Proteogenomic characterization of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 normal pancreatic ductal tissues. Proteomic, phosphoproteomic, and glycoproteomic analyses were used to characterize proteins and their modifications. In addition, whole-genome sequencing, whole-exome sequencing, methylation, RNA sequencing (RNA-seq), and microRNA sequencing (miRNA-seq) were performed on the same tissues to facilitate an integrated proteogenomic analysis and determine the impact of genomic alterations on protein expression, signaling pathways, and post-translational modifications. To ensure robust downstream analyses, tumor neoplastic cellularity was assessed via multiple orthogonal strategies using molecular features and verified via pathological estimation of tumor cellularity based on histological review. This integrated proteogenomic characterization of PDAC will serve as a valuable resource for the community, paving the way for early detection and identification of novel therapeutic targets.

PVT1 dependence in cancer with MYC copy-number increase.

'Gain' of supernumerary copies of the 8q24.21 chromosomal region has been shown to be common in many human cancers and is associated with poor prognosis. The well-characterized myelocytomatosis (MYC) oncogene resides in the 8q24.21 region and is consistently co-gained with an adjacent 'gene desert' of approximately 2 megabases that contains the long non-coding RNA gene PVT1, the CCDC26 gene candidate and the GSDMC gene. Whether low copy-number gain of one or more of these genes drives neoplasia is not known. Here we use chromosome engineering in mice to show that a single extra copy of either the Myc gene or the region encompassing Pvt1, Ccdc26 and Gsdmc fails to advance cancer measurably, whereas a single supernumerary segment encompassing all four genes successfully promotes cancer. Gain of PVT1 long non-coding RNA expression was required for high MYC protein levels in 8q24-amplified human cancer cells. PVT1 RNA and MYC protein expression correlated in primary human tumours, and copy number of PVT1 was co-increased in more than 98% of MYC-copy-increase cancers. Ablation of PVT1 from MYC-driven colon cancer line HCT116 diminished its tumorigenic potency. As MYC protein has been refractory to small-molecule inhibition, the dependence of high MYC protein levels on PVT1 long non-coding RNA provides a much needed therapeutic target.

Standard operating procedure for somatic variant refinement of sequencing data with paired tumor and normal samples.

PURPOSE: Following automated variant calling, manual review of aligned read sequences is required to identify a high-quality list of somatic variants. Despite widespread use in analyzing sequence data, methods to standardize manual review have not been described, resulting in high inter- and intralab variability. METHODS: This manual review standard operating procedure (SOP) consists of methods to annotate variants with four different calls and 19 tags. The calls indicate a reviewer's confidence in each variant and the tags indicate commonly observed sequencing patterns and artifacts that inform the manual review call. Four individuals were asked to classify variants prior to, and after, reading the SOP and accuracy was assessed by comparing reviewer calls with orthogonal validation sequencing. RESULTS: After reading the SOP, average accuracy in somatic variant identification increased by 16.7% (p value = 0.0298) and average interreviewer agreement increased by 12.7% (p value < 0.001). Manual review conducted after reading the SOP did not significantly increase reviewer time. CONCLUSION: This SOP supports and enhances manual somatic variant detection by improving reviewer accuracy while reducing the interreviewer variability for variant calling and annotation.

In silico epitope prediction analyses highlight the potential for distracting antigen immunodominance with allogeneic cancer vaccines.

Allogeneic cancer vaccines are designed to induce antitumor immune responses with the goal of impacting tumor growth. Typical allogeneic cancer vaccines are produced by expansion of established cancer cell lines, transfection with vectors encoding immunostimulatory cytokines, and lethal irradiation. More than 100 clinical trials have investigated the clinical benefit of allogeneic cancer vaccines in various cancer types. Results show limited therapeutic benefit in clinical trials and currently there are no FDA approved allogeneic cancer vaccines. We used recently developed bioinformatics tools including the pVAC-seq suite of software tools to analyze DNA/RNA sequencing data from the TCGA to examine the repertoire of antigens presented by a typical allogeneic cancer vaccine, and to simulate allogeneic cancer vaccine clinical trials. Specifically, for each simulated clinical trial we modeled the repertoire of antigens presented by allogeneic cancer vaccines consisting of three hypothetical cancer cell lines to 30 patients with the same cancer type. Simulations were repeated ten times for each cancer type. Each tumor sample in the vaccine and the vaccine recipient was subjected to HLA typing, differential expression analyses for tumor associated antigens (TAAs), germline variant calling, and neoantigen prediction. These analyses provided a robust, quantitative comparison between potentially beneficial TAAs and neoantigens versus distracting antigens present in the allogeneic cancer vaccines. We observe that distracting antigens greatly outnumber shared TAAs and neoantigens, providing one potential explanation for the lack of observed responses to allogeneic cancer vaccines. This analysis provides additional rationale for the redirection of efforts towards a personalized cancer vaccine approach.

A deep learning approach to automate refinement of somatic variant calling from cancer sequencing data.

Cancer genomic analysis requires accurate identification of somatic variants in sequencing data. Manual review to refine somatic variant calls is required as a final step after automated processing. However, manual variant refinement is time-consuming, costly, poorly standardized, and non-reproducible. Here, we systematized and standardized somatic variant refinement using a machine learning approach. The final model incorporates 41,000 variants from 440 sequencing cases. This model accurately recapitulated manual refinement labels for three independent testing sets (13,579 variants) and accurately predicted somatic variants confirmed by orthogonal validation sequencing data (212,158 variants). The model improves on manual somatic refinement by reducing bias on calls otherwise subject to high inter-reviewer variability.