Abstract 6196: Characteristics of individuals with early-onset multiple myeloma in South Carolina

Abstract Background: The incidence of early-onset cancers, defined as cancer in adults under 50 years, are on the rise. Early-onset multiple myeloma (MM), which is the second most common hematologic cancer in the U.S., is one of early-onset cancer types with increasing incidence. This age/race disparity is believed to be linked to genetic factors and social determinants of health (SDOH), though the precise causes are still under investigation. Data on risk factors for early-onset MM are limited. The incidence rate of MM in South Carolina is higher than the U.S. average. A geospatial analysis study showed differences in average county-level age-adjusted incidence rates (ASR) across U.S. counties, with high ASR found primarily in the Southeast and two major high-high local clusters of the ASR in Georgia and South Carolina. To address this gap, we first examined the characteristics of patients with early-onset MM in South Carolina. Purpose: To examine 1) the prevalence of patients with early-onset MM in South Carolina and 2) their characteristics and SDOH. Methods: A retrospective review of electronic medical records (EMR) from 1/1/2020 to 8/30/2023 was conducted to identify patients with early-onset MM (N=23) at a community oncology clinic in South Carolina. Potential cases were identified by EMR reports based on age and diagnosis. EMR confirmed eligibility, and socio-demographics and cancer-related information was entered into a REDCap database. SAS (v.9.4) was used for descriptive statistics. Results: The total number of MM cancer cases in all ages observed during the study period was 247. Among them, twenty-three cases were diagnosed before age 50 (prevalence: 10.7%). The average age at diagnosis was 44.5 (SD: ±5.29, range: 27-49). The average BMI was 33.87 (SD±9.81). MM stages based on the Revised International Staging System (R-ISS) were as follows: Stage I, N=6, stage II, N=7, stage III, N=4, and missing, N=6. Over half of the individuals with early-onset MM were male (52%). The ethnicity/race was Black (57%), White (39%), and Asian (4%). Other sociodemographic characteristics included unemployment status (35%), rurality (53%), and smoking history (43%). About 52% of early-onset MM patients had commercial insurance, 30% Medicare/Medicaid, 4% no insurance, 4% Department of Corrections, and 8% had veteran insurance. Notably, 43% of early-onset MM patients had high-risk cytogenetics (gain 1q21, gain 1q, del 1/p32/CDKN2C, IGH rearrangement, del 1p, del 17p, del 13q, del TP53, monosomy 13, Trisomy 1) and 18% received allogeneic stem cell transplant. Discussion: We demonstrated that patients with early-onset MM in South Carolina often have characteristics such as race, unemployment, and high-risk cytogenetics. Future research should explore how genetic, environmental, lifestyle factors and SDOH influence the risk and prognosis of early-onset MM, particularly in areas with persistent poverty and historically disadvantaged communities. Citation Format: Amy F. Ballou, Tomotaka Ugai, Abbas Tavakoli, Karen Wickersham. Characteristics of individuals with early-onset multiple myeloma in South Carolina [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6196.

Authors

Institutions

Publication Details

Journal
Cancer Research
Published
2025-04-21
DOI
https://doi.org/10.1158/1538-7445.am2025-6196
Primary Topic
T-cell and Retrovirus Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract 6196: Characteristics of individuals with early-onset multiple myeloma in South Carolina

Tomotaka Ugai, Abbas Tavakoli, Karen E. Wickersham, Amy F. Ballou
Cancer Research
T-cell and Retrovirus Studies
article

Abstract 6196: Characteristics of individuals with early-onset multiple myeloma in South Carolina

Tomotaka Ugai, Abbas Tavakoli, Karen E. Wickersham, Amy F. Ballou
article en

Abstract

Abstract Background: The incidence of early-onset cancers, defined as cancer in adults under 50 years, are on the rise. Early-onset multiple myeloma (MM), which is the second most common hematologic cancer in the U.S., is one of early-onset cancer types with increasing incidence. This age/race disparity is believed to be linked to genetic factors and social determinants of health (SDOH), though the precise causes are still under investigation. Data on risk factors for early-onset MM are limited. The incidence rate of MM in South Carolina is higher than the U.S. average. A geospatial analysis study showed differences in average county-level age-adjusted incidence rates (ASR) across U.S. counties, with high ASR found primarily in the Southeast and two major high-high local clusters of the ASR in Georgia and South Carolina. To address this gap, we first examined the characteristics of patients with early-onset MM in South Carolina. Purpose: To examine 1) the prevalence of patients with early-onset MM in South Carolina and 2) their characteristics and SDOH. Methods: A retrospective review of electronic medical records (EMR) from 1/1/2020 to 8/30/2023 was conducted to identify patients with early-onset MM (N=23) at a community oncology clinic in South Carolina. Potential cases were identified by EMR reports based on age and diagnosis. EMR confirmed eligibility, and socio-demographics and cancer-related information was entered into a REDCap database. SAS (v.9.4) was used for descriptive statistics. Results: The total number of MM cancer cases in all ages observed during the study period was 247. Among them, twenty-three cases were diagnosed before age 50 (prevalence: 10.7%). The average age at diagnosis was 44.5 (SD: ±5.29, range: 27-49). The average BMI was 33.87 (SD±9.81). MM stages based on the Revised International Staging System (R-ISS) were as follows: Stage I, N=6, stage II, N=7, stage III, N=4, and missing, N=6. Over half of the individuals with early-onset MM were male (52%). The ethnicity/race was Black (57%), White (39%), and Asian (4%). Other sociodemographic characteristics included unemployment status (35%), rurality (53%), and smoking history (43%). About 52% of early-onset MM patients had commercial insurance, 30% Medicare/Medicaid, 4% no insurance, 4% Department of Corrections, and 8% had veteran insurance. Notably, 43% of early-onset MM patients had high-risk cytogenetics (gain 1q21, gain 1q, del 1/p32/CDKN2C, IGH rearrangement, del 1p, del 17p, del 13q, del TP53, monosomy 13, Trisomy 1) and 18% received allogeneic stem cell transplant. Discussion: We demonstrated that patients with early-onset MM in South Carolina often have characteristics such as race, unemployment, and high-risk cytogenetics. Future research should explore how genetic, environmental, lifestyle factors and SDOH influence the risk and prognosis of early-onset MM, particularly in areas with persistent poverty and historically disadvantaged communities. Citation Format: Amy F. Ballou, Tomotaka Ugai, Abbas Tavakoli, Karen Wickersham. Characteristics of individuals with early-onset multiple myeloma in South Carolina [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6196.

Cancer ResearchVol. 85(8_Supplement_1)
Harvard University (US), University of South Carolina (US)
Openalex Percentile: Top 91%
T-cell and Retrovirus Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.