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  ["project_title"]=>
  string(128) "Development of PSA-based tumor growth rate (g-rate) as an early surrogate endpoint in metastatic prostate cancer clinical trials"
  ["project_narrative_summary"]=>
  string(856) "Tumors contain both treatment-sensitive and resistant cells; So even when tumors shrink after the start of therapy, resistant cells may continue to grow. We developed a mathematical method to estimate the growth rate of resistant tumor cells (g-rate) using serial PSA blood tests during treatment. Prior studies using older clinical trials and real-world data show that a faster g-rate on treatment is associated with worse survival.

In this study, we will use clinical trial data from YODA Project to determine 1)How PSA based g-rate compares to imaging-based g-rate, 2) how early g-rate can be reliably calculated while maintaining correlation with survival, and whether it can serve as an early indicator of treatment effectiveness. This could enable earlier treatment decisions and improve outcomes for patients with metastatic prostate cancer. "
  ["project_learn_source"]=>
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  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(8) "Harshraj"
    ["last_name"]=>
    string(5) "Leuva"
    ["degree"]=>
    string(4) "MBBS"
    ["primary_affiliation"]=>
    string(38) "University of Nebraska Medical Center "
    ["email"]=>
    string(15) "hleuva@unmc.edu"
    ["state_or_province"]=>
    string(2) "NE"
    ["country"]=>
    string(13) "United States"
  }
  ["project_key_personnel"]=>
  array(2) {
    [0]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(7) "Mengxi "
      ["p_pers_l_name"]=>
      string(4) "Zhou"
      ["p_pers_degree"]=>
      string(3) "MSC"
      ["p_pers_pr_affil"]=>
      string(38) "Memorial Sloan Kettering Cancer Center"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(3) "yes"
    }
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      ["p_pers_f_name"]=>
      string(12) "Antonio Tito"
      ["p_pers_l_name"]=>
      string(4) "Fojo"
      ["p_pers_degree"]=>
      string(5) "MDPhd"
      ["p_pers_pr_affil"]=>
      string(20) "Columbia University "
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(2) "no"
    }
  }
  ["project_ext_grants"]=>
  array(2) {
    ["value"]=>
    string(2) "no"
    ["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(1404) "Background: Changes in tumor burden during therapy reflect simultaneous regression of sensitive cells and growth of resistant clones. We have developed a mathematical model to estimate tumor growth rate (g-rate) and regression rate(d-rate), derived from serial PSA values. The g-rate has been shown to correlate strongly with overall survival (OS) in studies using older clinical trials and real-world data. 

Objective: To validate PSA-based g-rate as an early surrogate endpoint for treatment efficacy, correlate with imaging-based g-rate, and determine the minimum data required for stable estimation.

Study Design: Participant-level meta-analysis of randomized clinical trials available through the YODA Project.

Participants: Patients with metastatic/advanced prostate cancer enrolled in trials of novel hormone therapies with ≥2 PSA measurements during treatment.

Primary and Secondary Outcomes: Primary outcomes are g-rate and OS. Secondary outcomes include progression-free survival (PFS), time to next treatment (TTNT), and concordance with imaging-based response.

Statistical Analysis: g-rate will be estimated using the TUMGr package for r software. Associations with OS will be assessed using  Cox models and Harrell’s c-index. Stability will be evaluated using sequential PSA inputs. Kaplan-Meier and non-parametric tests will be applied as appropriate."
  ["project_brief_bg"]=>
  string(2848) "Metastatic prostate cancer remains a leading cause of cancer-related mortality despite advances in systemic therapies. A major limitation in oncology drug development and clinical decision-making is reliance on late endpoints such as overall survival (OS), which delay therapeutic optimization. Tumor dynamics during treatment can be modeled as the sum of the regression of treatment-sensitive cells and the growth of resistant clones. The tumor growth rate (g-rate) captures the biologically relevant resistant component and has demonstrated a strong and consistent correlation with OS across multiple datasets.

