Pancreatic cancer is projected to become the second leading cause of cancer-related death in the United States by 2020. A familial aggregation of pancreatic cancer has been established, but the cause of this aggregation in most families is unknown. To determine the genetic basis of susceptibility in these families, we sequenced the germline genome of 638 familial pancreatic cancer patients. We also sequenced the exomes of 39 familial pancreatic adenocarcinomas. Our analyses support the role of previously identified familial pancreatic cancer susceptibility genes such as BRCA2, CDKN2A and ATM, and identify novel candidate genes harboring rare, deleterious germline variants for further characterization. We also show how somatic point mutations that occur during hematopoiesis can affect the interpretation of genome-wide studies of hereditary traits. Our observations have important implications for the etiology of pancreatic cancer and for the identification of susceptibility genes in other common cancer types.

RNA sequencing (RNA-seq) is a powerful approach for measuring gene expression levels in cells and tissues, but it relies on highquality RNA. We demonstrate here that statistical adjustment using existing quality measures largely fails to remove the effects of RNA degradation when RNA quality associates with the outcome of interest. Using RNA-seq data from molecular degradation experiments of human primary tissues, we introduce a methodquality surrogate variable analysis (qSVA)-as a framework for estimating and removing the confounding effect of RNA quality in differential expression analysis. We show that this approach results in greatly improved replication rates (>3×) across two large independent postmortem human brain studies of schizophrenia and also removes potential RNA quality biases in earlier published work that compared expression levels of different brain regions and other diagnostic groups. Our approach can therefore improve the interpretation of differential expression analysis of transcriptomic data from human tissue.RNA sequencing | differential expression analysis | statistical modeling | RNA quality M icroarrays and RNA sequencing (RNA-seq) can measure gene expression levels across hundreds of samples in a single experiment. As gene expression levels are measured with error, normalization procedures have been implemented for both microarray (1) and RNA sequencing (2) data to reduce technical variability, including controlling for variability associated with how and when the samples are run, so-called "batch" effects (3). Recent research has further characterized this expression variability in RNA-seq data (4-6), including demonstrating variability associated with technical factors involved in the preparation, sequencing, and analysis of samples. Variability in gene expression is particularly influenced by RNA quality (7) because accurately measuring gene expression levels strongly depends on the quality of the input RNA. This suggests that a portion of traditionally measured latent "batch" effects could actually be attributed to the underlying quality of the input RNA.Postmortem studies typically extract RNA from tissue that has been susceptible to a wide variety of antemortem and postmortem factors. Several approaches exist for quantifying the quality of the input RNA before sequencing library construction, including UV absorption ratios of 280 nm to 260 nm and RNA integrity numbers (RINs). RIN is a machine learning-derived measurement resulting from placing RNA on a Bioanalyzer and obtaining a tracing of fragment sizes per sample. RIN ranges from 10 (very high quality RNA) to 0 (completely degraded RNA), and the apparent intactness of ribosomal RNAs (which are two large peaks in the fragment size tracing) is one of the most discriminating factors that distinguishes very high quality from moderate quality RNA (8). Recommended RIN thresholds for sample exclusion before data generation have been suggested as low as 5.0 for PCR (7) and 7.0 for RNA-seq. (9). However, even high quality samples (RIN ...

Despite advances in sequencing, structural variants (SVs) remain difficult to reliably detect due to the short read length (<300 bp) of 2nd generation sequencing. Not only do the reads (or paired-end reads) need to straddle a breakpoint, but repetitive elements often lead to ambiguities in the alignment of short reads. We propose to use the long-reads (up to 20 kb) possible with 3rd generation sequencing, specifically nanopore sequencing on the MinION. Nanopore sequencing relies on a similar concept to a Coulter counter, reading the DNA sequence from the change in electrical current resulting from a DNA strand being forced through a nanometer-sized pore embedded in a membrane. Though nanopore sequencing currently has a relatively high mismatch rate that precludes base substitution and small frameshift mutation detection, its accuracy is sufficient for SV detection because of its long reads. In fact, long reads in some cases may improve SV detection efficiency.We have tested nanopore sequencing to detect a series of well-characterized SVs, including large deletions, inversions, and translocations that inactivate the CDKN2A/p16 and SMAD4/DPC4 tumor suppressor genes in pancreatic cancer. Using PCR amplicon mixes, we have demonstrated that nanopore sequencing can detect large deletions, translocations and inversions at dilutions as low as 1:100, with as few as 500 reads per sample. Given the speed, small footprint, and low capital cost, nanopore sequencing could become the ideal tool for the low-level detection of cancer-associated SVs needed for molecular relapse, early detection, or therapeutic monitoring.

Objective To examine the clinicopathologic features and clonal relationship of multifocal intraductal papillary mucinous neoplasms (IPMNs) of the pancreas. Background Intraductal papillary mucinous neoplasms are increasingly diagnosed cystic precursor lesions of pancreatic cancer. Intraductal papillary mucinous neoplasms can be multifocal and a potential cause of recurrence after partial pancreatectomy. Methods Thirty four patients with histologically documented multifocal IPMNs were collected and their clinicopathologic features catalogued. In addition, thirty multifocal IPMNs arising in 13 patients from 3 hospitals were subjected to laser microdissection followed by KRAS pyrosequencing and loss of heterozygosity (LOH) analysis on chromosomes 6q and 17p. Finally, we sought to assess the clonal relationships among multifocal IPMNs. Results We identified 34 patients with histologically documented multifocal IPMNs. Synchronous IPMNs were present in 29 patients (85%), whereas 5 (15%) developed clinically significant metachronous IPMNs. Six patients (18%) had a history of familial pancreatic cancer. A majority of multifocal IPMNs (86% synchronous, 100% metachronous) were composed of branch duct lesions, and typically demonstrated a gastric-foveolar subtype epithelium with low or intermediate grades of dysplasia. Three synchronous IPMNs (10%) had an associated invasive cancer. Molecular analysis of multiple IPMNs from 13 patients demonstrated nonoverlapping KRAS gene mutations in 8 patients (62%) and discordant LOH profiles in 7 patients (54%); independent genetic alterations were established in 9 of the 13 patients (69%). Conclusions The majority of multifocal IPMNs arise independently and exhibit a gastric-foveolar subtype, with low to intermediate dysplasia. These findings underscore the importance of life-long follow-up after resection for an IPMN.