StemCell Therapy

Continuing Education Activity

Molecular diagnostics encompasses the analysis of human, viral, and microbial genomesand the products they encode. Molecular genetics utilizesmolecular biology's laboratory tools to relate genetic structureto protein function and, ultimately, health and disease.Variants identified during genetic testing are classified based on diverse evidence types, as the American College of Medical Genetics and Genomics recommends, emphasizing the need for board-certified geneticists to interpret the results.Integrating genetic testing methodologies with clinical expertise is crucial in translating molecular genetics advancements tobetter patient care.

The field of molecular genetic and genomic testing is undergoing rapid change due to improvements in our understanding of the molecular causes of uncommon and common illnesses and DNA analysis technologies.The advent of molecular genetics has revolutionized healthcare by offering unprecedented insights into the genetic basis of diseases, enabling personalized diagnostics, treatment strategies, and risk assessments. However, this progress brings with it the responsibility for healthcare providers to stay updated with the latest advancements and best practices in genetic testing.

This activity for healthcare professionals is designed to enhance learners' proficiency in identifying patients withindications for molecular genetics testing and interpreting genetic test results. Participants acquire a broader grasp of specimen collection, procedures, indications, potential diagnosis, normal and critical findings, interfering factors, and complications. Learners gain insights into thecomplexities of molecular genetics, preparing them to collaborate with an interprofessional team that aims to improve outcomes for patients who need molecular genetics testing.

Objectives:

Identify clinicalencounters appropriate for genetic molecular testing, distinguishing cases where such testing can contribute to diagnosis, prognosis, or treatment decisions.

Evaluate genetic test results accurately, discerning their clinical significance and relevance to patient management.

Differentiatebetween genetic testing methodologies, understanding their strengths, limitations, and optimal applications to diagnose patients.

Implement best interprofessional collaboration and communication practices to ensure that patientswho need molecular genetics testing receive comprehensive care that considers their medical, psychological, and social needs, thus improving outcomes.

Molecular genetics testing is fundamental in evaluating inherited disorders, somatic or acquired diseases with genetic associations, and pharmacogenetic responses. Genotyping can provide valuable disease diagnosis, prognosis, and progression indicators, guide treatment selection and response, and identify gene-specific therapeutic targets.[1]Human genetic material primarily consists of double-stranded, helical DNA. This molecule has a backbone composed of alternating sugar (deoxyribose) and phosphate groups, with hydrogen bonds linking nitrogenous base pairs. Specifically, adenine (purine) pairs with thymine (pyrimidine), while guanine (purine) pairs with cytosine (pyrimidine), forming the complementary base pairs within the DNA double helix.[2][3]

DNA in human cells is wrapped around histone proteins and packaged into nucleosome units, compacted further to form chromosomes.[4]Somatic cells normally have 23 chromosome pairs, with 1 pair comprised of the sex chromosomes X and Y. Each chromosome has DNA with a terminal stretch of short repeats called telomeres and additional repeats in the centromere region.[5]

Humans have 2 sets of 23 chromosomes, one derived from the mothers egg and the other from the father's sperm. Therefore, each egg and sperm is a single or haploid set of 23 chromosomes. Combining the 2 creates a diploid set of human DNA, allowing each individual to possess 2 different sequences, genes, and alleles on each chromosome.[6]Homologous recombination during meiosis generates unique allele combinations in gametes, leading to genetic diversity among offspring in the human population.[7]

The complete decoding of the human genome sequence and the development of powerful identification and cloning methods for genes linked to inherited diseases have transformed the practice of molecular genetics and molecular pathology. Advanced molecular analysis methods can now determine presymptomatic individuals' illness risk, detect asymptomatic recessive trait carriers, and prenatally diagnose conditions not yet evident in pregnancy.[8]Molecular genetics techniques are often the only approaches to these puzzles. Thus, genetic tests are powerful tools for diagnosis, genetic consultation, and prevention of heritable diseases.[9]

Many genetic testscan analyze gene, chromosome, and protein alterations. A clinician often considers several factors when selecting the appropriate test, including suspected conditions and their possible genetic variations. A broad genetic test is employed when a diagnosis is uncertain, while a targeted test is preferred for suspected specific conditions.[10]Molecular tests look for changes in 1 or more genes. These tests analyze the sequence of DNA building blocks (nucleotides) in an individual's genetic code, a process known as DNA sequencing, which can vary in scope.[11]

The targeted single variant test identifies a specific variant in a single gene known to cause a disorder, eg, the HBB gene variant causing-globin abnormalities that give rise to sickle cell disease. This test assesses the family members of an individual with the known variant to ascertain if they have the familial condition.[12]Single-gene tests examine genetic alterations in 1 gene to confirm or rule out a specific diagnosis, notably when many variants in the gene can cause the suspected condition. Gene panel tests look for variants in multiple genes to pinpoint a diagnosis when a person has symptoms that may fit various conditions or when many gene variants can cause the suspected condition.[13][14]

Whole-exome sequencing or whole-genome sequencing tests analyze the bulk of an individual's DNA to find genetic variations. This approach is useful when a single-gene or panel testing has not provided a diagnosis or when the suspected condition or genetic cause is unclear.[15]This sequencing method is often more cost- and time-effective than performing multiple single gene or panel tests.[16]

Chromosomal tests analyze whole chromosomes or long DNA lengths to identify significant alterations, including extra or missing chromosome copies (trisomy or monosomy), large chromosomal segment duplications or deletions, and segment rearrangements (translocations) (see Image. Trisomy 21on G-Banded Chromosomal Studies).[17]Chromosomal tests are employed when specific genetic conditions linked to chromosomal changes are suspected. For instance, Williams syndrome results from deleting a chromosome 7 segment.

Gene expression tests assess gene activation status in cells, indicating whether genes are active or inactive, with activated genes producing mRNA molecules that serve as templates for protein synthesis.[18]The mRNA produced helps determine which genes are highly active. Too much activity (overexpression) or too little activity (underexpression) of specific genes may suggest particular genetic disorders, including various cancer types.[19]Biochemical tests assess protein or enzyme levels and activity rather than directly analyzing DNA.[20]Abnormalities in these substances may indicate DNA changes underlying a genetic disorder.

Heritable mutations are detectable in all nucleated cells and are thus considered germline or constitutional genetic changes. Somatic genetic changes are characteristic of acquired or sporadic diseases like cancer.[21]Both scenarios are investigated using similar molecular biology methods to detect DNA and RNA variations, although the interpretation and utility of the laboratory results often differ significantly.[22]

Fluorescent in situ hybridization (FISH), chromosomal microarray analysis (CMA), and cytogenetic analysis (karyotyping) can be used to detect gross mutations like whole- and large-scale gene deletions, duplications, or rearrangements. Conventional karyotyping identifies rearrangements over 5 DNA megabases.[23]FISH has a resolution of 100 kilobases to 1 megabase. Minor alterations, such as single-base substitutions, insertions, and deletions, are detectable with single-strand conformation polymorphism (SSCP) and sequence analysis through next-generation sequencing (NGS). NGS uses genomic DNA (gDNA) or complementary DNA (cDNA) and has 3 modalities: whole genomic DNA, targeted, and exome sequencing.[24]

Denaturing high-performance liquid chromatography (DHPLC) can detect small deletions and duplications. Multiplex ligation-dependent probe amplification (MLPA) extends the range of deletions and duplications detected, bridging the gap between FISH or cytogenetic analysis and HPLC. MLPA is particularly useful in identifying complete or single and multiexon deletions or duplications.[25][26]

Peripheral blood is the specimen required for FISH, MLPA, DHPLC, and sequencing.Amniotic fluid cells and, more recently, cell-free fetal DNAmay be used for noninvasive prenatal testing.[27]Ethylenediaminetetraacetic acid is the most commonly used anticoagulant for molecular-based testing. However, acid citrate dextrose (ACD) is an acceptable alternative in cases where cellular form and function must be preserved.

