Clinical use of SNP-microarrays for the detection of genome-wide changes in haematological malignancies

One-base-mismatch CRISPR-based transistors for single nucleotide resolution assay

An effective strategy for accurately detecting single nucleotide variants (SNVs) is of great significance for genetic research and diagnostics. However, strict amplification conditions, complex experimental instruments, and specialized personnel are required to obtain a satisfactory tradeoff between sensitivity and selectivity for SNV discrimination. In this study, we present a CRISPR-based transistor biosensor for the rapid and highly selective detection of SNVs in viral RNA. By introducing a synthetic mismatch in the crRNA, the CRISPR-Cas13a protein can be engineered to capture the target SNV RNA directly on the surface of the graphene channel. This process induces a fast electrical signal response in the transistor, obviating the need for amplification or reporter molecules. The biosensor exhibits a detection limit for target RNA as low as 5 copies in 100 μL, which is comparable to that of real-time quantitative polymerase chain reaction (PCR). Its operational range spans from 10 to 5 × 105 copy mL-1 in artificial saliva solution. This capability enables the biosensor to discriminate between wild-type and SNV RNA within 15 min. By introducing 10 μL of swab samples during clinical testing, the biosensor provides specific detection of respiratory viruses in 19 oropharyngeal specimens, including influenza A, influenza B, and variants of SARS-CoV-2. This study emphasizes the CRISPR-transistor technique as a highly accurate and sensitive approach for field-deployable nucleic acid screening or diagnostics.

The association of FSCN1 (rs852479, rs1640233) and HOTAIR (rs920778) polymorphisms with the risk of breast cancer in Egyptian women

BACKGROUND

Breast cancer (BC) is one of the most prevalent cancers that contribute to mortality among women worldwide. Despite contradictory findings, considerable evidence suggests that single nucleotide polymorphisms (SNPs) in the FSCN1 and HOTAIR genes may have a causative impact on the development of BC. This case-control study was conducted to evaluate the association of genotype frequency in FSCN1 rs852479, rs1640233, and HOTAIR rs920778 with susceptibility and prognosis of BC, as well as the impact of clinical stages and hormonal features.

METHODS AND RESULTS

FSCN1 (rs852479, rs1640233) and HOTAIR (rs920778) were genotyped using TaqMan real-time PCR assay in 200 BC patients and 200 cancer-free controls, all representing Egyptian women. Genotypic analyses in association with clinicopathological factors and disease risk were assessed. As a result, a significant association with BC risk was observed for CC genotype frequency of FSCN1 rs852479 A > C (OR = 0.395, 95% CI 0.204-0.76, p-value = 0.005). However, no significant correlation was detected between the FSCN1 rs1640233 C > T and HOTAIR rs920778 C > T polymorphic variants and susceptibility to BC. Interestingly, CC genotype of FSCN1 rs1640233 was more likely to progress tumor size and lymph node invasion in BC cases (p-value = 0.04 and 0.02, respectively). Moreover, it was revealed that there was a non-significant correlation between the haplotype distributions of FSCN1 rs852479 and rs1640233 and the probability of BC.

CONCLUSIONS

Based on the sample size and genetic characteristics of the subjects involved in the present study, our findings indicated that FSCN1 rs852479 may contribute to BC susceptibility in a sample of the Egyptian population.

The yield of SNP microarray analysis for fetal ultrasound cardiac abnormalities

BACKGROUND

Chromosomal microarray analysis (CMA) has emerged as a critical instrument in prenatal diagnostic procedures, notably in assessing congenital heart diseases (CHD). Nonetheless, current research focuses solely on CHD, overlooking the necessity for thorough comparative investigations encompassing fetuses with varied structural abnormalities or those without apparent structural anomalies.

OBJECTIVE

This study sought to assess the relation of single nucleotide polymorphism-based chromosomal microarray analysis (SNP-based CMA) in identifying the underlying causes of fetal cardiac ultrasound abnormalities.

METHODS

A total of 2092 pregnant women who underwent prenatal diagnosis from 2017 to 2022 were included in the study and divided into four groups based on the presence of ultrasound structural abnormalities and the specific type of abnormality. The results of the SNP-Array test conducted on amniotic fluid samples from these groups were analyzed.

