A Whole Blood Gene Expression Signature Accurately Diagnoses Multiple Myeloma and Differentiates Patients in Remission from Those with Active and Progressive Disease

Introduction:

A variety of multiple myeloma (MM) gene expression (GEP) signatures have been developed based on plasma cell transcriptomic analyses. Although generally prognostic, these have limited utility for separating overt MM from earlier stages of plasma cell dyscrasias such as monoclonal gammopathy of undetermined significance (MGUS). A disadvantage is they are invasive and require collection of plasma cells by bone marrow biopsy. We investigated the potential of a customized whole blood-based gene expression assay to provide an effective tool for diagnosing MM, differentiate active from progressive or in remission disease and examine the effect of treatment on the signature.

Methods:

A 26 gene-GEP was obtained from 15 individual MM peripheral blood transcriptomes. Gene transcripts were functionally linked and captured a number of biological processes such as angiogenesis, apoptosis, immune responsiveness, phenotype definition, protein processing (secretion), proliferation, RNA processing and survival. Gene expression was confirmed in 3 human MM cell lines (IM-9, MM1R and NCI-H929). Selective signature expression in MM was confirmed by screening blood of controls (n=26) and 106 MM patients. This signature was then prospectively tested in 67 MM patients (newly diagnosed or progressive, n=35; in remission following treatment, n=32, including 26 complete remissions), 7 MGUS and controls (n=29). Among MM patients, 52% had ISS stage 1 disease, 13% had stage 2 and 22% stage 3. FISH cytogenetic analysis was available in 82% of patients. Hyperdiploidy and chromosome 1 abnormalities were detected in 16 patients each (24%), del13q was present in 43% and high-risk aberrations such as del17p and t(4;14) were found in 12% and 9% of patients, respectively. Median time from diagnosis was 3.5 years. The majority (79%) of pretreated patients had received an autologous stem cell transplant; the median number of prior treatment lines was 1 (range, 1-13). MM mRNA measurement was by PCR withTaqMAN probes and GEP converted to a linear score (MMscore) ranging from 0-100 using the XBG algorithm. Statistical analyses: non-parametric testing (Mann-Whitney), ROC analysis, decision curve analysis, multiple and logistic regression.

Results:

MMscores were significantly elevated in all MM samples (46±13, p <0.0001) vs. controls (10±11; AUC=0.97, accuracy 96%) and MGUS (36±3, p=0.02; AUC=0.76). The GEP diagnostic accuracy for MM was >95% by Decision curve analysis. MMscores were not related to ISS stage. Scores were significantly lower with prior immunomodulatory therapy (p=0.04) or prior ASCT (p=0.002). MMscores were not correlated with any cytogenetic parameter except for elevation in the 9% with t(4;14) (57±8, p=0.008 vs. no translocation). Levels were significantly increased in active/progressive disease (54±10, p <0.0001: AUC: 0.81) compared to patients in remission following treatment (39±12). The difference between MGUS (36±3) and smoldering MM (SMM: 61±14) was statistically significant (p=0.002). Complete remission had the lowest scores (37±11) and was not significantly different to MGUS (0.36±0.03) but greater than controls (10±11). Multiple regression analysis identified the MMscore to be the only relevant parameter as a predictor of clinical status (F-ratio: 27.2, p <0.0001). The logistic model demonstrated that the MMscore and t(4;14) status accurately (AUC: 0.85) identified the clinical status at the time of blood draw.

Conclusions:

A blood-based GEP assay accurately (>95%) diagnosed multiple myeloma. This signature was not related to staging or cytogenetic abnormalities except the t(4;14) translocation. Levels accurately identified active and progressive disease differentiating these from complete remission. Of note, MGUS and SMM samples differed significantly. These data thus demonstrate that blood-based monitoring of multiple myeloma transcript levels may facilitate therapeutic management of myeloma disease.