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Personalized Diagnostic Tools and Methods to Assess Genetic Predisposition in...
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Personalized Diagnostic Tools and Methods to Assess Genetic Predisposition in Human Disease

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: closed (10 June 2021) | Viewed by 34572

Special Issue Editor

Dr. Emil Alexov

E-Mail Website
Guest Editor
Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA
Interests: computational modeling of biological macromolecules; predicting molecular effects of disease-causing mutations; personalized medicine; macromolecular interactions; electrostatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There is a clear linkage between various human diseases and genetic variants of affected individuals. This has prompted the development of tools to predict and databases to collect pathogenic variants. This development was facilitated by enormous advances in gene sequencing techniques, providing the biomedical community with a huge amount of data and, at the same time, offering accessible genetic testing. Thus, nowadays, it is possible for interested individuals, either advised by their primary physician or driven by their curiosity, to have their DNA sequenced. The next step is the analysis of the individual’s DNA and assessing the risk of disease(s). This Special Issue in Genes titled “Personalized Diagnostic Tools and Methods to Assess Genetic Predisposition in Human Disease” will provide a platform for interested developers (both computational and experimental) to popularize their development and, at the same time, will provoke discussions regarding recent developments in specialized research topics and critical perspectives on upcoming challenges.

Dr. Emil Alexov
Guest Editor

Manuscript Submission Information

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Keywords

  • Personalized medicine
  • Disease-causing mutations
  • Genetic variations
  • Macromolecular interactions and networks
  • Single nucleotide polymorphism (SNP)
  • Genetic disease predisposition
  • Disorders

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Published Papers (7 papers)

