REFERENCES

1. Döhner H, Weisdorf DJ, Bloomfield CD. Acute myeloid leukemia. N Engl J Med. 2015;373:1136-52.

2. Vardiman JW, Thiele J, Arber DA, et al. The 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: rationale and important changes. Blood. 2009;114:937-51.

3. Zhang Z, Huang J, Zhang Z, et al. Application of omics in the diagnosis, prognosis, and treatment of acute myeloid leukemia. Biomark Res. 2024;12:60.

4. Shimony S, Stahl M, Stone RM. Acute myeloid leukemia: 2023 update on diagnosis, risk-stratification, and management. Am J Hematol. 2023;98:502-26.

5. Pereira MP, Herrity E, Kim DDH. TP53-mutated acute myeloid leukemia and myelodysplastic syndrome: biology, treatment challenges, and upcoming approaches. Ann Hematol. 2024;103:1049-67.

6. Negotei C, Colita A, Mitu I, et al. A review of FLT3 kinase inhibitors in AML. J Clin Med. 2023;12:6429.

7. Ediriwickrema A, Gentles AJ, Majeti R. Single-cell genomics in AML: extending the frontiers of AML research. Blood. 2023;141:345-55.

8. Karczewski KJ, Snyder MP. Integrative omics for health and disease. Nat Rev Genet. 2018;19:299-310.

9. Bottomly D, Long N, Schultz AR, et al. Integrative analysis of drug response and clinical outcome in acute myeloid leukemia. Cancer Cell. 2022;40:850-64.e9.

10. Zeng AGX, Bansal S, Jin L, et al. A cellular hierarchy framework for understanding heterogeneity and predicting drug response in acute myeloid leukemia. Nat Med. 2022;28:1212-23.

11. Severens JF, Karakaslar EO, van der Reijden BA, et al. Mapping AML heterogeneity - multi-cohort transcriptomic analysis identifies novel clusters and divergent ex-vivo drug responses. Leukemia. 2024;38:751-61.

12. Alharbi F, Vakanski A. Machine learning methods for cancer classification using gene expression data: a review. Bioengineering. 2023;10:173.

13. Addala V, Newell F, Pearson JV, et al. Computational immunogenomic approaches to predict response to cancer immunotherapies. Nat Rev Clin Oncol. 2024;21:28-46.

14. Sogbein O, Paul P, Umar M, Chaari A, Batuman V, Upadhyay R. Bortezomib in cancer therapy: mechanisms, side effects, and future proteasome inhibitors. Life Sci. 2024;358:123125.

15. Tomasson MH, Xiang Z, Walgren R, et al. Somatic mutations and germline sequence variants in the expressed tyrosine kinase genes of patients with de novo acute myeloid leukemia. Blood. 2008;111:4797-808.

16. Wang YH, Lin CC, Hsu CL, et al. Distinct clinical and biological characteristics of acute myeloid leukemia with higher expression of long noncoding RNA KIAA0125. Ann Hematol. 2021;100:487-98.

17. Taskesen E, Bullinger L, Corbacioglu A, et al. Prognostic impact, concurrent genetic mutations, and gene expression features of AML with CEBPA mutations in a cohort of 1182 cytogenetically normal AML patients: further evidence for CEBPA double mutant AML as a distinctive disease entity. Blood. 2011;117:2469-75.

18. Herold T, Jurinovic V, Batcha AMN, et al. A 29-gene and cytogenetic score for the prediction of resistance to induction treatment in acute myeloid leukemia. Haematologica. 2018;103:456-65.

19. Pei S, Pollyea DA, Gustafson A, et al. Monocytic subclones confer resistance to Venetoclax-based therapy in patients with acute myeloid leukemia. Cancer Discov. 2020;10:536-51.

20. Lu X, Meng J, Zhou Y, Jiang L, Yan F. MOVICS: an R package for multi-omics integration and visualization in cancer subtyping. Bioinformatics. 2021;36:5539-41.

21. Szklarczyk D, Kirsch R, Koutrouli M, et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023;51:D638-46.

22. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284-7.

23. van Galen P, Hovestadt V, Wadsworth Ii MH, et al. Single-cell RNA-seq reveals AML hierarchies relevant to disease progression and immunity. Cell. 2019;176:1265-81.e24.

24. Qiu X, Hill A, Packer J, Lin D, Ma YA, Trapnell C. Single-cell mRNA quantification and differential analysis with Census. Nat Methods. 2017;14:309-15.

25. Yang JM, Zhang N, Luo T, et al. TCellSI: a novel method for T cell state assessment and its applications in immune environment prediction. Imeta. 2024;3:e231.

26. Chen Y, He LN, Zhang Y, et al. tigeR: tumor immunotherapy gene expression data analysis R package. iMeta. 2024;3:e229.

27. Ianevski A, Giri AK, Aittokallio T. SynergyFinder 3.0: an interactive analysis and consensus interpretation of multi-drug synergies across multiple samples. Nucleic Acids Res. 2022;50:W739-43.

