Prevalence of Genetic Mutations in Patients with Metastatic Prostate Cancer in a Cohort of Mexican Patients
Bibliographic record
Abstract
Background: Prostate cancer is a malignant neoplasm of the male genitourinary system with the highest incidence worldwide. Susceptibility genes related to aggressiveness and prognosis, such as BRCA1/2, ATM, PTEN, have been identified. Currently, reports related to germline mutations in patients with prostate cancer in Latin American populations are very limited or absent. In the Mexican population, reports are also limited, especially in the context of metastatic prostate cancer. Determining the prevalence of these mutations is relevant to predict the potential aggressiveness of tumors and allow the use of targeted therapies, such as PARPi inhibitors. Objective: Determine the prevalence of germline mutations in patients with metastatic prostate cancer and establish their clinical characteristics at diagnosis. Material and Methods: Sixty-nine patients with metastatic PCa underwent testing and genetic analysis using the Comprehensive Multi-Cancer Hereditary Cancer Panel. The prevalence of germline mutations was assessed, and the cohort was divided into two groups for the evaluation and analysis of clinical characteristics between the mutated and non-mutated populations. Results: We identified mutations in 15 out of 69 patients (21.73%), while 54 patients (78.26%) had no mutations. Pathogenic mutations were observed in 15.9% of patients, Variants of Uncertain Significance (VUS) in 34.78%, and 5.79% had both. The most frequent mutations included ATM (11.54%), BRCA1 (11.54%), BRCA2 (7.69%), FANCA (7.69%), and FANCM (7.69%). No statistically significant differences were found in PSA levels, age at diagnosis, and resistance to castration between the two groups. Conclusions: Our study unveiled a mutation rate of 21.73%, marked by a significant prevalence of ATM, FANCA, FANCM, and Variants of Uncertain Significance (VUS). This pattern deviates from findings in other series, underscoring the necessity for improved access to clinical genetic testing in our population.
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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) | 0.000 | 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) | 0.000 | 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".