Gene expression array profiles identify subclasses of breast cancers with different clinical outcomes and different molecular features. The present study attempted to correlate genomic alterations (loss of heterozygosity; LOH) with subclasses of breast cancers having distinct gene expression signatures. Hierarchical clustering of expression array data from 89 invasive breast cancers identified four major expression subclasses. Thirty-four of these cases representative of the four subclasses were microdissected and allelotyped using genome-wide single nucleotide polymorphism detection arrays (Affymetrix, Inc.). LOH was determined by comparing tumor and normal single nucleotide polymorphism allelotypes. A newly developed statistical tool was used to determine the chromosomal regions of frequent LOH. We found that breast cancers were highly heterogeneous, with the proportion of LOH ranging widely from 0.3% to >60% of heterozygous markers. The most common sites of LOH were on 17p, 17q, 16q, 11q, and 14q, sites reported in previous LOH studies. Signature LOH events were discovered in certain expression subclasses. Unique regions of LOH on 5q and 4p marked a subclass of breast cancers with "basal-like" expression profiles, distinct from other subclasses. LOH on 1p and 16q occurred preferentially in a subclass of estrogen receptorpositive breast cancers. Finding unique LOH patterns in different groups of breast cancer, in part defined by expression signatures, adds confidence to newer schemes of molecular classification. Furthermore, exclusive association between biological subclasses and restricted LOH events provides rationale to search for targeted genes.

There is strong preclinical evidence that cancer, including breast cancer, undergoes immune surveillance. This continual monitoring, by both the innate and the adaptive immune systems, recognizes changes in protein expression, mutation, folding, glycosylation, and degradation. Local immune responses to tumor antigens are amplified in draining lymph nodes, and then enter the systemic circulation. The antibody response to tumor antigens, such as p53 protein, are robust, stable, and easily detected in serum, may exist in greater concentrations than their cognate antigens, and are potential highly specific biomarkers for cancer. However, antibodies have limited sensitivities as single analytes, and differences in protein purification and assay characteristics have limited their clinical application. For example, p53 autoantibodies in the sera are highly specific for cancer patients, but are only detected in the sera of 10-20% of patients with breast cancer. Detection of p53 autoantibodies is dependent on tumor burden, p53 mutation, rapidly decreases with effective therapy, but is relatively independent of breast cancer subtype. Although antibodies to hundreds of other tumor antigens have been identified in the sera of breast cancer patients, very little is known about the specificity and clinical impact of the antibody immune repertoire to breast cancer. Recent advances in proteomic technologies have the potential for rapid identification of immune response signatures for breast cancer diagnosis and monitoring. We have adapted programmable protein microarrays for the specific detection of autoantibodies in breast cancer. Here, we present the first demonstration of the application of programmable protein microarray ELISAs for the rapid identification of breast cancer autoantibodies.

In a select group of women undergoing neoadjuvant therapy for invasive breast cancer, MRI best predicted pathology response. The use of MRI in neoadjuvant therapy may allow for accurate prediction of patients eligible for breast conservation.