Integrated multidisciplinary approaches to improve prognosis prediction in multiple myeloma

Abstract Introduction: Despite the therapeutic advancements in the treatment of multiple myeloma (MM) a subset of patients shows dismal prognosis. Biomarkers of high-risk (HR) and multiomics-based techniques are in continuous implementation to intercept HR patients at diagnosis. Here we present a prospective monocentric study in which we collected clinical and molecular data of MM patients referring to our Institute with the aim to explore the potential of these approaches to distinguish the diverse phases of this disease and predict patient's outcome at diagnosis. Methods: We enrolled 123 patients with smoldering (SMM=36), newly diagnosed (NDMM=52) and relapse/refractory (RRMM=35) myeloma. Samples were processed by (i) SKY92 and (ii) Virtual Fluorescence in Situ Hybridization (vFISH) assays to detect HR gene expression signatures and cytogenetics [t(4;14), del(17p) and gain(1q)] respectively. RNA was extracted from bone marrow-enriched PCs samples (≥ 80%) and processed according to the manufacturer protocol (Skyline Dx). (iii) Ella™ automated ELISA (Bio-Techne) and (iv) Avida Methyl 3400 DMR Cancer Panel assay (Agilent) to determine Soluble B-cell maturation antigen (sBCMA) and cell free methylated DNA (cfDNAmet) levels in patient's peripheral blood plasma. DNA libraries were sequenced on a NovaSeq 6000 platform (Illumina, 20 x 106 reads/sample). Methylation Index (MI) and Differentially Methylated Regions (DMR) data were generated using Alissa Reporter software (Agilent). The ANOVA test (Tukey's correction), two-tailed unpaired t-test (Mann Whitney), Kaplan-Meier analysis and a chi-square (test for trend) were used. Results: We have detected HR SKY92 gene expression signature across all the MM disease stages observing a progressive increase in the proportion of HR patients from the asymptomatic phase of SMM (8.4%) to NDMM (36.7%) and RRMM (53.3%, p=0.0162). Consistently with the literature, NDMM classified as HR by SKY92 showed a poorer overall survival (OS) with respect to the standard risk group (p=0.0151). We have then assessed, in NDMM patients, the performance of vFISH predictions by comparing transcriptomic data derived from the SKY92 test and diagnostic FISH results. We observed a concordance rate of 100% (CI:100.0-100.0) for detecting both t(4;14) and gain(1q) and of 90.0% (CI:71.4-100.0) for del(17p). Looking at the plasma concentration of sBCMA in our patients and in healthy subjects (n=12, control) we observed increased sBCMA levels in the blood of NDMM and RRMM with respect to SMM (p=0.0009, p=0.0222) and control (p<0.0001, p=0.0010). No statistically significant differences were observed between NDMM and RRMM in accordance with literature data. We then evaluated the prognostic potential of sBCMA levels in the NDMM cohort by defining high and low expressing sBCMA patients based on the median plasmatic protein concentration (590.35 ng/mL). Interestingly, high expressing NDMM patients (n=21) showed a trend toward worst outcome compared with the low expressing group (n=20, p=0.1015). Using a similar approach, we analyzed the MI data, a surrogated of methylation status of cancer-related DNA regions, and we found that MI was increased in NDMM and RRMM with respect to SMM (p=0.0266, p=0.0003). Moreover, NDMM patients with MI above the cohort's median level of AU=0.4345 (n=13) showed a worst OS compared with cases characterized by MI values below this threshold (n=11, p=0.021). Furthermore, the analysis of individual DMRs revealed that, among the 3481 DNA regions interrogated by this assay, 515 showed statistically significant differences in methylation across SMM, NDMM and RRMM samples (p<0.05). Notably, 42 regions were found to be hypomethylated in the asymptomatic disease phase (SMM) compared to both NDMM and RRMM. Conclusion: In a context of massive development of new therapies for MM patients it is critical to identify the subset of patients that still does not benefit from this effort. This pilot study strengthens the prognostic relevance of SKY92 for the identification of HR MM patients at diagnosis and the prediction of HR cytogenetic biomarkers. Moreover, the results obtained from circulating biomarker profiling support the development of less invasive assays and in particular those based on DNA epigenetic modifications. An integrated multi-omics analysis including matched Whole-Body Magnetic Resonance (WB-MRI) imaging data is currently ongoing to maximize the predictive component of our approach.