Skip to main content
NCBI home page
Cambridge Prisms: Precision Medicine logoLink to Cambridge Prisms: Precision Medicine
. 2023 Feb 27;1:e15. doi: 10.1017/pcm.2023.3

Building a precision medicine infrastructure at a national level: The Swedish experience

Anders Edsjö 1,2, Anna Lindstrand 3,4,5, David Gisselsson 1,6, Paula Mölling 7, Mikaela Friedman 3, Lucia Cavelier 3,4, Maria Johansson 8, Hans Ehrencrona 1,6, Therese Fagerqvist 9, Tobias Strid 10,11, Lovisa Lovmar 12, Bo Jacobsson 13,14, Åsa Johansson 15, Lars Engstrand 16, Craig E Wheelock 17,18, Per Sikora 12,19,20, Valtteri Wirta 5,21,22, Thoas Fioretos 1,6,23, Richard Rosenquist 3,4,5,✉; Genomic Medicine Sweden (GMS)
PMCID: PMC10953755  PMID: 38550923

Abstract

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.

Keywords: genomic medicine, precision medicine, implementation, national infrastructure

Impact statement

Implementation of precision medicine in healthcare has been made possible thanks to the rapid development in high-throughput sequencing technologies coupled with new types of targeted drugs being introduced, hence realising personalised treatment and care. Genomic Medicine Sweden (GMS) was formed to enable an equal and resource-efficient implementation of precision medicine across the country. Through regional centres for genomic medicine at all Swedish university hospitals and a common infrastructure for data sharing, GMS has built up the capacity for precision diagnostics/medicine in rare diseases, cancer, infectious diseases and complex diseases. In this article, we summarise our experience with implementing precision medicine at a national level, discuss remaining challenges and opportunities, as well as future directions in this rapidly advancing field.

Introduction

The introduction of high-throughput sequencing (HTS) technologies at a broader scale more than 10 years ago provided entirely new research tools for the discovery of molecular events associated with disease (Koboldt et al., 2013). This led to a rapid increase of novel disease genes associated with rare inherited diseases (Gilissen et al., 2012; Stranneheim and Wedell, 2016), but also unravelled the genomic landscape in major cancer types (Ley et al., 2008; Stratton et al., 2009; Bailey et al., 2018; PCAWG-Consortium, 2020). In parallel, new types of therapies were developed that target disease-driving molecular alterations or key cellular pathways and/or processes with higher precision than before (Malone et al., 2020; Tsimberidou et al., 2020; Wasterlid et al., 2022). These developments led to the fast introduction of genomic technologies, such as whole-exome (WES)/genome (WGS) and gene panel sequencing, into clinical routine diagnostics for rare diseases, cancer and infectious diseases (Zehir et al., 2017; Lindstrand et al., 2019; Kobras et al., 2021; van der Sanden et al., 2022; Walter et al., 2022). In this way, genomic-based techniques paved the way for advanced molecular profiling as a basis for individualised treatment and follow-up, that is, precision medicine (Stranneheim and Wedell, 2016; Malone et al., 2020; Rosenquist et al., 2022; Wasterlid et al., 2022; Yang et al., 2022).

One of the first national genomic initiatives, Genomics England, was launched in 2013, with an ambition to sequence 100,000 genomes in patients with rare diseases or cancer (Turnbull, 2018; Investigators et al., 2021; Degasperi et al., 2022). After successfully finishing the project in 2018, genomic technologies were implemented in routine healthcare within National Health Services (NHS) through seven Genomic Medicine Services centres across the country (Turnbull et al., 2018), while also embarking on new pioneering projects, such as the new-born genome program and the population screening program for early cancer detection using liquid biopsy. Today, national precision medicine strategies/initiatives have been launched in many countries worldwide (more than 30 initiatives in Europe only), for example, the Danish National Genome Center, Genomic Medicine France 2025, Australian Genomics and Singapore’s National Precision Medicine program (Lethimonnier and Levy, 2018; Turnbull et al., 2018; Saunders et al., 2019; Stark et al., 2019; Kong et al., 2022; Stenzinger et al., 2022). In other countries, such as Sweden and Germany, with regionally organised healthcare, academia and healthcare have instead joined forces to establish regionally distributed genomic/precision medicine centres with a close national coordination (Fioretos et al., 2022; Stenzinger et al., 2022).

In this review, we will describe our efforts in Sweden to build a national infrastructure for the implementation of precision medicine, while also highlighting key disease areas for which precision medicine approaches are being implemented. Finally, we will discuss important challenges that we need to address to enable equal access to precision medicine nationally and internationally as well as future directions in this rapidly evolving field.

Building a national infrastructure for precision medicine

One important development in Sweden was the establishment in 2010 of an infrastructure for molecular biosciences, Science for Life Laboratory (SciLifeLab) that today provides a wide range of high-throughput technologies through ten technology platforms at a national level. One of these platforms, the Clinical Genomics platform, is focusing on the development, adaptation and implementation of genomic technologies for translational research and clinical utility. To secure national coverage, Clinical Genomics nodes are located at all seven universities with medical faculties in Sweden (Figure 1; Fioretos et al., 2022). In 2017, the Clinical Genomics platform initiated the formation of a national infrastructure for genomic medicine/precision medicine which was named Genomic Medicine Sweden (GMS). A Swelife-funded pre-study phase allowed for key stakeholders to lay the foundation of the overall organisation of GMS and the establishment of national working groups coordinating activities in key areas relevant for precision medicine. Funded by an implementation grant from the Swedish Innovation Agency Vinnova, GMS was officially inaugurated in 2018.

Figure 1.

Figure 1.

Open in a new tab

Regional distribution, key services and focus areas. Clinical Genomics (CG) units are located at the seven universities with medical faculties, and Genomic Medicine Centres (GMCs) at the seven university hospitals in Sweden. The National Genomics Platform (NGP), located in Western Sweden (Region Västra Götaland), is a highly competent data lake linked to a dynamic scale out high performance computing cluster. CG provides expertise and services to the research and industrial community, and to GMS. GMS currently encompasses seven diagnosis-specific working groups and five working groups supporting the GMS infrastructure.

In Sweden, the responsibility for healthcare is divided between the national government, the 21 healthcare regions and the municipalities, where the regions have the responsibility to organise healthcare in such a way that all citizens have access to good and equal healthcare. To overcome any possible regional differences and to be directly linked to healthcare, GMS is therefore organised as a regionally distributed infrastructure. The implementation of precision medicine is performed through the seven Genomic Medicine Centres (GMCs) that have been established at each of the university hospitals with close links to the technology-driven SciLifeLab Clinical Genomics node at each site, which provides a critical technological backbone for GMS (Fioretos et al., 2022).

The core partners of GMS are the seven university healthcare regions and the seven universities with a medical faculty. In addition, GMS works closely together with SciLifeLab, patient organisations, industry and government agencies. GMS is led by a national steering board, which consists of seven members appointed by each of Sweden’s seven university healthcare regions (usually the research director), two members jointly appointed by the remaining 14 regions, seven members appointed by each of seven universities with medical faculties (usually the dean of the faculty), two members from industry, two members from patient organisations as well as adjunct members from SciLifeLab and Biobank Sweden (Stenzinger et al., 2022). The strong national anchoring of the steering board, especially in regional healthcare, has been a key success factor in the implementation of genomic medicine in Swedish healthcare. The GMS operation is led by a director, together with the GMS collaboration office and the GMS management group with representations from all core partners.

The overarching aims of GMS are to (i) implement genome sequencing in healthcare for improved diagnostics and provide equal access to personalised treatment, (ii) establish a national IT-infrastructure and associated knowledge-bases to advance precision medicine in Sweden, (iii) increase the use of genomics and health data for research and innovation, (iv) increase participation in clinical trials and (v) implement genomics in prevention, diagnostics and patient stratification of complex diseases (in more detail in the GMS Strategy Plan 2021–2030). These goals are closely linked to the ambitions of the Swedish government’s life science strategy.

GMS is organised in seven disease-specific national working groups: rare diseases, solid tumours, haematological malignancies, childhood cancer, infectious diseases/microbiology, complex diseases, as well as pharmacogenomics (Figure 1). The disease-specific working groups are in turn supported by working groups in data management and informatics, health economy, ethical and legal issues, industry collaboration and education.

The National Genomics Platform

Because of the regionalised IT infrastructures at the university hospitals in Sweden, where information is kept in multiple non-interconnected systems, GMS informatics working group is building a dedicated infrastructure for data handling and storage, that is, the National Genomics Platform (NGP), also to standardise and organise genomic data across the GMCs (Fioretos et al., 2022; Stenzinger et al., 2022). The central part of the NGP is a data lake for genomic data and associated clinical metadata. By creating a unified infrastructure, it becomes feasible to enforce standards for data structuring as well as creating national analysis pipelines. Organising data within a joint national infrastructure also simplifies data availability for research purposes, as well as data interoperability through a single standardised interface.

The platform is divided into three subparts for storage, indexing and processing (Figure 2).

Figure 2.

Figure 2.

Open in a new tab

Schematic view of the National Genomics Platform. The platform is divided into three distinct parts covering storage (NGPr), indexing and metadata analysis (NGPi) and data processing (NGPc). Each GMC has its own tenant within the platform creating the possibility of logical separation between centres. The separation persists in the indexing layer, allowing a fine-grained control over what metadata is shared between centres. The indexing layer can then serve as a back-end for both interpretation tools and national and international data sharing. Data processing can be either local on-prem or provisioned on-demand in one or multiple cloud providers’ platforms.

The central storage within the NGP consists of an advanced object storage that is hosted in three separate geographic locations within Region Västra Götaland (located southwest in Sweden). All seven GMC are linked to the central storage through the Swedish hospital network. Using the GMS-developed middleware NGPiris, data and metadata can be culled from local LIMS and storage systems and uploaded to the central platform using the S3 protocol, ensuring data security and standardisation. Within the platform, data is logically separated by a tenant system where each GMC/university hospital has full control over their own data, including access and user control. Access rights can be set on individual objects, allowing targeted data sharing between GMCs.

For data to be utilised for interpretation and sharing, the NGP platform contains a powerful indexing engine based around Apache SOLR. Data is indexed in near real-time using multiple indexing pipelines, extracting information from various types of data files. Data points from multiple files are combined into metadata objects held within the index. Since the metadata object structure of the NGP is non-static, new information can be retrieved and indexed from the object store as new needs are identified. Access to the index can be tailored using index views that limit access to certain metadata points and certain indexes for certain applications, creating fine-grained access control. In other words, searches can be limited by what the respective user/application is allowed to “see” within the index and sensitive information can be hidden.

The NGP uses a combination of existing on-prem compute resources as well as cloud-based infrastructure to provide resource scaling. This is achieved through a cloud scale-out engine, based on the open-source Tortuga-project, and by engaging Swedish-owned cloud providers for scale-out, ensuring high security and compliance with Swedish law.

Implementation of precision diagnostics/medicine in healthcare

In the following sections, we will describe our efforts in implementing precision medicine within our focus disease areas, rare diseases, cancer, infectious diseases and complex diseases.

Rare diseases

Rare diseases constitute a large number of different disorders, of which the vast majority are severe genetic chromosomal or single-gene disorders. Although individually rare, these disorders are collectively common with an estimated population prevalence of 3.5–6% (Nguengang Wakap et al., 2020). The most important factor for effective treatment and preventive measures is a molecular diagnosis. With an exact diagnosis, patients can be offered personalised management, healthy relatives may receive carrier testing and prenatal diagnosis is possible.

To identify the disease-causing pathogenic variants in rare genetic diseases, WGS is emerging as a powerful first-line screening test regularly used in routine diagnostics (Lionel et al., 2018; Investigators et al., 2021; Stranneheim et al., 2021). The diagnostic yields vary between different disease categories and centres, depending on inclusion criteria and whether a trio or singleton approach was used (Figure 3). For individuals with intellectual disability, WGS yields diagnostic rates between 30 and 40% (Investigators et al., 2021; Stranneheim et al., 2021; Lindstrand et al., 2022; van der Sanden et al., 2022), which is comparable or even superior to the combined yield after multiple genetic tests such as WES, chromosomal microarray and targeted gene panels (Lindstrand et al., 2022; van der Sanden et al., 2022), while shortening the time to diagnosis substantially. Furthermore, WGS has the potential to improve the clinical yield even more in the future by adding analysis of novel genetic causes of disease, both coding and non-coding.

Figure 3.

Figure 3.

Open in a new tab

Number of analysed patients using WGS analysis in specific disease groups of rare disease. Positive (light colour) and negative (dark colour) genetic findings with corresponding diagnostic yield above each bar. Based on WGS analysis performed at three GMCs during 2021.

To maximise the diagnostic yield multiple variant types need to be called and interpreted clinically. As such, there is a need to optimise the analysis pipelines, databases and interpretation tools. Furthermore, the diagnosis of patients is still dependent on linking the genetic variants to clinical symptoms. This is reflected in the need for multidisciplinary collaborative environments, where laboratory specialists interact with both bioinformaticians and clinical specialists, a necessary prerequisite to implement genomics in healthcare (Investigators et al., 2021; Stranneheim et al., 2021).

