English | Russian | Kazakh
ONCOLOGY, NUCLEAR MEDICINE AND TRANSPLANTOLOGY
Review Article

CRISPR Functional Genomics in Precision Oncology: Integrating Single-Cell Multi-Omics for Cancer Vulnerability Discovery

Oncology, Nuclear Medicine and Transplantology, 2(3), 2026, onmt027, https://doi.org/10.63946/onmt/19194
Publication date: Aug 24, 2026
Full Text (PDF)

ABSTRACT

Precision oncology seeks to identify patient-specific therapeutic vulnerabilities; however, conventional genomic profiling is limited by intratumoral heterogeneity and its inability to distinguish functional driver alterations from passenger mutations, often resulting in incomplete prediction of therapeutic response. Recently, the combination of CRISPR functional genomics with single-cell multi-omics has proven to be a paradigm-shifting strategy for understanding context-specific cancer vulnerabilities by causal functional interrogation. The aim of this review is to critically examine recent progress in the integration of these technologies for discovering vulnerabilities in cancer, and introduces a new conceptual model, called the Integrated Functional Precision Oncology (IFPO) Framework, that brings together functional genomic perturbation, single-cell multi-omics, computational systems biology, and clinical translation. Literature was retrieved from Pubmed, Web of Science and Google Scholar and peer reviewed studies published between 2020 and 2025. Key findings from historic and recent research were analyzed to pinpoint methodological innovations, translational studies, limitations, and areas in need of further research. The results reveal that the integrated CRISPR–single-cell platforms, such as Perturb-seq, CROP-seq, and ECCITE-seq, can be used to causally interrogate gene function at the single-cell level, allowing for the identification of context-dependent essential genes, synthetic lethal interactions, regulatory networks, and therapeutic resistance mechanisms. All the evidence suggests that therapeutic response is not merely a function of genomic alterations but also the dynamic interplay between genomic alterations, cellular state, epigenetic plasticity, and the tumor microenvironment. The proposed IFPO Framework integrates these findings into a systems-level model that captures the mechanisms by which these functional perturbations, multimodal molecular profiling, and AI-driven integration of data converge to reveal clinically actionable cancer vulnerabilities. This integrated paradigm transforms precision oncology from descriptive molecular profiling to functional systems oncology and offers directions for further progress of precision cancer treatment based on enhanced biomarker discovery, therapeutic target identification, and prospective clinical translation.

KEYWORDS

CRISPR Functional Genomics Single-cell Multi-Omics Precision Oncology Cancer Vulnerability Therapeutic Resistance

CITATION (Vancouver)

Oluwadare OE, Frankpeace MS, Oluwaniran OM, Anatuanya JI, Onwuemelem LA. CRISPR Functional Genomics in Precision Oncology: Integrating Single-Cell Multi-Omics for Cancer Vulnerability Discovery. Oncology, Nuclear Medicine and Transplantology. 2026;2(3):onmt027. https://doi.org/10.63946/onmt/19194
