Chronic Lymphocytic Leukemia (CLL) is a malignancy of B lymphocytes with an incidence of 4.7/100,000 new cases per year. CLL is one of the more common adult-onset hematological malignancies. Around 200,000 people in the USA live with CLL. Better methods to identify which patients require early therapy are needed. SPECTRA is a new statistical technique to characterize tumors using global gene expression (the transcriptome). The technique represents a tumor using multiple quantitative variables, called ""spectra"", which can then be used in statistical modeling to identify high-risk groups. Transcriptome data from 249 CLL patients attending the Huntsman Cancer Hospital was used to derive 19 CLL spectra. Each patient has their unique values for the CLL spectra variables (their spectra ""barcode""). Similarities and differences among patients can be visualized with barcodes. Descriptive modeling showed spectra associated with important known clinical risk markers, such as IGHV mutation status. Predictive modeling using spectra identified risk groups for time to first treatment. In this way, a patient's tumor transcriptome can predict their need for early treatment. To replicate our initial findings, we are collecting and processing biological samples from more CLL patients in Huntsman Cancer Hospital. We collect peripheral blood and cell-sort to identify malignant cells (CD19+/CD5+). RNA is extracted from these sorted cells and sequenced to generate transcriptome data from which the spectra values are calculated. Non-malignant cells from the cell-sorting procedure are used to extract normal DNA. The SPECTRA technique provides a more complete understanding of CLL. Each spectra represents a different tumor characteristic. Our future research will include investigating whether inherited variations (in normal DNA) are associated with particular CLL spectra or other characteristics of CLL with the ultimate goal of early detection and prevention efforts. We are also using the SPECTRA technique in several other cancers.