Diffuse large cell lymphoma diagnosis and outcome prediction by expression analysis

#Classification of biological samples from individuals is not an exact science. In many instances, accurate diagnoses and safe and effective treatment of a disorder depend on being able to discern biological distinctions among morphologically similar samples, such as, for example, tumor samples. The classification of a sample from an individual into particular disease classes has proven to be difficult, incorrect or equivocal. Typically, using traditional methods such as histochemical analyses, immunophenotyping and cytogenetic analyses, only one or two characteristics of the sample are analyzed to determine the sample's classification, resulting in inconsistent and sometimes inaccurate results. Such results can lead to incorrect diagnoses and potentially ineffective or harmful treatment. Furthermore, important biological distinctions are likely to exist that have yet to be identified due to the lack of systematic and unbiased approaches for identifying or recognizing such classes. Thus, a need exists for an accurate and efficient method for identifying biological classes and classifying samples.

#The present invention relates to one or more sets of informative genes whose expression levels correlate with a class distinction between samples. In a particular embodiment, the class distinction is a lymphoma class distinction, such as a Non-Hodgkin's lymphoma class distinction (e.g., folicular lymphoma (FL) or diffuse large cell lymphoma (DLCL)). In another embodiment the class distinction can be a treatment outcome or survival class distinction.

#When classifying a sample as to its source, for example FL or DLCL, informative genes can be, for example, all or a subset of the genes shown in FIGS. 3A and 3B and/or the genes shown in FIGS. 4A and 4B. FIGS. 3A and 3B show informative genes whose expression is increased in DLCL and decreased in FL. FIGS. 4A and 4B show informative genes whose expression is decreased in DLCL and increased in FL.

#FIG. 1 shows a list of Large B-Cell Lymphoma treatment outcome gene markers whose expression is increased in low risk and decreased in high risk individuals. The genes are identified by GenBank Accession number followed by common name.

#FIGS. 2A-2B show a list of Large B-Cell Lymphoma treatment outcome gene markers whose expression is decreased in low risk and increased in high risk individuals. The genes are identified by GenBank Accession number followed by common name.

#FIGS. 3A-3B show a list of informative genes whose expression is increased in DLCL and decreased in FL. The genes are identified by GenBank Accession number followed by common name.

#The present invention relates to methods for classifying a sample according to the gene expression profile of the sample. In one embodiment, the present invention is directed to classifying a sample with respect to a phenotypic effect, e.g., lymphoma class or predicted treatment outcome, including the steps of isolating a gene expression product from one or more cells in the sample, and determining a gene expression profile of at least one informative gene, such that the gene expression profile is correlated with a phenotypic effect, thereby classifying the sample with respect to phenotypic effect.

#According to the methods of the invention, samples can be classified as belonging to (i. e., derived from) a particular type of lymphoma. For example, a sample can be classified as derived from a folicular lymphoma (FL) or from a diffuse large cell lymphoma (DLCL). This distinction is not readily discernable using traditional analytic methods.

#Alternatively, according to methods of the invention, samples can be classified as belonging to a particular class of treatment outcome. Such a classification sorts samples according to the likelihood of, for example, successful treatment. Samples can be sorted according to their responsiveness to drugs, therapy, or even generally to survival of the individual from whom the sample was derived. That is, a sample can be classified as belonging to a high risk class (e.g., a class with poor prognosis for survival after or without treatment) or a low risk class (e.g., a class with good prognosis for survival after or without treatment). Duration of illness, severity of symptoms and eradication of disease can also be used as the basis for differentiating, i.e., classifying, samples.