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BACKGROUND: the human leukocyte antigen (HLA) loci have been widely characterized to be associated with viral infectious diseases. Several studies including various ethnic groups and populations suggested associations between certain HLA alleles and SARS-CoV-2 infection. Despite the numerous associations identified, the role of HLA polymorphisms in determining the individual response to SARS-CoV-2 infection is controversial among different Saudi populations. METHOD: Here, we performed HLA typing by next-generation sequencing to investigate if variations in polymorphic HLA genes are linked to COVID-19 severity in the Saudi population. Namely, we analyzed HLA loci at allele level in 575 Saudi patients with SARS-CoV-2 infection. HLA class I and class II frequencies in patients were compared with allele frequency data from healthy Saudi population. RESULTS: in our cohort HLA-A* 02:01:01 G was associated with mild disease but was not associated with moderate and severe disease. HLA-B* 51:01:01 G was protective from severe disease while HLA-B* 50:01:01 G, HLA-C* 06:02:01 G and HLA-DRB1 * 07:01:01 G were associated with risk to severe disease as well as the total COVID-19 cohort. HLA-DRB1 * 15:01:01 G was associated with risk to all severity groups. CONCLUSION: in conclusion, we found significant associations between HLA alleles and COVID-19 disease severity in Saudis. Further studies are warranted to include HLA typing in the workup for any new COVID-19 patients.
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Cell-free fetal DNA (cffDNA) screening is a valuable tool in clinical practice for detecting chromosomal abnormalities and autosomal dominant (AD) conditions. This study introduces a novel proof-of-concept assay designed for autosomal recessive (AR) cffDNA screening, focusing on cases involving the NPC1 gene. We aim to illustrate the significant benefits of AR cffDNA screening in managing high-risk pregnancies, specifically where biallelic pathogenic variants in NPC1 cause Niemann-Pick disease, type C1 (NPC), a disorder marked by progressive neurodegeneration. Three participants for this study were recruited and gave consent to a hospital in Saudi Arabia. These participants were either carriers of NPC or had a first- or second-degree relative affected by the disorder. No specific criteria were set for the age of the participants. All were between 15 and 18 weeks of gestation. Using amplicon-based next-generation sequencing (NGS), we analyzed the zygosity and variants in cffDNA extracted from maternal peripheral blood. After amplicon NGS, analysis was completed by a custom data analysis pipeline that included in-house-built data processing scripts and commonly used software packages. Importantly, the results were not disclosed to the patients. Our findings showed that in all three cases, AR cffDNA screening results were consistent with standard invasive diagnostic testing. This screening method offers several advantages: it provides critical information to families earlier in the pregnancy compared to invasive diagnostic tests, and it helps to alleviate parental anxiety. Moreover, this non-invasive method can determine pregnancy status in the first trimester for known familial variants. Future research may extend this approach to screen for known disease-causing variants in common AR conditions.
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Computational data-centric research techniques play a prevalent and multi-disciplinary role in life science research. In the past, scientists in wet labs generated the data, and computational researchers focused on creating tools for the analysis of those data. Computational researchers are now becoming more independent and taking leadership roles within biomedical projects, leveraging the increased availability of public data. We are now able to generate vast amounts of data, and the challenge has shifted from data generation to data analysis. Here we discuss the pitfalls, challenges, and opportunities facing the field of data-centric research in biology. We discuss the evolving perception of computational data-driven research and its rise as an independent domain in biomedical research while also addressing the significant collaborative opportunities that arise from integrating computational research with experimental and translational biology. Additionally, we discuss the future of data-centric research and its applications across various areas of the biomedical field.
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Investigación Biomédica , Biología Computacional , Biología Computacional/métodos , HumanosRESUMEN
Data-driven computational analysis is becoming increasingly important in biomedical research, as the amount of data being generated continues to grow. However, the lack of practices of sharing research outputs, such as data, source code and methods, affects transparency and reproducibility of studies, which are critical to the advancement of science. Many published studies are not reproducible due to insufficient documentation, code, and data being shared. We conducted a comprehensive analysis of 453 manuscripts published between 2016-2021 and found that 50.1% of them fail to share the analytical code. Even among those that did disclose their code, a vast majority failed to offer additional research outputs, such as data. Furthermore, only one in ten articles organized their code in a structured and reproducible manner. We discovered a significant association between the presence of code availability statements and increased code availability. Additionally, a greater proportion of studies conducting secondary analyses were inclined to share their code compared to those conducting primary analyses. In light of our findings, we propose raising awareness of code sharing practices and taking immediate steps to enhance code availability to improve reproducibility in biomedical research. By increasing transparency and reproducibility, we can promote scientific rigor, encourage collaboration, and accelerate scientific discoveries. We must prioritize open science practices, including sharing code, data, and other research products, to ensure that biomedical research can be replicated and built upon by others in the scientific community.