RESULTS

Findings from the study revealed that the non-isolated CHD group exhibited the highest incidence of aneuploidy, overall chromosomal abnormalities, and trisomy 18, demonstrating statistically significant differences from the other groups (p < 0.001). Regarding the distribution frequency of copy number variation (CNV) segment size, no statistically significant distinctions were observed between the isolated CHD group and the non-isolated CHD group (p > 0.05). The occurrence rates of 22q11.2 and 15q11.2 were also not statistically different between the isolated CHD group and the non-isolated congenital heart defect group (p > 0.05).

CONCLUSION

SNP-based CMA enhances the capacity to detect abnormal CNVs in CHD fetuses, offering valuable insights for diagnosing chromosomal etiology and facilitating genetic counseling. This research contributes to the broader understanding of the utility of SNP-based CMA in the context of fetal cardiac ultrasound abnormalities.

A framework for the clinical implementation of optical genome mapping in hematologic malignancies

Optical Genome Mapping (OGM) is rapidly emerging as an exciting cytogenomic technology both for research and clinical purposes. In the last 2 years alone, multiple studies have demonstrated that OGM not only matches the diagnostic scope of conventional standard of care cytogenomic clinical testing but it also adds significant new information in certain cases. Since OGM consolidates the diagnostic benefits of multiple costly and laborious tests (e.g., karyotyping, fluorescence in situ hybridization, and chromosomal microarrays) in a single cost-effective assay, many clinical laboratories have started to consider utilizing OGM. In 2021, an international working group of early adopters of OGM who are experienced with routine clinical cytogenomic testing in patients with hematological neoplasms formed a consortium (International Consortium for OGM in Hematologic Malignancies, henceforth "the Consortium") to create a consensus framework for implementation of OGM in a clinical setting. The focus of the Consortium is to provide guidance for laboratories implementing OGM in three specific areas: validation, quality control and analysis and interpretation of variants. Since OGM is a complex technology with many variables, we felt that by consolidating our collective experience, we could provide a practical and useful tool for uniform implementation of OGM in hematologic malignancies with the ultimate goal of achieving globally accepted standards.

Chromosomal defects in multiple myeloma

Multiple myeloma is a plasma cell neoplasm driven by primary (e.g. hyperdiploidy; IGH translocations) and secondary (e.g. 1q21 gains/amplifications; del(17p); MYC translocations) chromosomal events. These are important to detect as they influence prognosis, therapeutic response and disease survival. Currently, cytogenetic testing is most commonly performed by interphase fluorescence in situ hybridisation (FISH) on aspirated bone marrow samples. A number of variations to FISH methodology are available, including prior plasma cell enrichment and incorporation of immunophenotypic plasma cell identification. Other molecular methods are increasingly being utilised to provide a genome-wide view at high resolution (e.g. single nucleotide polymorphism (SNP) microarray analysis) and these can detect abnormalities in most cases. Despite their wide application at diagnostic assessment, both FISH and SNP-array have relatively low sensitivity, limiting their use for identification of prognostically significant low-level sub-clones or for disease monitoring. Next-generation sequencing is increasingly being used to detect mutations and new FISH techniques such as by flow cytometry are in development and may address some of the current test limitations. Here we review the primary and secondary cytogenetic aberrations in myeloma and discuss the range of techniques available for their assessment.

MLPA in the initial genetic screening of patients with acute myeloid leukemia

INTRODUCTION

This study aimed to assess the effectiveness of multiplex ligase-dependent probe amplification (MLPA) in the initial genetic screening of patients with acute myeloid leukemia (AML) since current risk stratification and clinical management depend on molecular-genetic markers.

METHODS

We performed a prospective case-control study on newly diagnosed patients from the Clinical hematology clinic of UMHAT "St. Marina", Varna, for the period 02.2022 - 02.2023. MLPA - a semiquantitative PCR-based method, was implemented with probes for 40 AML/myelodysplastic syndrome-typical genetic changes. We compared these findings with a parallelly carried out cytogenetic analysis, part of the routine diagnostic process.

