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Abstract

Cancer genetics plays a revolutionary role in the field of oncology by providing novel insights into tumor evolution, heterogeneity, and treatment targets. Recent advances in sequencing technology, such as whole-genome and transcriptome sequencing, have enabled the precise molecular profiling of cancers, leading to the development of targeted and individualized treatment approaches. Liquid biopsy has emerged as a non-invasive approach for collecting samples, utilizing blood or other body fluids to identify tumor cells, molecular changes, and metabolites. Liquid biopsy helps to detect various biological markers, including ctDNA, CTCs, and exosomes. Immunogenomics has revolutionized cancer management through the use of immune checkpoint inhibitors and personalized neoantigen vaccines. Furthermore, the integration of machine learning facilitates the examination of extensive genomic datasets, enabling the detection of patterns, the prediction of treatment responses, and the identification of novel therapeutic targets. Innovative drug delivery technologies, such as nanoparticle-based and CRISPR-mediated genome editing, provide promise for more effective and less harmful treatments. This review discusses recent advancements in cancer genomics, including liquid biopsy, tumor heterogeneity, immunogenomics, and the function of machine learning in the analysis of genomic data. By understanding ethical and social issues, successful implementation can be possible. The combination of genomics, immunogenomics, and liquid biopsy holds promise for novel, personalized cancer treatments, indicating a new era in cancer drug development.

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2026-02-24
2026-03-07

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References

  1. Sriharikrishnaa S. Suresh P.S. Prasada K.S. An introduction to fundamentals of cancer biology. Optical Polarimetric Modalities for Biomedical Research. Mazumder N. Kistenev Y.V. Borisova E. Prasada K.S. Cham Springer International Publishing 2023 307 330 10.1007/978‑3‑031‑31852‑8_11
    [Google Scholar]
  2. Government of india ministry of health and family welfare department of health and family welfare 2025 Available from: https://sansad.in/getFile/annex/258/AU1555.pdf?source=pqars
  3. Jobanputra V. Wrzeszczynski K.O. Buttner R. Caldas C. Cuppen E. Grimmond S. Haferlach T. Mullighan C. Schuh A. Elemento O. 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]
  4. Roepman P. de Bruijn E. van Lieshout S. Schoenmaker L. Boelens M.C. Dubbink H.J. Geurts-Giele W.R.R. Groenendijk F.H. Huibers M.M.H. Kranendonk M.E.G. Roemer M.G.M. Samsom K.G. Steehouwer M. de Leng W.W.J. Hoischen A. Ylstra B. Monkhorst K. van der Hoeven J.J.M. Cuppen E. Clinical validation of whole genome sequencing for cancer diagnostics. J. Mol. Diagn. 2021 23 7 816 833 10.1016/j.jmoldx.2021.04.011 33964451
    [Google Scholar]
  5. Wrzeszczynski K.O. Felice V. Abhyankar A. Kozon L. Geiger H. Manaa D. London F. Robinson D. Fang X. Lin D. Lamendola-Essel M.F. Khaira D. Dikoglu E. Emde A.K. Robine N. Shah M. Arora K. Basturk O. Bhanot U. Kentsis A. Mansukhani M.M. Bhagat G. Jobanputra V. Analytical validation of clinical whole-genome and transcriptome sequencing of patient-derived tumors for reporting targetable variants in cancer. J. Mol. Diagn. 2018 20 6 822 835 10.1016/j.jmoldx.2018.06.007 30138725
    [Google Scholar]
  6. Duesberg P. Li R. Fabarius A. Hehlmann R. The chromosomal basis of cancer. Cell. Oncol. 2005 27 5-6 293 318 16373963
    [Google Scholar]
  7. Stratton M.R. Campbell P.J. Futreal P.A. The cancer genome. Nature 2009 458 7239 719 724 10.1038/nature07943 19360079
    [Google Scholar]
  8. Abstracts from the 53rd european society of human genetics (eshg) conference: Interactive e-posters. Eur J. Hum. Genet 2009 28 1 141 797 (Suppl. 1) 332624852
    [Google Scholar]
  9. Kim Y. Lee S. Cho S. Park J. Chae D. Park T. Minna J.D. Kim H.H. High-throughput functional evaluation of human cancer-associated mutations using base editors. Nat. Biotechnol. 2022 40 6 874 884 10.1038/s41587‑022‑01276‑4 35411116
    [Google Scholar]
  10. Krzyszczyk P. Acevedo A. Davidoff E.J. Timmins L.M. Marrero-Berrios I. Patel M. White C. Lowe C. Sherba J.J. Hartmanshenn C. O’Neill K.M. Balter M.L. Fritz Z.R. Androulakis I.P. Schloss R.S. Yarmush M.L. The growing role of precision and personalized medicine for cancer treatment. Technology (Singap) 2018 6 03n04 79 100 10.1142/S2339547818300020 30713991
    [Google Scholar]
  11. Cardarella S. Johnson B.E. The impact of genomic changes on treatment of lung cancer. Am. J. Respir. Crit. Care Med. 2013 188 7 770 775 10.1164/rccm.201305‑0843PP 23841470
    [Google Scholar]
  12. Spreafico A. Hansen A.R. Abdul Razak A.R. Bedard P.L. Siu L.L. The future of clinical trial design in oncology. Cancer Discov. 2021 11 4 822 837 10.1158/2159‑8290.CD‑20‑1301 33811119
    [Google Scholar]
  13. Prostate cancer genomic heterogeneity. NCT02022371 2017
    [Google Scholar]
  14. Deciphering antitumour response and resistance with intratumour heterogeneity (DARWINII). NCT02314481 2025
    [Google Scholar]
  15. OFS in premenopausal node+ breast cancer with low genomic risk (interstellar). NCT05333328 2025
    [Google Scholar]
  16. Study of trastuzumab deruxtecan (T-DXd) vs investigator's choice chemotherapy in HER2-low, hormone receptor positive, metastatic breast cancer (DB-06). NCT04494425 2025