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Primary effusion lymphoma (PEL) is an uncommon B-cell lymphoma associated with human herpesvirus 8 and comprises 3-4% of all HIV-related lymphomas. It traditionally presents as a pleural, pericardial, and/or peritoneal effusion, though it can occasionally manifest as an extracavitary or solid mass in the absence of an effusion. The extracavitary or solid variant of primary effusion lymphoma has been reported in the skin, gastrointestinal tract, lung, and lymph nodes. However, very few cases have been reported in the central nervous system. We describe a case of extracavitary or solid variant of primary effusion lymphoma presenting as a brain mass in an HIV-positive man, highlighting the clinicopathologic and immunophenotypic findings of a rare entity.
Primary effusion lymphoma (PEL) is an uncommon and aggressive form of large B-cell lymphoma with a grim outlook, making up less than 1% of all lymphomas. PEL is linked to human herpesvirus 8 and predominantly impacts individuals with HIV or weakened immune systems. The typical presentation of PEL involves cancerous fluid accumulating in the chest or abdominal cavities. Occasionally, PEL can appear as a solid mass outside these cavities, termed extracavitary PEL (EC-PEL). The case we are describing highlights the difficulties in diagnosing PEL/EC-PEL. It is crucial for healthcare providers to consider EC-PEL when dealing with human herpesvirus 8-positive B-cell lymphomas, especially when patients have weakened immune systems and an unusual clinical scenario involving a solid mass, as seen in this case.
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Neoplasias Encefálicas , Linfoma de Efusión Primaria , Humanos , Linfoma de Efusión Primaria/patología , Linfoma de Efusión Primaria/diagnóstico , Masculino , Neoplasias Encefálicas/patología , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/diagnóstico , Persona de Mediana EdadRESUMEN
Methods for analyzing the full complement of a biomolecule type, e.g., proteomics or metabolomics, generate large amounts of complex data. The software tools used to analyze omics data have reshaped the landscape of modern biology and become an essential component of biomedical research. These tools are themselves quite complex and often require the installation of other supporting software, libraries and/or databases. A researcher may also be using multiple different tools that require different versions of the same supporting materials. The increasing dependence of biomedical scientists on these powerful tools creates a need for easier installation and greater usability. Packaging and containerization are different approaches to satisfy this need by delivering omics tools already wrapped in additional software that makes the tools easier to install and use. In this systematic review, we describe and compare the features of prominent packaging and containerization platforms. We outline the challenges, advantages and limitations of each approach and some of the most widely used platforms from the perspectives of users, software developers and system administrators. We also propose principles to make the distribution of omics software more sustainable and robust to increase the reproducibility of biomedical and life science research.
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In the evolving landscape of ependymoma classification, which integrates histological, molecular, and anatomical context, we detail a rare case divergent from the usual histopathological spectrum. We present the case of a 37-year-old man with symptomatic spinal cord compression at the L3-L4 level. Neuroradiological evaluation revealed an intradural, encapsulated mass. Histologically, the tumor displayed atypical features: bizarre pleomorphic giant cells, intranuclear inclusions, mitotic activity, and a profusion of eosinophilic cytoplasm with hyalinized vessels, deviating from the characteristic perivascular pseudorosettes or myxopapillary patterns. Immunohistochemical staining bolstered this divergence, marking the tumor cells positive for glial fibrillary acidic protein and epithelial membrane antigen with a characteristic ring-like pattern, and CD99 but negative for Olig-2. These markers, alongside methylation profiling, facilitated its classification as a myxopapillary ependymoma (MPE), despite the atypical histologic features. This profile underscores the necessity of a multifaceted diagnostic process, especially when histological presentation is uncommon, confirming the critical role of immunohistochemistry and molecular diagnostics in classifying morphologically ambiguous ependymomas and exemplifying the histological diversity within MPEs.
