Transcriptome free energy can serve as a dynamic patient-specific biomarker in acute myeloid leukemia.
Journal
NPJ systems biology and applications
ISSN: 2056-7189
Titre abrégé: NPJ Syst Biol Appl
Pays: England
ID NLM: 101677786
Informations de publication
Date de publication:
25 Mar 2024
25 Mar 2024
Historique:
received:
07
08
2023
accepted:
26
02
2024
medline:
26
3
2024
pubmed:
26
3
2024
entrez:
26
3
2024
Statut:
epublish
Résumé
Acute myeloid leukemia (AML) is prevalent in both adult and pediatric patients. Despite advances in patient categorization, the heterogeneity of AML remains a challenge. Recent studies have explored the use of gene expression data to enhance AML diagnosis and prognosis, however, alternative approaches rooted in physics and chemistry may provide another level of insight into AML transformation. Utilizing publicly available databases, we analyze 884 human and mouse blood and bone marrow samples. We employ a personalized medicine strategy, combining state-transition theory and surprisal analysis, to assess the RNA transcriptome of individual patients. The transcriptome is transformed into physical parameters that represent each sample's steady state and the free energy change (FEC) from that steady state, which is the state with the lowest free energy.We found the transcriptome steady state was invariant across normal and AML samples. FEC, representing active molecular processes, varied significantly between samples and was used to create patient-specific barcodes to characterize the biology of the disease. We discovered that AML samples that were in a transition state had the highest FEC. This disease state may be characterized as the most unstable and hence the most therapeutically targetable since a change in free energy is a thermodynamic requirement for disease progression. We also found that distinct sets of ongoing processes may be at the root of otherwise similar clinical phenotypes, implying that our integrated analysis of transcriptome profiles may facilitate a personalized medicine approach to cure AML and restore a steady state in each patient.
Identifiants
pubmed: 38527998
doi: 10.1038/s41540-024-00352-6
pii: 10.1038/s41540-024-00352-6
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
32Subventions
Organisme : Israel Science Foundation (ISF)
ID : 1961/19
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : U01CA250067 S1
Organisme : U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
ID : U01CA250067 S1
Informations de copyright
© 2024. The Author(s).
Références
Papaemmanuil, E. et al. Genomic classification and prognosis in acute myeloid leukemia. N. Engl. J. Med. 374, 2209–2221 (2016).
pubmed: 27276561
pmcid: 4979995
doi: 10.1056/NEJMoa1516192
Jongen-Lavrencic, M. et al. Molecular minimal residual disease in acute myeloid leukemia. N. Engl. J. Med. 378, 1189–1199 (2018).
pubmed: 29601269
doi: 10.1056/NEJMoa1716863
Döhner, K. et al. Impact of NPM1/FLT3-ITD genotypes defined by the 2017 European LeukemiaNet in patients with acute myeloid leukemia. Blood 135, 371–380 (2020).
pubmed: 31826241
pmcid: 6993016
doi: 10.1182/blood.2019002697
Herold, T. et al. Isolated trisomy 13 defines a homogeneous AML subgroup with high frequency of mutations in spliceosome genes and poor prognosis. Blood 124, 1304–1311 (2014).
pubmed: 24923295
doi: 10.1182/blood-2013-12-540716
Remacle, F., Kravchenko-Balasha, N., Levitzki, A. & Levine, R. D. Information-theoretic analysis of phenotype changes in early stages of carcinogenesis. Proc. Natl Acad. Sci. USA 107, 10324–10329 (2010).
pubmed: 20479229
pmcid: 2890488
doi: 10.1073/pnas.1005283107
Vasudevan, S., Flashner-Abramson, E., Remacle, F., Levine, R. D. & Kravchenko-Balasha, N. Personalized disease signatures through information-theoretic compaction of big cancer data. Proc. Natl Acad. Sci. USA 115, 7694–7699 (2018).
pubmed: 29976841
pmcid: 6065026
doi: 10.1073/pnas.1804214115
Vasudevan, S. et al. Overcoming resistance to BRAFV600E inhibition in melanoma by deciphering and targeting personalized protein network alterations. npj Precis. Oncol. 5, 50 (2021).
pubmed: 34112933
pmcid: 8192524
doi: 10.1038/s41698-021-00190-3
Moris, N., Pina, C. & Arias, A. M. Transition states and cell fate decisions in epigenetic landscapes. Nat. Rev. Genet. 17, 693–703 (2016).
pubmed: 27616569
doi: 10.1038/nrg.2016.98
Rockne, R. C. et al. State-transition analysis of time-sequential gene expression identifies critical points that predict development of acute myeloid leukemia. Cancer Res. 80, 3157–3169 (2020).
pubmed: 32414754
pmcid: 7416495
doi: 10.1158/0008-5472.CAN-20-0354
Frankhouser, D. E. et al. Dynamic patterns of microRNA expression during acute myeloid leukemia state-transition. Sci. Adv. 8, 1664 (2022).
doi: 10.1126/sciadv.abj1664
GDC Data Portal Homepage. National Cancer Institute Office of Cancer Genomics. TARGET: Therapeutically Applicable Research to Generate Effective Treatments. https://portal.gdc.cancer.gov .
