RESUMO
Background: Genetic testing for hereditary cancers is inconsistently applied within the healthcare systems in Latin America. In Peru, the prevalence and spectrum of cancer-predisposing germline variants is thus poorly characterized. Purpose: To determine the spectrum and prevalence of cancer-predisposing germline variants and variants of uncertain significance (VUS) in high-risk individuals located in a Peruvian low-resource setting city. Methods: Individuals presenting clinical criteria for hereditary cancer syndromes or being unaffected with familial history of cancer were included in the study. Samples from a total of 84 individuals were subjected to a high-throughput DNA sequencing assay that targeted a panel of 94 cancer predisposition genes. The pathogenicity of detected germline variants was classified according to the established American College of Medical Genetics and Genomics (ACMG) criteria. All pathogenic variants were validated by cycling temperature capillary electrophoresis. Results: We identified a total of eight pathogenic variants, found in 19 out of 84 individuals (23%). Pathogenic variants were identified in 24% (10/42) of unaffected individuals with family history of cancer and in 21% (9/42) of individuals with a cancer diagnosis. Pathogenic variants were identified in eight genes: RET (3), BRCA1 (3), SBDS (2), SBDS/MLH1 (4), MLH1 (4), TP53 (1), FANCD2 (1), DDB2/FANCG (1). In cancer cases, all colon cancer cases were affected by pathogenic variants in MLH1 and SBDS genes, while 20% (2/10) of the thyroid cancer cases by RET c.1900T>C variants were affected. One patient with endometrial cancer (1/3) had a double heterozygous pathogenic variant in DDB2 and FANCG genes, while one breast cancer patient (1/14) had a pathogenic variant in TP53 gene. Overall, each individual presented at least 17 VUS, totaling 1926 VUS for the full study population. Conclusion: We describe the first genetic characterization in a low-resource setting population where genetic testing is not yet implemented. We identified multiple pathogenic germline variants in clinically actionable predisposition genes, that have an impact on providing an appropriate genetic counselling and clinical management for individuals and their relatives who carry these variants. We also reported a high number of VUS, which may indicate variants specific for this population and may require a determination of their clinical significance.
RESUMO
Outbreak investigations are essential to control and prevent the dissemination of pathogens. This study developed and validated a complete analysis protocol for faster and more accurate surveillance and outbreak investigations of antibiotic-resistant microbes based on Oxford Nanopore Technologies (ONT) DNA whole-genome sequencing. The protocol was developed using 42 methicillin-resistant Staphylococcus aureus (MRSA) isolates identified from former well-characterized outbreaks. The validation of the protocol was performed using Illumina technology (MiSeq, Illumina). Additionally, a real-time outbreak investigation of six clinical S. aureus isolates was conducted to test the ONT-based protocol. The suggested protocol includes: (1) a 20 h sequencing run; (2) identification of the sequence type (ST); (3) de novo genome assembly; (4) polishing of the draft genomes; and (5) phylogenetic analysis based on SNPs. After the sequencing run, it was possible to identify the ST in 2 h (20 min per isolate). Assemblies were achieved after 4 h (40 min per isolate) while the polishing was carried out in 7 min per isolate (42 min in total). The phylogenetic analysis took 0.6 h to confirm an outbreak. Overall, the developed protocol was able to at least discard an outbreak in 27 h (mean) after the bacterial identification and less than 33 h to confirm it. All these estimated times were calculated considering the average time for six MRSA isolates per sequencing run. During the real-time S. aureus outbreak investigation, the protocol was able to identify two outbreaks in less than 31 h. The suggested protocol enables identification of outbreaks in early stages using a portable and low-cost device along with a streamlined downstream analysis, therefore having the potential to be incorporated in routine surveillance analysis workflows. In addition, further analysis may include identification of virulence and antibiotic resistance genes for improved pathogen characterization.