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Review

New Insights into Therapy-Induced Progression of Cancer

by
Polina V. Shnaider
1,2,3,
Olga M. Ivanova
1,2,
Irina K. Malyants
2,4,
Ksenia S. Anufrieva
1,2,5,
Ilya A. Semenov
2,
Marat S. Pavlyukov
6,
Maria A. Lagarkova
1,2,
Vadim M. Govorun
7 and
Victoria O. Shender
1,2,8,*
1
Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Federal Research and Clinical Center of Physical-Chemical Medicine of Federal Medical Biological Agency, Moscow 119435, Russia
2
Laboratory of Cell Biology, Federal Research and Clinical Center of Physical-Chemical Medicine of the Federal Medical and Biological Agency, Moscow 119435, Russia
3
Faculty of Biology, Lomonosov Moscow State University, Moscow 119991, Russia
4
Faculty of Chemical-Pharmaceutical Technologies and Biomedical Drugs, Mendeleev University of Chemical Technology of Russia, Moscow 125047, Russia
5
Moscow Institute of Physics and Technology (State University), Dolgoprudny 141701, Russia
6
Laboratory of Membrane Bioenergetics, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow 117997, Russia
7
Laboratory of Simple Systems, Federal Research and Clinical Center of Physical-Chemical Medicine of the Federal Medical and Biological Agency, Moscow 119435, Russia
8
Laboratory of Molecular Oncology, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow 117997, Russia
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2020, 21(21), 7872; https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21217872
Submission received: 30 September 2020 / Revised: 19 October 2020 / Accepted: 21 October 2020 / Published: 23 October 2020
(This article belongs to the Special Issue Attacking Cancer Progression and Metastasis)

Abstract

:
The malignant tumor is a complex heterogeneous set of cells functioning in a no less heterogeneous microenvironment. Like any dynamic system, cancerous tumors evolve and undergo changes in response to external influences, including therapy. Initially, most tumors are susceptible to treatment. However, remaining cancer cells may rapidly reestablish the tumor after a temporary remission. These new populations of malignant cells usually have increased resistance not only to the first-line agent, but also to the second- and third-line drugs, leading to a significant decrease in patient survival. Multiple studies describe the mechanism of acquired therapy resistance. In past decades, it became clear that, in addition to the simple selection of pre-existing resistant clones, therapy induces a highly complicated and tightly regulated molecular response that allows tumors to adapt to current and even subsequent therapeutic interventions. This review summarizes mechanisms of acquired resistance, such as secondary genetic alterations, impaired function of drug transporters, and autophagy. Moreover, we describe less obvious molecular aspects of therapy resistance in cancers, including epithelial-to-mesenchymal transition, cell cycle alterations, and the role of intercellular communication. Understanding these molecular mechanisms will be beneficial in finding novel therapeutic approaches for cancer therapy.

1. Introduction

Due to existing advances in the development of anticancer drugs, most types of malignant tumors are susceptible to treatment. However, the results of patient treatment are not satisfying due to the development of metastasis and emergence of cancer cell resistance to applied therapy. Both of these processes often occur simultaneously [1,2,3]. The process of metastasis has been described in sufficient detail in earlier reviews [4,5]. Here we will focus our attention on how the therapy resistance emerges in the original tumors. Research shows that the incidence of recurrence varies greatly depending on the type of tumor [6]. Thus, glioblastoma relapses in nearly all patients, progression-free survival time for such patients is 2–7 months [7,8]. Similarly, in ovarian cancer patients, the relapse rate is about 85% with progression-free survival time less than two years [9,10]. Oncological diseases with a high relapse rate also include peripheral T-cell lymphoma, urinary bladder cancer, and soft tissue sarcoma [6]. On the other hand, tumors such as estrogen receptor-positive breast cancer, or kidney cancer, respond well to treatment and recur only in a small percentage of cases [11,12]. We assume that some types of malignant neoplasms possess the higher activity of certain signaling pathways or that the specific cellular organization of the tumor may allow cancers to develop resistance to treatment.
A tumor is a heterogeneous population of cells that changes significantly after exposure to drug therapy. Also, the tumor cells are in constant dynamic interaction with stromal and immune cells, as well as with other subpopulations of cancer cells. The therapeutic effect not only creates selective pressure but also changes the nature of intercellular communication, providing conditions for better survival of the remaining tumor cells [13,14]. In this review, we take a detailed look at the complex interplay of different processes that are activated in tumors and their microenvironment in response to chemo- and radiotherapy.
Mechanisms of acquired resistance can be subdivided based on the initial effect of therapy on cancer cells (Figure 1). In the simplest case, treatment can lead to the death of a large population of drug-sensitive cells and subsequent proliferation of pre-existing resistant cells. This mechanism is termed clonal selection. Secondly, a therapy induces temporary or permanent activation of various signaling pathways inside tumor cells enabling them to survive during treatment. Finally, the dying tumor cells can release a range of extracellular signals that trigger the formation of various mechanisms of resistance in neighboring cells (Figure 1 and Figure 2). However, it should be noted that, in a real tumor, in most cases, all three processes occur in parallel and are closely related to each other.

