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OBJECTIVE: B cells are important in the pathogenesis of primary Sjögren's syndrome (pSS). Patients positive for SSA/SSB autoantibodies are more prone to systemic disease manifestations and adverse outcomes. We aimed to determine the role of B cell composition, gene expression and B cell receptor (BCR) usage in pSS subgroups stratified for SSA/SSB antibodies. METHODS: Over 230 000 B cells were isolated from peripheral blood of pSS patients (n = 6 SSA-, n = 8 SSA+ single positive and n = 10 SSA/SSB+ double positive) and four healthy controls, and processed for single cell RNA and VDJ sequencing. RESULTS: We show that SSA/SSB+ patients present the highest and lowest proportion of naïve and memory B cells respectively, and the highest upregulation of interferon-induced genes across all B cell subtypes. Differential usage of IGHV showed that IGHV1-69 and IGHV4-30-4 were more often used in all pSS subgroups compared with controls. Memory B cells from SSA/SSB+ patients displayed a higher proportion of cells with unmutated VDJ transcripts compared to other pSS patient groups and controls, indicating altered somatic hypermutation processes. Comparison with previous studies revealed heterogeneous clonotype pools, with little overlap in CDR3 sequences. Joint analysis using scRNA-seq and scVDJ-seq data allowed unsupervised stratification pSS patients, and identified novel parameters that correlated to disease manifestations and antibody status. CONCLUSION: We describe heterogeneity and molecular characteristics in B cells from pSS patients, providing clues to intrinsic differences in B cells that affect the phenotype and outcome, allowing stratification of pSS patients at improved resolution. This article is protected by copyright. All rights reserved.

Functional precision medicine (FPM) aims to optimize patient-specific drug selection based on the unique characteristics of their cancer cells. Recent advancements in high throughput ex vivo drug profiling have accelerated interest in FPM. Here, we present a proof-of-concept study for an integrated experimental system that incorporates ex vivo treatment response with a single-cell gene expression output enabling barcoding of several drug conditions in one single-cell sequencing experiment. We demonstrate this through a proof-of-concept investigation focusing on the glucocorticoid-resistant acute lymphoblastic leukemia (ALL) E/R+ Reh cell line. Three different single-cell transcriptome sequencing (scRNA-seq) approaches were evaluated, each exhibiting high cell recovery and accurate tagging of distinct drug conditions. Notably, our comprehensive analysis revealed variations in library complexity, sensitivity (gene detection), and differential gene expression detection across the methods. Despite these differences, we identified a substantial transcriptional response to fludarabine, a highly relevant drug for treating high-risk ALL, which was consistently recapitulated by all three methods. These findings highlight the potential of our integrated approach for studying drug responses at the single-cell level and emphasize the importance of method selection in scRNA-seq studies. Finally, our data encompassing 27 327 cells are freely available to extend to future scRNA-seq methodological comparisons.

Acute lymphoblastic leukemia (ALL) is the most prevalent cancer in children, and despite considerable progress in treatment outcomes, relapses still pose significant risks of mortality and long-term complications. To address this challenge, we employed a supervised machine learning technique, specifically random survival forests, to predict the risk of relapse and mortality using array-based DNA methylation data from a cohort of 763 pediatric ALL patients treated in Nordic countries. The relapse risk predictor (RRP) was constructed based on 16 CpG sites, demonstrating c-indexes of 0.667 and 0.677 in the training and test sets, respectively. The mortality risk predictor (MRP), comprising 53 CpG sites, exhibited c-indexes of 0.751 and 0.754 in the training and test sets, respectively. To validate the prognostic value of the predictors, we further analyzed two independent cohorts of Canadian (n = 42) and Nordic (n = 384) ALL patients. The external validation confirmed our findings, with the RRP achieving a c-index of 0.667 in the Canadian cohort, and the RRP and MRP achieving c-indexes of 0.529 and 0.621, respectively, in an independent Nordic cohort. The precision of the RRP and MRP models improved when incorporating traditional risk group data, underscoring the potential for synergistic integration of clinical prognostic factors. The MRP model also enabled the definition of a risk group with high rates of relapse and mortality. Our results demonstrate the potential of DNA methylation as a prognostic factor and a tool to refine risk stratification in pediatric ALL. This may lead to personalized treatment strategies based on epigenetic profiling., Adrián Mosquera Orgueira and Olga Krali contributed equally to this work.

