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

Abstract

ABSTRACTComplex diseases, including cancer, are highly heterogeneous, and large molecular datasets are increasingly part of describing an individual’s unique experience. Gene expression is particularly attractive because it captures genetic, epigenetic and environmental consequences. SPECTRA is an approach to describe variation in a transcriptome as a set of unsupervised quantitative variables. Spectra variables provide a deep dive into the transcriptome, representing both large (prominent, high-level) and small (deeper, more subtle) sources of variance. Spectra variables are ideal for modeling alongside other variables for any outcome of interest. Each spectrum can also be considered a phenotypic trait, providing new avenues for disease characterization or to explore disease risk. We applied the SPECTRA approach to multiple myeloma (MM), the second most common blood cancer. Using RNA sequencing from malignant CD138+ cells, we derived 39 spectra in 767 patients from the MMRF CoMMpass study. We included spectra in prediction models for several clinical endpoints, compared to established expression-based risk scores, and used descriptive modeling to identify associations with patient characteristics. Spectra-based risk scores added predictive value beyond established clinical risk factors and other expression-based risk scores for overall survival, progression-free survival, and time to first-line treatment failure. We identified significant associations between CD138+ spectra and tumor cytogenetics, race, gender, and age at diagnosis. The SPECTRA approach provides quantitative measures of transcriptome variation to deeply profile tumors. This framework more comprehensively represents signals in the transcriptome and offers greater flexibility to model clinical outcomes and characteristics.