ACD A and ACD B are the only ACD tube designations recognized, differing only by their additive concentrations.[28]Both enhancewhite blood cell viability and recovery for several days after specimen collection, making them suitable for molecular diagnostic and cytogenetic testing.

FISH utilizes fluorescent DNA probes to target specific gene sequences in interphase or metaphase cells, enabling their visualization and detection. Housekeeping gene probes always serve as positive internal controls. The probe must be large enough to hybridize specifically with the target without impeding the hybridization process. Conventional FISH involves pipetting the hybridization mix onto the cytological sample and incubating them together.

The technique can be applied to suspended cells, cultured cells, and frozen or formalin-fixed paraffin-embedded tissue sections, with subsequent cell sorting for fluorescence signal separation.[29]Preserving nucleic acid integrity and cell morphology is necessary during sample fixation. The experimental FISH procedure includes several preparatory steps, the hybridization reaction itself, and the removal of unbound probes.[30]The probe may be directly labeled with fluorophores or targeted for fluorescent detection using labeled antibodies or similar substrates. Different tags may be used, and different targets may be detected in the same sample simultaneously (multi-color FISH). Tagging is performed in various ways, including nick translation or polymerase chain reaction (PCR) using tagged nucleotides (see Image. Polymerase Chain Reaction). Probes can vary from 20 to 30 nucleotides to much longer sequences.

Locus-specific probes provide insight into gene amplification, deletion, or normal copy number status. Dual-fusion probes are adept at identifying frequently translocated gene regions associated with cancer development. These probes target regions spanning the breakpoints of translocation partners. Intact green and red signals are determined when they are closer than one signal's width. Conversely, a break in the gene sequence results in separate green and red signals.[31]

Break-apart probes target 2 areas of a specific gene sequence, using a green fluorescent label on one end and a red fluorescent label on the other. Intact gene sequences typically produce a yellow signal, known as a fusion signal. Whole-chromosome probes consist of smaller probes, each binding to different sequences along a chromosome.[32]Multiple probes, labeled with fluorescent dyes, enable unique color labeling of each chromosome, creating a spectral karyotypea full-color chromosome map identifyingall chromosome pairs.[33]Whole-chromosome probes are useful for examining chromosomal abnormalities, such as translocations.

Chromosomal microarray (CMA) consists of thousands of tiny probes, each representing small DNA fragments from known locations on the 46 chromosomes. CMA detects imbalances in chromosomal material between patient and control DNA samples, identifying copy number differenceswhether gains (duplications) or losses (deletions)in specific DNA segments.[34]These differences pinpoint the cause of the patient's health condition based on the location and type of change detected.[35]

Denaturing high-performance liquid chromatography (DHPLC) relies on differential chromatography retention of DNA heteroduplexes post-denaturation and renaturation. DNA heteroduplex migration is influenced by both molecule length and melting temperature, which is crucial for test sensitivity. DHPLC typically compares 2 PCR products amplified from 2 genes: 1 wild type and 1 mutated. These PCR products can originate from either RNA (cDNA) or genomic DNA. The PCR products are denatured at 95 C and gradually reannealed by cooling from 95 C to 65 C before chromatography. A major advantage of this technology is that multiple samples can be pooled together for variant detection.[36]Sequencing detects single-base substitutions and small deletions and insertions in DNA fragments ranging from 80 to 1500 base pairs, with close to 100% accuracy within minutes.

When a mismatch is present, both the original homoduplexes and 2 heteroduplexes are simultaneously produced. The original homoduplexes form from the reannealing of perfectly matching sense and antisense strands (25% each). The heteroduplexes form from the reannealing of the sense strand of one homoduplex with the antisense strand of the other (also 25% each). Heteroduplexes denature more extensively than homoduplexes, resulting in earlier elution from the chromatography column. The separation of all 4 species is based on their differences in stacking interactions with the chromatography column (solid phase). More detailed theoretical explanations of DHPLC are available in the literature.[37]

MLPA utilizes genomic DNA samples, with specific MLPA probes hybridizing with denatured genomic DNA. These probes are uniquely designed to hybridize adjacent to each other on the target DNA region and confer a distinct length to each amplified MLPA probe pair. Detection and quantification occur via capillary electrophoresis.[38]All MLPA probes are amplified using the same primer pair, with the abundance of each fragment proportional to its target's copy number in the sample.

NGS amplifies DNA with random priming, providing a genome-wide view of the patient's genetic background through millions of reads. Library generation begins with nucleic acid fragmentation, representing the individual's entire genome or transcriptome. Whole-exome sequencing uses cDNA fragments, whereas the whole-genome modality includes complete genomic DNA. Fragments join using enriched sequence adaptors. Only some genes (gene panel) are analyzed in targeted libraries. Fragments hybridize with cDNA fragments for the region or genes of interest and are specifically enriched.[39]During sequencing, nucleotide addition is detected by fluorescent dyes or pH changes from hydrogen ion release during DNA polymerization.[40]

Sanger sequencing begins with PCR-based target DNA amplification, followed by removing excess deoxynucleotide triphosphates (dNTPs) and PCR primers. The Sanger method has 99.99% base accuracy and is thus the "gold standard" for validating DNA sequences, including those from NGS. The test's stepsinclude denaturing the double-stranded DNA (dsDNA) into 2 single-stranded DNA (ssDNA), attaching a primer corresponding to one end of the sequence, and sequencing 4 polymerase solutions with 4 dNTPs. Only one type of ddNTP is incorporated, initiating DNA synthesis until termination. The resulting DNA fragments are denatured into ssDNA.

Denatured fragments undergo gel electrophoresis for sequence determination. DNA polymerase synthesizes DNA only in the 5 to 3 direction, initiating at a provided primer. Each terminal ddNTP corresponds to a specific nucleotide in the original sequence. For example, the shortest fragment must terminate at the first nucleotide from the 5 end, the second-shortest fragment must terminate at the second nucleotide from the 5 end, and so on. Reading gel bands from smallest to largest reveals the 5 to 3 sequence of the original DNA strand.[41]

In manual Sanger sequencing, the user reads all 4 gel lanes simultaneously, moving from bottom to top to identify the terminal ddNTP for each band. For instance, if the bottom band is found in the ddGTP column, then the smallest PCR fragment terminates with ddGTP, and the first nucleotide from the 5 end of the original sequence has a guanine (G) base.[42]Automated Sanger sequencing employs a computer to read each capillary gel band sequentially, using fluorescence to determine the terminal ddNTP identity. Laser activation of fluorescent tags emits light, detected by the computer, with each ddNTP tagged with a unique fluorescent label. The output is a chromatogram displaying fluorescent peaks corresponding to each nucleotide along the template DNA's length.[43]

Third-generation sequencing enables sequencing long DNA or RNA stretches without fragmentation. Single strands of DNA or RNA are directed through protein nanopores, with nucleotide bases distinguished by characteristic changes in electric current to determine the sequence.[44]Compared to 2nd-generation sequencing, 3rd-generation sequencing requires minimal sample preprocessing, enabling the design of smaller and more portable equipment.[45]

Molecular genetic testing has distinct indications, differing from traditional clinical and molecular biological testing used for diagnosing other diseases.[46]This modalitys applications encompass newborn screening, diagnostic testing for genetic or chromosomal conditions, carrier testing, prenatal testing, predictive and presymptomatic testing for adult-onset disorders, and forensic testing for legal identification purposes.[47]

FISH is employed for patients with a family history of known deletions and has been utilized to detect deletions in single blastomeres during preimplantation genetic diagnosis. FISH tests use gene-specific probe panels to investigate deletions, amplifications, and translocations in hematologic and solid tumors. FISH can also identify intracellular microorganisms and parasites.

