Molecular Characterization of the Highest Risk Adult Patients With Acute Myeloid Leukemia (AML) Through Multi-Omics Clustering

Background: Acute myeloid leukemia (AML) is a clinically heterogeneous group of cancers. While some patients respond well to chemotherapy, we describe here a subgroup with distinct molecular features that has very poor prognosis under chemotherapy. The classification of AML relies substantially on cytogenetics, but most cytogenetic abnormalities do not offer targets for development of targeted therapeutics. Therefore, it is important to create a detailed molecular characterization of the subgroup most in need of new targeted therapeutics. Methods: We used a multi-omics approach to identify a molecular subgroup with the worst response to chemotherapy, and to identify promising drug targets specifically for this AML subgroup. Results: Multi-omics clustering analysis resulted in three primary clusters among 166 AML adult cancer cases in TCGA data. One of these clusters, which we label as the high-risk molecular subgroup (HRMS), consisted of cases that responded very poorly to standard chemotherapy, with only about 10% survival to 2 years. The gene TP53 was mutated in most cases in this subgroup but not in all of them. The top six genes over-expressed in the HRMS subgroup included E2F4, CD34, CD109, MN1, MMLT3, and CD200. Multi-omics pathway analysis using RNA and CNA expression data identified in the HRMS subgroup over-activated pathways related to immune function, cell proliferation, and DNA damage. Conclusion: A distinct subgroup of AML patients are not successfully treated with chemotherapy, and urgently need targeted therapeutics based on the molecular features of this subgroup. Potential drug targets include over-expressed genes E2F4, and MN1, as well as mutations in TP53, and several over-activated molecular pathways.

[1]  M. Konopleva,et al.  Acute myeloid leukemia: current progress and future directions , 2021, Blood Cancer Journal.

[2]  Tongqiang Zhang,et al.  Integrative Analysis of Multi-Omics Identified the Prognostic Biomarkers in Acute Myelogenous Leukemia , 2020, Frontiers in Oncology.

[3]  J. Taub,et al.  Targeting multiple signaling pathways: the new approach to acute myeloid leukemia therapy , 2020, Signal Transduction and Targeted Therapy.

[4]  Tin Nguyen,et al.  Disease subtyping using community detection from consensus networks , 2020, International Conference on Knowledge and Systems Engineering.

[5]  Tin Nguyen,et al.  Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer , 2020, Frontiers in Genetics.

[6]  P. Vyas,et al.  New directions for emerging therapies in acute myeloid leukemia: the next chapter , 2020, Blood Cancer Journal.

[7]  U. Dührsen,et al.  Inflammation-driven activation of JAK/STAT signaling reversibly accelerates acute myeloid leukemia in vitro. , 2020, Blood advances.

[8]  Steven D Green,et al.  Treatment of Acute Myeloid Leukemia in the Era of Genomics—Achievements and Persisting Challenges , 2020, Frontiers in Genetics.

[9]  Y. Liu,et al.  Therapeutic Targeting of TP53-mutated Acute Myeloid Leukemia by Inhibiting HIF-1α with Echinomycin , 2020, Oncogene.

[10]  Yan Du,et al.  E2F4 functions as a tumour suppressor in acute myeloid leukaemia via inhibition of the MAPK signalling pathway by binding to EZH2 , 2020, Journal of cellular and molecular medicine.

[11]  R. Schlenk,et al.  Targeting FLT3 mutations in AML: review of current knowledge and evidence , 2019, Leukemia.

[12]  G. Blandino,et al.  New therapeutic strategies to treat human cancers expressing mutant p53 proteins , 2018, Journal of Experimental & Clinical Cancer Research.

[13]  Yuan Ji,et al.  TCGA-Assembler 2: Software Pipeline for Retrieval and Processing of TCGA/CPTAC Data , 2017, bioRxiv.

[14]  J. Mesirov,et al.  The Molecular Signatures Database Hallmark Gene Set Collection , 2015 .

[15]  Benjamin J. Raphael,et al.  Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. , 2013, The New England journal of medicine.

[16]  H. Abdi,et al.  Multiple factor analysis: principal component analysis for multitable and multiblock data sets , 2013 .

[17]  T. Pardee Overexpression of MN1 Confers Resistance to Chemotherapy, Accelerates Leukemia Onset, and Suppresses p53 and Bim Induction , 2012, PloS one.

[18]  Benjamin E. Gross,et al.  The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. , 2012, Cancer discovery.

[19]  Matthew D. Wilkerson,et al.  ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking , 2010, Bioinform..

[20]  C. Bloomfield,et al.  The 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: rationale and important changes. , 2009, Blood.

[21]  E. Estey,et al.  Treatment of acute myeloid leukemia , 2009, Haematologica.

[22]  P. Grambsch,et al.  Modeling Survival Data: Extending the Cox Model , 2000 .

[23]  B. Quesnel,et al.  p53 Mutations Are Associated With Resistance to Chemotherapy and Short Survival in Hematologic Malignancies , 1994 .

[24]  A. Dreher Modeling Survival Data Extending The Cox Model , 2016 .

[25]  B. Ducommun,et al.  Experimental Therapeutics , Molecular Targets , and Chemical Biology Constitutive Activation of the DNA Damage Signaling Pathway in Acute Myeloid Leukemia with Complex Karyotype : Potential Importance for Checkpoint Targeting Therapy , 2009 .