Structural variant analysis of a cancer reference cell line sample using multiple sequencing technologies.

BACKGROUND: The cancer genome is commonly altered with thousands of structural rearrangements including insertions, deletions, translocation, inversions, duplications, and copy number variations. Thus, structural variant (SV) characterization plays a paramount role in cancer target identification, oncology diagnostics, and personalized medicine. As part of the SEQC2 Consortium effort, the present study established and evaluated a consensus SV call set using a breast cancer reference cell line and matched normal control derived from the same donor, which were used in our companion benchmarking studies as reference samples. RESULTS: We systematically investigated somatic SVs in the reference cancer cell line by comparing to a matched normal cell line using multiple NGS platforms including Illumina short-read, 10X Genomics linked reads, PacBio long reads, Oxford Nanopore long reads, and high-throughput chromosome conformation capture (Hi-C). We established a consensus SV call set of a total of 1788 SVs including 717 deletions, 230 duplications, 551 insertions, 133 inversions, 146 translocations, and 11 breakends for the reference cancer cell line. To independently evaluate and cross-validate the accuracy of our consensus SV call set, we used orthogonal methods including PCR-based validation, Affymetrix arrays, Bionano optical mapping, and identification of fusion genes detected from RNA-seq. We evaluated the strengths and weaknesses of each NGS technology for SV determination, and our findings provide an actionable guide to improve cancer genome SV detection sensitivity and accuracy. CONCLUSIONS: A high-confidence consensus SV call set was established for the reference cancer cell line. A large subset of the variants identified was validated by multiple orthogonal methods.

Whole genome and exome sequencing reference datasets from a multi-center and cross-platform benchmark study.

With the rapid advancement of sequencing technologies, next generation sequencing (NGS) analysis has been widely applied in cancer genomics research. More recently, NGS has been adopted in clinical oncology to advance personalized medicine. Clinical applications of precision oncology require accurate tests that can distinguish tumor-specific mutations from artifacts introduced during NGS processes or data analysis. Therefore, there is an urgent need to develop best practices in cancer mutation detection using NGS and the need for standard reference data sets for systematically measuring accuracy and reproducibility across platforms and methods. Within the SEQC2 consortium context, we established paired tumor-normal reference samples and generated whole-genome (WGS) and whole-exome sequencing (WES) data using sixteen library protocols, seven sequencing platforms at six different centers. We systematically interrogated somatic mutations in the reference samples to identify factors affecting detection reproducibility and accuracy in cancer genomes. These large cross-platform/site WGS and WES datasets using well-characterized reference samples will represent a powerful resource for benchmarking NGS technologies, bioinformatics pipelines, and for the cancer genomics studies.

Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing.

Clinical applications of precision oncology require accurate tests that can distinguish true cancer-specific mutations from errors introduced at each step of next-generation sequencing (NGS). To date, no bulk sequencing study has addressed the effects of cross-site reproducibility, nor the biological, technical and computational factors that influence variant identification. Here we report a systematic interrogation of somatic mutations in paired tumor-normal cell lines to identify factors affecting detection reproducibility and accuracy at six different centers. Using whole-genome sequencing (WGS) and whole-exome sequencing (WES), we evaluated the reproducibility of different sample types with varying input amount and tumor purity, and multiple library construction protocols, followed by processing with nine bioinformatics pipelines. We found that read coverage and callers affected both WGS and WES reproducibility, but WES performance was influenced by insert fragment size, genomic copy content and the global imbalance score (GIV; G > T/C > A). Finally, taking into account library preparation protocol, tumor content, read coverage and bioinformatics processes concomitantly, we recommend actionable practices to improve the reproducibility and accuracy of NGS experiments for cancer mutation detection.

Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing.

The lack of samples for generating standardized DNA datasets for setting up a sequencing pipeline or benchmarking the performance of different algorithms limits the implementation and uptake of cancer genomics. Here, we describe reference call sets obtained from paired tumor-normal genomic DNA (gDNA) samples derived from a breast cancer cell line-which is highly heterogeneous, with an aneuploid genome, and enriched in somatic alterations-and a matched lymphoblastoid cell line. We partially validated both somatic mutations and germline variants in these call sets via whole-exome sequencing (WES) with different sequencing platforms and targeted sequencing with >2,000-fold coverage, spanning 82% of genomic regions with high confidence. Although the gDNA reference samples are not representative of primary cancer cells from a clinical sample, when setting up a sequencing pipeline, they not only minimize potential biases from technologies, assays and informatics but also provide a unique resource for benchmarking 'tumor-only' or 'matched tumor-normal' analyses.