28. Döhner H, Estey E, Grimwade D, et al. Diagnosis and management of AML in adults: 2017 ELN recommendations from an international expert panel. Blood. 2017;129:424-47.

29. Aldoss I, Zhang J, Pillai R, et al. Venetoclax and hypomethylating agents in TP53-mutated acute myeloid leukaemia. Br J Haematol. 2019;187:e45-8.

30. Li K, Du Y, Cai Y, et al. Single-cell analysis reveals the chemotherapy-induced cellular reprogramming and novel therapeutic targets in relapsed/refractory acute myeloid leukemia. Leukemia. 2023;37:308-25.

31. Pei S, Shelton IT, Gillen AE, et al. A novel type of monocytic leukemia stem cell revealed by the clinical use of Venetoclax-based therapy. Cancer Discov. 2023;13:2032-49.

32. Rodriguez-Sevilla JJ, Ganan-Gomez I, Ma F, et al. Hematopoietic stem cells with granulo-monocytic differentiation state overcome venetoclax sensitivity in patients with myelodysplastic syndromes. Nat Commun. 2024;15:2428.

33. Zhao L, Yang J, Chen M, et al. Myelomonocytic and monocytic acute myeloid leukemia demonstrate comparable poor outcomes with venetoclax-based treatment: a monocentric real-world study. Ann Hematol. 2024;103:1197-209.

34. Sheth AI, Althoff MJ, Tolison H, et al. Targeting acute myeloid leukemia stem cells through perturbation of mitochondrial calcium. Cancer Discov. 2024;14:1922-39.

35. Stevens BM, Jones CL, Pollyea DA, et al. Fatty acid metabolism underlies venetoclax resistance in acute myeloid leukemia stem cells. Nat Cancer. 2020;1:1176-87.

36. Karjalainen R, Liu M, Kumar A, et al. Elevated expression of S100A8 and S100A9 correlates with resistance to the BCL-2 inhibitor venetoclax in AML. Leukemia. 2019;33:2548-53.

37. Mittal D, Gubin MM, Schreiber RD, Smyth MJ. New insights into cancer immunoediting and its three component phases - elimination, equilibrium and escape. Curr Opin Immunol. 2014;27:16-25.

38. Qiu J, Xu B, Ye D, et al. Cancer cells resistant to immune checkpoint blockade acquire interferon-associated epigenetic memory to sustain T cell dysfunction. Nat Cancer. 2023;4:43-61.

39. Vadakekolathu J, Minden MD, Hood T, et al. Immune landscapes predict chemotherapy resistance and immunotherapy response in acute myeloid leukemia. Sci Transl Med. 2020;12:eaaz0463.

40. Xie X, Zhang W, Zhou X, et al. Low doses of IFN-γ maintain self-renewal of leukemia stem cells in acute myeloid leukemia. Oncogene. 2023;42:3657-69.

41. Wang B, Reville PK, Yassouf MY, et al. Comprehensive characterization of IFNγ signaling in acute myeloid leukemia reveals prognostic and therapeutic strategies. Nat Commun. 2024;15:1821.

42. Yang R, Du Y, Zhang M, et al. Multi-omics analysis reveals interferon-stimulated gene OAS1 as a prognostic and immunological biomarker in pan-cancer. Front Immunol. 2023;14:1249731.

43. Nagai M, Vo NH, Shin Ogawa L, et al. The oncology drug elesclomol selectively transports copper to the mitochondria to induce oxidative stress in cancer cells. Free Radical Biol Med. 2012;52:2142-50.

44. Tsvetkov P, Coy S, Petrova B, et al. Copper induces cell death by targeting lipoylated TCA cycle proteins. Science. 2022;375:1254-61.

45. Cierlitza M, Chauvistré H, Bogeski I, et al. Mitochondrial oxidative stress as a novel therapeutic target to overcome intrinsic drug resistance in melanoma cell subpopulations. Exp Dermatol. 2015;24:155-7.

46. Corazao-Rozas P, Guerreschi P, Jendoubi M, et al. Mitochondrial oxidative stress is the Achille’s heel of melanoma cells resistant to Braf-mutant inhibitor. Oncotarget. 2013;4:1986-98.

47. Tsvetkov P, Detappe A, Cai K, et al. Mitochondrial metabolism promotes adaptation to proteotoxic stress. Nat Chem Biol. 2019;15:681-9.

48. Ohlstrom D, Bakhtiari M, Mumme H, et al. Longitudinal single-cell analysis reveals treatment-resistant stem and mast cells with potential treatments for pediatric AML. Leukemia. 2025;39:2721-34.

49. Kumar SK, Harrison SJ, Cavo M, et al. Venetoclax or placebo in combination with bortezomib and dexamethasone in relapsed or refractory multiple myeloma (BELLINI): final overall survival results from a randomised, phase 3 study. Lancet Haematol. 2025;12:e574-87.

Cancer Drug Resistance
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