This research trial studies molecular features and pathways in predicting drug resistance in patients with castration-resistant prostate cancer that has spread to other parts of the body and who are receiving enzalutamide. Studying samples of blood and tissue in the laboratory from patients receiving enzalutamide may help doctors learn more about molecular features and pathways that may cause prostate cancer to be resistant to the drug.
PRIMARY OBJECTIVES: I. To assess the correlations between baseline molecular features and pathways and prostate-specific antigen (PSA) response (\</\>= 50% decline) at 12 weeks versus (vs.) baseline. SECONDARY OBJECTIVES: I. To assess the correlations between the baseline molecular features and pathways and progression-free survival (defined as time from day 1 of study drug treatment to date of radiographic progression or clinical progression), disease-specific survival (defined as the time from day 1 of study drug to date of death from prostate cancer), and overall survival (defined as time from day 1 of study drug treatment to date of death from any cause). II. To assess the correlations between the baseline molecular features and pathways and time to PSA progression. III. To identify molecular features and cellular pathways present in tumors from men with metastatic castrate-resistant prostate cancer (CRPC) that are progressing despite enzalutamide treatment. IV. To explore correlation between baseline molecular features and pathways and objective response. V. To assess the correlations between the baseline molecular features and pathways and degree of PSA decline at 12 weeks and maximal PSA decline observed while on study. VI. To assess the correlations between the baseline molecular features and time on treatment. TERTIARY OBJECTIVES: I. To assess correlations between cell-free deoxyribonucleic acid (cfDNA) molecular features from blood and molecular features and pathways from the biopsy samples. II. To assess correlations between cfDNA molecular features and endpoints in the primary and secondary objectives listed above. III. To explore correlations with baseline molecular features and tissue histology. IV. To explore correlations with baseline tissue histology and PSA change, time to PSA progression, time on treatment, progression-free survival, and overall survival. OUTLINE: Patients undergo collection of blood and tissue samples at baseline, during administration of enzalutamide, and after the time of disease progression for analysis via immunohistochemistry, comparative genome hybridization, and sequencing. After completion of study, patients are followed up every 12 weeks.
Study Type
OBSERVATIONAL
Enrollment
41
UCLA / Jonsson Comprehensive Cancer Center
Los Angeles, California, United States
UCSF Medical Center-Mount Zion
San Francisco, California, United States
OHSU Knight Cancer Institute
Portland, Oregon, United States
PSA response, a binary variable indicating whether the PSA level has declined >= 50% within 12 weeks of beginning enzalutamide treatment
Will be reported with 95% exact confidence interval.
Time frame: Within 12 weeks
Degree of PSA decline
Simple linear regression will be used to assess the association between molecular biomarker predictors and the continuous endpoints. Logarithm transformation or other forms of transformation may be conducted to the continuous endpoints to address the skewedness of the distributions if necessary.
Time frame: 12 weeks
Disease-specific survival
Will be graphically illustrated using Kaplan-Meier plots of estimated survival distribution for patients (\</\>= 50%) PSA decline at 12 weeks. Log-rank test will be fitted to determine whether the estimated survival distribution of each time-to-event endpoint differs for patients with PSA decline \>= 50% versus patients with PSA decline \< 50%. Cox regression models will be fitted to obtain the estimated hazard ratio for each time-to-event endpoints for patients who had \>= 50% PSA decline at 12 weeks versus those who had not.
Time frame: Time from day 1 of the study drug to date of death from prostate cancer, assessed up to 4 years
Maximal PSA decline observed while on study
Simple linear regression will be used to assess the association between molecular biomarker predictors and the continuous endpoints. Logarithm transformation or other forms of transformation may be conducted to the continuous endpoints to address the skewedness of the distributions if necessary.
Time frame: Up to 4 years
Molecular features
Will assess correlations between the baseline molecular features and time on treatment.
Time frame: Up to 4 years
Objective response
Will be summarized using the estimated proportion and 95% confidence interval. Simple logistic regression model will be used to assess the association between molecular biomarker predictors and these binary secondary endpoints.
Time frame: Up to 4 years
Overall survival
Will be graphically illustrated using Kaplan-Meier plots of estimated survival distribution for patients (\</\>= 50%) PSA decline at 12 weeks. Log-rank test will be fitted to determine whether the estimated survival distribution of each time-to-event endpoint differs for patients with PSA decline \>= 50% versus patients with PSA decline \< 50%. Cox regression models will be fitted to obtain the estimated hazard ratio for each time-to-event endpoints for patients who had \>= 50% PSA decline at 12 weeks versus those who had not.
Time frame: Time from day 1 of study drug treatment to date of death from any cause, assessed up to 4 years
Progression for a subgroup of patients who have metastatic castration resistant prostate cancer and have received enzalutamide treatment
Will be summarized using the estimated proportion and 95% confidence interval. Simple logistic regression model will be used to assess the association between molecular biomarker predictors and these binary secondary endpoints.
Time frame: Up to 4 years
Progression-free survival
Will be graphically illustrated using Kaplan-Meier plots of estimated survival distribution for patients (\</\>= 50%) PSA decline at 12 weeks. Log-rank test will be fitted to determine whether the estimated survival distribution of each time-to-event endpoint differs for patients with PSA decline \>= 50% versus patients with PSA decline \< 50%. Cox regression models will be fitted to obtain the estimated hazard ratio for each time-to-event endpoints for patients who had \>= 50% PSA decline at 12 weeks versus those who had not.
Time frame: Time from day 1 of study drug treatment to date of first documented radiographic progression or clinical progression, assessed up to 4 years
Time to PSA progression
Will be graphically illustrated using Kaplan-Meier plots of estimated survival distribution for patients (\</\>= 50%) PSA decline at 12 weeks. Log-rank test will be fitted to determine whether the estimated survival distribution of each time-to-event endpoint differs for patients with PSA decline \>= 50% versus patients with PSA decline \< 50%. Cox regression models will be fitted to obtain the estimated hazard ratio for each time-to-event endpoints for patients who had \>= 50% PSA decline at 12 weeks versus those who had not.
Time frame: Up to 4 years
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