Toward personalized treatment in multiple myeloma based on molecular characteristics

Abstract

To date, the choice of therapy for an individual multiple myeloma patient has been based on clinical factors such as age and comorbidities. The widespread evolution, validation, and clinical utilization of molecular technologies, such as fluorescence in situ hybridization and next-generation sequencing has enabled the identification of a number of prognostic and predictive biomarkers for progression-free survival, overall survival, and treatment response. In this review, we argue that in order to continue to improve myeloma patient outcomes incorporating such biomarkers into the routine diagnostic workup of patients will allow for the use of personalized, biologically based treatments.

Figure 1.

Evolution of molecular analysis techniques in myeloma. Images from left to right show G-band karyotyping, FISH, GEP data, SNP array data, and NGS data. Karyotype images were reprinted from Panagopoulos et al. FISH images were reprinted from Fernando et al. GEP images were reprinted from André et al. SNP array and NGS images were reprinted from Bolli et al.

Figure 2.

Risk stratification systems and outcome. PFS as defined by the different risk stratification systems. (A) Ultrahigh risk defined by the presence of >1 adverse lesion (t(4;14), t(14;16), t(14;20), del(17p), and gain(1q)) in the analysis of 869 cases from the MRC Myeloma IX trial (published 2011). Reprinted from Boyd et al with permission. (B) Ultrahigh risk defined by the R-ISS (low-risk R-ISS group I [ISS stage I with no high-risk CA (del(17p) and/or t(4;14 and/or 14;16)) and normal LDH level] to high-risk R-ISS group III [ISS stage III and high-risk CA or high LDH level]) in a pooled study of 4445 patients with newly diagnosed multiple myeloma from 11 clinical studies (published 2016). Reprinted from Palumbo et al with permission. (C) Ultra high-risk defined as double-hit myeloma (either loss of both alleles of TP53 [by mutation, deletion or both] or with 2 extra copies of 1q, resulting in amplification rather than a single gain) by incorporating NGS data in the Myeloma Genome Project analysis of 784 patients (published 2018). Reprinted from Walker et al.

Figure 3.

Clinical trial design strategies for personalized treatment in myeloma. (A) Current standard approach with all patients recruited and treated as part of a clinical trial with subsequent subgroup analysis that may or may not be adequately powered to examine the effect of the novel strategy in high-risk patients. (B) Trial design for high-risk patients who are identified upfront and entered into dedicated protocols. These may be phase 2 or 3 randomized studies (as shown) or earlier-phase single-arm studies. (C) Umbrella trial design with patient molecular lesions identified upfront and entered into an arm examining a therapy appropriate to that lesion. (D) Basket trial design with patients with different cancers but with a shared molecular lesion entered into a study with an agent targeted to that lesion. Figures of men and women were downloaded from https://www.aiga.org/symbol-signs (free to use).