Genomics is already having a major impact on diagnostic precision and optimization of therapy in cancer. In this section we describe how one such approach, gene expression profiling, is used to guide diagnosis and treatment.

Gene Expression Profiling and Clustering to Identify Signatures

Comparative hybridization techniques can be used to simultaneously measure the level of mRNA expression of some or all of the genes in any human tissue sample.

A measurement of mRNA expression in a sample of tis sue constitutes a gene expression profile specific to that tissue. Fig. 1 depicts a hypothetical, idealized situation of eight samples, four from each of two types of tumor, A and B, profiled for 100 different genes. The expression profile derived from expression arrays for this simple example is already substantial, consisting of 800 expression values. In a real expression profiling experiment, however, hundreds of samples may be analyzed for the expression of all human genes, producing a massive data set of millions of expression values. Organizing the data and analyzing them to extract key information are challenging problems that have inspired the development of sophisticated statistical and bioinformatic tools. Using such tools, one can organize the data to find groups of genes whose expression seems to correlate (i.e., increase or decrease together) between and among the samples. Grouping genes by their pat terns of expression across samples is termed clustering.

Fig1. Schematic of an idealized gene expression profiling experiment of eight samples and 100 genes. (Left) Individual arrays of gene sequences spotted on glass or silicon chips are used for comparative hybridization of eight different samples relative to a common standard. Red indicates decreased expression compared with control, green indicates increased expression, and yellow is unchanged expression. (In this schematic, red, yellow, and green represent decreased, equal, or increased expression, whereas a real experiment would provide a continuous quantitative reading with shades of red and green.) (Center) All 800 expression measurements are organized so that the relative expression for each gene (1– 100) is put in order vertically in a column under the number of each sample. (Right) Clustering into signatures involves only those 13 genes that showed correlation across subsets of samples. Some genes have reciprocal (high vs low) expression in the two tumors; others show a correlated increase or decrease in one tumor and not the other.

Clusters of gene expression can then be tested to deter mine if any correlate with particular characteristics of the samples of interest. For example, profiling might indicate that a cluster of genes with a correlated expression profile is found more frequently in samples from tumor A than from tumor B, whereas another cluster of genes with a correlated expression profile is more frequent in samples derived from tumor B than from tumor A. Clusters of genes whose expression correlates with each other and with a particular set of samples constitute an expression signature characteristic of those samples. In the hypothetical profiles in Fig. 1, certain genes have a correlated expression that serves as a signature for tumor A; tumor B has a signature derived from the correlated expression of a different subset of these 100 genes.

Application of Gene Signatures

The application of gene expression profiles to characterize tumors is useful in several ways.

• First, it increases our ability to discriminate between different tumors in ways that complement the standard criteria applied by pathologists to characterize tumors, such as histologic appearance, cytogenetic markers, and expression of specific marker proteins. Once distinguishing signatures for different tumor types (e.g., tumor A vs tumor B) are defined using known samples, the expression pattern of unknown tumor samples can then be compared with the expression signatures for tumor A and tumor B and classified as A- like, B- like, or neither, depending on how well their expression profiles match the signatures of A and B. Pathologists have used expression pro filing to make difficult distinctions between tumors that require very different management approaches. These include distinguishing large B- cell lymphoma from Burkitt lymphoma, differentiating primary lung cancers from squamous cell carcinomas of the head and neck metastatic to lung, and identifying the tissue of origin of a cryptic primary tumor whose metastasis gives too little information to allow its classification.

• Second, different signatures may be found to correlate with known clinical outcomes, such as prognosis, response to therapy, or any other outcome of inter est. If validated, such signatures can be applied prospectively to help guide therapy in newly diagnosed patients.

 • Finally, for basic research, clustering may reveal previously unsuspected connections of functional importance among genes involved in a disease process.

Gene Expression Profiling in Cancer Prognosis

 Choosing the appropriate therapy for most cancers is difficult for patients and their physicians alike because recurrence is common and difficult to predict. Better characterization of each patient’s cancer as to recurrence risk and metastatic potential would clearly be beneficial for deciding between more or less aggressive courses of surgery and/ or chemotherapy. For example, in breast cancer— although presence of the estrogen and progesterone receptors, amplification of the HER2 oncogene, and absence of metastatic tumor in lymph nodes found on dissection of axillary lymphatics are strong predictors of better response to therapy and prognosis— they are still imprecise. Expression profiling (Fig. 2) is opening up a promising new avenue for clinical decision making in the management of breast cancer, as well as in other cancers, including lymphoma, prostate cancer, and metastatic adenocarcinomas of diverse tissue origins (lung, breast, colorectal, uterine, and ovarian).

