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      ["post_title"]=>
      string(257) "NCT01715285 - A Randomized, Double-blind, Comparative Study of Abiraterone Acetate Plus Low-Dose Prednisone Plus Androgen Deprivation Therapy (ADT) Versus ADT Alone in Newly Diagnosed Subjects With High-Risk, Metastatic Hormone-naive Prostate Cancer (mHNPC)"
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      string(193) "nct01715285-a-randomized-double-blind-comparative-study-of-abiraterone-acetate-plus-low-dose-prednisone-plus-androgen-deprivation-therapy-adt-versus-adt-alone-in-newly-diagnosed-subjects-with-h"
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      string(242) "https://dev-yoda.pantheonsite.io/clinical-trial/nct01715285-a-randomized-double-blind-comparative-study-of-abiraterone-acetate-plus-low-dose-prednisone-plus-androgen-deprivation-therapy-adt-versus-adt-alone-in-newly-diagnosed-subjects-with-h/"
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  ["project_title"]=>
  string(83) "Establishing a risk score for Skeletal Related Events (SRE) in patients with cancer"
  ["project_narrative_summary"]=>
  string(714) "Skeletal-related events (SREs), such as fractures, spinal cord compression, and severe bone pain, have serious implications for cancer patients, significantly reducing their quality of life and complicating treatment. These events can lead to hospitalization, loss of mobility, and increased need for medical interventions, impacting both the patient's physical and emotional well-being. Given the severe consequences of SREs, it's crucial to identify which patients are at higher risk to ensure early intervention and targeted treatment strategies.



Key serum-based markers found in the blood, like calcium and alkaline phosphatase, are valuable for predicting the risk of skeletal-related events in"
  ["project_learn_source"]=>
  string(9) "colleague"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(5) "Jonas"
    ["last_name"]=>
    string(4) "Saal"
    ["degree"]=>
    string(2) "MD"
    ["primary_affiliation"]=>
    string(24) "University Hospital Bonn"
    ["email"]=>
    string(20) "jonas.saal@ukbonn.de"
    ["state_or_province"]=>
    string(3) "NRW"
    ["country"]=>
    string(7) "Germany"
  }
  ["project_key_personnel"]=>
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  ["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."
  }
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1849) "Background



Skeletal-related events (SREs) such as fractures, spinal cord compression, and severe bone pain significantly impact the quality of life in cancer patients, especially those with bone metastases. Current clinical guidelines do not use blood-based biomarkers for SRE risk management.



Objective



Develop a laboratory-based risk score for predicting SREs in cancer patients by analyzing serum-based biomarkers.



Study Design



Retrospective analysis of clinical trial data to identify significant serum biomarkers, develop a risk score, and validate it using independent datasets.



Participants



Cancer patients from multiple clinical trials with documented serum biomarker levels and recorded SREs, including those with and without bone metastases.



Primary Outcome Measure:



	•	Time to skeletal-related event (SRE).



Secondary Outcome Measures:



	•	Number of SREs.

	•	Quality of life.

	•	SRE-free survival.



Main Predictor / Independent Variable



	•	Serum parameters (calcium, alkaline phosphatase, creatinine, albumin).

	•	Type of primary tumor and presence of bone metastases.



Other Variables of Interest



	•	Age (continuous and stratified >/< 65 years).

	•	Sex (male vs. female).

	•	Subgroup without baseline bone metastases.



Statistical Analysis



Stepwise regression will identify significant serum biomarkers (p < 0.05). Time-to-event analysis using Kaplan-Meier estimates and Cox proportional hazards regression will validate the risk score. Independent validation datasets will test model generalizability and robustness.

"
  ["project_brief_bg"]=>
  string(2299) "Background and Significance



Skeletal-related events (SREs) such as fractures, spinal cord compression, and severe bone pain are critical complications in cancer patients, particularly those with bone metastases. These events drastically reduce the quality of life, necessitate complex medical interventions, and can interrupt cancer treatment regimens. Despite their significant impact, current clinical guidelines do not incorporate blood-based biomarkers for the management of SRE risk.