T Cell-Mediated Antitumor Immunity Cooperatively Induced By TGFßR1 Antagonism and Gemcitabine Counteracts Reformation of the Stromal Barrier in Pancreatic Cancer.

The desmoplastic stroma of pancreatic cancers forms a physical barrier that impedes intratumoral drug delivery. Attempts to modulate the desmoplastic stroma to increase delivery of administered chemotherapy have not shown positive clinical results thus far, and preclinical reports in which chemotherapeutic drugs were coadministered with antistromal therapies did not universally demonstrate increased genotoxicity despite increased intratumoral drug levels. In this study, we tested whether TGFß antagonism can break the stromal barrier, enhance perfusion and tumoral drug delivery, and interrogated cellular and molecular mechanisms by which the tumor prevents synergism with coadministered gemcitabine. TGFß inhibition in genetically engineered murine models (GEMM) of pancreas cancer enhanced tumoral perfusion and increased intratumoral gemcitabine levels. However, tumors rapidly adapted to TGFß-dependent stromal modulation, and intratumoral perfusion returned to pre-treatment levels upon extended TGFß inhibition. Perfusion was governed by the phenotypic identity and distribution of cancer-associated fibroblasts (CAF) with the myelofibroblastic phenotype (myCAFs), and myCAFs which harbored unique genomic signatures rapidly escaped the restricting effects of TGFß inhibition. Despite the reformation of the stromal barrier and reversal of initially increased intratumoral exposure levels, TGFß inhibition in cooperation with gemcitabine effectively suppressed tumor growth via cooperative reprogramming of T regulatory cells and stimulation of CD8 T cell-mediated antitumor activity. The antitumor activity was further improved by the addition of anti-PD-L1 immune checkpoint blockade to offset adaptive PD-L1 upregulation induced by TGFß inhibition. These findings support the development of combined antistroma anticancer therapies capable of impacting the tumor beyond the disruption of the desmoplastic stroma as a physical barrier to improve drug delivery.

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples.

Gene expression analysis by RNA sequencing (RNA-seq) enables unique insights into clinical samples that can potentially lead to mechanistic understanding of the basis of various diseases as well as resistance and/or susceptibility mechanisms. However, FFPE tissues, which represent the most common method for preserving tissue morphology in clinical specimens, are not the best sources for gene expression profiling analysis. The RNA obtained from such samples is often degraded, fragmented, and chemically modified, which leads to suboptimal sequencing libraries. In turn, these generate poor quality sequence data that may not be reliable for gene expression analysis and mutation discovery. In order to make the most of FFPE samples and obtain the best possible data from low quality samples, it is important to take certain precautions while planning experimental design, preparing sequencing libraries, and during data analysis. This includes the use of appropriate metrics for precise sample quality control (QC), identifying the best methods for various steps during the sequencing library generation, and careful library QC. In addition, applying correct software tools and parameters for sequence data analysis is critical in order to identify artifacts in RNA-seq data, filter out contamination and low quality reads, assess uniformity of gene coverage, and measure the reproducibility of gene expression profiles among biological replicates. These steps can ensure high accuracy and reproducibility for profiling of very heterogeneous RNA samples. Here we describe the various steps for sample QC, library preparation and QC, sequencing, and data analysis that can help to increase the amount of useful data obtained from low quality RNA, such as that obtained from FFPE-RNA tissues.

Mannose receptor (CD206) activation in tumor-associated macrophages enhances adaptive and innate antitumor immune responses.

Solid tumors elicit a detectable immune response including the infiltration of tumor-associated macrophages (TAMs). Unfortunately, this immune response is co-opted into contributing toward tumor growth instead of preventing its progression. We seek to reestablish an antitumor immune response by selectively targeting surface receptors and endogenous signaling processes of the macrophage subtypes driving cancer progression. RP-182 is a synthetic 10-mer amphipathic analog of host defense peptides that selectively induces a conformational switch of the mannose receptor CD206 expressed on TAMs displaying an M2-like phenotype. RP-182-mediated activation of this receptor in human and murine M2-like macrophages elicits a program of endocytosis, phagosome-lysosome formation, and autophagy and reprograms M2-like TAMs to an antitumor M1-like phenotype. In syngeneic and autochthonous murine cancer models, RP-182 suppressed tumor growth, extended survival, and was an effective combination partner with chemo- or immune checkpoint therapy. Antitumor activity of RP-182 was also observed in CD206high patient-derived xenotransplantation models. Mechanistically, via selective reduction of immunosuppressive M2-like TAMs, RP-182 improved adaptive and innate antitumor immune responses, including increased cancer cell phagocytosis by reprogrammed TAMs.