APA
Oluwadare, O. E., Frankpeace, M. S., Oluwaniran, O. M., Anatuanya, J. I., & Onwuemelem, L. A. (2026). CRISPR Functional Genomics in Precision Oncology: Integrating Single-Cell Multi-Omics for Cancer Vulnerability Discovery. Oncology, Nuclear Medicine and Transplantology, 2(3), onmt027. https://doi.org/10.63946/onmt/19194
Harvard
Oluwadare, O. E., Frankpeace, M. S., Oluwaniran, O. M., Anatuanya, J. I., and Onwuemelem, L. A. (2026). CRISPR Functional Genomics in Precision Oncology: Integrating Single-Cell Multi-Omics for Cancer Vulnerability Discovery. Oncology, Nuclear Medicine and Transplantology, 2(3), onmt027. https://doi.org/10.63946/onmt/19194
AMA
Oluwadare OE, Frankpeace MS, Oluwaniran OM, Anatuanya JI, Onwuemelem LA. CRISPR Functional Genomics in Precision Oncology: Integrating Single-Cell Multi-Omics for Cancer Vulnerability Discovery. Oncology, Nuclear Medicine and Transplantology. 2026;2(3), onmt027. https://doi.org/10.63946/onmt/19194
Chicago
Oluwadare, Olaitan Ebenezer, Meveilleoux Soronuchi Frankpeace, Oluwabukunmi M Oluwaniran, Jane Ifeyinwa Anatuanya, and Lydia Amarachi Onwuemelem. "CRISPR Functional Genomics in Precision Oncology: Integrating Single-Cell Multi-Omics for Cancer Vulnerability Discovery". Oncology, Nuclear Medicine and Transplantology 2026 2 no. 3 (2026): onmt027. https://doi.org/10.63946/onmt/19194
MLA
Oluwadare, Olaitan Ebenezer et al. "CRISPR Functional Genomics in Precision Oncology: Integrating Single-Cell Multi-Omics for Cancer Vulnerability Discovery". Oncology, Nuclear Medicine and Transplantology, vol. 2, no. 3, 2026, onmt027. https://doi.org/10.63946/onmt/19194

REFERENCES

  1. Liu B, Zhou H, Tan L, Siu KTH, Guan XY. Exploring treatment options in cancer: tumor treatment strategies. Signal Transduct Target Ther. 2024;9(1):175. DOI: 10.1038/s41392-024-01856-7
  2. Qiao D, Wang RC, Wang Z. Precision oncology: current landscape, emerging trends, challenges, and future perspectives. Cells. 2025;14(22):1804. DOI: 10.3390/cells14221804
  3. Fu YC, Liang SB, Luo M, Wang XP. Intratumoral heterogeneity and drug resistance in cancer. Cancer Cell Int. 2025;25:103. DOI: 10.1186/s12935-025-03734-w
  4. Gupta P, Jindal A, Ahuja G, Jayadeva, Sengupta D. A new deep learning technique reveals the exclusive functional contributions of individual cancer mutations. J Biol Chem. 2022;298(8):102177. DOI: 10.1016/j.jbc.2022.102177
  5. Pandey V, Sharma S, Pokharel YR. Exploring CRISPR-Cas: the transformative impact of gene editing in molecular biology. Mol Ther Nucleic Acids. 2025;36(4):102717. DOI: 10.1016/j.omtn.2025.102717
  6. Holcomb EA, Pearson AN, Jungles KM, Tate A, James J, Jiang L, et al. High-content CRISPR screening in tumor immunology. Front Immunol. 2022;13:1041451. DOI: 10.3389/fimmu.2022.1041451
  7. Menon AV, Song B, Chao L, Sriram D, Chansky P, Bakshi I, et al. Unraveling the future of genomics: CRISPR, single-cell omics, and the applications in cancer and immunology. Front Genome Ed. 2025;7:1565387. DOI: 10.3389/fgeed.2025.1565387
  8. Tan WLW, Seow WQ, Zhang A, Rhee S, Wong WH, Greenleaf WJ, et al. Current and future perspectives of single-cell multi-omics technologies in cardiovascular research. Nat Cardiovasc Res. 2023;2(1):20-34. DOI: 10.1038/s44161-022-00205-7
