34PART 1 The Profession of Medicine

FIGURE 5-4 Gene expression and phenotype. A. The human protein-protein interactome is constructed, and a specific disease module is identified (I); gene expression

within this module is ascertained (II); and the tissue specificity of gene expression is determined (III). This analysis leads to a reduction of the total number of disease

module genes that govern phenotype in a specific organ, which is a reflection of the specific pathway (or pathways) that is (or are) expressed in their functional entirety in

that tissue. B. A disease-tissue bipartite network is constructed wherein specific tissues are placed within the circle and linked to diseases shown on the circumference.

Nodes are colored according to tissue classification, the sizes of nodes are proportional to the total number of genes expressed in them, and the widths (shades) of the

lines or edges correspond to the significance of the associations with specific diseases. (From M Kitsak et al: Tissue Specificity of Human Disease Module. Sci Rep 6: 35241,

2016, Figure 4.)

Non-disease genes DATA:

13,460 Proteins

141,296 Interactions

70 Diseases

64 Tissues

I. Human Interactome:

colored nodes are disease genes

II. Expression Data

Node size = expression level

III. Tissue-specific Interactome

Subgraph of significantly expressed genes

Genes of disease A

Genes of disease B

Genes of disease C

A

Significance

threshold

lowest

highest

Gene expression

Multiple

Total genes expressed in a tissue:

Cardiomyopathy, hypertrophic

Aneurysm

Crohn disease

Adrenal gland diseases

Anemia, aplastic

Blood coagulation disorders

Blood platelet disorders

Blood protein disorders

Anemia, hemolytic

Lupus erythematosus,

systemic

Psoriasis

Arthritis, rheumatoid

Nutritional and metabolic diseases

Muscular dystrophies

Lipid metabolism disorders

Multiple sclerosis Tauopathies

Macular degeneration

Disease-Tissue Network

Basal ganglia diseases

Cerebrovascular disorders

Alzheimer disease

Charcot-Marie-Tooth

disease

Peroxisomal disorders

Glomerulonephritis

Lung diseases, obstructive

Asthma

Mycobacterium infections

Sarcoma

Carbohydrate metabolism,

inborn errors

Amino acid metabolism,

inborn errors

Leukemia, myeloid, acute

Breast neoplasms

Appendix

Bonemarrow

Cingulate cortex

Medulla oblongata

Lysosomal storage diseases

Colorectal neoplasms

Adrenal cortex

Pancreatic islet

Cardiomyopathies

Association significance:

Bronchial epithelial cells

Skeletal muscle

Whole blood

z = 18.2

z = 1.6

Cardiovascular

Digestive

Endocrine

Immune

Integumentary

Musculoskeletal

Nervous

Reproductive

Respiratory

Classification

CD4 Tcells

Tongue

Prostate

CD105 Endothelial

Cardiac myocytes

CD34

CD8 Tcells Heart

CD56 NKCells Lung Thyroid

Placenta

Smooth muscle

Liver

CD14 Monocytes

Tonsil Lymphnode

Pituitary

Spinalcord

Hypothalamus

Prefrontal cortex

X721 B lymphoblasts

BDCA4 Dentritic cells

Thalamus

Amygdala

Whole brain

B

Precision Medicine and Clinical Care

35CHAPTER 5

Individual 1

Multi-omic

molecular

analysis

“Reticulotyping”

Individualized

targeted

therapeutics

Interrogation of

patient-specific

molecular

perturbations

in individualized

network contexts

Individual 2 Individual 3

Reticulotype

Genotype Phenotype Genotype Phenotype Genotype Phenotype

Reticulotype Reticulotype

DNA

RNAs

Proteins

Metabolites

Microbiome

Clinical/exposures

DNA

RNAs

Proteins

Metabolites

Microbiome

Clinical/exposures

DNA

RNAs

Proteins

Metabolites

Microbiome

Clinical/exposures

FIGURE 5-5 Reticulotype. Patient-specific genotype-phenotype relationships by multiomic network structures are depicted for three individuals. Each individual’s unique

molecular perturbations (genetic variants, differentially expressed genes) are examined within the context of the subject’s unique integrative biologic network or reticulome

derived from these multiomic analyses. These unique reticulotypes then serve as the basis for patient-specific, precision therapies. (Reproduced with permission from LYH

Lee, J Loscalzo: Network Medicine in Pathobiology. Am J Pathol 189:1311, 2019.)