In this issue of Blood, Kroeze et al1 provide an in-depth analysis of the genomic and transcriptomic landscape of pediatric B-cell precursor lymphoblastic lymphoma (BCP-LBL). Pediatric B-cell precursor malignancies present in 2 distinct forms: BCP acute lymphoblastic leukemia (BCP-ALL), predominantly affecting the bone marrow and blood, and the less common BCP-LBL, characterized by its origin in extramedullary tissues and presentation as a solid tumor rather than widespread bone marrow involvement (see figure). Despite their morphologic and immunophenotypic similarities, most studies to date have focused on BCP-ALL, leaving BCP-LBL relatively understudied.

Introduction The suitability of whole-genome sequencing (WGS) as the sole method to detect clinically relevant genomic aberrations in B-cell acute lymphoblastic leukemia (ALL) was investigated with the aim of replacing current diagnostic methods. Methods For this purpose, we assessed the analytical performance of 150 bp paired-end WGS (90x leukemia/30x germline). A set of 88 retrospective B-cell ALL samples were selected to represent established ALL subgroups as well as ALL lacking stratifying markers by standard-of-care (SoC), so-called B-other ALL. Results Both the analysis of paired leukemia/germline (L/N)(n=64) as well as leukemia-only (L-only)(n=88) detected all types of aberrations mandatory in the current ALLTogether trial protocol, i.e., aneuploidies, structural variants, and focal copy-number aberrations. Moreover, comparison to SoC revealed 100% concordance and that all patients had been assigned to the correct genetic subgroup using both approaches. Notably, WGS could allocate 35 out of 39 B-other ALL samples to one of the emerging genetic subgroups considered in the most recent classifications of ALL. We further investigated the impact of high (90x; n=58) vs low (30x; n=30) coverage on the diagnostic yield and observed an equally perfect concordance with SoC; low coverage detected all relevant lesions. Discussion The filtration of the WGS findings with a short list of genes recurrently rearranged in ALL was instrumental to extract the clinically relevant information efficiently. Nonetheless, the detection of DUX4 rearrangements required an additional customized analysis, due to multiple copies of this gene embedded in the highly repetitive D4Z4 region. We conclude that the diagnostic performance of WGS as the standalone method was remarkable and allowed detection of all clinically relevant genomic events in the diagnostic setting of B-cell ALL.

Therapy-resistant disease is a major cause of death in patients with acute lymphoblastic leukemia (ALL). Activation of the MYB oncogene is associated with ALL and leads to uncontrolled neoplastic cell proliferation and blocked differentiation. Here, we used RNA-seq to study the clinical significance of MYB expression and MYB alternative promoter (TSS2) usage in 133 pediatric ALLs. RNA-seq revealed that all cases analyzed overexpressed MYB and demonstrated MYB TSS2 activity. qPCR analyses confirmed the expression of the alternative MYB promoter also in seven ALL cell lines. Notably, high MYB TSS2 activity was significantly associated with relapse (p = 0.007). Moreover, cases with high MYB TSS2 usage showed evidence of therapy-resistant disease with increased expression of ABC multidrug resistance transporter genes (e.g., ABCA2, ABCB5, and ABCC10) and enzymes catalyzing drug degradation (e.g., CYP1A2, CYP2C9, and CYP3A5). Elevated MYB TSS2 activity was further associated with augmented KRAS signaling (p < 0.05) and decreased methylation of the conventional MYB promoter (p < 0.01). Taken together, our results suggest that MYB alternative promoter usage is a novel potential prognostic biomarker for relapse and therapy resistance in pediatric ALL.

