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

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

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

Somatic mutations in the chromatin remodeling gene ARID1A occur in several tumor types

Mutations in the chromatin remodeling gene ARID1A have recently been identified in the majority of ovarian clear cell carcinomas (OCCCs). To determine the prevalence of mutations in other tumor types, we evaluated 759 malignant neoplasms including those of the pancreas, breast, colon, stomach, lung, prostate, brain, and blood (leukemias). We identified truncating mutations in 6% of the neoplasms studied; nontruncating somatic mutations were identified in an additional 0.4% of neoplasms. Mutations were most commonly found in gastrointestinal samples with 12 of 119 (10%) colorectal and 10 of 100 (10%) gastric neoplasms, respectively, harboring changes. More than half of the mutated colorectal and gastric cancers displayed microsatellite instability (MSI) and the mutations in these tumors were out‐of‐frame insertions or deletions at mononucleotide repeats. Mutations were also identified in 2–8% of tumors of the pancreas, breast, brain (medulloblastomas), prostate, and lung, and none of these tumors displayed MSI. These findings suggest that the aberrant chromatin remodeling consequent to ARID1A inactivation contributes to a variety of different types of neoplasms. Hum Mutat 33:100–103, 2012. © 2011 Wiley Periodicals, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89516/1/humu_21633_sm_Mat.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/89516/2/21633_ftp.pd

A Six-Gene Signature Predicts Survival of Patients with Localized Pancreatic Ductal Adenocarcinoma

Jen Jen Yeh and colleagues developed and validated a six-gene signature in patients with pancreatic ductal adenocarcinoma that may be used to better stage the disease in these patients and assist in treatment decisions

Widespread somatic L1 retrotransposition occurs early during gastrointestinal cancer evolution

Somatic L1 retrotransposition events have been shown to occur in epithelial cancers. Here, we attempted to determine how early somatic L1 insertions occurred during the development of gastrointestinal (GI) cancers. Using L1-targeted resequencing (L1-seq), we studied different stages of four colorectal cancers arising from colonic polyps, seven pancreatic carcinomas, as well as seven gastric cancers. Surprisingly, we found somatic L1 insertions not only in all cancer types and metastases but also in colonic adenomas, well-known cancer precursors. Some insertions were also present in low quantities in normal GI tissues, occasionally caught in the act of being clonally fixed in the adjacent tumors. Insertions in adenomas and cancers numbered in the hundreds, and many were present in multiple tumor sections, implying clonal distribution. Our results demonstrate that extensive somatic insertional mutagenesis occurs very early during the development of GI tumors, probably before dysplastic growth

Genotype tunes pancreatic ductal adenocarcinoma tissue tension to induce matricellular fibrosis and tumor progression

Fibrosis compromises pancreatic ductal carcinoma (PDAC) treatment and contributes to patient mortality yet anti-stromal therapies are controversial. We found that human PDACs with impaired epithelial transforming growth factor β (TGF-β) signaling have elevated epithelial Stat3 activity and develop a stiffer, matricellular-enriched fibrosis associated with high epithelial tension and shorter patient survival. In several Kras-driven mouse models, both the loss of TGF-β signaling and elevated β1-integrin mechanosignaling engaged a positive feedback loop whereby Stat3 signaling promotes tumor progression by increasing matricellular fibrosis and tissue tension. In contrast, epithelial Stat3 ablation attenuated tumor progression by reducing the stromal stiffening and epithelial contractility induced by loss of TGF-β signaling. In PDAC patient biopsies, higher matricellular protein and activated Stat3 associated with SMAD4 mutation and shorter survival. The findings implicate epithelial tension and matricellular fibrosis in the aggressiveness of SMAD4 mutant pancreatic tumors, and highlight Stat3 and mechanics as key drivers of this phenotype