CMA is recommended for individuals lacking specific clinical indicators to identify genetic or nongenetic causes of intellectual disability, developmental delay, autism spectrum disorder, or multiple congenital anomalies.[48]CMA can be helpful if prenatal structural anomalies are linked to particular microdeletions or microduplications. This modality can also evaluate copy number variants in cases of de novo balanced rearrangements or marker chromosomes.[49]

MLPA has diverse applications, such as mutation detection, single nucleotide polymorphisms (SNP) analysis, DNA methylation analysis, mRNA quantification, chromosomal characterization, gene copy number detection, and identification of duplications and deletions in cancer predisposition genes like BRCA1, BRCA2, hMLH1, and hMSH2. MLPA also holds promise for prenatal diagnosis, both invasive and noninvasive.[50]

DHPLC is well-suited for scanning genes for novel mutations and analyzing large sample sizes cost-effectively. This test is also useful for genotyping specific mutations or polymorphisms. DHPLC offers various applications beyond detecting genetic variants, including size-based double-strand DNA separation, single-strand DNA separation, and DNA purification analysis.[51]

NGS rapidly sequences whole genomes and target regions, employs RNA sequencing to identify novel RNA variants and splice sites, quantifies mRNAs for gene expression analysis, and analyzes epigenetic factors like DNA methylation and DNA-protein interactions. Sequence cancer samples study rare somatic variant tumor subclones and identify novel pathogens. Sanger sequencing, or the "chain termination method," determines DNA nucleotide sequences.

FISH swiftly diagnoses common fetal aneuploidies but with reduced sensitivity compared to cytogenetic analysis. FISH cannot identify cytogenetic abnormalities beyond the most common ones, such as translocations, inversions, and markers. DHPLC detects single nucleotide changes, small deletions, or insertions requiring subsequent confirmation by sequencing. This method identifies unknown mutations, making it advantageous for diseases with a high proportion of de novo mutations. Neurofibromatosis type 1 (NF1) is an example, as approximately 50% of cases arise from new mutations. CMAs are first-tier tests for developmental delays, intellectual disabilities, autism spectrum disorders, or multiple congenital disabilities, replacing karyotyping.

MLPA detects gene abnormalities, particularly small deletions in diseases like multiple endocrine neoplasia type 1 (MEN1partial or complete deletion). MLPA can also assess methylation alterations, such as in pseudohypoparathyroidism 1b (PHP1b), where deletion of 1 or 4 of four differentially methylated regions is common.

NGS generates millions of sequences, which are then processed, analyzed, and interpreted to identify variants. Bioinformatics analysis begins with raw data generated by nucleotide incorporation signal detection. Read quality is evaluated during primary data analysis. Sequences are aligned or mapped against a reference genome, with computational algorithms searching for the best match for each read while allowing for some mismatches to detect genetic variants.[52]

Sanger sequencing is reliable in detecting point mutations, small deletions, or duplications. This method has a long history of use across various settings, including tumor mutational spectrum analysis and diagnostic testing for constitutional variants. Primers can cover multiple regions (amplicons) or any desired region size.

The increasing demand for genetic testing has led to greater availability. Ensuring uniformity and standardization in communicating the complex results to referring clinicians is essential. Failure to include pertinent information is considered a deficiency in the molecular pathology laboratory accreditation inspection.[53]All molecular genetic laboratories offering clinical testing should be accredited according to the Clinical Laboratory Improvement Amendments and actively participate in proficiency testing.[54]

A comprehensive genetic report must include essential patient details such as name, medical record number or birth date, sex, and ethnicity. The report should also specify the type of specimen received, identification number, laboratory test requested, the performing laboratorys name and address, and referring healthcare professional or hospital. The date of the report, analytic result interpretation using standard nomenclature, detailed method description (including literature citations if applicable), and assay sensitivity and specificity should be provided. For example, sensitivity and specificity should be reported regarding the number of variants analyzed, the proportion of variants not detected, and the possibility of genetic heterogeneity and recombination.[55]Reports from clinical DNA laboratories should include a disclaimer due to the prevalence of laboratory-developed tests (LDTs) or procedures (LDPs) designed, developed, and validated internally by each laboratory but remain unapproved by the FDA.[56]

Fluorescent tags binding to chromosomes reveal chromosomal abnormalities in FISH. MLPA detects copy number variations by correlating peak intensity during capillary electrophoresis with sample copy numbers. An MLPA probe's amplification signals the presence of a mutation in the sample.

An MLPA test can yield two outcomes:

DHPLC detects mutations by identifying heteroduplexes compared to the reference genome in the same sample. NGS identifies various genetic variants, including single nucleotides, small insertions or deletions, and some structural variants, but their role in the disease is not implied. Clinical analysis and assessment of the pathological potential of detected variants require consideration in different contexts.[58]Sanger sequencing results interpretation depends on the target DNA strand and primer availability. If strand A is of interest but the primer suits strand B better, the output matches strand A. Conversely, if the primer suits strand A better, the output aligns with strand B, necessitating conversion back to strand A.

FISH probe specificity prevents unintended hybridization with nontarget genes. Some FISH preparations may exhibit autofluorescence, necessitating thorough cell washing to remove fluorescent residues and reduce background fluorescence.

MPLA has limitations, including its ability to detect only known mutations designed into probes, making gene rearrangements like inversions and translocations undetectable. Sample purity is essential as contaminants such as phenol can interfere with the ligation step. MLPA may yield false positive or negative results due to rare sequence variants in target regions detected by probes. Reduced probe binding efficiency from point mutations or polymorphisms candiminishthe relative peak areas height. Confirmation of single exon deletions detected by MLPA is thus recommended using other methods like multiplex PCR or sequencing.[59]

DHPLC sensitivity relies on melting temperature. Computational algorithms can predict the melting temperature, and the procedure typically involves at least 2 melting temperatures for increased sensitivity. CMA does not detect point mutations, small DNA segment changes (eg, in Fragile X syndrome), or balanced chromosomal rearrangements (eg, balanced translocations, inversions).

NGS technologies continue to evolve to address various challenges. Some large sequencers can detect large insertions, duplications, and deletions, while sequencing long homopolymer regions remains problematic. However, establishing the infrastructure and expertise for data analysis remains a significant challenge in clinical settings. The primary limitation of implementing NGS in clinical settings is the requirement for adequate infrastructure, including computational resources, storage capacity, and skilled personnel for comprehensive data analysis and interpretation.

Despite automation, Sanger sequencing remains labor-intensive, time-consuming, and expensive, relying on specialized equipment. Sanger sequencing exhibits reduced sensitivity in detecting point mutations when 20% of mutant DNA is of a wild-type background. Additionally, it lacks quantifiability, making it impossible to differentiate mutation prevalence accurately based solely on peak sizes, necessitating supplementary testing approaches.

Peripheral blood collection via venipuncture infrequently leads to serious complications. Some patients, especially children, may experience hematomas, pain, and fear, which are expected. In contrast, procedures like amniocentesis are more invasive, thus posing more serious risks such as infection, preterm delivery, respiratory distress, trauma, and alloimmunization, though these complications are also infrequent.[60]Genetic tests using NGS of free-cell DNA from maternal peripheral blood offer an alternative to diagnosis using amniocentesis fluid.[61]

Molecular testing may give rise to legal, medical, psychological, and ethical issues besides the sampling procedures potential complications.[62]While molecular testing primarily aims to demonstrate a genetic trait associated with a disease, the current recommendation is to integrate the results into genetic counseling.[63]

Genetic counseling, led by a team including genetic counselors and other professionals, begins with clinically identifying suspected diseases to guide molecular testing. Patients are informed about the testing procedure, potential results, and legal considerations like informed consent, particularly for children.[64]Patient education is integral to this process.