Disease module

Disease gene

S1

S2

S3

t1 t2

Drug target

Shortest path to the

closest disease gene

Disease

module

Network-Based Drug Target ID Network-Based Drug Repurposing:

The Proximity Hypothesis

Drug target

FIGURE 5-6 Network-based precision drug repurposing. (Adapted from F Cheng et al: A genome-wide positioning

systems network algorithm for in silico drug repurposing. Nat Commun 10:3476, 2019.)

is determined by specific germline mutations present in the prion

protein (Chap. 438). Discovery of autoantibodies against aquaporin-4

(AQP-4) and myelin oligodendrocyte glycoprotein (MOG) has allowed

neuromyelitis optica, previously considered a multiple sclerosis–like

disorder, to be classified as a separate entity requiring different treatment (Chap. 445). Similarly, in myasthenia gravis, the identification

of novel autoantibodies now permits stratification and a more finely

tuned precision approach to therapy (Chap. 448).

Precision medicine approaches to cancers have, of course, become the

prime example of the opportunity that this strategy offers. In the pregenomic era, chemotherapy was widely used with variable success despite

continued efforts to characterize the molecular features of the specific

tumors and their semi-empiric responses to specific chemotherapeutic

agents. As cancer genome sequencing evolved, however, it became apparent that there are a limited number of oncogenic pathways (<20) that are

represented in the great majority of malignancies, without regard for the

organ in which the disease was primarily manifest. These genomic signatures served as a template for precisely targeted

therapies that have led to dramatic changes in

response to treatment, including, for example,

imatinib (and congeners) for Bcr-Abl tyrosine

kinase activity in chronic myelogenous leukemia, erlotinib for EGFR-mutant non-small

cell lung cancers, and ibrutinib for Bruton

tyrosine kinase in chronic lymphocytic leukemia, among many others.

As exciting as these approaches have been,

there are at least three primary challenges

associated with precision therapeutics that

are unique to cancer: (1) the mutational landscape continues to evolve as the disease progresses, and therapy often (if not invariably)

leads to selection for resistant clones; (2) the

likelihood that any cancer can be definitively

cured by any single agent, no matter its exquisite precision, is quite limited, necessitating

36PART 1 The Profession of Medicine

Health system data

‘Omic’ data

Exposome/social

determinants

Microbiome

Study-participantgenerated data

Motivations

and behaviours

Precision participant

descriptor

B C Electronic health-care

system of the future

Dynamic phenotype Data curation and

user-friendly display

A

FIGURE 5-7 Big data in precision medicine. A. Six dimensions by which individuals

may be characterized in the precision medicine era are described. B. The precision

participant descriptor integrates the data from these six dimensions and varies over

time. C. The electronic medical record increasingly must evolve to provide curated

precision data in a user-friendly way. (Reproduced with permission from EM Antman,

J Loscalzo: Precision medicine in cardiology. Nat Rev Cardiol 13:591, 2016.)

Disease

Sample

Decreased

heterogeneity

Prognostic

enrichment

Predictive

enrichment

Enrichment strategies

FIGURE 5-8 The basis for precision medicine. The notion of precision medicine

evolved, in part, from clinical trial design. From the entire population of patients

with the disease of interest, a sample cohort of individuals is enrolled in the trial that

ideally is representative of the entire distribution. Enrichment strategies developed

to decrease heterogeneity or increase the representation of individuals with a high

risk of observed outcomes (prognostic enrichment) facilitate trial conduct but do

not necessarily improve precision in defining treatment response. The predictive

enrichment strategy utilizes both trial participant characteristics and data from

experiments conducted before or during (adaptive design) the trial to improve the

prediction of who is likely to have a more pronounced response to the treatment

under study. (Reproduced with permission from EM Antman, J Loscalzo: Precision

medicine in cardiology. Nat Rev Cardiol 13:591, 2016.)

the development of rational polypharmaceutical approaches that take

into account alternative pathways that achieve the same oncogenic

goals as the primary targeted pathway, complicating drug development;

and (3) there is marked genomic heterogeneity in many malignancies

arguing that targeting a specific pathway—even with multiple drugs—

may not ultimately succeed over the long term owing to the continued

and heterogeneous evolution of the genomic landscape within a tumor

within a patient. Despite these serious shortcomings, the application of

progressively more refined and precisely targeted therapies used alone

and in combination, such as with immune modulators, continues to

offer great promise for the treatment of these diseases. In some ways,

these approaches in cancer mirror earlier strategies in the treatment of

infectious diseases in which the identification of the causative organism

and its sensitivity to potential antimicrobials allows precision approaches

to treatment. Combinatorial antimicrobial treatments represent an

effective strategy to address acquired resistance. These diagnostic and

therapeutic strategies can be applied without detailed knowledge of

personalized responses to the infection or treatment (aside from serious

adverse effects) with good outcomes in most cases. Yet, individuals do

respond differently to specific infections and their treatments, possibly driven by different endophenotypes (e.g., different inflammatory

responses), suggesting that more precise knowledge of these precise

mechanistic differences may yield improved prognosis and therapeutic approaches. As with cancer, immune modulation, particularly for

immune exhaustion in chronic infections, represents a new frontier,

again amenable to the personalized, precise analyses described above.

