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Aims and background Whole-genome sequencing (WGS) is increasingly applied in clinical practice and expected to replace standard-of-care (SoC) genetic diagnostics in hematological malignancies. This study aims to assess and compare the fully burdened cost (‘micro-costing’) per patient for Swedish laboratories using WGS and SoC, respectively, in pediatric and adult patients with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). Methods The resource use and cost details associated with SoC, e.g. chromosome banding analysis, fluorescent in situ hybridization, and targeted sequencing analysis, were collected via activity-based costing methods from four diagnostic laboratories. For WGS, corresponding data was collected from two of the centers. A simulation-based scenario model was developed for analyzing the WGS cost based on different annual sample throughput to evaluate economy of scale. Results The average SoC total cost per patient was €2,465 for pediatric AML and €2,201 for pediatric ALL, while in adults, the corresponding cost was €2,458 for AML and €1,207 for ALL. The average WGS cost (90x tumor/30x normal; sequenced on the Illumina NovaSeq 6000 platform) was estimated to €3,472 based on an annual throughput of 2,500 analyses, however, with an annual volume of 7,500 analyses the average cost would decrease by 23% to €2,671. Conclusion In summary, WGS is currently more costly than SoC, however the cost can be reduced by utilizing laboratories with higher throughput and by the expected decline in cost of reagents. Our data provides guidance to decision-makers for the resource allocation needed when implementing WGS in diagnostics of hematological malignancies.

QC 20240301

QC 20240304

Next-generation sequencing (NGS) has been increasingly popular in genomics studies over the last decade and is now commonly used in clinical applications for precision diagnostics. Many disease areas typically involve different kinds of sample specimens, sample qualities and quantities. The quality of the DNA can range from intact, high molecular weight molecules to degraded, damaged and very short molecules. The differences in quality and quantity pose challenges for downstream molecular analyses. To overcome the challenge with the need of different molecular methods for different types of samples, we have developed a joint procedure for preparing enriched DNA libraries from high molecular weight DNA and DNA from formalin-fixed, paraffin-embedded tissue, fresh frozen tissue material, as well as cell-free DNA., QC 20240613

Gene panel sequencing has become a common diagnostic tool for detecting somatically acquired mutations in myeloid neoplasms. However, many panels have restricted content, provide insufficient sensitivity levels, or lack clinically validated workflows. We here describe the development and validation of the Genomic Medicine Sweden myeloid gene panel (GMS-MGP), a capture-based 191 gene panel including mandatory genes in contemporary guidelines as well as emerging candidates. The GMS-MGP displayed uniform coverage across all targets, including recognized difficult GC-rich areas. The validation of 117 previously described somatic variants showed a 100% concordance with a limit-of-detection of a 0.5% variant allele frequency (VAF), achieved by utilizing error correction and filtering against a panel-of-normals. A national interlaboratory comparison investigating 56 somatic variants demonstrated highly concordant results in both detection rate and reported VAFs. In addition, prospective analysis of 323 patients analyzed with the GMS-MGP as part of standard-of-care identified clinically significant genes as well as recurrent mutations in less well-studied genes. In conclusion, the GMS-MGP workflow supports sensitive detection of all clinically relevant genes, facilitates novel findings, and is, based on the capture-based design, easy to update once new guidelines become available. The GMS-MGP provides an important step toward nationally harmonized precision diagnostics of myeloid malignancies., QC 20240823

Purpose: Tumor classification is a key component in personalized cancer care. For soft-tissue and bone tumors, this classification is currently based primarily on morphology assessment and IHC staining. However, these standard-of-care methods can pose challenges for pathologists. We therefore assessed how whole-genome and whole-transcriptome sequencing (WGTS) impacted tumor classification and clinical management when interpreted together with histomorphology.Experimental Design: We prospectively evaluated WGTS in routine diagnostics of 200 soft-tissue and bone tumors suspicious for malignancy, including DNA and RNA isolation from the tumor, and DNA isolation from a peripheral blood sample or any non-tumor tissue.Results: On the basis of specific genomic alterations or absence of presumed findings, WGTS resulted in reclassification of 7% (13/197) of the histopathologic diagnoses. Four cases were downgraded from low-grade sarcomas to benign lesions, and two cases were reclassified as metastatic malignant melanomas. Fusion genes associated with specific tumor entities were found in 30 samples. For malignant soft-tissue and bone tumors, we identified treatment relevant variants in 15% of cases. Germline pathogenic variants associated with a hereditary cancer syndrome were found in 22 participants (11%).Conclusions: WGTS provides an important dimension of data that aids in the classification of soft-tissue and bone tumors, correcting a significant fraction of clinical diagnoses, and identifies molecular targets relevant for precision medicine. However, genetic findings need to be evaluated in their morphopathologic context, just as germline findings need to be evaluated in the context of patient phenotype and family history., QC 20240703