NGS technologies applied to genetic counseling yield complex results surpassing traditional tests, necessitating informed patient discussions due to the considerable information and ethical implications involved.[65]Laboratories conducting molecular genetic tests should address preexamination, examination, and postexamination considerations, tailoring methodology and interpretation to each test's indication, application, and ethical implications.

Any permanent alteration in a gene's nucleotide sequence compared to a reference genome is deemed a genetic change or mutation. Variants identified through a tiered protocol must undergo sequencing confirmation, and their role in disease pathology must be assessed. Genetic testing may reveal variants classified as benign, likely benign, pathogenic, likely pathogenic, or of uncertain significance.[66]Variants must be rigorously classified based on various types of evidencepopulation, computational, functional, or segregation datato determine clinical significance.[67]

The American College of Medical Genetics and Genomics recommends this nomenclature and classification for genetic test findings, covering genotyping, single genes, panels, exomes, and genomes. NGS applications have deepened our understanding of genetic diseases and led to the discovery of variants requiring further study of their disease implications.[68]Interprofessional collaboration is essential for leveraging genetic tests for patient benefit, withan expertpanel advocating for results interpretation by a board-certified geneticist.[69]

Molecular genetic testing advanced significantly with PCR and NGS, providing genome-wide data.[70]Multidisciplinary teams collaborate to integrate various testing methods with clinical, pathological, functional, computational, ethical, and social aspects of diseases for patient benefit.[71]

Polymerase Chain Reaction. This diagram shows the polymerase chain reaction steps. Enzoklop,Public Domain via Wikimedia Commons

Trisomy 21 on G-Banded Chromosome Studies. This karyogram depicts trisomy 21 resulting from an inherited Robertsonian translocation between chromosomes 14 and 21. The infant's father was a carrier of the translocation in a balanced form. Crotwell PL, (more...)

Disclosure: Cecilia Ishida declares no relevant financial relationships with ineligible companies.

Disclosure: Muhammad Zubair declares no relevant financial relationships with ineligible companies.

Disclosure: Vikas Gupta declares no relevant financial relationships with ineligible companies.

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Molecular Genetics Testing - StatPearls - NCBI Bookshelf

Genetics is the study of genes, genetic variation, and heredity in living organisms. This online textbook covers major topics in molecular genetics in a problems-based approach. It grew out of teaching a course for upper level undergraduates and graduate students at the Pennsylvania State University.

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Working with Molecular Genetics (Hardison) - Biology LibreTexts

The complex molecular underpinnings of genetic and rare diseases offer a promising avenue for scientific exploration and innovation. This research topic explores the intricate molecular mechanisms driving these conditions, underlining the latest developments in diagnostic methodologies and therapeutic approaches. Due to the rapid advancement in molecular diagnostics, including Next-Generation Sequencing, our insights into rare and genetic diseases have expanded significantly.

Through this research topic, our aim is to uncover genetic and molecular mechanisms driving the onset and progression of genetic and rare diseases, with a particular focus on unraveling actionable therapeutic targets. By integrating advanced molecular biology methods, including Next-Generation Sequencing, CRISPR, and other cutting-edge technologies, this topic emphasizes the development of novel therapeutic approaches. The goal is to translate these molecular insights into innovative, personalized therapies that address the specific challenges of genetic and rare diseases, ultimately improving patient outcomes and advancing the field of precision medicine.

This Research Topic addresses significant challenges in accurately diagnosing and effectively treating genetic and rare diseases. Despite advances in molecular diagnostics, many of these conditions remain underdiagnosed or misdiagnosed, delaying crucial interventions. Recent developments in technologies like Next-Generation Sequencing have revolutionized our ability to detect genetic anomalies, but there is still a need to bridge the gap between these diagnostics and targeted therapeutic strategies. This topic seeks to explore how integrating advanced molecular tools with therapeutic innovations can lead to more precise and personalized treatments, ultimately improving outcomes for patients with genetic and rare diseases.

We welcome submissions of original research articles, in-depth reviews, case studies, and perspective pieces that advance the understanding of the genetic and molecular foundations of genetic and rare diseases. Contributions that explore novel diagnostic tools, therapeutic strategies, and translational research are particularly encouraged.

This Research Topic will cover a wide range of themes related to genetic and rare diseases, including but not limited to:

Identification and characterization of novel genetic mutations and their clinical implications;

Advances in molecular diagnostic technologies, including Next-Generation Sequencing and multi-omics approaches;

Development of targeted therapies and personalized treatment strategies for rare and genetic disorders;

Translational research bridging molecular diagnostics and therapeutic applications;

Ethical and clinical considerations in the treatment of genetic and rare diseases.

Keywords:Rare Diseases, Genetic Diseases, Next-Generation Sequencing, Molecular Diagnostics, Clinical Genomics

Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Through this research topic, our aim is to uncover genetic and molecular mechanisms driving the onset and progression of genetic and rare diseases, with a particular focus on unraveling actionable therapeutic targets. By integrating advanced molecular biology methods, including Next-Generation Sequencing, CRISPR, and other cutting-edge technologies, this topic emphasizes the development of novel therapeutic approaches. The goal is to translate these molecular insights into innovative, personalized therapies that address the specific challenges of genetic and rare diseases, ultimately improving patient outcomes and advancing the field of precision medicine.

This Research Topic addresses significant challenges in accurately diagnosing and effectively treating genetic and rare diseases. Despite advances in molecular diagnostics, many of these conditions remain underdiagnosed or misdiagnosed, delaying crucial interventions. Recent developments in technologies like Next-Generation Sequencing have revolutionized our ability to detect genetic anomalies, but there is still a need to bridge the gap between these diagnostics and targeted therapeutic strategies. This topic seeks to explore how integrating advanced molecular tools with therapeutic innovations can lead to more precise and personalized treatments, ultimately improving outcomes for patients with genetic and rare diseases.

We welcome submissions of original research articles, in-depth reviews, case studies, and perspective pieces that advance the understanding of the genetic and molecular foundations of genetic and rare diseases. Contributions that explore novel diagnostic tools, therapeutic strategies, and translational research are particularly encouraged.

This Research Topic will cover a wide range of themes related to genetic and rare diseases, including but not limited to:

Identification and characterization of novel genetic mutations and their clinical implications;

Advances in molecular diagnostic technologies, including Next-Generation Sequencing and multi-omics approaches;

Development of targeted therapies and personalized treatment strategies for rare and genetic disorders;

Translational research bridging molecular diagnostics and therapeutic applications;

Ethical and clinical considerations in the treatment of genetic and rare diseases.

Keywords:Rare Diseases, Genetic Diseases, Next-Generation Sequencing, Molecular Diagnostics, Clinical Genomics

Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Molecular Underpinnings of Genetic and Rare Diseases: From ... - Frontiers

The high heritability of schizophrenia has stimulated much work aimed at identifying susceptibility genes using positional genetics. However, difficulties in obtaining clear replicated linkages have led to the skepticism that such approaches would ever be successful. Fortunately, there are now signs of real progress. Several strong and well-established linkages have emerged. Three of the best ...

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The molecular genetics of schizophrenia: New findings promise new insights.

