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  ["project_title"]=>
  string(130) "Machine Learning to Predict Exceptional Survival in RRMM: A Pooled Individual Participant Data Analysis of BCMA-Targeted Therapies"
  ["project_narrative_summary"]=>
  string(856) "In relapsed/refractory multiple myeloma (RRMM), median survival with conventional salvage is just four months. While BCMA-targeted CAR-T (cilta-cel) and bispecifics offer unprecedented responses, outcomes are bifurcated: some patients achieve long-term survival while others relapse within months.



This study pools individual participant-level data (IPD) from CARTITUDE-1, KarMMa, and MagnetisMM-3, plus YODA Project benchmarks. Using machine learning and survival analysis, we aim to define drivers of "long-term survival" at 1- and 2-year thresholds and quantify "Functionally High Risk" patients—those relapsing early despite standard-risk genomics. By analyzing tumor genetics, prior therapy, and hematologic recovery, we will deliver a validated predictive framework to identify durable responders and personalize post-treatment care."
  ["project_learn_source"]=>
  string(9) "colleague"
  ["principal_investigator"]=>
  array(7) {
    ["first_name"]=>
    string(5) "Majid"
    ["last_name"]=>
    string(14) "Jaberi-Douraki"
    ["degree"]=>
    string(4) "Ph.D"
    ["primary_affiliation"]=>
    string(23) "Kansas State University"
    ["email"]=>
    string(14) "jaberi@ksu.edu"
    ["state_or_province"]=>
    string(2) "KS"
    ["country"]=>
    string(13) "United States"
  }
  ["project_key_personnel"]=>
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    [0]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(15) "Muhammad Talha "
      ["p_pers_l_name"]=>
      string(7) "Shaukat"
      ["p_pers_degree"]=>
      string(4) "MBBS"
      ["p_pers_pr_affil"]=>
      string(23) "Kansas State University"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(3) "yes"
    }
    [1]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(7) "Shahzad"
      ["p_pers_l_name"]=>
      string(4) "Raza"
      ["p_pers_degree"]=>
      string(8) "MD, FACP"
      ["p_pers_pr_affil"]=>
      string(43) "Cleveland Clinic Lerner College of Medicine"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(2) "no"
    }
    [2]=>
    array(6) {
      ["p_pers_f_name"]=>
      string(5) "Faiz "
      ["p_pers_l_name"]=>
      string(5) "Anwer"
      ["p_pers_degree"]=>
      string(8) "MD, FACP"
      ["p_pers_pr_affil"]=>
      string(43) "Cleveland Clinic Lerner College of Medicine"
      ["p_pers_scop_id"]=>
      string(0) ""
      ["requires_data_access"]=>
      string(2) "no"
    }
  }
  ["project_ext_grants"]=>
  array(2) {
    ["value"]=>
    string(3) "yes"
    ["label"]=>
    string(65) "External grants or funds are being used to support this research."
  }
  ["project_date_type"]=>
  string(18) "full_crs_supp_docs"
  ["property_scientific_abstract"]=>
  string(1570) "Background: For triple-class refractory RRMM, median OS with conventional salvage therapy approximates four months. BCMA-targeted therapies — cilta-cel, ide-cel (CAR-T), and elranatamab (bispecific antibody) — have transformed this outlook, yet a significant subset fails to achieve durable benefit. Determinants of the survival dichotomy between long-term survivors and early progressors remain incompletely defined.

Objective: To identify genomic, clinical, and treatment history variables distinguishing patients achieving ≥1- and ≥2-year survival following BCMA-targeted therapy; to define Functional High Risk (FHR) prevalence; and to benchmark outcomes against the ~4-month historical standard.

Study Design: Retrospective pooled IPD meta-analysis across multiple YODA Project and supplementary trials.

Participants: Heavily pretreated RRMM patients from CARTITUDE-1, KarMMa, and MagnetisMM-3, with historical controls (SIRIUS, CASTOR, POLLUX, APOLLO) and frontline genomic benchmarks (GRIFFIN, MAIA, ALCYONE).

