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Scd fingerprints

Scd fingerprints description/claims

This application is a continuation of Ser. No. 10/506,906, filed Jun. 27, 2006, which is a 371 national phase application of PCTGB03/00974 filed Mar. 7, 2003, which claims the benefit of GB0205394.0 filed Mar. 7, 2002; GB0207746.9 filed Apr. 3, 2002; and GB0228195.4, filed Dec. 3, 2002. Each of these applications in their entirety is incorporated by reference herein.

The present invention relates to the use of cluster of differentiation (CD) molecules in detecting the presence and/or assessing the progression and/or assessing the response to therapeutic intervention of one or more disease states in an individual. In particular I relates to the use of profile/fingerprints of shed CD (sCD) molecules in body fluids in detecting and/or assessing the progression of one or more disease states in an individual. Further uses of sCD profiles according to the present invention are also described.

Rapid and accurate diagnosis is essential in medicine as in many cases early diagnosis and successful treatment correlates with a better outcome and reduced hospitalisation. Currently, the clinical diagnosis and staging of many diseases of global significance involve different invasive procedures such as histopathological analysis of biopsy samples which are usually obtained when the disease process is at a relatively advanced stage. In many cases, a classic histopathological approach may not be sufficient to produce accurate diagnosis and any delay in confirming the diagnosis would have financial and morbidity repercussions for the healthcare institution and most importantly for the individual. Disease states and disease staging are also determined by different imaging techniques such as X-rays, nuclear magnetic resonance (NMR), CT analysis and others, however, these are expensive and impractical when dealing with large numbers of individuals, or when it is necessary to monitor disease progression closely, or in health institutes or clinical situations where such equipment is unavailable. Furthermore such investigations are impractical for individuals because it would result in such individuals obtaining high radiation doses. For this reason such tests cannot be carried out serially and are thus of little use in monitoring drug responses and monitoring disease progression.

A variety of diseases or the predisposition to such a disease can be characterised by changes in the overall patterns and/or expression levels of various genes and their proteins. For example, some cancers are associated with changes in the expression of oncogenes or tumour suppressor genes. Furthermore, disease conditions or disorders associated with disregulated cell cycle and development can be attributed to changes in transcriptional regulation of specific genes.

Although there are several genetic assays available to assess gene mutations, the identification of specific genetic changes may not always be a direct indicator of a disease or disorder and thus cannot be relied upon as an accurate prognostic indicator.

Certain genetic changes are exhibited by alterations in cell surface antigens. Again, however, prior attempts to develop a diagnostic assay for complex disease conditions or disorders based on the identification of single antigen or very small numbers of antigens have not been uniformly successful.

In addition, or alternatively, biochemical analysis of a patient may be used to diagnose a disease state. For example, the presence of Bence Jones proteins in urine is an indicator that an individual has multiple myeloma. However, classical biochemical methods are limited, for example an elevated cholesterol in serum indicates hypercholesterolaemia but does not definitively indicate atherosclerosis. A further disadvantage of biochemical methods of diagnosis is that they generally permit the measurement of only one or two indicator/s of disease in any one test. Consequently, they provide an incomplete picture of the disease state of an individual. Moreover, if several tests are performed in an attempt to provide a more complete picture, this inevitably increases the number of variables which complicates interpretation. Furthermore, for many diseases there are no reliable biochemical markers, especially for diseases of global importance such as breast cancer, colorectal cancer and lung cancer. In the case of solid tumours such as colorectal cancer, a number of carcinoembryonic antigen (CEA) markers have been identified, however they have poor sensitivity and very low specificity. The situation is similar with disease conditions requiring surgical intervention. There is still, for example, no marker for acute appendicitis and consequently, a great many patients undergo unnecessary invasive surgery. It has been estimated that more than 40,000 unnecessary appendicitis operations occur each year due to misdiagnosis with associated costs of $700 million. In a recent larger retrospective study, Flume and colleagues show that misdiagnosis occurs in 15% of instances.

