Learn How Our Researchers Are Unraveling The Genetics Behind Myeloma

By John

Supervised clustering with SAM/PAM subgroup–defined genes in training and test sets. A supervised clustergram of the expression of 700 genes (50 SAM-defined overexpressed and underexpressed genes from each of the 7 subgroups) across the training set of 256 cases (A) and the test set of 158 cases (B). Genes are indicated along the vertical axis and samples on the horizontal axis. The normalized expression value for each gene is indicated by a color, with red representing high expression and blue representing low expression.

Molecular subgroups show differences in event-free and overall survival. (A) Kaplan-Meier estimates of event-free (i) and overall (ii) survival in the 7 subgroups showed that the 3-year actuarial probabilities of event-free survival were favorable at 84% in low bone disease (LB); 72% in hyperdiploid (HY); 82% in CD-1; and 86% in CD-2. High-risk was associated with proliferation (PR), MMSET (MS), and MAF/MAFB (MF), with 3-year estimates of event-free survival of 44% in PR and 39% in MS and 50% in MF. With respect to overall survival, the 3-year actuarial probabilities were 55% for PR, 69% for MS, 71% for MF, 81% for CD1, 84% for HY, 87% for LB, and 88% in CD2. (B) Event-free (i) and overall (ii) survival analysis of low-risk (HR, CD1, CD2, LB) and high-risk (PR, MF, MS) groups.

| Through a generous donation from Donna D. and Donald M. Lambert and others, a laboratory dedicated to unraveling the genetics of multiple myeloma was established at the Myeloma Institute for Research and Therapy on March 18, 2000.  The lab applies state-of-the-art genomic technologies and bioinformatics to study the biology of multiple myeloma. We employ high-throughput techniques that utilize DNA microarrays and apply quantitative analyses of the data to identify the key genetic lesions causing the development and progression of myeloma. Our current research focuses on understanding the mechanisms, logic and evolution of multiple myeloma, as well as the development of diagnostics, prognostics and novel therapeutics through the application of genomic analyses of tumor samples.

The human genome (all the DNA in the 46 chromosomes of each cell) contains approximately 25,000 genes. Genes code for proteins that are tasked with carrying out specific functions related to growth and differentiation (specialization). However, only a proportion of all genes are active at any given time in any one of the variety of cells that make up an organism.  In other words, cells of different histological subtypes, e.g. liver, colon, breast, lung, express common genes, but many are only expressed within cells of each of the specific tissues, thus providing specialized functions. Differential regulation of gene expression patterns provides the instruction for directing cells to develop into the various organs with specialized functions. Importantly, genes also regulate cellular homeostasis – the rate of cell growth and cell death, which keeps cell numbers in proper balance. Virtually all cancers are caused by genetic defects that, for the most part, result in permanent alterations in the genes that regulate homeostasis. In simplest terms, genes that are off in normal cells are turned on in cancer cells and visa-versa. Sometimes, in addition to qualitative differences, the degree of activity of a gene can differentiate a tumor cell from its normal counterpart.

In the late 1990’s, scientists developed highly sensitive and quantitative DNA microarrays containing thousands of DNA fragments that comprise the entire human genome. They are synthesized in a highly ordered and reproducible manner on glass slides the size of a finger nail.  So called messenger RNA (mRNA), representing the intermediate copy of a gene, is used to translate the genetic code into a protein. As the number of mRNA copies determines the amount of protein made by a given cell and proteins ultimately control cell growth, microarrays allow an easy and reproducible way to stratify samples based on qualitative and quantitative differences between normal and malignant cells and variation within cancers. When labeled with a fluorescent dye (a dye that emits light when a specific  wavelength of ultraviolet light is shined upon it) and placed in a solution on the microarray overnight, all the mRNA fragments produced by the cell of interest find their corresponding gene’s DNA fragments on the array and hybridize, or stick, to specific spots on the array. Using laser light and complex computer tools, the fluorescence at each gene location on the microarray can be quantitated and thus the level of gene activity determined. This procedure is repeated on all the cases in an experiment, e.g. myeloma tumor samples from newly diagnosed patients entered in a clinical trial.  Given that the exact location of each gene on the microarray is known and the same from samples to sample, patterns that reveal similarities and differences of gene expression levels between samples can be determined.  In this way, the gene expression patterns in plasma cells derived from healthy donors can be compared to those from patients with myeloma.  The genes exhibiting differential expression represent those whose altered expression may contribute to the malignant phenotype and, as such, represent potential therapeutic targets.

In another type of analysis, differences within a patient population are compared and contrasted.  For example, it is known that outcome following the diagnosis of myeloma is highly variable. However, the molecular mechanisms underlying this variability are not known and our ability to predict outcome is limited. Molecular profiling of myeloma tumor cells from a group of newly diagnosed and uniformly treated patients has allowed us to begin to answer both of the above questions. Thus, the completion of the human genome and the development of novel technologies to leverage this knowledge have revolutionized cancer genetics.

The information gleaned from these studies is providing an unprecedented glimpse into the intricate changes associated with the transformation of normal plasma cells into malignant plasma cells. One of the fundamental changes in cancer is the deregulation of gene expression. Microarray technology has provided investigators an unprecedented opportunity to better understand such changes and develop improved methods of diagnosis, prognosis, and treatment. To more fully comprehend the molecular mechanisms of myeloma development we are using microarray technology to

  • Distinguish the critical gene expression changes that differentiate normal from myelomatous plasma cells;

  • Correlate gene expression patterns with clinical outcome in order to define myeloma subtypes;
  • Determine if the gene expression patterns in the non-tumor cell component of the bone marrow microenvironment is altered.
  • Identify the gene expression changes in the tumor cells and the cells of the microenvironment associated with response to drugs used in the treatment of the disease;
  • Identify gene expression patterns associated with development of drug resistance;
  • Identify genetic and biochemical pathways that may be targets for new drugs;
  • Understand how myeloma causes bone destruction.

We are also using microarrays to identify novel immunotherapeutic targets, that is, genes that are expressed in abnormal plasma cells but not expressed in any normal cells in the body. The proteins produced from these hyperactive genes can be used as ammunition to kill the very cells that produce it.

Identification of recurrent malignancy-specific changes in gene expression can provide critical information and insight into the biochemical pathways that are altered in the course of myeloma formation, disease progression and development of resistance to treatment modalities. We are also identifying patients whose gene expression profile would predict that they may benefit from drugs that are currently available and being used to treat other malignancies.

In addition to gene expression microarrays, the laboratory is investigating the relationship between genetic variation [as determined through analysis of Single Nucleotide Polymorphisms (SNPs)] in non-tumor cells at risk for developing Multiple Myeloma and its response to treatment. We are also using Comparative Genomic Hybridization (CGH) and Fluorescence In Situ Hybridization (FISH) to investigate how DNA copy number changes in tumor cells influence gene expression, myeloma biology, and response to treatment. The lab is particularly interested in fine mapping specific chromosomal abnormalities, particularly deletions of chromosome 13 and amplification chromosome 1q21, which, when present in myeloma patients, impart a poor prognosis.

We are using new technologies such as RNA interference and lentivirus vectors to knock down hyperactive genes in myeloma cells in order to investigate if these genes influence tumor growth.

Finally, based on the findings derived from our molecular studies, we are now developing new, innovative ways to treat myeloma.