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In 2010, the World Health Organization reclassified the entity originally described as intraductal oncocytic papillary neoplasm as the ‘oncocytic subtype' of intraductal papillary mucinous neoplasm. Although several key molecular alterations of other intraductal papillary mucinous neoplasm subtypes have been discovered, including common mutations in KRAS, GNAS, and RNF3, those of oncocytic subtype have not been well characterized. We analyzed 11 pancreatic ‘oncocytic subtype' of intraductal papillary mucinous neoplasms. Nine pancreatic ‘oncocytic subtype' of intraductal papillary mucinous neoplasms uniformly exhibited typical entity-defining morphology of arborizing papillae lined by layers of cells with oncocytic cytoplasm, prominent, nucleoli, and intraepithelial lumina. The remaining two were atypical. One lacked the arborizing papilla and had flat oncocytic epithelium only; the other one had focal oncocytic epithelium in a background of predominantly intestinal subtype intraductal papillary mucinous neoplasm. Different components of this case were analyzed separately. Formalin-fixed, paraffin-embedded specimens of all cases were microdissected and subjected to high-depth-targeted next-generation sequencing for a panel of 300 key cancer-associated genes in a platform that enabled the identification of sequence mutations, copy number alterations, and select structural rearrangements involving all targeted genes. Fresh frozen specimens of two cases were also subjected to whole-genome sequencing. For the nine typical pancreatic ‘oncocytic subtype' of intraductal papillary mucinous neoplasms, the number of mutations per case, identified by next-generation sequencing, ranged from 1 to 10 (median=4). None of these cases had KRAS or GNAS mutations and only one had both RNF43 and PIK3R1 mutations. ARHGAP26, ASXL1, EPHA8, and ERBB4 genes were somatically altered in more than one of these typical ‘oncocytic subtype' of intraductal papillary mucinous neoplasms but not in the other two atypical ones. In the neoplasm with flat oncocytic epithelium, the only mutated gene was KRAS. All components of the intestinal subtype intraductal papillary mucinous neoplasms with focal oncocytic epithelium manifested TP53, GNAS, and RNF43 mutations. In conclusion, this study elucidates that ‘oncocytic subtype' of intraductal papillary mucinous neoplasm is not only morphologically distinct but also genetically distinct from other intraductal papillary mucinous neoplasm subtypes. Considering that now its biologic behavior is also being found to be different than other intraductal papillary mucinous neoplasm subtypes, ‘oncocytic subtype' of intraductal papillary mucinous neoplasm warrants being recognized separately.

Intraductal tubulopapillary neoplasm is a relatively recently described member of the pancreatic intraductal neoplasm family. The more common member of this family, intraductal papillary mucinous neoplasm, often carries genetic alterations typical of pancreatic infiltrating ductal adenocarcinoma ( KRAS , TP53 , and CDKN2A ) but additionally has mutations in GNAS and RNF43 genes. However, the genetic characteristics of intraductal tubulopapillary neoplasm have not been well characterized. Twenty-two intraductal tubulopapillary neoplasms were analyzed by either targeted next-generation sequencing, which enabled the identification of sequence mutations, copy number alterations, and selected structural rearrangements involving all targeted (≥300) genes, or whole-exome sequencing. Three of these intraductal tubulopapillary neoplasms were also subjected to whole-genome sequencing. All intraductal tubulopapillary neoplasms revealed the characteristic histologic (cellular intraductal nodules of back-to-back tubular glands lined by predominantly cuboidal cells with atypical nuclei and no obvious intracellular mucin) and immunohistochemical (immunolabeled with MUC1 and MUC6 but were negative for MUC2 and MUC5AC) features. By genomic analyses, there was loss of CDKN2A in 5/20 (25%) of these cases. However, the majority of the previously reported intraductal papillary mucinous neoplasm-related alterations were absent. Moreover, in contrast to most ductal neoplasms of the pancreas, MAP-kinase pathway was not involved. In fact, 2/22 (9%) of intraductal tubulopapillary neoplasms did not reveal any mutations in the tested genes. However, certain chromatin remodeling genes ( MLL1, MLL2, MLL3, BAP1, PBRM1 , EED , and ATRX ) were found to be mutated in 7/22 (32%) of intraductal tubulopapillary neoplasms and 27% harbored phosphatidylinositol 3-kinase (PI3K) pathway ( PIK3CA , PIK3CB , INPP4A, and PTEN ) mutations. In addition, 4/18 (18%) of intraductal tubulopapillary neoplasms had FGFR2 fusions ( FGFR2-CEP55, FGFR2-SASS6, DISP1-FGFR2, FGFR2-TXLNA, and FGFR2-VCL ) and 1/18 (5.5%) had STRN-ALK fusion. Intraductal tubulopapillary neoplasm is a distinct clinicopathologic entity in the pancreas. Although its intraductal nature and some clinicopathologic features resemble those of intraductal papillary mucinous neoplasm, our results suggest that intraductal tubulopapillary neoplasm has distinguishing genetic characteristics. Some of these mutated genes are potentially targetable. Future functional studies will be needed to determine the consequences of these gene alterations.