Primary Outcomes: OS ≥24 months; OS ≥12 months post-treatment.

Secondary Outcomes: PFS; sustained MRD negativity (10⁻⁵); FHR prevalence; response to last therapy line.

Statistical Analysis: Kaplan-Meier and landmark analyses will define survivor strata. Cox regression and logistic models will identify independent predictors. LCMM will derive Kinetic Recovery Phenotypes. RSF and XGBoost will integrate genomic, treatment history, and KRP variables, with SHAP values providing interpretability."
  ["project_brief_bg"]=>
  string(3233) "For patients with triple-class refractory relapsed/refractory multiple myeloma (RRMM) — those who have exhausted immunomodulatory agents, proteasome inhibitors, and anti-CD38 monoclonal antibodies — the historical standard of care has offered a median overall survival of approximately four months [1,2]. Ciltacabtagene autoleucel (cilta-cel) demonstrated unprecedented response rates in CARTITUDE-1, with a growing subset now surviving beyond one and two years [3,4]. Similarly, idecabtagene vicleucel (ide-cel) in KarMMa and the BCMA-directed bispecific antibody elranatamab in MagnetisMM-3 have extended survival in populations with comparable prior treatment burdens [5,6]. Against the four-month historical baseline these outcomes represent a paradigm shift; yet a significant proportion still progresses within 12 months, highlighting a persistent and clinically meaningful dichotomy of response.

The concept of Functional High Risk (FHR) has emerged as a critical prognostic construct. Genomic risk stratification — classically defined by del(17p), t(4;14), t(14;16), and gain(1q) — fails to fully capture clinical behavior: a subset with standard-risk cytogenetics relapses early and behaves aggressively, while some patients with canonical high-risk features achieve unexpectedly durable remissions after BCMA-targeted therapy [7]. Quantifying FHR prevalence within BCMA-targeted therapy cohorts and characterizing its correlates is essential to refining prognostic models beyond purely cytogenetic classification.

The depth of response to the final conventional therapy line prior to BCMA-targeted treatment — and the cumulative burden of prior therapy (number of lines, agent class, refractoriness status) — represent underexplored predictors of long-term survival. Penta-refractory patients may carry a compromised immune microenvironment limiting the durability of T-cell redirecting therapy [8,9]. Conversely, patients achieving at least a partial response to their most recent prior therapy may retain favorable tumor biology that synergizes with immunological redirection. Systematically characterizing these treatment history variables alongside genomics and post-treatment kinetic recovery is essential to a comprehensive predictive model.

The most relevant existing prediction model is MyCARe (Gagelmann et al., J Clin Oncol 2024) [11], which identified four static baseline predictors of early relapse (≤5 months) after BCMA CAR-T. While an important proof of concept, MyCARe predicts early failure rather than long-term survival, omits treatment history variables and FHR, and was not designed to distinguish ≥1- or ≥2-year survival milestones. This study directly these gaps by targeting clinically actionable survival thresholds, incorporating FHR as both predictor and outcome, integrating last-line response and prior treatment burden, and extending analysis across CAR-T and bispecific modalities via a pooled IPD framework. To mitigate overfitting, we will employ nested cross-validation, L1/L2 regularization, and a pre-specified feature set; all performance metrics will be reported with 95% confidence intervals. External validation in real-world cohort is planned"
  ["project_specific_aims"]=>
  string(898) "Aim 1: To characterize the proportion of patients achieving long-term survival (≥1 and ≥2 years) following BCMA-targeted therapy across CAR-T (CARTITUDE-1, KarMMa) and bispecific antibody (MagnetisMM-3) cohorts, benchmarked against historical triple-class refractory RRMM (~4-month median OS).

Hypothesis: ≥1- and ≥2-year OS rates will significantly exceed historical benchmarks and will differ between CAR-T and bispecific modalities.