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This review delves into the most recent advancements in applying artificial intelligence (AI) within neuro-oncology, specifically emphasizing work on gliomas, a class of brain tumors that represent a significant global health issue. AI has brought transformative innovations to brain tumor management, utilizing imaging, histopathological, and genomic tools for efficient detection, categorization, outcome prediction, and treatment planning. Assessing its influence across all facets of malignant brain tumor management- diagnosis, prognosis, and therapy- AI models outperform human evaluations in terms of accuracy and specificity. Their ability to discern molecular aspects from imaging may reduce reliance on invasive diagnostics and may accelerate the time to molecular diagnoses. The review covers AI techniques, from classical machine learning to deep learning, highlighting current applications and challenges. Promising directions for future research include multimodal data integration, generative AI, large medical language models, precise tumor delineation and characterization, and addressing racial and gender disparities. Adaptive personalized treatment strategies are also emphasized for optimizing clinical outcomes. Ethical, legal, and social implications are discussed, advocating for transparency and fairness in AI integration for neuro-oncology and providing a holistic understanding of its transformative impact on patient care.
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The tragic COVID-19 pandemic, which has seen a total of 655 million cases worldwide and a death toll of over 6.6 million seems finally tailing off. Even so, new variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continue to arise, the severity of which cannot be predicted in advance. This is concerning for the maintenance and stability of public health, since immune evasion and increased transmissibility may arise. Therefore, it is crucial to continue monitoring antibody responses to SARS-CoV-2 in the general population. As a complement to polymerase chain reaction tests, multiplex immunoassays are elegant tools that use individual protein or peptide antigens simultaneously to provide a high level of sensitivity and specificity. To further improve these aspects of SARS-CoV-2 antibody detection, as well as accuracy, we have developed an advanced serological peptide-based multiplex assay using antigen-fused peptide epitopes derived from both the spike and the nucleocapsid proteins. The significance of the epitopes selected for antibody detection has been verified by in silico molecular docking simulations between the peptide epitopes and reported SARS-CoV-2 antibodies. Peptides can be more easily and quickly modified and synthesized than full length proteins and can, therefore, be used in a more cost-effective manner. Three different fusion-epitope peptides (FEPs) were synthesized and tested by enzyme-linked immunosorbent assay (ELISA). A total of 145 blood serum samples were used, compromising 110 COVID-19 serum samples from COVID-19 patients and 35 negative control serum samples taken from COVID-19-free individuals before the outbreak. Interestingly, our data demonstrate that the sensitivity, specificity, and accuracy of the results for the FEP antigens are higher than for single peptide epitopes or mixtures of single peptide epitopes. Our FEP concept can be applied to different multiplex immunoassays testing not only for SARS-CoV-2 but also for various other pathogens. A significantly improved peptide-based serological assay may support the development of commercial point-of-care tests, such as lateral-flow-assays.
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BACKGROUND: Acute myeloid leukemia (AML) is a hematological malignancy that remains a therapeutic challenge due to the high incidence of disease relapse. To better understand resistance mechanisms and identify novel therapies, robust preclinical models mimicking the bone marrow (BM) microenvironment are needed. This study aimed to achieve an automated fabrication process of a three-dimensional (3D) AML disease model that recapitulates the 3D spatial structure of the BM microenvironment and applies to drug screening and investigational studies. METHODS: To build this model, we investigated a unique class of tetramer peptides with an innate ability to self-assemble into stable hydrogel. An automated robotic bioprinting process was established to fabricate a 3D BM (niche-like) multicellular AML disease model comprised of leukemia cells and the BM's stromal and endothelial cellular fractions. In addition, monoculture and dual-culture models were also fabricated. Leukemia cell compatibility, functionalities (in vitro and in vivo), and drug assessment studies using our model were performed. In addition, RNAseq and gene expression analysis using TaqMan arrays were also performed on 3D cultured stromal cells and primary leukemia cells. RESULTS: The selected peptide hydrogel formed a highly porous network of nanofibers with mechanical properties similar to the BM extracellular matrix. The robotic bioprinter and the novel quadruple coaxial nozzle enabled the automated fabrication of a 3D BM niche-like AML disease model with controlled deposition of multiple cell types into the model. This model supported the viability and growth of primary leukemic, endothelial, and stromal cells and recapitulated cell-cell and cell-ECM interactions. In addition, AML cells in our model possessed quiescent characteristics with improved chemoresistance attributes, resembling more the native conditions as indicated by our in vivo results. Moreover, the whole transcriptome data demonstrated the effect of 3D culture on enhancing BM niche cell characteristics. We identified molecular pathways upregulated in AML cells in our 3D model that might contribute to AML drug resistance and disease relapse. CONCLUSIONS: Our results demonstrate the importance of developing 3D biomimicry models that closely recapitulate the in vivo conditions to gain deeper insights into drug resistance mechanisms and novel therapy development. These models can also improve personalized medicine by testing patient-specific treatments.