The identification of genetic and epigenetic alterations from primary tumor cells has become a common method to identify genes critical to the development and progression of cancer. We seek to identify those genetic and epigenetic aberrations that have the most impact on gene function within the tumor. First, we perform a bioinformatic analysis of copy number variation (CNV) and DNA methylation covering the genetic landscape of ovarian cancer tumor cells. We separately examined CNV and DNA methylation for 42 primary serous ovarian cancer samples using MOMA-ROMA assays and 379 tumor samples analyzed by The Cancer Genome Atlas. We have identified 346 genes with significant deletions or amplifications among the tumor samples. Utilizing associated gene expression data we predict 156 genes with altered copy number and correlated changes in expression. Among these genes CCNE1, POP4, UQCRB, PHF20L1 and C19orf2 were identified within both data sets. We were specifically interested in copy number variation as our base genomic property in the prediction of tumor suppressors and oncogenes in the altered ovarian tumor. We therefore identify changes in DNA methylation and expression for all amplified and deleted genes. We statistically define tumor suppressor and oncogenic features for these modalities and perform a correlation analysis with expression. We predicted 611 potential oncogenes and tumor suppressors candidates by integrating these data types. Genes with a strong correlation for methylation dependent expression changes exhibited at varying copy number aberrations include CDCA8, ATAD2, CDKN2A, RAB25, AURKA, BOP1 and EIF2C3. We provide copy number variation and DNA methylation analysis for over 11,500 individual genes covering the genetic landscape of ovarian cancer tumors. We show the extent of genomic and epigenetic alterations for known tumor suppressors and oncogenes and also use these defined features to identify potential ovarian cancer gene candidates.

Uveal melanoma (UM) is the most common primary intraocular malignancy in adults and leads to deadly metastases for which there is no approved treatment. Genetic events driving early tumor development are well-described, but those occurring later during metastatic progression remain poorly understood. We performed multiregional genomic sequencing on 22 tumors collected from two patients with widely metastatic UM who underwent rapid autopsy. We observed multiple seeding events from the primary tumors, metastasis-to-metastasis seeding, polyclonal seeding, and late driver variants in ATM, KRAS, and other genes previously unreported in UM. These findings reveal previously unrecognized temporal and anatomic complexity in the genetic evolution of metastatic uveal melanoma, and they highlight the distinction between early and late phases of UM genetic evolution with implications for novel therapeutic approaches.

Cutaneous and breast implant-associated anaplastic large-cell lymphomas (cALCLs and BI-ALCLs) are two localized forms of peripheral T-cell lymphomas (PTCLs) that are recognized as distinct entities within the family of ALCL. JAK-STAT signaling is a common feature of all ALCL subtypes, whereas DUSP22/IRF4, TP63 and TYK gene rearrangements have been reported in a proportion of ALK-negative sALCLs and cALCLs. Both cALCLs and BI-ALCLs differ in their gene expression profiles compared to PTCLs; however, a direct comparison of the genomic alterations and transcriptomes of these two entities is lacking. By performing RNA sequencing of 1385 genes (TruSight RNA Pan-Cancer, Illumina) in 12 cALCLs, 10 BI-ALCLs and two anaplastic lymphoma kinase (ALK)-positive sALCLs, we identified the previously reported TYK2-NPM1 fusion in 1 cALCL (1/12, 8%), and four new intrachromosomal gene fusions in 2 BI-ALCLs (2/10, 20%) involving genes on chromosome 1 (EPS15-GNG12 and ARNT-GOLPH3L) and on chromosome 17 (MYO18A-GIT1 and NF1-GOSR1). One of the two BI-ALCL samples showed a complex karyotype, raising the possibility that genomic instability may be responsible for intra-chromosomal fusions in BI-ALCL. Moreover, transcriptional analysis revealed similar upregulation of the PI3K/Akt pathway, associated with enrichment in the expression of neurotrophin signaling genes, which was more conspicuous in BI-ALCL, as well as differences, i.e., over-expression of genes involved in the RNA polymerase II transcription program in BI-ALCL and of the RNA splicing/processing program in cALCL.

