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  ["project_title"]=>
  string(106) "Evaluation of Bellmunt Risk Score as a prognostic score in metastatic castration-resistant prostate cancer"
  ["project_narrative_summary"]=>
  string(1990) "Metastatic castration-resistant prostate cancer (mCRPC) is an aggressive, incurable and often lethal condition. It is characterized by two factors: A prostate cancer which has spread beyond the prostate, forming so-called metastases. Moreover, the cancer no longer responds to anti-hormonal treatment, requiring further therapy. In a western population, about 6 in 1000 men suffer from mCRPC and approximately 2 in 1000 men are newly diagnosed per year.



Patients often want information about how long they will survive with this disease given their individual life plans or family and relationship bounds. Therefore, we aim to provide patients and their doctors an easy tool to predict the survival times.



The so-called Bellmunt Risk Score looks at the three factors: overall health status, blood count (hemoglobin) levels, and whether the cancer has spread to the liver. A score of 0 - 3 can quickly be built only from these factors.



In a small study involving 125 men with mCRPC, we already found that the Bellmunt Risk Score could be useful in predicting survival. While men with a score of 0 lived for approximately 4 years, men with scores larger than 2 only lived for about one year. With overall health status collected more accurate, a modified Bellmunt Risk score even improved these predictions. However, we need proof for these findings in studies with larger groups of patients. 



We aim at extracting Bellmunt Risk Score and survival times from the data of large controlled studies. We want to discover, if we can confirm our previous findings. This is possible by comparing the survival times of men with each score of 0, 1, 2 and 3 using the established statistical tools "Cox regression analysis" and "Log-Rank analysis". If we find significant differences between these groups, this score could become a valuable tool for doctors and patients, helping them to be informed and make better decisions about treatment."
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  array(7) {
    ["first_name"]=>
    string(6) "Thomas"
    ["last_name"]=>
    string(8) "Büttner"
    ["degree"]=>
    string(2) "MD"
    ["primary_affiliation"]=>
    string(47) "Department of Urology, University Hospital Bonn"
    ["email"]=>
    string(25) "Thomas.Buettner@ukbonn.de"
    ["state_or_province"]=>
    string(22) "North Rhine-Westphalia"
    ["country"]=>
    string(7) "Germany"
  }
  ["project_key_personnel"]=>
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    array(6) {
      ["p_pers_f_name"]=>
      string(6) "Niklas"
      ["p_pers_l_name"]=>
      string(8) "Klümper"
      ["p_pers_degree"]=>
      string(2) "MD"
      ["p_pers_pr_affil"]=>
      string(47) "Department of Urology, University Hospital Bonn"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(2) "no"
    }
  }
  ["project_ext_grants"]=>
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    ["value"]=>
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    ["label"]=>
    string(68) "No external grants or funds are being used to support this research."
  }
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  ["property_scientific_abstract"]=>
  string(716) "In our previous retrospective pilot analysis, the Bellmunt Risk Score as well as a slightly modified Bellmunt Risk Score (described below) provided easy and significant prognostic information in patients with metastatic castration-resistant prostate cancer (mCRPC). Attached Figures 1+2 show the corresponding plots.

Objective: To validate these findings in a large dataset

Study design: Pooled analysis of 4 trials, with each trial also analyzed seperately 

Participants: Men with mCRPC

Main Outcome Measure(s): Overall survival (OS)

Statistical Analysis: Kaplan-Meier-method, uni- and multivariate Cox regression analysis, time-dependent Area under the Curve, Concordance indices"
  ["project_brief_bg"]=>
  string(2402) "Metastatic castration-resistant prostate cancer (mCRPC) remains a disease of limited prognosis, however, overall survival is subject to individual factors (1). Several prognostic models have been proposed to refine the outcome prediction of men with mCRPC. Risk factors or models previously described include alkaline phosphatase (AP), lactate-dehydrogenase (LDH), Eastern Cooperative Oncology Group performance status (ECOG PS), Hemoglobin (Hb), Prostate-Specific Antigen (PSA) at mCRPC diagnosis, PSA response to therapy, duration until mCRPC development, and genomic landscape (1-12). However, to date, these models remain marginally used in clinical practice (13). The underlying causes for this are not well understood. However, conducting individual prognostic assessments for each patient could enhance decision-making, especially when considering treatment plans with significant side effects, such as those encountered in the mCRPC context.

