Detection of NTRK Fusions and TRK Expression and Performance of pan-TRK Immunohistochemistry in Routine Diagnostics:Results from a Nationwide Community-Based Cohort

Gene fusions involving NTRK1, NTRK2, and NTRK3 are rare drivers of cancer that can be targeted with histology-agnostic inhibitors. This study aimed to determine the nationwide landscape of NTRK/TRK testing in the Netherlands and the usage of pan-TRK immunohistochemistry (IHC) as a preselection tool to detect NTRK fusions. All pathology reports in 2017–2020 containing the search term ‘TRK’ were retrieved from the Dutch Pathology Registry (PALGA). Patient characteristics, tumor histology, NTRK/TRK testing methods, and reported results were extracted. NTRK/TRK testing was reported for 7457 tumors. Absolute testing rates increased from 815 (2017) to 3380 (2020). Tumors were tested with DNA/RNA-based molecular assay(s) (48%), IHC (47%), or in combination (5%). A total of 69 fusions involving NTRK1 (n = 22), NTRK2 (n = 6) and NTRK3 (n = 41) were identified in tumors from adult (n = 51) and pediatric (n = 18) patients. In patients tested with both IHC and a molecular assay (n = 327, of which 29 NTRK fusion-positive), pan-TRK IHC had a sensitivity of 77% (95% confidence interval (CI), 56–91) and a specificity of 84% (95% CI, 78–88%). These results showed that pan-TRK IHC has a low sensitivity in current routine practice and warrants the introduction of quality guidelines regarding the implementation and interpretation of pan-TRK IHC

Prevalence of KRAS p.(G12C) in stage IV NSCLC patients in the Netherlands:a nation-wide retrospective cohort study

OBJECTIVES: The recent accelerated FDA approval of sotorasib, a highly selective KRAS G12C inhibitor, offers new opportunities for the treatment of KRAS p.(G12C)-mutated non-squamous non-small cell lung cancer (NSCLC). The objective of the current study was to the determine the prevalence of KRAS mutations in stage IV non-squamous NSCLC in The Netherlands to reveal the potential impact of upcoming KRAS targeted therapy. MATERIALS AND METHODS: All patients diagnosed with stage IV non-squamous NSCLC in 2013, 2015 and 2017 in the Netherlands were selected by linking the nation-wide Netherlands Cancer Registry (NCR) and the Dutch Pathology Registry (PALGA). Demographic and pathological variables were retrieved from the pathology reports including sex, age, KRAS mutation status, molecular test method used, and the mutation status of other genes. RESULTS: Prevalence for any KRAS mutations in codon 12/13/61/146 was 39.1%. KRAS p.(G12C) was detected in 15.5% of all non-squamous NSCLC cases representing 39.6% of all KRAS-mutant cases. National testing rate for KRAS mutations increased from 70% in 2013 to 82% in 2017. Testing techniques changed significantly over time with next generation sequencing as the main used method in 2017 (71.6%) but did not affect prevalence of KRAS mutations over time. When KRAS was tested as part of a larger panel, the KRAS p.(G12C) mutation was frequently reported with a concurrent mutation in TP53 (47.7%) or STK11 (10.3%). CONCLUSION: The high prevalence for KRAS p.(G12C) offers a promising new specific treatment option for 15% of all stage IV non-squamous NSCLC patients

A Nationwide Study on the Impact of Routine Testing for EGFR Mutations in Advanced NSCLC Reveals Distinct Survival Patterns Based on EGFR Mutation Subclasses

SIMPLE SUMMARY: The presence of an EGFR activating mutation in tumors of non-small-cell lung cancer patients enables effective targeted therapy towards EGFR. Studies that describe a nationwide uptake of EGFR testing, the impact of the switch from single-gene EGFR to multi-gene testing, and the clinical response towards EGFR inhibitors in first-line treatment are limited. From 2013 to 2017 the percentage of patients routinely tested for EGFR mutations increased from 73% to 81% in the Netherlands. A strong shift towards EGFR testing as part of a multi-gene next generation sequencing analysis was observed. However, this did not change the percentage of EGFR mutations that were reported for this patient population, which remained stable at 12%. When treated with EGFR inhibitors that were available in a routine clinical setting prior to 2018, clear differences were observed between the type of EGFR mutation and survival. ABSTRACT: EGFR mutation analysis in non-small-cell lung cancer (NSCLC) patients is currently standard-of-care. We determined the uptake of EGFR testing, test results and survival of EGFR-mutant NSCLC patients in the Netherlands, with the overall objective to characterize the landscape of clinically actionable EGFR mutations and determine the role and clinical relevance of uncommon and composite EGFR mutations. Non-squamous NSCLC patients diagnosed in 2013, 2015 and 2017 were identified in the Netherlands Cancer Registry (NCR) and matched to the Dutch Pathology Registry (PALGA). Overall, 10,254 patients were included. Between 2013–2017, the uptake of EGFR testing gradually increased from 72.7% to 80.9% (p < 0.001). Multi-gene testing via next-generation sequencing (increased from 7.8% to 78.7% (p < 0.001), but did not affect the number of detected EGFR mutations (n = 925; 11.7%; 95% confidence interval (CI), 11.0–12.4) nor the distribution of variants. For patients treated with first-line EGFR inhibitors (n = 651), exon 19 deletions were associated with longer OS than L858R (HR 1.58; 95% CI, 1.30–1.92; p < 0.001) or uncommon, actionable variants (HR 2.13; 95% CI, 1.60–2.84; p < 0.001). Interestingly, OS for patients with L858R was similar to those with uncommon, actionable variants (HR 1.31; 95% CI, 0.98–1.75; p = 0.069). Our analysis indicates that grouping exon 19 deletions and L858R into one class of ‘common’ EGFR mutations in a clinical trial may mask the true activity of an EGFR inhibitor towards specific mutations

