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Genetic diseases are driven by aberrations of the human genome. Identification of such aberrations including structural variations (SVs) is key to our understanding. Conventional short-reads whole genome sequencing (cWGS) can identify SVs to base-pair resolution, but utilizes only short-range information and suffers from high false discovery rate (FDR). Linked-reads sequencing (10XWGS) utilizes long-range information by linkage of short-reads originating from the same large DNA molecule. This can mitigate alignment-based artefacts especially in repetitive regions and should enable better prediction of SVs. However, an unbiased evaluation of this technology is not available. In this study, we performed a comprehensive analysis of different types and sizes of SVs predicted by both the technologies and validated with an independent PCR based approach. The SVs commonly identified by both the technologies were highly specific, while validation rate dropped for uncommon events. A particularly high FDR was observed for SVs only found by 10XWGS. To improve FDR and sensitivity, statistical models for both the technologies were trained. Using our approach, we characterized SVs from the MCF7 cell line and a primary breast cancer tumor with high precision. This approach improves SV prediction and can therefore help in understanding the underlying genetics in various diseases.

Background Tumor models are critical for our understanding of cancer and the development of cancer therapeutics. Here, we present an integrated map of the genome, transcriptome and immunome of an epithelial mouse tumor, the CT26 colon carcinoma cell line. Results We found that Kras is homozygously mutated at p.G12D, Apc and Tp53 are not mutated, and Cdkn2a is homozygously deleted. Proliferation and stem-cell markers, including Top2a, Birc5 (Survivin), Cldn6 and Mki67, are highly expressed while differentiation and top-crypt markers Muc2, Ms4a8a (MS4A8B) and Epcam are not. Myc, Trp53 (tp53), Mdm2, Hif1a, and Nras are highly expressed while Egfr and Flt1 are not. MHC class I but not MHC class II is expressed. Several known cancer-testis antigens are expressed, including Atad2, Cep55, and Pbk. The highest expressed gene is a mutated form of the mouse tumor antigen gp70. Of the 1,688 non-synonymous point variations, 154 are both in expressed genes and in peptides predicted to bind MHC and thus potential targets for immunotherapy development. Based on its molecular signature, we predicted that CT26 is refractory to anti-EGFR mAbs and sensitive to MEK and MET inhibitors, as have been previously reported. Conclusions CT26 cells share molecular features with aggressive, undifferentiated, refractory human colorectal carcinoma cells. As CT26 is one of the most extensively used syngeneic mouse tumor models, our data provide a map for the rationale design of mode-of-action studies for pre-clinical evaluation of targeted- and immunotherapies.

Next generation sequencing (NGS) has enabled high throughput discovery of somatic mutations. Detection depends on experimental design, lab platforms, parameters and analysis algorithms. However, NGS-based somatic mutation detection is prone to erroneous calls, with reported validation rates near 54% and congruence between algorithms less than 50%. Here, we developed an algorithm to assign a single statistic, a false discovery rate (FDR), to each somatic mutation identified by NGS. This FDR confidence value accurately discriminates true mutations from erroneous calls. Using sequencing data generated from triplicate exome profiling of C57BL/6 mice and B16-F10 melanoma cells, we used the existing algorithms GATK, SAMtools and SomaticSNiPer to identify somatic mutations. For each identified mutation, our algorithm assigned an FDR. We selected 139 mutations for validation, including 50 somatic mutations assigned a low FDR (high confidence) and 44 mutations assigned a high FDR (low confidence). All of the high confidence somatic mutations validated (50 of 50), none of the 44 low confidence somatic mutations validated, and 15 of 45 mutations with an intermediate FDR validated. Furthermore, the assignment of a single FDR to individual mutations enables statistical comparisons of lab and computation methodologies, including ROC curves and AUC metrics. Using the HiSeq 2000, single end 50 nt reads from replicates generate the highest confidence somatic mutation call set.