Nanotechnology has many definitions but in general it is the use and application of materials with sizes in the nanometre range. Just as a millimetre is one-thousandth of a metre, a nanometre is one-millionth of a millimetre. In more understandable terms, a human hair is approximately 80,000 nanometres in diameter and the growing science and industry of nanotechnology utilises materials below 1000 nanometres. Benefits of working at this very small scale have been seen for many years over such diverse areas as electronics and energy storage to sunscreens and food packaging.

Nanomedicine is simply the application of nanotechnologies in a healthcare setting and the majority of benefits that have already been seen involve the use of nanoparticles to improve the behaviour of drug substances. Today, nanomedicines are used globally to improve the treatments and lives of patients suffering from a range of disorders including ovarian and breast cancer, kidney disease, fungal infections, elevated cholesterol, menopausal symptoms, multiple sclerosis, chronic pain, asthma and emphysema. The nanomedicines that are currently available are overcoming some of the difficulties experienced by normal medical approaches in delivering the benefit from the drug molecules used. In some cases the drugs have very little solubility in water and the human body struggles to absorb enough to treat the condition. In other cases, the drug molecule is absorbed well but the body removes the drug before it has had long enough to provide a benefit. Drugs may lead to side-effects due to poor delivery at the actual site of disease. For example, drugs that are targeting cancers must avoid healthy tissues and organs or damage can be caused. Nanomedicines therefore can play an important role in ensuring enough of the drug enters the body, that drug that does enter stays in the body for long periods and is targeted specifically to the areas that need treatment.

It has been known for many years that identifying illness or disease very early can help prevent long term damage or even death in patients. It is possible for many diseases that no symptoms are visible for many years but the human body does produce evidence of problems at the molecular level. Another important area of nanotechnology and nanomedicine is diagnostics. By studying and identifying individual molecules, it is possible to diagnose disease in time to improve the prognosis for the patient.

Over the coming years, the benefits of nanomedicines and new diagnostic tools will be felt by an increasing number of patients with considerable impact on global health.

This illustrative figure shows the different structures of nanomedicines and their approximate sizes. For comparison, the sizes of biological nanostructures are shown at the top of the figure.

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what is nanomedicine The British Society for Nanomedicine

Commensurate with technological improvements and the subsequent rise in genetic research and genetics-based tests and treatments, the term personalized medicine is being used more frequently. But what does it mean? This explainer outlines what personalized medicine is, its advantages and some concerns that have been raised in relation to it.

Also known as precision medicine, personalized medicine is a rapidly expanding field of practice that uses an individuals genetic profile to guide decisions about the prevention, diagnosis, and treatment of disease.

Although the concept of personalized medicine began in the 1990s, following advances in DNA sequencing technology, it pretty much remained just a concept and was rarely applied. Since then, continued advances have generated enormous amounts of new information. The discovery of genes, proteins, and pathways has enabled studies into the genetics underlying both rare and common diseases and aided the identification of new drug targets.

Whats good about personalized medicine

For a long time, the practice of medicine has largely been reactive, waiting for the onset of disease before treating or curing it. But were all unique in terms of genetic makeup, environment, and lifestyle factors. Our growing understanding of genetics and genomics the study of all of a persons genes and how they drive health, disease and treatment in individual people offers an opportunity to step away from a one size fits all approach based on broad population averages and adopt an individualized approach.

In addition to advances in the field of genomics, developments in the fields of science and technology play a crucial role in personalized medicine. For example, the development of high-resolution analytics, biotech research and chemistry, and the ability to decipher molecular structures, signaling pathways, and protein interactions that underpin the mechanisms of gene expression.

Personalized medicine is about more than prescribing the best drugs, although thats a large part of it. Proponents say it would shift medicines emphasis from reaction to prevention, better predict disease susceptibility and improve diagnosis, produce more effective drugs and reduce adverse side effects, and eliminate the inefficiency and cost of adopting a trial-and-error approach to healthcare.

Goetz & Schork

Weve already seen personalized medicine positively impact patient care in patients with diseases like breast cancer, melanoma, and cardiovascular disease. And the use of patient-derived cell and organoid avatars as disease models to identify beneficial treatments provides truly personalized medicine to individual patients. Then theres CRISPR, technology that allows genetic material to be added, removed, or altered at particular locations in the genome as a direct way of treating genetic and other conditions.

Concerns about personalized medicine

Despite its numerous benefits, the adoption of a personalized medicine approach raises several issues. For it to reach peak efficiency, a lot of genomic data must be collected from a large and diverse section of the population, and its critical that participants privacy and confidentiality are protected. Privacy issues extend to the collection, storage and sharing of that information.

Extensive changes to the healthcare system, including ethical changes, are likely needed to overcome the ethical obstacles of personalized medicine use, including knowledge gap and informed consent, privacy and confidentiality, and the availability of healthcare services. Social benefit versus science development and individual benefit needs to be considered and balanced. And there are concerns that data collected might be used unethically, such as insurance companies not offering some policies to people with a certain genetic predispositions.

Legally, a physician is negligent when they fail to follow generally accepted practice. In personalized medicine, when the interpretation of genetic information is at issue, there may be no generally accepted practice or standard. It begs the question, at what point does clinical genetic knowledge become a standard of practice?

Cost is another relevant factor. While the expense associated with large-scale DNA sequencing is decreasing, its still expensive. And drugs developed based on molecular or genetic variations are likely to be costly. Further, massive amounts of data require massive infrastructure changes, including changes to the mechanisms of data collection, storage and sharing, all of which require investment.

Will personalized medicine happen?

Personalized medicine is already happening in the form of things like CRISPR, mRNA vaccines and the large-scale genome sequencing of newborns. Its the kind of future that was envisioned when the Human Genome Project was first completed 20 years ago, and it certainly has its benefits.

However, the widespread adoption of personalized medicine may prove more difficult than first imagined. In addition to the concerns already mentioned, there needs to be a change in public attitudes and the way medical professionals, patients and health regulators view the approach. It may require a new approach to the way drugs are tested and a willingness to embrace risk.

Nonetheless, the potential benefits are so great and the march of technology and knowledge so inexorable that it is a near certainty that personalized medicine will continue to develop and become standard in healthcare systems at some point in the future it's just a matter of how fast the advances are made and how soon the aforementioned hurdles can be overcome as to exactly when this might be.

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Personalized medicine: The pros, cons and concerns - New Atlas

Abstract

The convergence of artificial intelligence (AI) and precision medicine promises to revolutionize health care. Precision medicine methods identify phenotypes of patients with lesscommon responses to treatment or unique healthcare needs. AI leverages sophisticated computation and inference to generate insights, enables the system to reason and learn, and empowers clinician decision making through augmented intelligence. Recent literature suggests that translational research exploring this convergence will help solve the most difficult challenges facing precision medicine, especially those in which nongenomic and genomic determinants, combined with information from patient symptoms, clinical history, and lifestyles, will facilitate personalized diagnosis and prognostication.

In a recent National Academy of Medicine report about the current and future state of artificial intelligence (AI) in health care, the authors noted unprecedented opportunities to augment the care of specialists and the assistance that AI provides in combating the realities of being human (including fatigue and inattention) and the risks of machine error. Importantly, the report notes that whereas care must be taken with the use of these technologies, much promise exists.1 The digitization of healthrelated data and the rapid uptake in technology are fueling transformation and progress in the development and use of AI in healthcare.2, 3, 4 However, multimodal data integration, security, federated learning (which requires fundamental advances in areas, such as privacy, largescale machine learning, and distributed optimization), model performance, and bias may pose challenges to the use of AI in health care.5

Three main principles for successful adoption of AI in health care include data and security, analytics and insights, and shared expertise. Data and security equate to full transparency and trust in how AI systems are trained and in the data and knowledge used to train them. As humans and AI systems increasingly work together, it is essential that we trust the output of these systems.

