Christopher Douville

A Combination of Molecular Markers and Clinical Features Improve the Classification of Pancreatic Cysts

BACKGROUND & AIMS The management of pancreatic cysts poses challenges to both patients and their physicians. We investigated whether a combination of molecular markers and clinical information could improve the classification of pancreatic cysts and management of patients. METHODS We performed a multi-center, retrospective study of 130 patients with resected pancreatic cystic neoplasms (12 serous cystadenomas, 10 solid pseudopapillary neoplasms, 12 mucinous cystic neoplasms, and 96 intraductal papillary mucinous neoplasms). Cyst fluid was analyzed to identify subtle mutations in genes known to be mutated in pancreatic cysts (BRAF, CDKN2A, CTNNB1, GNAS, KRAS, NRAS, PIK3CA, RNF43, SMAD4, TP53, and VHL); to identify loss of heterozygozity at CDKN2A, RNF43, SMAD4, TP53, and VHL tumor suppressor loci; and to identify aneuploidy. The analyses were performed using specialized technologies for implementing and interpreting massively parallel sequencing data acquisition. An algorithm was used to select markers that could classify cyst type and grade. The accuracy of the molecular markers was compared with that of clinical markers and a combination of molecular and clinical markers. RESULTS We identified molecular markers and clinical features that classified cyst type with 90%-100% sensitivity and 92%-98% specificity. The molecular marker panel correctly identified 67 of the 74 patients who did not require surgery and could, therefore, reduce the number of unnecessary operations by 91%. CONCLUSIONS We identified a panel of molecular markers and clinical features that show promise for the accurate classification of cystic neoplasms of the pancreas and identification of cysts that require surgery.

Mutational Signature of Aristolochic Acid Exposure as Revealed by Whole-Exome Sequencing

The mutational signature of aristolochic acid exemplifies how genome-wide sequencing can be used to identify environmental exposures leading to cancer. Carcinogen AAlert Aristolochic acid (AA) is a natural compound derived from plants in the Aristolochia genus. For centuries, Aristolochia has been used throughout Asia to treat a variety of ailments as a component of traditional Chinese medicine. In recent years, however, a more sinister side of this herb has come to light when it was linked to kidney damage and cancers of the urinary tract. Now, two studies by Poon et al. and Hoang et al. present a “molecular signature” of AA-induced DNA damage, which helps to explain the mutagenic effects of AA and may also be useful as a way to detect unsuspected AA exposure as a cause of cancer. The molecular signature seen in AA-associated tumors is characterized by a predominance of A:T-to-T:A transversions, a relatively unusual type of mutation that is infrequently seen in other types of cancer, including those caused by other carcinogens. These mutations concentrate at splice sites, causing the inappropriate inclusion or exclusion of entire exons in the resulting mRNA. The overall mutation rate is another notable feature of AA-associated cancers, because it is several times higher than the rate of mutations caused by other carcinogens such as tobacco and ultraviolet light. In both studies, the authors also used the molecular signature to discover that AA was a likely cause of tumors previously attributed to other carcinogens. In one case, a urinary tract cancer that had been attributed to smoking and, in the other case, a liver cancer previously attributed to a chronic hepatitis infection were both identified as having the telltale signature of AA mutagenesis. The identification of a specific molecular signature for AA has both clinical and public health implications. For individual patients, the molecular signature could help physicians identify which tumors were caused by AA. Although this information cannot yet be used to optimize the treatment of individual patients, those who are diagnosed with AA-associated cancers could be monitored more closely for the appearance of additional tumors. Meanwhile, a better understanding of the mutagenic effects of AA should also help to strengthen public health efforts to decrease exposure to this carcinogenic herb. In humans, exposure to aristolochic acid (AA) is associated with urothelial carcinoma of the upper urinary tract (UTUC). Exome sequencing of UTUCs from 19 individuals with documented exposure to AA revealed a remarkably large number of somatic mutations and an unusual mutational signature attributable to AA. Most of the mutations (72%) in these tumors were A:T-to-T:A transversions, located predominantly on the nontranscribed strand, with a strong preference for deoxyadenosine in a consensus sequence (T/CAG). This trinucleotide motif overlaps the canonical splice acceptor site, possibly accounting for the excess of splice site mutations observed in these tumors. The AA mutational fingerprint was found frequently in oncogenes and tumor suppressor genes in AA-associated UTUC. The AA mutational signature was observed in one patient’s tumor from a UTUC cohort without previous indication of AA exposure. Together, these results directly link an established environmental mutagen to cancer through genome-wide sequencing and highlight its power to reveal individual exposure to carcinogens.

