European Genome-phenome Archive Research Articles

PESSA: A web tool for pathway enrichment score-based survival analysis in cancer.

The activation levels of biologically significant gene sets are emerging tumor molecular markers and play an irreplaceable role in the tumor research field; however, web-based tools for prognostic analyses using it as a tumor molecular marker remain scarce. We developed a web-based tool PESSA for survival analysis using gene set activation levels. All data analyses were implemented via R. Activation levels of The Molecular Signatures Database (MSigDB) gene sets were assessed using the single sample gene set enrichment analysis (ssGSEA) method based on data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), The European Genome-phenome Archive (EGA) and supplementary tables of articles. PESSA was used to perform median and optimal cut-off dichotomous grouping of ssGSEA scores for each dataset, relying on the survival and survminer packages for survival analysis and visualisation. PESSA is an open-access web tool for visualizing the results of tumor prognostic analyses using gene set activation levels. A total of 238 datasets from the GEO, TCGA, EGA, and supplementary tables of articles; covering 51 cancer types and 13 survival outcome types; and 13,434 tumor-related gene sets are obtained from MSigDB for pre-grouping. Users can obtain the results, including Kaplan-Meier analyses based on the median and optimal cut-off values and accompanying visualization plots and the Cox regression analyses of dichotomous and continuous variables, by selecting the gene set markers of interest. PESSA (https://smuonco.shinyapps.io/PESSA/ OR http://robinl-lab.com/PESSA) is a large-scale web-based tumor survival analysis tool covering a large amount of data that creatively uses predefined gene set activation levels as molecular markers of tumors.

Comprehensive pan-cancer analysis: essential role of ABCB family genes in cancer.

The adenosine triphosphate-binding-cassette (ABC) transporter orchestrates the transmembrane transport of diverse substrates with the aid of ATP as an energy source. ABC transporter constitutes a widespread superfamily of transporters prominently present on the cellular membrane of organisms. Advancements in understanding have unveiled additional roles beyond mere intracellular or extracellular transport functions for the ABC protein family, encompassing involvement in DNA repair, protein translation, and gene expression regulation. Yet its role in tumors is still unknown. This study drew support from multiple databases, including Gene Expression Omnibus (GEO), European Genome-phenome Archive (EGA), The Cancer Genome Atlas (TCGA), and employed multidimensional bioinformatics analyses, incorporating online databases and the R-project. Through a comprehensive analysis, we seek to discern transcriptional-level disparities among genes and their consequential impacts on prognosis, tumor microenvironment (TME), stemness score, immune subtypes, clinical characteristics, and drug sensitivity across human cancers. ABC transporter subfamily B (ABCB) family genes exhibited heightened expression across diverse tumors, demonstrating a significant correlation with overall prognosis in pan-cancer contexts. Notably, gene expression levels manifested substantial associations with TME, stemness score, immune subtypes, clinical characteristics, and drug sensitivity in specific cancers, including kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), and pancreatic adenocarcinoma (PAAD). Within this subset, transporter associated with antigen processing 1 (TAP1), TAP2, and ABCB6 emerged as noteworthy oncogenes. The outcomes of this study contribute to a comprehensive understanding of the implications of ABCB family genes in tumor progression, offering insights into potential therapeutic targets for cancer. Notably, the identification of ABCB6 as a significant oncogene suggests promising avenues for targeted therapies in KIRP, LIHC, and PAAD.

FAIR data retrieval for sensitive clinical research data in Galaxy.

In clinical research, data have to be accessible and reproducible, but the generated data are becoming larger and analysis complex. Here we propose a platform for Findable, Accessible, Interoperable, and Reusable (FAIR) data access and creating reproducible findings. Standardized access to a major genomic repository, the European Genome-Phenome Archive (EGA), has been achieved with API services like PyEGA3. We aim to provide a FAIR data analysis service in Galaxy by retrieving genomic data from the EGA and provide a generalized "omics" platform for FAIR data analysis. To demonstrate this, we implemented an end-to-end Galaxy workflow to replicate the findings from an RD-Connect synthetic dataset Beyond the 1 Million Genomes (synB1MG) available from the EGA. We developed the PyEGA3 connector within Galaxy to easily download multiple datasets from the EGA. We added the gene.iobio tool, a diagnostic environment for precision genomics, to Galaxy and demonstrate that it provides a more dynamic and interpretable view for trio analysis results. We developed a Galaxy trio analysis workflow to determine the pathogenic variants from the synB1MG trios using the GEMINI and gene.iobio tool. The complete workflow is available at WorkflowHub, and an associated tutorial was created in the Galaxy Training Network, which helps researchers unfamiliar with Galaxy to run the workflow. We showed the feasibility of reusing data from the EGA in Galaxy via PyEGA3 and validated the workflow by rediscovering spiked-in variants in synthetic data. Finally, we improved existing tools in Galaxy and created a workflow for trio analysis to demonstrate the value of FAIR genomics analysis in Galaxy.

Inflammatory response signature score model for predicting immunotherapy response and pan-cancer prognosis

BackgroundInflammatory responses influence the outcome of immunotherapy and tumorigenesis by modulating host immunity. However, systematic inflammatory response assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers remain unexplored. Here, we investigated an inflammatory response score model to predict CIT responses and patient survival in a pan-cancer analysis. MethodsWe retrieved 12 CIT response gene expression datasets from the Gene Expression Omnibus database (GSE78220, GSE19423, GSE100797, GSE126044, GSE35640, GSE67501, GSE115821 and GSE168204), Tumor Immune Dysfunction and Exclusion database (PRJEB23709, PRJEB25780 and phs000452.v2.p1), European Genome-phenome Archive database (EGAD00001005738), and IMvigor210 cohort. The tumor samples from six cancers types: metastatic urothelial cancer, metastatic melanoma, gastric cancer, primary bladder cancer, renal cell carcinoma, and non-small cell lung cancer.We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm. FindingsThe model had high predictive accuracy in both the training and validation cohorts. During sub-group analysis, area under the curve (AUC) values of 0.82, 0.80, 0.71, 0.7, 0.67, and 0.64 were obtained for the non-small cell lung cancer, gastric cancer, metastatic urothelial cancer, primary bladder cancer, metastatic melanoma, and renal cell carcinoma cohorts, respectively. CIT response rates were higher in the high-scoring training cohort subjects (51%) than the low-scoring subjects (27%). The five-year survival rates in the high- and low score groups of the training cohorts were 62% and 21%, respectively, while those of the validation cohorts were 54% and 22%, respectively (P < 0·001 in all cases). Inflammatory response signature score derived from on-treatment tumor specimens are highly predictive of response to CIT in patients with metastatic melanoma. A significant correlation was observed between the inflammatory response scores and tumor purity. Regardless of the tumor purity, patients in the low score group had a significantly poorer prognosis than those in the high score group. Immune cell infiltration analysis indicated that in the high score cohort, tumor-infiltrating lymphocytes were significantly enriched, particularly effector and natural killer cells. Inflammatory response scores were positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may have benefited patients with high scores. Analysis of signature scores across different cancer types from The Cancer Genome Atlas revealed that the prognostic performance of inflammatory response scores for survival in patients who have not undergone immunotherapy can be affected by tumor purity. Interleukin 21 (IL21) had the highest weight in the inflammatory response model, suggesting its vital role in the prediction mode. Since the number of metastatic melanoma patients (n = 429) was relatively large among CIT cohorts, we further performed a co-culture experiment using a melanoma cell line and CD8 + T cell populations generated from peripheral blood monocytes. The results showed that IL21 therapy combined with anti-PD1 (programmed cell death 1) antibodies (trepril monoclonal antibodies) significantly enhanced the cytotoxic activity of CD8 + T cells against the melanoma cell line. ConclusionIn this study, we developed an inflammatory response gene signature model that predicts patient survival and immunotherapy response in multiple malignancies. We further found that the predictive performance in the non-small cell lung cancer and gastric cancer group had the highest value among the six different malignancy subgroups. When compared with existing signatures, the inflammatory response gene signature scores for on-treatment samples were more robust predictors of the response to CIT in metastatic melanoma.