Prior foundational work by stein et al. (2008) and Wilkerson et al. (2017) utilized clinical trial data from the Project Data Sphere repository to demonstrate that tumor burden during therapy can be decomposed into simultaneous regression (d-rate) and growth (g-rate) components, and importantly, that g-rate correlates with overall survival across various treatments in metastatic prostate cancer. Building on this, subsequent studies by Leuva et al. (2019, 2024, 2025)) using large real-world datasets from U.S. Veterans have validated the prognostic value of g-rate in diverse clinical contexts, demonstrating its robustness across heterogeneous patient populations and treatment exposures. This validates real world applicability of the g-rate method. 

However, real-world datasets are inherently limited by variability in assessment intervals, imaging, and treatment standardization. In contrast, clinical trial datasets provide protocol-defined treatment exposure, standardized follow-up schedules, and rigorously adjudicated outcomes. Leveraging participant-level data from the YODA Project, therefore, represents a critical next step to bridge prior findings from legacy clinical trial datasets and real-world evidence, enabling definitive validation of g-rate as an early surrogate endpoint under controlled conditions.

Unlike conventional PSA kinetics such as doubling time or velocity, g-rate can be calculated during both tumor regression and progression and is independent of assessment timing, making it particularly suitable for clinical trial and real-world data. Recently FDA independantly validated this method and noted it would be important to identify how quickly this can be calculated and maintain its correlation (Malinou et al. 2026).  Importantly, in prostate cancer g-rate can be estimated early in treatment using a limited number of PSA values, suggesting it may serve as an early surrogate endpoint for therapeutic efficacy.

Establishing g-rate as a reliable early surrogate endpoint could accelerate drug development, enable earlier treatment modification, and inform adaptive clinical trial designs, ultimately improving outcomes for patients with metastatic prostate cancer."
  ["project_specific_aims"]=>
  string(1407) "Aim 1: Utilization of PSA based tumor growth rate (g-rate) from clinical trial data to assess the efficacy of novel hormone therapy in metastatic prostate cancer via concordance with OS for validation.

Hypothesis: Patients with metastatic prostate cancer who received novel hormone therapy had a significantly lower g-rate and significantly better overall survival compared to patients in the control arms of these clinical trials.



Aim 2: Correlate concordance of PSA based tumor growth rate (g-rate) with current PSA based, imaging-based, or clinical-based progression-free survival criteria in patients with metastatic prostate cancer to predict OS.

Hypothesis: Slower g-rate correlates with longer progression-free survival in patients with metastatic prostate cancer, and PSA based g-rate will show concordance with imaging-based g-rate. Additionally, PSA based g-rate will have better concordance with OS than the current PSA response/progression metrics.



Aim 3: Identify the minimum amount and duration of PSA and imaging input required to assess g and d values with stable correlation with OS

Hypothesis: We can achieve a stable g-rate as long as we have 3 PSA values available, including a baseline obtained 2-3 weeks apart; thus, we can estimate g-rate within the first 3-6 months of treatment and enable early escalation/de-escalation of therapy."
  ["project_study_design"]=>
  array(2) {
    ["value"]=>
    string(8) "meth_res"
    ["label"]=>
    string(23) "Methodological research"
  }
  ["project_purposes"]=>
  array(3) {
    [0]=>
    array(2) {
      ["value"]=>
      string(56) "new_research_question_to_examine_treatment_effectiveness"
      ["label"]=>
      string(114) "New research question to examine treatment effectiveness on secondary endpoints and/or within subgroup populations"
    }
    [1]=>
    array(2) {
      ["value"]=>
      string(76) "confirm_or_validate previously_conducted_research_on_treatment_effectiveness"
      ["label"]=>
      string(76) "Confirm or validate previously conducted research on treatment effectiveness"
    }
    [2]=>
    array(2) {
      ["value"]=>
      string(37) "develop_or_refine_statistical_methods"
      ["label"]=>
      string(37) "Develop or refine statistical methods"
    }
  }
  ["project_research_methods"]=>
  string(1025) "We will include participant-level data from trials evaluating novel hormone therapies in metastatic prostate cancer (including both metastatic hormone-sensitive [mHSPC] and metastatic castration-resistant prostate cancer [mCRPC]).