Background Circulating tumor DNA (ctDNA) detection offers minimally invasive monitoring of cancer from blood samples. While tumor-informed approaches are sensitive, their clinical application is limited by cost, tissue availability, and turnaround time. In contrast, tumor-naïve assays offer faster turnaround times and broader applicability, but often sacrifice sensitivity. Methods Here, we developed DEEPctMUT, a computational pipeline for tumor-naïve ctDNA detection that integrates unique molecular identifiers (UMIs) with three complementary error-polishing strategies: (1) machine learning-based sequencing error suppression, (2) deep learning-based background noise filtering (DeepES), and (3) removal of clonal hematopoietic and germline variants using patient matched PBMC. We designed an efficient sequencing panel and developed and validated DEEPctMUT using cell lines, spike-in samples, healthy, and CRC plasma samples. Results DEEPctMUT accurately detected mutations down to 0.03% variant allele frequency (VAF), outperforming other tumor-naïve methods. In a head-to-head comparison, our pipeline identified pre-surgical CRC cases with 100% sensitivity, whereas the Roche Avenio Surveillance Kit achieved only 50% sensitivity. Furthermore, a panel-independent version of DEEPctMUT could improve the performance of Roche Avenio data without additional assay-specific training. We provide an extensive ground-truth dataset and make DEEPctMUT, including all trained models, available as an easy-to-use Nextflow pipeline. Conclusions By using advanced computational error-polishing techniques, DEEPctMUT can substantially reduce technical artifacts, allowing our tumor-naïve method to achieve a sensitivity comparable to tumor-informed approaches.

Background: Aspirin-exacerbated respiratory disease (AERD) is a syndrome characterised by chronic rhinosinusitis, nasal polyps, asthma and aspirin intolerance. An imbalance of eicosanoid metabolism with anover-production of cysteinyl leukotrienes (CysLTs) has been associated with AERD. However, the precise mechanisms underlying AERD are unknown. Objective: To establish the transcriptome of the nasal polyp airway epithelial cells derived from AERD patients to discover gene expression patterns in this disease. Methods: Nasal airway epithelial cells were isolated from 12 AERD polyps and 8 AERD non-polyp nasal mucosa samples as controls from the same subjects. Utilising the Illumina HiSeq 2500 platform, RNA samples were sequenced. Potential gene candidate DMRT3 was selected from the differentially-expressed genes for validation. Results: Comparative transcriptome profiling of nasal epithelial cells was accomplished in AERD. A total of 20 genes had twofold mean regulation expression differences or greater. In addition, 8 genes were upregulated, including doublesex and mab-3 related transcription factor 3 (DMRT3), and 12 genes were downregulated. Differentially regulated genes comprised roles in inflammation, defence and immunity. Metabolic process and embryonic development pathways were significantly enriched. Enzyme-linked immune sorbent assay (ELISA) results of DMRT3 in AERD patients were significantly upregulated compared to controls (p = 0.03). Immunohistochemistry (IHC) of AERD nasal polyps localised DMRT3 and was predominantly released in the airway epithelia. Conclusion: Findings suggest that DMRT3 could be potentially involved in nasal polyp development in AERD patients. Furthermore, several genes are downregulated, hinting at the dedifferentiation phenomenon in AERD polyps. However, further studies are imperative to confirm the exact mechanism of polyp formation in AERD patients.

The t(8;21) chromosomal translocation activates aberrant expression of the AML1‐ETO (AE) fusion protein and is commonly associated with core binding factor acute myeloid leukaemia (CBF AML). Combining a conditional mouse model that closely resembles the slow evolution and the mosaic AE expression pattern of human t(8;21) CBF AML with global transcriptome sequencing, we find that disease progression was characterized by two principal pathogenic mechanisms. Initially, AE expression modified the lineage potential of haematopoietic stem cells (HSCs), resulting in the selective expansion of the myeloid compartment at the expense of normal erythro‐ and lymphopoiesis. This lineage skewing was followed by a second substantial rewiring of transcriptional networks occurring in the trajectory to manifest leukaemia. We also find that both HSC and lineage‐restricted granulocyte macrophage progenitors (GMPs) acquired leukaemic stem cell (LSC) potential being capable of initiating and maintaining the disease. Finally, our data demonstrate that long‐term expression of AE induces an indolent myeloproliferative disease (MPD)‐like myeloid leukaemia phenotype with complete penetrance and that acute inactivation of AE function is a potential novel therapeutic option. Graphical Abstract This novel model system of AML1‐ETO driven acute myeloid leukaemia addresses the concept of ‘oncogene addiction’. Better understanding of AML1‐ETO need to maintain leukemia and rewire the transcriptome may help to design future therapies.