BurkHostGEN: a study protocol for evaluating variations in the Burkholderia pseudomallei and host genomes associated with melioidosis infection.

Melioidosis is a frequently fatal disease caused by an environmental bacterium Burkholderia pseudomallei. The disease is prevalent in northeast Thailand, particularly among rice field farmers who are at risk of bacterial exposure through contact with contaminated soil and water. However, not all exposure results in disease, and infection can manifest diverse outcomes. We postulate that genetic factors, whether from the bacterium, the host or the combination of both, may influence disease outcomes. To address this hypothesis, we aim to collect, sequence, and analyse genetic data from melioidosis patients and controls, along with isolates of B. pseudomallei obtained from patients. Additionally, we will study the metagenomics of the household water supply for both patients and controls, including the presence of B. pseudomallei. BurkHostGEN is an ongoing observational study being conducted at Sunpasitthiprasong Hospital, Ubon Ratchathani, Thailand. We are obtaining consent from 600 melioidosis patients and 700 controls, spanning both sexes, to collect 1 mL of blood for host DNA analysis, 3 mL of blood for RNA analysis, as well as 5 L of household water supply for metagenomic analysis. Additionally, we are isolating B. pseudomallei from the melioidosis patients to obtain bacterial DNA. This comprehensive approach will allow us to identify B. pseudomallei and their paired host genetic factors associated with disease acquisition and severity. Ethical approvals have been obtained for BurkHostGEN. Host and bacterial genetic data will be uploaded to European Genome-Phenome Archive (EGA) and European Nucleotide Archive (ENA), respectively. BurkHostGEN holds the potential to discover bacterial and host genetic factors associated with melioidosis infection and severity of illness. It can also support various study designs, including biomarker validation, disease pathogenesis, and epidemiological analysis not only for melioidosis but also for other infectious diseases.

German Human Genome-Phenome Archive in an International Context: Toward a Federated Infrastructure for Managing and Analyzing Genomics and Health Data

With increasing numbers of human omics data, there is an urgent need for adequate resources for data sharing while also standardizing and harmonizing data processing. As part of the National Research Data Infrastructure (NFDI), the German Human Genome-Phenome Archive (GHGA) strives to connect the data from German researchers and their institutions to the international landscape of genome research. To achieve this, GHGA partners up with international activities such as the federated European Genome-Phenome Archive (EGA) [1] and the recently funded European Genomic Data Infrastructure (GDI) project to enable participation in international studies while ensuring at the same time the proper protection of the sensitive patient data included in GHGA.

BurkHostGEN: a study protocol for evaluating variations in the Burkholderia pseudomallei and host genomes associated with melioidosis infection

Background: Melioidosis is a frequently fatal disease caused by an environmental bacterium Burkholderia pseudomallei. The disease is prevalent in northeast Thailand, particularly among rice field farmers who are at risk of bacterial exposure through contact with contaminated soil and water. However, not all exposure results in disease, and infection can have different infection outcomes. Our hypothesis is that the acquisition and outcomes of melioidosis may be influenced by genetic factors of the bacterium, the host, or a combination of both. To address this hypothesis, we aim to collect, sequence, and analyse genetic data from melioidosis patients and controls, along with isolates of B. pseudomallei obtained from patients. Additionally, we will study the metagenomics of the household water supply for both patients and controls, including the presence of B. pseudomallei. Methods: BurkHostGEN is an ongoing observational study being conducted at Sunpasitthiprasong Hospital, Ubon Ratchathani, Thailand. Weare obtaining consent from 600 melioidosis patients and 700 controls, spanning both sexes, to collect 1 mL of blood for host DNA analysis, 3 mL of blood for RNA analysis, as well as 5 L of household water supply for metagenomic analysis. Additionally, we are isolating B. pseudomallei from the melioidosis patients to obtain bacterial DNA. This comprehensive approach will allow us to identify B. pseudomallei and their paired host genetic factors associated with disease acquisition and severity. Ethical approvals have been obtained for BurkHostGEN. Host and bacterial genetic data will be uploaded to European Genome-Phenome Archive (EGA) and European Nucleotide Archive (ENA), respectively. Conclusions: BurkHostGEN holds the potential to discover bacterial and host genetic factors associated with melioidosis infection and severity of illness. It can also support various study designs, including biomarker validation, disease pathogenesis, and epidemiological analysis not only for melioidosis but also for other infectious diseases.

Identification of age- and immune-related gene signatures for clinical outcome prediction in lung adenocarcinoma.

The understanding of the factors causing decreased overall survival (OS) in older patients compared to younger patients in lung adenocarcinoma (LUAD) remains. Gene expression profiles of LUAD were obtained from publicly available databases by Kaplan-Meier analysis was performed to determine whether age was associated with patient OS. The immune cell composition in the tumor microenvironment (TME) was evaluated using CIBERSORT. The fraction of stromal and immune cells in tumor samples were also using assessed using multiple tools including ESTIMATE, EPIC, and TIMER. Differentially expressed genes (DEGs) from the RNA-Seq data that were associated with age and immune cell composition were identified using the R package DEGseq. A 22-gene signature composed of DEGs associated with age and immune cell composition that predicted OS were constructed using Least Absolute Shrinkage and Selection Operator (LASSO). In The Cancer Genome Atlas (TCGA)-LUAD dataset, we found that younger patients (≤70) had a significant better OS compared to older patients (>70). In addition, older patients had significantly higher expression of immune checkpoint proteins including inhibitory T cell receptors and their ligands. Moreover, analyses using multiple bioinformatics tools showed increased immune infiltration, including CD4+ T cells, in older patients compared to younger patients. We identified a panel of genes differentially expressed between patients >70 years compared to those ≤70 years, as well as between patients with high or low immune scores and selected 84 common genes to construct a prognostic gene signature. A risk score calculated based on 22 genes selected by LASSO predicted 1, 3, and 5-year OS, with an area under the curve (AUC) of 0.72, 0.72, 0.69, receptively, in TCGA-LUAD dataset and an independent validation dataset available from the European Genome-phenome Archive (EGA). Our results demonstrate that age contributes to OS of LUAD patients atleast in part through its association with immune infiltration in the TME.

Comprehensive genetic screening of early-onset dementia patients in an Austrian cohort-suggesting new disease-contributing genes

Early-onset dementia (EOD), with symptom onset before age 65, has a strong genetic burden. Due to genetic and clinical overlaps between different types of dementia, whole-exome sequencing (WES) has emerged as an appropriate screening method for diagnostic testing and novel gene-finding approaches. We performed WES and C9orf72 repeat testing in 60 well-defined Austrian EOD patients. Seven patients (12%) carried likely disease-causing variants in monogenic genes, PSEN1, MAPT, APP, and GRN. Five patients (8%) were APOE4 homozygote carriers. Definite and possible risk variants were detected in the genes TREM2, SORL1, ABCA7 and TBK1. In an explorative approach, we cross-checked rare gene variants in our cohort with a curated neurodegeneration candidate gene list and identified DCTN1, MAPK8IP3, LRRK2, VPS13C and BACE1 as promising candidate genes. Conclusively, 12 cases (20%) carried variants relevant to patient counseling, comparable to previously reported studies, and can thus be considered genetically resolved. Reduced penetrance, oligogenic inheritance and not yet identified high-risk genes might explain the high number of unresolved cases. To address this issue, we provide complete genetic and phenotypic information (uploaded to the European Genome-phenome Archive), enabling other researchers to cross-check variants. Thereby, we hope to increase the chance of independently finding the same gene/variant-hit in other well-defined EOD patient cohorts, thus confirming new genetic risk variants or variant combinations.

