Elsevier

Drug Resistance Updates

Volume 59, December 2021, 100797
Drug Resistance Updates

Impact of cancer metabolism on therapy resistance – Clinical implications

https://doi.org/10.1016/j.drup.2021.100797Get rights and content

Abstract

Despite an increasing arsenal of anticancer therapies, many patients continue to have poor outcomes due to the therapeutic failures and tumor relapses. Indeed, the clinical efficacy of anticancer therapies is markedly limited by intrinsic and/or acquired resistance mechanisms that can occur in any tumor type and with any treatment. Thus, there is an urgent clinical need to implement fundamental changes in the tumor treatment paradigm by the development of new experimental strategies that can help to predict the occurrence of clinical drug resistance and to identify alternative therapeutic options. Apart from mutation-driven resistance mechanisms, tumor microenvironment (TME) conditions generate an intratumoral phenotypic heterogeneity that supports disease progression and dismal outcomes. Tumor cell metabolism is a prototypical example of dynamic, heterogeneous, and adaptive phenotypic trait, resulting from the combination of intrinsic [(epi)genetic changes, tissue of origin and differentiation dependency] and extrinsic (oxygen and nutrient availability, metabolic interactions within the TME) factors, enabling cancer cells to survive, metastasize and develop resistance to anticancer therapies. In this review, we summarize the current knowledge regarding metabolism-based mechanisms conferring adaptive resistance to chemo-, radio-and immunotherapies as well as targeted therapies. Furthermore, we report the role of TME-mediated intratumoral metabolic heterogeneity in therapy resistance and how adaptations in amino acid, glucose, and lipid metabolism support the growth of therapy-resistant cancers and/or cellular subpopulations. We also report the intricate interplay between tumor signaling and metabolic pathways in cancer cells and discuss how manipulating key metabolic enzymes and/or providing dietary changes may help to eradicate relapse-sustaining cancer cells. Finally, in the current era of personalized medicine, we describe the strategies that may be applied to implement metabolic profiling for tumor imaging, biomarker identification, selection of tailored treatments and monitoring therapy response during the clinical management of cancer patients.

Introduction

Despite significant improvements in tumor prevention, diagnosis, and treatment, the prognosis of cancer patients remains frequently dismal due to drug resistance and consequent tumor relapse. Therapeutic failures in clinics can affect all types of tumors, hematological and solid tumors, and may occur virtually with all anticancer treatments, including conventional chemo/radiotherapy to targeted therapy or immunotherapy. Indeed, the clinical efficacy of anticancer therapies is strongly limited by drug resistance mechanisms that may exist at diagnosis or develop throughout treatment referred to as intrinsic and acquired drug resistance, respectively (Gonen and Assaraf, 2012; Li et al., 2016; Wang et al., 2019b; Wijdeven et al., 2016). The current clinical protocols, primarily based on the application of a maximum-tolerated dose, aim to kill the largest proportion of cancer cells in a short time but, at the same time, may select for resistant tumor cell phenotypes (Enriquez-Navas et al., 2015; Gillies et al., 2012). Apart from genetic alterations, intratumor phenotypic heterogeneity has been widely recognized to facilitate resistance to anticancer treatments in various cancer types (Marine et al., 2020). Therapy resistance is indeed often associated with the existence of specific tumor microenvironment (TME) conditions (the so-called niches) that can shape adaptive stem-like tumor cell phenotypes more prone to contribute to minimal residual disease and long-term clinical relapse (Boumahdi and de Sauvage, 2020). Acquired resistance may arise from a Darwinian selection of rare pre-existing resistant clones within the heterogeneous tumor cell population (Assaraf et al., 2019; De Angelis, 2018). Although therapy-resistant or drug-tolerant state may be found in any tumor cell, it is notoriously found in stem-like tumor cells that may be present within any type of malignancy (Balça-Silva et al., 2017; Freitas et al., 2014; Li et al., 2021).

