Competing roles of phenotype and genotype in prognosis of breast carcinoma: Enter the RNA expression profile

Phenotype is the physical, biochemical, and physiological makeup of an individual or neoplasm or cell. Genotype is the program that determines phenotype. The genotypic program produces phenotype through translation of nuclear genotypic DNA into messenger RNA. Messenger RNA contains code that directs protein synthesis in the cytoplasm, thereby expressing phenotype of normal or neoplastic cells. Advances in molecular biology have made feasible use of gene expression analysis for malignancy grading of breast carcinoma [1]. By analysis of RNA extracted from the tumor, activity of specific genes can be measured. Thereby, we intercept the code that regulates growth rate and other phenotypic features that determine behavior of the neoplasm and the morphologic features used in traditional malignancy grading. Progress in genotypic analysis has been accompanied pari passu by progress in cancer staging and analysis of phenotypic expression at the cellular level. Staging has been enhanced by developments in imaging utilizing radiologic, magnetic resonance, and positron emission technologies. Staging can be refined further by detection of cancer cells in bone marrow and blood. Invasive potential is another important contributor to tumor behavior. It is expressed phenotypically as vascular intravasation, cell motility, and secretion of proteases. Morphologic grading was conceived in the first half of the 20th century to predict natural history of breast carcinoma within staging categories. Features used in grading are nuclear pleomorphism, formation of glands or ducts, and frequency of mitotic figures in 10 high power fields (HPF). In the 1930s and subsequently, these features were combined with equal weights and roughly tertile cutoffs to define tumor grade by Bloom, Richardson, and associates and later were modified by Elston, Ellis, and associates in Nottingham. The three components of grade are correlated, and their relative importance in determining prognosis has been controversial. No clearly objective, quantitative technique for measuring nuclear pleomorphism has been devised. Recent opinion holds that mitotic index is the most important component of the Bloom–Richardson–Nottingham system. Addition of the other grading components (tubularity and nuclear pleomorphism) to mitosis per 10 HPF alone actually reduced the prognostic power of mitotic index alone [2]. Anomalously, grade has consensus recognition as a risk factor, but proliferation does not, although it is clearly superior to grade if properly measured. Mitosis is the final stage of the cell cycle. This implies that cells in mitosis may be more likely to complete the cell cycle than cells in earlier parts of the cycle (G1, S-phase, or G2), giving mitotic index an advantage over other measures of proliferation. Although superficially attractive, this argument lacks supporting data. Cell death could occur in mitosis, and completion of mitosis would be meaningless if the cell is destined to die in the ensuing cycle. Thus, we see no inherent superiority of measuring cell transit in mitosis versus S-phase, by DNA precursor labeling, nonspecific cell cycle-phase transit as by Ki-67 staining, or more recently, by measuring transit through terminal G2 and mitosis by immunostaining of phosphohistone H3 (PPH3) [2]. These alternative measurements have been developed to circumvent subjectivity involved in recognition of mitotic figures and alleged irreproducibility of mitotic index. Accuracy of the mitotic index per 10 HPF follows the binomial formula, and is poor at the level of the lower tertile cutoff, which can be below 2 mitoses per 10 HPF [3]. The lower tertile best defines risk for breast carcinoma. This problem can be addressed by counting five or more sets of HPF when the index is low, but few wish to do such tedious counting. Another problem is frequent distortion artefact present in needle core biopsies that can make accurate counting of mitotic figures impossible, although the tissue remains suitable for immunohistochemical stains such as Ki-67 and probably PPH3. We surveyed eight reports relating RNA expression profiles and outcome of breast cancer published in years 2004–2007. Numbers of genes in panels screened ranged from 2 to 534. Gene selection criteria were unsupervised hierarchical clustering (5 studies), cell of origin: basal/myoepithelial cell, luminal cell, Her2 (one study), genes active in wound response (one study), functional relationships: invasiveness, motility, proliferation (one study), genes active in breast tumor stem cells (one study). Three studies were restricted to patients without axillary metastasis, and five studies included both node-positive and node-negative patients. In the pooled studies, outcome results were favorable (no distant metastasis or death) in groups designated as low risk by RNA profile analysis in 88–98% (mean 92.85%) of patients of any lymph node status at 5 years. In the four studies reporting results for node-negative patients separately, the pooled 5 year metastasis-free survival was 94.8%. Patients not in the favorable groups had higher rates of relapse. In the one study that reported node-positive patients separately, distant metastasis-free survival was (a surprisingly high) 95.2% at 5 years in the group defined as favorable based on RNA profile. In these studies, 34.5–40.7% of patients were placed in the favorable group. Attempts were made to compare these genotypical results with phenotypical assessments, but were hindered by lack of control of entry level data with respect to size of tumor and malignancy grading, particularly mitotic counts. One key study failed to find significant relationship of size of tumor with outcome [1], suggesting that measurements may not have been controlled by microscopy,