Therefore, there is a pressing need in the art to provide a simple and complete picture of the disease state or condition of an individual. Such a ‘picture’ would be of use in predicting and/or detecting the presence of a disease or condition, in assessing the therapeutic strategies and the potential of various agents and in monitoring the progression and successful treatment of disease states or conditions

Lymphocytes and other leukocytes express a large number of different antigens associated with their outer plasma membranes that can be used to identify distinct functional cell subsets. Many of these antigens were “classically” known to be receptors for growth factors, cell-cell interactions, viruses eg CD4, CD 112 and CD 155 are the HIV, poliovirus receptor 2 and poliovirus receptor respectively), and immunoglobulins; molecules for cell adhesion or complement stimulation; enzymes and ion channels. A single systematic nomenclature has been adopted to classify monoclonal antibodies to human leukocyte cell surface antigens termed cluster of differentiation (CD) antigens, also referred to as CD molecules/antigens (Kishimoto et al., 1996 Proceedings of the Sixth International Workshop and Conference held in Kobe, Japan. 10-14 Garland Publishing Inc, NY, USA). This work originated as the direct result of the work of one of the inventors (Dr. César Milstein) of the present application who invented monoclonal antibody technology with his colleague Georges Kohler (Kohler and Milstein. Continuous cultures of fused cells secreting antibody of defined specificity (1975), Nature August 7, 256 (5517), 495-7) and who identified and raised the first monoclonal antibodies to both non-human and human CD antigens (McMichael et al. A human thymocyte antigen defined by a hydrid myeloma monoclonal antibody).

The data required in order to define a CD has changed over the years, not surprisingly in view of the advances in modem technology. Initially, clustering depended absolutely on the statistical revelation of similarities in reaction pattern of two or more antibodies, analysed on multiple tissues. It is now accepted that CD molecules may also be classified by molecular characteristics. Thus it has become customary to use the CD marker (for example CD21) to indicate the molecule recognised by each group of monoclonal antibodies. The current list of CD markers is constantly updated as new antigens are identified and eventually, the CD list will encompass all human lymphocyte cell surface antigens and their homologues in other mammalian and non-mammalian species (Mason et al., 2001, Immunology, 103, 401-406). It should be noted that although CD antigens were initially defined in the basis that they are expressed on the cell surface of leukocytes, a great many of them are also expressed on numerous other cell types including brain, liver, kidney, red blood cells, bone marrow, dendritic antigen presenting cells, epithelial cells, stem cells, thymocytes, osteoclasts, NK cells, B cells, macrophages, to name but a few.

Historically, CD cell surface antigens have been used as markers in diagnosis. Indeed leukemias are diagnosed on the basis of cell morphology, expression of specific CD antigens, lymphoid (LY) and myeloid (MY) antigens, enzyme activities and cytogenetic abnormalities such as chromosome translocations. The expression of up to three CD antigens on leukemia cells is determined using labelled antibodies to particular CD antigens with analysis by flow cytometry.

Significantly, however, it has been observed that often (if not always in normal or disease states) the surface bound CD immunological specificity molecules (intact CD molecules or fragments thereof) are found soluble in the serum and in other body fluids. Subsequent research has shown that indeed CD molecules can be secreted from cells as a result of “active” processes such as alternative splicing (Woolfson and Milstein, PNAS, 91 (14) 6683-6687 (1994)) or “passive” processes such as cell surface shedding. Thus, CD molecules can be found in three forms, membrane associated CD molecules, shed CD molecules (sCD) produced by alternative splicing or other mechanisms and intracellular CD molecules. Each of these can be complete molecules or fragments thereof.

It is generally accepted however that the change in levels of any one sCD is not specific to a given disease state and cannot therefore usefully be used in the diagnosis of disease states.