For rare disease patients, the diagnostic work is executed at the seven GMCs. As of 1 January 2022, more than 10,000 rare diseases patients have been analysed by WGS in Sweden. Of those, 22% were analysed as trios and 78% as singletons and the overall diagnostic yield was 36% and 33%, respectively. The projection is that over 5,000 additional individuals will undergo testing in 2022 resulting in a rapidly growing number of patients receiving a diagnosis.

One of the prioritised areas in the GMS rare diseases working group is to improve diagnostics by building a clinical database for genotype-phenotype matching within the NGP, as well as to establish classified variant databases and aggregated variant databases for improved variant prioritisation. This would also provide an important resource for Mendelian disease discovery as well as understanding other types of inheritance such as digenic, and mutational burden. To include as many individuals as possible we are also working on an electronic informed consent process at the national level.

The strategy for WGS-based diagnostics of rare disease patients has initially focused on well-known variant categories such as single-nucleotide variants and insertions/deletions, followed more recently by detection of structural variants, including copy-number variants (CNVs), repeat expansions and mtDNA (Dolzhenko et al., 2019; Lindstrand et al., 2019; Stranneheim et al., 2021; Ibanez et al., 2022). To develop the WGS-based precision diagnostics further, two national projects are conducted aiming at validating and implementing RNA-sequencing for confirmation of WGS findings and to improve understanding on regulatory, non-coding genomic variation and, secondly, long-read sequencing for improved resolution and calling of structural variants in challenging regions such as low-complexity regions.

Solid tumours

Solid tumours make up around 90% of the 62,000 cancer cases diagnosed yearly in Sweden (Socialstyrelsen, 2020). Of these, roughly 10,000 cases are currently analysed with focused gene panels, mainly for treatment-predictive purposes. Non-small cell lung cancer is the leading indication with up front reflex testing at all seven GMCs and almost 20 targeted drugs available. Patients with colorectal carcinomas and malignant melanomas are also commonly tested, along with a number of other solid tumours tested in select cases.

Given the rapidly increasing number of biomarkers and the need to assess structural variations and to generate assessments of more complex biomarkers, such as microsatellite instability (MSI), homologous recombination deficiency (HRD) and tumour mutational burden (TMB) based on mutational signatures (Malone et al., 2020; Mosele et al., 2020), a national gene panel (GMS560) for comprehensive genomic profiling of routine clinical samples was given the highest priority in the GMS solid tumour working group (Figure 4). The need was also underscored by the tumour-agnostic indications for MSI and NTRK fusions and ongoing clinical trials on additional tumour-agnostic drugs targeting mainly gene fusions, indicating an increased need for comprehensive genomic profiling (Le et al., 2015; Hong et al., 2020).

Figure 4.

Figure 4.

Open in a new tab

Genomic profiling of solid tumours illustrating first- and second-generation gene panels. CNV, copy-number variant; indels, insertions and deletions; SNVs, single nucleotide variants.

During the design phase, a structured review of current indications and biomarkers relevant for clinical studies was supplemented with input from the national clinical consensus groups for cancer treatment. In addition, variants relevant for tumour biology identified in the TCGA consortium were added for scientific purposes and for future-proofing of the design (Sanchez-Vega et al., 2018). Finally, a number of pharmacogenomic targets were included to enable identification of genetic aberrations impacting drug susceptibility. The responsibility for the development and validation of the various modules in the panel has been divided between the seven participating sites, using the combined resources of all involved clinical laboratories, GMCs and SciLifeLab Clinical Genomics nodes. To ensure equity of care and harmonised results for complex biomarkers, a modular bioinformatics solution has been developed as part of the national effort.

The implementation of the panel has gained considerable support via pilot projects for precision medicine funded by the Swedish Ministry of Health and Social Affairs. Starting in 2021, the pilots for solid tumours included the addition of a module for HRD testing, gene panel testing for breast cancer patients with stage 4 disease, as well as a pilot for tissue and liquid biopsy testing in lung cancer as the latest addition. The pilots have served as a valuable means to involve clinicians in each field and made it possible to discuss priorities as well as important issues on how best to convey results.

Liquid biopsies, the use of blood samples to test for biomarkers normally analysed in tissue samples, have the potential to address several important clinical questions (Markou et al., 2022). Currently used to detect resistance mutations, monitoring of treatment response, molecular characterisation in cases with a shortage of available material and a means to visualise molecular heterogeneity, are all potential applications in the clinical setting. A future next step includes early detection of cancer with the potential to improve treatment outcomes (Crosby et al., 2022).

With widened indications for targeted treatments in additional malignancies and increased need to follow treatment response and resistance development, the testing can be expected to double within the coming years. Another important trend is the focus on the diagnostic importance of molecular characterisation, mirroring the increased weight given to molecular alterations in the WHO classifications of malignant disease (WHO-Classification-of-Tumours-Editorial-Board, 2021a, 2021b).

Another challenge has been to establish precision medicine trials for cancer to ensure that the data generated from the comprehensive molecular profiling can be translated into treatment and that novel treatments can be evaluated. In this work, GMS and its founding partners has joined forces with Zero Vision Cancer, MEGALiT, SciLifeLab and several relevant authorities in the field of life science in Sweden to form the “Test Bed Sweden for Clinical Trials and Implementation of Precision Health in Cancer Care.” Through this network, Nordic and European collaborations have been established and support for joint projects have been secured, allowing for work on setting up drug repurposing studies in additional European countries and refining molecular diagnostics and molecular tumour boards.

Haematological malignancies

Haematologic malignancies represent highly molecularly heterogeneous disorders with an increasing number of disease-driving alterations being described and incorporated into classification and treatment guidelines (Alaggio et al., 2022; Arber et al., 2022; de Leval et al., 2022; Dohner et al., 2022; Khoury et al., 2022). Traditionally, these disorders are divided into myeloid and lymphoid neoplasms depending on the cellular lineage affected by the disease-driving alterations.

Early on, GMS recognised that in an evolving landscape with new genomic markers being described at a rapid pace in haematological malignancies, the design of a comprehensive and highly flexible gene panel was needed. Apart from allowing the detection of a high number of single nucleotide variants (SNVs), smaller insertions/deletions and CNVs, there was a need to seamlessly incorporate new diagnostic markers in the gene panel design. GMS decided to use a targeted capture-based approach, allowing an even and deep coverage of disease variants, as well as the detection of smaller insertions/deletions (Figure 5). The first gene panel introduced in clinical diagnostics was the GMS-Myeloid Gene Panel (GMS-MGP), which allows the detection of variants in 199 genes together with a backbone of single nucleotide polymorphisms (SNPs), allowing the detection of CNVs at a 10-Mb resolution, as chromosomal imbalances form an important basis for risk-stratification and treatment prediction in haematological malignancies. The gene panel also includes genes that are associated with hereditary predisposition to haematological malignancies, as well as pharmacogenetic genes of importance for drug susceptibility. The analytical and clinical performance of the GMS-MGP was recently investigated in a national interlaboratory study, showing a high concordance of the results between the participating GMCs (Orsmark-Pietras et al., manuscript in preparation). To date, >5,000 tests have been performed in a clinical diagnostic setting using the GMS-MGP.

Figure 5.

Figure 5.

Open in a new tab

National strategy for precision diagnostics in haematological malignancies. ALL, acute lymphoblastic leukaemia; AML, acute myeloid leukaemia; CLL, chronic lymphocytic leukaemia; CML, chronic myeloid leukaemia; indels, insertions and deletions; MDS, myelodysplastic syndrome; MPN, myeloproliferative neoplasias; SNVs, single nucleotide variants.

Using a similar capture-based protocol as the GMS-MGP, a lymphoid gene panel (GMS-LGP) was designed allowing the detection of variants in 252 genes. The design of the second version is ongoing for improved detection of CNVs relevant for lymphoid malignancies, and will also include structural variation and immunoglobulin/T-cell receptor genes.

In a national study, GMS is currently also evaluating if WGS in combination with whole-transcriptome sequencing (WTS) can replace current standard of care (SoC), including multiple, often time-consuming and costly diagnostic genetic tests (Figure 5; Berglund et al., 2022). In this study, 700 cases of adult and paediatric acute myeloid leukaemia (AML) and acute lymphoblastic leukaemia (ALL) are subjected to WGS and WTS and the results are compared to SoC using chromosome banding, fluorescence in situ hybridization (FISH), SNP-arrays, directed molecular tests and the GMS myeloid and lymphoid gene panels. In parallel to evaluating the ability of WGS and WTS to replace SoC as a diagnostic tool, health-economy studies are performed within the study. Other upcoming studies within GMS will include the evaluation of single-cell technologies for clinical applications, a rapidly advancing field (Pfisterer et al., 2021), as well as methods allowing ultrasensitive detection of measurable residual disease of critical importance for monitoring treatment effects and to predict disease relapse at an earlier stage (Chen et al., 2022; de Leval et al., 2022).

Childhood cancer

In children, leukaemia is the most common malignancy, while CNS tumours dominate among solid neoplasms. However, there is also a myriad of rare childhood cancers. Many of these rare entities have diagnostic markers at the genomic level, such as somatic gene fusions, point mutations or characteristic patterns of copy-number imbalances. Also, within diagnostic entities, genetic profiling is often used for prognostication and risk stratification. The diversity of diagnostic entities combined with the many types of clinically relevant genomic aberrations have made it difficult to set up comprehensive gene panel-based approaches for childhood cancer. The situation is further complicated by the constant discovery of new genomic markers that have to be included in diagnostic platforms. Taken together, this situation prompted a broad technical approach when considering a standardised clinical genomics pipeline for children with cancer.

The GMS Childhood Cancer working group resolved this issue by nationwide implementation of WGS coupled with WTS as part of the routine clinical work-up of paediatric malignancies. To achieve unbiased geographic coverage, patients from all of Sweden’s six paediatric oncology centres, of which some are very small, were included from the start of implementation in 2021. To create resilience by redundancy and to disseminate expertise though the country, WGS and WTS for childhood cancers was established at the GMCs at the large university hospitals in Stockholm, Gothenburg and Lund, as well as for leukaemia in Uppsala. The nationwide coverage and the fact that all paediatric malignancies are includible, makes our effort distinct from previous studies having focused on high-risk patients or large tertiary care centre cohorts (Wong et al., 2020; Newman et al., 2021; Trotman et al., 2022).

Irrespective of where the patient was diagnosed and cared for primarily, the same steps were taken from inclusion to clinical reporting (Figure 6). In a parallel project, germline variants of clinical importance are identified from normal sample WGS data (peripheral blood leukocytes for solid tumours, skin biopsies for leukaemias). Also, for CNS tumours, DNA methylation classification is performed on a regular basis. At the date of writing, around 150 children in Sweden have gone through this clinical pipeline. In the vast majority of cases, WGS and RNA sequencing provided information that corroborated, revised or refined the original diagnosis (manuscript in preparation).

Figure 6.

Figure 6.

Open in a new tab

Infographics of the GMS Childhood Cancer pipeline. The upper panel outlines the main steps for each patient’s sample and the resulting information: (1) inclusion based on informed consent and tumour cell content in biopsy >40%, (2) WGS of tumour DNA (minimum 90×), normal sample DNA (30×), and tumour RNA-sequencing, (3) filtering of tumour WGS data against normal sample WGS data to identify somatic mutations, (4) further filtering of non-synonymous coding variants against a flexible gene list of somatic mutations of clinical importance in childhood cancer, also including potential druggable targets, (5) fusion gene capture from RNA-sequencing data, (6) creation of whole genome profiles of copy numbers and allelic states, (7) discussion of findings at a molecular tumour board and (8) formulation of a written report, added as a complement to the standard pathology report. The bottom panel itemises passed milestones and future plans.

Microbiology/infectious diseases

The importance of implementing precision medicine in healthcare to prevent and treat infectious diseases is underscored by the drastic emergence of both antimicrobial resistance (AMR) and pandemics that affects and kills millions of people globally every year (Marani et al., 2021; Antimicrobial-Resistance-Collaborators, 2022; Fink et al., 2022). A functional infrastructure for pandemic preparedness, including AMR monitoring, should encompass a broad portfolio of genetic technologies that can be employed into the healthcare system. This requires new technical solutions and IT structures for data processing. The SARS-CoV-2 pandemic has accelerated this, where GMS has been setting up a joint automated workflow for bioinformatics analysis of SARS-CoV-2 on the NGP. The platform will also be the basis for other pathogenic microorganisms in future, where a nationwide implementation of WGS enables fast and reliable identification and typing of microorganisms, that is, bacteria, viruses, parasites and fungi. Methods and results can easily be shared in real time between regions affected by outbreaks, both to map the spread of infectious diseases but also to detect emergent spread of antibiotic resistance. International comparisons can thus be facilitated, which has been shown to be of great importance during the pandemic (Pires et al., 2022).

Putting the genetics into clinical practice to identify SNPs related to the severity of disease or antibiotic resistance will also pave the way for precision medicine in the managing and treatment of infectious diseases. The work within GMS has started with the bacterium multi-resistant methicillin Staphylococcus aureus (MRSA) where sequencing data from nine laboratories, generated with different sequencing technologies, have been compared regarding quality and different analysis pipelines to guide further harmonisation with the ultimate aim to set up one joint workflow on the NGP.

