Front Matter| Volume 5, ISSUE 1, Pix-xiv, November 2022


        Editorial Board,  iii
        Contributors,  v

        Editorial: Monkeypox: Another Virus Gone Viral!,  xv

        By Helen Fernandes

        Preface: Molecular Pathology: Life Beyond the Pandemic,  xvii

        By Gregory J. Tsongalis


        Polygenic Risk Scores in Clinical Care,  1

        By Emily R. Soper and Noura S. Abul-Husn
        Polygenic risk scores (PRS) combine the effect of common variants obtained from genome-wide association studies to estimate the risk for common diseases. PRS have the potential to identify large proportions of the population at high risk for disease, and there is growing interest in using this information to guide clinical decision-making. However, the clinical utility of PRS remains unknown. Here, we discuss how PRS are generated and validated, how they can be interpreted and used to guide clinical care, and ongoing challenges and considerations for widespread implementation.
         Introduction,  1
         Generation, validation, and evaluation of polygenic risk scores,  2
         Translating polygenic risk scores from research into clinical care,  3
         Reporting risk from polygenic risk scores,  3
         Considerations in communicating polygenic risk scores,  4
         Polygenic risk scores today and in the future,  4
         Clinics care points,  6
         Disclosure,  6

        Hereditary Cancer and Cancer Predisposition Syndromes,  9

        By Erfan Aref-Eshghi and Marylin M. Li
        Germline genetics influences cancer incidence in various ways. Traditional cancer predisposition syndromes are caused by inherited pathogenic variants in tumor suppressor genes in an autosomal dominant fashion and predispose individuals to cancer as the main phenotype. Many other genetic syndromes can also present with cancer as part of the phenotype, the underlying causes of which are broad and include cytogenetic abnormalities, imprinting defects, and single-gene disorders. More recently, non-syndromic germline contribution to malignancies has expanded the scope of hereditary cancer, and many sporadic tumors are being recognized to have an inherited basis. For most cancer occurrences, however, a multifactorial or polygenic contribution is still the predominantly accepted model. Advances in sequencing technology have enabled the discovery of novel contributors to hereditary cancer. With the increasing use of genomic testing in the diagnostic setting, demands for testing guidelines for hereditary cancer are on the rise.
         Introduction,  9
         Cancer predisposition syndromes in children and adults,  9
        Retinoblastoma,  10
        DICER1 tumor predisposition syndrome,  10
        Li-Fraumeni syndrome,  10
        Hereditary breast and ovarian cancers,  13
        Hereditary polyposis and non-polyposis colorectal cancer syndromes,  13
         Genetic syndromes with cancer predisposition as part of the phenotype,  14
        Constitutional chromosomal abnormalities,  14
        Beckwith-Wiedemann syndrome,  20
        RASopathies,  20
        Bone marrow failure and chromosomal instability syndromes,  20
         Germline mutation contributions to non-syndromic cancer predisposition,  21
         Polygenic hereditary contribution to cancer,  22
         Genomic testing in hereditary cancer,  22
         Concluding remarks,  23
         Disclosure,  24


        Expanding the Molecular Landscape of Cutaneous T-Cell Lymphoma,  29

        By Mark G. Evans and Carlos A. Torres-Cabala
        Cutaneous T-cell lymphoma represents a genetically heterogeneous group of diseases thought to be derived from skin-homing lymphocytes. The genomic landscape of these conditions has been recently redefined to incorporate findings from cutting-edge molecular and cytogenetic testing. Recurrent genetic alterations that have been detected include those associated with chromatin modification, DNA repair, cell cycle control, JAK/STAT pathway activation, TCR/NFκB signaling, and MAPK pathway regulation. Many of these changes can be targeted by current/upcoming personalized therapies.
         Introduction,  29
         Classical subtypes of cutaneous T-cell lymphoma: mycosis fungoides and Sézary syndrome,  29
         CD30-positive T-cell lymphoproliferative disorders,  33
         Uncommon subtypes of cutaneous T-cell lymphoma,  34
        Indolent Subtypes: Primary Cutaneous CD4-Positive Small/Medium Pleomorphic T-Cell Lymphoproliferative Disorder and Primary ...,  34
        Subcutaneous Subtype: Subcutaneous Panniculitis-like T-Cell Lymphoma,  34
        Aggressive Subtypes: Primary Cutaneous Gamma Delta T-Cell Lymphoma and Primary Cutaneous CD8-Positive Aggressive Epidermotr ...,  35
         Discussion,  36
         Summary,  37
         Clinics care points,  37
         Disclosure,  37

