Tissue detection of biomolecular predictors in breast cancer

One of the promises of modern biotechnology is to improve medical care by providing accurate diagnosis and targeted treatment to patients who will derive the maximum benefit. Delivery of this promise in the 21st century is the result of major advances in biotechnology over the past 20 years. Sequencing of the human genome and other high-volume data discovery has become possible, owing to relatively inexpensive computation power and automation. The same forces that drove the human genome project are now being focused on cataloging various disease processes at the DNA, RNA and protein levels. As these high-throughput technologies are entering the clinical care environment, the major task at hand is to integrate the complex data and derive clinically useful information. In spite of major breakthroughs in molecular approaches to the diagnosis and prognostication of cancer, there remain significant obstacles in applying these technologies to clinical samples. The time-honored conventional histopathology, for example, is still the backbone of tumor diagnosis and prognostication. The traditional fixation and processing methods are, however, rapidly losing ground, as they do not protect important tissue macromolecules. Formalin, the common universal fixative, is losing its place in histopathology. In addition to its toxicity, it alters macromolecules and renders the tissue unfit for most advanced molecular studies. This has prompted the use of fresh or fresh-frozen biopsy material for most biomolecular discoveries and clinical assays. This of course is impractical, or even impossible, in most clinical settings, particularly since tumors are being detected earlier and smaller. Also, many preneoplastic conditions are impossible to triage for freezing since their accurate diagnosis requires the use of the entire sample for detailed microscopic examination. The focus in this report is on breast cancer, where the value of the innovative approaches of the tissue detection of biomolecular predictors is examined. To this end, novel tissue handling platforms are introduced that are not only suitable for histological diagnosis, but allow the detection of tumor proteome and expression profiles on the same biopsy sample.