Genomic techniques such as metagenomics (sequencing of all DNA or RNA within a sample) is one way to identify and characterise a microorganism directly in a sample. The metagenomic approach offers rapid diagnostics and a possibility to detect unknown pathogens without culturing (Purushothaman et al., 2022). As part of the pandemic preparedness in Sweden, GMS has in collaboration with the SciLifeLab Pandemic Laboratory Preparedness capability, worked on establishing an assay and analysis pipeline for clinical metagenomics that is currently being established nationally and will be available on the NGP.

The same metagenomics technique is also used to characterise the human microbiome that will be of great importance to predict risk and treatment outcomes in various diseases including chronic inflammation and cancer. An integrative approach combining data sources will provide taxonomic profiles, diversity and functional annotations in such samples. The implementation of machine learning is needed to increase our capacity to classify and predict clinical outcomes and will be an important tool in precision medicine and infectious disease management (Peiffer-Smadja et al., 2020).

Complex diseases

While progress has been made in precision medicine approaches in cancer and rare diseases, most common diseases, including allergies, asthma, cardiovascular disease, diabetes, neurologic diseases, psychiatric disorders and rheumatic diseases, pose more of a challenge. These diseases are referred to as complex because most patients do not carry established single-gene mutations with large effect sizes, and the disease risk is instead explained by a complex set of genomic variations in combination with lifestyle and environmental factors (Wheelock and Rappaport, 2020; Franks et al., 2021). For instance, while a strong genome-wide genetic correlation has been identified between asthma and allergic diseases (Lionel et al., 2018), attempts to explain the incidence of obstructive lung disease or allergy solely by genetics have not been successful. Collectively, complex diseases account for ~70% of global deaths and the majority of healthcare costs (GBD-Disease-Injury-Incidence-Prevalence-Collaborators, 2017). To address this challenge, GMS has formed a working group to identify common complex diseases where a genetic link has been identified, but where this information is not yet used optimally in clinical practice.

During the last few years, the possibility of combining genetic associations across the genome into a polygenic risk score (PRS) has been investigated for many complex diseases (O’Sullivan et al., 2022). For example, in coronary artery disease, it has been shown that a high PRS confers an equal risk as monogenic effects (Khera et al., 2018), and a higher risk than conventional risk factors. For preventive purposes, many international organisations already recommend to estimate the 10-year cardiovascular risk for adult patients 40 to 75, and including the PRS will contribute to increased accuracy of the current risk prediction tools (O’Sullivan et al., 2022). PRS has also been used to further refine risk assessment among women with suspected predisposition to breast cancer (Mavaddat et al., 2019; Lakeman et al., 2020), and a web-based risk assessment tool, CanRisk Web Tool, based on PRS, family history and rare pathogenic variants in cancer susceptibility genes has been developed (Carver et al., 2021).

However, for most PRS the precision is too low to be clinically relevant for individual patients and there are no clear guidelines available on how to translate PRS to the benefit of individual patients. For example, the variance in asthma liability explained by 22 distinct genome-wide-significant variants is estimated to be as low as 3.5% (Demenais et al., 2018), and 12 common genetic variants associated with airflow obstruction and risk of chronic obstructive pulmonary disease only explain about 1.5% of the phenotypic variance (Hobbs et al., 2017). For many complex traits (Yengo et al., 2022), a dramatically larger sample size is therefore needed in the GWAS studies before a PRS will be of high enough precision to identify high- or low-risk individuals of complex diseases. However, it is worth highlighting that environmental risk factors, and environmental exposure, remain the predominant drivers for development of several complex diseases, including, for example, obstructive lung disease.

It is clear that substantially more research is required to be able to take genomic precision medicine into the clinic and many such initiatives are underway. There is a need to include other omics technologies, such as proteomics and metabolomics (Beger et al., 2016; Duarte and Spencer, 2016), which offers the advantage of being closest to the current phenotype of the patient, into precision medicine efforts to predict, diagnose and ultimately treat and prevent complex diseases. Following this approach, there have been recent efforts to investigate the utility of biomarker-based approaches to stratify asthmatics for treatment with biologics and patients with atrial fibrillation to receive oral anticoagulation (Benz et al., 2021; Israel et al., 2021).

In summary, while the healthcare organisation of Sweden is conducive to expanding precision medicine treatment approaches into the area of complex diseases, there remains a general lack of knowledge about how molecular information can be used to benefit the diagnosis, prevention and treatment of patients with complex diseases. It is most probable that eventual precision treatment efforts for complex diseases will involve a combination of genetic and other molecular information to create biomarker panels for optimising patient treatment strategies.

Challenges and opportunities working at a national level

In a regionally distributed healthcare setting, aligning work habits, analytical methods and processes and bioinformatic pipelines is essential to harmonise data generation nationally and fully enable the utilisation of the potential in the data. As an example, for cancer diagnostics, where molecular profiling of solid tumours, haematological malignancies and childhood cancer has evolved as separate fields, work has been initiated to create molecular assays utilising a common technical approach, as well as establish a modular bioinformatic framework addressing the needs of each disease area.

The NGP is of key importance to enable national data sharing, including databases of genetic variation, and real-time analysis for healthcare professionals, but will also create a trusted research environment for secondary use of genomic data for the research community and other stakeholders in the life science sector. However, to fully use and benefit from the technical solutions for data sharing established in the NGP, the current Swedish legislation relating to the secondary use of health data requires changes. To address this, an inquiry was recently initiated by the Swedish government.

Strategic collaboration with industry and other societal stakeholders is crucial to achieve the long-term goals of GMS and thus to enable the development and implementation of precision medicine. The core partners of GMS and the national trade associations of the life science industries have agreed on a framework for collaboration to provide guidance on legal issues and create improved conditions for collaboration for the mutual benefit of the partners, thus contributing to accelerate the transformation of the healthcare system towards precision diagnostics/medicine with the patient at centre stage. Industry collaboration is challenged by the organisational structure of GMS constituting 14 partners within academia and healthcare, partly governed by different laws and regulations. The rapid development of knowledge, and making research and healthcare intertwined, may also be challenging with respect to the format of collaboration. In the short-term perspective, increased collaboration with industry will accelerate development and innovation, based on joint learnings and active knowledge sharing between organisations.

Another important area concerns education and training, as well as increasing awareness. Here, all relevant stakeholders across the precision medicine ecosystem need education about genomic/precision medicine, including healthcare professionals, patients and the society at large. As a consequence of the fast technological development and shift in healthcare, an increased dialogue concerning patient/individual integrity and ethical issues, for example, in sharing healthcare data will be important. GMS is actively collaborating with the medical profession and patient associations to provide different types of educational activities, including webinars, workshops and online courses. Moving forward cooperation with patient representatives, from planning and design to evaluation and follow-up, will increasingly be important parts of GMS’ work. There is also a need to incorporate the precision medicine concept into undergraduate and graduate programs as well as to start new educational programs, for example, in clinical bioinformatics. Here, GMS will be an important partner.

Finally, to build a sustainable infrastructure for precision medicine, a strong national commitment is necessary in order to secure long-term financing. In Sweden, a roadmap for precision medicine and advanced therapy medicinal products was recently released by one of the government’s strategic cooperation programs that proposed the establishment of a national infrastructure for precision medicine where the state and healthcare regions join forces in a long-term partnership which in turn will enable equal access to precision medicine for all citizens across the country.

Future directions

Currently, a main driver of precision medicine is high-throughput sequencing and there is a substantial potential for additional clinical benefit from further developing these techniques. Additional indications and complex biomarkers can be expected to make their way into clinical routine (Alexandrov et al., 2013). New analyses are expected to be added both for blood with fractionation of, for example, thrombocytes, extracellular vesicles and circulating tumour cells as well as cerebrospinal fluid, urine and other body fluids in which nucleic acids can be enriched. The methods used for analysing circulating tumour cells can also be expected to be useful for single-cell analyses, adding new dimensions of molecular heterogeneity (Lin et al., 2021; Pfisterer et al., 2021).

In the coming years, other types of information and technologies can be expected to become increasingly clinically relevant, such as long-read sequencing, optical mapping of structural variations, multimodal single cell and spatial omics and genome-wide methylation analyses. All these new modalities need to be taken all the way from discovery to clinical implementation and routine diagnostics. To facilitate this, the SciLifeLab Clinical Genomics platform and GMS have established a research implementation framework (Fioretos et al., 2022), and other high-end technology platforms within SciLifeLab are likely to further promote Swedish precision in the near future using a similar strategy.

Regardless of the information modality, the vast amounts of data generated necessitates new approaches, including machine learning and artificial intelligence, to be able both to discern patterns and to find the individual details of future clinical relevance. The data from diagnostics and clinical studies also need to be linked to and cross-referenced with health data in quality registries and electronic health records, adding to the complexity and need for new data processing solutions. Here, a recently established large-scale national initiative in Data-Driven Life Sciences by SciLifeLab, funded by the Knut and Alice Wallenberg Foundation, is likely to promote the capability in Sweden to harness the massive amounts of data being generated for improved precision medicine research and diagnostics.

Furthermore, the in-depth data and targeted drugs of precision medicine stratify patients in smaller groups than handled by traditional clinical trials, calling for novel approaches to clinical study design. This is an important task, involving clinicians and diagnostic people alike, and the development of the field is driven by international collaborations to pool the data needed.

Finally, as evidenced by ongoing active collaboration between European precision medicine initiatives, the best way forward will be to form cross-border and cross-disciplinary collaborative networks to foster and create the healthcare of tomorrow for the benefit of our patients and the society at large.

Acknowledgements

The article was written on behalf of the GMS Strategic National Collaboration Group: Johan Botling, Lucia Cavelier, Anders Edsjö, Hans Ehrencrona, Lars Engstrand, Thoas Fioretos, Mikaela Friedman, David Gisselsson Nord, Sofia Gruvberger Saal, Ulf Gyllensten, Therese Fagerqvist, Gisela Helenius, Kina Höglund, Bo Jacobsson, Maria Johansson, Åsa Johansson, Maria Johansson Soller, Maréne Landström, Lars-Åke Levin, Anna Lindstrand, Lovisa Lovmar, Erik Melén, Paula Mölling, Lars Palmqvist, Richard Palmqvist, Dirk Repsilber, Richard Rosenquist Brandell, Per Sikora, Tobias Strid, Peter Söderqvist, Mia Wadelius, Anna Wedell, Craig Wheelock and Valtteri Wirta.

Open peer review

To view the open peer review materials for this article, please visit http://doi.org/10.1017/pcm.2023.3.

Author contributions

R.R. and A.E. coordinated the writing of the manuscript. All authors contributed to the writing and approved the final version.

Financial support

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.

Competing interest

A.E. has received honoraria from AstraZeneca, Amgen, Bayer, Diaceutics and Roche. R.R. has received honoraria from AbbVie, AstraZeneca, Illumina, Janssen and Roche. V.W. has received honoraria from Illumina and Roche. A.L. has received honoraria from Illumina. D.G. has received honoraria from Bayer AB. H.E. has received honoraria from AstraZeneca. T.F. is a co-founder, board member and scientific advisor of Qlucore AB and Cantargia AB. B.J. has performed clinical diagnostic trials on NIPT with Natera (ongoing), Vanadis (completed) and Hologic (ongoing) with expenditures reimbursed per patient. The other authors declare no conflicts of interest.