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Research

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8 pages, 711 KiB  
Article
Heritability of Low ER Staining/HER2-Breast Tumors: Are We Missing an Opportunity for Germline Testing?
by Leann A. Lovejoy, Clesson E. Turner, Justin M. Wells, Craig D. Shriver and Rachel E. Ellsworth
Genes 2020, 11(12), 1469; https://doi.org/10.3390/genes11121469 - 8 Dec 2020
Cited by 6 | Viewed by 2528
Abstract
In 2010, the genetic testing criteria was changed to allow women diagnosed ≤ 60 years old with triple negative breast cancer (TNBC) to undergo germline testing. In the same year, estrogen receptor (ER) positivity was defined as having ≥1% ER staining cells. While [...] Read more.
In 2010, the genetic testing criteria was changed to allow women diagnosed ≤ 60 years old with triple negative breast cancer (TNBC) to undergo germline testing. In the same year, estrogen receptor (ER) positivity was defined as having ≥1% ER staining cells. While tumors with 1–10% ER staining cells and HER2 negative (HER2-) status share characteristics with TNBC, the utility of germline testing in women with ER low positive/HER2- (ERLP/HER2-) tumors is not well-understood. To this end, all patients with hormone receptor positive staining cells ≤ 10% and negative HER2 status were identified. Clinical genetic test results were extracted for patients who underwent testing. Panel testing was performed for those women who had genomic DNA available for research purposes. ERLP/HER2-tumors constituted 2.7% of all tumors in the database. Patients did not differ significantly from those with TNBC by age at diagnosis, ethnicity, family history or tumor size, stage or grade (p > 0.05). Mutation frequency did not differ significantly (p = 0.757) between groups (ERLP/HER2- 16.1%; TNBC 16.7%). Hereditary forms of breast cancer were similar in both ERLP/HER2- and TNBC, thus current guidelines may result in the under testing of women with low ER tumors, resulting in missed opportunities to improve patient management. Full article
(This article belongs to the Special Issue Personalized Diagnostic Tools and Methods to Assess Genetic Predisposition in Human Disease)
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16 pages, 1971 KiB  
Article
Targeted Sequencing of Pancreatic Adenocarcinomas from Patients with Metachronous Pulmonary Metastases
by Viktor Hlavac, Beatrice Mohelnikova-Duchonova, Martin Lovecek, Jiri Ehrmann, Veronika Brynychova, Katerina Kolarova and Pavel Soucek
Genes 2020, 11(12), 1391; https://doi.org/10.3390/genes11121391 - 24 Nov 2020
Cited by 1 | Viewed by 2405
Abstract
Mutation spectra of 250 cancer driver, druggable, and actionable genes were analyzed in surgically resected pancreatic ductal adenocarcinoma (PDAC) patients who developed metachronous pulmonary metastases. Targeted sequencing was performed in DNA from blood and archival samples of 15 primary tumors and three paired [...] Read more.
Mutation spectra of 250 cancer driver, druggable, and actionable genes were analyzed in surgically resected pancreatic ductal adenocarcinoma (PDAC) patients who developed metachronous pulmonary metastases. Targeted sequencing was performed in DNA from blood and archival samples of 15 primary tumors and three paired metastases. Results were complemented with the determination of G12V mutation in KRAS by droplet digital PCR. The median number of protein-changing mutations was 52 per patient. KRAS and TP53 were significantly enriched in fractions of mutations in hotspots. Individual gene mutation frequencies or mutational loads accounting separately for drivers, druggable, or clinically actionable genes, did not significantly associate with patients’ survival. LRP1B was markedly mutated in primaries of patients who generalized (71%) compared to those developing solitary pulmonary metastases (0%). FLG2 was mutated exclusively in primary tumors compared to paired metastases. In conclusion, signatures of prognostically differing subgroups of PDAC patients were generated for further utilization in precision medicine. Full article
(This article belongs to the Special Issue Personalized Diagnostic Tools and Methods to Assess Genetic Predisposition in Human Disease)
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13 pages, 1218 KiB  
Article
Autophagy Genes for Wet Age-Related Macular Degeneration in a Finnish Case-Control Study
by Jussi J. Paterno, Ali Koskela, Juha M.T. Hyttinen, Elina Vattulainen, Ewelina Synowiec, Raimo Tuuminen, Cezary Watala, Janusz Blasiak and Kai Kaarniranta
Genes 2020, 11(11), 1318; https://doi.org/10.3390/genes11111318 - 6 Nov 2020
Cited by 15 | Viewed by 3284
Abstract