        High-Grade B-cell Lymphomas,  41

        By Anna Shestakova and Kristin H. Karner
        Accurate diagnosis of mature B-cell lymphomas is essential for adequate treatment. There is a morphologic and immunophenotypic overlap between high-grade B-cell lymphoma (HGBL) with MYC and BCL2 and/or BCL6 rearrangements, HGBL, not otherwise specified (NOS), and Burkitt-like lymphoma with 11q aberration. Therefore, cytogenetic and molecular testing are essential for an accurate diagnosis. HGBL with MYC and BCL2 and/or BCL6 rearrangements is defined by rearranged MYC and BCL2 and/or BCL6 genes, also known as double-hit and triple-hit lymphomas. Burkitt-like lymphoma with 11q is characterized by the absence of the rearrangement of the MYC gene and a peculiar aberration of 11q with proximal gain and terminal loss.
         Introduction,  41
        High-Grade B-Cell Lymphoma with MYC and BCL2 and/or BCL6 Rearrangements,  42
        High-Grade B-Cell Lymphoma, Not Otherwise Specified,  45
        Burkitt-Like Lymphoma with 11q Aberration,  45
         Discussion and future directions,  46
         Clinics care points,  48
         Disclosure,  48

        Molecular Advances in Nodal Peripheral T-Cell Lymphoma,  51

        By Safina Hafeez and Allison M. Cushman-Vokoun
        Peripheral T-cell lymphoma (PTCL) is a rare and heterogeneous group of mature T-cell malignancies. It is an aggressive form of the disease, which is frequently associated with a poor prognosis. It can be subdivided into four major categories based on the clinical presentation and location of the disease: nodal, extranodal, cutaneous, and leukemic. The diagnostic approach in PTCL requires multiple factors such as clinical presentation, morphology, immunophenotype, cytogenetics, and molecular genetic findings. The recent advances in gene expression profiling and next-generation sequencing technologies have opened up new insights into the pathogenesis and molecular genetic changes of different subtypes of PTCL. In this review article, we will focus on nodal PTCL.
         Introduction,  51
         Peripheral T-cell lymphoma—not otherwise specified,  52
         Nodal lymphoma of T follicular helper cell origin,  53
         Angioimmunoblastic T-cell lymphoma,  53
         Follicular T-cell lymphoma,  54
         Nodal peripheral T-cell lymphoma with a T follicular helper phenotype,  54
         Anaplastic large cell lymphoma,  54
        ALK Positive Anaplastic Large Cell Lymphoma,  54
        ALK-Negative Anaplastic Large Cell Lymphoma,  55
         Disclosure,  56

        Multiple Myeloma,  59

        By Loren J. Joseph
        Multiple myeloma is a malignant clonal proliferation of plasma cells; it evolves through several stages of progressive pathology, marked by one or more of several specific cytogenetic abnormalities and subsequent accumulation of mutations in one or more of 80 driver genes, possibly in conjunction with alterations in stromal cells. Multiple lines of therapy have been developed, directed at different pathways. Each case bears a unique rearrangement of the IGH/L genes that permits sensitive detection of residual disease by next-generation sequencing. Overall survival has improved so dramatically that some experts have started using the “C(ure)” word.
         Multiple myeloma overview,  59
         Plasma cell biology,  60
         Cytogenetics,  60
         Genomics,  61
         Gene expression profiles (transcriptomics),  63
         Single-cell analysis,  63
         (Macro) spatial heterogeneity,  66
         Single-cell spatial genomics,  66
        Treatment,  66
        Therapeutic Monoclonal Antibodies (Rx-mAbs),  66
         Other therapies,  67
         Minimal residual disease,  67
         Summary,  68
         Clinics care points,  68

