Inflammatory Breast Cancer typically includes a pathological finding of a high grade tumor with poorly differentiated cells. “In general, a higher grade means that there is a lesser degree of differentiation and the worse the biologic behavior of a malignant neoplasm will be. A well-differentiated neoplasm is composed of cells that closely resemble the cell of origin, while poorly differentiated neoplasms have cells that are difficult to recognize as to their cell of origin.” source
“The optimal treatment of patients with cancer depends on establishing accurate diagnoses by using a complex combination of clinical and histopathological data. In some instances, this task is difficult or impossible because of atypical clinical presentation or histopathology. To determine whether the diagnosis of multiple common adult malignancies could be achieved purely by molecular classification, we subjected 218 tumor samples, spanning 14 common tumor types, and 90 normal tissue samples to oligonucleotide microarray gene expression analysis. The expression levels of 16,063 genes and expressed sequence tags were used to evaluate the accuracy of a multiclass classifier based on a support vector machine algorithm. Overall classification accuracy was 78%, far exceeding the accuracy of random classification (9%). Poorly differentiated cancers resulted in low-confidence predictions and could not be accurately classified according to their tissue of origin, indicating that they are molecularly distinct entities with dramatically different gene expression patterns compared with their well differentiated counterparts. Taken together, these results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics.” Proc Natl Acad Sci U S A 2001 Dec 18;98(26):15149-54, Ramaswamy, S. et al.
A report of this research appeared as “One Size Fits All” in Nature Reviews Cancer 2, 78 (2002), Brooksbank,C: “Training this classifier using 144 primary tumour samples of known class allowed it to classify 78% of the samples correctly, and for half of the mistakes, the second or third most confident predication was correct. Increasing the number of tumours in the training set might improve this score. The classifier was then let loose on 54 test samples, with similar results. Interestingly, six of eight metastatic samples were correctly classified, indicating that metastatic tumours retain a gene-expression pattern similar to that of the tumour of origin. But the classifier fared less well on poorly differentiated (high-grade) carcinomas, indicating that their gene-expression patterns are fundamentally different from those of well-differentiated tumours from the same tissue. Might this reflect a different cellular origin for these tumours? Perhaps it’s time to refine our tumour classification systems to take these differences into account.”
“Classification of human tumors according to their primary anatomical site of origin is fundamental for the optimal treatment of patients with cancer. Here we describe the use of large-scale RNA profiling and supervised machine learning algorithms to construct a first-generation molecular classification scheme for carcinomas of the prostate, breast, lung, ovary, colorectum, kidney, liver, pancreas, bladder/ureter, and gastroesophagus, which collectively account for approximately 70% of all cancer-related deaths in the United States. The classification scheme was based on identifying gene subsets whose expression typifies each cancer class, and we quantified the extent to which these genes are characteristic of a specific tumor type by accurately and confidently predicting the anatomical site of tumor origin for 90% of 175 carcinomas, including 9 of 12 metastatic lesions. The predictor gene subsets include those whose expression is typical of specific types of normal epithelial differentiation, as well as other genes whose expression is elevated in cancer. This study demonstrates the feasibility of predicting the tissue origin of a carcinoma in the context of multiple cancer classes.” Cancer Res 2001 Oct 15;61(20):7388-93, Su, A. I. et al.