Inclusion criteria:

(1) Diagnosis of metastatic prostate cancer as defined within each parent clinical trial;

(2) Enrollment in a randomized clinical trial evaluating a novel hormonal therapy (e.g., abiraterone, apalutamide, or similar agents);

(3) Availability of at least 2 PSA measurements obtained during protocol-defined on-treatment follow-up;

(4) Availability of survival outcomes (overall survival and/or progression-free survival).



Exclusion criteria:

(1) Patients with missing or non-evaluable PSA values (e.g., all values below detection or constant values preventing model fitting).

 All analyses will be conducted  using participant-level data and the TumgR package for R software, which is already available."
  ["project_main_outcome_measure"]=>
  string(1127) "Primary Outcome Measures:

(1) Tumor growth rate (g-rate): estimated from serial PSA values using the TUMGr modeling framework. g-rate will be treated as a continuous variable (per unit time) and also evaluated categorically using data-driven thresholds.

(2) Overall survival (OS): defined as time from initiation of study treatment to death from any cause or last follow-up.



Secondary Outcome Measures:

(1) Progression-free survival (PFS): defined as time from treatment initiation to disease progression (radiographic, PSA, or clinical as defined in each trial) or death;

(2) Time to next treatment (TTNT), when available;

(3) Concordance between PSA-based g-rate and imaging-based response assessments (e.g., using RECIST where available);

(4) Stability of g-rate estimation based on sequential PSA inputs.



No changes to the primary or secondary outcome measures are anticipated in the final analysis. If trial-specific definitions of progression differ, harmonized definitions will be applied where feasible, and sensitivity analyses will be performed."
  ["project_main_predictor_indep"]=>
  string(671) "The primary independent variable is the PSA-derived tumor growth rate (g-rate), estimated from serial on-treatment PSA and imaging (RECIST) measurements using the TUMGr modeling framework.

g-rate will be analyzed as:

(1) A continuous variable representing exponential tumor growth rate (per unit time);

(2) A categorical variable based on data-driven thresholds (e.g., dichotomized or stratified into quantiles using methods such as the Contal and O’Quigley approach).



The primary objective is to assess the independent effect of g-rate on overall survival and secondary endpoints, adjusting for relevant clinical covariates.

"
  ["project_other_variables_interest"]=>
  string(1478) "We will also be calculating decay/regression rate (d-rate), which is independent of g-rate. 



The following variables will be included to characterize the study population and for multivariable adjustment:

Demographic variables:

Age (continuous and categorical), race/ethnicity (as defined in trial datasets), and geographic region (if available).

Disease characteristics:

Disease state (mHSPC vs mCRPC), baseline PSA level (continuous and categorical), Gleason score/grade group, and extent of disease (e.g., high vs low volume, visceral vs non-visceral metastases, if available).

Treatment-related variables:

Treatment assignment (experimental vs control arm), type of hormonal therapy, treatment start and stop dates, and dose intensity (if available).

Clinical status variables:

Performance status (e.g., ECOG), comorbidity indicators (if captured), and prior lines of therapy (if available).

Outcome-related variables:

Timing of PSA measurements, imaging assessments, and dates of progression or death.



These variables will be incorporated into multivariable Cox proportional hazards models and subgroup analyses to adjust for potential confounding and to evaluate effect modification. Missing data will be handled using appropriate statistical methods (e.g., multiple imputation or complete-case sensitivity analyses, depending on the extent and pattern of missing values)."
  ["project_stat_analysis_plan"]=>
  string(1681) "Baseline characteristics will be summarized using descriptive statistics. Group comparisons will be performed using chi-square or Fisher’s exact tests for categorical variables and Wilcoxon rank-sum tests for continuous variables.

Tumor growth and regression rates will be calculated using the TUMGr package in R. 