        Infectious Disease

        Prevalence and Clinical Disease Severity of Respiratory Coinfections During the Coronavirus Disease 2019 Pandemic,  73

        By Maxwell D. Weidmann, Gregory J. Berry, Daniel A. Green, and Fann Wu
        Respiratory tract infections represent a global health concern. More than 1 pathogenic organism in the respiratory tract has been widely recognized owing to the availability of molecular detection technologies. However, the association between the occurrence of multiple-pathogen infections and clinical disease severity remains unclear. Multiple infections fall into two broad categories: Coinfection occurs when a person is found to be infected by two or more micro-organisms, but which infection was established first cannot be clearly determined, while superinfection is an infection arising when one or more infections is already present. This review presents an overview of the prevalence and clinical disease severity of respiratory co-infections and superinfections and discusses possible mechanisms of the interactions between viral infections.
         Introduction,  73
         Respiratory viral coinfections,  74
        Prevalence of respiratory viral coinfections,  74
        Viral coinfection in adults,  74
        Viral coinfection in children,  74
        Clinical outcomes of respiratory viral coinfections,  77
        Coinfection with influenza,  77
        Coinfection with Respiratory Syncytial Virus,  77
        Virological mechanisms that determine viral persistence or exclusion during coinfections,  77
         Bacterial coinfection in the setting of respiratory viral illness,  78
        Influenza-associated bacterial coinfection,  78
        RSV-associated bacterial coinfection,  79
        SARS-CoV-2-associated bacterial coinfection,  80
         Disclosure,  81

        Applications of Long-Read Sequencing Technology in Clinical Genomics,  85

        By Shivaprasad H. Sathyanarayana, Sophie J. Deharvengt, Guohong Huang, Rachael E. Barney, Parth S. Shah, and Joel A. Lefferts
        Next-generation sequencing (NGS) using traditional short-read sequencing (SRS) has transformed the field of clinical genetics and genomics research. Newer long-read sequencing (LRS) technologies have emerged more recently that are able to reveal specific types of genetic information that traditional SRS technologies may not be able to detect. Detection of structural variants and highly repetitive sequences, differentiating homologous genomic sequences, and establishing allele phasing are some benefits of LRS. This review article highlights the power of LRS technology in different clinical testing areas and addresses some of its current limitations and challenges.
         Introduction,  85
         Long-read technology in genetics and genomics research,  89
         Structural variants detection and cancer,  90
         Structural variations and genetic disorders,  91
         Transposable elements,  93
         Haplotype resolution,  93
         Identifying pseudogenes,  94
         Tandem repeat expansion diseases,  94
         Applications of long-read technology in neurogenomics,  94
         Fragile X syndrome,  95
         Spinocerebellar ataxias,  95
         Huntington’s disease,  95
         Long-read technology in infectious disease and research,  96
         Long-read sequencing in metagenomics application,  97
         Challenges and issues in long-read technology,  98
         Data analytics,  98
         Summary and future prospects,  99


        SLCO1B1 Pharmacogenetics in Pediatrics,  109

        By Laura B. Ramsey, Jason A. Sprowl, J. Steven Leeder, and Jonathan B. Wagner
        Organic polypeptide transporting polypetide 1B1 (OATP1B1), encoded by the SLCO1B1 gene, is a major transporter involved in the hepatic uptake of several endogenous molecules and medications relevant to pediatrics. In children, the magnitude of effect of SLCO1B1 allelic variation on systemic exposure of statins is similar to that observed in adults, but is associated within genotype variability (>10-fold) that exceeds reported variability in adults; sources of the intra-genotype variability appear to be drug-specific. For methotrexate, the influence of rare variants appears to be comparable to common variants. Taken together, existing pharmacogenetic-driven dosing guidelines based on adult experience may not be directly applicable to pediatric age groups and should be used with caution. Integration of SLCO1B1 pharmacogenetics into clinical care will benefit from improved knowledge of non-genetic factors that contribute to inter-individual variability in drug disposition.
         Introduction,  109
        SLCO1B1 Gene,  109
        SLCO1B1 Genetic Variants and Nomenclature,  109
        OATP1B1 Protein,  110
        Ontogeny of OATP1B1,  110
        Endogenous Substrates as Biomarkers of OATP1B1 Activity,  110
        Drug Substrates,  111
        Clinical Implications,  113
        Infrequency of Statins Prescribed to Children,  113
         Summary,  115
         Conflicts of interest,  115