    [Google Scholar]
  17. CFI-400945 in patients with advanced/metastatic breast cancer. NCT03624543 2025
    [Google Scholar]
  18. Adjuvant PD-1 blockade for high-risk Stage-II DMMR/MSI-H colorectal cancer (smaller). NCT06520683 2024
    [Google Scholar]
  19. Doherty G.J. Petruzzelli M. Beddowes E. Ahmad S.S. Caldas C. Gilbertson R.J. Cancer treatment in the genomic era. Annu. Rev. Biochem. 2019 88 1 247 280 10.1146/annurev‑biochem‑062917‑011840 30901264
    [Google Scholar]
  20. Rivera-Concepcion J. Uprety D. Adjei A.A. Challenges in the use of targeted therapies in non–small cell lung cancer. Cancer Res. Treat. 2022 54 2 315 329 10.4143/crt.2022.078 35209703
    [Google Scholar]
  21. Chen Z. Fillmore C.M. Hammerman P.S. Kim C.F. Wong K.K. Non-small-cell lung cancers: A heterogeneous set of diseases. Nat. Rev. Cancer 2014 14 8 535 546 10.1038/nrc3775 25056707
    [Google Scholar]
  22. Satam H. Joshi K. Mangrolia U. Waghoo S. Zaidi G. Rawool S. Thakare R.P. Banday S. Mishra A.K. Das G. Malonia S.K. Next-generation sequencing technology: Current trends and advancements. Biology (Basel) 2023 12 7 997 10.3390/biology12070997 37508427
    [Google Scholar]
  23. Kamatani Y. Kaname T. Artificial intelligence in medical genomics. J. Hum. Genet. 2024 69 10 475 10.1038/s10038‑024‑01282‑1 39192016
    [Google Scholar]
  24. Avci C.B. Bagca B.G. Shademan B. Takanlou L.S. Takanlou M.S. Nourazarian A. Precision oncology: Using cancer genomics for targeted therapy advancements. Biochim. Biophys. Acta Rev. Cancer 2025 1880 1 189250 10.1016/j.bbcan.2024.189250 39701327
    [Google Scholar]
  25. Brown A.L. Li M. Goncearenco A. Panchenko A.R. Finding driver mutations in cancer: Elucidating the role of background mutational processes. PLOS Comput. Biol. 2019 15 4 e1006981 10.1371/journal.pcbi.1006981 31034466
    [Google Scholar]
  26. Gerlinger M. Rowan A.J. Horswell S. Larkin J. Endesfelder D. Gronroos E. Martinez P. Matthews N. Stewart A. Tarpey P. Varela I. Phillimore B. Begum S. McDonald N.Q. Butler A. Jones D. Raine K. Latimer C. Santos C.R. Nohadani M. Eklund A.C. Spencer-Dene B. Clark G. Pickering L. Stamp G. Gore M. Szallasi Z. Downward J. Futreal P.A. Swanton C. Swanton C. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 2012 366 10 883 892 10.1056/NEJMoa1113205 22397650
    [Google Scholar]
  27. Creighton C.J. Morgan M. Gunaratne P.H. Wheeler D.A. Gibbs R.A. Gordon Robertson A. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 2013 499 7456 43 49 [Cancer Genome Atlas Research Network.] 10.1038/nature12222 23792563
    [Google Scholar]
  28. Little S.E. Popov S. Jury A. Bax D.A. Doey L. Al-Sarraj S. Jurgensmeier J.M. Jones C. Receptor tyrosine kinase genes amplified in glioblastoma exhibit a mutual exclusivity in variable proportions reflective of individual tumor heterogeneity. Cancer Res. 2012 72 7 1614 1620 10.1158/0008‑5472.CAN‑11‑4069 22311673
    [Google Scholar]
  29. Patel A.P. Tirosh I. Trombetta J.J. Shalek A.K. Gillespie S.M. Wakimoto H. Cahill D.P. Nahed B.V. Curry W.T. Martuza R.L. Louis D.N. Rozenblatt-Rosen O. Suvà M.L. Regev A. Bernstein B.E. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 2014 344 6190 1396 1401 10.1126/science.1254257 24925914
    [Google Scholar]
  30. McLendon R. Friedman A. Bigner D. Van Meir E.G. Brat D.J.M. Mastrogianakis G. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 2008 455 7216 1061 1068 [Cancer Genome Atlas Research Network.] 10.1038/nature07385 18772890
    [Google Scholar]
  31. Janiszewska M. Liu L. Almendro V. Kuang Y. Paweletz C. Sakr R.A. Weigelt B. Hanker A.B. Chandarlapaty S. King T.A. Reis-Filho J.S. Arteaga C.L. Park S.Y. Michor F. Polyak K. In situ single-cell analysis identifies heterogeneity for PIK3CA mutation and HER2 amplification in HER2-positive breast cancer. Nat. Genet. 2015 47 10 1212 1219 10.1038/ng.3391 26301495
    [Google Scholar]
  32. Yates L.R. Gerstung M. Knappskog S. Desmedt C. Gundem G. Van Loo P. Aas T. Alexandrov L.B. Larsimont D. Davies H. Li Y. Ju Y.S. Ramakrishna M. Haugland H.K. Lilleng P.K. Nik-Zainal S. McLaren S. Butler A. Martin S. Glodzik D. Menzies A. Raine K. Hinton J. Jones D. Mudie L.J. Jiang B. Vincent D. Greene-Colozzi A. Adnet P.Y. Fatima A. Maetens M. Ignatiadis M. Stratton M.R. Sotiriou C. Richardson A.L. Lønning P.E. Wedge D.C. Campbell P.J. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat. Med. 2015 21 7 751 759 10.1038/nm.3886 26099045
    [Google Scholar]
  33. Koboldt D.C. Fulton R.S. McLellan M.D. Schmidt H. Kalicki-Veizer J. McMichael J.F. Comprehensive molecular portraits of human breast tumours. Nature 2012 490 7418 61 70 [Cancer Genome Atlas Network. 10.1038/nature11412 23000897
    [Google Scholar]
  34. Kuwai T. Nakamura T. Kim S.J. Sasaki T. Kitadai Y. Langley R.R. Fan D. Hamilton S.R. Fidler I.J. Intratumoral heterogeneity for expression of tyrosine kinase growth factor receptors in human colon cancer surgical specimens and orthotopic tumors. Am. J. Pathol. 2008 172 2 358 366 10.2353/ajpath.2008.070625 18202197
    [Google Scholar]
  35. Muzny D.M. Bainbridge M.N. Chang K. Dinh H.H. Drummond J.A. Fowler G. Comprehensive molecular characterization of human colon and rectal cancer. Nature 2012 487 7407 330 337 [Cancer Genome Atlas Network.] 10.1038/nature11252 22810696
    [Google Scholar]