Analytics and insights equate to purpose and people where augmented intelligence and actionable insights support what humans do, not replace them. AI can combine input from multiple structured and unstructured sources, reason at a semantic level, and use these abilities in computer vision, reading comprehension, conversational systems, and multimodal applications to help health professionals make more informed decisions (e.g., a physician making a diagnosis, a nurse creating a care plan, or a social services agency arranging services for an elderly citizen). Shared expertise equates to our complementary relationship with AI systems, which are trained by and are supporting human professionals, leading to workforce change, which leads to new skills. The ability to create cuttingedge AI models and build highquality business applications requires skilled experts with access to the latest hardware.

A vast amount of untapped data could have a great impact on our healthyet it exists outside medical systems.6 Our individual health is heavily influenced by our lifestyle, nutrition, our environment, and access to care. These behavioral and social determinants and other exogenous factors can now be tracked and measured by wearables and a range of medical devices. These factors account for about 60% of our determinants of health (behavioral, socioeconomical, physiological, and psychological data), our genes account for about 30%, and last our actual medical history accounts for a mere 10%.6 Over the course of our lifetimes, we will each generate the equivalent of over 300 million books of personal and healthrelated data that could unlock insights to a longer and healthier life.7

The phenomenon of big data can be described using the five Vs: volume, velocity, variety, veracity, and value. Volume refers to the vast amount of complex and heterogenous data, which makes data sets too large to store and analyze using traditional database technology. Velocity refers to the speed at which new data are generated and moves around. Variety refers to the different types of structured, semistructured, and unstructured data, such as social media conversations and voice recordings. Veracity refers to the certainty, accuracy, relevance, and predictive value of the data. Value refers to the conversion of data into business insights. Whereas the volume, variety, velocity, and veracity of data are contributing to the increasing complexity of data management and workloadscreating a greater need for advanced analytics to discover insightsmobile devices have made technology more consumable, creating user demand for interactive tools for visual analytics.

Big data analytics and AI are increasingly becoming omnipresent across the entire spectrum of health care, including the 5 Ps spanning: payer, provider, policy maker/government, patients, and product manufacturers. Up to 10% of global health care expenditure is due to fraud and abuse and AIbased tools help mitigate fraud, waste, and abuse in payer programs.8 Reliable identification of medical coding errors and incorrect claims positively impacts payers, providers, and governments by saving inordinate amounts of money, time, and efforts.9 As an example, IBM DataProbe, an AIbased business intelligence tool, was able to detect and recover US $41.5million in feeforservice payments over a 2year period in Medicaid fraud for Iowa Medicaid Enterprise.10 In the provider space, AI is used for evidencebased clinical decision support,11 detection of adverse events, and the usage of electronic health record (EHR) data to predict patients at risk for readmission.12 Healthcare policymakers and government use AIbased tools to control and predict infections and outbreaks. An example is FINDER, a machinelearned model for realtime detection of foodborne illness using anonymous and aggregated web search and location data.13 Another example is the integrated data hub and caremanagement solution using IBM Connect360 and IBM Watson Care Manager that Sonoma County, California government agencies used to transform health and healthcare for socially disadvantaged groups and other displaced individuals during a time of communitywide crisis.14 This solution enabled integration of siloed data and services into a unified citizen status view, identification of clinical and social determinants of health from structured and unstructured sources, construction of algorithms to match clients with services, and streamlining of care coordination during the 2017 and 2019 Sonoma County wildfires. With the advent of the global pandemic coronavirus disease 2019 (COVID19) in early 2020, such a model can be used to predict atrisk populations, and potentially provide additional risk information to clinicians caring for atrisk patients.15 The use of AI for patients and life sciences/healthcare products are addressed extensively in the sections that follow.

AI is not, however, the only datadriven field impacting health and health care. The field of precision medicine is providing an equal or even greater influence than AI on the direction of health care16 and has been doing so for more than a decade.17 Precision medicine aims to personalize care for every individual. This goal requires access to massive amounts of data, such as data collected through the United Kingdoms UK Biobank and the All of Us project, coupled with a receptive health care ecosystem willing to abandon the conventional approach to care in favor of a more highly individualized strategy. The convergence of these fields will likely accelerate the goals of personalized care and tightly couple AI to healthcare providers for the foreseeable future. In the sections that follow, we will briefly summarize the capabilities of existing AI technology, describe how precision medicine is evolving, and, through a series of examples, demonstrate the potentially transformative effect of AI on the rate and increasing breadth of application for precision medicine.

The past 10years have seen remarkable growth and acceptance of AI in a variety of domains and in particular by healthcare professionals. AI provides rich opportunities for designing intelligent products, creating novel services, and generating new business models. At the same time, the use of AI can introduce social and ethical challenges to security, privacy, and human rights.1

AI technologies in medicine exist in many forms, from the purely virtual (e.g., deeplearningbased health information management systems and active guidance of physicians in their treatment decisions) to cyberphysical (e.g., robots used to assist the attending surgeon and targeted nanorobots for drug delivery).18 The power of AI technologies to recognize sophisticated patterns and hidden structures has enabled many imagebased detection and diagnostic systems in healthcare to perform as well or better than clinicians, in some cases.19 AIenabled clinical decisionsupport systems may reduce diagnostic errors, augment intelligence to support decision making, and assist clinicians with EHR data extraction and documentation tasks.20 Emerging computational improvements in natural language processing (NLP), pattern identification, efficient search, prediction, and biasfree reasoning will lead to further capabilities in AI that address currently intractable problems.21, 22

Advances in the computational capability of AI have prompted concerns that AI technologies will eventually replace physicians. The term augmented intelligence, coined by W.R. Ashby in the 1950s,23 may be a more apt description of the future interplay among data, computation, and healthcare providers and perhaps a better definition for the abbreviation AI in healthcare. A version of augmented intelligence, described in the literature in Friedmans fundamental theorem of biomedical informatics,24 relates strongly to the role of AI in health care (depicted in Figure1). Consistent with Friedmans description of augmented intelligence, Langlotz at Stanford stated that Radiologists who use AI will replace radiologists who dont.25

A version of the Friedmans fundamental theorem of informatics describing the impact of augmented intelligence. The healthcare system with AI will be better than the healthcare system without it. AI, artificial intelligence.

An AI system exhibits four main characteristics that allow us to perceive it as cognitive: understanding, reasoning, learning, and empowering.26 An AI system understands by reading, processing, and interpreting the available structured and unstructured data at enormous scale and volume. An AI system reasons by understanding entities and relationships, drawing connections, proposing hypotheses, deriving inferences, and evaluating evidence that allows it to recognize and interpret the language of health and medicine. An AI system learns from human experts and realworld cases by collecting feedback, learning from outcomes at all levels and granularities of the system, and continuing to improve over time and experience. An AI system empowers and interacts clinicians and users by providing a more integrated experience in a variety of settings, combining dialog, visualization, collaboration, and delivering previously invisible data and knowledge into actionable insights. In contrast, humans excel at common sense, empathy, morals, and creativity.

Augmenting human capabilities with those provided by AI leads to actionable insights in areas such as oncology,27 imaging,28 and primary care.29 For example, a breast cancer predicting algorithm, trained on 38,444 mammogram images from 9,611 women, was the first to combine imaging and EHR data with associated health records. This algorithm was able to predict biopsy malignancy and differentiate between normal and abnormal screening results. The algorithm can be applied to assess breast cancer at a level comparable to radiologists, as well has having the potential to substantially reduce missed diagnoses of breast cancer.30 This combined machinelearning and deeplearning model trained on a dataset of linked mammograms and health records may assist radiologists in the detection of breast cancer as a second reader.