References

  1. Alaggio R, Amador C, Anagnostopoulos I, Attygalle AD, Araujo IBO, Berti E, Bhagat G, Borges AM, Boyer D, Calaminici M, Chadburn A, Chan JKC, Cheuk W, Chng WJ, Choi JK, Chuang SS, Coupland SE, Czader M, Dave SS, de Jong D, Du MQ, Elenitoba-Johnson KS, Ferry J, Geyer J, Gratzinger D, Guitart J, Gujral S, Harris M, Harrison CJ, Hartmann S, Hochhaus A, Jansen PM, Karube K, Kempf W, Khoury J, Kimura H, Klapper W, Kovach AE, Kumar S, Lazar AJ, Lazzi S, Leoncini L, Leung N, Leventaki V, Li XQ, Lim MS, Liu WP, Louissaint A, Jr, Marcogliese A, Medeiros LJ, Michal M, Miranda RN, Mitteldorf C, Montes-Moreno S, Morice W, Nardi V, Naresh KN, Natkunam Y, Ng SB, Oschlies I, Ott G, Parrens M, Pulitzer M, Rajkumar SV, Rawstron AC, Rech K, Rosenwald A, Said J, Sarkozy C, Sayed S, Saygin C, Schuh A, Sewell W, Siebert R, Sohani AR, Tooze R, Traverse-Glehen A, Vega F, Vergier B, Wechalekar AD, Wood B, Xerri L and Xiao W (2022) The 5th edition of the World Health Organization classification of haematolymphoid tumours: Lymphoid neoplasms. Leukemia 36, 1720–1748. 10.1038/s41375-022-01620-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Alexandrov LB, Nik-Zainal S, Wedge DC, Aparicio SA, Behjati S, Biankin AV, Bignell GR, Bolli N, Borg A, Borresen-Dale AL, Boyault S, Burkhardt B, Butler AP, Caldas C, Davies HR, Desmedt C, Eils R, Eyfjord JE, Foekens JA, Greaves M, Hosoda F, Hutter B, Ilicic T, Imbeaud S, Imielinski M, Jager N, Jones DT, Jones D, Knappskog S, Kool M, Lakhani SR, Lopez-Otin C, Martin S, Munshi NC, Nakamura H, Northcott PA, Pajic M, Papaemmanuil E, Paradiso A, Pearson JV, Puente XS, Raine K, Ramakrishna M, Richardson AL, Richter J, Rosenstiel P, Schlesner M, Schumacher TN, Span PN, Teague JW, Totoki Y, Tutt AN, Valdes-Mas R, van Buuren MM, van ‘t Veer L, Vincent-Salomon A, Waddell N, Yates LR, Australian Pancreatic Cancer Genome Initiative, ICGC Breast Cancer Consortium, ICGC MMML-Seq Consortium, ICGC PedBrain, Zucman-Rossi J, Futreal PA, McDermott U, Lichter P, Meyerson M, Grimmond SM, Siebert R, Campo E, Shibata T, Pfister SM, Campbell PJ and Stratton MR (2013) Signatures of mutational processes in human cancer. Nature 500, 415–421. 10.1038/nature12477. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Antimicrobial-Resistance-Collaborators (2022) Global burden of bacterial antimicrobial resistance in 2019: A systematic analysis. Lancet 399, 629–655. 10.1016/S0140-6736(21)02724-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Arber DA, Orazi A, Hasserjian RP, Borowitz MJ, Calvo KR, Kvasnicka HM, Wang SA, Bagg A, Barbui T, Branford S, Bueso-Ramos CE, Cortes JE, Dal Cin P, DiNardo CD, Dombret H, Duncavage EJ, Ebert BL, Estey EH, Facchetti F, Foucar K, Gangat N, Gianelli U, Godley LA, Gokbuget N, Gotlib J, Hellstrom-Lindberg E, Hobbs GS, Hoffman R, Jabbour EJ, Kiladjian JJ, Larson RA, Le Beau MM, Loh ML, Lowenberg B, Macintyre E, Malcovati L, Mullighan CG, Niemeyer C, Odenike OM, Ogawa S, Orfao A, Papaemmanuil E, Passamonti F, Porkka K, Pui CH, Radich JP, Reiter A, Rozman M, Rudelius M, Savona MR, Schiffer CA, Schmitt-Graeff A, Shimamura A, Sierra J, Stock WA, Stone RM, Tallman MS, Thiele J, Tien HF, Tzankov A, Vannucchi AM, Vyas P, Wei AH, Weinberg OK, Wierzbowska A, Cazzola M, Dohner H and Tefferi A (2022) International consensus classification of myeloid neoplasms and acute leukemias: Integrating morphologic, clinical, and genomic data. Blood 140, 1200–1228. 10.1182/blood.2022015850. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bailey MH, Tokheim C, Porta-Pardo E, Sengupta S, Bertrand D, Weerasinghe A, Colaprico A, Wendl MC, Kim J, Reardon B, Ng PK, Jeong KJ, Cao S, Wang Z, Gao J, Gao Q, Wang F, Liu EM, Mularoni L, Rubio-Perez C, Nagarajan N, Cortes-Ciriano I, Zhou DC, Liang WW, Hess JM, Yellapantula VD, Tamborero D, Gonzalez-Perez A, Suphavilai C, Ko JY, Khurana E, Park PJ, Van Allen EM, Liang H, MC3 Working Group, Lawrence MS, Godzik A, Lopez-Bigas N, Stuart J, Wheeler D, Getz G, Chen K, Lazar AJ, Mills GB, Karchin R and Ding L (2018) Comprehensive characterization of Cancer driver genes and mutations. Cell 173, 371–385. e318. 10.1016/j.cell.2018.02.060. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Beger RD, Dunn W, Schmidt MA, Gross SS, Kirwan JA, Cascante M, Brennan L, Wishart DS, Oresic M, Hankemeier T, Broadhurst DI, Lane AN, Suhre K, Kastenmuller G, Sumner SJ, Thiele I, Fiehn O, Kaddurah-Daouk R and for “Precision Medicine and Pharmacometabolomics Task Group”-Metabolomics Society Initiative (2016) Metabolomics enables precision medicine: “A white paper, community perspective”. Metabolomics 12, 149. 10.1007/s11306-016-1094-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Benz AP, Hijazi Z, Lindback J, Connolly SJ, Eikelboom JW, Oldgren J, Siegbahn A and Wallentin L (2021) Biomarker-based risk prediction with the ABC-AF scores in patients with atrial fibrillation not receiving Oral anticoagulation. Circulation 143, 1863–1873. 10.1161/CIRCULATIONAHA.120.053100. [DOI] [PubMed] [Google Scholar]
  8. Berglund E, Barbany G, Orsmark-Pietras C, Fogelstrand L, Abrahamsson J, Golovleva I, Hallbook H, Hoglund M, Lazarevic V, Levin LA, Nordlund J, Noren-Nystrom U, Palle J, Thangavelu T, Palmqvist L, Wirta V, Cavelier L, Fioretos T and Rosenquist R (2022) A study protocol for validation and implementation of whole-Genome and -transcriptome sequencing as a comprehensive precision diagnostic test in acute Leukemias. Front Med (Lausanne) 9, 842507. 10.3389/fmed.2022.842507. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Carver T, Hartley S, Lee A, Cunningham AP, Archer S, Babb de Villiers C, Roberts J, Ruston R, Walter FM, Tischkowitz M, Easton DF and Antoniou AC (2021) CanRisk tool: A web interface for the prediction of breast and ovarian cancer risk and the likelihood of carrying genetic pathogenic variants. Cancer Epidemiology, Biomarkers & Prevention 30, 469–473. 10.1158/1055-9965.EPI-20-1319. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Chen L, Eriksson A, Westrom S, Pandzic T, Lehmann S, Cavelier L and Landegren U (2022) Ultra-sensitive monitoring of leukemia patients using superRCA mutation detection assays. Nature Communications 13, 4033. 10.1038/s41467-022-31397-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Crosby D, Bhatia S, Brindle KM, Coussens LM, Dive C, Emberton M, Esener S, Fitzgerald RC, Gambhir SS, Kuhn P, Rebbeck TR and Balasubramanian S (2022) Early detection of cancer. Science 375, eaay9040. 10.1126/science.aay9040. [DOI] [PubMed] [Google Scholar]
  12. de Leval L, Alizadeh AA, Bergsagel PL, Campo E, Davies AJ, Dogan A, Fitzgibbon J, Horwitz SM, Melnick AM, Morice WG, Morin RD, Nadel B, Pileri SA, Rosenquist R, Rossi D, Salaverria I, Steidl C, Treon SP, Zelenetz AD, Advani R, Allen CE, Ansell SM, Chan WC, Cook JR, Cook LB, d’Amore F, Dirnhofer S, Dreyling M, Dunleavy K, Feldman A, Fend F, Gaulard P, Ghia P, Gribben JG, Hermine O, Hodson DJ, Hsi ED, Inghirami GG, Jaffe ES, Karube K, Kataoka K, Klapper W, Kim WS, King RL, Ko YH, LaCasce AS, Lenz G, Martin-Subero I, Piris MA, Pittaluga S, Pasqualucci L, Quintanilla-Martinez L, Rodig SJ, Rosenwald A, Salles GA, San-Miguel J, Savage KJ, Sehn LH, Semenzato G, Staudt LM, Swerdlow SH, Tam CS, Trotman J, Vose J, Weigert O, Wilson WH, Winter JN, Wu CJ, Zinzani PL, Zucca E, Bagg A and Scott DWW (2022) Genomic profiling for clinical decision making in lymphoid neoplasms. Blood 140, 2193–2227. 10.1182/blood.2022015854. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Degasperi A, Zou X, Amarante TD, Martinez-Martinez A, Koh GCC, Dias JML, Heskin L, Chmelova L, Rinaldi G, Wang VYW, Nanda AS, Bernstein A, Momen SE, Young J, Perez-Gil D, Memari Y, Badja C, Shooter S, Czarnecki J, Brown MA, Davies HR, Genomics England Research Consortium and Nik-Zainal S (2022) Substitution mutational signatures in whole-genome-sequenced cancers in the UK population. Science 376, science.abl9283. 10.1126/science.abl9283. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Demenais F, Margaritte-Jeannin P, Barnes KC, Cookson WOC, Altmuller J, Ang W, Barr RG, Beaty TH, Becker AB, Beilby J, Bisgaard H, Bjornsdottir US, Bleecker E, Bonnelykke K, Boomsma DI, Bouzigon E, Brightling CE, Brossard M, Brusselle GG, Burchard E, Burkart KM, Bush A, Chan-Yeung M, Chung KF, Couto Alves A, Curtin JA, Custovic A, Daley D, de Jongste JC, Del-Rio-Navarro BE, Donohue KM, Duijts L, Eng C, Eriksson JG, Farrall M, Fedorova Y, Feenstra B, Ferreira MA, Australian Asthma Genetics Consortium (AAGC) collaborators, Freidin MB, Gajdos Z, Gauderman J, Gehring U, Geller F, Genuneit J, Gharib SA, Gilliland F, Granell R, Graves PE, Gudbjartsson DF, Haahtela T, Heckbert SR, Heederik D, Heinrich J, Heliovaara M, Henderson J, Himes BE, Hirose H, Hirschhorn JN, Hofman A, Holt P, Hottenga J, Hudson TJ, Hui J, Imboden M, Ivanov V, Jaddoe VWV, James A, Janson C, Jarvelin MR, Jarvis D, Jones G, Jonsdottir I, Jousilahti P, Kabesch M, Kahonen M, Kantor DB, Karunas AS, Khusnutdinova E, Koppelman GH, Kozyrskyj AL, Kreiner E, Kubo M, Kumar R, Kumar A, Kuokkanen M, Lahousse L, Laitinen T, Laprise C, Lathrop M, Lau S, Lee YA, Lehtimaki T, Letort S, Levin AM, Li G, Liang L, Loehr LR, London SJ, Loth DW, Manichaikul A, Marenholz I, Martinez FJ, Matheson MC, Mathias RA, Matsumoto K, Mbarek H, McArdle WL, Melbye M, Melen E, Meyers D, Michel S, Mohamdi H, Musk AW, Myers RA, Nieuwenhuis MAE, Noguchi E, O’Connor GT, Ogorodova LM, Palmer CD, Palotie A, Park JE, Pennell CE, Pershagen G, Polonikov A, Postma DS, Probst-Hensch N, Puzyrev VP, Raby BA, Raitakari OT, Ramasamy A, Rich SS, Robertson CF, Romieu I, Salam MT, Salomaa V, Schlunssen V, Scott R, Selivanova PA, Sigsgaard T, Simpson A, Siroux V, Smith LJ, Solodilova M, Standl M, Stefansson K, Strachan DP, Stricker BH, Takahashi A, Thompson PJ, Thorleifsson G, Thorsteinsdottir U, Tiesler CMT, Torgerson DG, Tsunoda T, Uitterlinden AG, van der Valk RJP, Vaysse A, Vedantam S, von Berg A, von Mutius E, Vonk JM, Waage J, Wareham NJ, Weiss ST, White WB, Wickman M, Widen E, Willemsen G, Williams LK, Wouters IM, Yang JJ, Zhao JH, Moffatt MF, Ober C and Nicolae DL (2018) Multiancestry association study identifies new asthma risk loci that colocalize with immune-cell enhancer marks. Nature Genetics 50, 42–53. 10.1038/s41588-017-0014-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Dohner H, Wei AH, Appelbaum FR, Craddock C, DiNardo CD, Dombret H, Ebert BL, Fenaux P, Godley LA, Hasserjian RP, Larson RA, Levine RL, Miyazaki Y, Niederwieser D, Ossenkoppele G, Rollig C, Sierra J, Stein EM, Tallman MS, Tien HF, Wang J, Wierzbowska A and Lowenberg B (2022) Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN. Blood 140, 1345–1377. 10.1182/blood.2022016867. [DOI] [PubMed] [Google Scholar]
  16. Dolzhenko E, Deshpande V, Schlesinger F, Krusche P, Petrovski R, Chen S, Emig-Agius D, Gross A, Narzisi G, Bowman B, Scheffler K, van Vugt J, French C, Sanchis-Juan A, Ibanez K, Tucci A, Lajoie BR, Veldink JH, Raymond FL, Taft RJ, Bentley DR and Eberle MA (2019) ExpansionHunter: A sequence-graph-based tool to analyze variation in short tandem repeat regions. Bioinformatics 35, 4754–4756. 10.1093/bioinformatics/btz431. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Duarte TT and Spencer CT (2016) Personalized proteomics: The future of precision medicine. Proteomes 4, 29. 10.3390/proteomes4040029. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Fink G, Tediosi F and Felder S (2022) Burden of Covid-19 restrictions: National, regional and global estimates. EClinicalMedicine 45, 101305. 10.1016/j.eclinm.2022.101305. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Fioretos T, Wirta V, Cavelier L, Berglund E, Friedman M, Akhras M, Botling J, Ehrencrona H, Engstrand L, Helenius G, Fagerqvist T, Gisselsson D, Gruvberger-Saal S, Gyllensten U, Heidenblad M, Hoglund K, Jacobsson B, Johansson M, Johansson A, Soller MJ, Landstrom M, Larsson P, Levin LA, Lindstrand A, Lovmar L, Lyander A, Melin M, Nordgren A, Nordmark G, Molling P, Palmqvist L, Palmqvist R, Repsilber D, Sikora P, Stenmark B, Soderkvist P, Stranneheim H, Strid T, Wheelock CE, Wadelius M, Wedell A, Edsjo A and Rosenquist R (2022) Implementing precision medicine in a regionally organized healthcare system in Sweden. Nature Medicine 28, 1980–1982. 10.1038/s41591-022-01963-4. [DOI] [PubMed] [Google Scholar]
  20. Franks PW, Melen E, Friedman M, Sundstrom J, Kockum I, Klareskog L, Almqvist C, Bergen SE, Czene K, Hagg S, Hall P, Johnell K, Malarstig A, Catrina A, Hagstrom H, Benson M, Gustav Smith J, Gomez MF, Orho-Melander M, Jacobsson B, Halfvarson J, Repsilber D, Oresic M, Jern C, Melin B, Ohlsson C, Fall T, Ronnblom L, Wadelius M, Nordmark G, Johansson A, Rosenquist R and Sullivan PF (2021) Technological readiness and implementation of genomic-driven precision medicine for complex diseases. Journal of Internal Medicine 290, 602–620. 10.1111/joim.13330. [DOI] [PubMed] [Google Scholar]
