Skip to content
2000
image of Precision Medicine: Transforming Cancer Research through Targeted Therapies

Abstract

Precision medicine is a landmark strategy that has been changing the future of health care through matching treatment plans with each individual patient’s needs and requirements. It permits the discovery of certain genetic abnormalities that cause tumors in cancer research, resulting in tailored medicines and better outcomes. The new drug development process is facilitated by precision medicine, focusing on biomarkers and patient classification because they allow for faster identification of new treatments. Emerging trends in omics technologies and Artificial Intelligence for data processing have patient-centered telemedicine applications. Ethical and privacy issues are addressed, focusing on data security and informed consent. The additional development of precision medicine offers hope for bridging gaps in healthcare delivery systems, addressing rare disease challenges, and promoting global healthcare initiatives. The revolutionizing nature of healthcare and improved patient outcomes can only be fully realized through acceptance and support of precision medicine to its fullest extent. This review evaluates various applications of precision medicine with an emphasis on how it could potentially change the paradigm of cancer research.

Loading

Article metrics loading...

/content/journals/cg/10.2174/0113892029357275250721125929
2025-07-29
2025-09-26
Loading full text...

Full text loading...

References

  1. Tumour heterogeneity and resistance to cancer therapies. Nat. Rev. Clin. Oncol. 2018 15 2 81 94 10.1038/nrclinonc.2017.166 29115304
    [Google Scholar]
  2. Pattern recognition for predictive, preventive, and personalized medicine in cancer. EPMA J. 2017 8 1 51 60 10.1007/s13167‑017‑0083‑9 28620443
    [Google Scholar]
  3. High‐throughput sequencing for biology and medicine. Mol. Syst. Biol. 2013 9 1 640 10.1038/msb.2012.61 23340846
    [Google Scholar]
  4. Accurate segmentation of nuclear regions with multi-organ histopathology images using artificial intelligence for cancer diagnosis in personalized medicine. J. Pers. Med. 2021 11 6 515 10.3390/jpm11060515 34199932
    [Google Scholar]
  5. Potential prognostic biomarkers of NIMA (never in mitosis, Gene A)-related kinase (NEK) family members in breast cancer. J. Pers. Med. 2021 11 11 1089 10.3390/jpm11111089 34834441
    [Google Scholar]
  6. LASSO and bioinformatics analysis in the identification of key genes for prognostic genes of gynecologic cancer. J. Pers. Med. 2021 11 11 1177 10.3390/jpm11111177 34834529
    [Google Scholar]
  7. CDK1 and HSP90AA1 appear as the novel regulatory genes in non-small cell lung cancer: A Bioinformatics Approach. J. Pers. Med. 2022 12 3 393 10.3390/jpm12030393 35330393
    [Google Scholar]
  8. Signal transduction therapy of cancer. Mol. Aspects Med. 2010 31 4 287 329 10.1016/j.mam.2010.04.001 20451549
    [Google Scholar]
  9. Deciphering the role of mitochondrial DNA targeted therapy in hepatic cell carcinoma. Gene Expr. 2025 24 1 46 55 10.14218/GE.2023.00134
    [Google Scholar]
  10. Chronic myeloid leukemia: The paradigm of targeting oncogenic tyrosine kinase signaling and counteracting resistance for successful cancer therapy. Mol. Cancer 2018 17 1 49 10.1186/s12943‑018‑0780‑6 29455643
    [Google Scholar]
  11. Genetic cancer risk assessment and counseling: Recommendations of the national society of genetic counselors. J. Genet. Couns. 2004 13 2 83 114 10.1023/B:JOGC.0000018821.48330.77 15604628
