An Overview of the Use of Precision Population Medicine in Cancer Care: First of a Series

Abstract

Advances in science and technology in the past century and a half have helped improve disease management, prevention, and early diagnosis and better health maintenance. These have led to a longer life expectancy in most developed and middle-income countries. However, resource- and infrastructure-scarce countries and populations have not enjoyed these benefits. Furthermore, in every society, including in developed nations, the lag time from new advances, either in the laboratory or from clinical trials, to using those findings in day-to-day medical practice often takes many years and sometimes close to or longer than a decade. A similar trend is seen in the application of "precision medicine" (PM) in terms of improving population health (PH). One of the reasons for such lack of application of precision medicine in population health is the misunderstanding of equating precision medicine with genomic medicine (GM) as if they are the same. Precision medicine needs to be recognized as encompassing genomic medicine in addition to other new developments such as big data analytics, electronic health records (EHR), telemedicine, and information communication technology. By leveraging these new developments together and applying well-tested epidemiological concepts, it can be posited that population/public health can be improved. In this paper, we take cancer as an example of the benefits of recognizing the potential of precision medicine in applying it to population/public health. Breast cancer and cervical cancer are taken as examples to demonstrate these hypotheses. There exists significant evidence already to show the importance of recognizing "precision population medicine" (PPM) in improving cancer outcomes not only in individual patients but also for its applications in early detection and cancer screening (especially in high-risk populations) and achieving those goals in a more cost-efficient manner that can reach resource- and infrastructure-scarce societies and populations. This is the first report of a series that will focus on individual cancer sites in the future.

Keywords: artificial intelligence; big data; genomic medicine; multi-cancer early detection; precision medicine; precision population medicine.

Figure 1. Contexts to explore barriers and facilitators for clinical practice guidelines.

This image is reproduced from Correa et al. [2] and is available via Creative Commons Attribution 4.0 International License.

Figure 2. The components of precision medicine.

This image is a simplified depiction from the authors of this paper. EHR, electronic health records; mHealth, mobile health

Figure 3. The different axes of health data.

The complexity of large health datasets can be represented by distinct axes, each encompassing a quantifiable property of the data. This image is reproduced from Shilo et al. [12], and permission was obtained from the licensed content publisher Springer Nature.

Figure 4. The contributions of genes and social environment to health outcomes.

Genetic contributions to health occur along a continuum. The outcome in rare genetic diseases is determined primarily by genes; the outcome in other diseases, such as chicken pox, is determined primarily by the social environment. In the middle are diseases with a varying mix of genetic and environmental contributors, such as diabetes, heart disease, stroke, and most cancers; these conditions represent the major disease burdens in the United States. This image is reproduced from Burke [19] and is available via Creative Commons Attribution 4.0 International License.

Figure 5. Precision medicine in the era of artificial intelligence (AI): implications in chronic disease management.

Deep phenotyping and artificial intelligence for health promotion and chronic disease prevention. Deep phenotyping provides an entire molecular profile of an individual’s physiological status. When longitudinally tested, the pathways can be tracked to identify the transformation from a health to a disease. Various omics technologies along with other physiological measurements will be used to molecularly characterize an individual’s risk for disease. The further implementation of a systems approach to big data analysis and integration will provide a platform for machine learning and artificial intelligence in clinical decision-making for early disease risk identification and prevention. This image is reproduced from Subramanian et al. [33] and is available via Creative Commons Attribution 4.0 International License.

Figure 6. Relationship between artificial intelligence and its subtypes.

This image is reproduced from Scheetz et al. [34], and permission was obtained from licensed content publisher AMPCo Pty Ltd.

Figure 7. Radiogenomics in precision population health: the future envisioned.

