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Status: OngoingResearch Proposal
Project Title: Synthetic Control Arm Development in Mantle Cell Lymphoma
Scientific Abstract:
Background: MCL is a biologically and clinically heterogeneous lymphoma with poor long-term outcomes. Standard first-line regimens such as bendamustine + rituximab (BR) form the foundation of care, but outcomes vary widely depending on patient characteristics and disease biology.
Objective: Develop and validate computational methods for generating a synthetic control arm in MCL, using individual patient-level data from the SHINE trial BR arm as calibration material.
Study Design: Post-hoc methodological research using trial-level individual patient data. No re-analysis of trial efficacy will be conducted. Instead, the data will be used to train and evaluate a quantitative systems pharmacology (QSP)-inspired framework for disease progression modeling.
Participants: Patients randomized to BR in the SHINE trial (NCT01776840).
Main Outcome Measure(s): Progression-free survival (primary), overall survival (secondary), stratified by prognostic factors (e.g., MIPI risk status, age, cytogenetics, ECOG performance status).
Statistical Analysis: The framework will integrate patient-level covariates and survival outcomes using multivariate modeling and simulation approaches. The focus will be on replicating trial-level outcomes and subgroup heterogeneity.
Brief Project Background and Statement of Project Significance:
MCL is an uncommon lymphoma, representing 6--8% of non-Hodgkin lymphomas, with high unmet need. Synthetic control arms offer a promising way to maximize the utility of existing trial data, especially in rare diseases where conducting large randomized trials is challenging.
By using data from SHINE (the largest first-line MCL trial to date), this project will establish a validated computational benchmark for the BR regimen. This benchmark will then enable future research to compare novel therapies against standard-of-care in a rigorous and quantitative manner, helping regulators, clinicians, and researchers make better-informed decisions.
Specific Aims of the Project:
The aims of the projects are the follwoing:
1. To use patient-level data from SHINE (BR arm) to train and calibrate a computational SCA model for MCL.
2. To quantify the impact of baseline prognostic factors (e.g., MIPI risk status, cytogenetics, age, LDH, performance status) on Progression-Free Survival (PFS).
3. To evaluate how well synthetic arms can replicate trial outcomes across different patient subgroups.
4. To provide a methodological framework that can be extended to other MCL trials and novel therapies.
Study Design: Methodological research
What is the purpose of the analysis being proposed? Please select all that apply.: Research on clinical prediction or risk prediction Other
Software Used: Python, R, RStudio, Open Office
Data Source and Inclusion/Exclusion Criteria to be used to define the patient sample for your study:
Data Source: SHINE trial (NCT01776840), BR treatment arm only
Inclusion: All patients randomized to BR arm.
Exclusion: None
Primary and Secondary Outcome Measure(s) and how they will be categorized/defined for your study:
Primary: Progression-free survival.
Secondary:
- Patient demographics and baseline characteristics: age, sex, weight/body surface area, race, geographic region, ECOG performance status, time from diagnosis to randomization.
- MCL prognostic factors at baseline: MIPI score, histologic subtype (classical vs blastoid/pleomorphic), TP53 mutational status, Ki-67 index, Ann Arbor/Lugano stage, largest tumor bulk diameter, bone marrow involvement (%), extranodal disease.
- Baseline biomarkers: cyclin D1, LDH, beta-2 microglobulin, platelet count, hemoglobin, WBC, ALC, ANC.
- Longitudinal efficacy outcomes (all assessment time points): tumor burden (SPD or nodal measurements), MRD in peripheral blood and bone marrow, CR/PR assessments, time to response, time to progression, time of death, duration of response.
Main Predictor/Independent Variable and how it will be categorized/defined for your study:
Purpose: The model is calibrated to reproduce PFS (primary) and OS (secondary) as a function of prespecified baseline risk drivers and selected time-varying disease measures. We will not analyze treatment assignment (BR only).
Primary independent variable (baseline risk driver):
- MIPI risk category (Low / Intermediate / High), defined at baseline.
Key secondary independent variables (baseline):
- Histology: classical vs blastoid/pleomorphic.
- TP53 status: mutated vs wild-type.
- Ki-67 index: continuous (%) and categorical (<30% vs >=30%; sensitivity <50% vs >=50%).