  36. Tatematsu T. Sasaki H. Shimizu S. Hikosaka Y. Okuda K. Haneda H. Moriyama S. Yano M. Fujii Y. Intra-tumor heterogeneity of BRAF V600E mutation in lung adenocarcinomas. Exp. Ther. Med. 2015 9 5 1719 1722 10.3892/etm.2015.2298 26136882
    [Google Scholar]
  37. Collisson E.A. Campbell J.D. Brooks A.N. Berger A.H. Lee W. Chmielecki J. Comprehensive molecular profiling of lung adenocarcinoma. Nature 2014 511 7511 543 550 [Cancer Genome Atlas Research Network.] 10.1038/nature13385 25079552
    [Google Scholar]
  38. Mroz E.A. Tward A.D. Hammon R.J. Ren Y. Rocco J.W. Intra-tumor genetic heterogeneity and mortality in head and neck cancer: Analysis of data from the cancer genome atlas. PLoS Med. 2015 12 2 e1001786 10.1371/journal.pmed.1001786 25668320
    [Google Scholar]
  39. Lawrence M.S. Sougnez C. Lichtenstein L. Cibulskis K. Lander E. Gabriel S.B. Comprehensive genomic characterization of head and neck squamous cell carcinomas. Nature 2015 517 7536 576 582 [Cancer Genome Atlas Network.] 10.1038/nature14129 25631445
    [Google Scholar]
  40. Ramón y Cajal S. Sesé M. Capdevila C. Aasen T. De Mattos-Arruda L. Diaz-Cano S.J. Hernández-Losa J. Castellví J. Clinical implications of intratumor heterogeneity: Challenges and opportunities. J. Mol. Med. 2020 98 2 161 177 10.1007/s00109‑020‑01874‑2 31970428
    [Google Scholar]
  41. Beyes S. Bediaga N.G. Zippo A. An epigenetic perspective on intra-tumour heterogeneity: Novel insights and new challenges from multiple fields. Cancers 2021 13 19 4969 10.3390/cancers13194969 34638453
    [Google Scholar]
  42. El-Deiry W.S. Taylor B. Neal J.W. Tumor evolution, heterogeneity, and therapy for our patients with advanced cancer: How far have we come? Am. Soc. Clin. Oncol. Educ. Book 2017 37 37 e8 e15 10.1200/EDBK_175524 28746017
    [Google Scholar]
  43. E Sabaawy H. Genetic heterogeneity and clonal evolution of tumor cells and their impact on precision cancer medicine. J. Leuk. 2013 1 4 1000124 10.4172/2329‑6917.1000124 24558642
    [Google Scholar]
  44. Pucci C. Martinelli C. Ciofani G. Innovative approaches for cancer treatment: Current perspectives and new challenges. ecancermedicalscience 2019 13 961 10.3332/ecancer.2019.961 31537986
    [Google Scholar]
  45. Casagrande G.M.S. Silva M.O. Reis R.M. Leal L.F. Liquid biopsy for lung cancer: Up-to-date and perspectives for screening programs. Int. J. Mol. Sci. 2023 24 3 2505 10.3390/ijms24032505 36768828
    [Google Scholar]
  46. Li W. Liu J.B. Hou L.K. Yu F. Zhang J. Wu W. Tang X.M. Sun F. Lu H.M. Deng J. Bai J. Li J. Wu C.Y. Lin Q.L. Lv Z.W. Wang G.R. Jiang G.X. Ma Y.S. Fu D. Liquid biopsy in lung cancer: Significance in diagnostics, prediction, and treatment monitoring. Mol. Cancer 2022 21 1 25 10.1186/s12943‑022‑01505‑z 35057806
    [Google Scholar]
  47. Lone S.N. Nisar S. Masoodi T. Singh M. Rizwan A. Hashem S. El-Rifai W. Bedognetti D. Batra S.K. Haris M. Bhat A.A. Macha M.A. Liquid biopsy: A step closer to transform diagnosis, prognosis and future of cancer treatments. Mol. Cancer 2022 21 1 79 10.1186/s12943‑022‑01543‑7 35303879
    [Google Scholar]
  48. Ma L. Guo H. Zhao Y. Liu Z. Wang C. Bu J. Sun T. Wei J. Liquid biopsy in cancer: Current status, challenges and future prospects. Signal Transduct. Target. Ther. 2024 9 1 336 10.1038/s41392‑024‑02021‑w 39617822
    [Google Scholar]
  49. Nikanjam M. Kato S. Kurzrock R. Liquid biopsy: Current technology and clinical applications. J. Hematol. Oncol. 2022 15 1 131 10.1186/s13045‑022‑01351‑y 36096847
    [Google Scholar]
  50. Deng Z. Wu S. Wang Y. Shi D. Circulating tumor cell isolation for cancer diagnosis and prognosis. EBioMedicine 2022 83 104237 10.1016/j.ebiom.2022.104237 36041264
    [Google Scholar]
  51. Hu X. Zang X. Lv Y. Detection of circulating tumor cells: Advances and critical concerns.(Review) Oncol. Lett. 2021 21 5 422 10.3892/ol.2021.12683 33850563
    [Google Scholar]
  52. Marcuello M. Vymetalkova V. Neves R.P.L. Duran-Sanchon S. Vedeld H.M. Tham E. van Dalum G. Flügen G. Garcia-Barberan V. Fijneman R.J.A. Castells A. Vodicka P. Lind G.E. Stoecklein N.H. Heitzer E. Gironella M. Circulating biomarkers for early detection and clinical management of colorectal cancer. Mol. Aspects Med. 2019 69 107 122 10.1016/j.mam.2019.06.002 31189073
    [Google Scholar]
  53. Lozar T. Jesenko T. Kloboves Prevodnik V. Cemazar M. Hosta V. Jericevic A. Nolde N. Grasic Kuhar C. Preclinical and clinical evaluation of magnetic-activated cell separation technology for CTC isolation in breast cancer. Front. Oncol. 2020 10 554554 10.3389/fonc.2020.554554 33042837
    [Google Scholar]
  54. Petrik J. Verbanac D. Fabijanec M. Hulina-Tomašković A. Čeri A. Somborac-Bačura A. Petlevski R. Grdić Rajković M. Rumora L. Krušlin B. Štefanović M. Ljubičić N. Baršić N. Hanžek A. Bočkor L. Ćelap I. Demirović A. Barišić K. Circulating tumor cells in colorectal cancer: Detection systems and clinical utility. Int. J. Mol. Sci. 2022 23 21 13582 10.3390/ijms232113582 36362369
    [Google Scholar]
  55. Zhang Y. Liu Y. Liu H. Tang W.H. Exosomes: Biogenesis, biologic function and clinical potential. Cell Biosci. 2019 9 1 19 10.1186/s13578‑019‑0282‑2 30815248
    [Google Scholar]
  56. Henderson M.C. Azorsa D.O. The genomic and proteomic content of cancer cell-derived exosomes. Front. Oncol. 2012 2 38 10.3389/fonc.2012.00038 22649786
    [Google Scholar]