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.

QC 20231128

QC 20231128

QC 20231128

Molecular diagnostics is a cornerstone of modern precision medicine, broadly understood as tailoring an individual's treatment, follow-up, and care based on molecular data. In rare diseases (RDs), molecular diagnoses reveal valuable information about the cause of symptoms, disease progression, familial risk, and in certain cases, unlock access to targeted therapies. Due to decreasing DNA sequencing costs, genome sequencing (GS) is emerging as the primary method for precision diagnostics in RDs. Several ongoing European initiatives for precision medicine have chosen GS as their method of choice. Recent research supports the role for GS as first-line genetic investigation in individuals with suspected RD, due to its improved diagnostic yield compared to other methods. Moreover, GS can detect a broad range of genetic aberrations including those in noncoding regions, producing comprehensive data that can be periodically reanalyzed for years to come when further evidence emerges. Indeed, targeted drug development and repurposing of medicines can be accelerated as more individuals with RDs receive a molecular diagnosis. Multidisciplinary teams in which clinical specialists collaborate with geneticists, genomics education of professionals and the public, and dialogue with patient advocacy groups are essential elements for the integration of precision medicine into clinical practice worldwide. It is also paramount that large research projects share genetic data and leverage novel technologies to fully diagnose individuals with RDs. In conclusion, GS increases diagnostic yields and is a crucial step toward precision medicine for RDs. Its clinical implementation will enable better patient management, unlock targeted therapies, and guide the development of innovative treatments., QC 20231023

Precision medicine has the potential to transform healthcare by moving from one-size-fits-all to personalised treatment and care. This transition has been greatly facilitated through new high-throughput sequencing technologies that can provide the unique molecular profile of each individual patient, along with the rapid development of targeted therapies directed to the Achilles heels of each disease. To implement precision medicine approaches in healthcare, many countries have adopted national strategies and initiated genomic/precision medicine initiatives to provide equal access to all citizens. In other countries, such as Sweden, this has proven more difficult due to regionally organised healthcare. Using a bottom-up approach, key stakeholders from academia, healthcare, industry and patient organisations joined forces and formed Genomic Medicine Sweden (GMS), a national infrastructure for the implementation of precision medicine across the country. To achieve this, Genomic Medicine Centres have been established to provide regionally distributed genomic services, and a national informatics infrastructure has been built to allow secure data handling and sharing. GMS has a broad scope focusing on rare diseases, cancer, pharmacogenomics, infectious diseases and complex diseases, while also providing expertise in informatics, ethical and legal issues, health economy, industry collaboration and education. In this review, we summarise our experience in building a national infrastructure for precision medicine. We also provide key examples how precision medicine already has been successfully implemented within our focus areas. Finally, we bring up challenges and opportunities associated with precision medicine implementation, the importance of international collaboration, as well as the future perspective in the field of precision medicine., Funding agencies: GMS is supported by the strategic innovation program Swelife and Vinnova, the Swedish Innovation Agency; the Ministry of Health and Social Affairs; SciLifeLab; the Swedish Childhood Cancer Fund; funding from the participating healthcare regions Region Skåne, Region Västra Götaland, Region Östergötland, Region Stockholm, Region Uppsala, Region Västerbotten and Region Örebro län and the medical faculties at Gothenburg University, Linköping University, Lund University, Karolinska Institutet, Umeå University, Uppsala University and Örebro University. SciLifeLab provides funding to the Clinical Genomics platform and to several projects performed in collaboration with GMS as a partner. Illumina has provided reagents for sequencing a subset of the patients in the acute leukaemia combined WGS and WTS study.