The drawbacks of the models published so far may involve their validation in trial cohorts, potentially limiting their applicability in real-world settings. (1,3,8,11,14). Some models include genomic markers or liquid biopsy that are not collected as part of routine diagnostics and may not be included in the standard range of a budgeted physician (2,6,7). If an evaluation of PSA dynamics is necessary, the risk cannot be appraised before the commencement of treatment (4). Additional questionnaires, requirement of counting metastases and ultimately complex calculation formulas from the collected data may prevent utilization of existing risk models (3,13).

Therefore, a risk model, in addition to its prognostic importance, should enable straightforward application and incorporate only routinely assessed values. Several analyses have demonstrated a predictive value of the following factors in mCRPC: ECOG PS, Hb and the presence of liver metastases (3,8,9,15). Furthermore, these can be considered pan-cancer predictive markers 16-18. All three are summarized in the well-known Bellmunt Risk Score, which was initially developed for a different genitourinary cancer entity – urothelial carcinoma (19). Here, the Bellmunt Risk Score is routinely employed including major phase III trials (20,21). Its components (metastatic sites, Hb and ECOG PS) are routinely evaluated in nearly every cancer patient before initiating therapy. "
  ["project_specific_aims"]=>
  string(217) "We aim to validate our pilot study findings in the high-quality data of phase III clinical trials. The overall goal is to provide patients and physicians an easy-to-access score allowing for quick survival prediction."
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(5) "other"
    ["label"]=>
    string(5) "Other"
  }
  ["project_study_design_exp"]=>
  string(112) "We plan on both meta-analysis and individual trial analysis, given they cover different treatment lines in mCRPC"
  ["project_purposes"]=>
  array(3) {
    [0]=>
    array(2) {
      ["value"]=>
      string(22) "participant_level_data"
      ["label"]=>
      string(36) "Participant-level data meta-analysis"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(56) "participant_level_data_meta_analysis_from_yoda_and_other"
      ["label"]=>
      string(69) "Meta-analysis using data from the YODA Project and other data sources"
    }
    [2]=>
    array(2) {
      ["value"]=>
      string(50) "research_on_clinical_prediction_or_risk_prediction"
      ["label"]=>
      string(50) "Research on clinical prediction or risk prediction"
    }
  }
  ["project_software_used"]=>
  array(2) {
    ["value"]=>
    string(7) "rstudio"
    ["label"]=>
    string(7) "RStudio"
  }
  ["project_research_methods"]=>
  string(516) "Given the trial protocols, all participants should meet the inclusion criteria:



1. ECOG PS at Baseline documented

2. Hemoglobin levels at baseline documented

3. Presence of liver metastasis documented

4. Data on overall survival



There are no exclusion criteria defined.



Additionally to this request, we currently request data of NCT01193257 and NCT01308567 at Vivli (https://vivli.org/). All trial data will be analyzed in the R environment of Vivli."
  ["project_main_outcome_measure"]=>
  string(157) "The primary endpoint is overall survival (OS) and will be defined as the time from date of random assignment to date of death of any cause or last follow-up."
  ["project_main_predictor_indep"]=>
  string(284) "Bellmunt Risk Score: Determined by assigning one point each for



1. ECOG PS ≥1

2. Decreased serum hemoglobin (< 10 g/dL) 

3. Presence of liver metastasis. 



Patients are then stratified into the respective group of 0 – 3 score points. "
  ["project_other_variables_interest"]=>
  string(619) "Modified Bellmunt Risk Score: Determined by the semiquantitative ECOG PS (0 - 5), with one point each added for decreased serum hemoglobin (< 10 g/dL) and the presence of liver metastasis. This results in a score 0 – 7 stratifying the patient cohort.



Further Variables for multivariate risk adjustment:

1. Further sites of metastases

2. Opioid analgesic use (y/n)

3.  Measurable disease (y/n)

4. Age

5. LDH (and ULN for LDH)

6. Hemoglobin

7. PSA

8. Alkaline phosphatase

9. Albumin

10. C-reactive protein

11. Race/ethnicity"
  ["project_stat_analysis_plan"]=>
  string(1085) "The data of the 3 trials will be analyzed alongside NCT01193257 and NCT01308567 in the R environment provided by Vivli.