Age-related macular degeneration is an eye disease that is the main cause of legal blindness in the elderly in developed countries. Despite this, its pathogenesis is not completely known, and many genetic, epigenetic, environmental and lifestyle factors may be involved. Vision loss in [...] Read more.
Age-related macular degeneration is an eye disease that is the main cause of legal blindness in the elderly in developed countries. Despite this, its pathogenesis is not completely known, and many genetic, epigenetic, environmental and lifestyle factors may be involved. Vision loss in age-related macular degeneration (AMD) is usually consequence of the occurrence of its wet (neovascular) form that is targeted in the clinic by anti-VEGF (vascular endothelial growth factor) treatment. The wet form of AMD is associated with the accumulation of cellular waste in the retinal pigment epithelium, which is removed by autophagy and the proteosomal degradation system. In the present work, we searched for the association between genotypes and alleles of single nucleotide polymorphisms (SNPs) of autophagy-related genes and wet AMD occurrence in a cohort of Finnish patients undergoing anti-VEGF therapy and controls. Additionally, the correlation between treatment efficacy and genotypes was investigated. Overall, 225 wet AMD patients and 161 controls were enrolled in this study. Ten SNPs (rs2295080, rs11121704, rs1057079, rs1064261, rs573775, rs11246867, rs3088051, rs10902469, rs73105013, rs10277) in the mTOR (Mechanistic Target of Rapamycin), ATG5 (Autophagy Related 5), ULK1 (Unc-51-Like Autophagy Activating Kinase 1), MAP1LC3A (Microtubule Associated Protein 1 Light Chain 3 α), SQSTM1 (Sequestosome 1) were analyzed with RT-PCR-based genotyping. The genotype/alleles rs2295080-G, rs11121704-C, rs1057079-C and rs73105013-T associated with an increased, whereas rs2295080-TT, rs2295080-T, rs11121704-TT, rs1057079-TT, rs1057079-T, rs573775-AA and rs73105013-C with a decreased occurrence of wet AMD. In addition, the rs2295080-GG, rs2295080-GT, rs1057079-TT, rs11246867-AG, rs3088051-CC and rs10277-CC genotypes were a positively correlated cumulative number of anti-VEGF injections in 2 years. Therefore, variability in autophagy genes may have an impact on the risk of wet AMD occurrence and the efficacy of anti-VEGF treatment. Full article
(This article belongs to the Special Issue Personalized Diagnostic Tools and Methods to Assess Genetic Predisposition in Human Disease)
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10 pages, 620 KiB  
Article
An Ensemble Approach to Predict the Pathogenicity of Synonymous Variants
by Satishkumar Ranganathan Ganakammal and Emil Alexov
Genes 2020, 11(9), 1102; https://doi.org/10.3390/genes11091102 - 21 Sep 2020
Cited by 6 | Viewed by 3026
Abstract
Single-nucleotide variants (SNVs) are a major form of genetic variation in the human genome that contribute to various disorders. There are two types of SNVs, namely non-synonymous (missense) variants (nsSNVs) and synonymous variants (sSNVs), predominantly involved in RNA processing or gene regulation. sSNVs, [...] Read more.
Single-nucleotide variants (SNVs) are a major form of genetic variation in the human genome that contribute to various disorders. There are two types of SNVs, namely non-synonymous (missense) variants (nsSNVs) and synonymous variants (sSNVs), predominantly involved in RNA processing or gene regulation. sSNVs, unlike missense or nsSNVs, do not alter the amino acid sequences, thereby making challenging candidates for downstream functional studies. Numerous computational methods have been developed to evaluate the clinical impact of nsSNVs, but very few methods are available for understanding the effects of sSNVs. For this analysis, we have downloaded sSNVs from the ClinVar database with various features such as conservation, DNA-RNA, and splicing properties. We performed feature selection and implemented an ensemble random forest (RF) classification algorithm to build a classifier to predict the pathogenicity of the sSNVs. We demonstrate that the ensemble predictor with selected features (20 features) enhances the classification of sSNVs into two categories, pathogenic and benign, with high accuracy (87%), precision (79%), and recall (91%). Furthermore, we used this prediction model to reclassify sSNVs with unknown clinical significance. Finally, the method is very robust and can be used to predict the effect of other unknown sSNVs. Full article
(This article belongs to the Special Issue Personalized Diagnostic Tools and Methods to Assess Genetic Predisposition in Human Disease)
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Review