Reorganization of the nuclear lamina and cytoskeleton in adipogenesis

A thorough understanding of fat cell biology is necessary to counter the epidemic of obesity. Although molecular pathways governing adipogenesis are well delineated, the structure of the nuclear lamina and nuclear-cytoskeleton junction in this process are not. The identification of the ‘linker of nucleus and cytoskeleton’ (LINC) complex made us consider a role for the nuclear lamina in adipose conversion. We herein focused on the structure of the nuclear lamina and its coupling to the vimentin network, which forms a cage-like structure surrounding individual lipid droplets in mature adipocytes. Analysis of a mouse and human model system for fat cell differentiation showed fragmentation of the nuclear lamina and subsequent loss of lamins A, C, B1 and emerin at the nuclear rim, which coincides with reorganization of the nesprin-3/plectin/vimentin complex into a network lining lipid droplets. Upon 18 days of fat cell differentiation, the fraction of adipocytes expressing lamins A, C and B1 at the nuclear rim increased, though overall lamin A/C protein levels were low. Lamin B2 remained at the nuclear rim throughout fat cell differentiation. Light and electron microscopy of a subcutaneous adipose tissue specimen showed striking indentations of the nucleus by lipid droplets, suggestive for an increased plasticity of the nucleus due to profound reorganization of the cellular infrastructure. This dynamic reorganization of the nuclear lamina in adipogenesis is an important finding that may open up new venues for research in and treatment of obesity and nuclear lamina-associated lipodystrophy

A multi-platform approach to identify a blood-based host protein signature for distinguishing between bacterial and viral infections in febrile children (PERFORM): a multi-cohort machine learning study

BACKGROUND: Differentiating between self-resolving viral infections and bacterial infections in children who are febrile is a common challenge, causing difficulties in identifying which individuals require antibiotics. Studying the host response to infection can provide useful insights and can lead to the identification of biomarkers of infection with diagnostic potential. This study aimed to identify host protein biomarkers for future development into an accurate, rapid point-of-care test that can distinguish between bacterial and viral infections, by recruiting children presenting to health-care settings with fever or a history of fever in the previous 72 h. METHODS: In this multi-cohort machine learning study, patient data were taken from EUCLIDS, the Swiss Pediatric Sepsis study, the GENDRES study, and the PERFORM study, which were all based in Europe. We generated three high-dimensional proteomic datasets (SomaScan and two via liquid chromatography tandem mass spectrometry, referred to as MS-A and MS-B) using targeted and untargeted platforms (SomaScan and liquid chromatography mass spectrometry). Protein biomarkers were then shortlisted using differential abundance analysis, feature selection using forward selection-partial least squares (FS-PLS; 100 iterations), along with a literature search. Identified proteins were tested with Luminex and ELISA and iterative FS-PLS was done again (25 iterations) on the Luminex results alone, and the Luminex and ELISA results together. A sparse protein signature for distinguishing between bacterial and viral infections was identified from the selected proteins. The performance of this signature was finally tested using Luminex assays and by calculating disease risk scores. FINDINGS: 376 children provided serum or plasma samples for use in the discovery of protein biomarkers. 79 serum samples were collected for the generation of the SomaScan dataset, 147 plasma samples for the MS-A dataset, and 150 plasma samples for the MS-B dataset. Differential abundance analysis, and the first round of feature selection using FS-PLS identified 35 protein biomarker candidates, of which 13 had commercial ELISA or Luminex tests available. 16 proteins with ELISA or Luminex tests available were identified by literature review. Further evaluation via Luminex and ELISA and the second round of feature selection using FS-PLS revealed a six-protein signature: three of the included proteins are elevated in bacterial infections (SELE, NGAL, and IFN-γ), and three are elevated in viral infections (IL18, NCAM1, and LG3BP). Performance testing of the signature using Luminex assays revealed area under the receiver operating characteristic curve values between 89·4% and 93·6%. INTERPRETATION: This study has led to the identification of a protein signature that could be ultimately developed into a blood-based point-of-care diagnostic test for rapidly diagnosing bacterial and viral infections in febrile children. Such a test has the potential to greatly improve care of children who are febrile, ensuring that the correct individuals receive antibiotics. FUNDING: European Union's Horizon 2020 research and innovation programme, the European Union's Seventh Framework Programme (EUCLIDS), Imperial Biomedical Research Centre of the National Institute for Health Research, the Wellcome Trust and Medical Research Foundation, Instituto de Salud Carlos III, Consorcio Centro de Investigación Biomédica en Red de Enfermedades Respiratorias, Grupos de Refeencia Competitiva, Swiss State Secretariat for Education, Research and Innovation

