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

Background Childhood cancer predisposition (ChiCaP) syndromes are increasingly recognized as contributing factors to childhood cancer development. Yet, due to variable availability of germline testing, many children with ChiCaP might go undetected today. We report results from the nationwide and prospective ChiCaP study that investigated diagnostic yield and clinical impact of integrating germline whole-genome sequencing (gWGS) with tumor sequencing and systematic phenotyping in children with solid tumors. Methods gWGS was performed in 309 children at diagnosis of CNS (n = 123, 40%) or extracranial (n = 186, 60%) solid tumors and analyzed for disease -causing variants in 189 known cancer predisposing genes. Tumor sequencing data were available for 74% (227/309) of patients. In addition, a standardized clinical assessment for underlying predisposition was performed in 95% (293/309) of patients. Findings The prevalence of ChiCaP diagnoses was 11% (35/309), of which 69% (24/35) were unknown at inclusion (diagnostic yield 8%, 24/298). A second -hit and/or relevant mutational signature was observed in 19/21 (90%) tumors with informative data. ChiCaP diagnoses were more prevalent among patients with retinoblastomas (50%, 6/12) and high-grade astrocytomas (37%, 6/16), and in those with non -cancer related features (23%, 20/88), and >= 2 positive ChiCaP criteria (28%, 22/79). ChiCaP diagnoses were autosomal dominant in 80% (28/35) of patients, yet con fi rmed de novo in 64% (18/28). The 35 ChiCaP fi ndings resulted in tailored surveillance (86%, 30/35) and treatment recommendations (31%, 11/35). Interpretation Overall, our results demonstrate that systematic phenotyping, combined with genomics-based diagnostics of ChiCaP in children with solid tumors is feasible in large-scale clinical practice and critically guides personalized care in a sizable proportion of patients.

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

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

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

Background: The etiology of most mesenchymal tumors is unknown, and knowledge about syndromes with an increased risk of tumors in bone or soft tissue is sparse. Methods: We present a prospective germline analysis of 312 patients with tumors suspected of being sarcomas at a tertiary sarcoma center. Germline and tumor whole genome sequencing, tumor transcriptome, and methylome analyses were performed. Results: Germline pathogenic or likely pathogenic variants associated with an increased risk of tumors were detected in 24 patients (8%), of which 11 (4%) harbored a detectable second hit in the tumor. Second hits were confirmed in genes with (NF1, RB1, TP53, EXT2, and SDHC) and without (ATM, CDC73, MLH1, MSH6, POLG, and KCNQ1) known association with mesenchymal tumor predisposition. Sarcomas from two Lynch syndrome patients showed mismatch repair deficiency, predicting a treatment response to immune checkpoint inhibitors (Level 1 biomarker according to the FDA (Federal Drug Administration) and ESMO (European Society for Medical Oncology)). None of the three CHEK2 carriers had a second hit in the tumor, suggesting a weak link to sarcoma. Conclusions: We conclude that second-hit analyses can be used in standard of care to identify syndrome-related tumors. This approach can help distinguish true manifestations of tumor syndromes from unrelated germline findings and enhance the understanding of germline predisposition in soft tissue tumors. Prospective screening using germline whole genome sequencing should be considered when comprehensive somatic sequencing is introduced into clinical practice.

Massively parallel sequencing helps create new knowledge on genes, variants and their association with disease phenotype. This important technological advancement simultaneously makes clinical decision making, using genomic information for cancer patients, more complex. Currently, identifying actionable pathogenic variants with diagnostic, prognostic, or predictive impact requires substantial manual effort. Objective: The purpose is to design a solution for clinical diagnostics of lymphoma, specifically for systematic variant filtering and interpretation. Methods: A scoping review and demonstrations from specialists serve as a basis for a blueprint of a solution for massively parallel sequencing-based genetic diagnostics. Results: The solution uses machine learning methods to facilitate decision making in the diagnostic process. A validation round of interviews with specialists consolidated the blueprint and anchored it across all relevant expert disciplines. The scoping review identified four components of variant filtering solutions: algorithms and Artificial Intelligence (AI) applications, software, bioinformatics pipelines and variant filtering strategies. The blueprint describes the input, the AI model and the interface for dynamic browsing. Conclusion: An AI-augmented system is designed for predicting pathogenic variants. While such a system can be used to classify identified variants, diagnosticians should still evaluate the classification's accuracy, make corrections when necessary, and ultimately decide which variants are truly pathogenic.