Is It Time to Anticipate the Use of PARP Inhibition in Prostate Cancer Patients?
Bibliographic record
Abstract
The increasing diffusion of genetic analysis regarding the pathogenetic variants (PVs) of genes involved in DNA Damage Repair (DDR) mechanisms and the development of Poly ADP ribose polymerase (PARP) inhibitors (PARPis) led to the first valid precision medicine option tailored toward metastatic prostate cancer (mPC). The concept of anticipation in the systemic treatment of mPC was initially adopted for androgen receptor signaling inhibitors (ARSIs) to describe the expansion of their indications, from the setting of the late-stage second-line treatment of metastatic castration-resistant prostate cancer (mCRPC) to first-line therapy in selected cases. There is already mounting evidence in favor of the anticipation of PARPis in the first line of mCRPC therapy, and further evidence in favor of mHSPC is emerging. Many studies have demonstrated the synergism between ARSIs and PARP inhibitors. Recent discoveries regarding the crosstalk between the androgen receptor (AR) and DNA repair mechanisms are disconnecting the use of PARPis from genetic analysis. The new message emerging is that the combination of PARPis with ARSIs may work independently of DDR mutational status. As a matter of fact, most of the recent trials analyzing the combination of PARPis with abiraterone or enzalutamide as a first-line therapy enrolled mCRPC patients irrespective of their mutational status. The PROPEL trial concluded that the advantage of the combination was independent of PV status, despite a higher advantage being reported in the BRCA1/2 mutated subgroup. The MAGNITUDE trial, however, showed a significant advantage only in the DDR mutated subgroup, and the DDR non-mutated cohort was closed for further enrollment. The combination of PARPis with ARSIs represents a significant strategy with a view to the anticipation and intensification of care in mPC. However, it should not nullify the advantages of precision medicine linked to the genetic analysis of DDR genes.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".