Part I. Molecular and cellular characterization of high nitric oxide-adapted human breast adenocarcinoma cell lines.

There is a lack of understanding of the casual mechanisms behind the observation that some breast adenocarcinomas have identical morphology and comparatively different cellular growth behavior. This is exemplified by a differential response to radiation, chemotherapy, and other biological intervention therapies. Elevated concentrations of the free radical nitric oxide (NO), coupled with the up-regulated enzyme nitric oxide synthase (NOS) which produces NO, are activities which impact tumor growth. Previously, we adapted four human breast cancer cell lines: BT-20, Hs578T, T-47D, and MCF-7 to elevated concentrations of nitric oxide (or high NO [HNO]). This was accomplished by exposing the cell lines to increasing levels of an NO donor over time. Significantly, the HNO cell lines grew faster than did each respective ("PARENT") cell line even in the absence of NO donor-supplemented media. This was evident despite each "parent" being morphologically equivalent to the HNO adapted cell line. Herein, we characterize the HNO cells and their biological attributes against those of the parent cells. Pairs of HNO/parent cell lines were then analyzed using a number of key cellular activity criteria including: cell cycle distribution, DNA ploidy, response to DNA damage, UV radiation response, X-ray radiation response, and the expression of significant cellular enzymes. Other key enzyme activities studied were NOS, p53, and glutathione S-transferase-pi (GST-pi) expression. HNO cells were typified by a far more aggressive pattern of growth and resistance to various treatments than the corresponding parent cells. This was evidenced by a higher S-phase percentage, variable radioresistance, and up-regulated GST-pi and p53. Taken collectively, this data provides evidence that cancer cells subjected to HNO concentrations become resistant to free radicals such as NO via up-regulated cellular defense mechanisms, including p53 and GST-pi. The adaptation to NO may explain how tumor cells acquire a more aggressive tumor phenotype.

A framework for automated contour quality assurance in radiation therapy including adaptive techniques.

Contouring of targets and normal tissues is one of the largest sources of variability in radiation therapy treatment plans. Contours thus require a time intensive and error-prone quality assurance (QA) evaluation, limitations which also impair the facilitation of adaptive radiotherapy (ART). Here, an automated system for contour QA is developed using historical data (the 'knowledge base'). A pilot study was performed with a knowledge base derived from 9 contours each from 29 head-and-neck treatment plans. Size, shape, relative position, and other clinically-relevant metrics and heuristically derived rules are determined. Metrics are extracted from input patient data and compared against rules determined from the knowledge base; a computer-learning component allows metrics to evolve with more input data, including patient specific data for ART. Nine additional plans containing 42 unique contouring errors were analyzed. 40/42 errors were detected as were 9 false positives. The results of this study imply knowledge-based contour QA could potentially enhance the safety and effectiveness of RT treatment plans as well as increase the efficiency of the treatment planning process, reducing labor and the cost of therapy for patients.