Characterization of Non-Ischemic Dilated Cardiomyopathy in a Native Tanzanian Cohort: MOYO Study.

Background: Non-ischemic dilated cardiomyopathy (NIDCM) is a common cause of heart failure with progressive tendency. The disease occurs in one in every 2,500 individuals in the developed world, with high morbidity and mortality. However, detailed data on the role of NIDCM in heart failure in Tanzania is lacking. Aim: To characterize NIDCM in a Tanzanian cohort with respect to demographics, clinical profile, imaging findings and management. Methods: Characterization of non-ischemic dilated cardioMyOpathY in a native Tanzanian cOhort (MOYO) is a prospective cohort study of NIDCM patients seen at the Jakaya Kikwete Cardiac Institute. Patients aged ≥18 years with a clinical diagnosis of heart failure, an ejection fraction of ≤45% on echocardiography and no evidence of ischemia were enrolled. Clinical data, echocardiography, electrocardiography (ECG), coronary angiography and stress ECG information were collected from February 2020 to March 2022. Results: Of 402 patients, n = 220 (54.7%) were males with a median (IQR) age of 55.0 (41.0, 66.0) years. Causes of NIDCM were presumably hypertensive n = 218 (54.2%), idiopathic n = 116 (28.9%), PPCM n = 45 (11.2%), alcoholic n = 10 (2.5%) and other causes n = 13 (3.2%). The most common presenting symptoms were dyspnea n = 342 (85.1%), with the majority of patients presenting with New York Heart Association (NYHA) Class III n = 195 (48.5%). The mean (SD) left ventricular ejection fraction (LVEF) was 29.4% (±7.7), and severe systolic dysfunction (LVEF <30%) was common n = 208 (51.7%). Compared with other forms of DCM, idiopathic DCM patients were significantly younger, had more advanced NYHA class (p < 0.001) and presented more often with left bundle branch block on ECG (p = 0.0042). There was suboptimal use of novel guidelines recommended medications ARNI n = 10 (2.5%) and SGLT2 2-inhibitors n = 2 (0.5%). Conclusions: In our Tanzanian cohort, the majority of patients with NIDCM have an identified underlying cause, and they present at late stages of the disease. Patients with idiopathic DCM are younger with more severe disease compared to other forms of NIDCM.

Implementing Innovative Approaches to Improve Health Care Delivery Systems for Integrating Communicable and Non-Communicable Diseases Using Tuberculosis and Diabetes as a Model in Tanzania.

Background: Many evidence-based health interventions, particularly in low-income settings, have failed to deliver the expected impact. We designed an Adaptive Diseases Control Expert Programme in Tanzania (ADEPT) to address systemic challenges in health care delivery and examined the feasibility, acceptability and effectiveness of the model using tuberculosis (TB) and diabetes mellitus (DM) as a prototype. Methods: This was an effectiveness-implementation hybrid type-3 design that was implemented in Dar es Salaam, Iringa and Kilimanjaro regions. The strategy included a stepwise training approach with web-based platforms adapting the Gibbs' reflective cycle. Health facilities with TB services were supplemented with DM diagnostics, including glycated haemoglobin A1c (HbA1c). The clinical audit was deployed as a measure of fidelity. Retrospective and cross-sectional designs were used to assess the fidelity, acceptability and feasibility of the model. Results: From 2019-2021, the clinical audit showed that ADEPT intervention health facilities more often identified median 8 (IQR 6-19) individuals with dual TB and DM, compared with control health facilities, median of 1 (IQR 0-3) (p = 0.02). Likewise, the clinical utility of HbA1c on intervention sites was 63% (IQR:35-75%) in TB/DM individuals compared to none in the control sites at all levels, whereas other components of the standard of clinical management of patients with dual TB and DM did not significantly differ. The health facilities showed no difference in screening for additional comorbidities such as hypertension and malnutrition. The stepwise training enrolled a total of 46 nurse officers and medical doctors/specialists for web-based training and 40 (87%) attended the workshop. Thirty-one (67%), 18 nurse officers and 13 medical doctors/specialists, implemented the second step of training others and yielded a total of 519 additional front-line health care workers trained: 371 nurses and 148 clinicians. Overall, the ADEPT model was scored as feasible by metrics applied to both front-line health care providers and health facilities. Conclusions: It was feasible to use a stepwise training and clinical audit to support the integration of TB and DM management and it was largely acceptable and effective in differing regions within Tanzania. When adapted in the Tanzania health system context, the model will likely improve quality of services.