Background Prompted by the revolution in high-throughput sequencing and its potential impact for treating cancer patients, we initiated a clinical research study to compare the ability of different sequencing assays and analysis methods to analyze glioblastoma tumors and generate real-time potential treatment options for physicians. Methods A consortium of seven institutions in New York City enrolled 30 patients with glioblastoma and performed tumor whole genome sequencing (WGS) and RNA sequencing (RNA-seq; collectively WGS/RNA-seq); 20 of these patients were also analyzed with independent targeted panel sequencing. We also compared results of expert manual annotations with those from an automated annotation system, Watson Genomic Analysis (WGA), to assess the reliability and time required to identify potentially relevant pharmacologic interventions. Results WGS/RNAseq identified more potentially actionable clinical results than targeted panels in 90% of cases, with an average of 16-fold more unique potentially actionable variants identified per individual; 84 clinically actionable calls were made using WGS/RNA-seq that were not identified by panels. Expert annotation and WGA had good agreement on identifying variants [mean sensitivity = 0.71, SD = 0.18 and positive predictive value (PPV) = 0.80, SD = 0.20] and drug targets when the same variants were called (mean sensitivity = 0.74, SD = 0.34 and PPV = 0.79, SD = 0.23) across patients. Clinicians used the information to modify their treatment plan 10% of the time. Conclusion These results present the first comprehensive comparison of technical and machine augmented analysis of targeted panel and WGS/RNA-seq to identify potential cancer treatments.

Following publication of the original article [1], it was reported that the given name of the fourteenth author was incorrectly published. The incorrect and the correct names are given below.Following publication of the original article [1], it was reported that the given name of the fourteenth author was incorrectly published. The incorrect and the correct names are given below.

A rapid accumulation of protein sequences and structures through genomic and structure consortiums has presented researchers with a large number of proteins with none or limited functional annotation. Furthermore the ability to assign specificity to a known and established functional class of proteins and attribute each protein to a particular biological pathway or process is continually an experimental challenge. Therefore the complete annotation of any one protein in the proteome is a multi-step process over decades of laboratory (experimental) science. Our main goal as computational biologists is to understand how well we can alleviate some of this work through computational experiments. Machine learning techniques can classify functionally related proteins where homology-transfer as well as sequence and structure motifs fail. An understanding of the capabilities within computational biology that can discriminate enzymatic function and specificity on a biochemical and cellular level is essential to this goal. We foremost present a method that aimed at complementing homology-transfer in the identification of cell cycle control kinases from sequence alone. First, we identified functionally significant residues in cell cycle proteins through their high sequence conservation and biophysical properties. We then incorporated these residues and their features into support vector machines (SVM) to identify new kinases and more specifically to differentiate cell cycle kinases from other kinases and other proteins. By using these highly conserved, semi-buried residues and their biophysical properties we could distinguish cell cycle S/T kinases from other kinase families at levels of accuracy and coverage which outperform homology-transfer predictions. An application to the entire human proteome predicted several human proteins with limited previous annotations to be candidates for cell cycle kinases. We then wanted to better understand the ability of conserved functional residue features to aid in further enzymatic specificity predictions. We set our method on the computational prediction of another type of transferase, the histone methyltransferases. The histone methyltransferases presented a unique classification problem since many of the proteins contain a similar structurally conserved domain. We identify biophysical diversity among the methyltransferase family of proteins and use this diversity in our SVM feature based predictions. We show that conserved biophysical residue features also out perform full sequence features for prediction accuracy in this class of transferases. Furthermore SVM feature based identifications of histone methyltransferases provide higher accuracy and coverage than homology transfer annotations.