Modulating <scp>ATP</scp> binding cassette transporters in papillary renal cell carcinoma type 2 enhances its response to targeted molecular therapy
Bibliographic record
Abstract
Papillary renal cell carcinoma (PRCC) is the most common nonclear cell RCCs and is known to comprise two histological subtypes. PRCC2 is more aggressive and is molecularly distinct from the other subtypes. Despite this, PRCCs are treated together as one entity, and they show poor response to the current therapies that do not target pathways implicated in their pathogenesis. We have previously detected ABCC2 (an ABC transporter), VEGF, and mTOR pathways to be enriched in PRCC2. In this study, we assess the therapeutic potential of targeting these pathways in PRCC2. Twenty RCC cell lines from the Cancer Cell Encyclopedia were compared to the Cancer Genome Atlas PRCC cohort (290), to identify representative PRCC2 cell lines. Cell lines were further validated in xenograft models. Selected cell lines were treated in vitro and in vivo (mice models) under five different conditions, untreated, anti-VEGF (sunitinib), ABCC2 blocker (MK571), mTOR inhibitor (everolimus) and sunitinib + MK571. Sunitinib +ABCC2 blocker group showed a significant response to therapy compared to the other treatment groups both in vitro (P ≤ 0.0001) and in vivo (P = 0.0132). ABCC2 blockage resulted in higher sunitinib uptake, both in vitro (P = 0.0016) and in vivo (P = 0.0031). Everolimus group demonstrated the second best response in vivo. The double-treatment group showed the highest apoptotic rate and lowest proliferation rate. There is an urgent need for individualized therapies of RCC subtypes that take into account their specific biology. Our results demonstrate that combined targeted therapy with sunitinib and ABCC2 blocker in PRCC2 has therapeutic potential. The results are likewise potentially significant for other ABCC2 high tumors. However, the results are preliminary and clinical trials are needed to confirm these effects in PRCC2 patients.
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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 | 0.000 |
| 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".