SEPT-GD: A decision tree to prioritise potential RNA splice variants in cardiomyopathy genes for functional splicing assays in diagnostics.

BACKGROUND: Splice prediction algorithms currently used in routine DNA diagnostics have limited sensitivity and specificity, therefore many potential splice variants are classified as variants of uncertain significance (VUSs). However, functional assessment of VUSs to test splicing is labour-intensive and time-consuming. We developed a decision tree to prioritise potential splice variants for functional studies and functionally verified the outcome of the decision tree. MATERIALS AND METHODS: We built the decision tree, SEPT-GD, by setting thresholds for the splice prediction programs implemented in Alamut. A set of 343 variants with known effects on splicing was used as control for sensitivity and specificity. We tested SEPT-GD using variants from a Dutch cardiomyopathy cohort of 2002 patients that were previously classified as VUS and predicted to have a splice effect according to diagnostic rules. We then selected 12 VUSs ranked by SEPT-GD to functionally verify the predicted effect on splicing using a minigene assay: 10 variants predicted to have a strong effect and 2 with a weak effect. RT-PCR was performed for nine variants. Variant classification was re-evaluated based on the functional test outcome. RESULTS: Compared to similar individually tested algorithms, SEPT-GD shows higher sensitivity (91 %) and comparable specificity (88 %) for both consensus (dinucleotides at the start and end of the intron, GT at the 5' end and AG at the 3' end) and non-consensus splice-site variants (excluding middle of exon variants). Using clinical diagnostic criteria, 1295 unique variants in our cardiomyopathy cohort had originally been classified as VUSs, with 57 predicted by Alamut to have an effect on splicing. Using SEPT-GD, we prioritised 31 variants in 40 patients. In the minigene assay, all 12 variants showed results concordant with SEPT-GD predictions. RT-PCR confirmed the minigene results for two variants, TMEM43 c.1000 + 5G > T and TTN c.25922-6 T > G. Based on all outcomes, the SGCD c.4-1G > A and CSRP3 c.282-5_285del variants were reclassified as likely pathogenic. CONCLUSION: SEPT-GD outperforms the tools commonly used for RNA splicing prediction and improves prioritisation of variants in cardiomyopathy genes for functional splicing analysis in a diagnostic setting.

Feasibility of Follow-Up Studies and Reclassification in Spinocerebellar Ataxia Gene Variants of Unknown Significance.

Spinocerebellar ataxia (SCA) is a heterogeneous group of neurodegenerative disorders with autosomal dominant inheritance. Genetic testing for SCA leads to diagnosis, prognosis and risk assessment for patients and their family members. While advances in sequencing and computing technologies have provided researchers with a rapid expansion in the genetic test content that can be used to unravel the genetic causes that underlie diseases, the large number of variants with unknown significance (VUSes) detected represent challenges. To minimize the proportion of VUSes, follow-up studies are needed to aid in their reclassification as either (likely) pathogenic or (likely) benign variants. In this study, we addressed the challenge of prioritizing VUSes for follow-up using (a combination of) variant segregation studies, 3D protein modeling, in vitro splicing assays and functional assays. Of the 39 VUSes prioritized for further analysis, 13 were eligible for follow up. We were able to reclassify 4 of these VUSes to LP, increasing the molecular diagnostic yield by 1.1%. Reclassification of VUSes remains difficult due to limited possibilities for performing variant segregation studies in the classification process and the limited availability of routine functional tests.

Validation of New Gene Variant Classification Methods: a Field-Test in Diagnostic Cardiogenetics.