Fig2. Expression patterns for a series of genes (along the vertical axis at left) for series of patient tumors, with the tumors arranged along the horizontal axis at top so that tumors with more similar expression patterns are grouped more closely together. The tumors appear to generally cluster into two groups, which are then correlated with long- term survival. (Adapted from Reis- Filho J, Pusztai L: Gene expression profiling in breast cancer: Classification, prognostication, and prediction, Lancet 378:1812– 1823, 2011.)

Gene expression profiling of various sets of genes is clinically available for use in the management of breast, colon, and ovarian cancer; which genes and how many are included in the profile depends on the tumor type and vendor. Although the clinical utility and cost effective ness continue to be debated, there is a general consensus that combinations of clinical and gene expression data in patients newly diagnosed with cancer will provide better prospective estimates of prognosis and improved guidance of therapy. It is hoped that by improving the accuracy of prognosis with tumor expression profiling, oncologists can better tailor therapy and minimize exposure to toxic drugs when possible.

The fact that prognosis for practically every patient could be associated with a particular combination of clinical features, genome sequence, and expression signatures underscores a crucial point about cancer: each person’s cancer is a unique disorder. The genomic and gene expression heterogeneity among patients who all carry the same cancer diagnosis should not be surprising. Every patient is unique in the genetic variants carried, including those variants that will affect how the cancer develops and the body responds to it. Moreover, the clonal evolution of a cancer implies that chance mutational and epigenetic events will likely occur in different and unique combinations in every patient’s particular cancer.

Targeted Cancer Therapy

Until recently, most nonsurgical cancer treatments relied on cytotoxic agents, such as chemotherapeutic agents or radiation, designed to preferentially kill tumor cells while attempting to spare normal tissues. Despite tremendous successes in curing such diseases as childhood acute lymphocytic leukemia and Hodgkin lymphoma, most cancer patients in whom complete removal of the tumor with surgery is not possible achieve remission, not cure, of their disease, usually at the cost of substantial toxicity from cytotoxic agents. The discovery of specific driver genes and their mutations in cancers has opened a new avenue for precisely targeted, less toxic treatments. Activated oncogenes are tempting targets for cancer therapy through direct blockade of their aberrant function. This can include blocking an activated cell surface receptor by monoclonal antibodies, or targeted inhibition of intracellular constitutive kinase activity with drugs designed to specifically inhibit their enzymatic activities.

The proof of principle for this approach was established with the development of imatinib, a highly effective inhibitor of a number of tyrosine kinases, including the ABL1 kinase in CML. Prolonged remissions of this disease have been seen, in some cases with apparently indefinite postponement of the transformation into a virulent acute leukemia (blast crisis) that so often meant the end of a CML patient’s life. Additional kinase inhibitors have been developed to target other activated oncogene driver genes in a variety of tumor types. Furthermore, constitutional pathogenic variants have been the impetus for targeted therapies (e.g., BRCA1/ BRCA2, MLH1, MSH2, MSH6, PMS2, VHL1, NF1) (Table 1).

Table1. Cancer Treatments Targeted to Specific Driver Oncogenes

The initial results with targeted therapies, although very promising in some cases, have not led to permanent cures in most patients largely because tumors develop resistance to the targeted therapy. The outgrowth of resistant tumors is not surprising. First, as previously discussed, cancer cells are highly mutable, and their genomes undergo recurrent mutation. Even if only a small minority of cells acquire resistance through either mutation of the targeted oncogene itself or a compensatory mutation elsewhere, the tumor can progress even in the face of oncogene inhibition. Newer compounds that can overcome drug resistance are being developed and used in clinical trials. Ultimately, combination therapy that targets different driver genes may be required, based on the idea that a tumor cell is less likely to develop resistance in multiple unrelated pathways targeted by a combination of agents.

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مواضيع ذات صلة

Genetic Alterations Associated with Acromegaly

The Profession of Genetic Counseling

The Role of Epigenetics in Cancer

Screening for Genetic Susceptibility to Disease

Pharmacogenomics

Monogenic Diseases Show One of Three Patterns of Inheritance

Tumor Suppressor Genes in Autosomal Dominant Cancer Syndromes

Activated Oncogenes in Hereditary Cancer Syndromes

Genetic Basis of Cancer

Conditional Mutations Can Be Used to Study Essential Genes in Yeast

Segregation of Mutations in Breeding Experiments Reveals Their Dominance or Recessivity

Recessive and Dominant Mutant Alleles Generally Have Opposite Effects on Gene Function