Genetic and Microenvironment Features Do Not Distinguish Follicular Lymphoma Patients Requiring Immediate or Deferred Treatment.

Follicular lymphoma (FL) is the most common indolent non-Hodgkin lymphoma.1 Most patients present with asymptomatic lymphadenopathy, and only 20% present with B symptoms (night sweats, fevers, or unintentional weight loss).2 Although there is no evidence that overall survival (OS) improves if treatment is started before clinical symptoms or organ disfunction arises,3 some psychological benefit has been described for upfront treatment with rituximab.4 This latter benefit is considered insufficient to justify the side effects and cost of treatment, and therefore a Watch-and-Wait (W&W) approach is generally advised for asymptomatic patients with low tumor burden. The reported range of the W&W period before start of treatment is large (3–122 months, mean ± 24 months).4,5 Various tools have been developed to predict OS and failure-free survival, such as Follicular Lymphoma International Prognostic Index (FLIPI),2 and the M7-FLIPI.6 None of these tools is designed to predict at diagnosis, if the disease allows for a long W&W period before starting treatment. In this exploratory study, the aim of the Lunenburg Lymphoma Biomarker Consortium was to address whether genetic and microenvironmental features in diagnostic biopsy samples could differentiate which FL patients could be managed with a long W&W period (>5 years) versus those who required treatment immediately following diagnosis (based on clinical features). Stage III/IV, histologic grade 1–3A nodal FL cases as confirmed by central pathology review with complete clinical information at diagnosis and follow-up with availability of representative formalin-fixed paraffin-embedded diagnostic biopsy samples were included in this study in 2 subcohorts, representing the extremes of the clinical spectrum, with strictly applied inclusion criteria applied to patients from all sources, irrespective of sites and countries. For the W&W cohort, FL patients without any active treatment for ≥5 years after initial biopsy were included from German Low-Grade Lymphoma Study Group (GLSG, now German Lymphoma Alliance) GLSG2000 trial7 and population/institution-based registries; Haematological Malignancy Research network Registry,8 Swedish National Lymphoma Registry, St Bartholomew’s Hospital, London and Stanford University Medical Center, Stanford, USA. For the immediate treatment (IT) cohort FL patients from the above sources were supplemented with patients from the Lymphoma Study Association FL2000 study,9 with first-line treatment <3 months after initial diagnosis. To ensure that only patients were included that required immediate treatment for symptomatic disease, at least 2 of the following criteria were required for inclusion: high lactate dehydrogenase (LDH), tumor mass >7 cm, B symptoms, or hemoglobin (Hb) <10 g/dL (Suppl. Table S1: clinical study inclusion criteria; Suppl. Table S2A: cases per collaborator; Suppl. Table S2B: treatment modalities). Immunohistochemical (IHC) analysis for microenvironment-related biomarkers, included T-cell subsets (identified by CD8, CD4, CD3, PD1, and FOXP3) and macrophages (CD68, CD163), performed on tissue microarrays with semiautomated quantification by image analysis. Copy number aberrations (CNA) analysis was performed using the R-script QDNAseq (v1.12.0) and Gistic (v2.0) from 50-bp single read shallow whole genome sequencing data.10 Targeted next-generation sequencing panel for mutations (369 target genes) used for mutation and translocation analysis was conducted on 150-bp paired-end data of a 3 Mb SeqCapEZ capture panel (Roche NimbleGen, Madison, WI; order ID 43712) both sequenced on a HiSeq 4000 (Illumina, San Diego, CA)10 (see Suppl. Methods for more details). Patient’s clinical characteristics were summarized with descriptive statistics (median (range) for quantitative and frequency (percent) for qualitative variables) and compared using Chi-Square test and Mann-Whitney test. The IHC biomarker score used in the analysis was the average from 2 cores. Given the multiple tests performed, Bonferroni FWER correction was applied for the IHC biomarkers so the 2-sided α level was 0.05/7 = 0.007. Fisher exact test was used for frequencies of mutations and translocations. For comparisons between copy number regions, P values and false discovery rates (FDR) were calculated with comparative genomic hybridization test, which implements a Wilcoxon rank sum test. P values were not corrected for multiple testing, and FDR was controlled at 10% level for mutations and translocations. Statistical analysis was performed using various strategies in R (version 3.5.1) and SAS (SAS software version 9.4). The study and protocols to obtain human archival tissues and patient data were approved by the local ethical committee of the VU University Medical Center, Amsterdam (FWA00017598) and for all collaborating centers and complied with the Code for Proper Secondary Use of Human Tissue in the Netherlands (http://www.fmwv.nl. A total of 191 patients (W&W n = 66, IT n = 125) fulfilled the primary inclusion criteria. Of these, 159 patients had complete IHC data (W&W n = 60, IT n = 99) of which 90 patients with molecular data (W&W n = 44, IT n = 46) (Suppl. Figure S1). The median W&W period was 83 months (range 60–240 months). As a direct result of the pre-set inclusion criteria in this end-of-spectrum study, adverse parameters including >4 involved nodal sites, elevated LDH and Hb, bulky disease, B symptoms, and high FLIPI were overrepresented in the IT cohort (Table 1). It should be noted, that in the W&W cohort, 24% of the patients also presented with a high FLIPI. As expected, the differences in clinical presentation had an impact on 10 years OS W&W versus IT, 85% versus 63% (P = 0.001), underpinning the validity of patient selection (Suppl. Figure S2). W&W patients were primarily population-based patients, while IT patients were trial-derived. It is well known that patients treated in clinical trials have a better survival than age/stage matched real-world patients under similar treatment, but systematic tumor-biological differences between trial and real-world patients have not been described. Table 1 - Demographic and Clinical Characteristics From Time of Diagnose of Watch-and-Wait Versus Immediate Treatment Patients Included for Analysis in the Study Watch-and-Wait Immediate Treatment P Value (N = 60) (N = 99) Age at diagnosis 0.206 a Median years (range) 60.4 (28.4–85.5) 58.0 (25.3–83.3) Gender, n (%) 0.785 b Female 32 (53.3%) 55 (55.6%) Male 28 (46.7%) 44 (44.4%) Stage, n (%) 0.043 b III 31 (51.7%) 35 (35.4%) IV 29 (48.3%) 64 (64.6%) ECOG, n (%) 0.043 b ≤1 58 (98.3%) 88 (88.9%) >1 1 (1.7%) 11 (11.1%) Missing 1 0 FLIPI, n (%) <0.001 b High 13 (24.1%) 64 (67.4%) Intermediate 32 (59.3%) 25 (26.3%) Low 9 (16.7%) 6 (6.3%) Missing 6 4 B symptoms, n (%) <0.001 b Absent 54 (90.0%) 30 (30.3%) Present 6 (10.0%) 69 (69.7%) Bulky disease, n(%) <0.001 b <7 cm 56 (98.2%) 33 (34.4%) ≥7 cm 1 (1.8%) 63 (65.6%) Missing 3 3 Bone marrow involvement, n (%) 0.450 b No 28 (49.1%) 42 (42.9%) Yes 29 (50.9%) 56 (57.1%) Missing 3 1 Hemoglobin, n (%) 0.018 b <10 g/dL 0 (0.0%) 11 (11.5%) ≥10 g/dL 56 (100.0%) 85 (88.5%) Missing 4 3 Elevated LDH at diagnosis, n (%) <0.001 b No (≤ULN) 48 (88.9%) 33 (33.3%) Yes (>ULN) 6 (11.1%) 66 (66.7%) Missing 6 0 Number of nodal areas involved (Ann Arbor), n (%) 0.001 b 1 6 (10.3%) 1 (1.0%) 2 8 (13.9%) 14 (14.1%) 3 8 (13.9%) 10 (10.1%) 4 13 (22.4%) 7 (7.1%) >4 23 (39.7%) 67 (67.7%) Missing 2 0 aWilcoxon rank sum P value.bChi-square P value.ECOG = Eastern Cooperative Oncology Group; FLIPI = follicular lymphoma international prognostic index; LDH = lactate dehydrogenase; PS = performance score; ULN = upper limit of normal. IHC studies have shown conflicting results with regard to prognostic value of microenvironment-related biomarkers.11,12 We studied the composition of the immune microenvironment using T-cell and macrophage markers. No differences were seen for any of these cell population within the cohort with complete IHC data (W&W n = 60, IT n = 99) (Figure 1A, Suppl. Table S3A). While statistically significant on the 90-patient cohort, the absolute difference of CD68 (Suppl. Figure S3 and Suppl. Table S3B) was minor and insufficiently to have clinical relevance. Overall, the study was calibrated for a higher anticipated number of patients of 45 versus 250 for W&W versus IT patients with available samples to reach a 80% power to detect a difference