        Implementation of a Clinical Pharmacogenomics Service in a Large Freestanding Pediatric Health System,  119

        By Courtney Paetznick, David Gregornik, Lane Miller, Damon Olson, and Jacob Brown
        As a large, freestanding pediatric health system, Children’s Minnesota is faced with unique challenges and opportunities with pharmacogenomics (PGx) implementation as compared with adult health systems. The PGx program was initially developed to meet a growing desire to incorporate PGx information into clinical decision-making, particularly in hematology/oncology and behavioral health, and to lead the clinical PGx implementation. Since the launch of the PGx program, new possibilities have emerged with contributions to PGx research and on going development of an in-house genotyping assay as ways to better serve our diverse pediatric population.
         Introduction,  119
         History of Children’s Minnesota pharmacogenomics program,  120
         Pharmacogenomics clinic workflow and patient care process,  121
        Referral Patients to Pharmacogenomics Clinic,  121
        Transitions of Care,  124
         Provider perspective of pharmacogenomics implementation from a pediatric hematology/oncology physician,  125
         Pharmacogenomics research,  126
        Atomoxetine and CYP2D6,  126
        Fluoxetine and CYP2D6/CYP2C9,  126
        Sertraline and CYP2C19,  126
         Developing a pharmacogenomics genotyping assay,  126
         Summary,  128
         Disclosure,  128

        Artificial Intelligence/Machine Learning and Mechanistic Modeling Approaches as Translational Tools to Advance Personalized Medicine Decisions,  131

        By George A. Mystridis, Fani Chatzopoulou, George P. Patrinos, and Ioannis S. Vizirianakis
        Nowadays, advancements in omics-based methodologies, data analysis, informatics, and bioinformatics, as well as nanotechnology have continuously led to a more digitized, robust, as well as information-based environment in biomedicine and health care. Precision in therapy will be ensured through the application of computerized platforms, as well as interdisciplinary integrated analysis and decision-making. In this review, artificial intelligence/machine learning approaches and mechanistic modeling methodologies will be discussed as translational tools aiming to advance personalized medicine decisions and patient care.
         Introduction,  131
         Artificial intelligence-based and machine learning-based approaches for personalized cardiovascular medicine,  133
         Artificial intelligence-based and machine learning-based approaches for personalized cancer medicine,  134
         Tackling molecular pathology heterogeneity with physiologically based pharmacokinetic models to advance translational research efforts and personalized medicine decisions,  135
         Machine learning-based translational pharmacogenomics tools to advance personalized medicine,  136
         New drug development and drug repurposing through the application of artificial intelligence and machine learning approaches in drug development,  137
         Summary,  137
         Clinics care points,  137
         Disclosure,  137

        Solid tumors

        Advances in Cell-Free DNA,  141

        By Elizabeth S. Barrie and Andrea Ferreira-Gonzalez
        This chapter will focus on a subtype of liquid biopsy that uses cell-free DNA as a blood-based biomarker to detect and monitor solid tumors. While this assay has clear advantages in terms of patient safety, improved turnaround time, and the ability to sample more frequently over time, the complexity lies in its application and interpretation.
         Significance,  141
        Biological Basis,  141
        Preanalytic Factors,  141
        Test Metrics/Characteristics,  142
        Laboratory Techniques,  142
        Bladder Cancer,  143
        Brain Tumors,  143
        Breast Cancer,  143
        Colon Cancer,  144
        GIST,  144
        Lung Cancer,  145
        Present Relevance and Future Avenues to Consider,  145
         Summary,  146
         Clinics care points,  146
         Disclosure,  147