Huntsman Cancer Institute and School of Medicine, University of Utah, Salt Lake City, Utah.
Computational Biology, Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota.
Cancer Epidemiol Biomarkers Prev. 2023 May 1;32(5):708-717. doi: 10.1158/1055-9965.EPI-22-0798.
Transcriptome studies are gaining momentum in genomic epidemiology, and the need to incorporate these data in multivariable models alongside other risk factors brings demands for new approaches.
Here we describe SPECTRA, an approach to derive quantitative variables that capture the intrinsic variation in gene expression of a tissue type. We applied the SPECTRA approach to bulk RNA sequencing from malignant cells (CD138+) in patients from the Multiple Myeloma Research Foundation CoMMpass study.
A set of 39 spectra variables were derived to represent multiple myeloma cells. We used these variables in predictive modeling to determine spectra-based risk scores for overall survival, progression-free survival, and time to treatment failure. Risk scores added predictive value beyond known clinical and expression risk factors and replicated in an external dataset. Spectrum variable S5, a significant predictor for all three outcomes, showed pre-ranked gene set enrichment for the unfolded protein response, a mechanism targeted by proteasome inhibitors which are a common first line agent in multiple myeloma treatment. We further used the 39 spectra variables in descriptive modeling, with significant associations found with tumor cytogenetics, race, gender, and age at diagnosis; factors known to influence multiple myeloma incidence or progression.
Quantitative variables from the SPECTRA approach can predict clinical outcomes in multiple myeloma and provide a new avenue for insight into tumor differences by demographic groups.
The SPECTRA approach provides a set of quantitative phenotypes that deeply profile a tissue and allows for more comprehensive modeling of gene expression with other risk factors.
转录组研究在基因组流行病学中越来越受到关注，将这些数据与其他风险因素一起纳入多变量模型的需求带来了对新方法的需求。
在这里，我们描述了 SPECTRA 方法，该方法用于推导出定量变量，以捕获组织类型中基因表达的固有变化。我们将 SPECTRA 方法应用于多发性骨髓瘤研究基金会 CoMMpass 研究中患者的恶性细胞（CD138+）的批量 RNA 测序。
得出了一组 39 个谱变量，用于代表多发性骨髓瘤细胞。我们在预测模型中使用这些变量来确定用于总体生存、无进展生存期和治疗失败时间的基于谱的风险评分。风险评分除了已知的临床和表达风险因素外，还增加了预测价值，并在外部数据集得到了复制。谱变量 S5 是所有三个结果的重要预测因子，它显示出未折叠蛋白反应的预先排列的基因集富集，这是一种针对蛋白酶体抑制剂的机制，蛋白酶体抑制剂是多发性骨髓瘤治疗中常用的一线药物。我们进一步在描述性模型中使用了 39 个谱变量，发现与肿瘤细胞遗传学、种族、性别和诊断时的年龄有显著关联；这些因素已知会影响多发性骨髓瘤的发病率或进展。
SPECTRA 方法中的定量变量可预测多发性骨髓瘤的临床结果，并通过人口统计学群体为肿瘤差异提供了新的深入了解途径。
SPECTRA 方法提供了一组定量表型，可深度分析组织，并允许与其他风险因素更全面地建模基因表达。
