Identify high-risk disease in multiple myeloma through single-cell transcriptomic detection of highly proliferative (PR) cells

Abstract

Abstract Despite recent advances in multiple myeloma (MM) treatment, a subset of patients continues to experience early disease progression and poor outcomes and are classified as high-risk (HR). The recently proposed new classification of HR includes patients with 17p deletion, TP53 mutations, chromosome 1q/1p abnormalities, IgH translocation including t(4;14), t(14;16), or t(14;20) along with 1q/1p alterations and β2 microglobulin ≥5.5 mg/L with normal creatinine. However, these criteria do not fully capture the molecular heterogeneity of MM stemming from genetic, clonal diversity and subclonal disease. Furthermore, the RNA-based molecular classifications of MM into unique subtypes, including HP, MS, MF, CD1, CD2 and PR have been clinically useful for risk stratification, but have lacked the ability to identify prognostic and predictive markers of HR disease. In this study, we have developed a new method to identify HR MM based on the presence of highly proliferative (PR) subclones detected at single-cell level in bone marrow (BM) aspirates of newly diagnosed (ND) and relapsed/refractory (RR) MM patients. Single-cell RNA analyses were performed in BM sorted MM cells obtained from 118 patients (56 ND and 62 RR) collected in 3 independent cohorts (Calgary, Heidelberg, and Toulouse). Serial samples were available for 33 patients. Unbiased mRNA profiling and sequencing were conducted using the 10x Genomics and Illumina platforms. Cell Ranger and Seurat were used for data processing and downstream analysis. A single-cell Gene Set Enrichment Analysis (scGSEA) score obtained by using the Zhan dataset (Zhan et al, Blood 2006) was developed to classify cells into each MM subgroup. FISH data were used to ensure accurate calls. Kaplan-Meier survival analysis was performed to evaluate the effect of PR cells on progression-free survival (PFS). P values were calculated using the log-rank test. The Calgary cohort which included 37 RRMM patients contained 184,032 cells and was used as a training set to test our scGSEA score. A threshold of ≥22% positive cells was required to confidently (accuracy= 0.69 and error rate= 0.30) assign each patient into the different MM subgroups. Of note our score outperformed the FISH classification and was further validated by confirming the overexpression of genes of interest for each subgroup (NSD2 and FGFR3 for t(4;14), CCND1 for t(11;14), CCND3 for t(6;14), MAF for t(14;16) and MAFB for t(14;20)). We next used the R package cutpointr to estimate the optimal proportion of PR cells associated with poor outcomes. As such, we found that the presence of ≥13% PR cells in the tumor was predictive of poor prognosis in RRMM with a median PFS of 7.5 months in PR patients and 13 months in non-PR patients (p=0.013). This cutoff of 13% PR cells was validated in the 2 independent cohorts of 28 RRMM. Of interest, this cutoff was predictive of poor survival also in early-relapse patients (1-3 lines of therapy). The median PFS was 8.5 months in PR patients and 15.5 months in non-PR patients (p=0.013). In addition, regardless of the original MM subtype, all patients with PR cells retained their PR signature overtime and an enrichment of PR cells was observed in 48% of patients at progression, consistent with Darwinian clonal evolution under therapeutic pressure.In NDMM patients, we detected PR cells in 33% of patients and the presence of ≥5% PR cells in the tumor was associated with poor prognosis with a median PFS of 24.1 months in PR patients and 43 months in non-PR patients (p=0.0064). Importantly, in both RR and ND patients the presence of PR cells conferred poor outcomes even in patients with favorable cytogenetic (HP, CD1, and CD2 groups). Lastly, to better understand the biology of PR cells and identify potential therapeutic targets for HR patients, we characterized the transcriptomic signature of PR cells. Pathway enrichment analysis revealed consistent activation of the MTORC1 signature, MYC targets, E2F targets, and DNA repair pathways in PR cells across all three analyzed cohorts, suggesting their potential role in HR disease. Further evaluation of these pathways is currently ongoing. In conclusion, we have here defined an RNA-based method to identify HR disease in ND and RR MM based on the subclonal presence of PR cells at single-cell level. Future HR classifications should account for the subclonal presence of PR cells to improve disease prognostication and develop new targeted therapeutics for HR patients.