Survival outcomes will be analyzed using Kaplan-Meier methods and log-rank tests. Multivariable Cox proportional hazards models will be used to evaluate associations between g-rate and overall survival, adjusting for relevant covariates. Model discrimination will be assessed using Harrell’s c-index.

Time-dependent and landmark analyses will be performed to evaluate early predictive performance of g-rate. Stability of g-rate estimation will be assessed using sequential PSA modeling and confidence interval convergence.

Thresholds for clinically meaningful g-rate categories will be explored using the Contal and O’Quigley method. Of note, because the formulae used will include time (t), the analysis is not affected by assessment intervals such that if the intervals of two studies are different or if scheduling difficulties require some intervals to be longer or shorter the estimates of phi, g and d, are not affected since these estimates are a global average over all data points for that patient. This in turn allows the data to be presented as one output. Note also that estimates of phi are determined not only by the falling part of the tumor size curve (PSA as surrogate for this) but also by data form the re-growing phase. 



All analyses will be conducted using R within the YODA secure data platform."
  ["project_software_used"]=>
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  ["project_timeline"]=>
  string(216) "Months 0–2: Data access, cleaning, harmonization

Months 3–6: g-rate estimation and validation

Months 6–9: survival and subgroup analyses

Months 9–12: manuscript preparation and submission"
  ["project_dissemination_plan"]=>
  string(483) "Results will be disseminated through peer-reviewed publications (target journals: Journal of Clinical Oncology, JAMA Oncology, European Urology) and presentations at national meetings (ASCO, GU-ASCO).

If validated, g-rate could enable real-time treatment adaptation and support its integration as an early decision-making biomarker in adaptive clinical trials.

The target audience includes clinical oncologists, clinical trialists, and translational cancer researchers."
  ["project_bibliography"]=>
  string(2309) "

Stein WD, Figg WD, Dahut W, Stein AD, Hoshen MB, Price D, Bates SE, Fojo T. Tumor growth rates derived from data for patients in a clinical trial correlate strongly with patient survival: a novel strategy for evaluation of clinical trial data. Oncologist. 2008;13(10):1046-54.
Wilkerson J, Abdallah K, Hugh-Jones C, Curt G, Rothenberg M, Simantov R, et al. Estimation of tumor regression and growth rates during treatment in patients with advanced prostate cancer: a retrospective analysis. Lancet Oncol. 2017;18(1):143-54.
Leuva H, Sigel K, Zhou M, Wilkerson J, Aggen DH, Park YA, et al. A novel approach to assess real-world efficacy of cancer therapy in metastatic prostate cancer. Analysis of national data on Veterans treated with abiraterone and enzalutamide. Semin Oncol. 2019;46(4-5):351-61.
Leuva H, Zhou M, Jamaleddine N, Meseha M, Faiena I, Park YA, McWilliams G, Luhrs C, Maxwell KN, Von Hoff D, Bates SE, Fojo T. Assessing olaparib efficacy in U.S. Veterans with metastatic prostate cancer utilizing a time-indifferent g-rate method ideal for real-world analyses. EBioMedicine. 2024;107:105288.
Leuva H, Moran G, Jamaleddine N, Meseha M, Zhou M, Im Y, Rosenberg TM, Luhrs C, Bates SE, Park YA, Faiena I. Assessment of PSA responses and changes in the rate of tumor growth (g-rate) with immune checkpoint inhibitors in US Veterans with prostate cancer. Semin Oncol. 2024;S0093-7754(24)00037-X.
Leuva H, Zhou M, Teply BA, Park YA, Luhrs C, Mundi PS, Bates SE, Faiena I, Fojo T, Schoen MW. Abiraterone vs enzalutamide among US Veterans with metastatic hormone-sensitive prostate cancer. JAMA Netw Open. 2025;8(11):e2540730.
Malinou JN, Fan J, Cheng J, Gong Y, Shen YL, Larkins E. An FDA analysis of the association of tumor growth rate, overall survival, and progression-free survival in patients with metastatic non-small cell lung cancer. Oncologist. 2026;31(3):oyag009.

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