2. Therapy-Induced Clonal Selection

Modern techniques for the genome sequencing of small populations of cells located in different tumor zones [15,16,17,18,19], as well as single-cell sequencing, have enabled tracing tumor heterogeneity in vivo before and after therapy [20,21,22,23,24]. Moreover, the molecular barcoding approach made it possible to study the dynamics of how the ratio and abundance of the descendants of individual cancer cells change both in the case of normal tumor growth and under the influence of various methods of therapy [25,26]. These studies have demonstrated the presence of rare populations of cancer cells that are resistant to therapy. During initial tumor growth, these cells do not have significant advantages in the rate of proliferation, and, therefore, their population is low. However, therapy leads to the death of the bulk of cancer cells, and as a result, this small population of rare cells can form a new tumor.
Any therapeutic effect on a tumor becomes an additional factor providing selective conditions for the survival of cancer cells. In most cases, tumor cells carrying mutations in genes which belong to pathways targeted by the drug, gain selective advantage [27]. The acquisition of therapy resistance can be mediated by an advantage in proliferation of pre-existing clones under the therapy-induced selective conditions. On the other hand, therapy could lead to the emergence and accumulation of new genomic aberrations that provide advantages to the cells carrying them (acquired resistance). It is unclear, which scenario prevails in each case. Research shows that either of these scenarios can be implemented, or possibly both, depending on the type of cancer and the applied therapeutic strategy. Thus, DNA-damaging drugs like alkylating agents have a more mutagenic impact on cells [28]. Chemotherapy of high-grade serous ovarian cancer with platinum-based drugs causes a large number of new somatic mutations in cells [29]. A similar induction of the mutagenesis occurs in glioblastoma under temozolomide therapy [30]. By contrast, combined chemotherapy of bladder cancer with gemcitabine and cisplatin leads to the selection of pre-existing cell populations [31,32]. Likewise, slowly proliferating cells with elevated self-renewal potential can trigger the relapse of acute leukemia after therapy [33,34]. In the case of triple-negative breast cancer, it appears that cells with resistant genotypes had already existed in tumors and were selected by neoadjuvant chemotherapy consisting of epirubicin, docetaxel, and bevacizumab [20].
Particular importance among pre-existing cell populations is attached to a special group of cells with stem properties. A rare population of cancer stem cells is thought to be resistant to therapy and may repopulate the tumor through their self-renewing potential. Such cellular hierarchy models were proposed for several cancers including glioblastoma [35], leukemia [36], breast cancer [37], colorectal cancer [38], etc. However, the definition of cancer stem cells remains highly controversial because of their unstable phenotype [39,40]. Markers used to isolate cancer stem cells are not unique to a given cancer and might define populations of cells in different states. Growing body of evidence suggests that stemness features are associated mainly with intrinsic plasticity of cancer cells and are determined by a dynamic extracellular microenvironment [35,41,42,43]. Novel single-cell expression profiling studies and transcriptome-based lineage trajectory prediction algorithms may provide new insights for the understanding of cancer stem cells properties [35,44].
In addition to understanding which genotypes prone to be adaptively selected in response to chemotherapy, it is equally important to define how such cells evolve further and acquire an increasingly therapy-resistant phenotype. Due to the development of single-cell RNA sequencing technologies, it has become possible to study transcriptional profiles of individual cells and track programs of transcriptional reprogramming induced by therapy.
In the above-mentioned study, Kim et al. used single-cell transcriptome sequencing of triple-negative breast cancer samples to find that transcriptional changes are not initially programmed in tumor cells, but are activated under the effect of neoadjuvant chemotherapy [20]. Thus, it was shown that the expression of genes involved in extracellular matrix degradation, PI3K/AKT/mTOR pathway, angiogenesis, hypoxia signaling pathway, and epithelial-to-mesenchymal transition (EMT) increase in persistent tumor cells in response to chemotherapy.
Analysis of the transcriptional profiles of muscle-invasive urothelial bladder cancer samples before and after tipifarnib treatment made it possible to demonstrate that tumor cells that survived through therapy are in a state of dormancy, also characterized by increased expression levels of the IGFBP7, MDK, and B2M genes [24]. Such a slow-cycling, persistent quiescent state promotes tumor cell survival during therapy, and such cells can potentially give rise to actively proliferating resistant cells. Thus, B2M can induce EMT via the induction of RAS-independent activation of the PI3K/AKT/mTOR and ERK signaling pathways.
Transcriptional profiling of tumor samples is also important for molecular typing of the tumor and its microenvironment. For example, Izar and colleagues identified 18 separate clusters of malignant and non-malignant cells, differing in their transcriptional signatures in ovarian cancer ascites isolated from patients before and after chemotherapy [22]. Noteworthy, strong differences exist between cells of the same type. Chemotherapy activates the Jak/STAT pathway in some subpopulations of cancer cells and tumor-associated fibroblasts. This indicates the possibility of paracrine and/or autocrine signaling and, as a consequence, co-evolution and remodeling of the tumor environment towards a more aggressive and chemotherapy-resistant phenotype.
Single-cell RNA sequencing of metastatic lung cancer samples demonstrated differences in the transcriptional levels between cancer cells at different points in time: before therapy, in the course of the therapy, when the tumor was either regressing or stable, and upon subsequent progressive disease [21]. It turned out that activation of the WNT/β-catenin pathway in cancer cells contributes to their survival after the initial treatment. As the disease progresses, kynurenine, plasminogen, and gap-junction genes associated with inflammation and carcinogenesis pathways are activated. Besides, as a result of activation of the kynurenine pathway in cancer cells, a noticeable remodeling of the tumor microenvironment occurs, in particular, an antitumor immune response is suppressed.
In a study by Park et al. on a culture of colon cancer cells under the influence of a DNA-damaging drug 5-fluorouracil, the authors identify three unique transcriptome phenotypes and correlate them with the main DNA damage-induced cell-fate responses that include apoptosis, cell cycle arrest, and stress response [23]. In particular, differential regulation of CDKN1A or TP53 genes leads to one or another type of cellular response to therapy.
Most of the studies mentioned above combined single-cell RNA and DNA sequencing, which made it possible to notice that the heterogeneity between the samples at the transcriptome level is lower than at the level of gene mutation. This may suggest that, despite the high diversity of mutations, the transcriptional programs of cancer cells converged on some specific signaling pathways. Thus, using multiple types of cancer, it has been shown that several populations of tumor cells with different states arise after different types of chemotherapy. Therapy can trigger cell death, arrest of cancer cells at a certain phase of the cell cycle, or induce the emergence of a cancer cell population with activated cell injury repair signature due to signaling pathways PI3K/AKT/mTOR, Jak/STAT, WNT/β-catenin, and others. In turn, all of these signaling pathways are known to be capable of inducing EMT, which is often associated with more aggressive tumor behavior [45,46,47].

