Spencer Fox Eccles School of Medicine
88 Transcriptome-Based Risk Score Predicts Time to Treatment Failure for Multiple Myeloma Patients
Ishmael Elliott Molina-Zepeda; Nicola Camp; Brandt Jones; Myke Madsen; Douglas Sborov; and Brian Avery
Faculty Mentor: Nicola Camp (Internal Medicine, University of Utah)
Background
Multiple Myeloma (MM) is a malignancy of plasma cells and one of the more common hematological malignancies (6.3/100,000 new cases/year). Although treatments have improved, most patients fail their first line of treatment and ultimately do not survive beyond 5 years. Identifying patients at high risk of failing treatment early is a critical need. SPECTRA is a statistical technique developed by the Camp Lab to characterize global gene expression (the transcriptome) by representing it as multiple quantitative tumor variables. Spectra variables allow gene expression to be incorporated into predictive modeling to identify high-risk groups.
Methods
Transcriptome data for myeloma cells was available from 768 patients in the international CoMMpass study, where 39 spectra variables were derived. Each patient has a value for each of the 39 variables (their spectra “barcode”); patients can be compared for each bar in the barcode. To replicate the CoMMpass data findings, we collect and process local biological samples from MM patients at the Huntsman Cancer Institute (HCI). We collect bone marrow samples, which are then cell-sorted to identify tumor (CD138+) cells. RNA is extracted from these cells and sequenced to generate transcriptome data. Then, the spectra barcode is calculated.
Results
Predictive modeling using spectra variables was successful in identifying risk groups for time to treatment failure, such that a patient’s tumor transcriptome can be used to predict whether they are at high risk of having their treatment fail earlier.
Conclusion
Utilizing the SPECTRA technique provides a more complete understanding of MM by better characterizing the tumor. Each spectra is a tumor characteristic. Our future research includes investigating whether inherited variations (in normal DNA from saliva or whole blood) are associated with the transcriptome risk score. We are also pursuing the SPECTRA technique in several other cancers.
