Chapter 5_ Precision Medicine and Clinical Care

DISEASE NOSOLOGY AND PRECISION MEDICINE

Modern disease nosology arose in the late nineteenth century and represented a clear departure from the holistic, limited descriptions of disease dating to Galen. In this rubric, the definition of any disease is largely based on clinicopathologic observation. As the correlation between clinical signs and symptoms with pathoanatomy required autopsy material, diseases tended to be characterized by the end organ in which the primary syndrome was manifest and by late-stage presentations. Morgagni institutionalized this framework with the publication of De Sedibus et Causis Morborum per Anatomen Indagatis in 1761, correlating clinical features with more than 600 autopsies, demonstrating an anatomic basis for disease pathophysiology.

This approach employed inductive generalization and Occam’s razor, reducing disease complexity to its simplest possible form. While this Oslerian diagnostic approach has facilitated conquering many diseases, it suffers from shortcomings, including failure to distinguish underlying etiology of diseases with common pathophenotypes (e.g., many diseases causing end-stage kidney disease or heart failure). Over time, classifications of neurodegenerative disorders and lymphomas are becoming more refined as underlying etiologies are identified. These distinctions are important for providing predictable prognostic information and guiding therapy, as therapies may be ineffective without understanding subtle molecular complexities of specific disease drivers.

Beginning in the mid-twentieth century, the era of molecular medicine offered the idealized possibility of identifying the molecular basis of every disease. Using a conventional reductionist paradigm, physician-scientists explored disease mechanisms at increasing molecular depth, seeking single or limited molecular causes. However, few diseases could be explained by a single molecular mechanism (e.g., sickle cell disease’s β-globin mutation does not predict diverse manifestations like stroke, painful crises, hemolytic crisis).

The Human Genome Project provided tools to identify monogenic, oligogenic, or polygenic causes with environmental modulation. Yet, expanding genomic data revealed limited revelations, showing the need for new approaches beyond reductionism—returning to an integrative holism that accounts for genomic context in all dimensions. This leads to more precise and individualized disease definitions, setting the stage for precision medicine.

FIGURE 5-1

Arc of reductionism in medicine. (Greene & Loscalzo, N Engl J Med, 2017)

Oversimplification of phenotype stems from the observational scientific method, grouping individuals with reasonably similar symptoms for easier diagnosis and therapy. This served medicine well into the 21st century but has limitations such as predictive inaccuracies and non-response to drugs (up to 60%). A more nuanced approach to diagnosis and therapy is required for better outcomes.

Clinicians recognize subtle phenotypic differences among patients with the same disease—sometimes leading to subclassifications (e.g., heart failure with preserved vs. reduced ejection fraction). New technologies and refined serologies drive these distinctions. Experienced clinicians argue they have practiced personalized, precision medicine by characterizing each patient’s illness in detail to guide therapy.

Genomic variation, inherited or acquired, refines diagnostic precision. Modifier genes affect disease expression differently across individuals (e.g., why sickle cell patients develop different complications). This forms the ultimate basis of precision medicine.

To develop precision medicine strategies, clinicians must understand:

Convergent phenotypes: Different diseases showing similar pathophenotypes (e.g., hypertrophied myocardium in hypertrophic cardiomyopathy, aortic stenosis, hypertension).
Divergent phenotypes: Same disease manifesting different phenotypes (e.g., cystic fibrosis, sickle cell disease).

Both result from genomic context and unique environmental exposures over a lifetime.

FIGURE 5-2

Convergent and divergent phenotypes. (Greene & Loscalzo, 2017)

Complete knowledge of all genomic and environmental factors is impossible, but due to overdetermined molecular networks, precision medicine can be realized without full genome knowledge. Examples follow later in the chapter.

REQUIREMENTS FOR PRECISION MEDICINE

Essential elements include:

Phenotyping: Detailed history, family history, environmental exposures, functional studies, imaging (including molecular imaging).
Endophenotyping: Biochemical, immunologic, and molecular tests refining phenotype discriminants.
Genomic profiling: DNA sequencing, gene expression, metabolomics, epigenetics, proteomics, metagenome.

FIGURE 5-3

Universe of precision medicine. (Lee & Loscalzo, Am J Pathol, 2019)

Tissue-specific expression and coexpression of protein binding partners are critical for understanding the functional consequences of genetic variants driving disease.

FIGURE 5-4

Gene expression and phenotype. (Kitsak et al., Sci Rep, 2016)

Phenotype features vary over time; timed sampling for genomic and phenotypic data is important but currently limited by cost and feasibility.

Cancer uniquely allows frequent biopsy and genomic profiling, but:

High frequency of somatic mutations,
Single-cell variability,
Stromal interactions

make data interpretation complex and semi-empirical.

The goal of precision medicine is identifying therapeutic targets by analyzing:

DNA sequencing,
Gene expression in affected organs,
Functional consequences via protein-protein interactome mapping.

This leads to an individualized reticulome (reticulotype) linking genotype to phenotype.

FIGURE 5-5

Reticulotype for patient-specific therapies. (Lee & Loscalzo, 2019)

FIGURE 5-6

Network-based precision drug repurposing. (Cheng et al., Nat Commun, 2019)

Pharmacogenomics enhances drug utilization by understanding variants affecting metabolism (e.g., TPMT and azathioprine; CYP2C19 and clopidogrel).

EXAMPLES OF PRECISION MEDICINE APPLICATIONS

Heart failure: Therapies tailored to phenotypes (congestion, hypertension, contractility). Refinements include subclassifying heart failure by ejection fraction.
Pulmonary arterial hypertension: Pre- and postgenomic therapies targeting vascular function and fibrosis (e.g., calcium channel blockers, prostacyclins, endothelin receptor antagonists). Genomic insights reveal new endophenotypes.
Dementias: Classified by genotype and protein aggregation patterns (e.g., frontotemporal dementia linked to specific genotypes).
Neurological diseases: Autoantibodies identify new disease subtypes (e.g., neuromyelitis optica vs multiple sclerosis; myasthenia gravis antibody stratification).
Cancer: Oncogenic pathways (<20) guide targeted therapies (imatinib for Bcr-Abl, erlotinib for EGFR mutations, ibrutinib for Bruton tyrosine kinase).

Challenges in cancer precision therapeutics:

Evolving mutational landscape selects resistant clones.
Limited cure with single agents—need for polypharmaceutical approaches.
Tumor genomic heterogeneity complicates targeting.

Despite challenges, combined targeted and immune-modulating therapies show promise.

THE FUTURE OF PRECISION MEDICINE

Key requirements:

Refined personal phenotypic and genomic data: Large data sets require storage and complex analysis.
Expanded, dimensionally rich phenotyping: Clinical, environmental, social, and device data integrated through informatics.
Defining minimal predictive data sets: Using machine learning and AI to reduce redundant information and weight key determinants.

FIGURE 5-7

Big data in precision medicine. (Antman & Loscalzo, Nat Rev Cardiol, 2016)

Clinical trial design evolves from:

Population enrichment (decreasing heterogeneity),
Predictive enrichment using phenotypic and genomic features to identify likely responders.

FIGURE 5-8

Basis for precision medicine from clinical trials. (Antman & Loscalzo, 2016)