Targeting purine synthesis in ASS1-expressing tumors enhances the response to immune checkpoint inhibitors.

Argininosuccinate synthase (ASS1) downregulation in different tumors has been shown to support cell proliferation and yet, in several common cancer subsets ASS1 expression associates with poor patient prognosis. Here we demonstrate that ASS1 expression under glucose deprivation is induced by c-MYC, providing survival benefit by increasing nitric oxide synthesis and activating the gluconeogenic enzymes pyruvate carboxylase and phosphoenolpyruvate carboxykinase by S-nitrosylation. The resulting increased flux through gluconeogenesis enhances serine, glycine and subsequently purine synthesis. Notably, high ASS1-expressing breast cancer mice do not respond to immune checkpoint inhibitors and patients with breast cancer with high ASS1 have more metastases. We further find that inhibiting purine synthesis increases pyrimidine to purine ratio, elevates expression of the immunoproteasome and significantly enhances the response of autologous primary CD8+ T cells to anti-PD-1. These results suggest that treating patients with high-ASS1 cancers with purine synthesis inhibition is beneficial and may also sensitize them to immune checkpoint inhibition therapy.

Robustness of RNA sequencing on older formalin-fixed paraffin-embedded tissue from high-grade ovarian serous adenocarcinomas.

Formalin-fixed paraffin-embedded (FFPE) tissues are among the most widely available clinical specimens. Their potential utility as a source of RNA for transcriptome studies would greatly enhance population-based cancer studies. Although preliminary studies suggest FFPE tissue may be used for RNA sequencing, the effect of storage time on these specimens needs to be determined. We conducted this study to determine whether RNA in archived FFPE high-grade ovarian serous adenocarcinomas from Surveillance, Epidemiology and End Results (SEER) registries was present in sufficient quantity and quality for RNA-Seq analysis. FFPE tissues, stored from 7 to 32 years, were obtained from three SEER sites. RNA was extracted, quantified, quality assessed, and subjected to RNA-Seq (a whole transcriptome sequencing technology). FFPE specimens stored for longer periods of time had poorer RNA sample quality as indicated by negative correlations between specimen storage time and fragment distribution values (DV). In addition, sample contamination was a common issue among the RNA, with 41 of 67 samples having 5% to 48% bacterial contamination. However, regardless of specimen storage time and bacterial contamination, 60% of the samples yielded data that enabled gene expression quantification, identifying more than 10,000 genes, with the correlations among most biological replicates above 0.7. This study demonstrates that FFPE high-grade ovarian serous adenocarcinomas specimens stored in repositories for up to 32 years and under varying storage conditions are a promising source of RNA for RNA-Seq. We also describe certain caveats to be considered when designing RNA-Seq studies using archived FFPE tissues.

Genome Assembly and Annotation of the Trichoplusia ni Tni-FNL Insect Cell Line Enabled by Long-Read Technologies.

BACKGROUND: Trichoplusiani derived cell lines are commonly used to enable recombinant protein expression via baculovirus infection to generate materials approved for clinical use and in clinical trials. In order to develop systems biology and genome engineering tools to improve protein expression in this host, we performed de novo genome assembly of the Trichoplusiani-derived cell line Tni-FNL. METHODS: By integration of PacBio single-molecule sequencing, Bionano optical mapping, and 10X Genomics linked-reads data, we have produced a draft genome assembly of Tni-FNL. RESULTS: Our assembly contains 280 scaffolds, with a N50 scaffold size of 2.3 Mb and a total length of 359 Mb. Annotation of the Tni-FNL genome resulted in 14,101 predicted genes and 93.2% of the predicted proteome contained recognizable protein domains. Ortholog searches within the superorder Holometabola provided further evidence of high accuracy and completeness of the Tni-FNL genome assembly. CONCLUSIONS: This first draft Tni-FNL genome assembly was enabled by complementary long-read technologies and represents a high-quality, well-annotated genome that provides novel insight into the complexity of this insect cell line and can serve as a reference for future large-scale genome engineering work in this and other similar recombinant protein production hosts.