  21. GBD-Disease-Injury-Incidence-Prevalence-Collaborators (2017) Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: A systematic analysis for the global burden of disease study 2016. Lancet 390, 1211–1259. 10.1016/S0140-6736(17)32154-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Gilissen C, Hoischen A, Brunner HG and Veltman JA (2012) Disease gene identification strategies for exome sequencing. European Journal of Human Genetics 20, 490–497. 10.1038/ejhg.2011.258. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Hobbs BD, de Jong K, Lamontagne M, Bosse Y, Shrine N, Artigas MS, Wain LV, Hall IP, Jackson VE, Wyss AB, London SJ, North KE, Franceschini N, Strachan DP, Beaty TH, Hokanson JE, Crapo JD, Castaldi PJ, Chase RP, Bartz TM, Heckbert SR, Psaty BM, Gharib SA, Zanen P, Lammers JW, Oudkerk M, Groen HJ, Locantore N, Tal-Singer R, Rennard SI, Vestbo J, Timens W, Pare PD, Latourelle JC, Dupuis J, O’Connor GT, Wilk JB, Kim WJ, Lee MK, Oh YM, Vonk JM, de Koning HJ, Leng S, Belinsky SA, Tesfaigzi Y, Manichaikul A, Wang XQ, Rich SS, Barr RG, Sparrow D, Litonjua AA, Bakke P, Gulsvik A, Lahousse L, Brusselle GG, Stricker BH, Uitterlinden AG, Ampleford EJ, Bleecker ER, Woodruff PG, Meyers DA, Qiao D, Lomas DA, Yim JJ, Kim DK, Hawrylkiewicz I, Sliwinski P, Hardin M, Fingerlin TE, Schwartz DA, Postma DS, MacNee W, Tobin MD, Silverman EK, Boezen HM, Cho MH, COPDGene Investigators, ECLIPSE Investigators, LifeLines Investigators, SPIROMICS Research Group, International COPD Genetics Network Investigators, UK BiLEVE Investigators and International COPD Genetics Consortium (2017) Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis. Nature Genetics 49, 426–432. 10.1038/ng.3752. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Hong DS, DuBois SG, Kummar S, Farago AF, Albert CM, Rohrberg KS, van Tilburg CM, Nagasubramanian R, Berlin JD, Federman N, Mascarenhas L, Geoerger B, Dowlati A, Pappo AS, Bielack S, Doz F, McDermott R, Patel JD, Schilder RJ, Tahara M, Pfister SM, Witt O, Ladanyi M, Rudzinski ER, Nanda S, Childs BH, Laetsch TW, Hyman DM and Drilon A (2020) Larotrectinib in patients with TRK fusion-positive solid tumours: A pooled analysis of three phase 1/2 clinical trials. The Lancet Oncology 21, 531–540. 10.1016/S1470-2045(19)30856-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Ibanez K, Polke J, Hagelstrom RT, Dolzhenko E, Pasko D, Thomas ERA, Daugherty LC, Kasperaviciute D, Smith KR, WGS for Neurological Diseases Group, Deans ZC, Hill S, Fowler T, Scott RH, Hardy J, Chinnery PF, Houlden H, Rendon A, Caulfield MJ, Eberle MA, Taft RJ, Tucci A and Genomics England Research Consortium (2022) Whole genome sequencing for the diagnosis of neurological repeat expansion disorders in the UK: A retrospective diagnostic accuracy and prospective clinical validation study. Lancet Neurology 21, 234–245. 10.1016/S1474-4422(21)00462-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. The 100,000 Genomes Project Pilot Investigators, Smedley D, Smith KR, Martin A, Thomas EA, McDonagh EM, Cipriani V, Ellingford JM, Arno G, Tucci A, Vandrovcova J, Chan G, Williams HJ, Ratnaike T, Wei W, Stirrups K, Ibanez K, Moutsianas L, Wielscher M, Need A, Barnes MR, Vestito L, Buchanan J, Wordsworth S, Ashford S, Rehmstrom K, Li E, Fuller G, Twiss P, Spasic-Boskovic O, Halsall S, Floto RA, Poole K, Wagner A, Mehta SG, Gurnell M, Burrows N, James R, Penkett C, Dewhurst E, Graf S, Mapeta R, Kasanicki M, Haworth A, Savage H, Babcock M, Reese MG, Bale M, Baple E, Boustred C, Brittain H, de Burca A, Bleda M, Devereau A, Halai D, Haraldsdottir E, Hyder Z, Kasperaviciute D, Patch C, Polychronopoulos D, Matchan A, Sultana R, Ryten M, Tavares ALT, Tregidgo C, Turnbull C, Welland M, Wood S, Snow C, Williams E, Leigh S, Foulger RE, Daugherty LC, Niblock O, Leong IUS, Wright CF, Davies J, Crichton C, Welch J, Woods K, Abulhoul L, Aurora P, Bockenhauer D, Broomfield A, Cleary MA, Lam T, Dattani M, Footitt E, Ganesan V, Grunewald S, Compeyrot-Lacassagne S, Muntoni F, Pilkington C, Quinlivan R, Thapar N, Wallis C, Wedderburn LR, Worth A, Bueser T, Compton C, Deshpande C, Fassihi H, Haque E, Izatt L, Josifova D, Mohammed S, Robert L, Rose S, Ruddy D, Sarkany R, Say G, Shaw AC, Wolejko A, Habib B, Burns G, Hunter S, Grocock RJ, Humphray SJ, Robinson PN, Haendel M, Simpson MA, Banka S, Clayton-Smith J, Douzgou S, Hall G, Thomas HB, O’Keefe RT, Michaelides M, Moore AT, Malka S, Pontikos N, Browning AC, Straub V, Gorman GS, Horvath R, Quinton R, Schaefer AM, Yu-Wai-Man P, Turnbull DM, McFarland R, Taylor RW, O’Connor E, Yip J, Newland K, Morris HR, Polke J, Wood NW, Campbell C, Camps C, Gibson K, Koelling N, Lester T, Nemeth AH, Palles C, Patel S, Roy NBA, Sen A, Taylor J, Cacheiro P, Jacobsen JO, Seaby EG, Davison V, Chitty L, Douglas A, Naresh K, McMullan D, Ellard S, Temple IK, Mumford AD, Wilson G, Beales P, Bitner-Glindzicz M, Black G, Bradley JR, Brennan P, Burn J, Chinnery PF, Elliott P, Flinter F, Houlden H, Irving M, Newman W, Rahman S, Sayer JA, Taylor JC, Webster AR, Wilkie AOM, Ouwehand WH, Raymond FL, Chisholm J, Hill S, Bentley D, Scott RH, Fowler T, Rendon A and Caulfield M (2021) 100,000 genomes pilot on rare-disease diagnosis in health care—Preliminary report. The New England Journal of Medicine 385, 1868–1880. 10.1056/NEJMoa2035790. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Israel E, Denlinger LC, Bacharier LB, LaVange LM, Moore WC, Peters MC, Georas SN, Wright RJ, Mauger DT, Noel P, Akuthota P, Bach J, Bleecker ER, Cardet JC, Carr TF, Castro M, Cinelli A, Comhair SAA, Covar RA, Alexander LC, DiMango EA, Erzurum SC, Fahy JV, Fajt ML, Gaston BM, Hoffman EA, Holguin F, Jackson DJ, Jain S, Jarjour NN, Ji Y, Kenyon NJ, Kosorok MR, Kraft M, Krishnan JA, Kumar R, Liu AH, Liu MC, Ly NP, Marquis MA, Martinez FD, Moy JN, O’Neal WK, Ortega VE, Peden DB, Phipatanakul W, Ross K, Smith LJ, Szefler SJ, Teague WG, Tulchinsky AF, Vijayanand P, Wechsler ME, Wenzel SE, White SR, Zeki AA and Ivanova A (2021) PrecISE: Precision medicine in severe asthma: An adaptive platform trial with biomarker ascertainment. The Journal of Allergy and Clinical Immunology 147, 1594–1601. 10.1016/j.jaci.2021.01.037. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, Natarajan P, Lander ES, Lubitz SA, Ellinor PT and Kathiresan S (2018) Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nature Genetics 50, 1219–1224. 10.1038/s41588-018-0183-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Khoury JD, Solary E, Abla O, Akkari Y, Alaggio R, Apperley JF, Bejar R, Berti E, Busque L, Chan JKC, Chen W, Chen X, Chng WJ, Choi JK, Colmenero I, Coupland SE, Cross NCP, De Jong D, Elghetany MT, Takahashi E, Emile JF, Ferry J, Fogelstrand L, Fontenay M, Germing U, Gujral S, Haferlach T, Harrison C, Hodge JC, Hu S, Jansen JH, Kanagal-Shamanna R, Kantarjian HM, Kratz CP, Li XQ, Lim MS, Loeb K, Loghavi S, Marcogliese A, Meshinchi S, Michaels P, Naresh KN, Natkunam Y, Nejati R, Ott G, Padron E, Patel KP, Patkar N, Picarsic J, Platzbecker U, Roberts I, Schuh A, Sewell W, Siebert R, Tembhare P, Tyner J, Verstovsek S, Wang W, Wood B, Xiao W, Yeung C and Hochhaus A (2022) The 5th edition of the World Health Organization classification of Haematolymphoid Tumours: Myeloid and histiocytic/dendritic neoplasms. Leukemia 36, 1703–1719. 10.1038/s41375-022-01613-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Koboldt DC, Steinberg KM, Larson DE, Wilson RK and Mardis ER (2013) The next-generation sequencing revolution and its impact on genomics. Cell 155, 27–38. 10.1016/j.cell.2013.09.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Kobras CM, Fenton AK and Sheppard SK (2021) Next-generation microbiology: From comparative genomics to gene function. Genome Biology 22, 123. 10.1186/s13059-021-02344-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Kong D, Yu H, Sim X, White K, Tai ES, Wenk M and Teo AKK (2022) Multidisciplinary effort to drive precision-medicine for the future. Frontiers in Digital Health 4, 845405. 10.3389/fdgth.2022.845405. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Lakeman IMM, Rodriguez-Girondo M, Lee A, Ruiter R, Stricker BH, Wijnant SRA, Kavousi M, Antoniou AC, Schmidt MK, Uitterlinden AG, van Rooij J and Devilee P (2020) Validation of the BOADICEA model and a 313-variant polygenic risk score for breast cancer risk prediction in a Dutch prospective cohort. Genetics in Medicine 22, 1803–1811. 10.1038/s41436-020-0884-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD, Skora AD, Luber BS, Azad NS, Laheru D, Biedrzycki B, Donehower RC, Zaheer A, Fisher GA, Crocenzi TS, Lee JJ, Duffy SM, Goldberg RM, de la Chapelle A, Koshiji M, Bhaijee F, Huebner T, Hruban RH, Wood LD, Cuka N, Pardoll DM, Papadopoulos N, Kinzler KW, Zhou S, Cornish TC, Taube JM, Anders RA, Eshleman JR, Vogelstein B and Diaz LA Jr (2015) PD-1 blockade in tumors with mismatch-repair deficiency. The New England Journal of Medicine 372, 2509–2520. 10.1056/NEJMoa1500596. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Lethimonnier F and Levy Y (2018) Genomic medicine France 2025. Annals of Oncology 29, 783–784. 10.1093/annonc/mdy027. [DOI] [PubMed] [Google Scholar]
  36. Ley TJ, Mardis ER, Ding L, Fulton B, McLellan MD, Chen K, Dooling D, Dunford-Shore BH, McGrath S, Hickenbotham M, Cook L, Abbott R, Larson DE, Koboldt DC, Pohl C, Smith S, Hawkins A, Abbott S, Locke D, Hillier LW, Miner T, Fulton L, Magrini V, Wylie T, Glasscock J, Conyers J, Sander N, Shi X, Osborne JR, Minx P, Gordon D, Chinwalla A, Zhao Y, Ries RE, Payton JE, Westervelt P, Tomasson MH, Watson M, Baty J, Ivanovich J, Heath S, Shannon WD, Nagarajan R, Walter MJ, Link DC, Graubert TA, DiPersio JF and Wilson RK (2008) DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature 456, 66–72. 10.1038/nature07485. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Lin D, Shen L, Luo M, Zhang K, Li J, Yang Q, Zhu F, Zhou D, Zheng S, Chen Y and Zhou J (2021) Circulating tumor cells: Biology and clinical significance. Signal Transduction and Targeted Therapy 6, 404. 10.1038/s41392-021-00817-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Lindstrand A, Eisfeldt J, Pettersson M, Carvalho CMB, Kvarnung M, Grigelioniene G, Anderlid BM, Bjerin O, Gustavsson P, Hammarsjo A, Georgii-Hemming P, Iwarsson E, Johansson-Soller M, Lagerstedt-Robinson K, Lieden A, Magnusson M, Martin M, Malmgren H, Nordenskjold M, Norling A, Sahlin E, Stranneheim H, Tham E, Wincent J, Ygberg S, Wedell A, Wirta V, Nordgren A, Lundin J and Nilsson D (2019) From cytogenetics to cytogenomics: Whole-genome sequencing as a first-line test comprehensively captures the diverse spectrum of disease-causing genetic variation underlying intellectual disability. Genome Medicine 11, 68. 10.1186/s13073-019-0675-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Lindstrand A, Ek M, Kvarnung M, Anderlid BM, Bjorck E, Carlsten J, Eisfeldt J, Grigelioniene G, Gustavsson P, Hammarsjo A, Helgadottir HT, Hellstrom-Pigg M, Kuchinskaya E, Lagerstedt-Robinson K, Levin LA, Lieden A, Lindelof H, Malmgren H, Nilsson D, Svensson E, Paucar M, Sahlin E, Tesi B, Tham E, Winberg J, Winerdal M, Wincent J, Johansson Soller M, Pettersson M and Nordgren A (2022) Genome sequencing is a sensitive first-line test to diagnose individuals with intellectual disability. Genetics in Medicine 24, 2296–2307. 10.1016/j.gim.2022.07.022. [DOI] [PubMed] [Google Scholar]
  40. Lionel AC, Costain G, Monfared N, Walker S, Reuter MS, Hosseini SM, Thiruvahindrapuram B, Merico D, Jobling R, Nalpathamkalam T, Pellecchia G, Sung WWL, Wang Z, Bikangaga P, Boelman C, Carter MT, Cordeiro D, Cytrynbaum C, Dell SD, Dhir P, Dowling JJ, Heon E, Hewson S, Hiraki L, Inbar-Feigenberg M, Klatt R, Kronick J, Laxer RM, Licht C, MacDonald H, Mercimek-Andrews S, Mendoza-Londono R, Piscione T, Schneider R, Schulze A, Silverman E, Siriwardena K, Snead OC, Sondheimer N, Sutherland J, Vincent A, Wasserman JD, Weksberg R, Shuman C, Carew C, Szego MJ, Hayeems RZ, Basran R, Stavropoulos DJ, Ray PN, Bowdin S, Meyn MS, Cohn RD, Scherer SW and Marshall CR (2018) Improved diagnostic yield compared with targeted gene sequencing panels suggests a role for whole-genome sequencing as a first-tier genetic test. Genetics in Medicine 20, 435–443. 10.1038/gim.2017.119. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Malone ER, Oliva M, Sabatini PJB, Stockley TL and Siu LL (2020) Molecular profiling for precision cancer therapies. Genome Medicine 12, 8. 10.1186/s13073-019-0703-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Marani M, Katul GG, Pan WK and Parolari AJ (2021) Intensity and frequency of extreme novel epidemics. Proceedings of the National Academy of Sciences of the United States of America 118, e2105482118. 10.1073/pnas.2105482118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Markou A, Tzanikou E and Lianidou E (2022) The potential of liquid biopsy in the management of cancer patients. Seminars in Cancer Biology 84, 69–79. 10.1016/j.semcancer.2022.03.013. [DOI] [PubMed] [Google Scholar]
