Artificial intelligence in cancer transcriptomics: methods, applications in oncology, and limitations

Background. Transcriptomic data show which genes are active in cells and tissues. Machine-learning and deep-learning methods use these data to classify samples, integrate different datasets, and predict biological properties and treatment response. In oncology, a major challenge is transferring patterns identified in experimental models to tumor samples from patients.
Objective. To critically synthesize current artificial-intelligence methods for transcriptomic analysis, with emphasis on oncology applications, transfer of models to patient data, and requirements for clinical validation.
Materials and Methods. We performed a critical narrative review of primary studies and methodological papers and additionally described the authors' publication sample for 2021–2026. The literature review was updated through August 13, 2026.
Results. The most developed areas include single-cell data analysis, spatially resolved tumor diagnostics, and drug-response prediction. Independent comparative evaluations show that increasing model size and complexity does not guarantee better performance than simpler methods. Clinical translation requires accounting for cellular composition and technical differences between measurement batches, aligning experimental and clinical response measures, preventing information leakage, and performing external validation.
Conclusion. Artificial intelligence should be viewed as a data-analysis tool rather than a substitute for experimental or clinical validation. Its value depends on data quality, correct task definition, and the ability to maintain performance in independent cohorts.

1. Subramanian A, Narayan R, Corsello SM, et al. A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell. 2017;171(6):1437-1452.e17. doi: 10.1016/j.cell.2017.10.049.

2. Tirosh I, Izar B, Prakadan SM, et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science. 2016 Apr 8;352(6282):189-96. doi: 10.1126/science.aad0501.

3. Costello JC, Heiser LM, Georgii E, et al. A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotechnol. 2014 Dec;32(12):1202-12. doi: 10.1038/nbt.2877.

4. Theodoris CV, Xiao L, Chopra A, et al. Transfer learning enables predictions in network biology. Nature. 2023 Jun;618(7965):616-624. doi: 10.1038/s41586-023-06139-9.

5. Cui H, Wang C, Maan H, et al. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat Methods. 2024 Aug;21(8):1470-1480. doi: 10.1038/s41592-024-02201-0.

6. Zeng Y, Xie J, Shangguan N, et al. CellFM: a large-scale foundation model pre-trained on transcriptomics of 100 million human cells. Nat Commun. 2025 May 20;16(1):4679. doi: 10.1038/s41467-025-59926-5.

7. Kedzierska KZ, Crawford L, Amini AP, Lu AX. Zero-shot evaluation reveals limitations of single-cell foundation models. Genome Biol. 2025 Apr 18;26(1):101. doi: 10.1186/s13059-025-03574-x.

8. Ritter M, Blume C, Tang Y, et al. Spatially resolved transcriptomics and graph-based deep learning improve accuracy of routine CNS tumor diagnostics. Nat Cancer. 2025 Feb;6(2):292-306. doi: 10.1038/s43018-024-00904-z.

9. Barretina J, Caponigro G, Stransky N, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012 Mar 28;483(7391):603-7. doi: 10.1038/nature11003.

10. Yang W, Soares J, Greninger P, et al. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 2013 Jan;41(Database issue):D955-61. doi: 10.1093/nar/gks1111.

11. Basu A, Bodycombe NE, Cheah JH, et al. An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell. 2013 Aug 29;154(5):1151-1161. doi: 10.1016/j.cell.2013.08.003.

12. Corsello SM, Nagari RT, Spangler RD, et al. Discovering the anticancer potential of non-oncology drugs by systematic viability profiling. Nat Cancer. 2020;1(2):235–248. doi:10.1038/s43018-019-0018-6.

13. Ghandi M, Huang FW, Jané-Valbuena J, et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature. 2019 May;569(7757):503-508. doi: 10.1038/s41586-019-1186-3.

14. Haibe-Kains B, El-Hachem N, Birkbak NJ, et al. Inconsistency in large pharmacogenomic studies. Nature. 2013 Dec 19;504(7480):389-93. doi: 10.1038/nature12831.

15. Xia F, Allen J, Balaprakash P, et al. A cross-study analysis of drug response prediction in cancer cell lines. Brief Bioinform. 2022 Jan 17;23(1):bbab356. doi: 10.1093/bib/bbab356.

