Multi-Tiered Risk Stratification Model for MM May Perform Better Than Standard Systems

Because risk stratification in multiple myeloma (MM) is based on pretreatment patients and disease characteristics, implementation of a simple, additive multi-tiered model for patients with newly diagnosed MM could play an essential role in patient selection for clinical trials, investigators concluded.

The results of modeling study published in Blood Cancer Journal suggested that a 3-tiered or 5-tiered model could be easy to implement in practice and may improve upon the current revised International Staging System (R-ISS), the current standard risk stratification system that is based on clinical characteristics, laboratory studies, bone marrow cytogenetics, and genetic profiling.

The R-ISS was introduced in 2015 and assesses patient outcomes risk through the use of β₂ microglobulin, albumin, cytogenetic abnormalities, and lactate dehydrogenase (LDH), which are related to tumor burden and can represent high-risk markers. The ISS is a popular choice for risk stratification in MM because of its simplicity. However, its ability to discriminate among lower-risk patients is limited and the R-ISS involves the need for complex modeling and resources.

The investigators set out to develop a 3-tier model and a 5-tier model and test their ability to stratify risk in patients 18 years or older with newly diagnosed MM who presented at the Mayo Clinic in Rochester, Minnesota, between February 2004 and June 2019. Overall, 2556 patients identified from a preexisting database and electronic medical records were included into the cohort. The median age of the cohort was 64 years and 62% were men.

For model development, 1327 patients with simultaneous data for all high-risk abnormalities were included in a multivariate analysis. The population used for testing of the models’ validity included 502 patients enrolled in the MMRF CoMMpass study.

The multivariate analysis revealed that high-risk immunoglobulin heavy chain translocations and elevated LDH were independently associated with decreased overall survival. For patients with 0 high-risk factors (stage 1 MM), the overall survival was 11.0 (95% CI, 9.2-12.6) years. Patients with 1 high-risk factor (stage 2 MM) had an overall survival of 7.0 (95% CI, 6.3-9.2) years, and those with 2 or more factors (stage 3 MM) had an overall survival of 4.5 (95% CI, 3.7-5.2) years.

For the MMRF cohort, the median overall survival was 7.8, 6.0, and 4.3 years for patients with stage 1, stage 2, and stage 3 disease, respectively (P < .001).

Additionally, the system demonstrated prognostic utility over 2 time periods for patients who were 65 years or older and in patients who were younger than 65 years, as well as for patients who were transplant eligible or transplant ineligible. The system continued to be useful when used as a 4-tier risk stratification system, which could be used in cases where additional discrimination among patients who are high risk is desired.

“This application has the potential to serve as an important tool in the design of clinical trials exploring intensification of treatments in high-risk patients. Importantly, this model will lend itself for incorporation of other risk factors as they are identified in the future,” the investigators wrote.

The study has some limitations, including the long data collection period, a potential selection bias, and the lack of data on cytogenic abnormalities. The investigators said that future studies should explore whether clonal plasma cell percentage of cytogenetic abnormalities and genetic mutational profiles have an impact on risk stratification.

Reference

Abdallah NH, Binder M, Rajkumar SV, et al. A simple additive staging system for newly diagnosed multiple myeloma. Blood Cancer J. 2022;12:21. doi:10.1038/s41408-022-00611-x