Sunday, November 13, 2011

 Light targets and destroys cancer cells

National Cancer Institute researchers have designed a light-based therapy that allows for selective destruction of tumor cells in mice without harming surrounding normal tissue.

It could theoretically work against tumors in humans, such as those of the breast, lung, prostate, as well as cancer cells in the blood such as leukemias, say the scientists. Current photodynamic therapy is not specific for cancer cells, resulting in damage to surrounding normal tissue.

This new type of treatment, called photoimmunotherapy, or PIT, uses light to rapidly and selectively kill cancer cells. To create their PIT, the scientists coupled a monoclonal antibody or MAb, which recognizes specific proteins on the surface of cancer cells, with a photosensitizer — a molecule that, when exposed to light of the appropriate wavelength (near-infrared), rapidly damages cells.

Photoimmunotherapy using MAb-IR700 does not appear to harm normal cells, unlike conventional photosensitizers which can cause damage to healthy tissue. The light that is required to activate conventional photosensitizers can penetrate through only about 0.8 centimeters of tissue (about a third of an inch), while the near-infrared light used to activate IR700 can penetrate tissue to a depth of several centimeters.

The study also found antibody doses required for diagnosis were significantly lower than those required for therapy. Nevertheless, after MAb-IR700 exposure, the targeted tumors decreased in size and eventually disappeared, suggesting a potential means of controlling cancers with far lower doses of MAb than are usually administered to cancer patients, say the scientists.

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Computers found more accurate than doctors in breast-cancer diagnosis

Computer analyses of breast cancer microscopic images were found more accurate than those conducted by humans, computer scientists at the Stanford School of Engineering and pathologists at the Stanford School of Medicine report.

The researchers’ Computational Pathologist (C-Path) is a machine-learning-based method for automatically analyzing images of cancerous tissues and predicting patient survival.

Since 1928, the way breast cancer characteristics are evaluated and categorized has remained largely unchanged: done by hand, under a microscope. Pathologists examine the tumors visually and score them according to a scale first developed eight decades ago. These scores help doctors assess the type and severity of the cancer and calculate the patient’s prognosis and course of treatment.

Medical science has long used three specific features for evaluating breast cancer cells: what percentage of the tumor is comprised of tube-like cells, the diversity of the nuclei in the outermost (epithelial) cells of the tumor and the frequency with which those cells divide (a process known as mitosis). These three factors are judged by sight with a microscope and scored qualitatively to stratify breast cancer patients into three groups that predict survival rates.

Training C-Path: 6,642 cellular factors

To train C-Path, the researchers used existing tissue samples taken from patients whose prognosis was known. By comparing results against the known data, the computers adapted their models to better predict survival and gradually figured out what features of the cancers matter most and which matter less in predicting survival.

“Pathologists have been trained to look at and evaluate specific cellular structures of known clinical importance, which get incorporated into the grade. However, tumors contain innumerable additional features, whose clinical significance has not previously been evaluated,” said Andrew Beck, MD, a doctoral candidate in biomedical informatics and the paper’s first author.

C-Path assesses 6,642 cellular factors. C-Path yielded results that were a statistically significant improvement over human-based evaluation. What’s more, the computers identified structural features in cancers that matter as much or more than those that pathologists have focused on traditionally. In fact, they discovered that the characteristics of the cancer cells and the surrounding cells, known as the stroma, were both important in predicting patient survival.

C-Path could improve the accuracy of prognoses for all breast cancer victims. It could, likewise, improve the screening of pre-cancerous cells that could help many women avoid cancer altogether. It might even be applied to predict the effectiveness of various forms of treatment and drug therapies.

“If we can teach computers to look at a tumor tissue sample and predict survival, why not train them to predict from the same sample which courses of treatment or drugs a given patient might respond to best? Or even to look at samples of non-malignant cells to predict whether these benign tissues will turn cancerous,” said Daphne Koller, PhD, professor of computer science and senior author of the paper. “This is personalized medicine.”

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