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

Peiyu YangHaimin ChenXinyue LiangWeiling XuShanshan YuWenyang HuangXingcheng YiQiang GuoMengru TianTingting YueMengyao LiYingjie ZhangMengxue ZhangYurong YanZhongli HuShaji K KumarFan ZhouYun DaiFengyan Jin
Published in: American journal of hematology (2022)
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.
Keyphrases
  • newly diagnosed
  • end stage renal disease
  • chronic kidney disease
  • ejection fraction
  • emergency department
  • metabolic syndrome
  • multiple myeloma
  • machine learning
  • type diabetes
  • skeletal muscle
  • smoking cessation