Recent studies have highlighted the potential of serum-based biomarkers to predict SRE risk, suggesting that these markers can provide valuable insights into bone metabolism and cancer progression. Biomarkers such as calcium, alkaline phosphatase, creatinine, and albumin are particularly promising, given their association with bone health and metabolic activity in cancer patients  .



This project aims to develop a laboratory-based risk score for predicting SREs by analyzing these serum biomarkers. By utilizing a stepwise regression approach, the study will identify the most significant predictors of SREs and validate the risk score using independent datasets. The ultimate goal is to create a reliable, cost-effective tool that clinicians can use to identify high-risk patients, enabling early intervention and personalized treatment strategies.



Significance



The development of a serum-based risk score for SREs has several critical implications:



	1.	Improved Patient Outcomes: By identifying patients at high risk for SREs, clinicians can implement preventive measures and tailored treatments, potentially reducing the incidence and severity of these events.

	2.	Enhanced Clinical Decision-Making: The risk score will provide a data-driven basis for the use of bone-modifying agents, optimizing their use and improving patient care.

	3.	Cost-Effectiveness: Early identification and intervention can reduce healthcare costs associated with the treatment of SREs, such as hospitalizations and surgical procedures.

	4.	Generalizable Knowledge: The findings from this study will contribute to the broader understanding of SRE risk factors, informing future research and public health strategies."
  ["project_specific_aims"]=>
  string(1423) "Aim 1: Identify Significant Serum Biomarkers Associated with Skeletal-Related Events (SREs)



	•	Objective: To determine which serum biomarkers are significantly associated with the risk of SREs in cancer patients.

	•	Hypothesis: Specific serum biomarkers (e.g., calcium, alkaline phosphatase, creatinine, albumin) are predictive of SREs in cancer patients.



Aim 2: Develop a Risk Score Model for Predicting SREs



	•	Objective: To create a risk score model based on the identified serum biomarkers that can accurately predict the likelihood of SREs.

	•	Hypothesis: A composite risk score model using significant serum biomarkers can effectively stratify patients into high-risk and low-risk categories for SREs.



Aim 3: Validate the Risk Score Model Using Independent Datasets



	•	Objective: To validate the developed risk score model using independent clinical trial datasets to ensure its generalizability and robustness.

	•	Hypothesis: The risk score model will maintain its predictive accuracy and reliability across different patient populations and datasets.



Study Objectives



	1.	Retrospective Analysis: Conduct a retrospective analysis of clinical trial data to identify serum biomarkers significantly associated with SREs.

	2.	Model Development: Utilize a stepwise regression "
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(7) "meta_an"
    ["label"]=>
    string(52) "Meta-analysis (analysis of multiple trials together)"
  }
  ["project_purposes"]=>
  array(1) {
    [0]=>
    array(2) {
      ["value"]=>
      string(50) "research_on_clinical_prediction_or_risk_prediction"
      ["label"]=>
      string(50) "Research on clinical prediction or risk prediction"
    }
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  ["project_research_methods"]=>
  string(325) "Additional data will be obtained through vivli.org (NCT02200614, NCT01715285) and project data sphere (NCT00079001, NCT00869206, NCT00365105). Requests to both platforms are under review. 

All patients with available laboratory values and outcome data will be included in the analysis. There are no exclusion criteria."
  ["project_main_outcome_measure"]=>
  string(196) "Primary outcome variable: 

- time to skeletal related event (SRE)



Other outcome variables: 

- number of SRE

- Quality of Life

- SRE-free survival

"
  ["project_main_predictor_indep"]=>
  string(218) "Main Predictor / Independent Variable

- all available serum parameters will be screened (particular focus on bone-related factors)

- type of primary tumor and presence of bone metastases will be evaluated"
  ["project_other_variables_interest"]=>
  string(330) "Other Variables of Interest

- Age (continuous variable and stratified >/< 65y)

- Sex (categorized male vs. female)



- particular focus on subgroup without bone metastases at baseline (as there is currently no guideline recommendation for the use of bone modifying agents in this subgroup)