Fisher’s exact, Mann–Whitney U, and Kruskal–Wallis tests will be applied to perform intergroup comparisons. The overall survival (OS), including 95% confidence intervals will be estimated with the Kaplan–Meier method and compared with log-rank tests. To compare Bellmunt Risk Score groups, baseline patient (age, Body-Mass-Index) and tumor-related parameters (e.g, serum prostate-specific antigen levels, previous primary therapy) on OS, univariate and multiple Cox regressions will be conducted. Independent variables will only be included in the multiple regression if the respective effect is significant in the univariate analysis. Concordance indices and time-dependent Receiver Operating Characteristics will be conducted to test for predictive potential. Statistical analyses will be performed R-Studio via the vivli research environment.  All statistical tests will be two-sided, and p-values < 0.05 will be considered significant."
  ["project_timeline"]=>
  string(259) "Expected start of project: 01-SEP-2024

Expected end of Data analysis: 01-DEC-2024

Expected date of manuscript drafted: 01-MAR-2025

Expected date of submission for publication and data results reported bach to the YODA Project: 01-MAY-2025"
  ["project_dissemination_plan"]=>
  string(231) "The results will be presented at International meetings such as EAU and ESMO congress. The first abstract is planned for EAU in April 2025. Manuscripts will be published open-access aiming at journals like European Urology Oncology"
  ["project_bibliography"]=>
  string(5593) "