  57. Martinez-Arroyo O. Selma-Soriano E. Ortega A. Cortes R. Redon J. Small rab GTPases in intracellular vesicle trafficking: The case of Rab3A/Raphillin-3A complex in the kidney. Int. J. Mol. Sci. 2021 22 14 7679 10.3390/ijms22147679 34299299
    [Google Scholar]
  58. Li Y.Z. Kong S.N. Liu Y.P. Yang Y. Zhang H.M. Can liquid biopsy based on ctDNA/cfDNA replace tissue biopsy for the precision treatment of EGFR-Mutated NSCLC? J. Clin. Med. 2023 12 4 1438 10.3390/jcm12041438 36835972
    [Google Scholar]
  59. De Rubis G. Rajeev Krishnan S. Bebawy M. Liquid biopsies in cancer diagnosis, monitoring, and prognosis. Trends Pharmacol. Sci. 2019 40 3 172 186 10.1016/j.tips.2019.01.006 30736982
    [Google Scholar]
  60. Galoș D. Gorzo A. Balacescu O. Sur D. Clinical applications of liquid biopsy in colorectal cancer screening: Current challenges and future perspectives. Cells 2022 11 21 3493 10.3390/cells11213493 36359889
    [Google Scholar]
  61. Dao J. Conway P.J. Subramani B. Meyyappan D. Russell S. Mahadevan D. Using cfDNA and ctDNA as oncologic markers: A path to clinical validation. Int. J. Mol. Sci. 2023 24 17 13219 10.3390/ijms241713219 37686024
    [Google Scholar]
  62. Campos-Carrillo A. Weitzel J.N. Sahoo P. Rockne R. Mokhnatkin J.V. Murtaza M. Gray S.W. Goetz L. Goel A. Schork N. Slavin T.P. Circulating tumor DNA as an early cancer detection tool. Pharmacol. Ther. 2020 207 107458 10.1016/j.pharmthera.2019.107458 31863816
    [Google Scholar]
  63. Hitchins M.P. Methylated circulating tumor DNA biomarkers for the blood-based detection of cancer signals. epigenetics in precision medicine. Epigenetics in Precision Medicine. Elsevier 2022 471 512 10.1016/B978‑0‑12‑823008‑4.00001‑9
    [Google Scholar]
  64. Lin C. Liu X. Zheng B. Ke R. Tzeng C.M. Liquid biopsy, ctDNA diagnosis through NGS. Life 2021 11 9 890 10.3390/life11090890 34575039
    [Google Scholar]
  65. Zhao H. Chen K.Z. Hui B.G. Zhang K. Yang F. Wang J. Role of circulating tumor DNA in the management of early‐stage lung cancer. Thorac. Cancer 2018 9 5 509 515 10.1111/1759‑7714.12622 29528556
    [Google Scholar]
  66. Arisi M.F. Dotan E. Fernandez S.V. Circulating tumor DNA in precision oncology and its applications in colorectal cancer. Int. J. Mol. Sci. 2022 23 8 4441 10.3390/ijms23084441 35457259
    [Google Scholar]
  67. Motobayashi H. Kitahata Y. Okada K. Miyazawa M. Ueno M. Hayami S. Miyamoto A. Shimizu A. Sato M. Yoshimura T. Nakamura Y. Takemoto N. Nakai T. Hyo T. Matsumoto K. Yamaue H. Kawai M. Short-term serial circulating tumor DNA assessment predicts therapeutic efficacy for patients with advanced pancreatic cancer. J. Cancer Res. Clin. Oncol. 2024 150 2 35 10.1007/s00432‑023‑05594‑1 38277079
    [Google Scholar]
  68. Singh A.P. Cheng H. Guo X. Levy B. Halmos B. Circulating tumor DNA in non–small-cell lung cancer: A primer for the clinician. JCO Precis. Oncol. 2017 1 1 1 13 10.1200/PO.17.00054 35172511
    [Google Scholar]
  69. Khatami F. Tavangar S.M. Circulating tumor DNA (ctDNA) in the era of personalized cancer therapy. J. Diabetes Metab. Disord. 2018 17 1 19 30 10.1007/s40200‑018‑0334‑x 30288382
    [Google Scholar]
  70. Veldore V.H. Choughule A. Routhu T. Mandloi N. Noronha V. Joshi A. Dutt A. Gupta R. Vedam R. Prabhash K. Validation of liquid biopsy: Plasma cell-free DNA testing in clinical management of advanced non-small cell lung cancer. Lung Cancer 2018 9 1 11 10.2147/LCTT.S147841 29379323
    [Google Scholar]
  71. Kunnath A.P. Priyashini T. Potential applications of circulating tumor DNA technology as a cancer diagnostic tool. Cureus 2019 11 6 e4907 10.7759/cureus.4907 31423385
    [Google Scholar]
  72. Wang L. Wen X. Yang Y. Hu Z. Jiang J. Duan L. Liao X. He Y. Liu Y. Wang J. Liang Z. Zhu X. Liu Q. Liu T. Luo D. CRISPR/Cas13a-based supersensitive circulating tumor DNA assay for detecting EGFR mutations in plasma. Commun. Biol. 2024 7 1 657 10.1038/s42003‑024‑06368‑2 38806596
    [Google Scholar]
  73. Kamps R. Brandão R. Bosch B. Paulussen A. Xanthoulea S. Blok M. Romano A. Next-generation sequencing in oncology: Genetic diagnosis, risk prediction and cancer classification. Int. J. Mol. Sci. 2017 18 2 308 10.3390/ijms18020308 28146134
    [Google Scholar]
  74. Jiang S. Liu Y. Xu Y. Sang X. Lu X. Research on liquid biopsy for cancer: A bibliometric analysis. Heliyon 2023 9 3 e14145 10.1016/j.heliyon.2023.e14145 36915518
    [Google Scholar]
  75. Sivapalan L. Murray J.C. Canzoniero J.V. Landon B. Jackson J. Scott S. Lam V. Levy B.P. Sausen M. Anagnostou V. Liquid biopsy approaches to capture tumor evolution and clinical outcomes during cancer immunotherapy. J. Immunother. Cancer 2023 11 1 e005924 10.1136/jitc‑2022‑005924 36657818
    [Google Scholar]
  76. Fan J. Slowikowski K. Zhang F. Single-cell transcriptomics in cancer: Computational challenges and opportunities. Exp. Mol. Med. 2020 52 9 1452 1465 10.1038/s12276‑020‑0422‑0 32929226
    [Google Scholar]
  77. Addala V. Newell F. Pearson J.V. Redwood A. Robinson B.W. Creaney J. Computational immunogenomic approaches to predict response to cancer immunotherapies. Nat. Rev. Clin. Oncol. 2023 1 19 37907723
    [Google Scholar]
  78. Zhang T. Kurban E. Wang Z. Neoantigens: The novel precision cancer immunotherapy. Biologics 2023 3 4 321 334 10.3390/biologics3040017
    [Google Scholar]
  79. Attrill G.H. Ferguson P.M. Palendira U. Long G.V. Wilmott J.S. Scolyer R.A. The tumour immune landscape and its implications in cutaneous melanoma. Pigment Cell Melanoma Res. 2021 34 3 529 549 10.1111/pcmr.12926 32939993