    [Google Scholar]
  12. Legal, ethical, and social issues in human genome research. Annu. Rev. Anthropol. 1998 27 1 473 502 10.1146/annurev.anthro.27.1.473 15977340
    [Google Scholar]
  13. Anagallis arvensis induces apoptosis in HL-60 cells through ROS-mediated mitochondrial pathway. Nutr. Cancer 2021 73 11-12 2720 2731 10.1080/01635581.2020.1856893 33305590
    [Google Scholar]
  14. Nutraceutical phycocyanin nanoformulation for efficient drug delivery of paclitaxel in human glioblastoma U87MG cell line. J. Nanopart. Res. 2017 19 8 272 10.1007/s11051‑017‑3972‑x
    [Google Scholar]
  15. Diminution of free radical induced DNA damage by extracts/fractions from bark of Schleichera oleosa (Lour.). Oken. Drug Chem. Toxicol. 2010 33 4 329 336 10.3109/01480540903483433 20545578
    [Google Scholar]
  16. Vallaris solanacea induces mitochondrial mediated apoptosis in HL-60 human promyelocytic leukemia cells. Food Chem. Toxicol. 2024 189 114743 10.1016/j.fct.2024.114743 38763500
    [Google Scholar]
  17. The molecular biology of cancer. Mol. Aspects Med. 2000 21 6 167 223 10.1016/S0098‑2997(00)00007‑8 11173079
    [Google Scholar]
  18. The history and advances in cancer immunotherapy: Understanding the characteristics of tumor-infiltrating immune cells and their therapeutic implications. Cell. Mol. Immunol. 2020 17 8 807 821 10.1038/s41423‑020‑0488‑6 32612154
    [Google Scholar]
  19. Fundamentals of cancer metabolism. Sci. Adv. 2016 2 5 e1600200 10.1126/sciadv.1600200 27386546
    [Google Scholar]
  20. Next-generation sequencing informatics: Challenges and strategies for implementation in a clinical environment. Arch. Pathol. Lab. Med. 2016 140 9 958 975 10.5858/arpa.2015‑0507‑RA 26901284
    [Google Scholar]
  21. Cancer whole-genome sequencing: Present and future. Oncogene 2015 34 49 5943 5950 10.1038/onc.2015.90 25823020
    [Google Scholar]
  22. Whole-genome sequencing is more powerful than whole-exome sequencing for detecting exome variants. Proc. Natl. Acad. Sci. USA 2015 112 17 5473 5478 10.1073/pnas.1418631112 25827230
    [Google Scholar]
  23. Clinical interpretation of whole-genome and whole-transcriptome sequencing for precision oncology. Semin. Cancer Biol. 2022 84 23 31 10.1016/j.semcancer.2021.07.003 34256129
    [Google Scholar]
  24. Long-read sequencing of an advanced cancer cohort resolves rearrangements, unravels haplotypes, and reveals methylation landscapes. Cell. Genomics 2024 4 11 100674 10.1016/j.xgen.2024.100674 39406235
    [Google Scholar]
  25. Precision medicine integrating whole-genome sequencing, comprehensive metabolomics, and advanced imaging. Proc. Natl. Acad. Sci. USA 2020 117 6 3053 3062 10.1073/pnas.1909378117 31980526
    [Google Scholar]
  26. Precision medicine screening using whole-genome sequencing and advanced imaging to identify disease risk in adults. Proc. Natl. Acad. Sci. USA 2018 115 14 3686 3691 10.1073/pnas.1706096114 29555771
    [Google Scholar]
  27. Whole-genome and transcriptome analysis enhances precision cancer treatment options. Ann. Oncol. 2022 33 9 939 949 10.1016/j.annonc.2022.05.522 35691590
    [Google Scholar]
  28. Whole transcriptome sequencing of lung tissue to combine disease classification and identification of actionable. Ann Oncol. 2024 9 1 19 (Suppl. 6) 10.1016/esmoop/esmoop103743
    [Google Scholar]
  29. Translating cancer genomes and transcriptomes for precision oncology. CA Cancer J. Clin. 2016 66 1 75 88 10.3322/caac.21329 26528881
    [Google Scholar]