■ THE FUTURE OF PRECISION MEDICINE

Precision medicine clearly holds great promise for the future of the practice of medicine. For precision medicine to continue to evolve successfully,

however, several requirements will need to be met. First, both deeply

refined personal phenotypic data and genomic data are essential as the

information with which precision analysis is performed. These data sets

are quite large and require sufficient storage for analysis, especially for

individuals in whom time trajectories are acquired (as should be the case

for every person). Equally important, the analytical methods required to

extract useful information from these data sets are evolving and themselves quite complex. While great progress has been made in genomics

and biochemical testing, our ability to capture meaningful immunologic

endophenotypes and environmental exposures is limited by comparison.

Machine learning and artificial (auxiliary) intelligence methods will be

essential for extracting optimal information from these data sets, which

include not only pathways that can be uniquely targeted therapeutically

but also individualized genomic or phenotypic signatures that are highly

predictive of outcome, with or without therapy. Gathering sufficient information on the “normal” segments of the population is also required to

ensure appropriate comparison data sets for optimal prediction.

Second, phenotyping must continue to expand and become dimensionally richer. The phenotypic features included in this data gathering

must incorporate not only data relevant to the clinical presentation

but also orthogonal phenotypic data that may yield useful information

on disease trajectory or preclinical disease markers. Personal device

data, environmental exposure history, social network interactions, and

health system data will all be incorporated increasingly in defining

phenotype and will require great efforts on the part of the medical

informatics community to harmonize data sets, standardize data collection, and optimize/standardize data analysis (Fig. 5-7).

Third, perhaps the greatest challenge to making precision medicine

the standard approach to illness will be to determine the minimal data

set required to predict outcome and response to therapy. Gathering data

is comparatively simple; however, analyzing it to eliminate redundant

information in these overdetermined biologic systems, weighting the

determinants of an outcome, and using the data as phenomic/genomic

signatures that are easier to collect than comprehensive, unbiased data

sets are the ideal goals—a major challenge, but not insurmountable.

Rapidly evolving machine learning and artificial intelligence strategies

will also be essential for maximal success.

To return to the question of how precise precision medicine needs to

be in order to be useful, please refer to Fig. 5-8 where the approaches

Screening and Prevention of Disease

37CHAPTER 6

to clinical trial design meant to improve therapeutic signal are illustrated. Decreasing heterogeneity and enriching the study population will

enhance the effect size, but these strategies are based on analyses of prior

data sets that define those individuals who are more likely than not to

respond to a therapy. By contrast, the notion of predictive enrichment

follows from the information provided by a detailed, big data–driven

analysis of individuals that explores phenotypic and genomic features

used to predict response. These features need not be precisely met by each

patient; however, they can be collated or clustered to define a reasonably

sized cohort predicted to respond in a particular way within certain confidence bounds. In this way, the boundaries to the practice of precision

medicine are imprecise strictly speaking, but sufficiently predictive to

be practical from the perspectives of clinical care and cost-effectiveness.

■ FURTHER READING

Antman EM, Loscalzo J: Precision medicine in cardiology. Nat Rev

Cardiol 13:591, 2016.

Cheng F et al: Comprehensive characterization of protein-protein

interactions perturbed by disease mutations. Nat Genet 53:342, 2021.

Cheng F et al: A genome-wide positioning systems network algorithm

for in silico drug repurposing. Nat Commun 10:3476, 2019.

Greene JA, Loscalzo J: Putting the patient back together—Social

medicine, network medicine, and the limits of reductionism. N Engl

J Med 377:2493, 2017.

Kitsak M et al: Tissue specificity of human disease module. Sci Rep

6:35241, 2016.

Lee LY, Loscalzo J: Network medicine in pathobiology. Am J Pathol

189:1311, 2019.

Leopold JA et al: The application of big data to cardiovascular disease:

Paths to precision medicine. J Clin Invest 130:29, 2020.

Loscalzo J et al: Human disease classification in the postgenomic era:

A complex systems approach to human pathobiology. Mol Syst Biol

3:124, 2007.

Maron BA et al: Individualized interactomes for network-based precision medicine in hypertrophic cardiomyopathy with implications for

other clinical pathophenotypes. Nat Commun 12:873, 2021.