Huang, B. J. et al. Integrated stem cell signature and cytomolecular risk determination in pediatric acute myeloid leukemia. Nat. Commun. 13, 1–11 (2022).
Genomic and Epigenomic Landscapes of Adult De Novo Acute Myeloid Leukemia. N. Engl. J. Med. 368, 2059–2074 (2013).
Lowy et al. Toward a shared vision for cancer genomic data. N. Engl. J. Med. 375, 1109–1112 (2016).
pubmed: 27653561
pmcid: 6309165
doi: 10.1056/NEJMp1607591
Burd, A. et al. Precision medicine treatment in acute myeloid leukemia using prospective genomic profiling: feasibility and preliminary efficacy of the Beat AML Master Trial. Nat. Med. 26, 1852–1858 (2020).
pubmed: 33106665
pmcid: 8530434
doi: 10.1038/s41591-020-1089-8
Kaser, E. C. et al. The role of various interleukins in acute myeloid leukemia. Med. Oncol. 38, 55 (2032).
doi: 10.1007/s12032-021-01498-7
Nakase, K., Kita, K. & Katayama, N. IL-2/IL-3 interplay mediates growth of CD25 positive acute myeloid leukemia cells. Med. Hypotheses 115, 5–7 (2018).
pubmed: 29685196
doi: 10.1016/j.mehy.2018.03.007
Welch, J. S. et al. The origin and evolution of mutations in acute myeloid leukemia. Cell 150, 264–278 (2012).
pubmed: 22817890
pmcid: 3407563
doi: 10.1016/j.cell.2012.06.023
Rodrigues, A. C. Bd. C. et al. Cell signaling pathways as molecular targets to eliminate AML stem cells. Crit. Rev. Oncol. Hematol. 160, 103277 (2021).
pubmed: 33716201
doi: 10.1016/j.critrevonc.2021.103277
Cozzolino, F. et al. Interleukin 1 as an autocrine growth factor for acute myeloid leukemia cells. Proc. Natl Acad. Sci. USA 86, 2369–2373 (1989).
pubmed: 2522658
pmcid: 286914
doi: 10.1073/pnas.86.7.2369
Vijay, V. et al. Interleukin-8 blockade prevents activated endothelial cell mediated proliferation and chemoresistance of acute myeloid leukemia. Leuk. Res. 84, 106180 (2019).
pubmed: 31299413
pmcid: 6857733
doi: 10.1016/j.leukres.2019.106180
Nishioka, C., Ikezoe, T., Pan, B., Xu, K. & Yokoyama, A. MicroRNA-9 plays a role in interleukin-10-mediated expression of E-cadherin in acute myelogenous leukemia cells. Cancer Sci. 108, 685–695 (2017).
pubmed: 28107581
pmcid: 5406602
doi: 10.1111/cas.13170
Porcu, P. et al. Hyperleukocytic leukemias and leukostasis: a review of pathophysiology, clinical presentation and management. Leuk. Lymphoma 39, 1–18 (2000).
pubmed: 10975379
doi: 10.3109/10428190009053534
Nourshargh, S. & Alon, R. Leukocyte migration into inflamed tissues. Immunity 41, 694–707 (2014).
pubmed: 25517612
doi: 10.1016/j.immuni.2014.10.008
Yuan, T. L. & Cantley, L. C. PI3K pathway alterations in cancer: Variations on a theme. Oncogene 27, 5497–5510 (2008).
pubmed: 18794884
pmcid: 3398461
doi: 10.1038/onc.2008.245
Engelman, J. A. Targeting PI3K signalling in cancer: opportunities, challenges and limitations. Nat. Rev. Cancer 9, 550–562 (2009).
pubmed: 19629070
doi: 10.1038/nrc2664
Shlush, L. I. et al. Identification of pre-leukaemic haematopoietic stem cells in acute leukaemia. Nature 506, 328–333 (2014).