16. Geeleher P, Cox NJ, Huang RS. Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. Genome Biol. 2014 Mar 3;15(3):R47. doi: 10.1186/gb-2014-15-3-r47.

17. Hafner M, Niepel M, Chung M, Sorger PK. Growth rate inhibition metrics correct for confounders in measuring sensitivity to cancer drugs. Nat Methods. 2016 Jun;13(6):521-7. doi: 10.1038/nmeth.3853.

18. Eisenhauer EA, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009 Jan;45(2):228-47. doi: 10.1016/j.ejca.2008.10.026.

19. Aran D, Sirota M, Butte AJ. Systematic pan-cancer analysis of tumour purity. Nat Commun. 2015 Dec 4;6:8971. doi: 10.1038/ncomms9971.

20. Newman AM, Steen CB, Liu CL, et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol. 2019 Jul;37(7):773-782. doi: 10.1038/s41587-019-0114-2.

21. Warren A, Chen Y, Jones A, et al. Global computational alignment of tumor and cell line transcriptional profiles. Nat Commun. 2021 Jan 4; 12(1):22. doi: 10.1038/s41467-020-20294-x.

22. Mourragui S, Loog M, van de Wiel MA, et al. PRECISE: a domain adaptation approach to transfer predictors of drug response from pre-clinical models to tumors. Bioinformatics. 2019 Jul 15;35(14):i510-i519. doi: 10.1093/bioinformatics/btz372.

23. Sharifi-Noghabi H, Harjandi PA, Zolotareva O, et al. Out-ofdistribution generalization from labelled and unlabelled gene expression data for drug response prediction. Nat Mach Intell. 2021;3:962-972. doi: 10.1038/s42256-021-00408-w.

24. Mourragui SMC, Loog M, Vis DJ, et al. Predicting patient response with models trained on cell lines and patient-derived xenografts by nonlinear transfer learning. Proc Natl Acad Sci U S A. 2021 Dec 7;118(49):e2106682118. doi: 10.1073/pnas.2106682118.

25. He D, Liu Q, Wu Y, Xie L. A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening. Nat Mach Intell. 2022 Oct;4(10): 879-892. doi: 10.1038/s42256-022-00541-0.

26. Hsu YC, Chiu YC, Lu TP, et al. Predicting drug response through tumor deconvolution by cancer cell lines. Patterns (N Y). 2024 Mar 5;5(4):100949. doi: 10.1016/j.patter.2024.100949.

27. Zhou Y, Dai Q, Xu Y, et al. PharmaFormer predicts clinical drug responses through transfer learning guided by patient derived organoid. NPJ Precis Oncol. 2025 Aug 13;9(1):282. doi: 10.1038/s41698-025-01082-6.

28. Ma J, Fong SH, Luo Y, et al. Few-shot learning creates predictive models of drug response that translate from high-throughput screens to individual patients. Nat Cancer. 2021 Feb;2(2):233-244. doi: 10.1038/s43018-020-00169-2.

29. Pellecchia S, Viscido G, Franchini M, Gambardella G. Predicting drug response from single-cell expression profiles of tumours. BMC Med. 2023 Dec 1;21(1):476. doi: 10.1186/s12916-023-03182-1.

30. Sun X, Shen L, Wang L, et al. A disentangled transformer-based transfer learning framework to predict patient drug response from tumor single-cell transcriptomics. Bioinformatics. 2026 Jul 1;42(1):btag269. doi: 10.1093/bioinformatics/btag269.

31. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007 Jan;8(1):118-27. doi: 10.1093/biostatistics/kxj037.

32. Collins GS, Moons KGM, Dhiman P, et al. TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024 Apr 16;385:e078378. doi: 10.1136/bmj-2023-078378.

33. Moons KGM, Damen JAA, Kaul T, et al. PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods. BMJ. 2025 Mar 24;388:e082505. doi: 10.1136/bmj-2024-082505..

34. Liu X, Cruz Rivera S, Moher D, et al; SPIRIT-AI and CONSORTAI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med. 2020 Sep;26(9):1364-1374. doi: 10.1038/s41591-020-1034-x.