Main Article Content

Dr Alok Kumar Moulik
Regidi Swathi Ratnam
Dr. Sriushaswini B
Alin Bose Johnson
Dr Sukanta Bandyopadhyay
Dr. Mohammad Chand Jamali


Clinicians, genomics, genome sequencing, medicine, patients


In today's ever-evolving medical landscape, the groundbreaking concept of precision medicine is revolutionizing the way we approach healthcare. By focusing on the unique characteristics of each patient, precision medicine aims to deliver personalized and targeted treatments that can provide superior outcomes. This personalized healthcare breakthrough has the potential to transform the medical field, ushering in a new era of tailored and effective treatments. Traditional medicine has been largely empirical, where physicians rely on patterns and past experience to diagnose and treat patients. Treatment decisions are often made based on the physician's familiarity with similar cases. In this approach, a single treatment or medication may be prescribed for a "typical patient" with a specific disease, even though patient responses can vary widely. This can lead to unpredictable side effects and varying levels of efficacy. It aims to provide the right medicine, in the right dose, at the right time for each individual patient. It takes into account factors such as genetic predisposition, ethnic differences, metabolism rates, and disease stage to tailor treatments accordingly. The rapid advancement of genomics research in recent decades has ushered in a new era in biomedicine, offering the potential to revolutionize healthcare through personalized or precision medicine. This approach seeks to tailor medical interventions to individual patients by utilizing genetic tests, identifying biomarkers, and developing targeted drugs. However, the personalized medicine movement has not been without controversy, igniting a robust debate among its proponents and critics. This essay seeks to explore the assumptions, promises, limits, and possibilities of personalized or precision medicine by reviewing recent literature and situating the debate within this complex landscape.

Abstract 169 | PDF Downloads 41


1. Seyhan AA, Varadarajan U, Choe S, Liu Y, McGraw J, Woods M, Murray S, Eckert A, Liu W, Ryan TE. A genome-wide RNAi screen identifes novel targets of neratinib sensitivity leading to neratinib and paclitaxel combination drug treatments. Mol BioSyst. 2011;7:1974–89.
2. Chen DS, Mellman I. Elements of cancer immunity and the cancerimmune set point. Nature. 2017;541:321–30.
3. Nagarsheth N, Wicha MS, Zou WP. Chemokines in the cancer microenvironment and their relevance in cancer immunotherapy. Nat Rev Immunol. 2017;17:559–72.
4. Torous J, Andersson G, Bertagnoli A, Christensen H, Cuijpers P, Firth J, Haim A, Hsin H, Hollis C, Lewis S, et al. Towards a consensus around standards for smartphone apps and digital mental health. World Psychiatry. 2019;18:97–8.
5. Boulos MN, Brewer AC, Karimkhani C, Buller DB, Dellavalle RP. Mobile medical and health apps: state of the art, concerns, regulatory control and certifcation. Online J Public Health Inform. 2014;5:229.
6. Capper D, Jones DTW, Sill M, Hovestadt V, Schrimpf D, Sturm D, Koelsche C, Sahm F, Chavez L, Reuss DE, et al. DNA methylation-based classifcation of central nervous system tumours. Nature. 2018;555:469–74.
7. Moran S, Martinez-Cardus A, Sayols S, Musulen E, Balana C, Estival-Gonzalez A, Moutinho C, Heyn H, Diaz-Lagares A, de Moura MC, et al. Epigenetic profling to classify cancer of unknown primary: a multicentre, retrospective analysis. Lancet Oncol. 2016;17:1386–95.
8. Diniz BS, Pinto Junior JA, Forlenza OV. Do CSF total tau, phosphorylated tau, and beta-amyloid 42 help to predict progression of mild cognitive impairment to Alzheimer’s disease? A systematic review and meta-analysis of the literature. World J Biol Psychiatry. 2008;9:172–82.
9. Cavagnaro JA. Preclinical safety evaluation of biotechnology-derived pharmaceuticals. Nat Rev Drug Dis. 2002;1:469.
10. Zhao Z, Rocha NP, Salem H, Diniz BS, Teixeira AL. The association between systemic lupus erythematosus and dementia. A meta-analysis. Dement Neuropsychol. 2018; 12:143–51.
11. Leiserson MDM, Syrgkanis V, Gilson A, Dudik M, Gillett S, Chayes J, Borgs C, Bajorin DF, Rosenberg JE, Funt S, et al. A multifactorial model of T cell expansion and durable clinical beneft in response to a PD-L1 inhibitor. PLoS ONE. 2018;13:e0208422.
12. Snyder A, Nathanson T, Funt SA, Ahuja A, Buros Novik J, Hellmann MD, Chang E, Aksoy BA, Al-Ahmadie H, Yusko E, et al. Contribution of systemic and somatic factors to clinical response and resistance to PD-L1 blockade in urothelial cancer: an exploratory multi-omic analysis. PLoS Med. 2017;14:e1002309.
13. Breiman L. Random forests. Mach Learn. 2001;45:5–32.
14. National Academies of Sciences E. Medicine: artifcial intelligence and machine learning to accelerate translational research: proceedings of a workshop—in brief. Washington, DC: The National Academies Press; 2018.
15. Robinson PN. Deep phenotyping for precision medicine. Hum Mutat. 2012;33:777–80.
16. Schadt EE. Molecular networks as sensors and drivers of common human diseases. Nature. 2009;461:218–23.
17. Feldman I, Rzhetsky A, Vitkup D. Network properties of genes harboring inherited disease mutations. Proc Natl Acad Sci USA. 2008;105:4323–8.
18. Goh KI, Cusick ME, Valle D, Childs B, Vidal M, Barabasi AL. The human disease network. Proc Natl Acad Sci USA. 2007;104:8685–90.
19. Robinson PN, Kohler S, Bauer S, Seelow D, Horn D, Mundlos S. The human phenotype ontology: a tool for annotating and analyzing human hereditary disease. Am J Hum Genet. 2008;83:610–5.
20. Kohler S, Bauer S, Horn D, Robinson PN. Walking the interactome for prioritization of candidate disease genes. Am J Hum Genet. 2008;82:949–58.
21. Beck T, Gollapudi S, Brunak S, Graf N, Lemke HU, Dash D, Buchan I, Diaz C, Sanz F, Brookes AJ. Knowledge engineering for health: a new discipline required to bridge the “ICT gap” between research and healthcare. Hum Mutat. 2012; 33:797–802.
22. Johnson KB, Wei WQ, Weeraratne D, Frisse ME, Misulis K, Rhee K, Zhao J, Snowdon JL. Precision Medicine, AI, and the Future of Personalized Health Care. Clin Transl Sci. 2021 Jan;14(1):86-93. doi: 10.1111/cts.12884. Epub 2020 Oct 12. PMID: 32961010; PMCID: PMC7877825.