"
  ["project_stat_analysis_plan"]=>
  string(2546) "To generate, validate, and optimize a risk model for predicting skeletal-related events, we will employ stepwise regression modeling on time-to-event data, with a focus on serum-based biomarkers. This approach will begin by assessing a comprehensive set of candidate predictors, primarily serum-based markers such as calcium, alkaline phosphatase, and others. The stepwise method involves sequentially adding or removing predictors based on their statistical significance, with a threshold set at p < 0.05. In addition, Least Absolute Shrinkage and Selection Operator (LASSO) regression may be used for model selection. Different models will be compared in terms of performance and complexity (with less parameters being favourable for easy applicability). The most promising model will be selected for validation in independent datasets. 



To facilitate the modeling process, serum-based markers may be dichotomized based on clinically relevant cut-offs or medians, allowing for a clear differentiation between low- and high-risk groups. Once the initial risk model is constructed, it will be validated using independent datasets to ensure its reliability and generalizability across diverse populations. We will apply standard time-to-event methods to evaluate the model's performance. Kaplan-Meier estimates will provide a visual representation of survival probabilities across risk groups, while Cox proportional hazards regression will quantify the relative risk, adjusting for confounding variables.



We will use a single trial dataset for exploration and model fitting, while the other datasets will provide independent cohorts for validation purposes. This will ensure that our risk model generalizes beyond the trials analyzed. All trials will be analyzed independently, as trial populations and design vary. NCT00869206 will be used as the primary exploration set, as it is a large (n = 1822) trial including patients with different types of cancer. All other trials will be used for independent validation. 



Missing values will be excluded. The large number of patients included in the trials will provide sufficient power after excluding all patients with missing values.



We have selected studies in patients with cancer that include skeletal related events as an endpoint, thereby allowing analysis of this endpoint. We have included all cancer entities, as the goal is to generate a risk score with a broad applicability in patients with different types of cancer.  "
  ["project_software_used"]=>
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    [0]=>
    array(2) {
      ["value"]=>
      string(1) "r"
      ["label"]=>
      string(1) "R"
    }
  }
  ["project_timeline"]=>
  string(98) "Target Analysis Start Date

1/12/25

Estimated Analysis Completion Date

1/12/26"
  ["project_dissemination_plan"]=>
  string(313) "The research should be published in a peer reviewed, international medical journal (such as JAMA Oncology, Annals of Oncology, European Journal of Cancer) and may be presented at medical meetings (such as the European Society for Medical Oncology (ESMO) or American Society of Clinical Oncology (ASCO) congresses)"
  ["project_bibliography"]=>
  string(1199) "


(1) Coleman, R., Hadji, P., Body, J.-J., Santini, D., Chow, E., Terpos, E., Oudard, S., Bruland, Ø., Flamen, P., Kurth, A., Poznak, C. V., Aapro, M., Jordan, K. & Committee, E. G. (2020). Bone health in cancer: ESMO Clinical Practice Guidelines †. Annals of Oncology, 31(12), 1650–1663. https://doi.org/10.1016/j.annonc.2020.07.019 (2) Schini, M., Vilaca, T., Gossiel, F., Salam, S. & Eastell, R. (2022). Bone Turnover Markers: Basic Biology to Clinical Applications. Endocrine Reviews, 44(3), 417–473. https://doi.org/10.1210/endrev/bnac031 (3) Song, M.-K., Park, S. I. & Cho, S. W. (2023). Circulating biomarkers for diagnosis and therapeutic monitoring in bone metastasis. Journal of Bone and Mineral Metabolism, 41(3), 337–344. https://doi.org/10.1007/s00774-022-01396-6 (4) Coleman, R., Costa, L., Saad, F., Cook, R., Hadji, P., Terpos, E., Garnero, P., Brown, J., Body, J.-J., Smith, M., Lee, K.-A., Major, P., Dimopoulos, M. & Lipton, A. (2011). Consensus on the utility of bone markers in the malignant bone disease setting. Critical Reviews in Oncology/Hematology, 80(3), 411–432. https://doi.org/10.1016/j.critrevonc.2011.02.005


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