Aim 2: To identify genomic (del(17p), t(4;14), t(14;16), TP53, gain(1q)), treatment history (prior lines, agent class, refractoriness, depth of response to last therapy), and clinical variables distinguishing ≥2-year survivors from intermediate (≥1 to <2 years) and early progressors (0.75 for ≥2-year OS prediction, with KRP and treatment history variables ranking above individual cytogenetic variables in global SHAP importance."
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    ["label"]=>
    string(52) "Meta-analysis (analysis of multiple trials together)"
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  ["project_purposes"]=>
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      ["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]=>
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      string(22) "participant_level_data"
      ["label"]=>
      string(36) "Participant-level data meta-analysis"
    }
    [2]=>
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      ["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"
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    [3]=>
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      string(28) "research_on_comparison_group"
      ["label"]=>
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      ["label"]=>
      string(50) "Research on clinical prediction or risk prediction"
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  ["project_research_methods"]=>
  string(1861) "IPD will be obtained from the following sources:

YODA Project – Primary BCMA-Targeted Therapy:

NCT03548207 (CARTITUDE-1): Phase 1b/2 study of cilta-cel in heavily pretreated RRMM. Primary CAR-T discovery cohort [3,4].

Vivli Platform – Additional BCMA-Targeted Therapy:

KarMMa (idecabtagene vicleucel): Phase 2 registrational CAR-T trial in RRMM [5], pooled with CARTITUDE-1 for cross-product comparison. MagnetisMM-3 (elranatamab): Phase 2 registrational BCMA-targeted bispecific antibody trial in RRMM [6]; bispecific comparator cohort.

YODA Project – Historical RRMM Controls:

NCT01985126 (SIRIUS): Daratumumab monotherapy in triple-class refractory RRMM; primary historical benchmark [1,2]. NCT02136134 (CASTOR): DVd in RRMM. NCT02076009 (POLLUX): DRd in RRMM. NCT03180736 (APOLLO): DPd in late-line RRMM.

YODA Project – Frontline Genomic Benchmarks (Aim 2):

NCT02874742 (GRIFFIN), NCT02252172 (MAIA), NCT02195479 (ALCYONE): Provide reference genomic prevalence (del(17p), t(4;14), t(14;16), gain(1q)) in a relapse-unselected population for the Biological Equalizer sub-analysis. All analyses will be conducted within respective secure platforms; no data will be transferred between environments.

Inclusion: Patients who received assigned study treatment; available baseline cytogenetics and disease burden data; documented prior lines, agent class, and refractoriness status; documented response to last therapy line; longitudinal ANC/platelet/ALC data for ≥30 days post-treatment (Aim 3 KRP sub-analysis only).

Exclusion: Enrolled but uninfused/unadministered patients. For Aim 3 KRP sub-analysis only: patients lacking post-treatment laboratory data before Day 14; these patients are retained in Aims 1–2 analyses with censoring at event date to prevent immortal time bias."
  ["project_main_outcome_measure"]=>
  string(1829) "Primary Outcome Measures:

1. Long-Term Survival (≥2 Years): Overall Survival (OS) ≥24 months post-treatment initiation. Modeled as a binary classification endpoint for logistic regression and XGBoost analyses, and as a time-to-event endpoint for RSF and Cox proportional hazards models. Death from any cause defines the event; surviving patients will be right-censored at last follow-up.

2. Intermediate Long-Term Survival (≥1 Year): Overall Survival (OS) ≥12 months post-treatment initiation. Modeled analogously to the ≥2-year endpoint to enable stratification of the survivor population into three prognostically distinct groups: ≥2-year survivors, ≥1- to <2-year survivors, and early progressors/deaths (<1 year). Landmark analysis at the 12-month timepoint will be conducted to avoid immortal time bias.



Secondary Outcome Measures:

1. Progression-Free Survival (PFS): Time from treatment initiation to disease progression or death from any cause, per IMWG criteria within each source trial protocol.