RESULTS

We assessed 61 patients - 29 females and 32 males, median age of 61 years for females and 65 for males (min-max 20-89). 34 (56%) of all showed pathological results, while the rest 27 (44%) did not. Of the 34, 22 (65%) had a single gene variant in genes NPM1, DNMT3A, FLT3, and IDH2, isolated or in combination. 18 (53%) of the same 34 also had copy number aberration (CNA) in chromosomes 4, 5, 6, 7, 11, 14, 17, and 21. The latter were either isolated or in combination with other findings. 8 of the 18 cases also underwent cytogenetic analysis, with concordance between the two methods in 4.

CONCLUSION

MLPA is an informative method for initial genetic assessment in addition to cytogenetic analysis. Still, more patients are needed to draw finite conclusions on its eligibility for routine use. Given the significant percentage of normal results - 44%, simultaneous evaluation of more genetic markers, included in current guidelines, is reasonable.

Refined cytogenetic IPSS-R evaluation by the use of SNP array in a cohort of 290 MDS patients

Genetic testing plays a central role in myelodysplastic neoplasms (MDS) diagnosis, prognosis, and therapeutic decisions. The widely applied cytogenetic revised international prognostic scoring system (IPSS-R) was based on chromosome banding analysis (CBA). However, subsequently developed genetic methodologies, such as single nucleotide polymorphism (SNP) array, demonstrated to be a valid alternative test for MDS. SNP array is, in fact, able to detect the majority of MDS-associated cytogenetic aberrations, by providing further genomic information due to its higher resolution. In this study, 290 samples from individuals with a confirmed or suspected diagnosis of MDS were tested by both CBA and SNP array, in order to evaluate and compare their cytogenetic IPSS-R score in the largest MDS cohort reported so far. A concordant or better refined cytogenetic IPSS-R array-based score was obtained for 95% of cases (277). Therefore, this study confirms the effective applicability of SNP array toward the cytogenetic IPSS-R evaluation and consequently, toward the molecular international prognostic scoring system for MDS (IPSS-M) assessment, which ensures an improved MDS risk stratification refinement. Considering the advent of additional genetic technologies interrogating the whole genome with increased resolutions, counting cytogenetic abnormalities based on their size may result in a simplistic approach. On the contrary, assessing overall genomic complexity may provide additional crucial information. Independently of the technology used, genetic results should indeed aim at ensuring a highly refined stratification for MDS patients.

TP53 in AML and MDS: The new (old) kid on the block

MDS and AML are clonal hematopoietic stem cell disorders of increasing incidence, having a variable prognosis based, among others, on co-occurring molecular abnormalities. TP53 mutations are frequently detected in these myeloid neoplasms and portend a poor prognosis with known therapeutic resistance. This article provides a timely review of the complexity of TP53 alterations, providing updates in diagnosis and prognosis based on new 2022 International Consensus Classification (ICC) and World Health Organization (WHO) guidelines. The article addresses optimal testing strategies and reviews current and arising therapeutic approaches. While the treatment landscape for this molecular subgroup is under active development, further exploration is needed to optimize the care of this group of patients with unmet needs.

The Role of Single-Nucleotide Polymorphisms in Cholangiocarcinoma: A Systematic Review

Single-nucleotide polymorphisms (SNPs) play an essential role in various malignancies, but their role in cholangiocarcinoma (CCA) remains to be elucidated. Therefore, the purpose of this systematic review was to evaluate the association between SNPs and CCA, focusing on tumorigenesis and prognosis. A systematic literature search was carried out using PubMed, Embase, Web of Science and the Cochrane database for the association between SNPs and CCA, including literature published between January 2000 and April 2022. This systematic review compiles 43 SNPs in 32 genes associated with CCA risk, metastatic progression and overall prognosis based on 34 studies. Susceptibility to CCA was associated with SNPs in genes related to inflammation (PTGS2/COX2, IL6, IFNG/IFN-γ, TNF/TNF-α), DNA repair (ERCC1, MTHFR, MUTYH, XRCC1, OGG1), detoxification (NAT1, NAT2 and ABCC2), enzymes (SERPINA1, GSTO1, APOBEC3A, APOBEC3B), RNA (HOTAIR) and membrane-based proteins (EGFR, GAB1, KLRK1/NKG2D). Overall oncological prognosis was also related to SNPs in eight genes (GNB3, NFE2L2/NRF2, GALNT14, EGFR, XRCC1, EZH2, GNAS, CXCR1). Our findings indicate that multiple SNPs play different roles at various stages of CCA and might serve as biomarkers guiding treatment and allowing oncological risk assessment. Considering the differences in SNP detection methods, patient ethnicity and corresponding environmental factors, more large-scale multicentric investigations are needed to fully determine the potential of SNP analysis for CCA susceptibility prediction and prognostication.

