Classification of breast cancer patients using a combination of clinical criteria and informative genesets

#The present invention relates to the use of both phenotypic and genotypic aspects of a condition, such as a disease, in order to identify discrete subsets of patients for which specific sets of informative genes are then identified. The invention also relates to the classification of individuals, such as breast cancer patients, into a subset of the condition on the basis of clinical parameters and the status of markers, for example, of genes expression patterns, and the prognosis of those individuals on the basis of markers informative for prognosis within the subset of the condition. The invention also relates to methods of determining a course of treatment or therapy to an individual having, or suspected of having, a condition, such as breast cancer. The invention further relates to methods of structuring a clinical trial, particularly using five breast cancer-specific patient subsets and prognosis-informative genes for each, and of identifying patient populations for clinical trials or for other condition-related, for example, breast cancer-related, research. Finally, the invention relates to computer implementations of the above methods.

#The increased number of cancer cases reported in the United States, and, indeed, around the world, is a major concern. Currently there are only a handful of treatments available for specific types of cancer, and these provide no guarantee of success. In order to be most effective, these treatments require not only an early detection of the malignancy, but a reliable assessment of the severity of the malignancy.

#The incidence of breast cancer, a leading cause of death in women, has been gradually increasing in the United States over the last thirty years. Its cumulative risk is relatively high; 1 in 8 women are expected to develop some type of breast cancer by age 85 in the United States. In fact, breast cancer is the most common cancer in women and the second most common cause of cancer death in the United States. In 1997, it was estimated that 181,000 new cases were reported in the U.S., and that 44,000 people would die of breast cancer (Parker et al., CA Cancer J. Clin. 47:5-27 (1997); Chu et al., J. Nat. Cancer Inst. 88:1571-1579 (1996)). While mechanism of tumorigenesis for most breast carcinomas is largely unknown, there are genetic factors that can predispose some women to developing breast cancer (Miki et al., Science, 266:66-71 (1994)).

#FIG. 1 depicts the decision tree that resulted in the five patient subsets used to identify informative prognosis-related genes.

#FIG. 2: Relationship between ER level and age. (A) Scatter plot of ER vs. age for ER+ patients. Black dots indicate metastases free samples, and gray dots indicate metastases samples. It appears that patients of ER+ group can be subdivided into “ER+, ER/AGE high” group (above the black line) and “ER+, ER/AGE low” (below the black line) group. The black line is approximated by ER=0.1*(AGE-42.5), and the dashed line by ER=0.1*(age−50). Within each population, the ER level also increases with age. (B) Age distribution of all patients in ER+ samples. A bimodal distribution is observed. (C) ER-modulated age (age−10*) distribution of all patients in ER+ samples. A bimodal distribution is observed. (D) Age distribution of samples with metastasis. (E) ER-modulated age distribution of samples with metastasis. The three peaks appearing in this distribution suggest a polymorphism.

#FIG. 3. Performance of classifier for the “ER−/sporadic” group. (A) Error rate obtained from leave-one-out cross validation (LOOCV) for predicting the disease outcome as a function of the number of reporter genes used in the classifier. (B) Scatter plot between correlation to good group (X axis) and to poor group (Y axis). Circles indicate metastases-free samples, squares indicate samples with metastases. Dashed line: threshold for separating poor from good. (C) Error rate calculated with respect to good outcome group (good outcome misclassified as poor divided by total number of good), or poor outcome group (poor outcome misclassified as good divided by total number of poor), or the average of the two rates.

#5.1 Introduction

#The present invention provides methods for classifying individuals having a condition, such as a disease, into one or more subsets of individuals, where individuals in each subset are characterized by one or more phenotypic or genotypic characteristics of the condition. The individuals may be eukaryotes or prokaryotes, may be animals such as mammals, including but not limited to humans, primates, rodents, felines, canines, etc., birds, reptiles, fish, etc. “Individuals” as used herein also encompasses single-celled organisms, or colonies thereof, such as bacteria and yeast. The condition may be a disease, such as cancer, and may be a specific cancer, such as breast cancer. The condition may also be an environmental condition, such as exposure to a toxin, pollutant, drug, proximity to urban or industrial areas, etc.

#The present invention provides methods of determining the prognosis of individuals having a condition, such as cancer, for example, breast cancer, or who are suspected of having the condition, by the use of a combination of clinical, biological or biochemical parameters of the condition and gene expression pattern data. For prognosis, the parameters selected preferably relate to or affect the progression and/or outcome of the condition. The pattern of gene expression within a subset of individuals having the particular condition leads to the identification of sets of genes within a subset that is informative for that subset, for example, for prognosis within that subset. In general, the successful identification of sets of genes informative for prognosis within a particular subset justifies the selection of the plurality of clinical, biological or biochemical parameters of the condition on which division of individuals into condition subsets is based.