Menche J et al: Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Science 347:1257601, 2015.

Samokhin AO et al: NEDD9 targets COL3A1 to promote endothelial fibrosis and pulmonary arterial hypertension. Sci Transl Med

10:eaap7294, 2018.

A primary goal of health care is to prevent disease or detect it early

enough that intervention will be more effective. Tremendous progress

has been made toward this goal over the past 50 years. Screening tests

are available for many common diseases and encompass biochemical

(e.g., cholesterol, glucose), physiologic (e.g., blood pressure, growth

curves), radiologic (e.g., mammogram, bone densitometry), and cytologic (e.g., Pap smear) approaches. Effective preventive interventions

have resulted in dramatic declines in mortality from many diseases,

particularly infections. Preventive interventions include counseling

about risk behaviors, vaccinations, medications, and, in some relatively

uncommon settings, surgery. Preventive services (including screening

tests, preventive interventions, and counseling) are different than other

medical interventions because they are proactively administered to

healthy individuals instead of in response to a symptom, sign, or diagnosis. Thus, the decision to recommend a screening test or preventive

6 Screening and Prevention

of Disease

Katrina A. Armstrong, Gary J. Martin

intervention requires a particularly high bar of evidence that testing

and intervention are both practical and effective.

Because population-based screening and prevention strategies

must be extremely low risk to have an acceptable benefit-to-harm

ratio, the ability to target individuals who are more likely to

develop disease could enable the application of a wider set of potential

approaches and increase efficiency. Currently, there are many types of

data that can predict disease incidence in an asymptomatic individual.

Germline genomic data have received the most attention to date, at least

in part because mutations in high-penetrance genes have clear implications for preventive care (Chap. 467). Women with mutations in either

BRCA1 or BRCA2, the two major breast cancer susceptibility genes

identified to date, have a markedly increased risk (five- to twentyfold) of

breast and ovarian cancer. Screening and prevention recommendations

include prophylactic oophorectomy and breast magnetic resonance

imaging (MRI), both of which are considered to incur too much harm

for women at average cancer risk. Some women with BRCA mutations

opt for prophylactic mastectomy to dramatically reduce their breast

cancer risk. Although the proportion of common disease explained by

high-penetrance genes appears to be relatively small (5–10% of most

diseases), mutations in rare, moderate-penetrance genes, and variants

in low-penetrance genes, also contribute to the prediction of disease

risk. Most recently, polygenic risk scores combining information about

variants across hundreds of genes are being evaluated for identifying

individuals at high risk of coronary heart disease and other conditions.

The advent of affordable whole exome/whole genome sequencing is

likely to speed the dissemination of these tests into clinical practice and

may transform the delivery of preventive care.

Other forms of “omic” data also have the potential to provide important predictive information. Proteomics and metabolomics can provide

insight into gene function, but it has proven challenging to develop

reliable, predictive measures using these platforms. More recently, it has

become possible to measure the presence of mutations in DNA circulating in the bloodstream and in stool, with early promising evidence that

these assays can be used to detect cancer before existing screening tests.

In addition to “omic” data, imaging data are increasingly being

integrated into risk-stratified prevention approaches as evidence grows

about the predictive ability of these data. For example, coronary computed tomography (CT) scans are used in many preventive cardiology

programs to inform decisions about beginning statin therapy when

there is conflicting or uncertain information from other risk assessment approaches. Of course, these data may also be helpful in predicting the risk of harms from screening or prevention, such as the risk of

a false-positive mammogram.

In addition to advances in risk prediction, there are several other

reasons that screening and prevention are likely to gain importance

in medical care in the near term. New imaging modalities are being

developed that promise to detect changes at the cellular and subcellular

levels, greatly increasing the probability that early detection improves

outcomes. The rapidly growing understanding of the biologic pathways

underlying initiation and progression of many common diseases has

the potential to transform the development of preventive interventions,

including chemoprevention. Furthermore, screening and prevention

offer the promise of both improving health and sparing the costs of disease treatment, an issue that will continue to gain importance as long

as health care costs in the United States remain a concern to patients,

government agencies, and insurers.

This chapter will review the basic principles of screening and prevention in the primary care setting. Recommendations for specific

disorders such as cardiovascular disease, diabetes, and cancer are provided in the chapters dedicated to those topics.

■ BASIC PRINCIPLES OF SCREENING

The basic principles of screening populations for disease were published by the World Health Organization in 1968 (Table 6-1).

In general, screening is most effective when applied to relatively

common disorders that carry a large disease burden (Table 6-2). The

five leading causes of mortality in the United States are heart diseases, malignant neoplasms, chronic obstructive pulmonary disease,