| Literature DB >> 33806036 |
Tanja Belčič Mikič1,2, Tadej Pajič3,4, Samo Zver1,2, Matjaž Sever1,2.
Abstract
CALR mutations are a revolutionary discovery and represent an important hallmark of myeloproliferative neoplasms (MPN), especially essential thrombocythemia and primary myelofibrosis. To date, several CALR mutations were identified, with only frameshift mutations linked to the diseased phenotype. It is of diagnostic and prognostic importance to properly define the type of CALR mutation and subclassify it according to its structural similarities to the classical mutations, a 52-bp deletion (type 1 mutation) and a 5-bp insertion (type 2 mutation), using a statistical approximation algorithm (AGADIR). Today, the knowledge on the pathogenesis of CALR-positive MPN is expanding and several cellular mechanisms have been recognized that finally cause a clonal hematopoietic expansion. In this review, we discuss the current basis of the cellular effects of CALR mutants and the understanding of its implementation in the current diagnostic laboratorial and medical practice. Different methods of CALR detection are explained and a diagnostic algorithm is shown that aids in the approach to CALR-positive MPN. Finally, contemporary methods joining artificial intelligence in accordance with molecular-genetic biomarkers in the approach to MPN are presented.Entities:
Keywords: artificial intelligence; calcium; calreticulin; chaperone; diagnostics; myeloproliferative neoplasm; thrombocythemia
Year: 2021 PMID: 33806036 PMCID: PMC8038093 DOI: 10.3390/ijms22073371
Source DB: PubMed Journal: Int J Mol Sci ISSN: 1422-0067 Impact factor: 5.923
Figure 1The schematic structure of CALR. KDEL, endoplasmic reticulum-retention signal.
Figure 2The pathogenic effects of CALR mutants.
Comparison of different molecular genetic tests for CALR mutations detection in patients with suspected myeloproliferative neoplasm.
| Method | Advantage | Critical Remarks | Sensitivity | Reference |
|---|---|---|---|---|
| Sanger sequencing | Known and unknown genetic variant detection. | Low sensitivity; Not quantitative; Moderate cost. | 10 to 20% | [ |
| PCR and fragment analysis | Known and unknown genetic variant detection; Qualitative and quantitative; Simple to perform; Low cost and rapid. | Moderate to low sensitivity.; Preferential amplification of shorter amplicons may lead to over- or underestimation of the | 1 to 10% | [ |
| High-resolution Melt | Known and unknown genetic variant detection; Simple to perform; Low cost and rapid. | Moderate to low sensitivity; Not quantitative; Sanger sequencing needed for correctly genotype the | 1 to 5% | [ |
| Quantitative PCR (real-time PCR) (qPCR) | High sensitivity; Quantitative; Rapid. | Detects only target genetic variants; Moderate cost. | 0.01 to 1% | [ |
| Digital PCR | High sensitivity; Quantitative; Rapid. | Detects only target genetic variants; Moderate cost. | 0.01 to 1% | [ |
| NGS | Known and unknown genetic variant detection; Simultaneous screening of multiple genes in multiple samples. | Complex genetic variants and large indels need in some instances confirmation by alternate molecular genetic methods; Complex workflow and result interpretation; Moderate to high cost. | 1 to 5% | [ |
Abbreviations: PCR, polymerase chain reaction; NGS, next generation sequencing.
Figure 3Algorithmic approach to patients with suspected CALR-positive MPN.