Meier R, Graw S, Usset J, et al. An ensemble-based Cox proportional hazards regression framework for predicting survival in metastatic castration-resistant prostate cancer (mCRPC) patients. F1000Res. 2016;5:2677. doi:10.12688/f1000research.8226.1
Abida W, Cyrta J, Heller G, et al. Genomic correlates of clinical outcome in advanced prostate cancer. Proc Natl Acad Sci U S A. Jun 4 2019;116(23):11428-11436. doi:10.1073/pnas.1902651116
Armstrong AJ, Lin P, Tombal B, et al. Five-year Survival Prediction and Safety Outcomes with Enzalutamide in Men with Chemotherapy-naïve Metastatic Castration-resistant Prostate Cancer from the PREVAIL Trial. Eur Urol. Sep 2020;78(3):347-357. doi:10.1016/j.eururo.2020.04.061
Mendonça Macedo A, Gameiro Marques R, Cunha André M, Silva Figueira N, Leal Carvalho M. Prostate-specific antigen response after Abiraterone treatment in mCRPC: PSA as a predictor of overall survival. Arch Ital Urol Androl. Feb 22 2023;95(1):11052. doi:10.4081/aiua.2023.11052
Kawahara T, Saigusa Y, Yoneyama S, et al. Development and validation of a survival nomogram and calculator for male patients with metastatic castration-resistant prostate cancer treated with abiraterone acetate and/or enzalutamide. BMC Cancer. 2023/03/07 2023;23(1):214. doi:10.1186/s12885-023-10700-0
Song W, Kwon GY, Kim JH, et al. Immunohistochemical staining of ERG and SOX9 as potential biomarkers of docetaxel response in patients with metastatic castration-resistant prostate cancer. Oncotarget. Dec 13 2016;7(50):83735-83743. doi:10.18632/oncotarget.13407
Tolmeijer SH, Boerrigter E, Sumiyoshi T, et al. Early on-treatment changes in circulating tumor DNA fraction and response to enzalutamide or abiraterone in metastatic castration-resistant prostate cancer. Clinical Cancer Research. 2023:OF1-OF10.
Halabi S, Lin CY, Kelly WK, et al. Updated prognostic model for predicting overall survival in first-line chemotherapy for patients with metastatic castration-resistant prostate cancer. J Clin Oncol. Mar 1 2014;32(7):671-7. doi:10.1200/jco.2013.52.3696
Halabi S, Lin C-Y, Small EJ, et al. Prognostic Model Predicting Metastatic Castration-Resistant Prostate Cancer Survival in Men Treated With Second-Line Chemotherapy. JNCI: Journal of the National Cancer Institute. 2013;105(22):1729-1737. doi:10.1093/jnci/djt280
Armstrong AJ, Tannock IF, Wit Rd, George DJ, Eisenberger M, Halabi S. The development of risk groups in men with metastatic castration-resistant prostate cancer based on risk factors for PSA decline and survival. European Journal of Cancer. 2010;46(3):517-525. doi:10.1016/j.ejca.2009.11.007
Chi KN, Kheoh T, Ryan CJ, et al. A prognostic index model for predicting overall survival in patients with metastatic castration-resistant prostate cancer treated with abiraterone acetate after docetaxel. Annals of Oncology. 2016;27(3):454-460. doi:10.1093/annonc/mdv594
Templeton AJ, Pezaro C, Omlin A, et al. Simple prognostic score for metastatic castration-resistant prostate cancer with incorporation of neutrophil-to-lymphocyte ratio. Cancer. 2014;120(21):3346-3352. doi:https://doi.org/10.1002/cncr.28890
Pinart M, Kunath F, Lieb V, et al. Prognostic models for predicting overall survival in metastatic castration-resistant prostate cancer: a systematic review. World Journal of Urology. 2020/03/01 2020;38(3):613-635. doi:10.1007/s00345-018-2574-2
Moreira DM, Howard LE, Sourbeer KN, et al. Predicting Time From Metastasis to Overall Survival in Castration-Resistant Prostate Cancer: Results From SEARCH. Clin Genitourin Cancer. Feb 2017;15(1):60-66.e2. doi:10.1016/j.clgc.2016.08.018
Chen W-J, Kong D-M, Li L. Prognostic value of ECOG performance status and Gleason score in the survival of castration-resistant prostate cancer: a systematic review. Asian Journal of Andrology. 2021;23(2):163-169.
Jang RW, Caraiscos VB, Swami N, et al. Simple prognostic model for patients with advanced cancer based on performance status. Journal of oncology practice. 2014;10(5):e335-e341.
Caro JJ, Salas M, Ward A, Goss G. Anemia as an independent prognostic factor for survival in patients with cancer: a systematic, quantitative review. Cancer. 2001;91(12):2214-2221.
Horn SR, Stoltzfus KC, Lehrer EJ, et al. Epidemiology of liver metastases. Cancer Epidemiology. 2020/08/01/ 2020;67:101760. doi:https://doi.org/10.1016/j.canep.2020.101760
Bellmunt J, Choueiri TK, Fougeray R, et al. Prognostic factors in patients with advanced transitional cell carcinoma of the urothelial tract experiencing treatment failure with platinum-containing regimens. Journal of Clinical Oncology. 2010;28(11):1850-1855.
Powles T, Rosenberg JE, Sonpavde GP, et al. Enfortumab Vedotin in Previously Treated Advanced Urothelial Carcinoma. New England Journal of Medicine. 2021;384(12):1125-1135. doi:10.1056/NEJMoa2035807
Fradet Y, Bellmunt J, Vaughn DJ, et al. Randomized phase III KEYNOTE-045 trial of pembrolizumab versus paclitaxel, docetaxel, or vinflunine in recurrent advanced urothelial cancer: results of &gt;2 years of follow-up. Annals of Oncology. 2019;30(6):970-976. doi:10.1093/annonc/mdz127