  30. Recent trends in computer-assisted diagnosis (CAD) systems for breast cancer diagnosis using histopathological images. IRBM 2019 40 4 211 227 10.1016/j.irbm.2019.06.001
    [Google Scholar]
  31. HER2-amplified breast cancer: Mechanisms of trastuzumab resistance and novel targeted therapies. Expert Rev. Anticancer Ther. 2011 11 2 263 275 10.1586/era.10.226 21342044
    [Google Scholar]
  32. Insights of breast cancer and barriers to its therapy. J. Pharm. Technol Res. Manag. 2019 7 2 73 86 10.15415/jptrm.2019.72010
    [Google Scholar]
  33. The evolving landscape of HER2 targeting in breast cancer. JAMA Oncol. 2015 1 8 1154 1161 10.1001/jamaoncol.2015.2286 26204261
    [Google Scholar]
  34. Circulating tumor cells and circulating tumor DNA. n: Principles and Applications of Molecular Diagnostics; Elsevier 2018 235 281 10.1016/B978‑0‑12‑816061‑9.00009‑6
    [Google Scholar]
  35. Enhancing clinical potential of liquid biopsy through a multi-omic approach: A systematic review. Front. Genet. 2023 14 1152470 10.3389/fgene.2023.1152470 37077538
    [Google Scholar]
  36. Liquid biopsies come of age: Towards implementation of circulating tumour DNA. Nat. Rev. Cancer 2017 17 4 223 238 10.1038/nrc.2017.7 28233803
    [Google Scholar]
  37. Liquid biopsies: The future of cancer early detection. J. Transl. Med. 2023 21 1 118 10.1186/s12967‑023‑03960‑8 36774504
    [Google Scholar]
  38. Prognostic value of circulating tumor DNA (ctDNA) in oncogene-driven NSCLC: Current knowledge and future perspectives. Cancers 2022 14 19 4954 10.3390/cancers14194954 36230877
    [Google Scholar]
  39. Research progress of CTC, ctDNA, and EVs in cancer liquid biopsy. Front. Oncol. 2024 14 14 1303335 10.3389/fonc.2024.1303335 38333685
    [Google Scholar]
  40. Systems biology of cancer metastasis. Cell Syst. 2019 9 2 109 127 10.1016/j.cels.2019.07.003 31465728
    [Google Scholar]
  41. Circulating tumor cells: Technologies and their clinical potential in cancer metastasis. Biomedicines 2021 9 9 1111 10.3390/biomedicines9091111 34572297
    [Google Scholar]
  42. Gauging the impact of cancer treatment modalities on circulating tumor cells (CTCs). Cancers 2020 12 3 743 10.3390/cancers12030743 32245166
    [Google Scholar]
  43. Liquid biopsy: Monitoring cancer-genetics in the blood. Nat. Rev. Clin. Oncol. 2013 10 8 472 484 10.1038/nrclinonc.2013.110 23836314
    [Google Scholar]
  44. Sequence variant classification and reporting: recommendations for improving the interpretation of cancer susceptibility genetic test results. Hum. Mutat. 2008 29 11 1282 1291 10.1002/humu.20880 18951446
    [Google Scholar]
  45. Precision medicine, genomics and drug discovery. Hum. Mol. Genet. 2016 25 R2 R166 R172 10.1093/hmg/ddw246 27538422
    [Google Scholar]
  46. Current challenges of metastatic breast cancer. Cancer Metastasis Rev. 2016 35 4 495 514 10.1007/s10555‑016‑9636‑y 27933405
    [Google Scholar]
  47. Targeted therapy moves to earlier stages of non-small-cell lung cancer: Emerging evidence, controversies and future challenges. Future Oncol. 2021 17 30 4011 4025 10.2217/fon‑2020‑1255 34337973
    [Google Scholar]
  48. The function and therapeutic targeting of anaplastic lymphoma kinase (ALK) in non-small cell lung cancer (NSCLC). Mol. Cancer 2018 17 1 52 10.1186/s12943‑018‑0810‑4 29455675
    [Google Scholar]
  49. First-Line Lorlatinib or Crizotinib in advanced ALK-positive lung cancer. N. Engl. J. Med. 2020 383 21 2018 2029 10.1056/NEJMoa2027187 33207094
    [Google Scholar]
