Molecular Diagnosis of the Hematologic Cancers

Abstract

he diagnosis of the hematologic cancers presents a daunting challenge. The many stages of normal hematopoietic differentiation give rise to a number of biologically and clinically distinct cancers. Inherited DNA-sequence variants do not appear to have a prominent causative role; rather, these diverse cancers are typically initiated by acquired alterations to the genome of the cancer cell, such as chromosomal translocations, mutations, and deletions. The diagnosis of the hemato-logic cancers is commonly based on morphologic evaluation supplemented by analysis of a few molecular markers. However, in some diagnostic categories defined in this fash-ion, the response of patients to treatment is markedly heterogeneous, arousing the sus-picion that there can be several molecularly distinct diseases within the same morpho-logic category. Gene-expression profiling is a genomics technique that has proved effective in de-ciphering this biologic and clinical diversity. The approach relies on the fact that only a fraction of the genes encoded in the genome of each cell are expressed — that is, actively transcribed into messenger RNA (mRNA) (Fig. 1A). The abundance of mRNA for each gene depends on a cell's lineage and stage of differentiation, on the activity of intracel-lular regulatory pathways, and on the influence of extracellular stimuli. To a large extent, the complement of mRNAs in a cell dictates its complement of proteins, and conse-quently, gene expression is a major determinant of the biology of normal and malig-nant cells. In the process of expression profiling, robotically printed DNA microarrays are used to measure the expression of tens of thousands of genes at a time; this creates a molecular profile of the RNA in a tumor sample 1 (Fig. 1B). A variety of analytic techniques are used to classify cancers on the basis of their gene-expression profiles. 2,3 There are two general approaches. In an unsupervised approach, pattern-recognition algorithms are used to identify subgroups of tumors that have related gene-expression profiles (Fig. 2A). In a supervised approach, statistical methods are used to relate gene-expression data and clinical data (Fig. 2B). These methods have revealed unexpected subgroups within the diagnostic categories of the hematologic cancers that are based on morphology and have demonstrated that the response to therapy is dictated by multiple independent biologic features of a tumor. This is not a comprehensive review of hematologic cancers; rather, it will provide examples of how gene-expression profiling has been used to provide a framework for the molecular diagnosis of these cancers. diffuse large-b-cell lymphoma Some cases of diffuse large-B-cell lymphoma respond well to multiagent chemothera-py, 5 but this lymphoma nonetheless remains a perplexing clinical puzzle, since roughly t molecular diagnosis of non-hodgkin's lymphoma The New England Journal of Medicine Downloaded from nejm.org on December 23, 2015. For personal use only. No other uses without permission.