Multiple Myeloma Immunophenotype Related to Chromosomal Abnormalities Used in Risk Assessment

(1) Background: At diagnosis, multiplemyeloma risk estimation includes disease burden, end-organ damage, and biomarkers, with increasing emphasis on genetic abnormalities. Multicolor flow cytometry (MFC) is not always considered in risk estimation. We demonstrate associations found between genetic abnormalities and antigen expression of plasma cells measured by MFC. (2) Methods: Single nucleotide polymorphism microarray (SNP-A) karyotyping as well as MFC using standardized next-generation flow (NGF) panels and instrument settings were performed from bone marrow aspirates at the time of diagnosis. (3) Results: We uncovered specific immunophenotype features related to different genetic risk factors. Specifically, we found higher malignant/normal plasma cell ratio and lower expression of CD27, CD38, CD45, CD56, CD117 and CD138 in higher-risk genetic groups or risk categories.

Acute myelogenous leukemia – current recommendations and approaches in molecular-genetic assessment

Acute myelogenous leukemia is a multi-step hematological malignancy, affecting function, growth, proliferation and cell cycle of myeloid precursors. Overall assessment of patients with the disease requires among everything else, a comprehensive investigation of the genetic basis through various methods such as cytogenetic and molecular-genetic ones. This clarification provides diagnostic refinement and carries prognostic and predictive value in respect of essential therapeutic choices.

With this review of the literature, we focus on summarizing the latest recommendations and preferred genetic methods, as well as on emphasizing on their general benefits and limitations. Since none of these methods is actually totipotent, we also aim to shed light over the often-difficult choice of appropriate genetic analyses.

A Novel Quality-Control Procedure to Improve the Accuracy of Rare Variant Calling in SNP Arrays

Background: Single-nucleotide polymorphism (SNP) arrays are an ideal technology for genotyping genetic variants in mass screening. However, using SNP arrays to detect rare variants [with a minor allele frequency (MAF) of <1%] is still a challenge because of noise signals and batch effects. An approach that improves the genotyping quality is needed for clinical applications. Methods: We developed a quality-control procedure for rare variants which integrates different algorithms, filters, and experiments to increase the accuracy of variant calling. Using data from the TWB 2.0 custom Axiom array, we adopted an advanced normalization adjustment to prevent false calls caused by splitting the cluster and a rare het adjustment which decreases false calls in rare variants. The concordance of allelic frequencies from array data was compared to those from sequencing datasets of Taiwanese. Finally, genotyping results were used to detect familial hypercholesterolemia (FH), thrombophilia (TH), and maturity-onset diabetes of the young (MODY) to assess the performance in disease screening. All heterozygous calls were verified by Sanger sequencing or qPCR. The positive predictive value (PPV) of each step was estimated to evaluate the performance of our procedure. Results: We analyzed SNP array data from 43,433 individuals, which interrogated 267,247 rare variants. The advanced normalization and rare het adjustment methods adjusted genotyping calling of 168,134 variants (96.49%). We further removed 3916 probesets which were discordant in MAFs between the SNP array and sequencing data. The PPV for detecting pathogenic variants with 0.01%10,000 are available. The results demonstrated our procedure could perform correct genotype calling of rare variants. It provides a solution of pathogenic variant detection through SNP array. The approach brings tremendous promise for implementing precision medicine in medical practice.

Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles

BACKGROUND AND OBJECTIVES

This paper presents an in-depth review of the state-of-the-art genetic variations analysis to discover complex genes associated with the brain's genetic disorders. We first introduce the genetic analysis of complex brain diseases, genetic variation, and DNA microarrays. Then, the review focuses on available machine learning methods used for complex brain disease classification. Therein, we discuss the various datasets, preprocessing, feature selection and extraction, and classification strategies. In particular, we concentrate on studying single nucleotide polymorphisms (SNP) that support the highest resolution for genomic fingerprinting for tracking disease genes. Subsequently, the study provides an overview of the applications for some specific diseases, including autism spectrum disorder, brain cancer, and Alzheimer's disease (AD). The study argues that despite the significant recent developments in the analysis and treatment of genetic disorders, there are considerable challenges to elucidate causative mutations, especially from the viewpoint of implementing genetic analysis in clinical practice. The review finally provides a critical discussion on the applicability of genetic variations analysis for complex brain disease identification highlighting the future challenges.

METHODS

We used a methodology for literature surveys to obtain data from academic databases. Criteria were defined for inclusion and exclusion. The selection of articles was followed by three stages. In addition, the principal methods for machine learning to classify the disease were presented in each stage in more detail.

RESULTS

It was revealed that machine learning based on SNP was widely utilized to solve problems of genetic variation for complex diseases related to genes.

CONCLUSIONS

Despite significant developments in genetic diseases in the past two decades of the diagnosis and treatment, there is still a large percentage in which the causative mutation cannot be determined, and a final genetic diagnosis remains elusive. So, we need to detect the variations of the genes related to brain disorders in the early disease stages.

Prognostic impact of pre-transplant chromosomal aberrations in peripheral blood of patients undergoing unrelated donor hematopoietic cell transplant for acute myeloid leukemia

To improve risk stratification and treatment decisions for patients with acute myeloid leukemia (AML) undergoing hematopoietic cell transplantation (HCT). We used SNP-array data from the DISCOVeRY-BMT study to detect chromosomal aberrations in pre-HCT peripheral blood (collected 2–4 weeks before the administration of conditioning regimen) from 1974 AML patients who received HCT between 2000 and 2011. All aberrations detected in ≥ 10 patients were tested for their association with overall survival (OS), separately by remission status, using the Kaplan–Meier estimator. Cox regression models were used for multivariable analyses. Follow-up was through January 2019. We identified 701 unique chromosomal aberrations in 285 patients (7% of 1438 in complete remission (CR) and 36% of 536 not in CR). Copy-neutral loss-of-heterozygosity (CNLOH) in chr17p in CR patients (3-year OS = 20% vs. 50%, with and without chr17p CNLOH, p = 0.0002), and chr13q in patients not in CR (3-year OS = 4% vs. 26%, with and without chr13q CNLOH, p < 0.0001) are risk factors for poor survival. Models adjusted for clinical factors showed approximately three-fold excess risk of post-HCT mortality with chr17p CNLOH in CR patients (hazard ratio, HR = 3.39, 95% confidence interval CI 1.74–6.60, p = 0.0003), or chr13q CNLOH in patients not in CR (HR = 2.68, 95% CI 1.75–4.09, p < 0.0001). The observed mortality was mostly driven by post-HCT relapse (HR = 2.47, 95% CI 1.01–6.02, p = 0.047 for chr17p CNLOH in CR patients, and HR = 2.58, 95% CI 1.63–4.08, p < 0.0001 for chr13q CNLOH in patients not in CR. Pre-transplant CNLOH in chr13q or chr17p predicts risk of poor outcomes after unrelated donor HCT in AML patients. A large prospective study is warranted to validate the results and evaluate novel strategies to improve survival in those patients.

Intrachromosomal Amplification of Chromosome 21 in Childhood Acute Lymphoblastic Leukemia: Study of 3 Cases

Acute lymphoblastic leukemia (ALL) is the most common malignancy of childhood. The presence or absence of a characteristic genetic abnormality usually observed in childhood ALL plays a very important role in determining the prognosis and stratification for treatment. Intrachromosomal amplification of chromosome 21 (iAMP21) is an uncommon high-risk chromosomal abnormality than can occur only in 2% of childhood B-cell precursor lymphoblastic leukemia. Molecular genetic analysis and the fluorescence in situ hybridization (FISH) technique are the basic methods used to detect the presence of the most common genetic abnormalities, the presence or absence of which has an impact on the patient's classification into the appropriate risk group. This work presents 3 BCP-ALL iAMP21-positive patients who were detected during routine genetic diagnostics using the FISH method and microarray test. iAMP21 is associated with a poor prognosis and high risk for relapse. Children with B-cell precursor lymphoblastic leukemia with this genetic entity are associated with a delayed treatment response. The FISH method and single-nucleotide polymorphism array provides a useful method to detect characteristic genetic changes.