Detection and localization of surgically resectable cancers with a multi-analyte blood test

SEEK and you may find cancer earlier Many cancers can be cured by surgery and/or systemic therapies when detected before they have metastasized. This clinical reality, coupled with the growing appreciation that cancers rapid genetic evolution limits its response to drugs, have fueled interest in methodologies for earlier detection of the disease. Cohen et al. developed a noninvasive blood test, called CancerSEEK that can detect eight common human cancer types (see the Perspective by Kalinich and Haber). The test assesses eight circulating protein biomarkers and tumor-specific mutations in circulating DNA. In a study of 1000 patients previously diagnosed with cancer and 850 healthy control individuals, CancerSEEK detected cancer with a sensitivity of 69 to 98% (depending on cancer type) and 99% specificity. Science, this issue p. 926; see also p. 866 A blood test that combines protein and DNA markers may allow earlier detection of eight common cancer types. Earlier detection is key to reducing cancer deaths. Here, we describe a blood test that can detect eight common cancer types through assessment of the levels of circulating proteins and mutations in cell-free DNA. We applied this test, called CancerSEEK, to 1005 patients with nonmetastatic, clinically detected cancers of the ovary, liver, stomach, pancreas, esophagus, colorectum, lung, or breast. CancerSEEK tests were positive in a median of 70% of the eight cancer types. The sensitivities ranged from 69 to 98% for the detection of five cancer types (ovary, liver, stomach, pancreas, and esophagus) for which there are no screening tests available for average-risk individuals. The specificity of CancerSEEK was greater than 99%: only 7 of 812 healthy controls scored positive. In addition, CancerSEEK localized the cancer to a small number of anatomic sites in a median of 83% of the patients.

Identifying Mendelian disease genes with the Variant Effect Scoring Tool

BackgroundWhole exome sequencing studies identify hundreds to thousands of rare protein coding variants of ambiguous significance for human health. Computational tools are needed to accelerate the identification of specific variants and genes that contribute to human disease.ResultsWe have developed the Variant Effect Scoring Tool (VEST), a supervised machine learning-based classifier, to prioritize rare missense variants with likely involvement in human disease. The VEST classifier training set comprised ~ 45,000 disease mutations from the latest Human Gene Mutation Database release and another ~45,000 high frequency (allele frequency > 1%) putatively neutral missense variants from the Exome Sequencing Project. VEST outperforms some of the most popular methods for prioritizing missense variants in carefully designed holdout benchmarking experiments (VEST ROC AUC = 0.91, PolyPhen2 ROC AUC = 0.86, SIFT4.0 ROC AUC = 0.84). VEST estimates variant score p-values against a null distribution of VEST scores for neutral variants not included in the VEST training set. These p-values can be aggregated at the gene level across multiple disease exomes to rank genes for probable disease involvement. We tested the ability of an aggregate VEST gene score to identify candidate Mendelian disease genes, based on whole-exome sequencing of a small number of disease cases. We used whole-exome data for two Mendelian disorders for which the causal gene is known. Considering only genes that contained variants in all cases, the VEST gene score ranked dihydroorotate dehydrogenase (DHODH) number 2 of 2253 genes in four cases of Miller syndrome, and myosin-3 (MYH3) number 2 of 2313 genes in three cases of Freeman Sheldon syndrome.ConclusionsOur results demonstrate the potential power gain of aggregating bioinformatics variant scores into gene-level scores and the general utility of bioinformatics in assisting the search for disease genes in large-scale exome sequencing studies. VEST is available as a stand-alone software package at http://wiki.chasmsoftware.org and is hosted by the CRAVAT web server at http://www.cravat.us