2. Sustained MRD Negativity: Achievement and maintenance of MRD negativity at the 10⁻⁵ sensitivity threshold, per NGS or NGF as available in source trial protocols.

3. Functional High Risk (FHR) Prevalence: Defined operationally as patients who meet the following criteria: (i) absence of canonical high-risk cytogenetic features (del(17p), t(4;14), t(14;16)) by FISH (i.e., cytogenetically standard-risk); and (ii) failure to achieve ≥1-year OS post-BCMA-targeted treatment (i.e., clinically high-risk behavior). The proportion of patients classified as FHR will be reported as a secondary descriptive outcome across the three survival strata, providing a quantitative estimate of the discordance between genomic and clinical risk.



"
  ["project_main_predictor_indep"]=>
  string(1777) "1. Genomic/Cytogenetic Variables: High-risk FISH status (del(17p), t(4;14), t(14;16)); TP53 mutational status; gain(1q21); double/triple-hit designation per IMWG criteria. Each variable treated as a binary indicator and aggregated into a composite Cytogenetic Risk Score. This domain tests whether canonical genomic risk retains prognostic discrimination at ≥1- and ≥2-year thresholds and enables FHR identification (standard-risk genomics with early progression).

2. Treatment History Variables: Number of prior lines (continuous; categorized as 1–2, 3–4, ≥5); class of prior agents (IMiD, PI, anti-CD38, BCMA-targeted, XPO1 inhibitor, alkylating agent — each binary); refractoriness status (triple-class vs. penta-refractory); depth of response to the last conventional therapy prior to enrollment (no response/PD; minimal response; PR or better). This domain tests whether cumulative immune microenvironment depletion and tumor resistance profile at BCMA-targeted therapy initiation independently determine durable remission.

3. Baseline Clinical Variables: R-ISS stage; extramedullary disease — true EMD vs. paraskeletal plasmacytoma vs. absent [12]; bone marrow plasma cell percentage; ECOG performance status; serum LDH; ferritin and CRP at lymphodepletion [6]; bridging therapy utilization and response.

4. Kinetic Recovery Phenotype (KRP): A derived categorical variable mapping ANC, platelet, and ALC [10] trajectories from Day 0 to Day 100 post-treatment via Latent Class Mixed Models (LCMM). This dynamic variable operationalizes the hypothesis that treatment-related cytopenia depth and duration reflects in vivo immune effector engagement magnitude — a novel predictor class absent from all existing risk stratification models."
  ["project_other_variables_interest"]=>
  string(769) "•	Development and maximum grade of Cytokine Release Syndrome (CRS) and Immune Effector Cell-Associated Neurotoxicity Syndrome (ICANS), per ASTCT 2019 criteria (CAR-T cohorts) and per trial-specific criteria (bispecific cohort).

•	Best overall response (sCR, CR, VGPR, PR, MR, SD, PD) and time to best response, per IMWG criteria within each source trial.

•	Treatment modality (cilta-cel CAR-T vs. ide-cel CAR-T vs. elranatamab bispecific antibody): categorical variable to enable cross-modality comparison in pooled analyses.

•	Trial-level variables: study phase, sponsor, geographic region of enrollment, and calendar year of enrollment — included as covariates in multivariable models to account for between-trial heterogeneity.

"
  ["project_stat_analysis_plan"]=>
  string(4934) "The analysis will proceed in four integrated phases utilizing R and Python within the YODA and Vivli secure research environments.



Preliminary: Missing Data Handling. Prior to all analytical phases, missing baseline and longitudinal values will be addressed using multiple imputation by chained equations (MICE, via the mice package in R), generating 20 imputed datasets pooled using Rubin’s rules. For the LCMM trajectory models (Phase 2), the mixed-effects framework inherently accommodates irregular and partially missing longitudinal observations under a Missing-At-Random (MAR) assumption. The proportion of missing data per variable will be reported in the final manuscript.



