Faculty Publications

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https://hdl.handle.net/1911/75173
This collection includes faculty journal articles deposited per Rice's Open Access Policy and additional faculty work. Items found in this collection can also be found in the authors' departmental faculty publication collections.

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Browsing Faculty Publications by Author "Abbas, Hussein A."

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    Decoupling Lineage-Associated Genes in Acute Myeloid Leukemia Reveals Inflammatory and Metabolic Signatures Associated With Outcomes
    (Frontiers, 2021) Abbas, Hussein A.; Mohanty, Vakul; Wang, Ruiping; Huang, Yuefan; Liang, Shaoheng; Wang, Feng; Zhang, Jianhua; Qiu, Yihua; Hu, Chenyue W.; Qutub, Amina A.; Dail, Monique; Bolen, Christopher R.; Daver, Naval; Konopleva, Marina; Futreal, Andrew; Chen, Ken; Wang, Linghua; Kornblau, Steven M.
    Acute myeloid leukemia (AML) is a heterogeneous disease with variable responses to therapy. Cytogenetic and genomic features are used to classify AML patients into prognostic and treatment groups. However, these molecular characteristics harbor significant patient-to-patient variability and do not fully account for AML heterogeneity. RNA-based classifications have also been applied in AML as an alternative approach, but transcriptomic grouping is strongly associated with AML morphologic lineages. We used a training cohort of newly diagnosed AML patients and conducted unsupervised RNA-based classification after excluding lineage-associated genes. We identified three AML patient groups that have distinct biological pathways associated with outcomes. Enrichment of inflammatory pathways and downregulation of HOX pathways were associated with improved outcomes, and this was validated in 2 independent cohorts. We also identified a group of AML patients who harbored high metabolic and mTOR pathway activity, and this was associated with worse clinical outcomes. Using a comprehensive reverse phase protein array, we identified higher mTOR protein expression in the highly metabolic group. We also identified a positive correlation between degree of resistance to venetoclax and mTOR activation in myeloid and lymphoid cell lines. Our approach of integrating RNA, protein, and genomic data uncovered lineage-independent AML patient groups that share biologic mechanisms and can inform outcomes independent of commonly used clinical and demographic variables; these groups could be used to guide therapeutic strategies.
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    Proteomic Profiling of Acute Promyelocytic Leukemia Identifies Two Protein Signatures Associated with Relapse
    (Wiley, 2019) Hoff, Fieke W.; Hu, Chenyue W.; Qutub, Amina A.; Qiu, Yihua; Hornbaker, Marisa J.; Bueso‐Ramos, Carlos; Abbas, Hussein A.; Post, Sean M.; Bont, Eveline S.J.M. de; Kornblau, Steven M.
    Purpose: Acute promyelocytic leukemia (APL) is the most prognostically favorable subtype of Acute myeloid leukemia (AML). Defining the features that allow identification of APL patients likely to relapse after therapy remains challenging. Experimental Design: Proteomic profiling is performed on 20 newly diagnosed APL, 205 non‐APL AML, and 10 normal CD34+ samples using Reverse Phase Protein Arrays probed with 230 antibodies. Results: Comparison between APL and non‐APL AML samples identifies 8.3% of the proteins to be differentially expressed. Proteins higher expressed in APL are involved in the pro‐apoptotic pathways or are linked to higher proliferation. The “MetaGalaxy” approach that considers proteins in relation to other assayed proteins stratifies the APL patients into two protein signatures. All of the relapse patients (n = 4/4) are in protein signature 2 (S2). Comparison of proteins between the signatures shows significant differences in relative expression for 38 proteins. Protein expression summary plots suggest less translational activity in combination with a less proliferative character for S2 compared to signature 1. Conclusions and Clinical Relevance: This study provides a potential proteomic‐based classification of APL patients that may be useful for risk stratification and therapeutic guidance. Validation in a larger independent cohort is required.