Background: In the molecular genetic diagnostics of Mendelian disorders, solutions are needed for the major challenge of dealing with the large number of variants of uncertain significance (VUSs) identified using next-generation sequencing (NGS). Recently, promising approaches using constraint metrics to calculate case excess scores (CE), etiological fractions (EF), and gnomAD-derived constraint scores have been reported that estimate the likelihood of rare variants in specific genes or regions that are pathogenic. Our objective is to study the usability of these constraint data into variant interpretation in a diagnostic setting, using our cardiomyopathy cohort. Methods and Results: Patients (N = 2002) referred for clinical genetic diagnostics underwent NGS testing of 55-61 genes associated with cardiomyopathies. Previously classified likely pathogenic (LP) and pathogenic (P) variants were used to validate the use of data from CE, EF, and gnomAD constraint analyses for (re)classification of associated variant types in specific cardiomyopathy subtype-related genes. The classifications corroborated in 94% (354/378) of cases. Next, we reclassified 23 unique VUSs to LP, increasing the diagnostic yield by 1.2%. In addition, 106 unique VUSs (5.3% of patients) were prioritized for co-segregation or functional analyses. Conclusions: Our analysis confirms that the use of constraint metrics data can improve variant interpretation, and we, therefore, recommend using constraint scores on other cohorts and disorders and its inclusion in variant interpretation protocols.

Diagnostic yield of targeted next generation sequencing in 2002 Dutch cardiomyopathy patients.

BACKGROUND: Next-generation sequencing (NGS) is increasingly used for clinical evaluation of cardiomyopathy patients as it allows for simultaneous screening of multiple cardiomyopathy-associated genes. Adding copy number variant (CNV) analysis of NGS data is not routine yet and may contribute to the diagnostic yield. OBJECTIVES: Determine the diagnostic yield of our targeted NGS gene panel in routine clinical diagnostics of Dutch cardiomyopathy patients and explore the impact of exon CNVs on diagnostic yield. METHODS: Patients (N = 2002) referred for clinical genetic analysis underwent diagnostic testing of 55-61 genes associated with cardiomyopathies. Samples were analyzed and evaluated for single nucleotide variants (SNVs), indels and CNVs. CNVs identified in the NGS data and suspected of being pathogenic based on type, size and location were confirmed by additional molecular tests. RESULTS: A (likely) pathogenic (L)P variant was detected in 22.7% of patients, including 3 with CNVs and 25 where a variant was identified in a gene currently not associated with the patient's cardiomyopathy subtype. Only 15 out of 2002 patients (0.8%) were found to carry two (L)P variants. CONCLUSION: The yield of routine clinical diagnostics of cardiomyopathies was relatively low when compared to literature. This is likely due to the fact that our study reports the outcome of patients in daily routine diagnostics, therefore also including patients not fully fulfilling (subtype specific) cardiomyopathy criteria. This may also explain why (L)P variants were identified in genes not associated with the reported subtype. The added value of CNV analysis was shown to be limited but not negligible.

Clinical Value of EGFR Copy Number Gain Determined by Amplicon-Based Targeted Next Generation Sequencing in Patients with EGFR-Mutated NSCLC.

BACKGROUND: The clinical relevance of epidermal growth factor receptor (EGFR) copy number gain in patients with EGFR mutated advanced non-small cell lung cancer on first-line tyrosine kinase inhibitor treatment has not been fully elucidated. OBJECTIVE: We aimed to estimate EGFR copy number gain using amplicon-based next generation sequencing data and explored its prognostic value. PATIENTS AND METHODS: Next generation sequencing data were obtained for 1566 patients with non-small cell lung cancer. EGFR copy number gain was defined based on an increase in EGFR read counts relative to internal reference amplicons and normal controls in combination with a modified z-score ≥ 3.5. Clinical follow-up data were available for 60 patients treated with first-line EGFR-tyrosine kinase inhibitors. RESULTS: Specificity and sensitivity of next generation sequencing-based EGFR copy number estimations were above 90%. EGFR copy number gain was observed in 27.9% of EGFR mutant cases and in 7.4% of EGFR wild-type cases. EGFR gain was not associated with progression-free survival but showed a significant effect on overall survival with an adjusted hazard ratio of 3.14 (95% confidence interval 1.46-6.78, p = 0.003). Besides EGFR copy number gain, osimertinib in second or subsequent lines of treatment and the presence of T790M at relapse revealed significant effects in a multivariate analysis with adjusted hazard ratio of 0.43 (95% confidence interval 0.20-0.91, p = 0.028) and 0.24 (95% confidence interval 0.1-0.59, p = 0.001), respectively. CONCLUSIONS: Pre-treatment EGFR copy number gain determined by amplicon-based next generation sequencing data predicts worse overall survival in EGFR-mutated patients treated with first-line EGFR-tyrosine kinase inhibitors. T790M at relapse and subsequent treatment with osimertinib predict longer overall survival.