Phase 1: Descriptive Survival Analysis and Benchmark Comparison (Aim 1). Kaplan-Meier curves will be generated for OS and PFS across all BCMA-targeted therapy cohorts (CARTITUDE-1, KarMMa, MagnetisMM-3) and the historical RRMM control cohorts (SIRIUS, CASTOR, POLLUX, APOLLO). Landmark analyses will be conducted at the 12- and 24-month timepoints to define the ≥1-year and ≥2-year survivor populations while avoiding immortal time bias. Rates of OS ≥12 months and OS ≥24 months will be compared between (i) BCMA-targeted therapy cohorts and (ii) historical control cohorts using log-rank tests, with hazard ratios estimated by univariable Cox proportional hazards regression. Between-cohort comparisons will account for differences in patient-level covariates using propensity score matching (MatchIt package in R).



Phase 2: Feature Engineering and Trajectory Modeling (Aim 3 – KRP). Kinetic Recovery Phenotypes (KRPs) will be established using Latent Class Mixed Models (LCMM, lcmm package in R) [9], applied to post-treatment ANC, platelet, and ALC trajectories (Days 0–100) across all RRMM cohorts with available longitudinal laboratory data. The optimal number of latent classes (K) will be determined by Bayesian Information Criterion (BIC) and clinical interpretability, with the expectation of identifying 3–4 trajectory classes (e.g., "Rapid Recovery," "Delayed Recovery," "Prolonged Cytopenia"). Modal KRP class membership will be assigned to each patient for use as a predictor in downstream analyses.



Phase 3: Multivariable Regression and FHR Analysis (Aim 2). To identify independent predictors of the three survival strata (≥2 years, ≥1 to <2 years, and <1 year), multivariable Cox proportional hazards regression and multivariable logistic regression will be applied. Candidate predictors will be pre-specified from the four variable domains (genomic, treatment history, clinical, KRP) and will not be selected by stepwise or other data-driven methods. All pre-specified variables will be entered simultaneously in the multivariable model. Treatment history variables will be assessed both individually and as composite scores (e.g., total prior lines as a continuous variable). To quantify FHR prevalence: patients will be classified as FHR if they are cytogenetically standard-risk (absence of del(17p), t(4;14), t(14;16)) but fail to achieve ≥1-year OS. FHR prevalence will be reported with 95% confidence intervals across each survival stratum and modality cohort. Chi-squared and Fisher’s exact tests will compare FHR proportions between cohorts. Kaplan-Meier analyses stratified by response to last line of therapy (no response / progressive disease vs. any response) will be conducted to characterize the survival impact of pre-treatment response depth.



Phase 4: Survival-Adapted Machine Learning and Interpretability (Aim 3). To develop a validated predictive model for ≥1-year and ≥2-year OS, we will deploy Random Survival Forests (RSF) [8] and Extreme Gradient Boosting (XGBoost), both optimized for right-censored time-to-event data. Class imbalance between long-term survivors and early progressors will be addressed via inverse probability of censoring weighting (IPCW). Model performance will be assessed using the Concordance index (C-index) and time-dependent Area Under the Curve (tAUC) via nested 5-fold cross-validation. To mitigate overfitting, L1/L2 regularization will be applied to XGBoost, hyperparameter tuning will be conducted within the inner fold of nested cross-validation, and the feature set will be restricted to pre-specified clinically meaningful variables. All performance metrics will be reported with 95% confidence intervals from 1,000 bootstrap iterations. SHAP (SHapley Additive exPlanations) values [13] will be extracted to quantify the global and local predictive contribution of each feature, directly comparing the relative importance of genomic variables, treatment history variables (prior lines, refractoriness, last-line response), and KRP class in predicting ≥2-year OS. SHAP summary and dependence plots will be included as primary figures in the manuscript.



"
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      ["label"]=>
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  ["project_timeline"]=>
  string(1642) "•	Target Project Start Date: 1st June, 2026.