The field of precision medicine is similarly experiencing rapid growth. Precision medicine is perhaps best described as a health care movement involving what the National Research Council initially called the development of a New Taxonomy of human disease based on molecular biology, or a revolution in health care triggered by knowledge gained from sequencing the human genome.31 The field has since evolved to recognize how the intersection of multiomic data combined with medical history, social/behavioral determinants, and environmental knowledge precisely characterizes health states, disease states, and therapeutic options for affected individuals.32 For the remainder of this paper, we will use the term precision medicine to describe the health care philosophy and research agenda described above, and the term personalized care to reflect the impact of that philosophy on the individual receiving care.

Precision medicine offers healthcare providers the ability to discover and present information that either validates or alters the trajectory of a medical decision from one that is based on the evidence for the average patient, to one that is based upon individuals unique characteristics. It facilitates a clinicians delivery of care personalized for each patient. Precision medicine discovery empowers possibilities that would otherwise have been unrealized.

Advances in precision medicine manifest into tangible benefits, such as early detection of disease33 and designing personalized treatments are becoming more commonplace in health care.34 The power of precision medicine to personalize care is enabled by several data collection and analytics technologies. In particular, the convergence of highthroughput genotyping and global adoption of EHRs gives scientists an unprecedented opportunity to derive new phenotypes from realworld clinical and biomarker data. These phenotypes, combined with knowledge from the EHR, may validate the need for additional treatments or may improve diagnoses of disease variants.

Perhaps the most wellstudied impact of precision medicine on health care today is genotypeguided treatment. Clinicians have used genotype information as a guideline to help determine the correct dose of warfarin.35 The Clinical Pharmacogenetics Implementation Consortium published genotypebased drug guidelines to help clinicians optimize drug therapies with genetic test results.36 Genomic profiling of tumors can inform targeted therapy plans for patients with breast or lung cancer.34 Precision medicine, integrated into healthcare, has the potential to yield more precise diagnoses, predict disease risk before symptoms occur, and design customized treatment plans that maximize safety and efficiency. The trend toward enabling the use of precision medicine by establishing data repositories is not restricted to the United States; examples from Biobanks in many countries, such as the UK Biobank,37 BioBank Japan,38 and Australian Genomics Health Alliance39 demonstrate the power of changing attitudes toward precision medicine on a global scale.

Although there is much promise for AI and precision medicine, more work still needs to be done to test, validate, and change treatment practices. Researchers face challenges of adopting unified data formats (e.g., Fast Healthcare Interoperability Resources), obtaining sufficient and high quality labeled data for training algorithms, and addressing regulatory, privacy, and sociocultural requirements.

AI and precision medicine are converging to assist in solving the most complex problems in personalized care. Figure2 depicts five examples of personalized healthcare dogma that are inherently challenging but potentially amenable to progress using AI.40, 41, 42

Dimensions of synergy between AI and precision medicine. Both precision medicine and artificial intelligence (AI) techniques impact the goal of personalizing care in five ways: therapy planning using clincal, genomic or social and behavioral determinants of health, and risk prediction/diagnosis, using genomic or other variables.

Genomeinformed prescribing is perhaps one of the first areas to demonstrate the power of precision medicine at scale.43 However, the ability to make realtime recommendations hinges on developing machinelearning algorithms to predict which patients are likely to need a medication for which genomic information. The key to personalizing medications and dosages is to genotype those patients before that information is needed.44

This use case was among the earliest examples of the convergence between AI and precision medicine, as AI techniques have proven useful for efficient and highthroughput genome interpretation.45 As noted recently by Zou and colleagues,46 deep learning has been used to combine knowledge from the scientific literature with findings from sequencing to propose 3D protein configurations, identify transcription start sites, model regulatory elements, and predict gene expression from genotype data. These interpretations are foundational to identifying links among genomic variation and disease presentation, therapeutic success, and prognosis.

In medulloblastoma, the emergence of discrete molecular subgroups of the disease following AImediated analysis of hundreds of exomes, has facilitated the administration of the right treatment, at the right dosage, to the right cohort of pediatric patients.47 Although conventional treatment of this disease involved multimodal treatment, including surgery, chemotherapy, and whole brain radiation, precision genomics has enabled treatment of the wingless tumor subgroup, which is more common in children, with chemotherapy aloneobviating the need for radiation.48 Avoiding radiotherapy is particularly impactful for mitigating potential neurocognitive sequelae and secondary cancers from wholebrain radiation among disease survivors.49, 50

The initial successful paradigm of AI in imaging recognition has also given rise to radiogenomics. Radiogenomics, as a novel precision medicine research field, focuses on establishing associations between cancer imaging features and gene expression to predict a patients risk of developing toxicity following radiotherapy.51, 52, 53 For example, Chang et al. proposed a framework of multiple residual convolutional neural networks to noninvasively predict isocitrate dehydrogenase genotype in grades IIIV glioma using multiinstitutional magnetic resonance imaging datasets. Besides, AI has been used in discovering radiogenomic associations in breast cancer,52 liver cancer,54 and colorectal cancer.53 Currently, limited data availability remains the most formidable challenge for AI radiogenomics.51

Knowing the response to therapy can help clinicians choose the right treatment plan. AI demonstrates potential applications in this area. For example, McDonald et al. trained a support vector machine using patients gene expression data to predict their response to chemotherapy. Their data show encouraging outcomes across multiple drugs.55 Sadanandam et al. proposed approaches of discovering patterns in gene sequences or molecular signatures that are associated with better outcomes following nontraditional treatment. Their findings may assist clinicians in selecting a treatment that is most likely to be effective.56 Although tremendous progress has been made using AI techniques and genomics to predict treatment outcome, more prospective and retrospective clinical research and clinical studies still need to be conducted to generate the data that can then train the algorithms.

Incorporating environmental considerations into management plans require sufficient personal and environmental information, which may affect a patients risk for a poor outcome, knowledge about care alternatives, and conditions under which each alternative may be optimal.

One such example has been the challenge of identifying homelessness in some patients.57, 58, 59 These patients may require care in varying locations over a short period, requiring frequent reassessments of patient demographic data. Related issues, such as transportation, providing medications that require refrigeration, or using diagnostic modalities that require electricity (for monitoring), need to be modified accordingly.

Another environmental consideration is the availability of expertise in remote locations, including the availability of trained professionals at the point of need. AI has provided numerous examples of augmenting diagnostic capabilities in resourcepoor locations, which may translate into better patient classification and therefore more personalized therapy planning. Examples include the use of deep learning to identify patients with malaria60 and cervical cancer,61 as well as predicting infectious disease outbreaks,62 environmental toxin exposure,63 and allergen load.64

Finally, in addition to genomic considerations and social determinants of health, clinical factors are imperative to successful therapy planning. Age, comorbidities, and organ function in particular predicate treatment considerations and AI has emerged as a central pillar in stratifying patients for therapy. In one study, machine learning classifiers were used to analyze 30 comorbidities to identify critically ill patients who will require prolonged mechanical ventilation and tracheostomy placement.65 Other studies have used AI algorithms to analyze bedside monitored adverse events and other clinical parameters to predict organ dysfunction and failure.66, 67

Actress Angelina Jolies response to her inheritance of the BRCA gene illustrates the potential impact of more advanced genomic information on disease risk and prevention options.68 This case is not novel; the case of Woodie Guthrie and Huntingtons disease disclosed a similar conundrum for health care.69 Although the ethics of genetic testing without a clear cure continues to be debated, the broad availability of genetic information offered by nextgeneration sequencing and directtoconsumer testing renders personalized prevention and management of serious diseases a reality.70