Whole Genome Sequencing Defines the Genetic Heterogeneity of Familial Pancreatic Cancer

UNLABELLED 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 genomes of 638 patients with familial pancreatic cancer and the tumor 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. SIGNIFICANCE The genetic basis of disease susceptibility in the majority of patients with familial pancreatic cancer is unknown. We whole genome sequenced 638 patients with familial pancreatic cancer and demonstrate that the genetic underpinning of inherited pancreatic cancer is highly heterogeneous. This has significant implications for the management of patients with familial pancreatic cancer.

CRAVAT: cancer-related analysis of variants toolkit

Summary: Advances in sequencing technology have greatly reduced the costs incurred in collecting raw sequencing data. Academic laboratories and researchers therefore now have access to very large datasets of genomic alterations but limited time and computational resources to analyse their potential biological importance. Here, we provide a web-based application, Cancer-Related Analysis of Variants Toolkit, designed with an easy-to-use interface to facilitate the high-throughput assessment and prioritization of genes and missense alterations important for cancer tumorigenesis. Cancer-Related Analysis of Variants Toolkit provides predictive scores for germline variants, somatic mutations and relative gene importance, as well as annotations from published literature and databases. Results are emailed to users as MS Excel spreadsheets and/or tab-separated text files. Availability: http://www.cravat.us/ Contact: karchin@jhu.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Assessing the Pathogenicity of Insertion and Deletion Variants with the Variant Effect Scoring Tool (VEST-Indel)

Insertion/deletion variants (indels) alter protein sequence and length, yet are highly prevalent in healthy populations, presenting a challenge to bioinformatics classifiers. Commonly used features—DNA and protein sequence conservation, indel length, and occurrence in repeat regions—are useful for inference of protein damage. However, these features can cause false positives when predicting the impact of indels on disease. Existing methods for indel classification suffer from low specificities, severely limiting clinical utility. Here, we further develop our variant effect scoring tool (VEST) to include the classification of in‐frame and frameshift indels (VEST‐indel) as pathogenic or benign. We apply 24 features, including a new “PubMed” feature, to estimate a genes importance in human disease. When compared with four existing indel classifiers, our method achieves a drastically reduced false‐positive rate, improving specificity by as much as 90%. This approach of estimating gene importance might be generally applicable to missense and other bioinformatics pathogenicity predictors, which often fail to achieve high specificity. Finally, we tested all possible meta‐predictors that can be obtained from combining the four different indel classifiers using Boolean conjunctions and disjunctions, and derived a meta‐predictor with improved performance over any individual method.

Experimental Assessment of Splicing Variants Using Expression Minigenes and Comparison with In Silico Predictions

Assessment of the functional consequences of variants near splice sites is a major challenge in the diagnostic laboratory. To address this issue, we created expression minigenes (EMGs) to determine the RNA and protein products generated by splice site variants (n = 10) implicated in cystic fibrosis (CF). Experimental results were compared with the splicing predictions of eight in silico tools. EMGs containing the full‐length Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) coding sequence and flanking intron sequences generated wild‐type transcript and fully processed protein in Human Embryonic Kidney (HEK293) and CF bronchial epithelial (CFBE41o‐) cells. Quantification of variant induced aberrant mRNA isoforms was concordant using fragment analysis and pyrosequencing. The splicing patterns of c.1585–1G>A and c.2657+5G>A were comparable to those reported in primary cells from individuals bearing these variants. Bioinformatics predictions were consistent with experimental results for 9/10 variants (MES), 8/10 variants (NNSplice), and 7/10 variants (SSAT and Sroogle). Programs that estimate the consequences of mis‐splicing predicted 11/16 (HSF and ASSEDA) and 10/16 (Fsplice and SplicePort) experimentally observed mRNA isoforms. EMGs provide a robust experimental approach for clinical interpretation of splice site variants and refinement of in silico tools.