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Ameloblastic fibrosarcoma (AFS) is considered a malignant progression resulting from dysplastic changes in an ameloblastic fibroma (AF). Both tumors are extremely rare, with only a few cases reported in the scientific literature. Notably, BRAF mutations have been identified in ameloblastomas, suggesting a connection between ameloblastic morphology and BRAF mutations, as AF is believed to be the precursor neoplasm leading to AFS. In this study, we present a case of AFS in a 25-year-old male. The tumor tissue underwent molecular analysis, specifically next-generation sequencing (NGS) using the Oncomine Comprehensive Assay v3 System. The analysis revealed pathogenic mutations in TP53 and RB genes, as well as copy number gains in NTRK1, MDM4, and BRAF. Additionally, we provide a summary of the literature's findings from the analysis of 107 previously reported AFS cases. Our findings suggest the existence of a molecularly distinct subtype, emphasizing the importance of comprehensive molecular testing for these patients.
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Non-invasive prenatal testing (NIPT) is a pioneering technique that has consistently advanced the field of prenatal testing to detect genetic abnormalities and conditions with the aim of decreasing the incidence and prevalence of inherited conditions. NIPT remains a method of choice for common autosomal aneuploidies, mostly trisomy 21, and several monogenic disorders. The advancements in gene sequencing techniques have expanded the panel of conditions where NIPT could be offered. However, basic research on the impact of several genetic conditions lags behind the methods of detection of these sequence aberrations, and the impact of the expansion of NIPT should be carefully considered based on its utility. With interest from commercial diagnostics and a lack of regulatory oversight, there remains a need for careful validation of the predictive values of different tests offered. NIPT comes with many challenges, including ethical and economic issues. The scientific evidence, technical feasibility, and clinical benefit of NIPT need to be carefully investigated before new tests and developments are translated into clinical practice. Moreover, the implementation of panel expansion of NIPT should accompany expert genetic counseling pre- and post-testing.
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PURPOSE: Next-generation sequencing (NGS) technology detects specific mutations that can provide treatment opportunities for colorectal cancer (CRC) patients. PATIENTS AND METHODS: We analyzed the mutation frequencies of common actionable genes and their association with clinicopathological characteristics and oncologic outcomes using targeted NGS in 107 Saudi Arabian patients without a family history of CRC. RESULTS: Approximately 98% of patients had genetic alterations. Frequent mutations were observed in BRCA2 (79%), CHEK1 (78%), ATM (76%), PMS2 (76%), ATR (74%), and MYCL (73%). The APC gene was not included in the panel. Statistical analysis using the Cox proportional hazards model revealed an unusual positive association between poorly differentiated tumors and survival rates (p = 0.025). Although no significant univariate associations between specific mutations or overall mutation rate and overall survival were found, our preliminary analysis of the molecular markers for CRC in a predominantly Arab population can provide insights into the molecular pathways that play a significant role in the underlying disease progression. CONCLUSIONS: These results may help optimize personalized therapy when drugs specific to a patient's mutation profile have already been developed.
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Data-driven computational analysis is becoming increasingly important in biomedical research, as the amount of data being generated continues to grow. However, the lack of practices of sharing research outputs, such as data, source code and methods, affects transparency and reproducibility of studies, which are critical to the advancement of science. Many published studies are not reproducible due to insufficient documentation, code, and data being shared. We conducted a comprehensive analysis of 453 manuscripts published between 2016-2021 and found that 50.1% of them fail to share the analytical code. Even among those that did disclose their code, a vast majority failed to offer additional research outputs, such as data. Furthermore, only one in ten papers organized their code in a structured and reproducible manner. We discovered a significant association between the presence of code availability statements and increased code availability (p=2.71×10-9). Additionally, a greater proportion of studies conducting secondary analyses were inclined to share their code compared to those conducting primary analyses (p=1.15*10-07). In light of our findings, we propose raising awareness of code sharing practices and taking immediate steps to enhance code availability to improve reproducibility in biomedical research. By increasing transparency and reproducibility, we can promote scientific rigor, encourage collaboration, and accelerate scientific discoveries. We must prioritize open science practices, including sharing code, data, and other research products, to ensure that biomedical research can be replicated and built upon by others in the scientific community.