However, the mechanisms mediating drug resistance are multifactorial, often interconnected, and typically involved in tumor progression. Most of them have been recognized as either associated with cancer hallmarks or with interactions between the TME and tumor cells (Assaraf et al., 2019). These mechanisms include, among others, enhanced escape from cell death (Lima et al., 2004) tumor intracellular genetic instability and tumor dynamics due to mutations (Dagogo-Jack and Shaw, 2018), along with epigenetic alterations (Garnier-Suillerot et al., 2001; Ozyerli-Goknar and Bagci-Onder, 2021) or alterations in microRNAs (miRs) expression (Hugo et al., 2013; Lima et al., 2011; Alves et al., 2019). Furthermore, intercellular communication with stromal and immune cells from TME (Bu et al., 2020; Kadel et al., 2019; Xavier et al., 2021), escape from immune surveillance (Sharma et al., 2017; Vasan et al., 2019), induction of (partial) epithelial-to-mesenchymal transition (EMT) (Faheem et al., 2020; Zheng et al., 2015), alterations in intracellular drug concentration mediated by several mechanisms (Alves et al., 2015; Joyce et al., 2015; Law et al., 2021; Namee and O’Driscoll, 2018; Sousa et al., 2015) and metabolic alterations (Boedtkjer and Pedersen, 2020; Wang et al., 2021b) are other mechanisms involved in cancer drug resistance (Fig. 1). Tumor metabolism perfectly illustrates how TME peculiarities strongly influence tumor cell phenotypes and hence treatment outcomes in patients (Faubert et al., 2020; McCann and Kerr, 2021). In this review, we summarize the current knowledge about metabolism-based mechanisms of adaptive resistance to anticancer therapies. Furthermore, we report on the role of TME-mediated intratumor metabolic heterogeneity in drug resistance and the reliance of many therapy-resistant cancer types and/or subpopulations on amino acid (AA), glucose, and lipid metabolism. We also discuss the therapeutic avenues of interference with tumor metabolism that may achieve the eradication of relapse-sustaining cancer cells. This review finally explores the strategies that may be applied to implement metabolic profiling of tumors for rational clinical decision-making in cancer patients.

Section snippets

Metabolic reprogramming in cancer and therapy resistance

Metabolic needs and preferences evolve along disease progression to facilitate cancer cell survival, proliferation, metastasis, and the development of resistance to anticancer therapies. Compelling evidence has shown that tumor cells display high metabolic flexibility, i.e., the capacity to use different nutrients as well as plasticity reflected in their capacity to metabolize the same nutrient differently. Additionally, tumor cells can either cooperate (i.e., metabolic symbiosis) or compete

Microenvironment-mediated intratumoral metabolic heterogeneity and therapy resistance

Cancer is now undoubtedly viewed as a dynamic ecosystem in which subclonal cancer cell populations behave cooperatively with non-cancer stromal cells to support disease progression (Tabassum and Polyak, 2015). Cancer cells can metabolically cooperate or compete with other cell types, such as cancer-associated fibroblasts (CAFs), cancer-associated adipocytes (CAA), and immune cells to support tumor progression and therapy resistance. Metabolic reprogramming has also been reported to be

Intricate interplay between tumor signaling and metabolic pathways in therapy resistance

Cell signaling pathways are critical in metabolic regulation for the maintenance of homeostasis. The aberrant dysregulation of one or more signaling pathways is strongly associated with cancer progression and plays a crucial role in therapy resistance (Fig. 3). One of the most commonly dysregulated pathways in human cancer is the PI3K/AKT/mTOR network, contributing to cancer cell growth and survival. The activation of the PI3K/AKT/mTOR pathway in cancer can occur through different processes,

Tumor metabolism: more than a clinical illusion?

Since the development and clinical introduction, more than 70 years ago, of the antimetabolite drugs to impair the activity of key enzymes in the nucleotide biosynthetic pathways (e.g., dihydrofolate reductase and thymidylate synthase), tumor metabolism has become a significant source of inspiration to develop new anticancer drugs (Fendt et al., 2020; Luengo et al., 2017). The antimetabolites represent a relatively large group of anticancer agents that include folic acid antagonists (e.g.,

Implementing tumor metabolic profiling for clinical decision-making in cancer patients

The identification of individual patients most likely to benefit from a metabolic therapy may speed up the entrance of these drugs into clinical practice, but the majority of metabolic therapies lack reliable biomarkers (Faubert et al., 2020). An exception to this is IDH1/2 inhibitors. As mentioned before, mutations in IDH1/2 genes lead to neomorphic activities that convert α-ketoglutarate (α-KG) to 2-hydroxyglutarate (2-HG). The detection of this oncometabolite represents not only a potential

Conclusions and future perspectives

Reprogramming of energy metabolism by tumor cells is currently considered one of the hallmarks of cancer (Hanahan and Weinberg, 2011) and metabolic rewiring is regarded as a novel and important mechanism of adaptive resistance (Zaal and Berkers, 2018). The mechanisms involved in therapy resistance are multifactorial. They are often interconnected and typically also involved in tumor progression, and most have been recognized as being either associated with the cancer hallmarks or associated

Declaration of Competing Interest

The authors have no conflict of interest to disclose.

Acknowledgments

This article is based upon work from COST Action STRATAGEM, CA17104, supported by COST (European Cooperation in Science and Technology) (www.cost.eu).

Foundation for Science and Technology (FCT), Portugal, and Fundo Social Europeu (FSE) supported ACG, JJ, ABSR, and CX [UID/NEU/04539/2019, UIDB/ 04539/2020, UIDP/04539/2020, SFRH/BD/145531/2019 (JJ) and SFRH/BPD/122871/2016 (CX)]. CC and ER are supported by the Fonds Joseph Maisin 2020-2022 and grants from the Fonds de la Recherche Scientifique

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