  9. Rabaan AA, AlSaihati H, Bukhamsin R, Bakhrebah MA, Nassar MS, Alsaleh AA, et al. Application of CRISPR/Cas9 technology in cancer treatment: a future direction. Curr Oncol. 2023;30(2):1954-1976. DOI: 10.3390/curroncol30020152
  10. Meng H, Nan M, Li Y, Ding Y, Yin Y, Zhang M. Application of CRISPR-Cas9 gene editing technology in basic research, diagnosis and treatment of colon cancer. Front Endocrinol. 2023;14:1148412. DOI: 10.3389/fendo.2023.1148412
  11. Gervais NC, La Bella AA, Wensing LF, Sharma J, Acquaviva V, Best M, et al. Development and applications of a CRISPR activation system for facile genetic overexpression in Candida albicans. G3 (Bethesda). 2023;13(2):jkac301. DOI: 10.1093/g3journal/jkac301
  12. Kantor A, McClements ME, MacLaren RE. CRISPR-Cas9 DNA base-editing and prime-editing. Int J Mol Sci. 2020;21(17):6240. DOI: 10.3390/ijms21176240
  13. Li YR, Lyu Z, Tian Y, Fang Y, Zhu Y, Chen Y, et al. Advancements in CRISPR screens for the development of cancer immunotherapy strategies. Mol Ther Oncolytics. 2023;31:100733. DOI: 10.1016/j.omto.2023.100733
  14. Ravichandran M, Maddalo D. Applications of CRISPR-Cas9 for advancing precision medicine in oncology: from target discovery to disease modeling. Front Genet. 2023;14:1273994. DOI: 10.3389/fgene.2023.1273994
  15. Yang W, Zhang T, Song X, Dong G, Xu L, Jiang F. SNP-target genes interaction perturbing the cancer risk in the post-GWAS. Cancers (Basel). 2022;14(22):5636. DOI: 10.3390/cancers14225636
  16. Arora HL, Sekar G, Phadnis A, Bahot A, Bomle D, Patel V, et al. Emerging hallmarks and the rise of complexities and heterogeneity of tumor. Biochem Biophys Rep. 2025;44:102347. DOI: 10.1016/j.bbrep.2025.102347
  17. Zhu Z, Shen J, Ho PCL, Hu Y, Ma Z, Wang L. Transforming cancer treatment: integrating patient-derived organoids and CRISPR screening for precision medicine. Front Pharmacol. 2025;16:1563198. DOI: 10.3389/fphar.2025.1563198
  18. Le J, Dian Y, Zhao D, Guo Z, Luo Z, Chen X, et al. Single-cell multi-omics in cancer immunotherapy: from tumor heterogeneity to personalized precision treatment. Mol Cancer. 2025;24:221. DOI: 10.1186/s12943-025-02426-3
  19. Ortega-Batista A, Jaén-Alvarado Y, Moreno-Labrador D, Gómez N, García G, Guerrero EN. Single-cell sequencing: genomic and transcriptomic approaches in cancer cell biology. Int J Mol Sci. 2025;26(5):2074. DOI: 10.3390/ijms26052074
  20. Butterfield GL, Reisman SJ, Iglesias N, Gersbach CA. Gene regulation technologies for gene and cell therapy. Mol Ther. 2025;33(5):2104-2122. DOI: 10.1016/j.ymthe.2025.04.004
  21. Wu X, Yang X, Dai Y, Zhao Z, Zhu J, Guo H, et al. Single-cell sequencing to multi-omics: technologies and applications. Biomark Res. 2024;12:110. DOI: 10.1186/s40364-024-00643-4
  22. Pfohl U, Pflaume A, Regenbrecht M, Finkler S, Graf Adelmann Q, Reinhard C, et al. Precision oncology beyond genomics: the future is here—it is just not evenly distributed. Cells. 2021;10(4):928. DOI: 10.3390/cells10040928
  23. An Y, Wang Q, Gao K, Zhang C, Ouyang Y, Li R, et al. Epigenetic regulation of aging and its rejuvenation. MedComm. 2025;6(9):e70369. DOI: 10.1002/mco2.70369