  44. Mavaddat N, Michailidou K, Dennis J, Lush M, Fachal L, Lee A, Tyrer JP, Chen TH, Wang Q, Bolla MK, Yang X, Adank MA, Ahearn T, Aittomaki K, Allen J, Andrulis IL, Anton-Culver H, Antonenkova NN, Arndt V, Aronson KJ, Auer PL, Auvinen P, Barrdahl M, Beane Freeman LE, Beckmann MW, Behrens S, Benitez J, Bermisheva M, Bernstein L, Blomqvist C, Bogdanova NV, Bojesen SE, Bonanni B, Borresen-Dale AL, Brauch H, Bremer M, Brenner H, Brentnall A, Brock IW, Brooks-Wilson A, Brucker SY, Bruning T, Burwinkel B, Campa D, Carter BD, Castelao JE, Chanock SJ, Chlebowski R, Christiansen H, Clarke CL, Collee JM, Cordina-Duverger E, Cornelissen S, Couch FJ, Cox A, Cross SS, Czene K, Daly MB, Devilee P, Dork T, Dos-Santos-Silva I, Dumont M, Durcan L, Dwek M, Eccles DM, Ekici AB, Eliassen AH, Ellberg C, Engel C, Eriksson M, Evans DG, Fasching PA, Figueroa J, Fletcher O, Flyger H, Forsti A, Fritschi L, Gabrielson M, Gago-Dominguez M, Gapstur SM, Garcia-Saenz JA, Gaudet MM, Georgoulias V, Giles GG, Gilyazova IR, Glendon G, Goldberg MS, Goldgar DE, Gonzalez-Neira A, Grenaker Alnaes GI, Grip M, Gronwald J, Grundy A, Guenel P, Haeberle L, Hahnen E, Haiman CA, Hakansson N, Hamann U, Hankinson SE, Harkness EF, Hart SN, He W, Hein A, Heyworth J, Hillemanns P, Hollestelle A, Hooning MJ, Hoover RN, Hopper JL, Howell A, Huang G, Humphreys K, Hunter DJ, Jakimovska M, Jakubowska A, Janni W, John EM, Johnson N, Jones ME, Jukkola-Vuorinen A, Jung A, Kaaks R, Kaczmarek K, Kataja V, Keeman R, Kerin MJ, Khusnutdinova E, Kiiski JI, Knight JA, Ko YD, Kosma VM, Koutros S, Kristensen VN, Kruger U, Kuhl T, Lambrechts D, Le Marchand L, Lee E, Lejbkowicz F, Lilyquist J, Lindblom A, Lindstrom S, Lissowska J, Lo WY, Loibl S, Long J, Lubinski J, Lux MP, MacInnis RJ, Maishman T, Makalic E, Maleva Kostovska I, Mannermaa A, Manoukian S, Margolin S, Martens JWM, Martinez ME, Mavroudis D, McLean C, Meindl A, Menon U, Middha P, Miller N, Moreno F, Mulligan AM, Mulot C, Munoz-Garzon VM, Neuhausen SL, Nevanlinna H, Neven P, Newman WG, Nielsen SF, Nordestgaard BG, Norman A, Offit K, Olson JE, Olsson H, Orr N, Pankratz VS, Park-Simon TW, Perez JIA, Perez-Barrios C, Peterlongo P, Peto J, Pinchev M, Plaseska-Karanfilska D, Polley EC, Prentice R, Presneau N, Prokofyeva D, Purrington K, Pylkas K, Rack B, Radice P, Rau-Murthy R, Rennert G, Rennert HS, Rhenius V, Robson M, Romero A, Ruddy KJ, Ruebner M, Saloustros E, Sandler DP, Sawyer EJ, Schmidt DF, Schmutzler RK, Schneeweiss A, Schoemaker MJ, Schumacher F, Schurmann P, Schwentner L, Scott C, Scott RJ, Seynaeve C, Shah M, Sherman ME, Shrubsole MJ, Shu XO, Slager S, Smeets A, Sohn C, Soucy P, Southey MC, Spinelli JJ, Stegmaier C, Stone J, Swerdlow AJ, Tamimi RM, Tapper WJ, Taylor JA, Terry MB, Thone K, Tollenaar R, Tomlinson I, Truong T, Tzardi M, Ulmer HU, Untch M, Vachon CM, van Veen EM, Vijai J, Weinberg CR, Wendt C, Whittemore AS, Wildiers H, Willett W, Winqvist R, Wolk A, Yang XR, Yannoukakos D, Zhang Y, Zheng W, Ziogas A, ABCTB Investigators, kConFab/AOCS Investigators, NBCS Collaborators, Dunning AM, Thompson DJ, Chenevix-Trench G, Chang-Claude J, Schmidt MK, Hall P, Milne RL, Pharoah PDP, Antoniou AC, Chatterjee N, Kraft P, Garcia-Closas M, Simard J and Easton DF (2019) Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. American Journal of Human Genetics 104, 21–34. 10.1016/j.ajhg.2018.11.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Mosele F, Remon J, Mateo J, Westphalen CB, Barlesi F, Lolkema MP, Normanno N, Scarpa A, Robson M, Meric-Bernstam F, Wagle N, Stenzinger A, Bonastre J, Bayle A, Michiels S, Bieche I, Rouleau E, Jezdic S, Douillard JY, Reis-Filho JS, Dienstmann R and Andre F (2020) Recommendations for the use of next-generation sequencing (NGS) for patients with metastatic cancers: A report from the ESMO precision medicine working group. Annals of Oncology 31, 1491–1505. 10.1016/j.annonc.2020.07.014. [DOI] [PubMed] [Google Scholar]
  46. Newman S, Nakitandwe J, Kesserwan CA, Azzato EM, Wheeler DA, Rusch M, Shurtleff S, Hedges DJ, Hamilton KV, Foy SG, Edmonson MN, Thrasher A, Bahrami A, Orr BA, Klco JM, Gu J, Harrison LW, Wang L, Clay MR, Ouma A, Silkov A, Liu Y, Zhang Z, Liu Y, Brady SW, Zhou X, Chang TC, Pande M, Davis E, Becksfort J, Patel A, Wilkinson MR, Rahbarinia D, Kubal M, Maciaszek JL, Pastor V, Knight J, Gout AM, Wang J, Gu Z, Mullighan CG, McGee RB, Quinn EA, Nuccio R, Mostafavi R, Gerhardt EL, Taylor LM, Valdez JM, Hines-Dowell SJ, Pappo AS, Robinson G, Johnson LM, Pui CH, Ellison DW, Downing JR, Zhang J and Nichols KE (2021) Genomes for kids: The scope of pathogenic mutations in pediatric cancer revealed by comprehensive DNA and RNA sequencing. Cancer Discovery 11, 3008–3027. 10.1158/2159-8290.CD-20-1631. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Nguengang Wakap S, Lambert DM, Olry A, Rodwell C, Gueydan C, Lanneau V, Murphy D, Le Cam Y and Rath A (2020) Estimating cumulative point prevalence of rare diseases: Analysis of the Orphanet database. European Journal of Human Genetics 28, 165–173. 10.1038/s41431-019-0508-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. O’Sullivan JW, Raghavan S, Marquez-Luna C, Luzum JA, Damrauer SM, Ashley EA, O’Donnell CJ, Willer CJ, Natarajan P, on behalf of the American Heart Association Council on Genomic and Precision Medicine, Council on Clinical Cardiology, Council on Arteriosclerosis, Thrombosis and Vascular Biology, Council on Cardiovascular Radiology and Intervention, Council on Lifestyle and Cardiometabolic Health and Council on Peripheral Vascular Disease (2022) Polygenic risk scores for cardiovascular disease: A scientific statement from the American Heart Association. Circulation 146, e93–e118. 10.1161/CIR.0000000000001077. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. PCAWG-Consortium (2020) Pan-cancer analysis of whole genomes. Nature 578, 82–93. 10.1038/s41586-020-1969-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Peiffer-Smadja N, Delliere S, Rodriguez C, Birgand G, Lescure FX, Fourati S and Ruppe E (2020) Machine learning in the clinical microbiology laboratory: Has the time come for routine practice? Clinical Microbiology and Infection 26, 1300–1309. 10.1016/j.cmi.2020.02.006. [DOI] [PubMed] [Google Scholar]
  51. Pfisterer U, Braunig J, Brattas P, Heidenblad M, Karlsson G and Fioretos T (2021) Single-cell sequencing in translational cancer research and challenges to meet clinical diagnostic needs. Genes, Chromosomes & Cancer 60, 504–524. 10.1002/gcc.22944. [DOI] [PubMed] [Google Scholar]
  52. Pires SM, Wyper GMA, Wengler A, Penalvo JL, Haneef R, Moran D, Cuschieri S, Redondo HG, De Pauw R, McDonald SA, Moon L, Shedrawy J, Pallari E, Charalampous P, Devleesschauwer B and Von Der Lippe E (2022) Burden of disease of COVID-19: Strengthening the collaboration for National Studies. Frontiers in Public Health 10, 907012. 10.3389/fpubh.2022.907012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Purushothaman S, Meola M and Egli A (2022) Combination of whole genome sequencing and metagenomics for microbiological diagnostics. International Journal of Molecular Sciences 23, 9834. 10.3390/ijms23179834. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Rosenquist R, Frohling S and Stamatopoulos K (2022) Precision medicine in cancer: A paradigm shift. Seminars in Cancer Biology 84, 1–2. 10.1016/j.semcancer.2022.05.008. [DOI] [PubMed] [Google Scholar]
  55. Sanchez-Vega F, Mina M, Armenia J, Chatila WK, Luna A, La KC, Dimitriadoy S, Liu DL, Kantheti HS, Saghafinia S, Chakravarty D, Daian F, Gao Q, Bailey MH, Liang WW, Foltz SM, Shmulevich I, Ding L, Heins Z, Ochoa A, Gross B, Gao J, Zhang H, Kundra R, Kandoth C, Bahceci I, Dervishi L, Dogrusoz U, Zhou W, Shen H, Laird PW, Way GP, Greene CS, Liang H, Xiao Y, Wang C, Iavarone A, Berger AH, Bivona TG, Lazar AJ, Hammer GD, Giordano T, Kwong LN, McArthur G, Huang C, Tward AD, Frederick MJ, McCormick F, Meyerson M, Cancer Genome Atlas Research N, Van Allen EM, Cherniack AD, Ciriello G, Sander C and Schultz N (2018) Oncogenic signaling pathways in the cancer genome atlas. Cell 173, 321–337, e310. 10.1016/j.cell.2018.03.035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Saunders G, Baudis M, Becker R, Beltran S, Beroud C, Birney E, Brooksbank C, Brunak S, Van den Bulcke M, Drysdale R, Capella-Gutierrez S, Flicek P, Florindi F, Goodhand P, Gut I, Heringa J, Holub P, Hooyberghs J, Juty N, Keane TM, Korbel JO, Lappalainen I, Leskosek B, Matthijs G, Mayrhofer MT, Metspalu A, Navarro A, Newhouse S, Nyronen T, Page A, Persson B, Palotie A, Parkinson H, Rambla J, Salgado D, Steinfelder E, Swertz MA, Valencia A, Varma S, Blomberg N and Scollen S (2019) Leveraging European infrastructures to access 1 million human genomes by 2022. Nature Reviews Genetics 20, 693–701. 10.1038/s41576-019-0156-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Socialstyrelsen (2020) Statistics on cancer incidence, 2020. Available at https://www.socialstyrelsen.se/globalassets/sharepoint-dokument/artikelkatalog/statistik/2021-12-7701.pdf (accessed 12 October 2022).
  58. Stark Z, Boughtwood T, Phillips P, Christodoulou J, Hansen DP, Braithwaite J, Newson AJ, Gaff CL, Sinclair AH and North KN (2019) Australian genomics: A federated model for integrating genomics into healthcare. American Journal of Human Genetics 105, 7–14. 10.1016/j.ajhg.2019.06.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Stenzinger A, Edsjo A, Ploeger C, Friedman M, Frohling S, Wirta V, Seufferlein T, Botling J, Duyster J, Akhras M, Thimme R, Fioretos T, Bitzer M, Cavelier L, Schirmacher P, Malek N, Rosenquist R, GMS working group and ZPM working group (2022) Trailblazing precision medicine in Europe: A joint view by Genomic Medicine Sweden and the centers for personalized medicine. ZPM, in Germany. Seminars in Cancer Biology 84, 242–254. 10.1016/j.semcancer.2021.05.026. [DOI] [PubMed] [Google Scholar]
  60. Stranneheim H and Wedell A (2016) Exome and genome sequencing: A revolution for the discovery and diagnosis of monogenic disorders. Journal of Internal Medicine 279, 3–15. 10.1111/joim.12399. [DOI] [PubMed] [Google Scholar]
  61. Stranneheim H, Lagerstedt-Robinson K, Magnusson M, Kvarnung M, Nilsson D, Lesko N, Engvall M, Anderlid BM, Arnell H, Johansson CB, Barbaro M, Bjorck E, Bruhn H, Eisfeldt J, Freyer C, Grigelioniene G, Gustavsson P, Hammarsjo A, Hellstrom-Pigg M, Iwarsson E, Jemt A, Laaksonen M, Enoksson SL, Malmgren H, Naess K, Nordenskjold M, Oscarson M, Pettersson M, Rasi C, Rosenbaum A, Sahlin E, Sardh E, Stodberg T, Tesi B, Tham E, Thonberg H, Tohonen V, von Dobeln U, Vassiliou D, Vonlanthen S, Wikstrom AC, Wincent J, Winqvist O, Wredenberg A, Ygberg S, Zetterstrom RH, Marits P, Soller MJ, Nordgren A, Wirta V, Lindstrand A and Wedell A (2021) Integration of whole genome sequencing into a healthcare setting: High diagnostic rates across multiple clinical entities in 3219 rare disease patients. Genome Medicine 13, 40. 10.1186/s13073-021-00855-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Stratton MR, Campbell PJ and Futreal PA (2009) The cancer genome. Nature 458, 719–724. 10.1038/nature07943. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Trotman J, Armstrong R, Firth H, Trayers C, Watkins J, Allinson K, Jacques TS, Nicholson JC, Burke GAA, Genomics England Research C, Behjati S, Murray MJ, Hook CE and Tarpey P (2022) The NHS England 100,000 genomes project: Feasibility and utility of centralised genome sequencing for children with cancer. British Journal of Cancer 127, 137–144. 10.1038/s41416-022-01788-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Tsimberidou AM, Fountzilas E, Nikanjam M and Kurzrock R (2020) Review of precision cancer medicine: Evolution of the treatment paradigm. Cancer Treatment Reviews 86, 102019. 10.1016/j.ctrv.2020.102019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Turnbull C (2018) Introducing whole-genome sequencing into routine cancer care: The genomics England 100 000 genomes project. Annals of Oncology 29, 784–787. 10.1093/annonc/mdy054. [DOI] [PubMed] [Google Scholar]
  66. Turnbull C, Scott RH, Thomas E, Jones L, Murugaesu N, Pretty FB, Halai D, Baple E, Craig C, Hamblin A, Henderson S, Patch C, O’Neill A, Devereau A, Smith K, Martin AR, Sosinsky A, McDonagh EM, Sultana R, Mueller M, Smedley D, Toms A, Dinh L, Fowler T, Bale M, Hubbard T, Rendon A, Hill S, Caulfield MJ and Genomes P (2018) The 100 000 genomes project: Bringing whole genome sequencing to the NHS. BMJ 361, k1687. 10.1136/bmj.k1687. [DOI] [PubMed] [Google Scholar]
