The majority of cells in the human body reside in non-proliferating, out-of-cycle states and only a minority population is actively cycling. These cycling cells are mainly located in stem-transit amplifying compartments of self-renewing tissues such as cervix, colon or skin. In contrast, most functional cells (e.g. hepatocycles) reside in a quiescent (G0), reversibly arrested state, or have irreversibly withdrawn from the mitotic cell division cycle into terminally differentiated states (e.g. neurons, myocytes, or surface colonic epithelial cells). Cancers, on the contrary, are characterized by uncontrolled cell growth and therefore contain a high proportion of cycling cells.
Cells are responsive to mitogens in their environment at a discrete time in G1, referred to as the restriction point. The absence of mitogens does not affect cell cycle progression through S, G2 and M phase until cells return to their sensitive window in G1. In response to high cell density or mitogen deprivation, cells accumulate with a 2N DNA content and exit into G0. The cell cycle phase transitions are driven by changes in cyclin-CDK pairs. Cyclin D-CDK4, cyclin D-CDK6 and cyclin E-CDK2 regulate G0/G1 and are required for full E2F activity, S phase is initiated by cyclinA-CDK2, and cyclin B-CDK1 regulates progression through G2 and entry into mitosis. What distinguishes cells in out-of-cycle states from cells engaged in cycle remains to be elucidated.
Cancer is a complex group of heterogeneous diseases caused by the accumulation of gene mutations, which increase the activity of regulatory genes that stimulate cell proliferation and decrease the activity of proteins that normally restrain it. Activation of dominant stimulatory oncogenes or inactivation of recessive tumour suppressor genes, through point mutation, gene amplification, hypermethylation, translocation, or interaction with viral oncoproteins, can affect all levels of growth signalling pathways including mitogens, mitogen growth factor receptors, Ras, Raf, ABL, PI3 kinase AKt upstream to molecules such as p16INK4A, Myc, cyclin D, cyclin E, pRB and p53 downstream.
Microarray gene expression profiling is ideally suited for analysis of the complex multifactorial, interactive and stepwise alterations in gene expression that characterise tumourigenesis and is currently an intensive area of investigation aimed at identifying unique molecular signatures that can be exploited for cancer diagnosis and prognosis. Interestingly, the expression arrays include a proliferation signature, genes whose expression pattern correlates with tumour grade (differentiation status), cell cycle status and doubling times. This proliferation signature is one of the most prominent gene-expression patterns observed in tumour datasets, regardless of the tissue from which it is derived and includes many cell cycle regulated genes such as E2F1, BUB1, PLK1, cyclins E1, D1 and B1.
Unfortunately, the actual performance of prediction rules using gene expression has not turned out to be as informative as initially suggested for many tumour types and the list of genes identified can be highly unstable. For example, most predictive rules using gene expression have not provided a significantly improved prognostic classification for breast cancer when compared to conventional clinicopathological criteria such as tumour differentiation status, extent of spread and proliferation index. Indeed, many of the published gene signatures predicting distant-metastasis free survival in cancers have been found to correlate significantly with differentiation status.
The global microarray approach for identification of clinically useful proliferation signatures is potentially constrained. Firstly, the microarray approach in some part assumes a single compartment tumour model, in which cancers are composed of proliferating, exponentially growing cells. Neoplasms, however, are highly heterogeneous with regard to the cell cycle state of individual tumour cells. For example, in well-differentiated, low-grade tumours only a very small fraction of clonogenic tumour cells may be cycling, but in which the majority have executed their differentiation programmes and irreversibly withdrawn from cycle into a differentiated state (sterile compartment). Thus benign hyperproliferative conditions (e.g. hyperplasia) and physiological reparative growth, reactive pathological conditions containing large numbers of mitotic cells, may give higher proliferation signatures than well-differentiated cancers.
Secondly, tumour cells in vivo might also withdraw reversibly from cycle into a non-proliferating G0 state. Indeed, in many tumours the non-proliferating cells are the majority; that is the growth fraction (the ratio of proliferating to total cells) is less than 0.5. This situation is perhaps not surprising, because normal tissues are composed of mixed proliferating and non-proliferating elements and some remnant of this complex behaviour is hardwired into most cancers. The presence of contaminating benign neoplastic cells, tumour stroma, lymphoid follicles, intra- and peri-tumoural inflammatory infiltrates, and other connective tissues such as blood vessels also distorts the analysis by adding large numbers of cells with additional complex cell cycle kinetics. Hence the complex and heterogeneous cell cycle kinetics within individual tumours are likely to hinder identification of clinically useful microarray proliferation signatures, a problem that has also constrained the use of flow cytometry in routine clinical practice.
The use of flow cytometry has also had a limited impact since clinical samples are often not suitable for such analysis. This is partly due to fixation artifacts, inadequate amounts of tissue, and because of interpretation difficulties due to contaminating populations from reactive stroma and/or benign elements.
Assessment of cell proliferation markers has not previously provided any prognostic and predictive solution, and experts in the field have been sceptical that proliferation markers will provide useful clinical information. There is a belief that measuring parameters of cell proliferation will provide objective information about tumours, but despite numerous studies there is little direct evidence that the use of certain cell proliferation markers are really an improvement on conventional histological assessment optimally employed. Few studies have even addressed the critical issue of the relative value of proliferation markers compared to the standard histopathological grading and staging.
While gene expression has been applied to predicting the outcome in cancers, the actual performance of prediction rules using gene expression has not turned out to be as informative as initially suggested. For example, currently most predictive rules using gene expression have not provided a significantly improved prognostic classification when compared to the conventional NPI prognostic factors in breast cancer. The identification of new biomarkers to improve prognostic assessment in common cancers is therefore urgently required.
Breast cancer is an example of a common cancer and is a complex disease due to its morphological and biological heterogeneity, its tendency to acquire chemo-resistance and the existence of several molecular mechanisms underlying its pathogenesis. Half of women who receive loco-regional treatment for breast cancer will never relapse, whereas the other half will eventually die from metastatic disease. It is therefore imperative to distinguish clearly between these two groups of patients for optimal clinical management. Unfortunately, prognostic markers for breast cancers are at present limited. There is also lack of a test (predictive test) which allows selection of the most appropriate anti-cancer drugs particularly in the context of the new generation of small molecule inhibitors that target critical kinases involved in growth control and cell cycle transitions. For example in breast cancer predictive testing is presently limited to Her2 immunoexpression profiling.
Epithelial ovarian carcinoma (EOC) is another common cancer and is the fourth most common cancer in women in the U.S. and the U.K. Patients often presents with advanced disease, and despite improvements in drug therapy, survival is poor. At present, tumour stage is the most important prognostic factor. Residual disease after surgery, histologic subtype, and tumour grade also predict survival, but give little information about the biological variables responsible for stage progression and outcome.
Their remains a need for improved diagnostic, prognostic and predictive approaches to diseases caused by abnormal proliferation, such as cancerous or pre-cancerous conditions.
The present invention is directed to alleviating at least one disadvantage associated with the prior art.
Any discussion of documents, devices, acts or knowledge in this specification is included to explain the context of the invention. It should not be taken as an admission that any of the material forms a part of the prior art base or the common general knowledge in the relevant art on or before the priority date of the disclosure and claims herein.