    [Google Scholar]
  80. Yue T. Wang Y. Zhang L. Gu C. Xue H. Wang W. Lyu Q. Dun Y. Deep learning for genomics: A concise overview. arXiv 2018 1 10.48550/arXiv.1802.00810
    [Google Scholar]
  81. Widschwendter M. Jones A. Evans I. Reisel D. Dillner J. Sundström K. Steyerberg E.W. Vergouwe Y. Wegwarth O. Rebitschek F.G. Siebert U. Sroczynski G. de Beaufort I.D. Bolt I. Cibula D. Zikan M. Bjørge L. Colombo N. Harbeck N. Dudbridge F. Tasse A.M. Knoppers B.M. Joly Y. Teschendorff A.E. Pashayan N. Epigenome-based cancer risk prediction: Rationale, opportunities and challenges. Nat. Rev. Clin. Oncol. 2018 15 5 292 309 10.1038/nrclinonc.2018.30 29485132
    [Google Scholar]
  82. Pfeifer G.P. Mechanisms of UV-induced mutations and skin cancer. Genome Instab Disease 2020 1 3 99 113 10.1007/s42764‑020‑00009‑8 34589668
    [Google Scholar]
  83. Dammeijer F. Lau S.P. van Eijck C.H.J. van der Burg S.H. Aerts J.G.J.V. Rationally combining immunotherapies to improve efficacy of immune checkpoint blockade in solid tumors. Cytokine Growth Factor Rev. 2017 36 5 15 10.1016/j.cytogfr.2017.06.011 28693973
    [Google Scholar]
  84. Ziogas D.C. Theocharopoulos C. Lialios P.P. Foteinou D. Koumprentziotis I.A. Xynos G. Gogas H. Beyond CTLA-4 and PD-1 inhibition: Novel immune checkpoint molecules for melanoma treatment. Cancers 2023 15 10 2718 10.3390/cancers15102718 37345056
    [Google Scholar]
  85. Punekar S.R. Shum E. Grello C.M. Lau S.C. Velcheti V. Immunotherapy in non-small cell lung cancer: Past, present, and future directions. Front. Oncol. 2022 12 877594 10.3389/fonc.2022.877594 35992832
    [Google Scholar]
  86. Rother C. John T. Wong A. Biomarkers for immunotherapy resistance in non-small cell lung cancer. Front. Oncol. 2024 14 1489977 10.3389/fonc.2024.1489977 39749035
    [Google Scholar]
  87. Ott P.A. Hu Z. Keskin D.B. Shukla S.A. Sun J. Bozym D.J. Zhang W. Luoma A. Giobbie-Hurder A. Peter L. Chen C. Olive O. Carter T.A. Li S. Lieb D.J. Eisenhaure T. Gjini E. Stevens J. Lane W.J. Javeri I. Nellaiappan K. Salazar A.M. Daley H. Seaman M. Buchbinder E.I. Yoon C.H. Harden M. Lennon N. Gabriel S. Rodig S.J. Barouch D.H. Aster J.C. Getz G. Wucherpfennig K. Neuberg D. Ritz J. Lander E.S. Fritsch E.F. Hacohen N. Wu C.J. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 2017 547 7662 217 221 10.1038/nature22991 28678778
    [Google Scholar]
  88. Abelin J.G. Keskin D.B. Sarkizova S. Hartigan C.R. Zhang W. Sidney J. Stevens J. Lane W. Zhang G.L. Eisenhaure T.M. Clauser K.R. Hacohen N. Rooney M.S. Carr S.A. Wu C.J. Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction. Immunity 2017 46 2 315 326 10.1016/j.immuni.2017.02.007 28228285
    [Google Scholar]
  89. Connolly K.A. Fitzgerald B. Damo M. Joshi N.S. Novel mouse models for cancer immunology. Annu. Rev. Cancer Biol. 2022 6 1 269 291 10.1146/annurev‑cancerbio‑070620‑105523 36875867
    [Google Scholar]
  90. Filipovic A. Miller G. Bolen J. Progress toward identifying exact proxies for predicting response to immunotherapies. Front. Cell Dev. Biol. 2020 8 155 10.3389/fcell.2020.00155 32258034
    [Google Scholar]
  91. Fu C. Jiang A. Dendritic cells and CD8 T cell immunity in tumor microenvironment. Front. Immunol. 2018 9 3059 10.3389/fimmu.2018.03059 30619378
    [Google Scholar]
  92. Li S. Simoni Y. Zhuang S. Gabel A. Ma S. Chee J. Islas L. Cessna A. Creaney J. Bradley R.K. Redwood A. Robinson B.W. Newell E.W. Characterization of neoantigen-specific T cells in cancer resistant to immune checkpoint therapies. Proc. Natl. Acad. Sci. USA 2021 118 30 e2025570118 10.1073/pnas.2025570118 34285073
    [Google Scholar]
  93. Kiyotani K. Toyoshima Y. Nakamura Y. Immunogenomics in personalized cancer treatments. J. Hum. Genet. 2021 66 9 901 907 10.1038/s10038‑021‑00950‑w 34193979
    [Google Scholar]
  94. Bracci L. Fragale A. Gabriele L. Moschella F. Towards a systems immunology approach to unravel responses to cancer immunotherapy. Front. Immunol. 2020 11 582744 10.3389/fimmu.2020.582744 33193392
    [Google Scholar]
  95. Yang J. Zhao S. Wang J. Sheng Q. Liu Q. Shyr Y. A pan-cancer immunogenomic atlas for immune checkpoint blockade immunotherapy. Cancer Res. 2021 82 4 539 542 10.1158/0008‑5472.CAN‑21‑2335 34903605
    [Google Scholar]
  96. Mukherjee S. Genomics-guided immunotherapy for precision medicine in cancer. Cancer Biother. Radiopharm. 2019 34 8 487 497 10.1089/cbr.2018.2758 31314580
    [Google Scholar]
  97. Wolf Y. Samuels Y. Cancer research in the era of immunogenomics. ESMO Open 2018 3 7 e000475 10.1136/esmoopen‑2018‑000475 30622743
    [Google Scholar]
  98. Chen S. Yu J. Chamouni S. Wang Y. Li Y. Integrating machine learning and artificial intelligence in life-course epidemiology: Pathways to innovative public health solutions. BMC Med. 2024 22 1 354 10.1186/s12916‑024‑03566‑x 39218895
    [Google Scholar]
  99. Nilius H. Tsouka S. Nagler M. Masoodi M. Machine learning applications in precision medicine: Overcoming challenges and unlocking potential. Trends Analyt. Chem. 2024 179 117872 10.1016/j.trac.2024.117872
    [Google Scholar]
  100. Reel P.S. Reel S. Pearson E. Trucco E. Jefferson E. Using machine learning approaches for multi-omics data analysis: A review. Biotechnol. Adv. 2021 49 107739 10.1016/j.biotechadv.2021.107739 33794304
    [Google Scholar]
  101. Koumakis L. Deep learning models in genomics; are we there yet? Comput. Struct. Biotechnol. J. 2020 18 1466 1473 10.1016/j.csbj.2020.06.017 32637044