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Family history is an important factor in determining hereditary cancer risk for many cancer types. The emergence of next-generation sequencing (NGS) has expedited the discovery of many hereditary cancer susceptibility genes and the development of rapid, affordable testing kits. Here, a 30-gene targeted NGS panel for hereditary cancer risk assessment was tested and validated in a Saudi Arabian population. A total of 310 subjects were screened, including 57 non-cancer patients, 110 index patients with cancer and 143 of the cancer patients' family members, 16 of which also had cancer. Of the 310 subjects, 119 (38.4%) were carriers of pathogenic or likely pathogenic variants (PVs) affecting one or more of the following genes: TP53, ATM, CHEK2, CDH1, CDKN2A, BRCA1, BRCA2, PALB2, BRIP1, RAD51D, APC, MLH1, MSH2, MSH6, PMS2, PTEN, NBN/NBS1 and MUTYH. Among 126 patients and relatives with a history of cancer, 49 (38.9%) were carriers of PVs or likely PVs. Two variants in particular were significantly associated with the occurrence of a specific cancer in this population (APC c.3920T>A - colorectal cancer/Lynch syndrome (p = 0.026); TP53 c.868C>T; - multiple colon polyposis (p = 0.048)). Diverse variants in BRCA2, the majority of which have not previously been reported as pathogenic, were found at higher frequency in those with a history of cancer than in the general patient population. There was a higher background prevalence of genetic variants linked to familial cancers in this cohort than expected based on prevalence in other populations.
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Neoplasias Colorrectales , Neoplasias Nasofaríngeas , Humanos , Arabia Saudita , Secuenciación de Nucleótidos de Alto Rendimiento , Prevalencia , Predisposición Genética a la EnfermedadRESUMEN
The ability to identify and track T-cell receptor (TCR) sequences from patient samples is becoming central to the field of cancer research and immunotherapy. Tracking genetically engineered T cells expressing TCRs that target specific tumor antigens is important to determine the persistence of these cells and quantify tumor responses. The available high-throughput method to profile TCR repertoires is generally referred to as TCR sequencing (TCR-Seq). However, the available TCR-Seq data are limited compared with RNA sequencing (RNA-Seq). In this paper, we have benchmarked the ability of RNA-Seq-based methods to profile TCR repertoires by examining 19 bulk RNA-Seq samples across 4 cancer cohorts including both T-cell-rich and T-cell-poor tissue types. We have performed a comprehensive evaluation of the existing RNA-Seq-based repertoire profiling methods using targeted TCR-Seq as the gold standard. We also highlighted scenarios under which the RNA-Seq approach is suitable and can provide comparable accuracy to the TCR-Seq approach. Our results show that RNA-Seq-based methods are able to effectively capture the clonotypes and estimate the diversity of TCR repertoires, as well as provide relative frequencies of clonotypes in T-cell-rich tissues and low-diversity repertoires. However, RNA-Seq-based TCR profiling methods have limited power in T-cell-poor tissues, especially in highly diverse repertoires of T-cell-poor tissues. The results of our benchmarking provide an additional appealing argument to incorporate RNA-Seq into the immune repertoire screening of cancer patients as it offers broader knowledge into the transcriptomic changes that exceed the limited information provided by TCR-Seq.
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Benchmarking , Neoplasias , Humanos , Receptores de Antígenos de Linfocitos T/genética , Linfocitos T , Neoplasias/genética , Análisis de Secuencia de ARNRESUMEN
Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte-macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).
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COVID-19 , Enfermedad Crítica , Predisposición Genética a la Enfermedad , Variación Genética , Estudio de Asociación del Genoma Completo , Humanos , COVID-19/genética , Predisposición Genética a la Enfermedad/genética , Variación Genética/genética , Genotipo , Técnicas de Genotipaje , Monocitos/metabolismo , Fenotipo , Proteínas de Unión al GTP rab/genética , Transcriptoma , Secuenciación Completa del GenomaRESUMEN
Central nervous system (CNS) metastasis is the most common brain tumor type in adults. Compared to their primary tumors, these metastases undergo a variety of genetic changes to be able to survive and thrive in the complex tissue microenvironment of the brain. In clinical settings, the majority of traditional chemotherapies have shown limited efficacy against CNS metastases. However, the discovery of potential driver mutations, and the development of drugs specifically targeting affected signaling pathways, could change the treatment landscape of CNS metastasis. Genetic studies of brain tumors have so far focused mainly on common cancers in western populations. In this study, we performed Next Generation Sequencing (NGS) on 50 pairs of primary tumors, including but not limited to colorectal, breast, renal and thyroid tumors, along with their brain metastatic tumor tissue counterparts, from three different local tertiary centers in Saudi Arabia. We identified potentially clinically relevant mutations in brain metastases that were not detected in corresponding primary tumors, including mutations in the PI3K, CDK, and MAPK pathways. These data highlight the differences between primary cancers and brain metastases and the importance of acquiring and analyzing brain metastatic samples for further clinical management.