  67. van der Sanden B, Schobers G, Corominas Galbany J, Koolen DA, Sinnema M, van Reeuwijk J, Stumpel C, Kleefstra T, de Vries BBA, Ruiterkamp-Versteeg M, Leijsten N, Kwint M, Derks R, Swinkels H, den Ouden A, Pfundt R, Rinne T, de Leeuw N, Stegmann AP, Stevens SJ, van den Wijngaard A, Brunner HG, Yntema HG, Gilissen C, Nelen MR and Vissers L (2022) The performance of genome sequencing as a first-tier test for neurodevelopmental disorders. European Journal of Human Genetics 31, 81–88. 10.1038/s41431-022-01185-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Walter W, Pfarr N, Meggendorfer M, Jost P, Haferlach T and Weichert W (2022) Next-generation diagnostics for precision oncology: Preanalytical considerations, technical challenges, and available technologies. Seminars in Cancer Biology 84, 3–15. 10.1016/j.semcancer.2020.10.015. [DOI] [PubMed] [Google Scholar]
  69. Wasterlid T, Cavelier L, Haferlach C, Konopleva M, Frohling S, Ostling P, Bullinger L, Fioretos T and Smedby KE (2022) Application of precision medicine in clinical routine in haematology-challenges and opportunities. Journal of Internal Medicine 292, 243–261. 10.1111/joim.13508. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Wheelock CE and Rappaport SM (2020) The role of gene-environment interactions in lung disease: The urgent need for the exposome. The European Respiratory Journal 55, 1902064. 10.1183/13993003.02064-2019. [DOI] [PubMed] [Google Scholar]
  71. WHO-Classification-of-Tumours-Editorial-Board. (2021a) Central Nervous System Tumours: WHO Classification of Tumours, 5th Edn, Vol. 6 (IARC).
  72. WHO-Classification-of-Tumours-Editorial-Board. (2021b) Thoracic Tumours: WHO Classification of Tumours, 5th Edn, Vol. 5 (IARC).
  73. Wong M, Mayoh C, Lau LMS, Khuong-Quang DA, Pinese M, Kumar A, Barahona P, Wilkie EE, Sullivan P, Bowen-James R, Syed M, Martincorena I, Abascal F, Sherstyuk A, Bolanos NA, Baber J, Priestley P, Dolman MEM, Fleuren EDG, Gauthier ME, Mould EVA, Gayevskiy V, Gifford AJ, Grebert-Wade D, Strong PA, Manouvrier E, Warby M, Thomas DM, Kirk J, Tucker K, O’Brien T, Alvaro F, McCowage GB, Dalla-Pozza L, Gottardo NG, Tapp H, Wood P, Khaw SL, Hansford JR, Moore AS, Norris MD, Trahair TN, Lock RB, Tyrrell V, Haber M, Marshall GM, Ziegler DS, Ekert PG and Cowley MJ (2020) Whole genome, transcriptome and methylome profiling enhances actionable target discovery in high-risk pediatric cancer. Nature Medicine 26, 1742–1753. 10.1038/s41591-020-1072-4. [DOI] [PubMed] [Google Scholar]
  74. Yang SR, Schultheis AM, Yu H, Mandelker D, Ladanyi M and Buttner R (2022) Precision medicine in non-small cell lung cancer: Current applications and future directions. Seminars in Cancer Biology 84, 184–198. 10.1016/j.semcancer.2020.07.009. [DOI] [PubMed] [Google Scholar]
  75. Yengo, L., Vedantam, S., Marouli, E., Sidorenko, J., Bartell, E., Sakaue, S., Graff, M., Eliasen, A.U., Jiang, Y., Raghavan, S., Miao, J., Arias, J.D., Mukamel, R.E., Spracklen, C.N., Yin, X., Chen, S.-H., Ferreira, T., Yingjie, J., Karedera, T., Lüll, K., Lin, K., Malden, D.E., Medina-Gomez, C., Machado, M., Moore, A., Rüeger, S., Ahluwalia, T.S., Akiyama, M., Allison, M.A., Alvarez, M., Andersen, M.K., Ani, A., Appadurai, V., Arbeeva, L., Bhaskar, S., Bielak, L.F., Bollepalli, S., Bonnycastle, L.L., Bork-Jensen, J., Bradfield, J.P., Bradford, Y., Braund, P.S., Brody, J.A., Burgdorf, K.S., Cade, B.E., Cai, H., Cai, Q., Campbell, A., Cañadas-Garre, M., Catamo, E., Chai, J.-F., Chai, X., Chang, L.-C., Chang, Y.-C., Chen, C.-H., Chesi, A., Choi, S.H., Chung, R.-H., Cocca, M., Concas, M.P., Couture, C., Cuellar-Partida, G., Danning, R., Daw, E.W., Degenhard, F., Delgado, G.E., Delitala, A., Demirkan, A., Deng, X., Devineni, P., Dietl, A., Dimitriou, M., Dimitrov, L., Dorajoo, R., Ekici, A.B., Engmann, J.E., Fairhurst-Hunter, Z., Farmaki, A.-E., Faul, J.D., Fernandez-Lopez, J.-C., Forer, L., Francescatto, M., Freitag-Wolf, S., Fuchsberger, C., Galesloot, T.E., Gao, Y., Gao, Z., Geller, F., Giannakopoulou, O., Giulianini, F., Gjesing, A.P., Goel, A., Gordon, S.D., Gorski, M., Graham, S.E., Grove, J., Guo, X., Gustafsson, S., Haessler, J., Hansen, T.F., Havulinna, A., Haworth, S.J., He, J., Heard-Costa, N., Hebbar, P., Hindy, G., Ho, Y.-L.A., Hofer, E., Holliday, E., Horn, K., Hornsby, W.E., Hottenga, J.-J., Huang, H., Huang, J., Huerta-Chagoya, A., Huffman, J.E., Hung, Y.-J., Huo, S., Hwang, M.Y., Iha, H., Ikeda, D.D., Isono, M., Jackson, A.U., Jäger, S., Jansen, I.E., Johansson, I., Jonas, J.B., Jonsson, A., Jørgensen, T., Kalafati, I.-P., Kanai, M., Kanoni, S., Kårhus, L.L., Kasturiratne, A., Katsuya, T., Kawaguchi, T., Kember, R.L., Kentistou, K.A., Kim, H.-N., Kim, Y.J., Kleber, M.E., Knol, M.J., Kurbasic, A., Lauzon, M., Le, P., Lea, R., Lee, J.-Y., Leonard, H.L., Li, S.A., Li, X., Li, X., Liang, J., Lin, H., Lin, S.-Y., Liu, J., Liu, X., Lo, K.S., Long, J., Lores-Motta, L., Luan, J.A., Lyssenko, V., Lyytikäinen, L.-P., Mahajan, A., Mamakou, V., Mangino, M., Manichaikul, A., Marten, J., Mattheisen, M., Mavarani, L., McDaid, A.F., Meidtner, K., Melendez, T.L., Mercader, J.M., Milaneschi, Y., Miller, J.E., Millwood, I.Y., Mishra, P.P., Mitchell, R.E., Møllehave, L.T., Morgan, A., Mucha, S., Munz, M., Nakatochi, M., Nelson, C.P., Nethander, M., Nho, C.W., Nielsen, A.A., Nolte, I.M., Nongmaithem, S.S., Noordam, R., Ntalla, I., Nutile, T., Pandit, A., Christofidou, P., Pärna, K., Pauper, M., Petersen, E.R.B., Petersen, L.V., Pitkänen, N., Polašek, O., Poveda, A., Preuss, M.H., Pyarajan, S., Raffield, L.M., Rakugi, H., Ramirez, J., Rasheed, A., Raven, D., Rayner, N.W., Riveros, C., Rohde, R., Ruggiero, D., Ruotsalainen, S.E., Ryan, K.A., Sabater-Lleal, M., Saxena, R., Scholz, M., Sendamarai, A., Shen, B., Shi, J., Shin, J.H., Sidore, C., Sim, X., Sitlani, C.M., Slieker, R.C., Smit, R.A.J., Smith, A.V., Smith, J.A., Smyth, L.J., Southam, L., Steinthorsdottir, V., Sun, L., Takeuchi, F., Priyanka Tallapragada, D.S., Taylor, K.D., Tayo, B.O., Tcheandjieu, C., Terzikhan, N., Tesolin, P., Teumer, A., Theusch, E., Thompson, D.J., Thorleifsson, G., Timmers, P.R.H.J., Trompet, S., Turman, C., Vaccargiu, S., van der Laan, S.W., van der Most, P.J., van Klinken, J.B., van Setten, J., Verma, S.S., Verweij, N., Veturi, Y., Wang, C.A., Wang, C., Wang, L., Wang, Z., Warren, H.R., Wei, W.B., Wickremasinghe, A.R., Wielscher, M., Wiggins, K.L., Winsvold, B.S., Wong, A., Wu, Y., Wuttke, M., Xia, R., Xie, T., Yamamoto, K., Yang, J., Yao, J., Young, H., Yousri, N.A., Yu, L., Zeng, L., Zhang, W., Zhang, X., Zhao, J.-H., Zhao, W., Zhou, W., Zimmermann, M.E., Zoledziewska, M., Adair, L.S., Adams, H.H.H., Aguilar-Salinas, C.A., Al-Mulla, F., Arnett, D.K., Asselbergs, F.W., Åsvold, B.O., Attia, J., Banas, B., Bandinelli, S., Bennett, D.A., Bergler, T., Bharadwaj, D., Biino, G., Bisgaard, H., Boerwinkle, E., Böger, C.A., Bønnelykke, K., Boomsma, D.I., Børglum, A.D., Borja, J.B., Bouchard, C., Bowden, D.W., Brandslund, I., Brumpton, B., Buring, J.E., Caulfield, M.J., Chambers, J.C., Chandak, G.R., Chanock, S.J., Chaturvedi, N., Chen, Y.-D.I., Chen, Z., Cheng, C.-Y., Christophersen, I.E., Ciullo, M., Cole, J.W., Collins, F.S., Cooper, R.S., Cruz, M., Cucca, F., Cupples, L.A., Cutler, M.J., Damrauer, S.M., Dantoft, T.M., Borst, G.J.D., de Groot, L.C.P.G.M., De Jager, P.L., de Kleijn, D.P.V., de Silva, H.J., Dedoussis, G.V., den Hollander, A.I., Du, S., Easton, D.F., Elders, P.J.M., Eliassen, A.H., Ellinor, P.T., Elmståhl, S., Erdmann, J., Evans, M.K., Fatkin, D., Feenstra, B., Feitosa, M.F., Ferrucci, L., Ford, I., Fornage, M., Franke, A., Franks, P.W., Freedman, B.I., Gasparini, P., Gieger, C., Girotto, G., Goddard, M.E., Golightly, Y.M., Gonzalez-Villalpando, C., Gordon-Larsen, P., Grallert, H., Grant, S.F.A., Grarup, N., Griffiths, L., Groop, L., Gudnason, V., Haiman, C., Hakonarson, H., Hansen, T., Hartman, C.A., Hattersley, A.T., Hayward, C., Heckbert, S.R., Heng, C.-K., Hengstenberg, C., Hewitt, A.W., Hishigaki, H., Hoyng, C.B., Huang, P.L., Huang, W., Hunt, S.C., Hveem, K., Hyppönen, E., Iacono, W.G., Ichihara, S., Ikram, M.A., Isasi, C.R., Jackson, R.D., Jarvelin, M.-R., Jin, Z.-B., Jöckel, K.-H., Joshi, P.K., Jousilahti, P., Jukema, J.W., Kähönen, M., Kamatani, Y., Kang, K.D., Kaprio, J., Kardia, S.L.R., Karpe, F., Kato, N., Kee, F., Kessler, T., Khera, A.V., Khor, C.C., Kiemeney, L.A.L.M., Kim, B.-J., Kim, E.K., Kim, H.-L., Kirchhof, P., Kivimaki, M., Koh, W.-P., Koistinen, H.A., Kolovou, G.D., Kooner, J.S., Kooperberg, C., Köttgen, A., Kovacs, P., Kraaijeveld, A., Kraft, P., Krauss, R.M., Kumari, M., Kutalik, Z., Laakso, M., Lange, L.A., Langenberg, C., Launer, L.J., Marchand, L.L., Lee, H., Lee, N.R., Lehtimäki, T., Li, H., Li, L., Lieb, W., Lin, X., Lind, L., Linneberg, A., Liu, C.-T., Liu, J., Loeffler, M., London, B., Lubitz, S.A., Lye, S.J., Mackey, D.A., Mägi, R., Magnusson, P.K.E., Marcus, G.M., Vidal, P.M., Martin, N.G., März, W., Matsuda, F., McGarrah, R.W., McGue, M., McKnight, A.J., Medland, S.E., Mellström, D., Metspalu, A., Mitchell, B.D., Mitchell, P., Mook-Kanamori, D.O., Morris, A.D., Mucci, L.A., Munroe, P.B., Nalls, M.A., Nazarian, S., Nelson, A.E., Neville, M.J., Newton-Cheh, C., Nielsen, C.S., Nöthen, M.M., Ohlsson, C., Oldehinkel, A.J., Orozco, L., Pahkala, K., Pajukanta, P., Palmer, C.N.A., Parra, E.J., Pattaro, C., Pedersen, O., Pennell, C.E., Penninx, B.W.J.H., Perusse, L., Peters, A., Peyser, P.A., Porteous, D.J., Posthuma, D., Power, C., Pramstaller, P.P., Province, M.A., Qi, Q., Qu, J., Rader, D.J., Raitakari, O.T., Ralhan, S., Rallidis, L.S., Rao, D.C., Redline, S., Reilly, D.F., Reiner, A.P., Rhee, S.Y., Ridker, P.M., Rienstra, M., Ripatti, S., Ritchie, M.D., Roden, D.M., Rosendaal, F.R., Rotter, J.I., Rudan, I., Rutters, F., Sabanayagam, C., Saleheen, D., Salomaa, V., Samani, N.J., Sanghera, D.K., Sattar, N., Schmidt, B., Schmidt, H., Schmidt, R., Schulze, M.B., Schunkert, H., Scott, L.J., Scott, R.J., Sever, P., Shiroma, E.J., Shoemaker, M.B., Shu, X.-O., Simonsick, E.M., Sims, M., Singh, J.R., Singleton, A.B., Sinner, M.F., Smith, J.G., Snieder, H., Spector, T.D., Stampfer, M.J., Stark, K.J., Strachan, D.P., t Hart, L.M., Tabara, Y., Tang, H., Tardif, J.-C., Thanaraj, T.A., Timpson, N.J., Tönjes, A., Tremblay, A., Tuomi, T., Tuomilehto, J., Tusié-Luna, M.-T., Uitterlinden, A.G., van Dam, R.M., van der Harst, P., Van der Velde, N., van Duijn, C.M., van Schoor, N., Vitart, V., Völker, U., Vollenweider, P., Völzke, H., Vrieze, S., Wacher-Rodarte, N.H., Walker, M., Wang, Y.X., Wareham, N.J., Watanabe, R.M., Watkins, H., Weir, D.R., Werge, T.M., Widen, E., Wilkens, L.R., Willemsen, G., Willett, W.C., Wilson, J.F., Wong, T.-Y., Woo, J.-T., Wright, A.F., Wu, J.-Y., Xu, H., Yajnik, C.S., Yokota, M., Yuan, J.-M., Zeggini, E., Zemel, B.S., Zheng, W., Zhu, X., Zmuda, J.M., Zonderman, A.B., Zwart, J.-A., Chasman, D.I., Cho, Y.S., Heid, I.M., McCarthy, M.I., Ng, M.C.Y., O’Donnell, C.J., Rivadeneira, F., Thorsteinsdottir, U., Sun, Y.V., Tai, E.S., Boehnke, M., Deloukas, P., Justice, A.E., Lindgren, C.M., Loos, R.J.F., Mohlke, K.L., North, K.E., Stefansson, K., Walters, R.G., Winkler, T.W., Young, K.L., Loh, P.-R., Yang, J., Esko, T., Assimes, T.L., Auton, A., Abecasis, G.R., Willer, C.J., Locke, A.E., Berndt, S.I., Lettre, G., Frayling, T.M., Okada, Y., Wood, A.R., Visscher, P.M., and Hirschhorn, J.N. (2022) A saturated map of common genetic variants associated with human height from 5.4 million individuals of diverse ancestries. Nature 610, 704–712. 10.1101/2022.01.07.475305. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Zehir A, Benayed R, Shah RH, Syed A, Middha S, Kim HR, Srinivasan P, Gao J, Chakravarty D, Devlin SM, Hellmann MD, Barron DA, Schram AM, Hameed M, Dogan S, Ross DS, Hechtman JF, DeLair DF, Yao J, Mandelker DL, Cheng DT, Chandramohan R, Mohanty AS, Ptashkin RN, Jayakumaran G, Prasad M, Syed MH, Rema AB, Liu ZY, Nafa K, Borsu L, Sadowska J, Casanova J, Bacares R, Kiecka IJ, Razumova A, Son JB, Stewart L, Baldi T, Mullaney KA, Al-Ahmadie H, Vakiani E, Abeshouse AA, Penson AV, Jonsson P, Camacho N, Chang MT, Won HH, Gross BE, Kundra R, Heins ZJ, Chen HW, Phillips S, Zhang H, Wang J, Ochoa A, Wills J, Eubank M, Thomas SB, Gardos SM, Reales DN, Galle J, Durany R, Cambria R, Abida W, Cercek A, Feldman DR, Gounder MM, Hakimi AA, Harding JJ, Iyer G, Janjigian YY, Jordan EJ, Kelly CM, Lowery MA, Morris LGT, Omuro AM, Raj N, Razavi P, Shoushtari AN, Shukla N, Soumerai TE, Varghese AM, Yaeger R, Coleman J, Bochner B, Riely GJ, Saltz LB, Scher HI, Sabbatini PJ, Robson ME, Klimstra DS, Taylor BS, Baselga J, Schultz N, Hyman DM, Arcila ME, Solit DB, Ladanyi M and Berger MF (2017) Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nature Medicine 23, 703–713. 10.1038/nm.4333. [DOI] [PMC free article] [PubMed] [Google Scholar]
Camb Prism Precis Med. doi: 10.1017/pcm.2023.3.pr2