Genetic diversity influences the response of the brain to developmental lead exposure.

Although extrinsic factors, such as nutritional status, and some intrinsic genetic factors may modify susceptibility to developmental lead (Pb) poisoning, no studies have specifically examined the influence of genetic background on outcomes from Pb exposure. In this study, we used gene microarray profiling to identify Pb-responsive genes in rats of different genetic backgrounds, including inbred (Fischer 344 (F344)) and outbred (Long Evans (LE), Sprague Dawley (SD)) strains, to investigate the role that genetic variation may play in influencing outcomes from developmental Pb exposure. Male and female animals received either perinatal (gestation through lactation) or postnatal (birth through weaning) exposure to Pb in food (0, 250, or 750 ppm). RNA was extracted from the hippocampus at day 55 and hybridized to Affymetrix Rat Gene 1.0 ST Arrays. There were significant strain-specific effects of Pb on the hippocampal transcriptome with 978 transcripts differentially expressed in LE rats across all experimental groups, 269 transcripts differentially expressed in F344 rats, and only 179 transcripts differentially expressed in SD rats. These results were not due to strain-related differences in brain accumulation of Pb. Further, no genes were consistently differentially regulated in all experimental conditions. There was no set of "Pb toxicity" genes that are a molecular signature for Pb neurotoxicity that transcended sex, exposure condition, and strain. These results demonstrate the influence that strain and genetic background play in modifying the brain's response to developmental Pb exposure and may have relevance for better understanding the molecular underpinnings of the lack of a neurobehavioral signature in childhood Pb poisoning.

Effects of developmental lead exposure on the hippocampal transcriptome: influences of sex, developmental period, and lead exposure level.

Developmental lead (Pb) exposure has profound effects on cognition and behavior. Much is known about effects of Pb on hippocampal-mediated behaviors, but little is known about the molecular consequences of Pb exposure and the influences of developmental timing of exposure, level of exposure, and sex as effect modifiers of Pb exposure on the brain. The aim of this study was to examine the effects of different levels of Pb exposure (250 and 750 ppm Pb acetate) during perinatal (gestation/lactation) and postnatal (through postnatal day 45) periods on the hippocampal transcriptome in male and female Long Evans rats. Total RNA was extracted from hippocampus from four animals per experimental condition. RNA was hybridized to Affymetrix Rat Gene RNA Arrays using standard methods. Pb exposure per se influenced the expression of 717 transcripts (328 unique annotated genes), with many influenced in a sex-independent manner. Significant differences in gene expression patterns were also influenced by timing and level of exposure, with generally larger effects at the lower level of exposure across all groups. Statistically enriched biological functions included ion binding, regulation of RNA metabolic processes, and positive regulation of macromolecule biosynthetic processes. Processes of regulation of transcription and regulation of gene expression were preferentially enriched in males, regardless of timing or amount of Pb exposure. The effect on transcription factors and the diverse pathways or networks affected by Pb suggest a substantial effect of developmental Pb exposure on plasticity and adaptability, with these effects significantly modified by sex, developmental window of exposure, and level of Pb exposure.

SKDM human leukocyte antigen (HLA) tool: A comprehensive HLA and disease associations analysis software.

The immensely polymorphic and gene-rich landscape of the major histocompatibility complex on chromosome 6 necessitates a thorough and consistent investigation of its constituting elements. The human leukocyte antigens (HLAs) are an example of such polymorphic elements, implicated in many immune-based diseases. So far, analyses of HLA molecules in the context of diseases have been ad hoc, frequently incomplete, and extremely cumbersome. SKDM provides a comprehensive and automated workflow for detecting and dissecting HLA associations in diseases. We created a Java application to consistently perform our proposed method of analysis of HLAs in case-control datasets. The SKDM HLA tool can test for HLA allele differences between two populations and, by retrieving amino acid sequences, evaluates each polymorphic amino acid residue or a pocket of amino acids as an independent variant. Once primary associations are identified, the program examines zygosity and tests for strongest association, interaction, and linkage disequilibrium among amino acid epitopes of the same HLA molecule or between HLA isotypes. A summary of the analysis is output in plain language. The software and a user's manual are freely available at http://sourceforge.net/projects/skdm.