ntroduction While novel approaches for treatment of acute lymphoblastic leukemia (ALL) in children have resulted in significant improvements in survival rates, survivors are faced with potentially life-long challenges as a result of the highly toxic treatment and the outcome for patients who relapse remains poor. The current risk-adapted treatment for ALL in children includes multiple drugs administered sequentially or in combination, with little variation in timing or intensity. Factors that affect varying treatment response between patients and cellular mechanisms that underly response to individual drugs are still under exploration. In this study, we investigate the relationship between ex-vivo drug resistance, transcriptomes, epigenomes and clinical outcome, with the aim of better understanding underlying determinants of treatment response. Methods Pediatric patients diagnosed with B-ALL between 1992 and 2008 in the Nordic countries were included in the study (n = 598). For these patients, ex-vivo drug response to ten clinically used drugs was determined using the fluorometric microculture cytotoxicity assay (FMCA). Clinical data, including follow-up information (overall survival and relapse free survival) was available for all patients. Additionally, DNA-methylation (450K array, n = 383) and gene expression profiling (RNA-seq, n = 119) was performed on a subset of diagnostic patient samples. The patients were stratified into low, medium and high resistance groups based on ex-vivo drug response. The low and high resistance groups were compared with regard to differentially methylated CpG sites or expressed genes associated with individual drug response profiles. Results Overall survival was significantly lower for patients with higher ex-vivo drug resistance to dexamethasone, doxorubicin and thioguanine compared to patients that displayed sensitivity to these drugs (p < 0.05). Similarly, relapse free survival was s

Background: Whole-genome sequencing (WGS) and whole-transcriptome sequencing (WTS), with the ability to provide comprehensive genomic information, have become the focal point of research interest as novel techniques that can support precision diagnostics in routine clinical care of patients with various cancer types, including hematological malignancies. This national multi-center study, led by Genomic Medicine Sweden, aims to evaluate whether combined application of WGS and WTS (WGTS) is technically feasible and can be implemented as an efficient diagnostic tool in patients with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). In addition to clinical impact assessment, a health-economic evaluation of such strategy will be performed. Methods and Analysis: The study comprises four phases (i.e., retrospective, prospective, real-time validation, and follow-up) including approximately 700 adult and pediatric Swedish AML and ALL patients. Results of WGS for tumor (90×) and normal/germline (30×) samples as well as WTS for tumors only will be compared to current standard of care diagnostics. Primary study endpoints are diagnostic efficiency and improved diagnostic yield. Secondary endpoints are technical and clinical feasibility for routine implementation, clinical utility, and health-economic impact. Discussion: Data from this national multi-center study will be used to evaluate clinical performance of the integrated WGTS diagnostic workflow compared with standard of care. The study will also elucidate clinical and health-economic impacts of a combined WGTS strategy when implemented in routine clinical care., On behalf of the Clinical Genomics Platform at SciLifeLab and Genomic Medicine Sweden.Funding: This work was supported by Vinnova, the Swedish Innovation Agency, the Swedish Childhood Cancer Fund, the Swedish Cancer Society, the Swedish Research Council, regional ALF funding from Region Skåne, Region Västra Götaland, Karolinska Institutet, and Karolinska University Hospital. The sequencing of the retrospective cohort was funded by the SciLifeLab National Projects (NP00043).

BACKGROUND: Ovarian cancer is the eighth most common cancer among women and has a 5-year survival of only 30-50%. The survival is close to 90% for patients in stage I but only 20% for patients in stage IV. The presently available biomarkers have insufficient sensitivity and specificity for early detection and there is an urgent need to identify novel biomarkers. METHODS: We employed the Explore PEA technology for high-precision analysis of 1463 plasma proteins and conducted a discovery and replication study using two clinical cohorts of previously untreated patients with benign or malignant ovarian tumours (N = 111 and N = 37). RESULTS: The discovery analysis identified 32 proteins that had significantly higher levels in malignant cases as compared to benign diagnoses, and for 28 of these, the association was replicated in the second cohort. Multivariate modelling identified three highly accurate models based on 4 to 7 proteins each for separating benign tumours from early-stage and/or late-stage ovarian cancers, all with AUCs above 0.96 in the replication cohort. We also developed a model for separating the early-stage from the late-stage achieving an AUC of 0.81 in the replication cohort. These models were based on eleven proteins in total (ALPP, CXCL8, DPY30, IL6, IL12, KRT19, PAEP, TSPAN1, SIGLEC5, VTCN1, and WFDC2), notably without MUCIN-16. The majority of the associated proteins have been connected to ovarian cancer but not identified as potential biomarkers. CONCLUSIONS: The results show the ability of using high-precision proteomics for the identification of novel plasma protein biomarker candidates for the early detection of ovarian cancer.