  24. Shevade K, Yang YA, Feng K, Mader K, Sevim V, Parsons J, et al. Simultaneous capture of single cell RNA-seq, ATAC-seq, and CRISPR perturbation enables multiomic screens to identify gene regulatory relationships. Cell Rep Methods. 2025;5(12):101222. DOI: 10.1016/j.crmeth.2025.101222
  25. Wang C, Zhou J, Zhang H, Zhuang Z, Bai G, Tang M, et al. Computational analyses and challenges of single-cell ATAC-seq. Genomics Proteomics Bioinformatics. 2025;23(6):qzaf115. DOI: 10.1093/gpbjnl/qzaf115
  26. Abdul-Hussin IF, Alkhalidi MHO, Al-Musawi S, Alshalah LAM, Sheykhhasan M. CRISPR-Cas9 in functional genomics: implications for target validation in precision oncology. Trends Pharm Biotechnol. 2025;3(1):36-48. DOI: 10.57238/tpb.2025.153196.1026
  27. Srivastava K, Pandit B. Genome-wide CRISPR screens and their applications in infectious disease. Front Genome Ed. 2023;5:1243731. DOI: 10.3389/fgeed.2023.1243731
  28. Tang N, Li J, Gu A, Li M, Liu Y. Single-cell multi-omics in biliary tract cancers: decoding heterogeneity, microenvironment, and treatment strategies. Mol Biomed. 2025;6:82. DOI: 10.1186/s43556-025-00330-2
  29. Liu SJ, Zou C, Pak J, Morse A, Pang D, Casey-Clyde T, et al. In vivo perturb-seq of cancer and microenvironment cells dissects oncologic drivers and radiotherapy responses in glioblastoma. Genome Biol. 2024;25:256. DOI: 10.1186/s13059-024-03404-6
  30. Finkbeiner S. Functional genomics, genetic risk profiling and cell phenotypes in neurodegenerative disease. Neurobiol Dis. 2020;146:105088. DOI: 10.1016/j.nbd.2020.105088
  31. Lalla M, Ratnani A, Yang J, Wang M, Cheng H. Drug-tolerant persister cells and tumor dormancy in NSCLC: a new frontier in overcoming therapeutic resistance. Cancers (Basel). 2026;18(5):779. DOI: 10.3390/cancers18050779
  32. Binan L, Danquah S, Valakh V, Simonton B, Bezney J, Nehme R, et al. Simultaneous CRISPR screening and spatial transcriptomics reveals intracellular, intercellular, and functional transcriptional circuits. bioRxiv. 2023 Nov 30 [preprint]. DOI: 10.1101/2023.11.30.569494
  33. Șerban M, Toader C, Covache-Busuioc RA. CRISPR and artificial intelligence in neuroregeneration: closed-loop strategies for precision medicine, spinal cord repair, and adaptive neuro-oncology. Int J Mol Sci. 2025;26(19):9409. DOI: 10.3390/ijms26199409
  34. Sannigrahi MK, Cao AC, Rajagopalan P, Sun L, Brody RM, Raghav L, et al. A novel pipeline for prioritizing cancer type‐specific therapeutic vulnerabilities using DepMap identifies PAK2 as a target in head and neck squamous cell carcinomas. Mol Oncol. 2024;18(2):336-349. DOI: 10.1002/1878-0261.13558
  35. Bharadwaj S, Mierzwicka JM, Vaňková L, Malý P. Unraveling the molecular-pathological characteristics and cellular complexity of the tumor immune microenvironment in metastatic non-small cell lung cancer. Cell Commun Signal. 2025;23:400. DOI: 10.1186/s12964-025-02410-w
  36. Prindle V, Richardson AE, Sher KR, Kongpachith S, Kentala K, Petiwala S, et al. Synthetic lethality of mRNA quality control complexes in cancer. Nature. 2025;638(8052):1095-1103. DOI: 10.1038/s41586-024-08398-6
