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Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes.

Rosalie Griffin1,2, Heidi A Hanson1, Brian J Avery1

  • 1Huntsman Cancer Institute and School of Medicine, University of Utah, Salt Lake City, Utah.

Cancer Epidemiology, Biomarkers & Prevention : a Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology
|March 1, 2023
PubMed
Summary

SPECTRA quantifies gene expression variation for improved multiple myeloma risk prediction. This approach enhances understanding of tumor biology and patient outcomes.

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Area of Science:

  • Genomic epidemiology
  • Transcriptome analysis
  • Biostatistics

Background:

  • Transcriptome studies are increasingly vital in genomic epidemiology.
  • Integrating gene expression data with other risk factors requires novel analytical methods.

Purpose of the Study:

  • To introduce SPECTRA, a novel approach for deriving quantitative gene expression variables.
  • To apply SPECTRA to multiple myeloma data for predictive modeling of clinical outcomes.
  • To explore associations between SPECTRA variables and demographic/clinical factors.

Main Methods:

  • SPECTRA was developed to capture intrinsic gene expression variation within tissue types.
  • Applied to bulk RNA sequencing data from multiple myeloma (CD138+) patients.
  • Derived 39 SPECTRA variables used in predictive and descriptive modeling.

Main Results:

  • SPECTRA-derived risk scores accurately predicted overall survival, progression-free survival, and time to treatment failure.
  • These scores improved predictions beyond existing clinical and expression factors, validated on an external dataset.
  • SPECTRA variables showed significant associations with tumor cytogenetics, race, gender, and age, and identified the unfolded protein response pathway.

Conclusions:

  • Quantitative variables derived by SPECTRA effectively predict clinical outcomes in multiple myeloma.
  • SPECTRA offers new insights into tumor heterogeneity across different demographic groups.
  • This approach facilitates more comprehensive multivariable modeling of gene expression data.