    [Google Scholar]
  102. Johnson K.B. Wei W.Q. Weeraratne D. Frisse M.E. Misulis K. Rhee K. Zhao J. Snowdon J.L. Precision medicine, AI, and the future of personalized health care. Clin. Transl. Sci. 2021 14 1 86 93 10.1111/cts.12884 32961010
    [Google Scholar]
  103. Allen T.A. The role of circulating tumor cells as a liquid biopsy for cancer: advances, biology, technical challenges, and clinical relevance. Cancers 2024 16 7 1377 10.3390/cancers16071377 38611055
    [Google Scholar]
  104. Ahmad R.M. Ali B.R. Al-Jasmi F. Sinnott R.O. Al Dhaheri N. Mohamad M.S. A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer. Brief. Bioinform. 2023 25 1 bbad479 10.1093/bib/bbad479 38149678
    [Google Scholar]
  105. Hunt C. Montgomery S. Berkenpas J.W. Sigafoos N. Oakley J.C. Espinosa J. Justice N. Kishaba K. Hippe K. Si D. Hou J. Ding H. Cao R. Recent progress of machine learning in gene therapy. Curr. Gene Ther. 2022 22 2 132 143 10.2174/1566523221666210622164133 34161210
    [Google Scholar]
  106. Vikal A. Maurya R. Bhowmik S. Patel P. Singh R. Gupta G.D. Kurmi B.D. Precision genome editing: The synergy of CRISPR and nanotechnology in cancer treatment. Curr. Cancer Ther. Rev. 2025 21 3 265 277 10.2174/0115733947290474240315062214
    [Google Scholar]
  107. Sadique M.A. Yadav S. Ranjan P. Verma S. Salammal S.T. Khan M.A. Kaushik A. Khan R. High-performance antiviral nano-systems as a shield to inhibit viral infections: SARS-CoV-2 as a model case study. J. Mater. Chem. B Mater. Biol. Med. 2021 9 23 4620 4642 10.1039/D1TB00472G 34027540
    [Google Scholar]
  108. Quazi S. Retracted article: Artificial intelligence and machine learning in precision and genomic medicine. Med. Oncol. 2022 39 8 120 10.1007/s12032‑022‑01711‑1 35704152
    [Google Scholar]
  109. Dias R. Torkamani A. Artificial intelligence in clinical and genomic diagnostics. Genome Med. 2019 11 1 70 10.1186/s13073‑019‑0689‑8 31744524
    [Google Scholar]
  110. Vamathevan J. Clark D. Czodrowski P. Dunham I. Ferran E. Lee G. Li B. Madabhushi A. Shah P. Spitzer M. Zhao S. Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discov. 2019 18 6 463 477 10.1038/s41573‑019‑0024‑5 30976107
    [Google Scholar]
  111. Vuong N.A. Mai T.T. Unveiling the synergy: Exploring the intersection of AI and NLP in redefining modern marketing for enhanced consumer engagement and strategy optimization. Q J. Emerg. Technol Innov. 2023 8 3 103 118 10.29099/ijair.v7i2.1147
    [Google Scholar]
  112. Waqaas M Afzal U Shehzad A Anwar U Salman M The machine learning revolution in biology, microscopy, and innovative technology. preprint 2023 10.13140/RG.2.2.16511.59044
    [Google Scholar]
  113. Crossa J. Montesinos-Lopez O.A. Costa-Neto G. Vitale P. Martini J.W.R. Runcie D. Fritsche-Neto R. Montesinos-Lopez A. Pérez-Rodríguez P. Gerard G. Dreisigacker S. Crespo-Herrera L. Pierre C.S. Lillemo M. Cuevas J. Bentley A. Ortiz R. Machine learning algorithms translate big data into predictive breeding accuracy. Trends Plant Sci. 2025 30 2 167 184 10.1016/j.tplants.2024.09.011 39462718
    [Google Scholar]
  114. Arjmand B. Alavi-Moghadam S. Parhizkar-Roudsari P. Rezaei-Tavirani M. Tayanloo-Beik A. Goodarzi P. Mehrdad N. Mohamadi-Jahani F. Larijani B. Metabolomics signatures of SARS-CoV-2 infection. Adv. Exp. Med. Biol. 2022 1376 45 59 10.1007/5584_2021_674 34735713
    [Google Scholar]
  115. Hosseinkhani S. Arjmand B. Bandarian F. Aazami H. Hadizadeh N. Najjar N. Pasalar P. Razi F. Omics experiments in iran, a review in endocrine and metabolism disorders studies. J. Diabetes Metab. Disord. 2021 23 2 1539 1544 10.1007/s40200‑021‑00727‑0 39610495
    [Google Scholar]
  116. Tayanloo-Beik A. Sarvari M. Payab M. Gilany K. Alavi-Moghadam S. Gholami M. Goodarzi P. Larijani B. Arjmand B. OMICS insights into cancer histology; metabolomics and proteomics approach. Clin. Biochem. 2020 84 13 20 10.1016/j.clinbiochem.2020.06.008 32589887
    [Google Scholar]
  117. Menyhárt O. Győrffy B. Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis. Comput. Struct. Biotechnol. J. 2021 19 949 960 10.1016/j.csbj.2021.01.009 33613862
    [Google Scholar]
  118. Arjmand B. Hamidpour S.K. Tayanloo-Beik A. Goodarzi P. Aghayan H.R. Adibi H. Larijani B. Machine learning: A new prospect in multi-omics data analysis of cancer. Front. Genet. 2022 13 824451 10.3389/fgene.2022.824451 35154283
    [Google Scholar]
  119. Chakraborty S. Sharma G. Karmakar S. Banerjee S. Multi-OMICS approaches in cancer biology: New era in cancer therapy. Biochim. Biophys. Acta Mol. Basis Dis. 2024 1870 5 167120 10.1016/j.bbadis.2024.167120 38484941
    [Google Scholar]
  120. Tran B. Dancey J.E. Kamel-Reid S. McPherson J.D. Bedard P.L. Brown A.M.K. Zhang T. Shaw P. Onetto N. Stein L. Hudson T.J. Neel B.G. Siu L.L. Cancer genomics: Technology, discovery, and translation. J. Clin. Oncol. 2012 30 6 647 660 10.1200/JCO.2011.39.2316 22271477
    [Google Scholar]
  121. Mardis E.R. The impact of next-generation sequencing on cancer genomics: From discovery to clinic. Cold Spring Harb. Perspect. Med. 2019 9 9 a036269 10.1101/cshperspect.a036269 30397020
    [Google Scholar]
  122. Tam V. Patel N. Turcotte M. Bossé Y. Paré G. Meyre D. Benefits and limitations of genome-wide association studies. Nat. Rev. Genet. 2019 20 8 467 484 10.1038/s41576‑019‑0127‑1 31068683
    [Google Scholar]