  37. Konda P, Garinet S, Van Allen EM, Viswanathan SR. Genome-guided discovery of cancer therapeutic targets. Cell Rep. 2023;42(8):112978. DOI: 10.1016/j.celrep.2023.112978
  38. Tan SYX, Zhang J, Tee WW. Epigenetic regulation of inflammatory signaling and inflammation-induced cancer. Front Cell Dev Biol. 2022;10:931493. DOI: 10.3389/fcell.2022.931493
  39. McLean B, Istadi A, Clack T, Vankan M, Schramek D, Neely GG, et al. A CRISPR path to finding vulnerabilities and solving drug resistance: targeting the diverse cancer landscape and its ecosystem. Adv Genet. 2022;3(4):2200014. DOI: 10.1002/ggn2.202200014
  40. Ng CX, Lee SY, Yap XY, Wong YH, Loh JS, Ang KP, et al. Epigenetic reprogramming as the nexus of cancer stemness and therapy resistance: implications for biomarker discovery. Discov Oncol. 2025;16:2220. DOI: 10.1007/s12672-025-04085-8
  41. Guo D, Guo Y, Zhu C, Liao Y, Lin Z, Zhang H, et al. Programmed cell death network in cancer drug resistance: a framework for therapeutic intervention. Drug Resist Updat. 2026;86:101387. DOI: 10.1016/j.drup.2026.101387
  42. Khan SU, Fatima K, Aisha S, Malik F. Unveiling the mechanisms and challenges of cancer drug resistance. Cell Commun Signal. 2024;22(1):109. DOI: 10.1186/s12964-023-01302-1
  43. Han H, Sun X, Guo X, Wen J, Zhao X, Zhou W. CRISPR/Cas9 technology in tumor research and drug development application progress and future prospects. Front Pharmacol. 2025;16:1552741. DOI: 10.3389/fphar.2025.1552741
  44. Jamalinia M, Weiskirchen R. Advances in personalized medicine: translating genomic insights into targeted therapies for cancer treatment. Ann Transl Med. 2025;13(2):18. DOI: 10.21037/atm-25-34
  45. Passaro A, Al Bakir M, Hamilton EG, Diehn M, André F, Roy-Chowdhuri S, et al. Cancer biomarkers—emerging trends and clinical implications for personalized treatment. Cell. 2024;187(7):1617-1635. DOI: 10.1016/j.cell.2024.02.041
  46. Liang A, Kong Y, Chen Z, Qiu Y, Wu Y, Zhu X, et al. Advancements and applications of single-cell multi-omics techniques in cancer research: unveiling heterogeneity and paving the way for precision therapeutics. Biochem Biophys Rep. 2024;37:101589. DOI: 10.1016/j.bbrep.2023.101589
  47. Dong M, Wang L, Hu N, Rao Y, Wang Z, Zhang Y. Integration of multi-omics approaches in exploring intra-tumoral heterogeneity. Cancer Cell Int. 2025;25(1):317. DOI: 10.1186/s12935-025-03944-2
  48. Shuai Y, Huang H. Transcriptional and epigenetic reprogramming, lineage plasticity and therapy resistance in prostate cancer. J Natl Cancer Cent. 2026;6(1):73-87. DOI: 10.1016/j.jncc.2025.06.001
  49. Motohashi S, Katsuta E, Ban D. Advances and challenges in drug screening for cancer therapy: a comprehensive review. Bioengineering (Basel). 2025;12(12):1315. DOI: 10.3390/bioengineering12121315
  50. Cao H, Oghenemaro EF, Latypova A, Abosaoda MK, Zaman GS, Devi A. Advancing clinical biochemistry: addressing gaps and driving future innovations. Front Med (Lausanne). 2025;12:1521126. DOI: 10.3389/fmed.2025.1521126
  51. Leming MJ, Bron EE, Bruffaerts R, Ou Y, Iglesias JE, Gollub RL, et al. Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting. NPJ Digit Med. 2023;6:129. DOI: 10.1038/s41746-023-00868-x