•	Weeks 1–4 (Data Integration and Cleaning): Complete onboarding to the YODA and Vivli secure research environments. Extract and harmonize baseline genomic, treatment history, clinical, and outcome variables across all YODA trials and Vivli datasets (CARTITUDE-1, KarMMa, MagnetisMM-3, SIRIUS, CASTOR, POLLUX, APOLLO, GRIFFIN, MAIA, ALCYONE). Define and apply the FHR classification algorithm to the pooled dataset.

•	Weeks 5 (Descriptive Survival Analysis – Aim 1): Generate Kaplan-Meier estimates, conduct landmark analyses at 12 and 24 months, and perform benchmark comparisons between BCMA-targeted therapy and historical RRMM control cohorts.

•	Weeks 6 (KRP Trajectory Modeling – Phase 2): Execute LCMM models to define KRP clusters. Finalize KRP class assignments for all RRMM patients with available longitudinal laboratory data.

•	Weeks 7 (Multivariable Regression and FHR Analysis – Aim 2): Conduct multivariable Cox and logistic regression models. Quantify FHR prevalence and characterize its genomic and treatment history correlates. Perform Kaplan-Meier analyses stratified by response to last line of therapy.

•	Weeks 8 (Machine Learning and SHAP – Aim 3): Train RSF and XGBoost models, conduct nested cross-validation, extract SHAP values, and generate final feature importance visualizations.

•	Weeks 9-13: Draft and revise primary manuscript; submit to target journal. Submit findings abstract to ASH Annual Meeting and ASTCT. Report final results to the YODA Project. Analysis Completion Target: 1st September 2026"
  ["project_dissemination_plan"]=>
  string(1337) "The primary product of this research will be a full-length original research manuscript characterizing the genomic, treatment history, and kinetic recovery determinants of long-term survival (≥1 and ≥2 years) following BCMA-targeted therapy in RRMM, with explicit quantification of Functional High Risk prevalence and its clinical correlates. The target audience includes clinical hematologists, translational researchers, and cellular therapy specialists. We plan to submit the manuscript to a high-tier peer-reviewed journal such as the Journal of Clinical Oncology (JCO) or Blood, both of which have published the foundational CARTITUDE-1, KarMMa, and MyCARe data that motivate this analysis. A secondary manuscript focusing specifically on the FHR analysis and its implications for genomic risk classification in the context of modern immunotherapy may be submitted to a focused journal such as Blood Advances or Blood Cancer Journal. Findings will be submitted as an abstract for oral or poster presentation at the American Society of Hematology (ASH) Annual Meeting and the American Society for Transplantation and Cellular Therapy (ASTCT) annual meeting, platforms chosen for their direct reach to the clinical hematology and cellular therapy community most likely to implement the predictive framework developed by this work."
  ["project_bibliography"]=>
  string(4745) "