  123. Sugita S. Enokida H. Yoshino H. Miyamoto K. Yonemori M. Sakaguchi T. Osako Y. Nakagawa M. HRAS as a potential therapeutic target of salirasib RAS inhibitor in bladder cancer. Int. J. Oncol. 2018 53 2 725 736 10.3892/ijo.2018.4435 29901113
    [Google Scholar]
  124. Thomas R. Weihua Z. Rethink of EGFR in cancer with its kinase independent function on board. Front. Oncol. 2019 9 800 10.3389/fonc.2019.00800 31508364
    [Google Scholar]
  125. Yang J. Nie J. Ma X. Wei Y. Peng Y. Wei X. Targeting PI3K in cancer: Mechanisms and advances in clinical trials. Mol. Cancer 2019 18 1 26 10.1186/s12943‑019‑0954‑x 30782187
    [Google Scholar]
  126. Singh D. Dhiman V.K. Pandey M. Dhiman V.K. Sharma A. Pandey H. Verma S.K. Pandey R. Personalized medicine: An alternative for cancer treatment. Cancer Treat Res. Commun 2024 42 100860 10.1016/j.ctarc.2024.100860 39827574
    [Google Scholar]
  127. Su J. Yang L. Sun Z. Zhan X. Personalized drug therapy: Innovative concept guided with proteoformics. Mol. Cell. Proteomics 2024 23 3 100737 10.1016/j.mcpro.2024.100737 38354979
    [Google Scholar]
  128. Lee Y.T. Tan Y.J. Oon C.E. Molecular targeted therapy: Treating cancer with specificity. Eur. J. Pharmacol. 2018 834 188 196 10.1016/j.ejphar.2018.07.034 30031797
    [Google Scholar]
  129. Stern, HM Improving treatment of HER2-positive cancers: Opportunities and challenges. Sci. Transl. Med. 2012 4 127 127rv2 10.1126/scitranslmed.3001539 22461643
    [Google Scholar]
  130. Guo P. Yang J. Huang J. Auguste D.T. Moses M.A. Therapeutic genome editing of triple-negative breast tumors using a noncationic and deformable nanolipogel. Proc. Natl. Acad. Sci. USA 2019 116 37 18295 18303 10.1073/pnas.1904697116 31451668
    [Google Scholar]
  131. Sun B. Wu W. Narasipura E.A. Ma Y. Yu C. Fenton O.S. Song H. Engineering nanoparticle toolkits for mRNA delivery. Adv. Drug Deliv. Rev. 2023 200 115042 10.1016/j.addr.2023.115042 37536506
    [Google Scholar]
  132. Chehelgerdi M. Chehelgerdi M. Allela O.Q.B. Pecho R.D.C. Jayasankar N. Rao D.P. Thamaraikani T. Vasanthan M. Viktor P. Lakshmaiya N. Saadh M.J. Amajd A. Abo-Zaid M.A. Castillo-Acobo R.Y. Ismail A.H. Amin A.H. Akhavan-Sigari R. Progressing nanotechnology to improve targeted cancer treatment: overcoming hurdles in its clinical implementation. Mol. Cancer 2023 22 1 169 10.1186/s12943‑023‑01865‑0 37814270
    [Google Scholar]
  133. Zhong L. Li Y. Xiong L. Wang W. Wu M. Yuan T. Yang W. Tian C. Miao Z. Wang T. Yang S. Small molecules in targeted cancer therapy: Advances, challenges, and future perspectives. Signal Transduct. Target. Ther. 2021 6 1 201 10.1038/s41392‑021‑00572‑w 34054126
    [Google Scholar]
  134. Nayerossadat N. Maedeh T. Ali P. Viral and nonviral delivery systems for gene delivery. Adv. Biomed. Res. 2012 1 1 27 10.4103/2277‑9175.98152 23210086
    [Google Scholar]
  135. Jahangir M.A. Mohanty D. Choudhury A. Imam S.S. Theranostic applications of functionalized vesicular carriers: theranostic applications of functionalized vesicular carriers (liposomes, niosomes, virosomes, ethosomes, phytosomes). multifunctional and targeted theranostic nanomedicines: Formulation, design and applications. Multifunctional And Targeted Theranostic Nanomedicines. Springer 2023 49 76 10.1007/978‑981‑99‑0538‑6_3
    [Google Scholar]
  136. Zhang W. Sadeghi A. Karaca A.C. Zhang J. Jafari S.M. Carbohydrate polymer-based carriers for colon targeted delivery of probiotics. Crit. Rev. Food Sci. Nutr. 2023 64 33 12759 12779 10.1080/10408398.2023.2257321 37702799
    [Google Scholar]
  137. Liu B.Y. He X.Y. Xu C. Xu L. Ai S.L. Cheng S.X. Zhuo R.X. A dual-targeting delivery system for effective genome editing and in situ detecting related protein expression in edited cells. Biomacromolecules 2018 19 7 2957 2968 10.1021/acs.biomac.8b00511
    [Google Scholar]
  138. Jin H. Wang L. Bernards R. Rational combinations of targeted cancer therapies: Background, advances and challenges. Nat. Rev. Drug Discov. 2023 22 3 213 234 10.1038/s41573‑022‑00615‑z 36509911
    [Google Scholar]
  139. Ye J. Wu J. Liu B. Therapeutic strategies of dual-target small molecules to overcome drug resistance in cancer therapy. Biochim. Biophys. Acta Rev. Cancer 2023 1878 3 188866 10.1016/j.bbcan.2023.188866 36842765
    [Google Scholar]
  140. Jiang H. Zuo J. Li B. Chen R. Luo K. Xiang X. Lu S. Huang C. Liu L. Tang J. Gao F. Drug-induced oxidative stress in cancer treatments: Angel or devil? Redox Biol. 2023 63 102754 10.1016/j.redox.2023.102754 37224697
    [Google Scholar]
  141. Park S. Olsen S. Ku B.M. Lee M.S. Jung H.A. Sun J.M. Lee S.H. Ahn J.S. Park K. Choi Y.L. Ahn M.J. High concordance of actionable genomic alterations identified between circulating tumor dna–based and tissue‐based next‐generation sequencing testing in advanced non–small cell lung cancer: The korean lung liquid versus invasive biopsy program. Cancer 2021 127 16 3019 3028 10.1002/cncr.33571 33826761
    [Google Scholar]
  142. Johanns TM Bowman-Kirigin JA Liu C Dunn GP Targeting neoantigens in glioblastoma: An overview of cancer immunogenomics and translational implications. Neurosurgery 2017 64 CN_suppl_1 165 176 10.1093/neuros/nyx321 28899059
    [Google Scholar]
  143. Mollanoori H. Shahraki H. Rahmati Y. Teimourian S. CRISPR/Cas9 and CAR-T cell, collaboration of two revolutionary technologies in cancer immunotherapy, an instruction for successful cancer treatment. Hum. Immunol. 2018 79 12 876 882 10.1016/j.humimm.2018.09.007 30261221