  52. Clark AJ, Lillard JW Jr. A comprehensive review of bioinformatics tools for genomic biomarker discovery driving precision oncology. Genes (Basel). 2024;15(8):1036. DOI: 10.3390/genes15081036
  53. Tsimberidou AM, Fountzilas E, Nikanjam M, Kurzrock R. Review of precision cancer medicine: evolution of the treatment paradigm. Cancer Treat Rev. 2020;86:102019. DOI: 10.1016/j.ctrv.2020.102019
  54. Jiang Z, Zhang H, Gao Y, Sun Y. Multi-omics strategies for biomarker discovery and application in personalized oncology. Mol Biomed. 2025;6:115. DOI: 10.1186/s43556-025-00340-0
  55. Ghoreyshi N, Heidari R, Farhadi A, Chamanara M, Farahani N, Vahidi M, et al. Next-generation sequencing in cancer diagnosis and treatment: clinical applications and future directions. Discov Oncol. 2025;16:578. DOI: 10.1007/s12672-025-01816-9
  56. Colonna G. Overcoming barriers in cancer biology research: current limitations and solutions. Cancers (Basel). 2025;17(13):2102. DOI: 10.3390/cancers17132102
  57. Varshney GK, Burgess SM. CRISPR-based functional genomics tools in vertebrate models. Exp Mol Med. 2025;57(7):1355-1372. DOI: 10.1038/s12276-025-01514-0
  58. Castells-Roca L, Tejero E, Rodríguez-Santiago B, Surrallés J. CRISPR screens in synthetic lethality and combinatorial therapies for cancer. Cancers (Basel). 2021;13(7):1591. DOI: 10.3390/cancers13071591
  59. Ali SIM, Alrashid SZ. A review of methods for gene regulatory networks reconstruction and analysis. Artif Intell Rev. 2025;58(8):256. DOI: 10.1007/s10462-025-11257-z
  60. Heumos L, Ji Y, May L, Green TD, Peidli S, Zhang X, et al. Pertpy: an end-to-end framework for perturbation analysis. Nat Methods. 2026;23(2):350-359. DOI: 10.1038/s41592-025-02909-7
  61. Haddadin L, Sun X. Stem cells in cancer: from mechanisms to therapeutic strategies. Cells. 2025;14(7):538. DOI: 10.3390/cells14070538
  62. Al-Kabani A, Huda B, Haddad J, Yousuf M, Bhurka F, Ajaz F, et al. Exploring experimental models of colorectal cancer: a critical appraisal from 2D cell systems to organoids, humanized mouse avatars, organ-on-chip, CRISPR engineering, and AI-driven platforms—challenges and opportunities for translational precision oncology. Cancers (Basel). 2025;17(13):2163. DOI: 10.3390/cancers17132163
  63. Garg P, Singhal G, Pareek S, Kulkarni P, Horne D, Nath A, et al. Unveiling the potential of gene editing techniques in revolutionizing cancer treatment: a comprehensive overview. Biochim Biophys Acta Rev Cancer. 2025;1880(1):189233. DOI: 10.1016/j.bbcan.2024.189233
  64. Brancato V, Esposito G, Coppola L, Cavaliere C, Mirabelli P, Scapicchio C, et al. Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine. J Transl Med. 2024;22:136. DOI: 10.1186/s12967-024-04891-8
  65. Seijas A, Cora D, Novo M, Al-Soufi W, Sánchez L, Arana ÁJ. CRISPR/Cas9 delivery systems to enhance gene editing efficiency. Int J Mol Sci. 2025;26(9):4420. DOI: 10.3390/ijms26094420
  66. Krejcar O, Abdullah J, Namazi H. Implementing XAI in life sciences: key challenges and pathways to solutions. Artif Intell Life Sci. 2026;9:100153. DOI: 10.1016/j.ailsci.2026.100153
  67. Chen JF, Yan Q. The roles of epigenetics in cancer progression and metastasis. Biochem J. 2021;478(17):3373-3393. DOI: 10.1042/BCJ20210084
