Treatment resistance in prostate cancer: Rationale of combination therapy (85.2)
Bibliographic record
Abstract
Despite advances in targeting the androgen receptor in prostate cancer patients which have led to unprecedented improvements in both overall survival and disease free, challenges still remain. Recently FDA approved second generation anti‐androgen Enzalutamide (ENZ) has limited efficacy in a substantial percentage of men and even those showing dramatic responses still develop resistance. These clinical results underscore the importance of understanding the mechanisms of resistance to ENZ. As seen in patients, we found that the resistance also occurs in our preclinical LNCaP xenograft model; the maximum androgen blockade (castration plus ENZ) leads to cancer recurrence in 80% of the ENZ treated tumors while only 20% responded to treatment. Targeting the AR in ENZ resistant tumors with a 3rd generation AR inhibitor was short lived. These data highlight that unexplored signalling pathways drive treatment resistance beyond classical AR reactivation underscore the need of more effective therapies. Using unbiased approaches of gene profiling and sequencing, we found that resistance is heterogeneous and displays cell plasticity indicating the existence of the epithelial cells driven by AR, the emergence of cancer stem cells (CSCs) and cells that may have progressed through an epithelial‐to mesenchymal transition (EMT). We will discuss the involvement of oncogenic and survival pathways driving the resistance as well as the rational of co‐targeting therapy to enhance the efficacy of Enzalutamide for better and long anti‐cancer response. This work is supported by Prostate Cancer Canada Movember Team grant, Prostate Cancer Foundation USA and NCI SPORE Pilot grant
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".