Development of an RNA sequencing‐based prognostic gene signature in multiple myeloma

Abstract

Several prognostic gene signatures have been developed to predict the clinical outcome in patients with multiple myeloma (MM). The most salient disadvantage of the previous signatures is their non-reproducibility in external datasets. Given the disadvantages and the superiority of RNA sequencing over microarrays in transcriptome profiling to produce more reliable outputs, we sought to develop a reproducible RNA sequencingbased prognostic gene signature for MM. Genes significantly associated with survival were detected in The Cancer Genome Atlas (TCGA) MM RNA sequencing dataset (MMRF-CoMMpass) (n = 412) through a strict pipeline containing four rigid filters. The reproducibility of the selected genes was checked in an independent dataset (GSE24080), containing 559 newly diagnosed patients with MM. The RNA sequencing-based prognostic signature was reconstructed based on the final genes in the training dataset (MMRF-CoMMpass) and externally validated in five independent datasets (i.e. GSE2658, GSE13624, GSE9782, GSE6477 and GSE57317), containing 1461 MM cases. The RNA sequencing-based signature was reconstructed using finally five reproducible genes: CCT2, CKS1B, PRKDC, NONO and UBE2A. This signature was able to robustly discriminate between low-and high-risk patients in both training and validation datasets (Ps ≤ 0Á001). Our signature was also independent of and more powerful than the routine MM prognostic factors (i.e. b2-microglobulin, albumin, age and sex) (Ps ≤ 0Á01). Treatment regimens had no effect on RNA sequencing-based signature insofar as this signature succeeded in predicting the clinical outcome in various treatment groups (Ps ≤ 0Á001).

Deep transcriptome profiling of multiple myeloma with quantitative measures using the SPECTRA approach

“…We applied SPECTRA to transcriptome data for CD138+ cells from the MMRF CoMMpass study. 26 We investigated associations of CD138+ spectra with 1) existing expression-based risk scores; 27,28 2) clinically-relevant DNA aberrations; 3) clinical prognostic outcomes, and 4) patient demographic groups with elevated myeloma risk. Also, we illustrate the potential to track transcriptome changes over time.…”

“…We compare to the state-of-the-art risk scores: 1) the most widely adopted and first supervised expression risk score in myeloma, from the University of Arkansas for Medical Sciences (UAMS-70); and 2) one of the most recent supervised risk scores, from the Shahid Bahonar University of Kerman (SBUK-17). 28 Both risk scores were derived from the entire transcriptome, reducing dimensionality by restricting to selected genes, and providing a multi-gene risk metric based on those genes.…”

“…The SBUK-17 prognosis score was developed in RNA sequencing data. 28 All genes in the entire transcriptome were tested for association with survival. 28 A multi-step process, including univariate Cox analysis, intersection in six Class Prediction algorithms, and multivariate Cox analysis, was used to select 17 genes consistent across multiple methods.…”

“…28 All genes in the entire transcriptome were tested for association with survival. 28 A multi-step process, including univariate Cox analysis, intersection in six Class Prediction algorithms, and multivariate Cox analysis, was used to select 17 genes consistent across multiple methods. 28 These were entered in a multivariable Cox regression to define the SBUK-17 score.…”

“…28 A multi-step process, including univariate Cox analysis, intersection in six Class Prediction algorithms, and multivariate Cox analysis, was used to select 17 genes consistent across multiple methods. 28 These were entered in a multivariable Cox regression to define the SBUK-17 score. 28 The 75 th percentile was used to classify patients into a high-risk and low-risk categories.…”