Proposed risk-scoring model for estimating the prognostic impact of 1q gain in patients with newly diagnosed multiple myeloma

Am J Hematol. 2023 Feb;98(2):251-263. doi: 10.1002/ajh.26774. Epub 2022 Nov 8.

Abstract

1q gain (+1q) is the most common high-risk cytogenetic abnormality (HRCA) in patients with multiple myeloma (MM). However, its prognostic value remains unclear in the era of novel agents. Here, we retrospectively analyzed the impact of +1q on the outcomes of 934 patients newly diagnosed with MM. +1q was identified in 53.1% of patients and verified as an independent variate for inferior overall survival (OS) (hazard ratio, 1.400; 95% confidence interval, 1.097-1.787; p = .007). Concurrence of other HRCAs (particularly t(14;16) and del(17p)) further exacerbated the outcomes of patients with +1q, suggesting prognostic heterogeneity. Thus, a risk-scoring algorithm based on four risk variates (t(14;16), hypercalcemia, ISS III, and high LDH) was developed to estimate the outcomes of patients with +1q. Of the patients, 376 evaluable patients with +1q were re-stratified into low (31.6%), intermediate (61.7%), and high risk (6.7%) groups, with significantly different progression-free survival and OS (p < .0001), in association with early relapse of the disease. The prognostic value of this model was validated in the CoMMpass cohort. While attaining undetectable MRD largely circumvented the adverse impact of +1q, it scarcely ameliorated the outcome of the patients with high risk, who likely represent a subset of patients with extremely poor survival. Hence, patients with +1q are a heterogeneous group of high-risk patients, therefore underlining the necessity for their re-stratification. The proposed simple risk-scoring model can estimate the outcomes of patients with +1q, which may help guide risk-adapted treatment for such patients.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Chromosome Aberrations
  • Humans
  • Multiple Myeloma* / diagnosis
  • Multiple Myeloma* / genetics
  • Prognosis
  • Proportional Hazards Models
  • Retrospective Studies