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Grant Details

Grant Number: 5U01CA265735-03 Interpret this number
Primary Investigator: Chang, Su-Hsin
Organization: Washington University
Project Title: Comparative Modeling of Multiple Myeloma Across Myeloma Control Continuuum: Prevention, Treatment, and Disparity Reduction
Fiscal Year: 2023


Abstract

PROJECT SUMMARY/ABSTRACT Multiple myeloma (MM) is a common and lethal hematologic malignancy. Treatments of MM have been rapidly evolving. While these new treatments improve survival considerably, the median survival still ranges from 43- 83 months at diagnosis. Among all cancer sites, the management of MM is the most costly, which in part can be attributable to guideline recommended multidrug regimens. Despite such significant health and economic burdens and rapid changing landscape for MM treatments, MM is not one of the cancer sites in the Cancer Intervention and Surveillance Modeling Network (CISNET). Therefore, MM lacks comparative modeling to set goals and policy prioritization in MM prevention and control. Moreover, unlike breast cancer or colorectal cancer, there exists no population-based screening for MM or risk managed strategies for those with premalignant conditions (MGUS and smoldering MM). MM requires comparative modeling to evaluate promising intervention strategies, particularly at premalignant stages. To prevent/control this devastating disease, it is imperative to demonstrate the potentials of these interventions before implementation. Moreover, marked racial disparities in MM (both incidence and survival) is long-established. Without any value-based strategies for prevention and treatment, MM health disparities will continue to worsen. This Incubator Program will include two modeling groups to conduct comparative modeling under the coordination of the coordinating center. Our Program will evaluate novel strategies in preventing or treating MM with the goals of reducing the burden of MM and mitigating MM disparities. We plan to comparatively build, calibrate, and validate evidence- based MM modeling across the MM care continuum (Aim 1). Using the proposed comparatively modeling, we will (1) assess the impacts of novel MM prevention strategies in high-risk patients diagnosed with MGUS (Aim 2); (2) evaluate the cost-effectiveness of novel treatment regimens as well as guideline-recommended treatments in patients diagnosed with MM (Aim 3); and (3) assess whether, under what conditions, and in which ways the goal of eliminating racial disparities can be achieved through the proposed novel intervention strategies (Aim 4). The proposed MM Incubator Program is significant in its capability to 1) build evidence- based comparative modeling for MM, a disease area that lacks of such modeling, relative to the areas of solid tumors already with such modeling, to guide interventions and policies; 2) provide evidence-based evaluation before implementation of any costly clinical trial; 3) explore novel interventions/treatments at various stages of MM; and 4) examine the value of guideline-recommended therapies, providing evidence to inform changes in guidelines and thus a shift in current clinical practice of MGUS and MM management. The proposed intervention strategies for MGUS and MM patients are innovative, with the goals to prevent and control MM and reduce MM disparities. Successful completion of this study will provide evidence in tangible metrics to urge a paradigm shift from current MGUS/MM management. It is therefore a vital step to move the field forward.



Publications

Racial differences in treatment and survival among older patients with multiple myeloma.
Authors: Wang R. , Neparidze N. , Ma X. , Colditz G.A. , Chang S.H. , Wang S.Y. .
Source: Cancer medicine, 2024 Feb; 13(3), p. e6915.
EPub date: 2024-01-17.
PMID: 38234237
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The Association of Agent Orange Exposure with the progression of monoclonal gammopathy of undetermined significance to multiple myeloma: a population-based study of Vietnam War Era Veterans.
Authors: Liu L.W. , Wang M. , Grandhi N. , Schroeder M.A. , Thomas T. , Vargo K. , Gao F. , Sanfilippo K.M. , Chang S.H. .
Source: Journal of hematology & oncology, 2024-01-08; 17(1), p. 3.
EPub date: 2024-01-08.
PMID: 38191467
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Approaches to developing de novo cancer population models to examine questions about cancer and race in bladder, gastric, and endometrial cancer and multiple myeloma: the Cancer Intervention and Surveillance Modeling Network incubator program.
Authors: Sereda Y. , Alarid-Escudero F. , Bickell N.A. , Chang S.H. , Colditz G.A. , Hur C. , Jalal H. , Myers E.R. , Layne T.M. , Wang S.Y. , et al. .
Source: Journal of the National Cancer Institute. Monographs, 2023-11-08; 2023(62), p. 219-230.
PMID: 37947329
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The Association of Agent Orange (AO) Exposure with Monoclonal Gammopathy of Undetermined Significance (MGUS) to Multiple Myeloma (MM) Progression: A Population-based Study of Vietnam War Era Veterans.
Authors: Liu L.W. , Wang M. , Grandhi N. , Schroeder M.A. , Thomas T. , Vargo K. , Gao F. , Sanfilippo K.M. , Chang S.H. .
Source: Research square, 2023-10-09; , .
EPub date: 2023-10-09.
PMID: 37886452
Related Citations

Disentangling age, gender, and racial/ethnic disparities in multiple myeloma burden: a modeling study.
Authors: Huber J.H. , Ji M. , Shih Y.H. , Wang M. , Colditz G. , Chang S.H. .
Source: Nature communications, 2023-09-20; 14(1), p. 5768.
EPub date: 2023-09-20.
PMID: 37730703
Related Citations

Natural Language Processing-Assisted Classification Models to Confirm Monoclonal Gammopathy of Undetermined Significance and Progression in Veterans' Electronic Health Records.
Authors: Wang M. , Yu Y.C. , Liu L. , Schoen M.W. , Kumar A. , Vargo K. , Colditz G. , Thomas T. , Chang S.H. .
Source: JCO clinical cancer informatics, 2023 Sep; 7, p. e2300081.
PMID: 38048516
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Mortality in the US Populations With Monoclonal Gammopathy of Undetermined Significance.
Authors: Ji M. , Huber J.H. , Schoen M.W. , Sanfilippo K.M. , Colditz G.A. , Wang S.Y. , Chang S.H. .
Source: JAMA oncology, 2023-09-01; 9(9), p. 1293-1295.
PMID: 37498610
Related Citations

A moment kernel machine for clinical data mining to inform medical decision making.
Authors: Yu Y.C. , Zhang W. , O'Gara D. , Li J.S. , Chang S.H. .
Source: Scientific reports, 2023-06-28; 13(1), p. 10459.
EPub date: 2023-06-28.
PMID: 37380721
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