- Bulky disease (largest diameter): continuous (cm) and categorical (>=5 cm vs <5 cm; sensitivity >=10 cm).
- LDH: ratio to ULN (continuous and >=ULN vs <ULN).
- Time-varying independent variables used to calibrate intermediate states (not hypothesis-tested):
- Tumor burden over time (SPD or nodal measurements at each assessment).
- MRD in PB/BM over time (status and value, when available).
- Longitudinal labs (e.g., β2-microglobulin, CBC components) as available.
- response/progression status
Interpretation note: "Independent effect" here means the model estimates the contribution of each prespecified driver to PFS/OS while holding other drivers constant via fitted parameters; we will report model fit/validation and parameter/sensitivity summaries rather than classical significance tests.
Other Variables of Interest that will be used in your analysis and how they will be categorized/defined for your study: Demographics (age, sex, weight/BSA, race, region, ECOG, time from diagnosis), staging (Ann Arbor/Lugano), bone marrow involvement %, extranodal disease, baseline labs (LDH [ratio to ULN], β2-microglobulin, cyclin D1), CBC (platelets, hemoglobin, WBC, ALC, ANC); longitudinal efficacy (tumor burden,/SPD, MRD PB/BM, CR/PR, time-to-events); treatment exposure (arm, dosing dates, modifications, discontinuations).
Statistical Analysis Plan: We will train a computational model to reproduce the observed individual patient data from the SHINE BR arm. We will fit longitudinal and survival outcomes at the patient level, capturing between-patient variability and within-patient trajectories. When available, longitudinal biomarkers and tumor measurements (e.g., cyclin D1, LDH, β2-microglobulin, blood counts, MRD) will be modeled as patient-level trajectories to calibrate intermediate disease states and inform time-to-event dynamics. The main objective is to replicate trial-level endpoints (e.g., PFS) while preserving subgroup heterogeneity; internal validation will compare simulated outputs against observed trial data.
Narrative Summary: Mantle Cell Lymphoma (MCL) is a rare, aggressive B-cell malignancy with limited first-line options and variable prognosis. Robust evaluation of new therapies often requires comparison with standard-of-care, but suitable control groups are not always available. This project, supported by Nova In Silico, will use anonymized patient-level data from the bendamustine + rituximab (BR) arm of the SHINE trial (NCT01776840) to develop and validate synthetic control arm methods. The data will not be re-analyzed for efficacy but will calibrate models linking patient characteristics, prognostic markers, and outcomes such as progression-free survival. Results will inform future trial design and decision-making in MCL.
Project Timeline:
Month 0--1 -- Knowledge investigation & modeling
- Literature synthesis; define model scope and assumptions.
- Map variables to model inputs;
- Specify model structure (longitudinal submodels + survival link placeholders); code scaffold.
Milestone M1: Model is implemented
Month 1--2 -- Data processing
- Process the data and transform it in a format compatible for calibration algorithm.
- Design calibration and validation strategy
Milestone 2: Data is processed and ready for the calibration, calibration and validation strategy have been designed
Month 3 -- Longitudinal calibration (phase 1)
- Calibrate longitudinal variables: tumor burden (SPD/nodes), MRD (PB/BM), and labs (LDH, β2M, CBC) with individual patient data so that the model learns the relationships between these and the main outcomes.
Milestone D3: Model can reproduce observed longitudinal variables
Month 4--5 -- PFS calibration
- Calibrate survival component using longitudinal latent states as drivers.
- Reproduce trial-level PFS and subgroup heterogeneity (MIPI, histology, TP53, Ki-67).
Milestone D4: Calibrated PFS model
Month 5--6 -- Validation & reporting
- Run model validation as defined in the validation strategy design (month 2)
- Run sensitivity analyses on the model to assess impact of each parameter on the main outcome (PFS)
- Prepare figures/tables; draft manuscript/abstract; report.
Milestone D5 (Final): Technical Report + Manuscript Draft
Prepare figures/tables; draft manuscript/abstract; YODA results report.
Archive code a
Dissemination Plan: Results could be shared at major hematology and oncology meetings and submitted for publication in peer-reviewed journals (e.g., Blood, Leukemia, Lancet Hematology). All publications will acknowledge the YODA Project.
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