  68. Michael B, Veerasami H, Jayaprakash N. Artificial intelligence and big data for decoding infectious disease transmission dynamics and outbreak prediction. Decod Infect Transm. 2026;4:100079. DOI: 10.1016/j.dcit.2026.100079
  69. Fahim YA, Hasani IW, Kabba S, Ragab WM. Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives. Eur J Med Res. 2025;30:848. DOI: 10.1186/s40001-025-03196-w
  70. Far BF. Artificial intelligence ethics in precision oncology: balancing advancements in technology with patient privacy and autonomy. Explor Target Antitumor Ther. 2023;4(4):685-689. DOI: 10.37349/etat.2023.00160
  71. Liu Y, Zhu K, Peng W, Liu Z, Mao X. Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications. Signal Transduct Target Ther. 2026;11(1):210. DOI: 10.1038/s41392-026-02631-6
  72. Haider S, Singh AP, Panthi B, Sindhu SR, Safa NT, Malik S, et al. Advances in CRISPR/Cas9 genome editing for crop improvement and global food security. Curr Plant Biol. 2026;46:100593. DOI: 10.1016/j.cpb.2026.100593
  73. Banushi B, Collova J, Milroy H. Epigenetic echoes: bridging nature, nurture, and healing across generations. Int J Mol Sci. 2025;26(7):3075. DOI: 10.3390/ijms26073075
  74. Baek SW, Yoon SY, Kim SK, Leem SH. Therapy-driven molecular evolution of bladder cancer: roles of cellular plasticity and tumor microenvironment. Int J Mol Sci. 2026;27(12):5152. DOI: 10.3390/ijms27125152
  75. Cen X, Huang X, Deng E, Gong X, Tan N, Ye J, et al. Single‐cell and spatial omics: methods and applications. MedComm. 2026;7(4):e70713. DOI: 10.1002/mco2.70713
  76. Ansori ANM, Antonius Y, Susilo RJK, Hayaza S, Kharisma VD, Parikesit AA, et al. Application of CRISPR-Cas9 genome editing technology in various fields: a review. Narra J. 2023;3(2):e184. DOI: 10.52225/narra.v3i2.184
  77. Boehm KM, Khosravi P, Vanguri R, Gao J, Shah SP. Harnessing multimodal data integration to advance precision oncology. Nat Rev Cancer. 2022;22(2):114-126. DOI: 10.1038/s41568-021-00408-3
  78. Agbo Eje O, Azim SM, Dehzangi I. Explainable AI applications in healthcare: a systematic review. Algorithms. 2026;19(6):488. DOI: 10.3390/a19060488
  79. Uddin F, Rudin CM, Sen T. CRISPR gene therapy: applications, limitations, and implications for the future. Front Oncol. 2020;10:1387. DOI: 10.3389/fonc.2020.01387
  80. Oliva A, Kaphle A, Reguant R, Sng LMF, Twine NA, Malakar Y, et al. Future-proofing genomic data and consent management: a comprehensive review of technology innovations. GigaScience. 2024;13:giae021. DOI: 10.1093/gigascience/giae021
  81. Freidlin B, Korn EL, Maki RG. Molecular profiling for precision oncology: moving beyond feasibility and safety. J Clin Oncol. 2026;44(7):515-517. DOI: 10.1200/JCO-25-02642
  82. Dogiparthi LK, Bukke SPN, Thalluri C, Thalamanchi B, Vidya KP, Sree GN, et al. The role of genomics and proteomics in drug discovery and its application in pharmacy. Discov Appl Sci. 2025;7(6):552. DOI: 10.1007/s42452-025-07155-2
  83. Rusciano D. Molecular oncodiagnostics in precision oncology: integrating tumor transcriptomics, patient pharmacogenetics, and ex vivo chemoresistance testing to improve individual chemotherapy response. J Pers Med. 2026;16(4):176. DOI: 10.3390/jpm16040176
