Classification of cancers has been dominated by the fields of histology and histopathology which aim to leverage morphological markers for accurate identification of a tumour type. Histological methods rely on chemical staining of tissues with pigments such as haematoxylin and eosin and microscopy-based visualization by a pathologist. The identification of tumor subtypes is based on established classification schemes such as the International Classification of Diseases published by the World Health Organization which provides codes to classify diseases and a wide variety of signs, symptoms, abnormal findings, complaints, social circumstances, and external causes of injury or diseases. For some types of cancer, these methods are unable to distinguish between subclasses; for example, defining subgroups of diffuse large B-cell lymphoma (DLBCL) have largely failed due to discrepancies between inter- and intra-observer reproducibility. Furthermore, the clinical outcomes of tumors classified as DLBCLs is highly variable suggesting that there are multiple subtypes of DLBCL that cannot be distinguished based on these histological markers. Breast tumor classification too has largely failed based on these predictors. Development of effective therapies depends on accurate diagnosis; additionally, poor diagnosis can lead to patient suffering due to needless side-effects from non-targeted treatments and to increased health care expenditure. Most telling perhaps is that 70-80% of breast cancer patients receiving chemotherapy based on traditional predictors would have survived without it.

Of note, similar gene expression patterns associated with metastatic behaviour of breast cancer tumor cells have also been found in breast cancer of dog, the most common tumor of the female dog. Below are ways that gene expression profiling has been used to more precisely classify tumors into subgroups, often with clinical effect.

Molecular Signature

In a particular type of cell or tissue, only a small subset of an organism’s genomic DNA will be expressed as mRNAs at any given time. The unique pattern of gene expression for a given cell or tissue is referred to as its molecular signature. For example, the expression of genes in skin cells would be very different compared to those expressed in blood cells. Microarray analysis can provide quantitative gene expression information allowing for the generation of a molecular signature, each unique to a particular class of tumor. This idea was first shown experimentally^[7] in 2000 by researchers at Stanford University published in Nature Genetics. The authors measured the relative expression of 9,703 human cDNAs in sixty cancer cell lines previously studied and characterized by the National Cancer Institute’s Developmental Therapeutics Program. A hierarchical clustering algorithm was used to group cell lines based on the similarity by which the pattern of gene expression varied. In this study by Ross et al., the majority of cell lines with common organs of origin (based on information from the National Institutes of Health) clustered together at terminal branches, suggesting that cancer cells arising from the same tissue share many molecular characteristics. This allows for reliable identification of tumour type based on gene expression.

Anticipating your valuable response towards the invitation.

Regards,
John George
Associate Editor
Journal of Molecular cancer