  50. Lorlatinib versus crizotinib in patients with advanced ALK-positive non-small cell lung cancer: 5-year outcomes from the Phase III CROWN study. J. Clin. Oncol. 2024 42 29 3400 3409 10.1200/JCO.24.00581 38819031
    [Google Scholar]
  51. Anti-EGFR therapy to treat metastatic colorectal cancer: Not for all. Adv. Exp. Med. Biol. 2018 1110 113 131 10.1007/978‑3‑030‑02771‑1_8 30623369
    [Google Scholar]
  52. Trastuzumab deruxtecan in patients with HER2-positive advanced colorectal cancer (DESTINY-CRC02): Primary results from a multicentre, randomised, phase 2 trial. Lancet Oncol. 2024 25 9 1147 1162 10.1016/S1470‑2045(24)00380‑2 39116902
    [Google Scholar]
  53. Deficient mismatch repair and the role of immunotherapy in metastatic colorectal cancer. Curr. Treat. Options Oncol. 2016 17 8 41 10.1007/s11864‑016‑0414‑4 27315067
    [Google Scholar]
  54. XELOX (capecitabine plus oxaliplatin) plus bevacizumab (anti-VEGF-A antibody) with or without adoptive cell immunotherapy in the treatment of patients with previously untreated metastatic colorectal cancer: A multicenter, open-label, randomized, controlled, phase 3 trial. Signal Transduct. Target. Ther. 2024 9 1 79 10.1038/s41392‑024‑01788‑2 38565886
    [Google Scholar]
  55. BRAF gene and melanoma: Back to the future. Int. J. Mol. Sci. 2021 22 7 3474 10.3390/ijms22073474 33801689
    [Google Scholar]
  56. Contribution of MEK inhibition to BRAF/MEK inhibitor combination treatment of BRAF-mutant melanoma: Part 2 of the randomized, open-label. Phase III COLUMBUS Trial. J. Clin. Oncol. 2023 41 29 4621 4631 10.1200/JCO.22.02322 37506329
    [Google Scholar]
  57. Translational research in the era of precision medicine: Where we are and where we will go. J. Pers. Med. 2021 11 3 216 10.3390/jpm11030216 33803592
    [Google Scholar]
  58. Advantages of targeting the tumor immune microenvironment over blocking immune checkpoint in cancer immunotherapy. Signal Transduct. Target. Ther. 2021 6 1 72 10.1038/s41392‑020‑00449‑4 33608497
    [Google Scholar]
  59. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat. Rev. Cancer 2019 19 3 133 150 10.1038/s41568‑019‑0116‑x 30755690
    [Google Scholar]
  60. Immune checkpoint inhibitors in cancer therapy. Curr. Oncol. 2022 29 5 3044 3060 10.3390/curroncol29050247 35621637
    [Google Scholar]
  61. Tumor mutational burden as a predictive biomarker for non-small cell lung cancer treated with immune checkpoint inhibitors of PD-1/PD-L1. Clin. Transl. Oncol. 2024 26 6 1446 1458 10.1007/s12094‑023‑03370‑8 38190035
    [Google Scholar]
  62. PD-L1, tumor mutational burden (TMB) and long-term survival in patients with non-small cell lung cancer (NSCLC) and brain metastases. J. Clin. Oncol. 2024 42 16 Suppl. 2034 2034 10.1200/JCO.2024.42.16_suppl.2034
    [Google Scholar]
  63. Engineering the next-generation of CAR T-cells with CRISPR-Cas9 gene editing. Mol. Cancer 2022 21 1 78 10.1186/s12943‑022‑01559‑z 35303871
    [Google Scholar]
  64. The journey of CAR-T therapy in hematological malignancies. Mol. Cancer 2022 21 1 194 10.1186/s12943‑022‑01663‑0 36209106
    [Google Scholar]
  65. Cholecystokinin receptor antagonist improves efficacy of chemotherapy in murine models of pancreatic cancer by altering the tumor microenvironment. Cancers 2021 13 19 4949 10.3390/cancers13194949 34638432
    [Google Scholar]
  66. Cholecystokinin receptor antagonist induces pancreatic stellate cell plasticity rendering the tumor microenvironment less oncogenic. Cancers 2023 15 10 2811 10.3390/cancers15102811 37345148
    [Google Scholar]
