Anaemia affects 1.6 billion people worldwide, with roughly 10% of these individuals affected by rare anaemias, of which 80% are hereditary.1 Hereditary anaemias (HA) encompass a highly heterogeneous group of disorders characterised by anaemia of variable degrees and by complex genotype–phenotype correlations. Differential diagnosis, classification, and patient stratification among HA is often very difficult.
To date, the major current application of next-generation sequencing (NGS) in diagnostics is through disease-targeted tests, for which multiple causal genes are known. Some studies have already demonstrated the utility of a targeted NGS (t-NGS) approach in the study of specific subtypes of HA patients. In this study, we described the diagnostic workflow based on t-NGS that we developed for the diagnosis of patients affected by HA. Within this wide group of disorders, we included a) hyporegenerative anaemias, such as congenital dyserythropoietic anaemias (CDA); b) haemolytic anaemias due to red cell membrane defects, such as hereditary spherocytosis (HS) and stomatocytosis (HSt); and c) haemolytic anaemias due to enzymatic defects, such as pyruvate kinase (PK) deficiency.2-5
We generated two consecutive versions of the same custom gene panel: the first included 34 genes, the second 71 genes. The probe design was performed by SureDesign (Agilent Technologies, Santa Clara, California, USA). Sample preparation was obtained by HaloPlex Target Enrichment kit for Illumina Sequencing (Agilent Technologies), and high-throughput sequencing was performed by Illumina NextSeq 500 (Illumina Inc., San Diego, California, USA). For bioinformatic analyses, we used Agilent SureCall software (v 220.127.116.11, Agilent Technologies). The pathogenicity of each variant was evaluated according to the guidelines of the American College of Medical Genetics and Genomics (ACMG).6,7
We investigated 74 probands with clinical suspicion of HA. Our approach revealed a diagnostic yield of 64.9% of analysed patients. Genetic data by t-NGS analysis confirmed the clinical suspicion in 54.2% of patients. Of note, most of these patients were originally suspected to have red cell membrane disorders (HSt or HS).
Conversely, t-NGS analysis modified the original diagnosis in 45.8% of patients; 81.8% of these patients were clinically suspected to have CDA. Of note, among the 22 patients originally classified as CDA, we identified 45.5% of cases with a conclusive genetic diagnosis of congenital haemolytic anaemias due to enzymatic defects. Indeed, we diagnosed one case with biallelic mutations in GPI, the causative gene of haemolytic non-spherocytic anaemia due to glucose phosphate isomerase deficiency; another case due to mutations in AK1, the causative locus of haemolytic anaemia due to adenylate kinase deficiency; and eight cases due to mutations in PKLR, the causative gene of PK deficiency.7
Our observation regarding congenital haemolytic anaemia patients misdiagnosed as CDA is highly relevant; it underlines how t-NGS analysis is valuable not only for achieving a correct and conclusive diagnosis but also for guiding possible treatment of HA patients. This is mainly true for the treatment of PK deficient-patients, for whom there is an allosteric activator of PK enzyme available that can increase the enzymatic activity of patient erythrocytes treated ex vivo.8