    [Google Scholar]
  144. Berger M.F. Mardis E.R. The emerging clinical relevance of genomics in cancer medicine. Nat. Rev. Clin. Oncol. 2018 15 6 353 365 10.1038/s41571‑018‑0002‑6 29599476
    [Google Scholar]
  145. Liu M.C. Oxnard G.R. Klein E.A. Swanton C. Seiden M.V. Liu M.C. Oxnard G.R. Klein E.A. Smith D. Richards D. Yeatman T.J. Cohn A.L. Lapham R. Clement J. Parker A.S. Tummala M.K. McIntyre K. Sekeres M.A. Bryce A.H. Siegel R. Wang X. Cosgrove D.P. Abu-Rustum N.R. Trent J. Thiel D.D. Becerra C. Agrawal M. Garbo L.E. Giguere J.K. Michels R.M. Harris R.P. Richey S.L. McCarthy T.A. Waterhouse D.M. Couch F.J. Wilks S.T. Krie A.K. Balaraman R. Restrepo A. Meshad M.W. Rieger-Christ K. Sullivan T. Lee C.M. Greenwald D.R. Oh W. Tsao C-K. Fleshner N. Kennecke H.F. Khalil M.F. Spigel D.R. Manhas A.P. Ulrich B.K. Kovoor P.A. Stokoe C. Courtright J.G. Yimer H.A. Larson T.G. Swanton C. Seiden M.V. Cummings S.R. Absalan F. Alexander G. Allen B. Amini H. Aravanis A.M. Bagaria S. Bazargan L. Beausang J.F. Berman J. Betts C. Blocker A. Bredno J. Calef R. Cann G. Carter J. Chang C. Chawla H. Chen X. Chien T.C. Civello D. Davydov K. Demas V. Desai M. Dong Z. Fayzullina S. Fields A.P. Filippova D. Freese P. Fung E.T. Gnerre S. Gross S. Halks-Miller M. Hall M.P. Hartman A-R. Hou C. Hubbell E. Hunkapiller N. Jagadeesh K. Jamshidi A. Jiang R. Jung B. Kim T.H. Klausner R.D. Kurtzman K.N. Lee M. Lin W. Lipson J. Liu H. Liu Q. Lopatin M. Maddala T. Maher M.C. Melton C. Mich A. Nautiyal S. Newman J. Newman J. Nicula V. Nicolaou C. Nikolic O. Pan W. Patel S. Prins S.A. Rava R. Ronaghi N. Sakarya O. Satya R.V. Schellenberger J. Scott E. Sehnert A.J. Shaknovich R. Shanmugam A. Shashidhar K.C. Shen L. Shenoy A. Shojaee S. Singh P. Steffen K.K. Tang S. Toung J.M. Valouev A. Venn O. Williams R.T. Wu T. Xu H.H. Yakym C. Yang X. Yecies J. Yip A.S. Youngren J. Yue J. Zhang J. Zhang L. Zhang L.Q. Zhang N. Curtis C. Berry D.A. Sensitive and specific multi-cancer detection and localization using methylation signatures in cell-free DNA. Ann. Oncol. 2020 31 6 745 759 10.1016/j.annonc.2020.02.011 33506766
    [Google Scholar]
  146. Hoang L.N. Gilks B.C. Hereditary breast and ovarian cancer syndrome: Moving beyond BRCA1 and BRCA2. Adv. Anat. Pathol. 2018 25 2 85 95 10.1097/PAP.0000000000000177 28914618
    [Google Scholar]
  147. Wang S. Jiang X. Singh S. Marmor R. Bonomi L. Fox D. Dow M. Ohno-Machado L. Genome privacy: Challenges, technical approaches to mitigate risk, and ethical considerations in the united states. Ann. N. Y. Acad. Sci. 2017 1387 1 73 83 10.1111/nyas.13259 27681358
    [Google Scholar]
  148. Storm C. Agarwal R. Offit K. Ethical and legal implications of cancer genetic testing: do physicians have a duty to warn patients’ relatives about possible genetic risks? J. Oncol. Pract. 2008 4 5 229 230 10.1200/JOP.0858504 20856700
    [Google Scholar]
  149. Spector-Bagdady K. Governing secondary research use of health data and specimens: The inequitable distribution of regulatory burden between federally funded and industry research. J. Law Biosci. 2021 8 1 lsab008 10.1093/jlb/lsab008 34055367
    [Google Scholar]
  150. Smit A.K. Gokoolparsadh A. McWhirter R. Newett L. Milch V. Hermes A. McInerney-Leo A. Newson A.J. Ethical, legal, and social issues related to genetics and genomics in cancer: A scoping review and narrative synthesis. Genet. Med. 2024 26 12 101270 10.1016/j.gim.2024.101270 39282688
    [Google Scholar]
  151. Braverman G. Shapiro Z.E. Bernstein J.A. Ethical issues in contemporary clinical genetics. Mayo Clin. Proc. Innov. Qual. Outcomes 2018 2 2 81 90 10.1016/j.mayocpiqo.2018.03.005 30225437
    [Google Scholar]
  152. Martinez-Martin N. Magnus D. Privacy and ethical challenges in next-generation sequencing. Expert Rev. Precis. Med. Drug Dev. 2019 4 2 95 104 10.1080/23808993.2019.1599685 32775691
    [Google Scholar]
  153. Zee T.W. Abdul Aziz M.F.B. Wei P.C. Ethical challenges of conducting and reviewing human genomics research in malaysia: An exploratory study. Developing World Bioeth. 2023 37997006
    [Google Scholar]
  154. Soulier A. Niemiec E. Howard H.C. D2. 4: Ethical analysis of human genetics and genomics. SIENNA project. 2019 Available from: http://www.philosophylab.philosophy.uoa.gr/fileadmin/philosophylab.ppp.uoa.gr/uploads/D2.4_Ethical_analysis__genomics_.pdf
  155. Lehmann L.S. Snyder Sulmasy L. Burke W. Opole I.O. Deep N.N. Abraham G.M. Burnett J. Callister T.B. Carney J.K. Cooney T.G. Esbensen K.L. Fins J.J. Harp T. Holbrook A.K. Huddle T.S. Levine M.A. Prager K.M. Ethical considerations in precision medicine and genetic testing in internal medicine practice: A position paper from the american college of physicians. Ann. Intern. Med. 2022 175 9 1322 1323 [ACP Ethics, Professionalism and Human Rights Committee.] 10.7326/M22‑0743 35878403
    [Google Scholar]
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Transforming Cancer Care: Genomic Profiling, AI, and Next-Generation Drug Delivery Systems
Bentham Science Publishers ; https://doi.org/10.2174/0113892029406752251110100414
/content/journals/cg/10.2174/0113892029406752251110100414
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  • Article Type:     Review Article
Keywords: CRISPR ; oncology ; immunogenomics ; Cancer genomics ; liquid biopsy

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