  84. Ahmad Z, Rahim S, Zubair M, Abdul-Ghafar J. The age of molecular biomarkers: cancer in the era of personalized medicine. What do pathologists in developing countries need to know and understand? Int J Gen Med. 2026;19:590285. DOI: 10.2147/IJGM.S590285
  85. Baumann AA, Buribayev Z, Wolkenhauer O, Salybekov AA, Wolfien M. Epigenomic echoes—decoding genomic and epigenetic instability to distinguish lung cancer types and predict relapse. Epigenomes. 2025;9(1):5. DOI: 10.3390/epigenomes9010005
  86. Ramón y Cajal S, Sesé M, Capdevila C, Aasen T, De Mattos-Arruda L, Diaz-Cano SJ, et al. Clinical implications of intratumor heterogeneity: challenges and opportunities. J Mol Med (Berl). 2020;98(2):161-177. DOI: 10.1007/s00109-020-01874-2
  87. Proietto M, Crippa M, Damiani C, Pasquale V, Sacco E, Vanoni M, et al. Tumor heterogeneity: preclinical models, emerging technologies, and future applications. Front Oncol. 2023;13:1164535. DOI: 10.3389/fonc.2023.1164535
  88. Smirnov D, Konstantinovskiy N, Prokisch H. Integrative omics approaches to advance rare disease diagnostics. J Inherit Metab Dis. 2023;46(5):824-838. DOI: 10.1002/jimd.12663
  89. Park BS, Lee M, Kim J, Kim T. Perturbomics: CRISPR–Cas screening-based functional genomics approach for drug target discovery. Exp Mol Med. 2025;57(7):1443-1454. DOI: 10.1038/s12276-025-01487-0
  90. Singh SR, Bhaskar R, Ghosh S, Yarlagadda B, Singh KK, Verma P, et al. Exploring the genetic orchestra of cancer: the interplay between oncogenes and tumor-suppressor genes. Cancers (Basel). 2025;17(7):1082. DOI: 10.3390/cancers17071082
  91. Mahgoub EO, Cho WC, Sharifi M, Falahati M, Zeinabad HA, Mare HE, et al. Role of functional genomics in identifying cancer drug resistance and overcoming cancer relapse. Heliyon. 2023;10(1):e22095. DOI: 10.1016/j.heliyon.2023.e22095
  92. Huber A, Djajawi TM, Rivera IS, Vervoort SJ, Kearney CJ. CRISPR screens define unified hallmarks of cancer cell-autonomous immune evasion. Cell Rep. 2026;45(1):116738. DOI: 10.1016/j.celrep.2025.116738
  93. Patel SK, George B, Rai V. Artificial intelligence to decode cancer mechanism: beyond patient stratification for precision oncology. Front Pharmacol. 2020;11:1177. DOI: 10.3389/fphar.2020.01177
  94. Satam H, Joshi K, Mangrolia U, Waghoo S, Zaidi G, Rawool S, et al. Next-generation sequencing technology: current trends and advancements. Biology (Basel). 2023;12(7):997. DOI: 10.3390/biology12070997
  95. Giovannoni C, Metta C, Monreale A, Rinzivillo S. A survey on multimodal explainable artificial intelligence. Intell Syst Appl. 2026;31:200671. DOI: 10.1016/j.iswa.2026.200671
  96. Xu D, Tang Y, Luo J, Wen C. Computational approaches to multimodal data integration in rheumatoid arthritis: from data landscape to clinical translation. Brief Bioinform. 2026;27(1):bbag073. DOI: 10.1093/bib/bbag073
  97. Djelti F, Hani M, Chetbani Y, Belaadi A, Ammarullah MI. Surviving the siege: a review on the metabolic hallmarks of cancer dormancy. Cancer Treat Res Commun. 2026;47:101123. DOI: 10.1016/j.ctarc.2026.101123

LICENSE

Creative Commons License
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.