Targeted RNA-Sequencing Enables Detection of Relevant Translocations and Single Nucleotide Variants and Provides a Method for Classification of Hematological Malignancies-RANKING.

BACKGROUND: Patients with hematological malignancies (HMs) carry a wide range of chromosomal and molecular abnormalities that impact their prognosis and treatment. Since no current technique can detect all relevant abnormalities, technique(s) are chosen depending on the reason for referral, and abnormalities can be missed. We tested targeted transcriptome sequencing as a single platform to detect all relevant abnormalities and compared it to current techniques. MATERIAL AND METHODS: We performed RNA-sequencing of 1385 genes (TruSight RNA Pan-Cancer, Illumina) in bone marrow from 136 patients with a primary diagnosis of HM. We then applied machine learning to expression profile data to perform leukemia classification, a method we named RANKING. Gene fusions for all the genes in the panel were detected, and overexpression of the genes EVI1, CCND1, and BCL2 was quantified. Single nucleotide variants/indels were analyzed in acute myeloid leukemia (AML), myelodysplastic syndrome and patients with acute lymphoblastic leukemia (ALL) using a virtual myeloid (54 genes) or lymphoid panel (72 genes). RESULTS: RANKING correctly predicted the leukemia classification of all AML and ALL samples and improved classification in 3 patients. Compared to current methods, only one variant was missed, c.2447A>T in KIT (RT-PCR at 10-4), and BCL2 overexpression was not seen due to a t(14; 18)(q32; q21) in 2% of the cells. Our RNA-sequencing method also identified 6 additional fusion genes and overexpression of CCND1 due to a t(11; 14)(q13; q32) in 2 samples. CONCLUSIONS: Our combination of targeted RNA-sequencing and data analysis workflow can improve the detection of relevant variants, and expression patterns can assist in establishing HM classification.

Genetic Screening Test to Detect Translocations in Acute Leukemias by Use of Targeted Locus Amplification.

BACKGROUND: Over 500 translocations have been identified in acute leukemia. To detect them, most diagnostic laboratories use karyotyping, fluorescent in situ hybridization, and reverse transcription PCR. Targeted locus amplification (TLA), a technique using next-generation sequencing, now allows detection of the translocation partner of a specific gene, regardless of its chromosomal origin. We present a TLA multiplex assay as a potential first-tier screening test for detecting translocations in leukemia diagnostics. METHODS: The panel includes 17 genes involved in many translocations present in acute leukemias. Procedures were optimized by using a training set of cell line dilutions and 17 leukemia patient bone marrow samples and validated by using a test set of cell line dilutions and a further 19 patient bone marrow samples. Per gene, we determined if its region was involved in a translocation and, if so, the translocation partner. To balance sensitivity and specificity, we introduced a gray zone showing indeterminate translocation calls needing confirmation. We benchmarked our method against results from the 3 standard diagnostic tests. RESULTS: In patient samples passing QC, we achieved a concordance with benchmarking tests of 81% in the training set and 100% in the test set, after confirmation of 4 and nullification of 3 gray zone calls (in total). In cell line dilutions, we detected translocations in 10% aberrant cells at several genetic loci. CONCLUSIONS: Multiplex TLA shows promising results as an acute leukemia screening test. It can detect cryptic and other translocations in selected genes. Further optimization may make this assay suitable for diagnostic use.