Review: Building a precision medicine infrastructure at a national level: The Swedish experience — R0/PR2

Theodora Katsila 1

Comments to Author: Edsjo et al present GMS via regional centres for genomic medicine at all Swedish university hospitals and a common infrastructure for data sharing with the aim of precision diagnostics and/or precision medicine in rare diseases and complex traits, such as cancer and infectious diseases. This is a well written manuscript with a clear rationale. Challenges and opportunities are clearly and eloquently described. The authors include their views and comments on current strategies based on their national experience. Such findings are of benefit to the readership in particular when it comes to implementation

That said it would be nice for the authors to elaborate on the tasks that could be taken by information technologies to ease translation to the clinic considering limitations and future opportunities.

Camb Prism Precis Med. doi: 10.1017/pcm.2023.3.pr3

Recommendation: Building a precision medicine infrastructure at a national level: The Swedish experience — R0/PR3

Editor: Mark Caulfield1

No accompanying comment.

Camb Prism Precis Med. doi: 10.1017/pcm.2023.3.pr4

Decision: Building a precision medicine infrastructure at a national level: The Swedish experience — R0/PR4

Editor: Anna Dominiczak1

No accompanying comment.

Articles from Cambridge Prisms: Precision Medicine are provided here courtesy of Cambridge University Press

RESOURCES