of 0.65 standard deviation with a 2-sided corrected alpha threshold of 0.007 (=0.05/7 targeted biomarkers). Despite the lower sample size and resulting reduced power, the observed difference was significantly below the threshold of clinical applicability which indicate that this study had sufficient power to detect clinically relevant differences.Figure 1.: Microenvironment, mutations, translocations, and copy number landscape of Watch-and-Wait vs Immediate Treatment patients with follicular lymphoma. (A) For W&W (n = 60) and IT (n = 99) CD4, CD8, CD3, FOXP3, and PD1 are computer assisted scored and the percentage of positive nucleated cells of all nucleated cells are depicted as boxplots. CD163 and CD68 are computer assisted scored and the percentage of positive area of the total cell area scored are plotted in the boxplots. None of the markers show a significant difference. (B) Frequency of top 25 alterations including BLC2 and BLC6 translocations and mutated genes, W&W (n = 44) is depicted in yellow and IT (n = 46) in blue, no significant differences were found (P < 0.05, Fisher exact test and FDR using Benjamini&Hochberg method). (C) Comparison plots for CNAs between W&W (filled n = 44) and IT (line n = 46) depicted are percentages of the number of cases with gains (positive value red) and losses (negative value blue), sorted for chromosome position (x-axis). (D) P values (orange) calculated with a 2-sided rank sum test with 10,000 permutations and FDR (striped blue segments) of the difference in CNAs, the horizontal red dotted lines show the significance thresholds P value <0.05, and the FDR in blue <0.1. No significant differences were found. (E) Total number of nonsynonymous and splice-site mutations per patient are depicted in boxplots. With a median of 9 nonsynonymous and splice-site mutations (mean 9.52, range 2–22) in the W&W cohort (yellow) vs a median of 12.5 nonsynonymous and splice-site mutations (mean 13.07, range 4–32) in the IT cohort (blue), this is a significant difference, Wilcoxon test P value = 0.003. (F) Copy number load is depicted in boxplots. For the W&W cohort (yellow), a median of 7.79% (mean 13.22%) vs a median of 12.11% (mean 19.18%) for the IT cohort (blue), this is a significant difference, Wilcoxon test P value = 0.045. CNAs = copy number aberrations; FDR = false discover rates; IT = immediate treatment; W&W = Watch-and-Wait.Immune microenvironment interactions and genome alterations are considered complementary drivers of FL lymphomagenesis with impact on outcome and clinical course.13 In depth next generation sequencing (NGS) characterization showed a spectrum of genetic alterations consistent with previously published data in FL.6 The most frequent rearrangements in W&W (n = 44) versus IT (n = 46) were BCL2 (89% versus 98%) and BCL6 (21% versus 9%) translocations, KMT2D (66% versus 63%), CREBBP (61% versus 61%), TNFRSF14 (25% versus 30%), and EZH2 (16% versus 30%) mutations and somatic hypermutation (SHM) in BCL2 (Figure 1B, Suppl. Tables S4, S5, S6 and Suppl. Figure S4). No significant differences in the overall spectrum or in individual markers between the cohorts were observed. Specifically, no significant differences were observed for markers frequently associated with more aggressive clinical behavior in FL such as TP53 mutations (W&W 4.5% versus IT 4.2%) (Figure 1B, Suppl. Table S3). Similar results were seen in the W&W (n = 47) versus IT (n = 55) with only complete NGS data (data not shown). Analysis of CNAs showed that the most frequent losses found in chromosomes 1p36, 6q, 10q23 and gains of 1p, 2p, 6p, 7, 8, 12, and 18 (Figure 1C, D). Further investigation with GISTIC yielded candidate driver deletions in IT cohort: 6q16, 6q23.3, 8p23.3, 9p21.3 (containing CDKN2A), and 10q23.3 and in W&W cohort: 6q23.3, 8p23.3, and 10q23.3. Gains in IT cohort was 2p16.1, and in W&W cohort 1q24.2, 2p16.1, and 8q24.2 (Suppl. Figure S5). A systematic comparison showed no significant differences of CNA frequencies between both cohorts. Although significant differences were not seen at the level of individual mutated genes, structural alterations and CNAs, the overall number of nonsynonymous and splice-site mutations per case (median W&W 9 versus IT 12.5, P = 0.003) and the CNA load (median W&W 7.8% versus IT 12.1%, P = 0.045) were both significantly higher in the IT cohort. (Figure 1E, F). In a study by Mamessier et al14 similar lower mutational and CNA loads were observed and associated with so-called “early phase” FL (in situ follicular neoplasia, duodenal-type FL, and partial involvement) as compared with overt FL. The W&W cohort showed a similar lower mutational and CNA load which may suggest a common biology underlying the protracted clinical behavior. We compared FL patients in whom treatment was deferred for >5 years (W&W) from initial diagnosis to a cohort with an IT indication. Despite major clinical differences, we observed a remarkable similarity of all investigated microenvironmental and/or molecular features in the diagnostic biopsy samples. Although overall higher number of CD68+ macrophages, mutational and CNA loads were statistically significantly related to the need to start treatment within 3 months after diagnosis, the observed differences were minor and as such insufficient to provide a basis for decision making between a W&W versus IT approach in individual patients. Whether other tumor-related factors and/or constitutional patient characteristics may contribute to prediction of treatment timing needs further study.15 ACKNOWLEDGMENTS The authors thank all pathologists and pathology laboratories for providing tissue materials and data about patients who have been under their care. Matias Mendeville, Nathalie J. Hijmering, and Jurriaan Janssen for their help with performing the experiments and analysis and interpreted of the data. Prof Dr Anton Hagenbeek, the deceased founding father of the Lunenburg Lymphoma Biomarker Consortium. We thank the Hartwig Medical Foundation (Amsterdam, the Netherlands) for generating, analyzing, and providing access to reference whole genome sequencing data of the Netherlands population. AUTHOR CONTRIBUTIONS The Lunenburg Lymphoma Biomarker Consortium, MJK, BY, and DdJ designed the study; WBCS, GTL-dV, EvD, CLJ, AJC, PS performed experiments; WBCS, GTL-dV, EvD, CLJ, BS, AR, DMB, BY, and DdJ analyzed and interpreted the data; WBCS, GTL-dV, EvD, DMB, CLJ, BY, and DdJ wrote the manuscript; and all authors critically revised the manuscript and were involved in its editing and gave final approval of the submitted and published versions. The Lunenburg Lymphoma Biomarker Consortium (LLBC) is a collaboration of 9 international lymphoma research groups, each represented by one or more clinicians and hematopathologists and some also by one or more statisticians. Foundation of the LLBC was made possible with a grant from the van Vlissingen Lymphoma Foundation. EORTC Lymphoma group: Daphne de Jong, John Raemaekers. HOVON Lymphoma group: Daphne de Jong, Marie José Kersten. LYSA: Philippe Gaulard, Gilles Salles, Luc Xerri. Delphine Maucort-Boulch, Carole Langois-Jacques. British Columbia Cancer Agency: Laurie H. Sehn, David W. Scott. GLA: Andreas Rosenwald, Wolfram Klapper, Christian Buske, Wolfgang Hiddemann, Eva Hoster. Nordic lymphoma group: Birgitta Sander, Eva Kimby. Barts Cancer Institute: Maria Calaminici, John Gribben, Andrew J. Clear. Haematological Malignancy Research Network: Catherine Burton, Reuben M. Tooze, Alexandra G. Smith. Stanford Cancer Institute: Yasodha Natkunam, Ranjana Advani. DATA AVAILABILITY All sequence data has been uploaded to the European Genome-phenome Archive (EGA; accession number EGAS00001005755). DISCLOSURES The authors have no conflicts of interest to disclose. SOURCES OF FUNDING This study was supported by the Dutch Cancer Society grants KWF 2015-7925 and by unrestricted grants from: van Vlissingen Lymfoom Fonds, Genentech/Roche, GlaxoSmithKline, Pfizer Pharma, Teva, Pharmaceuticals/Cephalon, Millennium Pharmaceuticals Inc and Celgene. Haematological Malignancy Research Network is funded by Cancer Research UK, grant numbers 29685; and Blood Cancer UK, grant number 1503. GTLdV, DdJ, BY, and MJK received funding from the Dutch Cancer Society grants KWF 2015-7925. WBCS, EvD, CL-J, and DM-J were funded by unrestricted grants from: van Vlissingen Lymfoom Fonds, Genentech/Roche, GlaxoSmithKline, Pfizer Pharma, Teva, Pharmaceuticals/Cephalon, Millennium Pharmaceuticals Inc and Celgene. AGS was funded by Cancer Research UK and Blood Cancer UK.