Formalin-fixed, paraffin-embedded (FFPE) specimens are an underutilized resource in medical research, particularly in the setting of transcriptome sequencing, as RNA from these samples is often degraded. We took advantage of an exome capture-based RNA-sequencing protocol to explore global gene expression in paired fresh–frozen (FF) and FFPE samples from 16 diffuse large B-cell lymphoma (DLBCL) patients. While FFPE samples generated fewer mapped reads compared to their FF counterparts, these reads captured the same library complexity and had a similar number of genes expressed on average. Furthermore, gene expression demonstrated a high correlation when comparing housekeeping genes only or across the entire transcriptome (r = 0.99 for both comparisons). Differences in gene expression were primarily seen in lowly expressed genes and genes with small or large coding sequences. Using cell-of-origin classifiers and clinically relevant gene expression signatures for DLBCL, FF, and FFPE samples from the same biopsy paired nearly perfectly in clustering analysis. This was further confirmed in a validation cohort of 50 FFPE DLBCL samples. In summary, we found the biological differences between tumors to be far greater than artifacts created as a result of degraded RNA. We conclude that exome capture transcriptome sequencing data from archival samples can confidently be used for cell-of-origin classification of DLBCL samples.

Personalised medicine (PM) presents a great opportunity to improve the future of individualised healthcare. Recent advances in -omics technologies have led to unprecedented efforts characterising the biology and molecular mechanisms that underlie the development and progression of a wide array of complex human diseases, supporting further development of PM. This article reflects the outcome of the 2021 EATRIS-Plus Multi-omics Stakeholder Group workshop organised to 1) outline a global overview of common promises and challenges that key European stakeholders are facing in the field of multi-omics research, 2) assess the potential of new technologies, such as artificial intelligence (AI), and 3) establish an initial dialogue between key initiatives in this space. Our focus is on the alignment of agendas of European initiatives in multi-omics research and the centrality of patients in designing solutions that have the potential to advance PM in long-term healthcare strategies.

B-cell lineage acute lymphoblastic leukaemia (B-ALL) is the most common paediatric malignancy. Transcription factor B-cell lymphoma 6 (BCL6) is essential to germinal centre formation and antibody affinity maturation and plays a major role in mature B-cell malignancies. More recently, it was shown to act as a critical downstream regulator in pre-BCR+ B-ALL. We investigated the expression of the BCL6 protein in a population-based cohort of paediatric B-ALL cases and detected moderate to strong positivity through immunohistochemistry in 7% of cases (8/117); however, only two of eight BCL6 cases (25%) co-expressed the ZAP70 protein. In light of these data, the subtype with active pre-BCR signalling constitutes a rare entity in paediatric B-ALL. In three independent larger cohorts with gene expression data, high BCL6 mRNA levels were associated with the TCF3-PBX1, Ph-like, NUTM1, MEF2D and PAX5-alt subgroups and the ‘metagene’ signature for pre-BCR-associated genes. However, higher-than-median BCL6 mRNA level alone was associated with favourable event free survival in the Nordic paediatric cohort, indicating that using BCL6 as a diagnostic marker requires careful design, and evaluation of protein level is needed alongside the genetic or transcriptomic data.

Tumor-normal paired DNA samples from a breast cancer cell line and a matched lymphoblastoid cell line enable calibration of clinical sequencing pipelines and benchmarking 'tumor-only' or 'matched tumor-normal' analyses. 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.