Lonial, S., Weiss, B. M., Usmani, S. Z., et al. (2016). Daratumumab monotherapy in patients with treatment-refractory multiple myeloma (SIRIUS): an open-label, randomised, phase 2 trial. The Lancet, 387(10027), 1551–1560. https://doi.org/10.1016/S0140-6736(15)01120-4
Kumar, S. K., Dispenzieri, A., Lacy, M. Q., et al. (2012). Continued improvement in survival in multiple myeloma: changes in early mortality and outcomes in older patients. Leukemia, 28(5), 1122–1128. https://doi.org/10.1038/leu.2013.313
Berdeja, J. G., Madduri, D., Usmani, S. Z., et al. (2021). Ciltacabtagene autoleucel, a B-cell maturation antigen-directed chimeric antigen receptor T-cell therapy in patients with relapsed or refractory multiple myeloma (CARTITUDE-1): a phase 1b/2 open-label study. The Lancet, 398(10297), 314–324.
Jagannath, S., Martin, T. G., Lin, Y., et al. (2025). Long-term (≥5-year) remission and survival after treatment with ciltacabtagene autoleucel in CARTITUDE-1 patients with relapsed/refractory multiple myeloma. Journal of Clinical Oncology, 43(25), 2766–2771.
Munshi, N. C., Anderson, L. D., Shah, N., et al. (2021). Idecabtagene vicleucel in relapsed and refractory multiple myeloma. New England Journal of Medicine, 384(8), 705–716. https://doi.org/10.1056/NEJMoa2024850
Lesokhin, A. M., Tomasson, M. H., Arnulf, B., et al. (2023). Elranatamab in relapsed or refractory multiple myeloma: phase 2 MagnetisMM-3 trial results. Nature Medicine, 29(9), 2259–2267. https://doi.org/10.1038/s41591-023-02528-9
Skerget, S., Penaherrera, D., Chari, A., et al. (2024). Comprehensive molecular profiling of multiple myeloma identifies refined copy number and expression subtypes. Nature Genetics, 56, 1878–1889. https://doi.org/10.1038/s41588-024-01853-0
Rejeski, K., Hansen, D. K., Bansal, R., et al. (2023). The CAR-HEMATOTOX score as a prognostic model of toxicity and response in patients receiving BCMA-directed CAR-T for relapsed/refractory multiple myeloma. Journal of Hematology & Oncology, 16(1), 88. https://doi.org/10.1186/s13045-023-01465-x
Liu, Y., Jie, X., Nian, L., et al. (2023). A combination of pre-infusion serum ferritin, CRP and IL-6 predicts outcome in relapsed/refractory multiple myeloma patients treated with CAR-T cells. Frontiers in Immunology, 14, 1169071. https://doi.org/10.3389/fimmu.2023.1169071
Mejia Saldarriaga, M., Pan, D., Unkenholz, C., et al. (2024). Absolute lymphocyte count after BCMA CAR-T therapy is a predictor of response and outcomes in relapsed multiple myeloma. Blood Advances, 8(15), 3859–3869. https://doi.org/10.1182/bloodadvances.2023012470
Gagelmann, N., Dima, D., Merz, M., Hashmi, H., Ahmed, N., Tovar, N., …, Anwer, F., …, & Fernández de Larrea, C. (2024). Development and validation of a prediction model of outcome after B-cell maturation antigen-directed chimeric antigen receptor T-cell therapy in relapsed/refractory multiple myeloma. Journal of Clinical Oncology, 42(14), 1665–1675. https://doi.org/10.1200/JCO.23.02232
Afrough, A., Dima, D., Razzo, B., Goel, U., Sannareddy, A., Pasvolsky, O., …, Anwer, F., …, & Lee, H. C. (2025). The impact of extramedullary and paraskeletal plasmacytomas on treatment outcomes in multiple myeloma treated with teclistamab: U.S. Myeloma Immunotherapy Consortium real-world experience. Blood Cancer Journal, 16(1), 12. https://doi.org/10.1038/s41408-025-01414-6
Proust-Lima, C., Saulnier, T., Philipps, V., et al. (2023). Describing complex disease progression using joint latent class models for multivariate longitudinal markers and clinical endpoints. Statistics in Medicine, 42(22), 3996–4014. https://doi.org/10.1002/sim.9844
Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008). Random survival forests. The Annals of Applied Statistics, 2(3), 841–860. https://doi.org/10.1214/08-AOAS169
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.
Dima, D., Abdallah, A. O., Davis, J. A., et al. (2024). Impact of extraosseous extramedullary disease on outcomes of patients with relapsed-refractory multiple myeloma receiving standard-of-care chimeric antigen receptor T-cell therapy. Blood Cancer Journal, 14(1), 90. https://doi.org/10.1038/s41408-024-01068-w
Wesson, W., Dima, D., Suleman, N., et al. (2024). Timing of toxicities and non-relapse mortality following CAR T therapy in myeloma. Transplantation and Cellular Therapy, 30(9), 876–884. https://doi.org/10.1016/j.jtct.2024.06.012

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data partner
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pi affil
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products
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res
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}