Current studies in Multiple Myeloma suggest that patient tumors and cell lines cluster separately based on gene expression profiles. Hyperdiploid patients are also extremely underrepresented in established human myeloma cell lines (HMCLs). This suggests that the average HMCL model system does not accurately represent the average myeloma patient. To investigate this question we performed a combined CNA and SNV evolutionary comparison between four myeloma tumors and their established HMCLs (JMW-1, VP-6, KAS-6/1-KAS-6/2 and KP-6). We identified copy number changes shared between the tumors and their cell lines (mean of 74 events - 59%), those unique to patients (mean of 21.25 events - 17%), and those only in the cell lines (mean of 30.75 events \u2014 24%). A relapse sample from the JMW-1 patient showed 58% similarity to the primary diagnostic tumor. These data suggest that, on the level of copy number abnormalities, HMCLs show equal levels of evolutionary divergence as that observed within patients. By exome sequencing, patient tumors were 71% similar to their representative HMCLs, with ~12.5% and ~16.5% of SNVs unique to the tumors and HMCLs respectively. The HMCLs studied appear highly representative of the patient from which they were derived, with most differences associated with an enrichment of sub-populations present in the primary tumor. Additionally, our analysis of the KP-6 aCGH data showed that the patient's hyperdiploid karyotype was maintained in its respective HMCL. This discovery confirms the establishment and validation of a novel and potentially clinically relevant hyperdiploid HMCL that could provide a major advance in our ability to understand the pathogenesis and progression of this prominent patient population.

Multiple myeloma is a genetically heterogeneous disease, which can be divided into several genetic subtypes based upon gene expression profiles and chromosomal abnormalities. Unlike older techniques employed in myeloma research, such as cytogenetics, FISH, and microarray technologies, RNA sequencing offers a unique approach to examine the aforementioned genetic characteristics in that it allows for gene expression profiling and the detection of novel fusion transcripts arising from chromosomal rearrangements. This study utilized RNA sequencing to analyze the transcriptomes of 84 multiple myeloma patients and 69 human myeloma cell lines. FCHSD2 was found to be involved in five novel fusion events along with known oncogenes, MMSET and MYC, as well as three previously unreported genes in myeloma, including CHMP4B, NCF2, and CARNS1. An analysis of FCHSD2 expression within myeloma cell lines indicated that it is highly expressed in comparison to other tissues, suggesting that FCHSD2 translocations could lead to promoter replacement events in which the expression of partnering genes is dysregulated. The presence of the five FCHSD2 hybrid transcripts was confirmed by reverse transcription-PCR and Sanger sequencing. Overexpression of the FCHSD2 fusion transcripts in HEK293 cells resulted in the production of N-terminally truncated fusion partner proteins and a novel FCHSD2-CARNS1 fusion protein.

The processes of a human somatic cell are very complex with various genetic mechanisms governing its fate. Such cells undergo various genetic mutations, which translate to the genetic aberrations that we see in cancer. There are more than 100 types of cancer, each having many more subtypes with aberrations being unique to each. In the past two decades, the widespread application of high-throughput genomic technologies, such as micro-arrays and next-generation sequencing, has led to the revelation of many such aberrations. Known types and subtypes can be readily identified using gene-expression profiling and more importantly, high-throughput genomic datasets have helped identify novel sub-types with distinct signatures. Recent studies showing usage of gene-expression profiling in clinical decision making in breast cancer patients underscore the utility of high-throughput datasets. Beyond prognosis, understanding the underlying cellular processes is essential for effective cancer treatment. Various high-throughput techniques are now available to look at a particular aspect of a genetic mechanism in cancer tissue. To look at these mechanisms individually is akin to looking at a broken watch; taking apart each of its parts, looking at them individually and finally making a list of all the faulty ones. Integrative approaches are needed to transform one-dimensional cancer signatures into multi-dimensional interaction and regulatory networks, consequently bettering our understanding of cellular processes in cancer. Here, I attempt to (i) address ways to effectively identify high quality variants when multiple assays on the same sample samples are available through two novel tools, snpSniffer and NGSPE; (ii) glean new biological insight into multiple myeloma through two novel integrative analysis approaches making use of disparate high-throughput datasets. While these methods focus on multiple myeloma datasets, the informatics approaches are applicable to all cancer datasets and will thus help advance cancer genomics.