(A) Hepatocellular carcinoma, preventive interventions in the natural history of HCC development in progressive fibrotic liver diseases. (B) Individual risk-based tailored hepatocellular carcinoma screening and chemoprevention. (C) Clinical hepatocellular carcinoma/fibrosis risk indicators. (D) Molecular targets of potential hepatocellular carcinoma chemoprevention therapies (the illustration in panel D is unclear, so we added a separate figure: Figure 8). This image is reproduced from Fujiwara et al. [58], and permission was obtained from the licensed content publisher Elsevier. HCC, hepatocellular carcinoma, IEN, intraepithelial neoplasia; HBV, hepatitis B virus; HCV, hepatitis C virus; IGF 1, insulin-like growth factor 1; SVR, sustained virologic response; NASH, non-alcoholic steatohepatitis; EGF, epidermal growth factor; MPO, myeloperoxidase; SNP, single nucleotide polymorphism; TLL1, tolloid-like protein 1; HSC, hepatic stellate cell; HIR, hepatic injury and regeneration

Figure 8. Molecular targets of potential hepatocellular carcinoma (HCC) chemoprevention therapies.

Intra- and extracellular targets of potential HCC chemopreventive therapies are summarized. Solid line with arrowhead or bar: activation or inhibition; dotted line with arrowhead: translocation between intracellular compartments. This image is reproduced from Fujiwara et al. [58], and permission was obtained from the licensed content publisher Elsevier. ACE, angiotensin-converting enzyme; AMPK, adenosine monophosphate-activated protein kinase; Ang, angiotensin; ATX, autotaxin; AT1, angiotensin type 1 receptor; BCAA, branched chain amino acid; COX2, cyclooxygenase 2; DAMPs, damage-associated molecular patterns; EGFR, epidermal growth factor receptor; ER, endoplasmic reticulum; ERK, extracellular signal-regulated kinase; FGF21, fibroblast growth factor 21; GSK3, glycogen synthase kinase 3; HIF, hypoxia inducible factor; HMG-CoA, 3-hydroxy-3-methyl-glutaryl-coenzyme A; IFNR, interferon receptor; IGFR, insulin-like growth factor 1 receptor; JAK, Janus kinase; JNK, c-Jun N-terminal kinase; LKB1, liver kinase B1; LPA, lysophosphatidic acid; LPAR, lysophosphatidic acid receptor; MDM, mouse double minute; mTOR, mammalian target of rapamycin; NF-κB, nuclear factor-kappa B; PDGFR, platelet-derived growth factor receptor; PGE2, prostaglandin E2; PI3K, phosphoinositide 3-kinase; RAR, retinoic acid receptor; ROS, reactive oxygen species; RXR, retinoid X receptor; STAT, signal transducers and activator of transcription; TDZ, thidiazuron; TLRs, Toll-like receptors; TNFR, tumor necrosis factor receptor; TSC, tuberous sclerosis complex; VEGFR, vascular endothelial growth factor receptor; YAP, Yes-associated protein; TAZ, transcriptional coactivator with PDZ-binding motif; MEK, mitogen-activated protein kinase enzyme; AKT, serine/threonine kinase; IKK, IkB kinase complex; IκB, inhibitor of nuclear factor-kappa B

Figure 9. Peripheral blood-based biopsy for breast cancer risk prediction and early detection.

This image is reproduced from Nassar et al. [43] and is available via Creative Commons Attribution 4.0 International License.

Figure 10. Age-standardized incidence (A) and mortality rates (B) of cervical cancer by country in 2020.

Data are from the GLOBOCAN database, collated by the International Agency for Research on Cancer and hosted by the Global Cancer Observatory. This image is reproduced from Singh et al. [45], and permission was obtained from the licensed content publisher Elsevier.

Figure 11. Schematic of the components of a data registry to track the utilization and outcomes of patients receiving MCED tests.

This image is reproduced from Etzioni et al. [48], and permission was obtained from licensed content publisher Oxford University Press. TOO, tissue of origin; MCED: multi-cancer early detection

Figure 12. Potential areas for developing precision population medicine.

This image is a simplified depiction from the authors of this paper.