Investigating additional malignancy rates and prognostic factors in multiple myeloma patients: a Surveillance, Epidemiology, and End Results (SEER) database retrospective cohort study.

Abstract

Background: Effective treatments have improved survival in multiple myeloma (MM), but the extension of survival has increased the risk of additional malignancies. Existing staging systems, such as the Durie-Salmon (D-S) staging system and the Revised International Staging System (R-ISS), lack comprehensive data on malignancy-related complications. With the aim of improving outcomes, in this study, we investigated additional malignancy rates, latency periods, and prognostic factors in MM patients using the Surveillance, Epidemiology, and End Results (SEER) database.

Methods: Data from MM patients with additional malignancies [1992-2020] were extracted from SEER. Patients meeting International Classification of Diseases for Oncology, Third Edition (ICD-O-3) criteria were included, excluding those with MM as the sole primary malignancy (PM). Variables analyzed included age, sex, race, latency period, tumor number, sequence, and malignancies sites. Standardized incidence ratio (SIR) and Cox regressions were used to assess risks and survival factors. Two nomograms were developed for prognosis prediction.

Results: Among 60,550 MM patients, 3,676 (6.07%) developed second primary malignancies (SPMs), and 1,663 (2.75%) had primary malignancies (PMs). Prostate cancer was the most solid tumor, whereas non-Hodgkin's lymphoma (NHL) was the leading hematologic malignancy. Among MM patients with SPMs, the median follow-up duration was 60 months, and the overall survival (OS) rate was 28.63%. The following key risk factors were identified: older age at MM diagnosis [≥80 vs. <60 years, hazard ratio (HR) =2.101, P<0.001], higher sequence number (second of two or more primaries vs. first, HR =2.006, P<0.001; third or more vs. first, HR =5.483, P<0.001), and specific cancer sites (e.g., thyroid vs. prostate). Tumor number (4-9 vs. 2 malignant tumors, HR =0.650, P=0.01), specific cancer sites (e.g., NHL vs. prostate), and latency period (2-3 vs. 0-1 year, HR =0.610, P<0.001; ≥4 vs. 0-1 year, HR =0.306, P<0.001) were identified as protective factors influencing the survival. A nomogram for SPMs showed high predictive accuracy [area under the curve (AUC): 0.944 for 3-year survival]. For PMs, median follow-up was 26 months, with a 31.11% survival rate. Risk factors included advanced age, NHL, and higher sequence number. A PM nomogram demonstrated moderate accuracy (AUC: 0.622 for 3-year survival). For PMs, median follow-up was 26 months, with a 31.11% survival rate. Risk factors included advanced age, NHL, and higher sequence number. A PM nomogram demonstrated moderate accuracy (AUC: 0.622 for 3-year survival).

Conclusions: This study highlights key risk factors for additional malignancies in MM patients, including advanced age, NHL, and higher sequence numbers. The developed nomograms aid in predicting survival outcomes, enabling personalized clinical decisions and improved patient management.