP1 (P < 0.0001) in established tRCC cell lines compared with clear cell RCC cells. Simultaneous vertical targeting of both PI3K/AKT and mTOR axis provided a greater antiproliferative effect both in vitro (P < 0.0001) and in vivo (P < 0.01) compared with single-node inhibition. Knockdown of TFE3 in RP-R07 resulted in decreased expression of IRS-1 and inhibited cell proliferation. Conclusions: These results identify TFE3/IRS-1/PI3K/AKT/mTOR as a potential dysregulated pathway in TFE3–tRCC, and suggest a therapeutic potential of vertical inhibition of this axis by using a dual PI3K/mTOR inhibitor for patients with TFE3–tRCC.
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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".