Cleaved NOTCH1 expression pattern in head and neck squamous cell carcinoma is associated with NOTCH1 mutation, HPV status and high-risk features

The Notch pathway is frequently altered in head and neck squamous cell carcinomas (HNSCC); however, the clinical significance of NOTCH1 dysregulation is poorly understood. This study was designed to characterize expression of the transcriptionally active NOTCH1 intracellular domain (NICD1) in HNSCCs and evaluate its association with NOTCH1 mutation status and clinical parameters. IHC for NICD1 was performed on 79 previously sequenced archival HNSCCs with known NOTCH1 mutation status. Three distinct immunohistochemical staining patterns were identified: positive/peripheral (47%), positive/nonperipheral (34%), and negative (19%). NICD1 expression was associated with NOTCH1 mutation status (P < 0.001). Most NOTCH1–wild-type tumors were peripheral (55%), whereas mutated NOTCH1 tumors were most commonly negative (47%). Nonperipheral tumors were more likely than peripheral tumors to have extracapsular spread [adjusted odds ratio (aOR), 16.01; 95% confidence interval (CI), 1.92–133.46; P = 0.010] and poor differentiation (aOR, 5.27; 95% CI, 0.90–30.86; P = 0.066). Negative staining tumors tended to be poorly differentiated (aOR, 24.71; 95% CI, 1.53–399.33; P = 0.024) and were less likely to be human papillomavirus (HPV) positive (aOR, 0.043; 95% CI, 0.001–1.59; P = 0.087). NOTCH1 mutagenesis was significantly associated with HPV status, with NOTCH1–wild-type tumors more likely to be HPV positive than NOTCH1-mutated tumors (aOR, 19.06; 95% CI, 1.31–276.15; P = 0.031). TP53 disruptive mutations were not associated with NICD1 expression or NOTCH1 mutation. In conclusion, NICD1 is expressed in three distinct patterns in HNSCC that are significantly associated with high-risk features. These findings further support a dual role for NOTCH1 as both tumor suppressor and oncogene in HNSCC. Further research is necessary to clarify the role of NOTCH1 in HNSCC and understand the clinical and therapeutic implications therein. Cancer Prev Res; 8(4); 287–95. ©2015 AACR.

Proteins linked to autosomal dominant and autosomal recessive disorders harbor characteristic rare missense mutation distribution patterns

The role of rare missense variants in disease causation remains difficult to interpret. We explore whether the clustering pattern of rare missense variants (MAF < 0.01) in a protein is associated with mode of inheritance. Mutations in genes associated with autosomal dominant (AD) conditions are known to result in either loss or gain of function, whereas mutations in genes associated with autosomal recessive (AR) conditions invariably result in loss-of-function. Loss-of-function mutations tend to be distributed uniformly along protein sequence, whereas gain-of-function mutations tend to localize to key regions. It has not previously been ascertained whether these patterns hold in general for rare missense mutations. We consider the extent to which rare missense variants are located within annotated protein domains and whether they form clusters, using a new unbiased method called CLUstering by Mutation Position. These approaches quantified a significant difference in clustering between AD and AR diseases. Proteins linked to AD diseases exhibited more clustering of rare missense mutations than those linked to AR diseases (Wilcoxon P = 5.7 × 10(-4), permutation P = 8.4 × 10(-4)). Rare missense mutation in proteins linked to either AD or AR diseases was more clustered than controls (1000G) (Wilcoxon P = 2.8 × 10(-15) for AD and P = 4.5 × 10(-4) for AR, permutation P = 3.1 × 10(-12) for AD and P = 0.03 for AR). The differences in clustering patterns persisted even after removal of the most prominent genes. Testing for such non-random patterns may reveal novel aspects of disease etiology in large sample studies.