Non-Invasive Ultrasound Assessment of Endometrial Cancer Progression in Pax8-Directed Deletion of the Tumor Suppressors Arid1a and Pten in Mice.

Uterine cancers can be studied in mice due to the ease of handling and genetic manipulation in these models. However, these studies are often limited to assessing pathology post-mortem in animals euthanized at multiple time points in different cohorts, which increases the number of mice needed for a study. Imaging mice in longitudinal studies can track the progression of disease in individual animals, reducing the number of mice needed. Advances in ultrasound technology have allowed for the detection of micrometer-level changes in tissues. Ultrasound has been used to study follicle maturation in ovaries and xenograft growth but has not been applied to morphological changes in the mouse uterus. This protocol examines the juxtaposition of pathology with in vivo imaging comparisons in an induced endometrial cancer mouse model. The features observed by ultrasound were consistent with the degree of change seen by gross pathology and histology. Ultrasound was found to be highly predictive of the observed pathology, supporting the incorporation of ultrasonography into longitudinal studies of uterine diseases such as cancer in mice.

Integrative network analysis to identify aberrant pathway networks in ovarian cancer.

Ovarian cancer is often called the 'silent killer' since it is difficult to have early detection and prognosis. Understanding the biological mechanism related to ovarian cancer becomes extremely important for the purpose of treatment. We propose an integrative framework to identify pathway related networks based on large-scale TCGA copy number data and gene expression profiles. The integrative approach first detects highly conserved copy number altered genes and regards them as seed genes, and then applies a network-based method to identify subnetworks that can differentiate gene expression patterns between different phenotypes of ovarian cancer patients. The identified subnetworks are further validated on an independent gene expression data set using a network-based classification method. The experimental results show that our approach can not only achieve good prediction performance across different data sets but also identify biological meaningful subnetworks involved in many signaling pathways related to ovarian cancer.

Assessment of plasma DNA levels, allelic imbalance, and CA 125 as diagnostic tests for cancer.

BACKGROUND: Allelic imbalance (AI), the loss or gain of chromosomal regions, is found in many cancers. AI can be detected in genomic tumor DNA released into the blood after necrosis or apoptosis. We evaluated plasma DNA concentration, allelic status in plasma DNA, and serum CA 125 level as screening tests for ovarian and other cancers. METHODS: Plasma samples were obtained from 330 women (44 normal healthy control individuals, 122 patients with various cancers, and 164 control patients with non-neoplastic diseases). Plasma DNA concentration was determined in all samples. Allelic status was determined by digital single nucleotide polymorphism (SNP) analysis with eight SNP markers in plasma DNA from 54 patients with ovarian cancer and 31 control patients. CA 125 was determined in 63 samples. Receiver-operating characteristic (ROC) curves were plotted, and the areas under the ROC curves--a measure of the overall ability of a diagnostic test with multiple cutoffs to distinguish between diseased and nondiseased individuals--were determined. RESULTS: The area under the ROC curve for plasma DNA concentration was 0.90 for patients with neoplastic disease versus healthy control individuals and 0.74 for patients with neoplastic diseases versus control patients with non-neoplastic diseases. For control subjects given a specificity of 100% (95% confidence interval [CI] = 92% to 100%), the highest sensitivity achieved was 57% (95% CI = 49% to 67%). AI in at least one SNP was found in 87% (95% CI = 60% to 98%) of patients with stage I/II ovarian cancer and 95% (95% CI = 83% to 99%) of patients with stage III/IV ovarian cancer, but AI was not found in 31 patients with non-neoplastic diseases (specificity = 100%, 95% CI = 89% to 100%). The area under the ROC curve assessing AI was 0.95. Combining the serum CA 125 level with the plasma DNA concentration increased the area under the ROC curve from 0.78 (CA 125 alone) to 0.84. CONCLUSION: Plasma DNA concentration may not be sensitive or specific enough for cancer screening or diagnosis, even when combined with CA 125. AI was detected with high specificity in plasma DNA from patients with ovarian cancer and should be studied further as a screening tool.