Cardiovascular medicine is an area with a long history of embracing predictive modeling to assess patient risk.71 Recent work has uncovered methods to predict heart failure72 and other serious cardiac events in asymptomatic individuals.73 When combined with personalized prevention strategies,74, 75 these models may positively impact disease incidence and sequela. Complex diseases, such as cardiovascular disease, often involve the interplay among gender, genetic, lifestyle, and environmental factors. Integrating these attributes requires attention to the heterogeneity of the data.76 AI approaches that excel at discovering complex relationships among a large number of factors provide such opportunities. A study from Vanderbilt demonstrated early examples of combining EHR and genetic data, with positive results in cardiovascular disease prediction.77 AIenabled recognition of phenotype features through EHR or images and matching those features with genetic variants may allow faster genetic disease diagnosis.78 For example, accurate and fast diagnosis for seriously ill infants that have a suspected genetic disease can be attained by using rapid wholegenome sequencing and NLPenabled automated phenotyping.79

Automated speech analytics have benefited from improvements in the technical performance of NLP, understanding, and generation. Automated speech analytics may provide indicators for assessment and detection of earlystage dementia, minor cognitive impairment, Parkinsons disease, and other mental disorders.80, 81, 82, 83 Efforts also are underway to detect changes in mental health using smartphone sensors.84

AIassisted monitoring may also be used in realtime to assess the risk of intrapartum stress during labor, guiding the decision of cesarean section vs. normal vaginal deliveries, in an effort to decrease perinatal complications and stillbirths.85 This exemplifies realtime AIassisted monitoring of streaming data to reduce manual error associated with human interpretation of cardiotocography data during childbirth.

AI is also being used in the detection and characterization of polyps in colonoscopy.86 Wider adaptation of AI during endoscopy may lead to a higher rate of benign adenoma detection and reduction of cost and risk for unwarranted polypectomy.87 AImediated image analysis aimed at improving disease risk prediction and diagnosis will likely continue to increase in use for detection of diabetic retinopathy88 and metastasis in cancer,89 as well as for identification of benign melanoma.90 AIbased image analysis has become a part of a directtoconsumer diagnostic tool for anemia as well.91

The widespread use of home monitoring and wearable devices has long been accompanied by the expectation that collected data could help detect disease at an earlier state. Indeed, these advances have fueled new, noninvasive, wearable applications for monitoring and detecting specific health conditions, such as diabetes, epilepsy, pain management, Parkinsons disease, cardiovascular disease, sleep disorders, and obesity.92 Digital biomarkers are expected to facilitate remote disease monitoring outside of the physical confines of a hospital setting and can support decentralized clinical trials.93 Wearable tools that provide continuous multidimensional measurements of preselected biomarkers would enable the detection of minimum residual disease and monitor disease progression.94 In the field of cancer care, evolving technology using wearable devices continuously analyzes circulating tumor cells to screen for relapsed disease.95

We have observed increasing efforts to implement AI in precision medicine to perform tasks such as disease diagnosis, predicting risk, and treatment response. Although most of these studies showed promising experimental results, how AI improved health care is not fully demonstrated. In reality, the success of transforming an AI system to a realworld application not only depends on the accuracy but also relies on the capability of working accurately in a reliable, safe, and generalizable manner.5 For example, the difference among institutions in coding definitions, report formats, or cohort diversity, may result in a model trained using onesite data to not work well in another site (https://www.bmj.com/content/368/bmj.l6927). Here, we highlighted three main challenges that would impact the success of transitions to realworld healthcare.

Fairness and bias. The health data can be biased while building and processing the dataset (e.g., a lack of diverse sampling, missing values, and imputation methods; https://datasociety.net/library/fairnessinprecisionmedicine/). An AI model trained on the data might amplify the bias and make nonfavorable decisions toward a particular group of people characterized by age, gender, race, geographic, or economic level. Such unconscious bias may harm clinical applicability and health quality. Thus, it is crucial to detect and mitigate the bias in data and models. Some potential solutions include improving the diversity of the data, such as the All of Us program that aimed to recruit participants with diverse backgrounds. AI communities also proposed several techniques to fight against bias (https://arxiv.org/abs/1908.09635). IBM has developed an online toolkit (AI Fairness 360) that implemented a comprehensive set of fairness metrics to help researchers examine the bias among datasets and models, and algorithms to mitigate bias in classifiers (https://doi.org/10.1147/JRD.2019.2942287). However, fairness and protected attributes are closely related to the domain context and applications. More work is needed in biomedical research to define and explore the fairness and bias in AI models trained with historical patient data. To address the challenge, a collaborative effort that involves the AI and biomedical community is needed.

Socioenvironmental factors. The environmental factors and workflows where the AI model would be deployed may impact model performance and clinical efficacy. A recent prospective study carried out by Google Health evaluated an AI system for screening diabetic retinopathy in a real clinical environment. The AI system was developed to augment diabetic retinopathy screening by providing intime assessment, before this the process may take several weeks. Despite a specialistlevel accuracy (>90% sensitivity and specificity) achieved on retrospective patient data; however, the system has undergone unexpected challenges when applied to Thailand clinics (https://doi.org/10.1145/3313831.3376718). For example, the variety of conditions and workflows in clinics impaired the quality of the images that did not meet the system' high standards, resulting in a high rejection rate of images. The unstable internet connection restricted the processing speed of the AI models and caused a longer waiting time for the patients. Travel and travel costs may deter participants from remaining in the study. Such prospective studies highlighted the importance of validating the AI models in the clinical environment and considering an iteration loopthat collects users feedback as new input for learning and system improvement96 before applying the AI system widely. Of note, in healthcare, obtaining such feedback would take a long time at a high cost. It may take a longer time to evaluate a therapys effect and associated longterm health outcomes than what is required to validate whether a product is appealing to a customer. There is a need to explore other ways to facilitate creation of highperforming AI systems, for example, generating synthetic data that carries similar distributions and variances as the realworld data, or leveraging a simulated environment. Early examples by groups, such as Baowaly and colleagues,89 demonstrate much promise, but more AI research efforts are needed.

Data safety and privacy. Data is crucial to an AIdriven system. As AI and precision medicine are converging, data (e.g., genomics, medical history, behaviors, and social data that covers peoples daily lives) will be increasingly collected and integrated. Individuals concerns for data privacy are closely related to trust when they use AIenabled services. Building a safe and wellcontrolled ecosystem for data storage, management, and sharing is essential, requiring new technology adoptions, and collaborations, as well as the creation of new regulations and business models.

The training of AI methods and validation of AI models using large data sets prior to applying the methods to personal data may address many of the challenges facing precision medicine today. The cited examples reinforce the importance of another potential use of augmented intelligence, namely that of the role of technology in the hands of consumers to help communicate justintime risk or as an agent of behavior change. Although most studies to date are small and the data are limited, the ability to identify atrisk patients will translate into personalized care when identification is combined with strategies to notify and intervene. Researchers are actively pursuing the use of mobile apps, wearables, voice assistants, and other technology to create personspecific interfaces to intelligent systems. A review of these approaches is beyond the scope of this paper.

Active research in both AI and precision medicine is demonstrating a future where healthrelated tasks of both medical professionals and consumers are augmented with highly personalized medical diagnostic and therapeutic information. The synergy between these two forces and their impact on the healthcare system aligns with the ultimate goal of prevention and early detection of diseases affecting the individual, which could ultimately decrease the disease burden for the public at large, and, therefore, the cost of preventable health care for all.

This work was funded by a partnership between IBM Watson Health and Vanderbilt University Medical Center.

Drs. Weeraratne, Rhee, and Snowdon are employed by IBM Watson Health. All other authors declared no competing interests for this work.

The authors thank Karlis Draulis for his assistance with the figures.

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