Abstract The Multiple Myeloma Research Foundation (MMRF) and the Multiple Myeloma Research Consortium (MMRC) have pioneered efforts towards understanding the genomic underpinnings of multiple myeloma. The MMRC centers have submitted over 1900 tumor samples to the MMRC tissue bank, from which >400 have been used in the Multiple Myeloma Genomics Initiative (MMGI), which is a multi-institutional comprehensive study to unveil the complete genomic blueprint of myeloma with public data access. In the initial phase 238 myeloma tumors were analyzed for somatic copy number alterations using array-based comparative genomic hybridization (aCGH) and array-based gene expression profiling (GEP). The large majority of this reference collection was also analyzed by array-based methylation profiling. We have also performed whole genome or exome sequencing on >200 normal/tumor pairs. These studies led to several key discoveries including global hypomethylation, homozygous deletion of UTX, novel mutations in FAM46C (13%) and DIS3/RRP44 (11%), and potentially therapeutically actionable BRAF mutations (4%). To improve our understanding of the transcriptional activities in myeloma, we have performed RNA-sequencing on 84 myeloma patient samples and 65 human myeloma cell lines for which whole genome or exome sequencing data is also available. RNA-seq has a number of advantages over GEP including improved dynamic range and the detection of exon and isoform levels, allele specific expression, and fusion transcripts. There are a number of robust gene expression classifications and indexes with prognostic value for myeloma samples tested by GEP. Within the cohort of 84 patient samples there are 42 samples with matched GEP data. Using this subgroup we tested if the current GEP methods could be directly moved to RNA-seq data by matching the current probesets to genes. We then compared the results across the patient set for each index or classification. For classifications we determined that the TC classification could be directly transitioned to RNA-seq data with 100% concordance. For the indexes we showed a strong correlation for the proliferation index (R2=0.971) and the NFKB index (R2=0.961) but only a moderate correlation for the 70-gene index (R2=0.761). The decreased correlation in the 70-gene index is clearly due to the large number of probesets used, which are associated with genes that are clearly not expressed by RNA-seq. Integration of the DNA and RNA sequencing data showed that 42% of the mutations detected in the DNA are expressed. Interestingly we observed the progressive loss of wildtype FAM46C alleles, which are often heterozygously mutated and expressed in patients but is exclusively homozogous in cell lines. Additional integrated analysis will improve out understanding of myelomagenesis and might lead to new and improved classification and prognostic models. Citation Format: Jonathan Keats, Venkata Yellapantula, Winnie Liang, Ahmet Kurdoglu, Angela Baker, Joan Levy, Todd Golub, David Craig, John Carpten. RNA sequencing based prognostic indexes show a high degree of correlation with historic array based gene expression measurements in multiple myeloma. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 806. doi:10.1158/1538-7445.AM2013-806