The transcription of tumor mutations from DNA into RNA has implications for biology, epigenetics and clinical practice. It is not clear if mutations are in general transcribed and, if so, at what proportion to the wild-type allele. Here, we examined the correlation between DNA mutation allele frequency and RNA mutation allele frequency. We sequenced the exome and transcriptome of tumor cell lines with large copy number variations, identified heterozygous single nucleotide mutations and absolute DNA copy number and determined the corresponding DNA and RNA mutation allele fraction. We found that 99% of the DNA mutations in expressed genes are expressed as RNA. Moreover, we found a high correlation between the DNA and RNA mutation allele frequency. Exceptions are mutations that cause premature termination codons and therefore activate nonsense-mediated decay. Beyond this, we did not find evidence of any wide-scale mechanism, such as allele-specific epigenetic silencing, preferentially promoting mutated or wild-type alleles. In conclusion, our data strongly suggest that genes are equally transcribed from all alleles, mutated and wild-type and thus transcribed in proportion to their DNA allele frequency.

Inflammation influences iron balance in the whole organism. A common clinical manifestation of these changes is anemia of chronic disease (ACD; also called anemia of inflammation). Inflammation reduces duodenal iron absorption and increases macrophage iron retention, resulting in low serum iron concentrations (hyposideremia). Despite the protection hyposideremia provides against proliferating microorganisms, this 'iron withholding' reduces the iron available to maturing red blood cells and eventually contributes to the development of anemia 1 . Hepcidin antimicrobial peptide (Hamp) is a hepatic defensin-like peptide hormone 2 , 3 that inhibits duodenal iron absorption and macrophage iron release 4 , 5 , 6 , 7 . Hamp is part of the type II acute phase response 8 and is thought to have a crucial regulatory role in sequestering iron in the context of ACD 7 , 9 . Mice with deficiencies in the hemochromatosis gene product, Hfe, mounted a general inflammatory response after injection of lipopolysaccharide but lacked appropriate Hamp expression and did not develop hyposideremia. These data suggest a previously unidentified role for Hfe in innate immunity and ACD.

The t(8;21) chromosomal translocation activates aberrant expression of the AML1‐ETO (AE) fusion protein and is commonly associated with core binding factor acute myeloid leukaemia (CBF AML). Combining a conditional mouse model that closely resembles the slow evolution and the mosaic AE expression pattern of human t(8;21) CBF AML with global transcriptome sequencing, we find that disease progression was characterized by two principal pathogenic mechanisms. Initially, AE expression modified the lineage potential of haematopoietic stem cells (HSCs), resulting in the selective expansion of the myeloid compartment at the expense of normal erythro‐ and lymphopoiesis. This lineage skewing was followed by a second substantial rewiring of transcriptional networks occurring in the trajectory to manifest leukaemia. We also find that both HSC and lineage‐restricted granulocyte macrophage progenitors (GMPs) acquired leukaemic stem cell (LSC) potential being capable of initiating and maintaining the disease. Finally, our data demonstrate that long‐term expression of AE induces an indolent myeloproliferative disease (MPD)‐like myeloid leukaemia phenotype with complete penetrance and that acute inactivation of AE function is a potential novel therapeutic option.

Autosomal dominant chronic mucocutaneous candidiasis (CMC) is a severe immunodeficiency characterized by mucosal infections with fungi. Mutations affecting a particular region of the STAT1 signaling protein cause autosomal dominant CMC, probably through denting of the responses of type 1 and type 17 helper T cells. Chronic mucocutaneous candidiasis (CMC) is a primary immunodeficiency disorder that is characterized by susceptibility to infection of the skin, nails, and mucous membranes by candida species and dermatophytes. 1 There are several CMC subtypes: autosomal recessive autoimmune polyendocrinopathy candidiasis with ectodermal dystrophy (APECED), autosomal dominant CMC with or without thyroid disease, and autosomal recessive, isolated CMC. The defect in APECED resides in the autoimmune regulator AIRE, which has a key role in immunotolerance. 2 The susceptibility to candida in patients with APECED is attributed to autoantibodies to interleukin-17 and interleukin-22, 3 since type 17 helper T cells (Th17) are crucial for mucosal antifungal . . .