  67. Cholecystokinin receptor-targeted polyplex nanoparticle inhibits growth and metastasis of pancreatic cancer. Cell. Mol. Gastroenterol. Hepatol. 2018 6 1 17 32 10.1016/j.jcmgh.2018.02.013 29928669
    [Google Scholar]
  68. Heterodimerization of cholecystokinin 1 and cholecystokinin 2 receptors in gallbladder cancer: A new mechanism for carcinogenesis. J. Cancer Res. Clin. Oncol. 2023 149 10 7069 7078 10.1007/s00432‑023‑04653‑x 36871090
    [Google Scholar]
  69. Associations between LSAMP gene polymorphisms and major depressive disorder and panic disorder. Transl. Psychiatry 2012 2 8 e152 10.1038/tp.2012.74 22892717
    [Google Scholar]
  70. Long non-coding RNA LSAMP-1 is down-regulated in non-small cell lung cancer and predicts a poor prognosis. Cancer Cell Int. 2022 22 1 181 10.1186/s12935‑022‑02592‑0 35524253
    [Google Scholar]
  71. Targeting tumor microenvironment for cancer therapy. Int. J. Mol. Sci. 2019 20 4 840 10.3390/ijms20040840 30781344
    [Google Scholar]
  72. Predictive biomarkers: A paradigm shift towards personalized cancer medicine. Nat. Rev. Clin. Oncol. 2011 8 10 587 596 10.1038/nrclinonc.2011.121 21862978
    [Google Scholar]
  73. Are innovation and new technologies in precision medicine paving a new era in patients centric care? J. Transl. Med. 2019 17 1 114 10.1186/s12967‑019‑1864‑9 30953518
    [Google Scholar]
  74. The challenges of the expanded availability of genomic information: An agenda-setting paper. J. Community Genet. 2018 9 2 103 116 10.1007/s12687‑017‑0331‑7 28952070
    [Google Scholar]
  75. Precision and personalized medicine: How genomic approach improves the management of cardiovascular and neurodegenerative disease. Genes 2020 11 7 747 10.3390/genes11070747 32640513
    [Google Scholar]
  76. Advancing personalized medicine through the application of whole exome sequencing and big data analytics. Front. Genet. 2019 10 49 10.3389/fgene.2019.00049 30809243
    [Google Scholar]
  77. Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing. Nat. Biotechnol. 2021 39 9 1151 1160 10.1038/s41587‑021‑00993‑6 34504347
    [Google Scholar]
  78. The known unknown: the challenges of genetic variants of uncertain significance in clinical practice. J. Law Biosci. 2017 4 3 648 657 10.1093/jlb/lsx038 29868193
    [Google Scholar]
  79. Understanding and targeting resistance mechanisms in cancer. MedComm 2023 4 3 e265 10.1002/mco2.265 37229486
    [Google Scholar]
  80. Dynamic cancer cell heterogeneity: Diagnostic and therapeutic implications. Cancers 2022 14 2 280 10.3390/cancers14020280 35053446
    [Google Scholar]
  81. Attitudes of patients with cancer about personalized medicine and somatic genetic testing. J. Oncol. Pract 2012 8 6 329-335, 2, 335 10.1200/JOP.2012.000626 23598841
    [Google Scholar]
  82. Genomic big data and privacy: Challenges and opportunities for precision medicine. Comput. Support. Coop. Work 2016 25 2-3 115 136 10.1007/s10606‑016‑9248‑7
    [Google Scholar]
  83. Advancing precision medicine: A review of innovative in silico approaches for drug development, clinical pharmacology and personalized healthcare. Pharmaceutics 2024 16 3 332 10.3390/pharmaceutics16030332 38543226
    [Google Scholar]
/content/journals/cg/10.2174/0113892029357275250721125929
Loading
/content/journals/cg/10.2174/0113892029357275250721125929
Loading

Data & Media loading...


  • Article Type:
    Review Article
Keywords: CTCs ; immunotherapy ; biomarkers ; cancer research ; Precision medicine
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test