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        string(4) "1753"
        ["description"]=>
        string(0) ""
        ["caption"]=>
        string(0) ""
        ["name"]=>
        string(40) "sv_57kskakadt3u9aq-r_2qkzmrmc6zrs3p5-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(14563)
        ["date"]=>
        string(19) "2024-04-08 12:30:10"
        ["modified"]=>
        string(19) "2024-04-08 12:30:12"
        ["menu_order"]=>
        int(0)
        ["mime_type"]=>
        string(15) "application/pdf"
        ["type"]=>
        string(11) "application"
        ["subtype"]=>
        string(3) "pdf"
        ["icon"]=>
        string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
      }
    }
    [1]=>
    array(1) {
      ["file_coi"]=>
      array(21) {
        ["ID"]=>
        int(14612)
        ["id"]=>
        int(14612)
        ["title"]=>
        string(40) "SV_57KskaKADT3U9Aq-R_2Vjg8hEnN3c3JYZ.pdf"
        ["filename"]=>
        string(40) "SV_57KskaKADT3U9Aq-R_2Vjg8hEnN3c3JYZ.pdf"
        ["filesize"]=>
        int(20826)
        ["url"]=>
        string(89) "https://yoda.yale.edu/wp-content/uploads/2024/04/SV_57KskaKADT3U9Aq-R_2Vjg8hEnN3c3JYZ.pdf"
        ["link"]=>
        string(86) "https://yoda.yale.edu/data-request/2024-0424/sv_57kskakadt3u9aq-r_2vjg8henn3c3jyz-pdf/"
        ["alt"]=>
        string(0) ""
        ["author"]=>
        string(4) "1753"
        ["description"]=>
        string(0) ""
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        string(0) ""
        ["name"]=>
        string(40) "sv_57kskakadt3u9aq-r_2vjg8henn3c3jyz-pdf"
        ["status"]=>
        string(7) "inherit"
        ["uploaded_to"]=>
        int(14563)
        ["date"]=>
        string(19) "2024-04-14 09:31:08"
        ["modified"]=>
        string(19) "2024-04-14 09:31:09"
        ["menu_order"]=>
        int(0)
        ["mime_type"]=>
        string(15) "application/pdf"
        ["type"]=>
        string(11) "application"
        ["subtype"]=>
        string(3) "pdf"
        ["icon"]=>
        string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
      }
    }
  }
  ["data_use_agreement_training"]=>
  bool(true)
  ["certification"]=>
  bool(true)
  ["search_order"]=>
  string(1) "0"
  ["project_send_email_updates"]=>
  bool(false)
  ["project_publ_available"]=>
  bool(true)
  ["project_year_access"]=>
  string(0) ""
  ["project_rep_publ"]=>
  bool(false)
  ["project_assoc_data"]=>
  array(0) {
  }
  ["project_due_dil_assessment"]=>
  bool(false)
  ["project_title_link"]=>
  array(21) {
    ["ID"]=>
    int(14716)
    ["id"]=>
    int(14716)
    ["title"]=>
    string(28) "Data Request Approved Notice"
    ["filename"]=>
    string(32) "Data-Request-Approved-Notice.pdf"
    ["filesize"]=>
    int(195663)
    ["url"]=>
    string(81) "https://yoda.yale.edu/wp-content/uploads/2024/04/Data-Request-Approved-Notice.pdf"
    ["link"]=>
    string(77) "https://yoda.yale.edu/data-request/2024-0400/data-request-approved-notice-10/"
    ["alt"]=>
    string(0) ""
    ["author"]=>
    string(3) "190"
    ["description"]=>
    string(0) ""
    ["caption"]=>
    string(0) ""
    ["name"]=>
    string(31) "data-request-approved-notice-10"
    ["status"]=>
    string(7) "inherit"
    ["uploaded_to"]=>
    int(14453)
    ["date"]=>
    string(19) "2024-04-25 15:38:15"
    ["modified"]=>
    string(19) "2024-04-25 15:38:15"
    ["menu_order"]=>
    int(0)
    ["mime_type"]=>
    string(15) "application/pdf"
    ["type"]=>
    string(11) "application"
    ["subtype"]=>
    string(3) "pdf"
    ["icon"]=>
    string(62) "https://yoda.yale.edu/wp/wp-includes/images/media/document.png"
  }
  ["project_review_link"]=>
  bool(false)
  ["project_highlight_button"]=>
  string(0) ""
  ["request_overridden_res"]=>
  string(1) "3"
  ["request_data_partner"]=>
  string(15) "johnson-johnson"
}
data partner
array(1) {
  [0]=>
  string(15) "johnson-johnson"
}


pi country
array(0) {
}


pi affil
array(0) {
}


products
array(2) {
  [0]=>
  string(6) "zytiga"
  [1]=>
  string(7) "erleada"
}


num of trials
array(1) {
  [0]=>
  string(1) "3"
}


res
array(1) {
  [0]=>
  string(1) "3"
}