Deep Transcriptome Profiling of Multiple Myeloma Using Quantitative Phenotypes.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. IMPACT: 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.

Shared genomic segment analysis in a large high-risk chronic lymphocytic leukemia pedigree implicates CXCR4 in inherited risk.

AIM: Chronic lymphocytic leukemia (CLL) has been shown to cluster in families. First-degree relatives of individuals with CLL have an ~8 fold increased risk of developing the malignancy. Strong heritability suggests pedigree studies will have good power to localize pathogenic genes. However, CLL is relatively rare and heterogeneous, complicating ascertainment and analyses. Our goal was to identify CLL risk loci using unique resources available in Utah and methods to address intra-familial heterogeneity. METHODS: We identified a six-generation high-risk CLL pedigree using the Utah Population Database. This pedigree contains 24 CLL cases connected by a common ancestor. We ascertained and genotyped eight CLL cases using a high-density SNP array, and then performed shared genomic segment (SGS) analysis - a method designed for extended high-risk pedigrees that accounts for heterogeneity. RESULTS: We identified a genome-wide significant region (P = 1.9 × 10-7, LOD-equivalent 5.6) at 2q22.1. The 0.9 Mb region was inherited through 26 meioses and shared by seven of the eight genotyped cases. It sits within a ~6.25 Mb locus identified in a previous linkage study of 206 small CLL families. Our narrow region intersects two genes, including CXCR4 which is highly expressed in CLL cells and implicated in maintenance and progression. CONCLUSION: SGS analysis of an extended high-risk CLL pedigree identified the most significant evidence to-date for a 0.9 Mb CLL disease locus at 2q22.1, harboring CXCR4. This discovery contributes to a growing literature implicating CXCR4 in inherited risk to CLL. Investigation of the segregating haplotype in the pedigree will be valuable for elucidating risk variant(s).

Molecular identification of microorganisms associated with the brine shrimp Artemia franciscana.

BACKGROUND: Prior research on the microorganisms associated with the brine shrimp, Artemia franciscana, has mainly been limited to culture-based identification techniques or feeding studies for aquaculture. Our objective was to identify bacteria and archaea associated with Artemia adults and encysted embryos to understand the role of microbes in the Artemia life cycle and, therefore, their importance in a hypersaline food chain. RESULTS: We used small subunit (SSU) 16S ribosomal RNA gene sequencing to identify bacteria and archaea associated with adults and encysted Artemia embryos from one of their natural environments - Great Salt Lake (GSL), Utah, USA. We found that bacterial sequences most closely related to the genera Halomonas and Vibrio were commonly extracted from GSL adult Artemia, while bacterial sequences most similar to the genera Halomonas, Psychroflexus and Alkalilimnicola dominate in GSL water. Encysted embryos (cysts) yielded bacterial sequences from the genera Idiomarina and Salinivibrio, which were absent from adults and water. Common archaeal sequences in adults were most closely related to the genera Haloterrigena and Haloarcula, while all of the archaeal sequences from GSL water were most similar to the genus Halogeometricum. Cyst derived archaeal sequences were most closely related to the genera Halorubrum and Haloarcula. CONCLUSIONS: In addition to identifying microbial rRNA sequences that are specific to different stages of the Artemia life cycle, we observed striking differences in the sequences associated with the adult Artemia population in samples collected from GSL at different times and locations. While our study was limited in scope and the sample was small, our findings provide a foundation for future research into how the bacteria and archaea associated with Artemia influence the Artemia life cycle, and GSL food web.