A Tangle of Genomic Aberrations Drives Multiple Myeloma and Correlates with Clinical Aggressiveness of the Disease: A Comprehensive Review from a Biological Perspective to Clinical Trial Results

Multiple myeloma (MM) is a genetically heterogeneous disease, in which the process of tumorigenesis begins and progresses through the appearance and accumulation of a tangle of genomic aberrations. Several are the mechanisms of DNA damage in MM, varying from single nucleotide substitutions to complex genomic events. The timing of appearance of aberrations is well studied due to the natural history of the disease, that usually progress from pre-malignant to malignant phase. Different kinds of aberrations carry different prognostic significance and have been associated with drug resistance in some studies. Certain genetic events are well known to be associated with prognosis and are incorporated in risk evaluation in MM at diagnosis in the revised International Scoring System (R-ISS). The significance of some other aberrations needs to be further explained. Since now, few phase 3 randomized trials included analysis on patient's outcomes according to genetic risk, and further studies are needed to obtain useful data to stratify the choice of initial and subsequent treatment in MM.

All-in-one approaches for rapid and highly specific quantifcation of single nucleotide polymorphisms based on ligase detection reaction using molecular beacons as turn-on probes

Rapid, simple, specific and sensitive approaches for single nucleotide polymorphisms (SNPs) detection are essential for clinical diagnosis. In this study, all-in-one approaches, consisting of the whole detection process including ligase detection reaction (LDR) and real time quantitative polymerase chain reaction performed in one PCR tube by a one-step operation on a real-time PCR system using molecular beacon (MB) as turn-on probe, were developed for rapid, simple, specific and sensitive quantifcation of SNPs. High specificity of the all-in-one approach was achieved by using the LDR, which employs a thermostable and single-base discerning Hifi Taq DNA ligase to ligate adjacently hybridized LDR-specific probes. In addition, a highly specific probe, MB, was used to detect the products of all-in-one approach, which doubly enhances the specificity of the all-in-one approach. The linear dynamic range and high sensitivity of mutant DNA (MutDNA) and wild-type DNA (WtDNA) all-in-one approaches for the detection of MutDNA and WtDNA were studied in vitro, with a broad linear dynamic range of 0.1 fM to 1 pM and detection limits of 65.3 aM and 31.2 aM, respectively. In addition, the MutDNA and WtDNA all-in-one approaches were able to accurately detect allele frequency changes as low as 0.1%. In particular, the epidermal growth factor receptor T790M MutDNA frequency in the tissue of five patients with non-small cell lung cancer detected by all-in-one approaches were in agreement with clinical detection results, indicating the excellent practicability of the developed approaches for the quantification of SNPs in real samples. In summary, the developed all-in-one approaches exhibited promising potential for further applications in clinical diagnosis.

Cancer classification from microarray data for genomic disorder research using optimal discriminant independent component analysis and kernel extreme learning machine

One of the challenging tasks in the medicinal field is genomic disorder investigation and its classification from the microarray dataset. The microarray dataset reorganization and its classification is more complex and expensive in the biomedical research area due to the larger number of features in the microarray dataset. In this paper, we construct a hybrid feature selection method such as t test, Fisher ration, and Bayesian logistic regression to select genes and that reduce the time cost. Based on the features, the top-ranked features are selected via the best hybrid rank method. Thereafter, the features are extracted using the modified firefly optimization-based discriminant independent component analysis (MF-DICA). Especially, the modified firefly optimization algorithm is capable of improving the search efficiency of DICA. From the high dimensional microarray dataset, MF-DICA is used to obtain the best features within the entire search space. The kernel extreme learning machine classifies the gene features depending upon the most relevant class. Experimentally, six datasets namely Leukemia dataset, Diffuse Larger B-cell Lymphomas, Lung cancer, Breast cancer, Prostate tumor, and Colon dataset are chosen to evaluate the performance of proposed approaches. Finally, the experimental data demonstrate that the proposed method is well suitable to classify the microarray data.