Decoding Acute Myeloid Leukemia Heterogeneity: A Multi-Omics Factor Analysis of Therapeutic Vulnerabilities and Clinical Outcomes

Abstract

Acute myeloid leukemia (AML) is characterized by profound biological and clinical heterogeneity, where traditional mutation-centric frameworks often fail to fully capture the complex interplay between genomic drivers and functional phenotypes. In this study, we employed Multi-Omic Factor Analysis (MOFA) to integrate transcriptomic, mutational, pharmacological, and clinical data from a focused cohort of treatment-naïve de novo AML specimens. By utilizing a Bayesian framework, we identified nine latent factors that collapse high-dimensional data into distinct biological axes, explaining the majority of variance in the transcriptomic (54.5%) and clinical (23.7%) modalities. Our results characterized Factor 1 as a monocytic differentiation axis defined by high expression of mature myeloid markers such as CD14 and S100A8/9. Furthermore, the integration of MOFA-derived factors with the European LeukemiaNet (ELN) 2022 risk classification improved predictive accuracy, increasing the Harrell’s C-index from 0.66 to 0.72. These findings conclude that "molecularly silent" variance—biology not captured by somatic mutations alone—is a critical determinant of chemotherapy response and clinical outcome. Ultimately, this work provides a robust framework for transitioning toward a functional, multi-omic approach for personalized therapeutic selection and more precise risk assessment in AML.

Keywords

• Acute myeloid leukemia
• Multi-omics integration
• Therapeutic vulnerabilities
• Prognostic biomarkers
• Drug sensitivity
• Computational biology

References

1. Argelaguet, R., Velten, B., Arnol, D., Dietrich, S., Zenz, T., Marioni, J. C., Buettner, F., Huber, W., & Stegle, O. (2018). Multi‐Omics Factor Analysis—A framework for unsupervised integration of multi‐omics data sets. Molecular Systems Biology, 14(6), MSB178124. https://doi.org/10.15252/msb.20178124
2. Behbehani, G. K., Samusik, N., Bjornson, Z. B., Fantl, W. J., Medeiros, B. C., & Nolan, G. P. (2015). Mass cytometric functional profiling of acute myeloid leukemia defines cell-cycle and immunophenotypic properties that correlate with known responses to therapy. Cancer Discovery, 5(9), 988–1003. https://doi.org/10.1158/2159-8290.CD-15-0298
3. Bottomly, D., Long, N., Schultz, A. R., Kurtz, S. E., Tognon, C. E., Johnson, K., Abel, M., Agarwal, A., Avaylon, S., Benton, E., Blucher, A., Borate, U., Braun, T. P., Brown, J., Bryant, J., Burke, R., Carlos, A., Chang, B. H., Cho, H. J., … Tyner, J. W. (2022). Integrative analysis of drug response and clinical outcome in acute myeloid leukemia. Cancer Cell, 40(8), 850–864.e9. https://doi.org/10.1016/j.ccell.2022.07.002
4. Dolgalev, I. (2025). msigdbr: MSigDB Gene Sets for Multiple Organisms in a Tidy Data Format (Version 25.1.1) [Computer software]. https://cran.r-project.org/web/packages/msigdbr/index.html
5. Döhner, H., Wei, A. H., Appelbaum, F. R., Craddock, C., DiNardo, C. D., Dombret, H., Ebert, B. L., Fenaux, P., Godley, L. A., Hasserjian, R. P., Larson, R. A., Levine, R. L., Miyazaki, Y., Niederwieser, D., Ossenkoppele, G., Röllig, C., Sierra, J., Stein, E. M., Tallman, M. S., … Löwenberg, B. (2022). Diagnosis and management of AML in adults: 2022 recommendations from an international expert panel on behalf of the ELN. Blood, 140(12), 1345–1377. https://doi.org/10.1182/blood.2022016867
6. Karakaslar, E. O., Severens, J. F., Sánchez-López, E., van Veelen, P. A., Zlei, M., van Dongen, J. J. M., Otte, A. M., Halkes, C. J. M., van Balen, P., Veelken, H., Reinders, M. J. T., Griffioen, M., & van den Akker, E. B. (2024). A transcriptomic based deconvolution framework for assessing differentiation stages and drug responses of AML. NPJ Precision Oncology, 8(1), 105. https://doi.org/10.1038/s41698-024-00596-9
7. Lachowiez, C. A., Long, N., Saultz, J., Gandhi, A., Newell, L. F., Hayes-Lattin, B., Maziarz, R. T., Leonard, J., Bottomly, D., McWeeney, S., Dunlap, J., Press, R., Meyers, G., Swords, R., Cook, R. J., Tyner, J. W., Druker, B. J., & Traer, E. (2023). Comparison and validation of the 2022 European LeukemiaNet guidelines in acute myeloid leukemia. Blood Advances, 7(9), 1899–1909. https://doi.org/10.1182/bloodadvances.2022009010
8. Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P. (2015). The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Systems, 1(6), 417–425. https://doi.org/10.1016/j.cels.2015.12.004

9. Pollyea, D. A., Amaya, M., Strati, P., & Konopleva, M. Y. (2019). Venetoclax for AML: Changing the treatment paradigm. Blood Advances, 3(24), 4326–4335. https://doi.org/10.1182/bloodadvances.2019000937
10. Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550. https://doi.org/10.1073/pnas.0506580102
11. Tyner, J. W., Tognon, C. E., Bottomly, D., Wilmot, B., Kurtz, S. E., Savage, S. L., Long, N., Schultz, A. R., Traer, E., Abel, M., Agarwal, A., Blucher, A., Borate, U., Bryant, J., Burke, R., Carlos, A., Carpenter, R., Carroll, J., Chang, B. H., … Druker, B. J. (2018). Functional genomic landscape of acute myeloid leukaemia. Nature, 562(7728), 526–531. https://doi.org/10.1038/s41586-018-0623-z
12. Vakiti, A., Reynolds, S. B., & Mewawalla, P. (2024). Acute Myeloid Leukemia. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK507875/
13. Wachter, F., & Pikman, Y. (2024). Pathophysiology of Acute Myeloid Leukemia. Acta Haematologica, 147(2), 229–246. https://doi.org/10.1159/000536152
14. Wang, Y., Ji, P., Liu, J., Broaddus, R. R., Xue, F., & Zhang, W. (2009). Centrosome-associated regulators of the G2/M checkpoint as targets for cancer therapy. Molecular Cancer, 8(1), 8. https://doi.org/10.1186/1476-4598-8-8
15. Wei, A. H., & Roberts, A. W. (2023). BCL2 inhibition: A new paradigm for the treatment of AML and beyond. HemaSphere, 7(6), e912. https://doi.org/10.1097/HS9.0000000000000912
16. Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012). clusterProfiler: An R package for comparing biological themes among gene clusters. Omics: A Journal of Integrative Biology, 16(5), 284–287. https://doi.org/10.1089/omi.2011.0118
17. Zhou, J., Zhang, T., Xu, Z., Gu, Y., Ma, J., Li, X., Guo, H., Wen, X., Zhang, W., Yang, L., Liu, X., Lin, J., & Qian, J. (2019). BCL2 overexpression: Clinical implication and biological insights in acute myeloid leukemia. Diagnostic Pathology, 14(1), 68. https://doi.org/10.1186/s13000-019-0841-1