When Should One Substract Background Fluorescence in Two Color Microarrays?

Two color microarrays are a powerful tool for genomic analysis, but have noise components that make inferences regarding gene expression inefficient and potentially misleading. Background fluorescence,whether attributable to non-specific binding or other sources,is an important component of noise. The decision to subtract fluorescence surrounding spots of hybridization from spot fluorescence has been controversial, with no clear criteria for determining circumstances that may favor, or disfavor, background subtraction. While it is generally accepted that subtracting background reduces bias but increases variance in the estimates of the ratios of interest, no formal analysis of the bias-variance trade off of background subtraction has been undertaken. In this paper, we use simulation to systematically examine the bias-variance trade off under a variety of possible experimental conditions. Our simulation is based on data obtained from two self versus self microarray experiments and is free of distributional assumptions. Our results identify factors that are important for determining whether to background subtract, including the correlation of foreground to background intensity ratios. Using these results we develop recommendations for diagnostic visualizations that can help decisions about background subtraction

Cronkhite-Canada Syndrome: Gastric Involvement Diagnosed by MDCT

Chronkhite-Canada is a rare nonfamilial polyposis syndrome that usually presents as chronic malabsorption in adults. We present a case of a-73-year old woman with chronic gastrointestinal bleeding and malnutrition. On CT imaging she was found to have massive gastric polyps, which on biopsy was most consistent with Cronkhite-Canada syndrome

Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) remains a lethal disease with a 5-year survival of 4%. A key hallmark of PDAC is extensive stromal involvement, which makes capturing precise tumor-specific molecular information difficult. Here, we have overcome this problem by applying blind source separation to a diverse collection of PDAC gene expression microarray data, which includes primary, metastatic, and normal samples. By digitally separating tumor, stroma, and normal gene expression, we have identified and validated two tumor-specific subtypes including a “basal-like” subtype which has worse outcome, and is molecularly similar to basal tumors in bladder and breast cancer. Furthermore, we define “normal” and “activated” stromal subtypes which are independently prognostic. Our results provide new insight into the molecular composition of PDAC which may be used to tailor therapies or provide decision support in a clinical setting where the choice and timing of therapies is critical

IST Austria Technical Report

A comprehensive understanding of the clonal evolution of cancer is critical for understanding neoplasia. Genome-wide sequencing data enables evolutionary studies at unprecedented depth. However, classical phylogenetic methods often struggle with noisy sequencing data of impure DNA samples and fail to detect subclones that have different evolutionary trajectories. We have developed a tool, called Treeomics, that allows us to reconstruct the phylogeny of a cancer with commonly available sequencing technologies. Using Bayesian inference and Integer Linear Programming, robust phylogenies consistent with the biological processes underlying cancer evolution were obtained for pancreatic, ovarian, and prostate cancers. Furthermore, Treeomics correctly identified sequencing artifacts such as those resulting from low statistical power; nearly 7% of variants were misclassified by conventional statistical methods. These artifacts can skew phylogenies by creating illusory tumor heterogeneity among distinct samples. Importantly, we show that the evolutionary trees generated with Treeomics are mathematically optimal

Cross-platform Comparison of Two Pancreatic Cancer Phenotypes

Model-based approaches for combining gene expression data from multiple high throughput platforms can be sensitive to technological artifacts when the number of samples in each platform is small. This paper proposes simple tools for quantifying concordance in a small study of pancreatic cancer cells lines with an emphasis on visualizations that uncover intra- and inter-platform variation. Using this approach, we identify several transcripts from the integrative analysis whose over-or under-expression in pancreatic cancer cell lines was validated by qPCR

MAP2K4/MKK4 Expression in Pancreatic Cancer

Abstract MKK4 (MAP2K4/SEK1) is a member of the mitogen-activated protein kinase family, originally identified as a kinase involved in the stress-activated protein kinase pathway by directly phosphorylating c-Jun NH2-terminal kinase. MKK4 genetic inactivation has been observed in a subset of pancreatic carcinomas, implicating deregulation of the stress-activated protein kinase pathway in pancreatic carcinogenesis. We evaluated Mkk4 protein expression patterns by immunohistochemical labeling in a series of 60 resected primary infiltrating pancreatic adenocarcinomas (24 cases with known MKK4 genetic status), and 14 different tissue arrays representing the primary carcinoma and all of the gross metastases from 26 patients that died of metastatic pancreatic cancer. Among the surgically resected carcinomas, focal or diffuse-positive immunolabeling for Mkk4 protein was found in 52 of 60 cases (86.7%). Among the eight carcinomas with negative Mkk4 immunolabeling, three harbored a homozygous deletion or intragenic mutation of the MKK4 gene, in contrast to none of the 52 cases with positive Mkk4 immunolabeling (P < 0.01). Loss of Mkk4 immunolabeling showed a trend toward shorter survival, with Mkk4-positive carcinomas having half the risk of death than Mkk4-negative carcinomas (P = 0.09). Mkk4 immunolabeling patterns were also evaluated among unresectable primary and metastatic cancer tissues from autopsy specimens, indicating intact Mkk4 immunolabeling in 88.8% of the unresectable primary carcinomas as compared with 63.3% of distant metastases (P < 0.001). Our data indicate that the loss of Mkk4 protein expression in pancreatic carcinomas may be more frequent than suggested by the rates of genetic inactivation alone and that MKK4 loss may contribute to disease progression. The correlation of MKK4 genetic status with immunolabeling patterns validate this approach for the evaluation of MKK4 status in routine histologic sections and may provide useful information regarding patient prognosis

Abstract 2374: Reconstructing the evolutionary history of metastatic cancers

Reconstructing the evolutionary history of metastases is critical for understanding their basic biological principles and has profound clinical implications. Genome-wide sequencing data has enabled modern phylogenomic methods to accurately dissect subclones and their phylogenies from noisy and impure bulk tumor samples at unprecedented depth. However, existing methods are not designed to infer metastatic seeding patterns. We have developed a tool, called Treeomics, that utilizes Bayesian inference and Integer Linear Programming to reconstruct the phylogeny of metastases. Treeomics allowed us to infer comprehensive seeding patterns for pancreatic, ovarian, and prostate cancers. Moreover, Treeomics correctly disambiguated true seeding patterns from sequencing artifacts; 7% of variants were misclassified by conventional statistical methods. These artifacts can skew phylogenies by creating illusory tumor heterogeneity among distinct samples. Last, we performed in silico benchmarking on simulated tumor phylogenies across a wide range of sample purities (30-90%) and sequencing depths (50-800x) to demonstrate the high accuracy of Treeomics compared to existing methods.Mathematic

Limited heterogeneity of known driver gene mutations among the metastases of individual patients with pancreatic cancer

The extent of heterogeneity among driver gene mutations present in naturally occurring metastases - that is, treatment-naive metastatic disease - is largely unknown. To address this issue, we carried out 60× whole-genome sequencing of 26 metastases from four patients with pancreatic cancer. We found that identical mutations in known driver genes were present in every metastatic lesion for each patient studied. Passenger gene mutations, which do not have known or predicted functional consequences, accounted for all intratumoral heterogeneity. Even with respect to these passenger mutations, our analysis suggests that the genetic similarity among the founding cells of metastases was higher than that expected for any two cells randomly taken from a normal tissue. The uniformity of known driver gene mutations among metastases in the same patient has critical and encouraging implications for the success of future targeted therapies in advanced-stage disease