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15 pages, 837 KiB  
Review
Making Sense of Genetic Information: The Promising Evolution of Clinical Stratification and Precision Oncology Using Machine Learning
by Mahaly Baptiste, Sarah Shireen Moinuddeen, Courtney Lace Soliz, Hashimul Ehsan and Gen Kaneko
Genes 2021, 12(5), 722; https://doi.org/10.3390/genes12050722 - 12 May 2021
Cited by 9 | Viewed by 4462
Abstract
Precision medicine is a medical approach to administer patients with a tailored dose of treatment by taking into consideration a person’s variability in genes, environment, and lifestyles. The accumulation of omics big sequence data led to the development of various genetic databases on [...] Read more.
Precision medicine is a medical approach to administer patients with a tailored dose of treatment by taking into consideration a person’s variability in genes, environment, and lifestyles. The accumulation of omics big sequence data led to the development of various genetic databases on which clinical stratification of high-risk populations may be conducted. In addition, because cancers are generally caused by tumor-specific mutations, large-scale systematic identification of single nucleotide polymorphisms (SNPs) in various tumors has propelled significant progress of tailored treatments of tumors (i.e., precision oncology). Machine learning (ML), a subfield of artificial intelligence in which computers learn through experience, has a great potential to be used in precision oncology chiefly to help physicians make diagnostic decisions based on tumor images. A promising venue of ML in precision oncology is the integration of all available data from images to multi-omics big data for the holistic care of patients and high-risk healthy subjects. In this review, we provide a focused overview of precision oncology and ML with attention to breast cancer and glioma as well as the Bayesian networks that have the flexibility and the ability to work with incomplete information. We also introduce some state-of-the-art attempts to use and incorporate ML and genetic information in precision oncology. Full article
(This article belongs to the Special Issue Personalized Diagnostic Tools and Methods to Assess Genetic Predisposition in Human Disease)
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22 pages, 381 KiB  
Review
Pharmacogenomic Biomarkers and Their Applications in Psychiatry
by Heejin Kam and Hotcherl Jeong
Genes 2020, 11(12), 1445; https://doi.org/10.3390/genes11121445 - 30 Nov 2020
Cited by 20 | Viewed by 8448
Abstract
Realizing the promise of precision medicine in psychiatry is a laudable and beneficial endeavor, since it should markedly reduce morbidity and mortality and, in effect, alleviate the economic and social burden of psychiatric disorders. This review aims to summarize important issues on pharmacogenomics [...] Read more.
Realizing the promise of precision medicine in psychiatry is a laudable and beneficial endeavor, since it should markedly reduce morbidity and mortality and, in effect, alleviate the economic and social burden of psychiatric disorders. This review aims to summarize important issues on pharmacogenomics in psychiatry that have laid the foundation towards personalized pharmacotherapy and, in a broader sense, precision medicine. We present major pharmacogenomic biomarkers and their applications in a variety of psychiatric disorders, such as depression, attention-deficit/hyperactivity disorder (ADHD), narcolepsy, schizophrenia, and bipolar disorder. In addition, we extend the scope into epilepsy, since antiepileptic drugs are widely used to treat psychiatric disorders, although epilepsy is conventionally considered to be a neurological disorder. Full article
(This article belongs to the Special Issue Personalized Diagnostic Tools and Methods to Assess Genetic Predisposition in Human Disease)
24 pages, 1687 KiB  
Review
Precision and Personalized Medicine: How Genomic Approach Improves the Management of Cardiovascular and Neurodegenerative Disease
by Oriana Strianese, Francesca Rizzo, Michele Ciccarelli, Gennaro Galasso, Ylenia D’Agostino, Annamaria Salvati, Carmine Del Giudice, Paola Tesorio and Maria Rosaria Rusciano
Genes 2020, 11(7), 747; https://doi.org/10.3390/genes11070747 - 6 Jul 2020
Cited by 70 | Viewed by 9519
Abstract
Life expectancy has gradually grown over the last century. This has deeply affected healthcare costs, since the growth of an aging population is correlated to the increasing burden of chronic diseases. This represents the interesting challenge of how to manage patients with chronic [...] Read more.
Life expectancy has gradually grown over the last century. This has deeply affected healthcare costs, since the growth of an aging population is correlated to the increasing burden of chronic diseases. This represents the interesting challenge of how to manage patients with chronic diseases in order to improve health care budgets. Effective primary prevention could represent a promising route. To this end, precision, together with personalized medicine, are useful instruments in order to investigate pathological processes before the appearance of clinical symptoms and to guide physicians to choose a targeted therapy to manage the patient. Cardiovascular and neurodegenerative diseases represent suitable models for taking full advantage of precision medicine technologies applied to all stages of disease development. The availability of high technology incorporating artificial intelligence and advancement progress made in the field of biomedical research have been substantial to understand how genes, epigenetic modifications, aging, nutrition, drugs, microbiome and other environmental factors can impact health and chronic disorders. The aim of the present review is to address how precision and personalized medicine can bring greater clarity to the clinical and biological complexity of these types of disorders associated with high mortality, involving tremendous health care costs, by describing in detail the methods that can be applied. This might offer precious tools for preventive strategies and possible clues on the evolution of the disease and could help in predicting morbidity, mortality and detecting chronic disease indicators much earlier in the disease course. This, of course, will have a major effect on both improving the quality of care and quality of life of the patients and reducing time efforts and healthcare costs. Full article
(This article belongs to the Special Issue Personalized Diagnostic Tools and Methods to Assess Genetic Predisposition in Human Disease)
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