QC 20230817

Simple Summary Cancer in the stomach and oesophagus is deadly when discovered at a late stage. There are no good biomarkers for its detection or for making a prognostic prediction. In this study, we evaluate the analysis of cell-free DNA as a prognostic cancer biomarker. Cell-free DNA is DNA released from any tissue to a body fluid. When there is a tumour in the body, some of the cell-free DNA will come from that tumour, and it can be detected in a blood sample. We show that the detection of cell-free DNA from the cancer correlates to a worse prognosis than when no tumour DNA is detected. We also show that the method of analysis is important. Either a tissue biopsy must be included as a validation of the genetic variants detected or analysis of the blood cells or another blood sample after tumour resection needs to be analysed to improve detection. In this longitudinal study, cell-free tumour DNA (a liquid biopsy) from plasma was explored as a prognostic biomarker for gastro-oesophageal cancer. Both tumour-informed and tumour-agnostic approaches for plasma variant filtering were evaluated in 47 participants. This was possible through sequencing of DNA from tissue biopsies from all participants and cell-free DNA from plasma sampled before and after surgery (n = 42), as well as DNA from white blood cells (n = 21) using a custom gene panel with and without unique molecular identifiers (UMIs). A subset of the plasma samples (n = 12) was also assayed with targeted droplet digital PCR (ddPCR). In 17/31 (55%) diagnostic plasma samples, tissue-verified cancer-associated variants could be detected by the gene panel. In the tumour-agnostic approach, 26 participants (59%) had cancer-associated variants, and UMIs were necessary to filter the true variants from the technical artefacts. Additionally, clonal haematopoietic variants could be excluded using the matched white blood cells or follow-up plasma samples. ddPCR detected its targets in 10/12 (83%) and provided an ultra-sens, QC 20230321

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).

Over the last decades, rapid technological and scientific advances have led to a merge of molecular sciences and clinical medicine, resulting in a better understanding of disease mechanisms and the development of novel therapies that exploit specific molecular lesions or profiles driving disease. Precision oncology is here used as an example, illustrating the potential of precision/personalized medicine that also holds great promise in other medical fields. Real-world implementation can only be achieved by dedicated healthcare connected centers which amass and build up interdisciplinary expertise reflecting the complexity of precision medicine. Networks of such centers are ideally suited for a nation-wide outreach offering access to precision medicine to patients independent of their place of residence. Two of these multicentric initiatives, Genomic Medicine Sweden (GMS) and the Centers for Personalized Medicine (ZPM) initiative in Germany have teamed up to present and share their views on core concepts, potentials, challenges, and future developments in precision medicine. Together with other initiatives worldwide, GMS and ZPM aim at providing a robust and sustainable framework, covering all components from technology development to clinical trials, ethical and legal aspects as well as involvement of all relevant stakeholders, including patients and policymakers in the field.

The amount of data available from genomic medicine has revolutionized the approach to identify the determinants underlying many rare diseases. The task of confirming a genotype–phenotype causality for a patient affected with a rare genetic disease is often challenging. In this context, the establishment of the Matchmaker Exchange (MME) network has assumed a pivotal role in bridging heterogeneous patient information stored on different medical and research servers. MME has made it possible to solve rare disease cases by “matching” the genotypic and phenotypic characteristics of a patient of interest with patient data available at other clinical facilities participating in the network. Here, we present PatientMatcher (https://github.com/Clinical-Genomics/patientMatcher), an open-source Python and MongoDB-based software solution developed by Clinical Genomics facility at the Science for Life Laboratory in Stockholm. PatientMatcher is designed as a standalone MME server, but can easily communicate via REST API with external applications managing genetic analyses and patient data. The MME node is being implemented in clinical routine in collaboration with the Genomic Medicine Center Karolinska at the Karolinska University Hospital. PatientMatcher is written to implement the MME API and provides several customizable settings, including a custom-fit similarity score algorithm and adjustable matching results notifications., QC 20221125