pubmed: 24522528
pmcid: 4991939
doi: 10.1038/nature13038
Flashner-Abramson, E., Vasudevan, S., Adejumobi, I. A., Sonnenblick, A. & Kravchenko-Balasha, N. Decoding cancer heterogeneity: Studying patient-specific signaling signatures towards personalized cancer therapy. Theranostics 9, 5149–5165 (2019).
pubmed: 31410207
pmcid: 6691586
doi: 10.7150/thno.31657
Gross, A., Li, C. M., Remacle, F. & Levine, R. D. Free energy rhythms in Saccharomyces cerevisiae: a dynamic perspective with implications for ribosomal biogenesis. Biochemistry 52, 1641–1648 (2013).
pubmed: 23379300
doi: 10.1021/bi3016982
Kravchenko-Balasha, N., Wang, J., Remacle, F., Levine, R. D. & Heath, J. R. Glioblastoma cellular architectures are predicted through the characterization of two-cell interactions. Proc. Natl Acad. Sci. USA 111, 6521–6526 (2014).
pubmed: 24733941
pmcid: 4035957
doi: 10.1073/pnas.1404462111
Alkhatib, H. et al. Patient-specific signaling signatures predict optimal therapeutic combinations for triple negative breast cancer. Mol. Cancer 23, 1–7 (2024).
doi: 10.1186/s12943-023-01921-9
Klein, S. & Levitzki, A. Targeted cancer therapy: promise and reality. Adv. Cancer Res. 97, 295–319 (2007).
pubmed: 17419951
doi: 10.1016/S0065-230X(06)97013-4
Levitzki, A. & Klein, S. Signal transduction therapy of cancer. Mol Asp. Med. 31, 287–329 (2010).
doi: 10.1016/j.mam.2010.04.001
Huang, S., Ernberg, I. & Kauffman, S. Cancer attractors: a systems view of tumors from a gene network dynamics and developmental perspective. Semin. Cell Dev. Biol. 20, 869 (2009).
pubmed: 19595782
pmcid: 2754594
doi: 10.1016/j.semcdb.2009.07.003
Kang, X. & Li, C. A dimension reduction approach for energy landscape: identifying intermediate states in metabolism‐EMT network. Adv. Sci. 8, 2003133 (2021).
doi: 10.1002/advs.202003133
Li, R. et al. GDCRNATools: An R/Bioconductor package for integrative analysis of lncRNA, miRNA and mRNA data in GDC. Bioinformatics 34, 2515–2517 (2018).
pubmed: 29509844
doi: 10.1093/bioinformatics/bty124
Alter, O., Brown, P. O. & Botstein, D. Singular value decomposition for genome-Wide expression data processing and modeling. Proc. Natl Acad. Sci. USA. 97, 10101–10106 (2000).
pubmed: 10963673
pmcid: 27718
doi: 10.1073/pnas.97.18.10101
Kraskov, A., Stögbauer, H. & Grassberger, P. Estimating mutual information. Phys. Rev. E: Stat. Physics Plasmas Fluids Relat. Interdiscip. Top. 69, 16 (2004).
doi: 10.1103/PhysRevE.69.066138
Gao, W., Kannan, S., Oh, S. & Viswanath, P. Estimating mutual information for discrete-continuous mixtures. Adv. Neural Inf. Process. Syst. 2017-Decem, 5987–5998 (2017).
Hirano, T. et al. Long noncoding RNA, CCDC26, controls myeloid leukemia cell growth through regulation of KIT expression. Mol. Cancer 14, 90 (2015).
pubmed: 25928165
pmcid: 4423487
doi: 10.1186/s12943-015-0364-7
Ishikawa, Y. et al. Prospective evaluation of prognostic impact of KIT mutations on acute myeloid leukemia with RUNX1-RUNX1T1 and CBFB-MYH11. Blood Adv. 4, 66–75 (2020).
pubmed: 31899799
pmcid: 6960455
doi: 10.1182/bloodadvances.2019000709
Sherman, B. T. et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 50, W216–W221 (2022).
pubmed: 35325185
pmcid: 9252805
doi: 10.1093/nar/gkac194
Dennis, G. et al. DAVID: Database for annotation, visualization, and integrated discovery. Genome Biol. 4, P3 (2003).
pubmed: 12734009
doi: 10.1186/gb-2003-4-5-p3
Szklarczyk, D. et al. STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).
pubmed: 30476243
doi: 10.1093/nar/gky1131
Shannon, P. et al. Cytoscape: a software environment for integrated models. Genome Res. 13, 426 (1971).