Identifying and treating <i>ROBO1</i><sup>−ve</sup>/<i>DOCK1</i><sup>+ve</sup> prostate cancer: An aggressive cancer subtype prevalent in African American patients
Bibliographic record
Abstract
Abstract Background There is a need to develop novel therapies which could be beneficial to patients with prostate cancer (CaP) including those who are predisposed to poor outcome, such as African‐Americans. This study investigates the role of ROBO1 ‐pathway in predicting outcome and race‐based disparity in patients with CaP. Methods and Results Aided by RNA sequencing‐based DECIPHER‐testing and immunohistochemical (IHC) analysis of tumors we show that ROBO1 is lost during the progressive stages of CaP, a prevalent feature in African‐Americans. We show that the loss of ROBO1 predicts high‐risk of recurrence, metastasis and poor outcome of androgen‐deprivation therapy in radical prostatectomy‐treated patients. These data identified an aggressive ROBO1 deficient /DOCK1 +ve sub‐class of CaP. Combined genetic and IHC data showed that ROBO1 loss is accompanied by DOCK1 / Rac1 elevation in grade‐III/IV primary‐tumors and Mets. We observed that the hypermethylation of ROBO1 ‐promoter contributes to loss of expression that is highly prevalent in African‐Americans. Because of limitations in restoring ROBO1 function, we asked if targeting the DOCK1 could be an ideal strategy to inhibit progression or treat ROBO1 deficient metastatic‐CaP. We tested the pharmacological efficacy of CPYPP, a selective inhibitor of DOCK1 under in vitro and in vivo conditions. Using ROBO1 −ve and ROBO1 +ve CaP models, we determined the median effective concentration of CPYPP for growth. DOCK1‐inhibitor treatment significantly decreased the (a) Rac1‐GTP/β‐catenin activity, (b) transmigration of ROBO1 deficient cells across endothelial lining, and (c) metastatic spread of ROBO1 deficient cells through the vasculature of transgenic fl Zebrafish model. Conclusion We suggest that ROBO1 status forms as predictive biomarker of outcome in high‐risk populations such as African‐Americans and DOCK1‐targeting therapy has a clinical potential for treating metastatic‐CaP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".