EGAsubmitter: A software to automate submission of nucleic acid sequencing data to the European Genome-phenome Archive.

Making raw data available to the research community is one of the pillars of Findability, Accessibility, Interoperability, and Reuse (FAIR) research. However, the submission of raw data to public databases still involves many manually operated procedures that are intrinsically time-consuming and error-prone, which raises potential reliability issues for both the data themselves and the ensuing metadata. For example, submitting sequencing data to the European Genome-phenome Archive (EGA) is estimated to take 1month overall, and mainly relies on a web interface for metadata management that requires manual completion of forms and the upload of several comma separated values (CSV) files, which are not structured from a formal point of view. To tackle these limitations, here we present EGAsubmitter, a Snakemake-based pipeline that guides the user across all the submission steps, ranging from files encryption and upload, to metadata submission. EGASubmitter is expected to streamline the automated submission of sequencing data to EGA, minimizing user errors and ensuring higher end product fidelity.

Short term starvation potentiates the efficacy of chemotherapy in triple negative breast cancer via metabolic reprogramming

BackgroundChemotherapy (CT) is central to the treatment of triple negative breast cancer (TNBC), but drug toxicity and resistance place strong restrictions on treatment regimes. Fasting sensitizes cancer cells to a range of chemotherapeutic agents and also ameliorates CT-associated adverse effects. However, the molecular mechanism(s) by which fasting, or short-term starvation (STS), improves the efficacy of CT is poorly characterized.MethodsThe differential responses of breast cancer or near normal cell lines to combined STS and CT were assessed by cellular viability and integrity assays (Hoechst and PI staining, MTT or H2DCFDA staining, immunofluorescence), metabolic profiling (Seahorse analysis, metabolomics), gene expression (quantitative real-time PCR) and iRNA-mediated silencing. The clinical significance of the in vitro data was evaluated by bioinformatical integration of transcriptomic data from patient data bases: The Cancer Genome Atlas (TCGA), European Genome-phenome Archive (EGA), Gene Expression Omnibus (GEO) and a TNBC cohort. We further examined the translatability of our findings in vivo by establishing a murine syngeneic orthotopic mammary tumor-bearing model.ResultsWe provide mechanistic insights into how preconditioning with STS enhances the susceptibility of breast cancer cells to CT. We showed that combined STS and CT enhanced cell death and increased reactive oxygen species (ROS) levels, in association with higher levels of DNA damage and decreased mRNA levels for the NRF2 targets genes NQO1 and TXNRD1 in TNBC cells compared to near normal cells. ROS enhancement was associated with compromised mitochondrial respiration and changes in the metabolic profile, which have a significant clinical prognostic and predictive value. Furthermore, we validate the safety and efficacy of combined periodic hypocaloric diet and CT in a TNBC mouse model.ConclusionsOur in vitro, in vivo and clinical findings provide a robust rationale for clinical trials on the therapeutic benefit of short-term caloric restriction as an adjuvant to CT in triple breast cancer treatment.

Mutated axon guidance gene PLXNB2 sustains growth and invasiveness of stem cells isolated from cancers of unknown primary.

The genetic changes sustaining the development of cancers of unknown primary (CUP) remain elusive. The whole-exome genomic profiling of 14 rigorously selected CUP samples did not reveal specific recurring mutation in known driver genes. However, by comparing the mutational landscape of CUPs with that of most other human tumor types, it emerged a consistent enrichment of changes in genes belonging to the axon guidance KEGG pathway. In particular, G842C mutation of PlexinB2 (PlxnB2) was predicted to be activating. Indeed, knocking down the mutated, but not the wild-type, PlxnB2 in CUP stem cells resulted in the impairment of self-renewal and proliferation in culture, as well as tumorigenic capacity in mice. Conversely, the genetic transfer of G842C-PlxnB2 was sufficient to promote CUP stem cell proliferation and tumorigenesis in mice. Notably, G842C-PlxnB2 expression in CUP cells was associated with basal EGFR phosphorylation, and EGFR blockade impaired the viability of CUP cells reliant on the mutated receptor. Moreover, the mutated PlxnB2 elicited CUP cell invasiveness, blocked by EGFR inhibitor treatment. In sum, we found that a novel activating mutation of the axon guidance gene PLXNB2 sustains proliferative autonomy and confers invasive properties to stem cells isolated from cancers of unknown primary, in EGFR-dependent manner.

