The differentiation and stress response factor XBP-1 drives multiple myeloma pathogenesis.

Multiple myeloma (MM) evolves from a highly prevalent premalignant condition termed MGUS. The factors underlying the malignant transformation of MGUS are unknown. We report a MGUS/MM phenotype in transgenic mice with Emu-directed expression of the XBP-1 spliced isoform (XBP-1s), a factor governing unfolded protein/ER stress response and plasma-cell development. Emu-XBP-1s elicited elevated serum Ig and skin alterations. With age, Emu-xbp-1s transgenics develop features diagnostic of human MM, including bone lytic lesions and subendothelial Ig deposition. Furthermore, transcriptional profiles of Emu-xbp-1s lymphoid and MM cells show aberrant expression of known human MM dysregulated genes. The similarities of this model with the human disease, coupled with documented frequent XBP-1s overexpression in human MM, serve to implicate XBP-1s dysregulation in MM pathogenesis.

High-resolution genomic profiles define distinct clinico-pathogenetic subgroups of multiple myeloma patients.

To identify genetic events underlying the genesis and progression of multiple myeloma (MM), we conducted a high-resolution analysis of recurrent copy number alterations (CNAs) and expression profiles in a collection of MM cell lines and outcome-annotated clinical specimens. Attesting to the molecular heterogeneity of MM, unsupervised classification using nonnegative matrix factorization (NMF) designed for array comparative genomic hybridization (aCGH) analysis uncovered distinct genomic subtypes. Additionally, we defined 87 discrete minimal common regions (MCRs) within recurrent and highly focal CNAs. Further integration with expression data generated a refined list of MM gene candidates residing within these MCRs, thereby providing a genomic framework for dissection of disease pathogenesis, improved clinical management, and initiation of targeted drug discovery for specific MM patients.

RNA-Seq analysis in MeV.

SUMMARY: RNA-Seq is an exciting methodology that leverages the power of high-throughput sequencing to measure RNA transcript counts at an unprecedented accuracy. However, the data generated from this process are extremely large and biologist-friendly tools with which to analyze it are sorely lacking. MultiExperiment Viewer (MeV) is a Java-based desktop application that allows advanced analysis of gene expression data through an intuitive graphical user interface. Here, we report a significant enhancement to MeV that allows analysis of RNA-Seq data with these familiar, powerful tools. We also report the addition to MeV of several RNA-Seq-specific functions, addressing the differences in analysis requirements between this data type and traditional gene expression data. These tools include automatic conversion functions from raw count data to processed RPKM or FPKM values and differential expression detection and functional annotation enrichment detection based on published methods.

Marked genomic differences characterize primary and secondary glioblastoma subtypes and identify two distinct molecular and clinical secondary glioblastoma entities.

Glioblastoma is classified into two subtypes on the basis of clinical history: "primary glioblastoma" arising de novo without detectable antecedent disease and "secondary glioblastoma" evolving from a low-grade astrocytoma. Despite their distinctive clinical courses, they arrive at an indistinguishable clinical and pathologic end point highlighted by widespread invasion and resistance to therapy and, as such, are managed clinically as if they are one disease entity. Because the life history of a cancer cell is often reflected in the pattern of genomic alterations, we sought to determine whether primary and secondary glioblastomas evolve through similar or different molecular pathogenetic routes. Clinically annotated primary and secondary glioblastoma samples were subjected to high-resolution copy number analysis using oligonucleotide-based array comparative genomic hybridization. Unsupervised classification using genomic nonnegative matrix factorization methods identified three distinct genomic subclasses. Whereas one corresponded to clinically defined primary glioblastomas, the remaining two stratified secondary glioblastoma into two genetically distinct cohorts. Thus, this global genomic analysis showed wide-scale differences between primary and secondary glioblastomas that were previously unappreciated, and has shown for the first time that secondary glioblastoma is heterogeneous in its molecular pathogenesis. Consistent with these findings, analysis of regional recurrent copy number alterations revealed many more events unique to these subclasses than shared. The pathobiological significance of these shared and subtype-specific copy number alterations is reinforced by their frequent occurrence, resident genes with clear links to cancer, recurrence in diverse cancer types, and apparent association with clinical outcome. We conclude that glioblastoma is composed of at least three distinct molecular subtypes, including novel subgroups of secondary glioblastoma, which may benefit from different therapeutic strategies.

Common and distinct genomic events in sporadic colorectal cancer and diverse cancer types.

Colorectal cancer (CRC) is a major cause of cancer morbidity and mortality, and elucidation of its underlying genetics has advanced diagnostic screening, early detection, and treatment. Because CRC genomes are characterized by numerous non-random chromosomal structural alterations, we sought to delimit regions of recurrent amplifications and deletions in a collection of 42 primary specimens and 37 tumor cell lines derived from chromosomal instability neoplasia and microsatellite instability neoplasia CRC subtypes and to compare the pattern of genomic aberrations in CRC with those in other cancers. Application of oligomer-based array-comparative genome hybridization and custom analytic tools identified 50 minimal common regions (MCRs) of copy number alterations, 28 amplifications, and 22 deletions. Fifteen were highly recurrent and focal (<12 genes) MCRs, five of them harboring known CRC genes including EGFR and MYC with the remaining 10 containing a total of 65 resident genes with established links to cancer. Furthermore, comparisons of these delimited genomic profiles revealed that 22 of the 50 CRC MCRs are also present in lung cancer, glioblastoma, and/or multiple myeloma. Among 22 shared MCRs, nine do not contain genes previously shown genetically altered in cancer, whereas the remaining 13 harbor 35 known cancer genes, of which only 14 have been linked to CRC pathogenesis. Together, these observations point to the existence of many yet-to-be discovered cancer genes driving CRC development, as well as other human cancers, and show the utility of high-resolution copy number analysis in the identification of genetic events common and specific to the development of various tumor types.

High-resolution characterization of the pancreatic adenocarcinoma genome.

The pancreatic adenocarcinoma genome harbors multiple amplifications and deletions, pointing to the existence of numerous oncogenes and tumor suppressor genes driving the genesis and progression of this lethal cancer. Here, array comparative genomic hybridization on a cDNA microarray platform and informatics tools have been used to define the copy number alterations in a panel of 24 pancreatic adenocarcinoma cell lines and 13 primary tumor specimens. This high-resolution genomic analysis has identified all known regional gains and losses as well as many previously uncharacterized highly recurrent copy number alterations. A systematic prioritization scheme has selected 64 focal minimal common regions (MCRs) of recurrent copy number change. These MCRs possess a median size of 2.7 megabases (Mb), with 21 (33%) MCRs spanning 1 Mb or less (median of 0.33 Mb) and possessing an average of 15 annotated genes. Furthermore, complementary expression profile analysis of a significant fraction of the genes residing within these 64 prioritized MCRs has enabled the identification of a subset of candidates with statistically significant association between gene dosage and mRNA expression. Thus, the integration of DNA and RNA profiles provides a highly productive entry point for the discovery of genes involved in the pathogenesis of pancreatic adenocarcinoma.