Genetic biomarkers of drug resistance: A compass of prognosis and targeted therapy in acute myeloid leukemia
Introduction
Acute myeloid leukemia (AML) is a prevalent and aggressive hematopoietic malignancy accounting for ∼1% of newly diagnosed cancers with a 5–year overall survival (OS) of <40%, predominantly due to the failure to achieve a durable remission (Dohner et al., 2015; Eisfeld et al., 2018; Roloff and Griffiths, 2018). Refractory and relapsed AML occurs in approximately 50% of patients <60 years old and ∼ 80–90% of patients which are >60 years old after complete remission (Schlenk et al., 2017a, Schlenk et al., 2017b). The high recurrence of disease is mainly caused by chemoresistance, leading to dismal prognosis (Murphy and Yee, 2017; Briot et al., 2018). Advanced genetic analyses have been implemented for personalized drug treatment. However, the current treatment regimens are changing due to a better molecular understanding of the genetics and pathophysiology of AML. In this respect, since 2017, as many as eight new drugs have been approved by the U.S. Food and Drug Administration (FDA) for the treatment of AML; these include the FLT3 inhibitors midostaurin and gilteritinib, the IDH inhibitors ivosidenib and enasidenib, the anti-CD33 monoclonal antibody gemtuzumab ozogamicin, liposomal daunorubicin and cytarabine, the hedgehog pathway inhibitor glasdegib and the BCL-2 inhibitor venetoclax (DiNardo, 2019; Guerra et al., 2019). Despite this remarkable advance, intrinsic (primary) and acquired (secondary) anticancer drug resistance continue to be major impediments towards curative cancer therapy in both hematological malignancies like AML as well as various solid tumors (Gonen and Assaraf, 2012; Niewerth et al., 2015; Taylor et al., 2015; Wijdeven et al., 2016; Zhitomirsky and Assaraf, 2016; Gacche and Assaraf, 2018; Assaraf et al., 2019; Leonetti et al., 2019; Zhang et al., 2019; Kopecka et al., 2020; Short et al., 2020). As such, the development of novel targeted therapeutic modalities may readily surmount well-defined intrinsic and acquired mechanisms of chemoresistance (Livney and Assaraf, 2013; Li et al., 2016b; Guerra et al., 2019; Levin et al., 2019; Patel and Gerber, 2020).
Quantitative sequencing or profiling extensively unveiled the linkage between the molecular heterogenous genomic events of AML and chemoresistance (Tyner et al., 2018; McNeer et al., 2019). Clinically, it has been well accepted that genetic profiling is a feasible tool for the assessment of prognosis. Risk stratification based on cytogenetic and molecular aberrations has classified patients into three risk groups to guide different treatment regimens on whether to conduct hematopoietic stem cell transplantation (Dohner et al., 2017; O’Donnell et al., 2017). However, new discoveries including mutations in genes associated with chromatin regulation, cohesin modifications or RNA splicing have not been fully understood in the AML prognostic field (Meyer et al., 2017; Saez et al., 2017). Cytogenetically normal AML (CN-AML) has been placed in the dilemma of prognostic assessment. Validation of various risk models reveals different prediction of clinical outcome in patients and needs further refinements (Wang et al., 2017a). Therefore, there is a burning need to develop novel and reliable prognostic AML biomarkers.
The specific vulnerability of mutational status constitutes the molecular basis for chemotherapeutic sensitivity to different anticancer drugs (Tyner et al., 2018). For example, the inhibitor of spleen tyrosine kinase (SYK) entospletinib is substantially more active in patients with fms-like tyrosine kinase 3-internal tandem duplication (FLT3-ITD), which has been first demonstrated in preclinical research (Puissant et al., 2014; Tyner et al., 2018). Numerous preclinical studies have delineated various mechanisms that elucidate how leukemia cells escape the cytotoxic activity of various chemotherapeutics. Well defined molecular mechanisms of drug resistance were recognized including the overexpression of the multidrug resistance efflux transporter P-glycoprotein (P-gp/ABCB1) and multidrug resistance associated protein 1 (MRP1/ABCC1) as well as breast cancer resistance protein (ABCG2) (Kathawala et al., 2015). Currently, multiple mechanisms involved in both intracellular as well as extracellular alterations have been identified to be associated with the development of anticancer drug resistance (Levin et al., 2019). Targeting proteins in mediating well-defined chemo-resistant mechanisms offers a promising avenue for the development of targeted pharmacological agents, which could be more effective for specific mutational status or bona fide target gene products in individual patients, hence paving the way towards the rational overcoming of chemoresistance in individual AML patients (Tyner et al., 2018).
In this review, we overview the general mechanisms of drug resistance with novel prognostic or targetable therapeutic strategies in AML. Moreover, we delineate the underlying mechanisms involved in certain AML mutations to provide a better understanding of the association between drug resistance and their clinical prognostic significance. We also suggest some novel and potential targeted therapeutics for individual AML patients.
Section snippets
Mechanisms of anticancer drug resistance
Whole genome sequencing revealed that genetic aberrations in AML can result in primary drug resistance with consequent expansion of dominant subclones; moreover, acquired drug resistance can also emerge via secondary mutations during and/or following chemotherapy (Ding et al., 2012; McNeer et al., 2019). In response to treatment in AML, the dynamic and heterogeneous genetic abnormalities of AML develop a sophisticated chemo-resistant interaction network, causing the frequent occurrence of
Prognostic biomarkers from preclinical studies
Despite the increased understanding of the molecular basis of chemoresistance, only a small number of clinical prognostic biomarkers have been developed to predict prognosis of patients and help therapeutic decision making. Previous study has identified gene expression of some functional molecules and mutations that could constitute a comprehensive prognostic score (Lucena-Araujo et al., 2019). Various indicators present potential prognostic value, including molecules related to signaling
Drug resistance-associated mutations and clinical prognosis
Genetic aberrations have been revealed to have critical roles in clinical prognosis, some of which have been incorporated into the risk stratification proposed by the European Leukemia Net (ELN) and the National Comprehensive Cancer Network (NCCN) (Dohner et al., 2017; O’Donnell et al., 2017). Extended genomic landscape of AML leads to the discovery of more mutations in driver genes including DNA methyltransferase 3 alpha (DNMT3A), IDH1/2, splicing factors or chromatin modification proteins (
Conclusion
The multi-factorial molecular abnormalities associated with chemoresistance, that involve an intricate and dynamic signaling network and cause adaptive responses, are the basis for prognostic assessment and targeted chemotherapies in AML. Here, we have outlined the complex molecular mechanisms of MDR and the various prognostic biomarkers in AML. As the current risk stratification mainly incorporates genetic mutations or fusions, biomarkers arising from preclinical chemoresistance studies are of
Future perspectives
With the increasing understanding of the molecular basis underlying MDR, newly approved targeted drugs with notable curative effects have been developed to improve therapeutic efficacy since 2017. The significant clinical benefits of BCL-2 inhibitor venetoclax highlight the promising effects of multi-targeted therapies against pathways or molecules that have broad biological functions. In this regard, the UPR pathway, which comprehensively regulates hematopoietic cell differentiation, apoptosis
Declaration of Competing Interest
The authors declare no conflict of interest.
Acknowledgements
This work was supported by grants from the National Natural Science Foundation of China (No.81670161, 81872901, 81773755), Beijing Natural Science Foundation (No. J190014), Hebei Province Natural Science Foundation (H2019206713) and CAMS Innovation Fund for Medical Sciences (No. 2018-I2M-1-002). We thank the partial support from the Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, St. John’s University.
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