Abstract 2392: The use of transcriptomic analysis to identify novel drug targets for DSA drug combination studies in acute myeloid leukemia

Abstract Acute myeloid leukemia (AML) is a heterogeneous hematological malignancy that is commonly characterized by uncontrolled proliferation of myeloid blasts that overwhelm the bone marrow, leading to hematopoietic failure. AML is the most common form of acute leukemia in adults and its incidence increases with age. Despite recent advances, the overall prognosis of AML patients remains poor and relapse rates are high. Thus, there remains a need to identify potential therapeutic candidates that can improve patient survival outcomes and reduce the risk of relapse in AML patients. The objective of this study was to utilize transcriptomics to identify potential genes that can be targeted by drug candidates for future use in Duocarmycin SA (DSA)-targeted therapy combination studies in AML. DSA is a DNA alkylating agent that binds to the minor groove of DNA forming DNA adducts that lead to DNA damage. Previously, our lab showed that DSA reduced proliferation, induced cell cycle arrest at the G2/M phase, and triggered apoptosis in AML cells in a dose- and time-dependent manner. Therefore, we hypothesized that RNA sequencing analysis of genes obtained from DSA-treated AML cells will identify novel genes that can targeted by potential drug candidates. To test our hypothesis, human AML cell lines (Molm-14 and HL-60) were treated with high (Molm-14: 100 pM; HL-60: 500 pM) and low (Molm-14: 11.12 pM; HL-60: 112.7 pM) concentrations of DSA for 36 hours, cells were harvested, RNA was isolated and RNA sequencing and gene expression profiling analysis were performed by the Hartwell Center for Biotechnology and the Center for Applied Bioinformatics at St. Jude Children’s Research Hospital, respectively. Pathway analysis was performed using the NIH’s Database for Annotation, Visualization and Integrated Discovery (DAVID) tool to identify the top DSA-induced pathways and their associated genes. Differentially expressed genes (DEGs) for all four DSA treatment groups were identified using the cutoffs: P-value <0.05, Log2Fold Change >0.5 and <-0.5, and then the overlapping DEGs were visualized using Venny 2.1.0. Our results showed that the top pathways that were activated in all four DSA treatment groups included oxidative phosphorylation, chemical carcinogenesis - reactive oxygen species, and metabolic pathways. Genes that were upregulated within these pathways primarily included mitochondrial associated genes that play a role primarily in apoptosis (MT-ATP6, MT-CO2, and MT-ND5). Additionally, some of the pathway genes play a role in proliferation (MT-ND2 and TPM2) and chemoresistance (MT-ND2, MT-ND4 and TPM2). Our findings suggest that the genes associated with proliferation and chemoresistance can serve as potential therapeutic targets that can be used to develop novel targeted therapies to be evaluated in combination with DSA for the treatment of AML. Citation Format: William A. Chen, Valery Filippov, Jie Fang, Pedro Ochoa, Carlos A. Casiano, Kristopher E. Boyle, Sinisa Dovat, Hongjian Jin, Jun Yang, Olivia L. Francis-Boyle. The use of transcriptomic analysis to identify novel drug targets for DSA drug combination studies in acute myeloid leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2392.

Authors

Institutions

Publication Details

Journal
Cancer Research
Published
2025-04-21
DOI
https://doi.org/10.1158/1538-7445.am2025-2392
Primary Topic
Acute Myeloid Leukemia Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract 2392: The use of transcriptomic analysis to identify novel drug targets for DSA drug combination studies in acute myeloid leukemia

Kristopher E. Boyle, Carlos A. Casiano, William A. Chen, Valery Filippov et al.
Cancer Research
Acute Myeloid Leukemia Research
article

Abstract 2392: The use of transcriptomic analysis to identify novel drug targets for DSA drug combination studies in acute myeloid leukemia

Kristopher E. Boyle, Carlos A. Casiano, William A. Chen, Valery Filippov, Pedro T. Ochoa, Olivia L. Francis-Boyle, Jun Yang, Hongjian Jin, Sinisa Dovat, Jie Fang
article en

Abstract

Abstract Acute myeloid leukemia (AML) is a heterogeneous hematological malignancy that is commonly characterized by uncontrolled proliferation of myeloid blasts that overwhelm the bone marrow, leading to hematopoietic failure. AML is the most common form of acute leukemia in adults and its incidence increases with age. Despite recent advances, the overall prognosis of AML patients remains poor and relapse rates are high. Thus, there remains a need to identify potential therapeutic candidates that can improve patient survival outcomes and reduce the risk of relapse in AML patients. The objective of this study was to utilize transcriptomics to identify potential genes that can be targeted by drug candidates for future use in Duocarmycin SA (DSA)-targeted therapy combination studies in AML. DSA is a DNA alkylating agent that binds to the minor groove of DNA forming DNA adducts that lead to DNA damage. Previously, our lab showed that DSA reduced proliferation, induced cell cycle arrest at the G2/M phase, and triggered apoptosis in AML cells in a dose- and time-dependent manner. Therefore, we hypothesized that RNA sequencing analysis of genes obtained from DSA-treated AML cells will identify novel genes that can targeted by potential drug candidates. To test our hypothesis, human AML cell lines (Molm-14 and HL-60) were treated with high (Molm-14: 100 pM; HL-60: 500 pM) and low (Molm-14: 11.12 pM; HL-60: 112.7 pM) concentrations of DSA for 36 hours, cells were harvested, RNA was isolated and RNA sequencing and gene expression profiling analysis were performed by the Hartwell Center for Biotechnology and the Center for Applied Bioinformatics at St. Jude Children’s Research Hospital, respectively. Pathway analysis was performed using the NIH’s Database for Annotation, Visualization and Integrated Discovery (DAVID) tool to identify the top DSA-induced pathways and their associated genes. Differentially expressed genes (DEGs) for all four DSA treatment groups were identified using the cutoffs: P-value <0.05, Log2Fold Change >0.5 and <-0.5, and then the overlapping DEGs were visualized using Venny 2.1.0. Our results showed that the top pathways that were activated in all four DSA treatment groups included oxidative phosphorylation, chemical carcinogenesis - reactive oxygen species, and metabolic pathways. Genes that were upregulated within these pathways primarily included mitochondrial associated genes that play a role primarily in apoptosis (MT-ATP6, MT-CO2, and MT-ND5). Additionally, some of the pathway genes play a role in proliferation (MT-ND2 and TPM2) and chemoresistance (MT-ND2, MT-ND4 and TPM2). Our findings suggest that the genes associated with proliferation and chemoresistance can serve as potential therapeutic targets that can be used to develop novel targeted therapies to be evaluated in combination with DSA for the treatment of AML. Citation Format: William A. Chen, Valery Filippov, Jie Fang, Pedro Ochoa, Carlos A. Casiano, Kristopher E. Boyle, Sinisa Dovat, Hongjian Jin, Jun Yang, Olivia L. Francis-Boyle. The use of transcriptomic analysis to identify novel drug targets for DSA drug combination studies in acute myeloid leukemia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2392.

Cancer ResearchVol. 85(8_Supplement_1)
St. Jude Children's Research Hospital (US), Loma Linda University (US)
Openalex Percentile: Top 90%
Acute Myeloid Leukemia Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.