Remote visualization of large-scale genomic alignments for collaborative clinical research and diagnosis of rare diseases

The Solve-RD project objectives include solving undiagnosed rare diseases (RD) through collaborative research on shared genome-phenome datasets. The RD-Connect Genome-Phenome Analysis Platform (GPAP), for data collation and analysis, and the European Genome-Phenome Archive (EGA), for file storage, are two key components of the Solve-RD infrastructure. Clinical researchers can identify candidate genetic variants within the RD-Connect GPAP and, thanks to the developments presented here as part of joint ELIXIR activities, are able to remotely visualize the corresponding alignments stored at the EGA. The Global Alliance for Genomics and Health (GA4GH) htsget streaming application programming interface (API) is used to retrieve alignment slices, which are rendered by an integrated genome viewer (IGV) instance embedded in the GPAP. As a result, it is no longer necessary for over 11,000 datasets to download large alignment files to visualize them locally. This work highlights the advantages, from both the user and infrastructure perspectives, of implementing interoperability standards for establishing federated genomics data networks.

Successful Retreatment With Venetoclax in a Patient With Chronic Lymphocytic Leukemia.

The B-cell lymphoma 2 (BCL2) specific inhibitor, venetoclax, has demonstrated remarkable clinical activity in the treatment of chronic lymphocytic leukemia (CLL), alone or in combination with CD20 antibodies, and is now standard of care for frontline and relapsed/refractory disease.1,2 Venetoclax promotes rapid CLL cell death in vitro in a tumor protein 53 (TP53) independent manner and in vivo with comparable overall response irrespective of TP53 mutational status.3,4 However, recent data suggest that loss of TP53 may impair venetoclax efficacy with suboptimal doses.5 Nevertheless, some patients relapse and optimal treatment strategies after venetoclax progression remain unclear, particularly if they have also relapsed on tyrosine kinase inhibitors. Acquired resistance mechanisms to venetoclax include mutations to BCL26 in CLL and the upregulation of alternative anti-apoptotic proteins myeloid leukemia 1 (MCL1) and/or B-cell lymphoma-extra large (BCLXL) in models of lymphoma.7 Venetoclax retreatment has been successful in CLL where patients have taken “treatment holidays” prior to disease progression.8 However, the outcome of retreatment following acquired resistance is unknown. Recent data have shown that for some CLL patients, mechanisms of resistance to multiple inhibitors may occur independently in mutually exclusive cells,9 suggesting retreatment with previously efficacious therapies may be effective in these cases. Here, we report the case of a CLL patient successfully retreated with venetoclax following prior venetoclax relapse and intermediate therapy with a combination of 2 B-cell receptor (BCR) inhibitors (BCRi). To our knowledge, this is the first time such a case has been reported. This study was approved by local Research Ethics Committee and the University Hospitals of Leicester Nation Health Service Trust (06/Q2501/122). Samples were obtained after written informed consent. Mononuclear cells were separated from whole blood or bone marrow using Ficoll histopaque (10771, Sigma-Aldrich). The proportion of CD19-positive B-cells was confirmed by flow cytometry using anti-human CD19-PE (982402, BioLegend, San Jose, CA). Cells were maintained in RPMI-1640 media (21870076, Gibco; Life Technologies) with 10% fetal calf serum (10270-106, Gibco). Treatment with venetoclax, BCLXL inhibitor A1331852, or MCL1 inhibitor S63845 (Selleck Chemicals, Houston, TX) was carried out 24 hours before analysis using CellTiter-Glo viability assay.10 Immunoblot was performed as previously described11 using antibodies: mouse anti-BCL-2 (M088701-2, Dako Agilent, Hamburg), rabbit anti-BCL-XL (2762S, Cell Signaling, Beverly, MA), rabbit anti-MCL-1 (5453S, Cell Signaling), rabbit anti-Bcl-2-like protein 11 (3183S, Cell Signaling), mouse anti-aTubulin (ab15895, Abcam, Cambridge, United Kingdom). Interphase fluorescence in situ hybridization (FISH) on mononuclear cells was performed as previously described.12 Details of FISH probes used are given in Suppl. Methods Table S1. DNA was extracted from mononuclear cells and bone marrow using DNeasy tissue kit (Qiagen, Crawley, United Kingdom) according to manufacturer’s instructions. Extracted DNA was used to prepare libraries for whole exome sequencing (WES) to assess somatic mutations and copy number variation (CNV) and hybridizing to a CytoScan HD Array (Affymetrix, Santa Clara, CA) for further CNV analysis. Details of sequencing, array and bioinformatics pipelines are provided in SDC Methods. WES data are available in the European Genome-Phenome Archive (EGA; https://ega-archive.org); EGA study accession number: EGAS00001006158. A 57-year-old lady with refractory CLL to Fludarabine, cyclophosphamide and rituximab, rituximab, cyclophosphamide, hydroxydaunorubicin hydrochloride (doxorubicin hydrochloride), vincristine (Oncovin) and prednisone, and high-dose methylprednisolone was referred to our center unwell with massive lymphadenopathy (Figure 1A-i). FISH analysis with the TP53/myeloperoxidase (MPO) probe showed a heterozygous deletion in 17p13 in 72% of interphase nuclei (Suppl. Table S1) and TP53 mutant disease [NM_000546.6(TP53):c.329G>T(p.Arg110Leu)] shown by WES. She was entered into a clinical trial assessing single-agent venetoclax in March 2014 (NCT01889186).Figure 1.: Clinical counts and in vitro analysis. (A), CT images taken before initial venetoclax treatment (i) and 14 wk later (ii) demonstrating clearance of CLL cells observed by reduction in lymphadenopathy. (B), WBC over the course of treatment. Samples used for in vitro studies are indicated with sample numbers (1–5) and times taken are indicated by dashed lines. Venetoclax treatment periods indicated in blue and BCRi indicated in green. (C), CLL cells collected at samples 1–5 (and a bone marrow sample taken at the same time as sample 4) were incubated with increasing concentrations of venetoclax for 24 h before analysis of cell viability by CellTiter-Glo assay (n = 3). (D), EC50 values of venetoclax treatment in samples 1–5, calculated using GraphPad Prism. (E), Rate of decrease of WBC over period of venetoclax dose escalation for both initial treatment and retreatment. (F), CLL cells collected at samples 1, 2, and 4 were incubated with increasing concentrations of A1331852 or S63845 for 24 h before analysis of cell viability by CellTiter-Glo assay (n = 2). (G), Immunoblot analysis of lysates prepared from PBMCs of samples 1, 2, and 4. BCL2 = B-cell lymphoma 2; BCLxL = B-cell lymphoma-extra large; BCRi = B-cell receptor inhibitor; BIM = Bcl-2-like 11; BM = bone marrow; CLL = chronic lymphocytic leukemia; CT = computerised tomography; CTG = CellTiter-Glo; EC50 = half maximal effective concentration; MCL1 = myeloid leukemia 1; MW = molecular weight; PBMC = peripheral blood mononuclear cells; WBC = white cell count.The patient began treatment with 20 mg venetoclax once daily (OD), escalating to 400 mg OD over 4 weeks. Clinical response was observed with a rapid decrease in white cell count (WBC) from 32.5 × 109/L to 2.4 × 109/L in 4 weeks (Figure 1A, Suppl. Table S2) with rapid resolution of lymphadenopathy (Figure 1A-ii). Venetoclax sensitivity was confirmed in vitro (sample 1, half maximal effective concentration 1.4 nM) (Figure 1C and D). The patient was treated with venetoclax for 21 months, with low-level (0.01%) disease remaining in the bone marrow, as assessed by multicolor flow cytometry. However, she subsequently progressed very rapidly in August 2015 while still on drug. Disease progression was associated with an increase in lymphocyte count from 