Diagnosis of childhood febrile illness using a multi-class blood RNA molecular signature

BACKGROUND: Appropriate treatment and management of children presenting with fever depend on accurate and timely diagnosis, but current diagnostic tests lack sensitivity and specificity and are frequently too slow to inform initial treatment. As an alternative to pathogen detection, host gene expression signatures in blood have shown promise in discriminating several infectious and inflammatory diseases in a dichotomous manner. However, differential diagnosis requires simultaneous consideration of multiple diseases. Here, we show that diverse infectious and inflammatory diseases can be discriminated by the expression levels of a single panel of genes in blood. METHODS: A multi-class supervised machine-learning approach, incorporating clinical consequence of misdiagnosis as a "cost" weighting, was applied to a whole-blood transcriptomic microarray dataset, incorporating 12 publicly available datasets, including 1,212 children with 18 infectious or inflammatory diseases. The transcriptional panel identified was further validated in a new RNA sequencing dataset comprising 411 febrile children. FINDINGS: We identified 161 transcripts that classified patients into 18 disease categories, reflecting individual causative pathogen and specific disease, as well as reliable prediction of broad classes comprising bacterial infection, viral infection, malaria, tuberculosis, or inflammatory disease. The transcriptional panel was validated in an independent cohort and benchmarked against existing dichotomous RNA signatures. CONCLUSIONS: Our data suggest that classification of febrile illness can be achieved with a single blood sample and opens the way for a new approach for clinical diagnosis. FUNDING: European Union's Seventh Framework no. 279185; Horizon2020 no. 668303 PERFORM; Wellcome Trust (206508/Z/17/Z); Medical Research Foundation (MRF-160-0008-ELP-KAFO-C0801); NIHR Imperial BRC

Diagnosis of multisystem inflammatory syndrome in children by a whole-blood transcriptional signature

BACKGROUND: To identify a diagnostic blood transcriptomic signature that distinguishes multisystem inflammatory syndrome in children (MIS-C) from Kawasaki disease (KD), bacterial infections, and viral infections. METHODS: Children presenting with MIS-C to participating hospitals in the United Kingdom and the European Union between April 2020 and April 2021 were prospectively recruited. Whole-blood RNA Sequencing was performed, contrasting the transcriptomes of children with MIS-C (n = 38) to those from children with KD (n = 136), definite bacterial (DB; n = 188) and viral infections (DV; n = 138). Genes significantly differentially expressed (SDE) between MIS-C and comparator groups were identified. Feature selection was used to identify genes that optimally distinguish MIS-C from other diseases, which were subsequently translated into RT-qPCR assays and evaluated in an independent validation set comprising MIS-C (n = 37), KD (n = 19), DB (n = 56), DV (n = 43), and COVID-19 (n = 39). RESULTS: In the discovery set, 5696 genes were SDE between MIS-C and combined comparator disease groups. Five genes were identified as potential MIS-C diagnostic biomarkers (HSPBAP1, VPS37C, TGFB1, MX2, and TRBV11-2), achieving an AUC of 96.8% (95% CI: 94.6%-98.9%) in the discovery set, and were translated into RT-qPCR assays. The RT-qPCR 5-gene signature achieved an AUC of 93.2% (95% CI: 88.3%-97.7%) in the independent validation set when distinguishing MIS-C from KD, DB, and DV. CONCLUSIONS: MIS-C can be distinguished from KD, DB, and DV groups using a 5-gene blood RNA expression signature. The small number of genes in the signature and good performance in both discovery and validation sets should enable the development of a diagnostic test for MIS-C