Recent studies (those of WO 00/39580) have described a system for the diagnosis of haematological maligancies, whereby immunoglobulins are immobilised on a solid support and are used to detect cell-surface antigen levels, in particular cell-surface CD antigen levels in samples of cells. Using this approach, a pattern of expression of cell-surface bound CD antigens is generated which the inventors have shown to be indicative of the presence of various defined leukemias in a patient. However, there are several disadvantages with this technique. Firstly and importantly, it is a cell-based technique. Such techniques have many disadvantages associated with them, for example that of background noise and the difficulty of measuring antigen levels accurately. Such methods only allow semiquantitative determination of the relative densities of sub-populations of cells of distinct immunophemotypes, indeed absolute quantification using this method may not be possible. Another problem with this prior art method is that at equilibrium, the number of cells captured by the immobilised antibody dot depends not only on the affinities of the interactions, the concentration of the antibody dot, the level of expression of the CD antigen on the cell surface and in addition to this the stereochemical availability and accessibility of the monoclonal antibody immobilised on the nitrocellulose membrane of the CD antibody array. Furthermore computerised quantification of the cell density as indicated by the pixel intensity corresponding to each dot of arrayed antibody, depends not only on the number of cells in the test sample, but in addition to the cell size and morphology. In addition to all of these factors, the absolute requirement for purification of cells from whole blood and the possible need to fractionate blood cells still further makes such an approach both labour intensive and time consuming.

Therefore, there still exists a need in the art for a simple method for diagnosis of different diseases and conditions by the measurement of CD antigens wherein such method produces a complete, sensitive, specific and accurate picture of disease.

The present inventors have surprisingly found that particular disease states can be characterised by specific patterns of levels of shed/soluble/secreted (sCD) (as herein defined) CD molecules derived from the body fluids of an individual. That is, the profile or ‘sCD print’ of the levels of sCD antigens correlate with particular diseases or disorders or physiological states such as those induced by administration of a drug or toxin. This finding is especially surprising since the levels of sCDs found in the body fluids of an individual are generally very low, and the sCD released by cells would only be expected to change in some, and not all cell types of an individual when affected by one or more diseases, the change of levels of shed CD levels, as herein defined detectable in the body fluids of diseased individuals as compared with non-diseased individuals would be expected to be minimal.

Thus, in a first aspect, the present invention provides a shed CD (sCD) fingerprint (sCD print) of one or more disease states.

In the context of the present invention, the term ‘CD’ refers to a different cell surface leukocyte molecule recognised by a given monoclonal or group of monoclonal antibodies which specifically ‘cluster’ to the antigen/molecule in question. Many, if not all of these molecules produce forms which are released from the cell surface by alternative splicing, proteolytic cleavage, dissociation or other mechanisms. Thus in the context of the present invention, the term ‘shed CD molecule (sCD)’ is synonymous with the term secreted/soluble CD (sCD) and refers to a released form of a cell surface leukocyte molecule in which at least a portion of that molecule is recognised by a given monoclonal or group of monoclonal antibodies as herein described. It should be noted however, that the antibody used to recognise the CD molecule may not be monoclonal. It may be engineered, an artificial construct consisting of an expressed fragment derived from an antibody molecule with intact recognition, or it may be a non-protein molecular recognition agent, or a protein recognition agent which is not an antibody, or is an antibody hydrid, for example made by introducing antibody binding sites into a different scaffolding. Advantageously, as herein defined a shed form of sCD is generated by various mechanisms including but not limited to any of those selected from the group consisting of the following: alternative splicing, proteolytic cleavage and dissociation.

In the context of the present invention it is important to note that the CD nomenclature is a simple method for representing a whole range of molecules. For example: CD14 is the lipopolysaccharide receptor, LP5-R; CD21 is the EBV receptor, CD 25 is the IL-2Ralpha receptor; CD 31 is PECAM-1; CD 44 is H-CAM; CD 50 is ICAM-3; CD 54 ICAM-1; CD 62E is LECAM-2; CD 62L is LECAM-1; CD 86 is B 70; CD 95 is FAS apoptosis antigen; CD 102 is ICAM-2; CD 106 is VCAM-1; CD 116 is GM-CSFR alpha; CD 117 is c-kit stem cell factor receptor; CD 124 is IL-4R alpha; CD 126 is IL-6Ralpha; CD 130 is gp 130; CD 138 is syndican-1; CD 141 is thrombomodulin; CD 91 is low density lipoprotein receptor-related antigen; CD 132 is common cytokine receptor gamma), CD 89 is IgA Fc receptor, CD 74 is class II specific chaperone, CD 95 is apoptosis antigen; CD220 is the insulin receptor and CD 184 is the chemokine receptor 4. (CXCR4) CD8 is Lin 2; CD 27 is low affinity IgE-R; CD 30 is Ki-1.

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