17.7 × 109/L to 196.6 × 109/L in just 8 weeks. Clinical resistance was mirrored by an almost 1000-fold increased resistance to venetoclax in vitro (sample 2, EC50 9445 nM). She next received a combination of tirabrutinib (20 mg twice daily [BD]) and idelalisib (50 mg BD) (NCT02968563). She remained well without significant toxicity for 47 months, attaining stable partial remission. However, she developed pancytopenia due to progressive bone marrow infiltration in November 2019. At relapse to BCRi, there was no recurrence of the bulky lymphadenopathy observed prior to initial venetoclax therapy. In vitro analysis on sample 3 taken 11 months after starting BCRi treatment, while the patient was still responding, showed a partial regain of sensitivity to venetoclax (EC50 227.9 nM). Furthermore, blood and bone marrow samples taken at BCRi progression showed comparable venetoclax sensitivity as sample 1 (sample 4, EC50 1.3 nM) (Figure 1C and D). Based on these results, she was retreated with venetoclax in December 2019. Within 4 weeks, her WBC recovered with rapid clearance of CLL cells at a comparable rate to that observed initially (Figure 1E). There was a recovery of normal hematological indices and resolution of splenomegaly. Unfortunately, she died in April 2020 due to coronavirus disease 2019-associated pneumonitis. A timeline of WBC count with duration of treatments is presented (Figure 1B). To understand the causes of variable venetoclax sensitivities, WES was performed on the same CLL samples as in vitro venetoclax sensitivity analyses. A productive unmutated IGHV rearrangement (1–69), possible driver mutations in SF3B1, KMT2C, FANCA, and SPEN, and copy number alterations to chromosomes 4p16.3-p15.1, 5q34-q35.3, 7q21.11-q32.3, and Xq28-q28 were detected at comparable frequencies across all samples (Figure 2A and B, Suppl. Figures S1 and S2, Suppl. Tables S3 and S4). Differences observed between samples included the variant allele frequency variant allele frequency (VAF) of the loss-of-function TP53 R110L mutation 13 and the size of the chromosome 17p deletion. Prior to initial venetoclax treatment (sample 1), TP53 R110L had a VAF of 42.7%. CNV analysis demonstrated a heterozygous chromosome 17p13 deletion extending across the centromere (Figure 2C centromere (Figure 2C-i). At venetoclax relapse, TP53 R110L was not detected by WES (sample 2). CNV analysis at this time showed a TP53 focal copy number loss, and not the previously observed arm loss, extending from base pairs (bp) 7,168,109-7,593,927 (425,818bp, hg19), which was detected in all subsequent samples (Figure 2C-ii). This likely represents the emergence of a new subclone selected for during venetoclax exposure. This was not detected by FISH as the commercial probes used (TP53/MPO and locus specific identifier TP53) extended beyond the focal loss. Venetoclax resistance was not associated with detectable mutations in BCL2 or genes of BCL2 interacting proteins. Similar findings have been observed in the MURANO (venetoclax and rituxumab in relapsed/refractory CLL, NCT02005471) trial, where they observed a lack of acquired BCL2 mutations.14 In addition, there were no changes in protein expression of BCL2 or other anti-apoptotic proteins (Figure 1G), nor altered sensitivity to BCLXL or MCL1 specific inhibitors (Figure 1F).Figure 2.: Genomic analysis of PBMC samples. (A), WES was carried out on sequential PBMC samples, with VAFs of driver mutations plotted for each sample. (B), Whole Genome View generated from CytoScan HD Array CNV analysis of sample 1 (i) and sample 2 (ii), with regions of gain indicated by upward deflections and loss by downward deflections in the weighted Log2 ratio. LOH is presented by deviations from the 3 standard lines (0, 0.5, and 1) in the BAF. (C), CNV analysis of chromosome 17 generated from WES data of sample 1 (i) sample 2 (ii) with regions of gain indicated by upward deflections and loss by downward deflections presented as a Log R ratio (a normalized measure of the total signal intensity for 2 alleles). BAF = B-allele frequency; CNV = copy number variation; LOH = loss of heterozygosity; PBMC = peripheral blood mononuclear cells; VAF = variant allele frequency; WES = whole exome sequencing.After 11 months of BCRi therapy during clinical response (sample 3), TP53 R110L became detectable again at low level (VAF of 4.2%). This coincided with the increased in vitro venetoclax sensitivity observed. These changes in VAF occurred despite observing no altered VAFs in other driver mutations, allowing a distinction between the subclonal TP53 R110L mutation and other likely clonal driver mutations (Figure 2A, Suppl. Table S3). Unexpectedly the reemergence of TP53 R110L was not accompanied by the reemergence of the 17p13 deletion. Instead, the TP53 focal copy number loss remained, suggesting greater subclonal complexity than just 2 competing subclonal populations. At this point, no BTK inhibitor (BTKi)-associated BTK or PLCG2 mutations were detected. After another 36 months of therapy at BCRi disease progression (sample 4), BTK T474I (associated with BTKi resistance15) was detectable at a VAF of 16.9%. The VAF of TP53 R110L increased to 35.0% and remained stable until time of death. In summary, we have demonstrated that clonal evolution and genetic complexity in a patient with CLL allowed for sensitivity to venetoclax to be regained after initial venetoclax progression and subsequent prolonged BCRi therapy. The venetoclax-sensitive subclone could be characterized by WES, and its reemergence tracked throughout BCRi treatment. Retreatment with venetoclax was informed by in vitro drug testing,16 which may therefore have clinical utility to monitor and predict in vivo responses. We note that these observations were made during intermediate combination therapy of 2 BCRi, and it is unclear if similar clonal selection would occur with single-agent BCRi. This study is limited to observations in just 1 case of venetoclax progression. Retrospective in vitro analysis of CLL cases treated with sequential targeted therapies may help to provide further evidence of treatment resensitization in a larger patient cohort. In addition, there was only a short follow-up of 4 months after commencing venetoclax retreatment, so it remains unclear how durable and comparable to the initial venetoclax treatment the response would have been. These data suggest that consecutive exposure to different classes of targeted agents may allow the reemergence of drug-sensitive subclones, providing rationale for retreatment with previously efficacious therapies, or the use of combination therapies early to target multiple subclones with distinct sensitivities. ACKNOWLEDGMENTS We would like to thank the research nurses at the Hope Clinical Trial Unit, Leicester Royal Infirmary, as well as the staff of the tumor genetics laboratories of the Universities Kiel and Ulm. This research used the ALICE High Performance Computing Facility at the University of Leicester. AUTHOR CONTRIBUTIONS RAJ and VMS performed research. All authors involved in data analysis and interpretation. RAJ and VMS drafted the article that was revised and approved by all authors (article writing). MJSD and HSW designed and supervised the study. DISCLOSURES MJSD has received research funding from Gilead Sciences, and honorarium and travel grants from AbbVie. HSW has received research funding from Gilead Sciences, and honorarium and travel grants from AbbVie. All the other authors have no conflicts of interest to disclose. SOURCES OF FUNDING This work was supported by funds from the Scott Waudby Trust, the Hope Against Cancer charity, the University of Leicester, the Kay Kendall Leukemia Fund, Gilead Sciences, Inc, and the Deutsche Forschungsgemeinschaft through SFB1074 (project B10).

Claudin 18.2 is a potential therapeutic target for zolbetuximab in pancreatic ductal adenocarcinoma.

BACKGROUNDPancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed and treated in advanced tumor stages with poor prognosis. More effective screening programs and novel therapeutic means are urgently needed. Recent studies have regarded tight junction protein claudin 18.2 (CLDN18.2) as a candidate target for cancer treatment, and zolbetuximab (formerly known as IMAB362) has been developed against CLDN18.2. However, there are few data reported thus far related to the clinicopathological characteristics of CLDN18.2 expression for PDAC.AIMTo investigate the expression of CLDN18.2 in PDAC patients and subsequently propose a new target for the treatment of PDAC.METHODSThe Cancer Genome Atlas, Genotype-Tissue Expression, Gene Expression Omnibus, and European Genome-phenome Archive databases were first employed to analyze the CLDN18 gene expression in normal pancreatic tissue compared to that in pancreatic cancer tissue. Second, we analyzed the expression of CLDN18.2 in 93 primary PDACs, 86 para-cancer tissues, and 13 normal pancreatic tissues by immunohistochemistry. Immunostained tissues were assessed applying the histoscore. subsequently, they fell into two groups according to the expression state of CLDN18.2. Furthermore, the correlations between CLDN18.2 expression and diverse clinicopathological characteristics, including survival, were investigated.RESULTSThe gene expression of CLDN18 was statistically higher (P < 0.01) in pancreatic tumors than in normal tissues. However, there was no significant correlation between CLDN18 expression and survival in pancreatic cancer patients. CLDN18.2 was expressed in 88 (94.6%) of the reported PDACs. Among these tumors, 50 (56.8%) cases showed strong immunostaining. The para-cancer tissues were positive in 81 (94.2%) cases, among which 32 (39.5%) of cases were characterized for strong staining intensities. Normal pancreatic tissue was identified solely via weak immunostaining. Finally, CLDN18.2 expression significantly correlated with lymph node metastasis, distant metastasis, nerve invasion, stage, and survival of PDAC patients, while there was no correlation between CLDN18.2 expression and localization, tumor size, patient age and sex, nor any other clinicopathological characteristic. CONCLUSIONCLDN18.2 expression is frequently increased in PDAC patients. Thus, it may act as a potential therapeutic target for zolbetuximab in PDAC.

A quality control portal for sequencing data deposited at the European genome-phenome archive.

Since its launch in 2008, the European Genome–Phenome Archive (EGA) has been leading the archiving and distribution of human identifiable genomic data. In this regard, one of the community concerns is the potential usability of the stored data, as of now, data submitters are not mandated to perform any quality control (QC) before uploading their data and associated metadata information. Here, we present a new File QC Portal developed at EGA, along with QC reports performed and created for 1 694 442 files [Fastq, sequence alignment map (SAM)/binary alignment map (BAM)/CRAM and variant call format (VCF)] submitted at EGA. QC reports allow anonymous EGA users to view summary-level information regarding the files within a specific dataset, such as quality of reads, alignment quality, number and type of variants and other features. Researchers benefit from being able to assess the quality of data prior to the data access decision and thereby, increasing the reusability of data (https://ega-archive.org/blog/data-upcycling-powered-by-ega/).

SmMIP-tools: a computational toolset for processing and analysis of single-molecule molecular inversion probes-derived data.

MotivationSingle-molecule molecular inversion probes (smMIPs) provide an exceptionally cost-effective and modular approach for routine or large-cohort next-generation sequencing. However, processing the derived raw data to generate highly accurate variants calls remains challenging.ResultsWe introduce SmMIP-tools, a comprehensive computational method that promotes the detection of single nucleotide variants and short insertions and deletions from smMIP-based sequencing. Our approach delivered near-perfect performance when benchmarked against a set of known mutations in controlled experiments involving DNA dilutions and outperformed other commonly used computational methods for mutation detection. Comparison against clinically approved diagnostic testing of leukaemia patients demonstrated the ability to detect both previously reported variants and a set of pathogenic mutations that did not pass detection by clinical testing. Collectively, our results indicate that increased performance can be achieved when tailoring data processing and analysis to its related technology. The feasibility of using our method in research and clinical settings to benefit from low-cost smMIP technology is demonstrated.Availability and implementationThe source code for SmMIP-tools, its manual and additional scripts aimed to foster large-scale data processing and analysis are all available on github (https://github.com/abelson-lab/smMIP-tools). Raw sequencing data generated in this study have been submitted to the European Genome-Phenome Archive (EGA; https://ega-archive.org) and can be accessed under accession number EGAS00001005359.Supplementary information Supplementary data are available at Bioinformatics online.

The European Genome-phenome Archive in 2021.

The European Genome-phenome Archive (EGA - https://ega-archive.org/) is a resource for long term secure archiving of all types of potentially identifiable genetic, phenotypic, and clinical data resulting from biomedical research projects. Its mission is to foster hosted data reuse, enable reproducibility, and accelerate biomedical and translational research in line with the FAIR principles. Launched in 2008, the EGA has grown quickly, currently archiving over 4,500 studies from nearly one thousand institutions. The EGA operates a distributed data access model in which requests are made to the data controller, not to the EGA, therefore, the submitter keeps control on who has access to the data and under which conditions. Given the size and value of data hosted, the EGA is constantly improving its value chain, that is, how the EGA can contribute to enhancing the value of human health data by facilitating its submission, discovery, access, and distribution, as well as leading the design and implementation of standards and methods necessary to deliver the value chain. The EGA has become a key GA4GH Driver Project, leading multiple development efforts and implementing new standards and tools, and has been appointed as an ELIXIR Core Data Resource.

A Systematic Review of Transcriptional Dysregulation in Huntington's Disease Studied by RNA Sequencing.

Huntington’s disease (HD) is a chronic neurodegenerative disorder caused by an expansion of polyglutamine repeats in exon 1 of the Huntingtin gene. Transcriptional dysregulation accompanied by epigenetic alterations is an early and central disease mechanism in HD yet, the exact mechanisms and regulators, and their associated gene expression programs remain incompletely understood. This systematic review investigates genome-wide transcriptional studies that were conducted using RNA sequencing (RNA-seq) technology in HD patients and models. The review protocol was registered at the Open Science Framework (OSF). The biomedical literature and gene expression databases, PubMed and NCBI BioProject, Array Express, European Nucleotide Archive (ENA), European Genome-Phenome Archive (EGA), respectively, were searched using the defined terms specified in the protocol following the PRISMA guidelines. We conducted a complete literature and database search to retrieve all RNA-seq-based gene expression studies in HD published until August 2020, retrieving 288 articles and 237 datasets from PubMed and the databases, respectively. A total of 27 studies meeting the eligibility criteria were included in this review. Collectively, comparative analysis of the datasets revealed frequent genes that are consistently dysregulated in HD. In postmortem brains from HD patients, DNAJB1, HSPA1B and HSPB1 genes were commonly upregulated across all brain regions and cell types except for medium spiny neurons (MSNs) at symptomatic disease stage, and HSPH1 and SAT1 genes were altered in expression in all symptomatic brain datasets, indicating early and sustained changes in the expression of genes related to heat shock response as well as response to misfolded proteins. Specifically in indirect pathway medium spiny neurons (iMSNs), mitochondria related genes were among the top uniquely dysregulated genes. Interestingly, blood from HD patients showed commonly differentially expressed genes with a number of brain regions and cells, with the highest number of overlapping genes with MSNs and BA9 region at symptomatic stage. We also found the differential expression and predicted altered activity of a set of transcription factors and epigenetic regulators, including BCL6, EGR1, FOSL2 and CREBBP, HDAC1, KDM4C, respectively, which may underlie the observed transcriptional changes in HD. Altogether, our work provides a complete overview of the transcriptional studies in HD, and by data synthesis, reveals a number of common and unique gene expression and regulatory changes across different cell and tissue types in HD. These changes could elucidate new insights into molecular mechanisms of differential vulnerability in HD. Systematic Review Registration: https://osf.io/pm3wq