3. Intracellular Mechanisms of Acquired Therapy Resistance

As described above, therapy creates conditions promoting the selection of a pre-existing population of tumor cells. However, numerous studies have shown that the progeny of cells that survived after therapy significantly differs from the parental cells. These changes can occur both at the genome level (new mutations) and at the transcriptome level (gene expression changes which are not associated with DNA mutations) [48] (Figure 1 and Figure 2). Several mechanisms of such de novo adaptation of tumors to the therapy are already well described: regulation of chemotherapeutic drug concentration in the cell, suppression of apoptotic pathways, modification of target proteins, or activation of alternative signaling cascades. Hence, therapy induces intracellular processes allowing the cell to adapt to its action, survive treatment, and subsequently form a new tumor.
For example, the role of drug uptake, efflux, and inactivation in acquired resistance has been demonstrated [49]. Often therapy induces mutations that change the activity or decrease the expression of drug receptors and transporter genes, which ultimately leads to a decrease in the rate of chemotherapeutic drug absorption and development of chemoresistance [50,51]. Besides, the expression of ABC transporters (MDR1, MRP1, MXR, etc.) can increase in response to pharmaceutical drugs, forming the basis for resistance to many other drugs [52,53,54]. Another mechanism is based on the inhibition of proteasomal degradation of drug-target proteins in cancer cells. It has been recently shown that overexpression of disintegrin/metalloproteinase ADAM10 in cancer cells leads to the increased cleavage of JAM-A [55,56]. The cleaved form of JAM-A protein inhibits the proteasomal degradation of HER-2 contributing to the acquired resistance of breast cancer cells to anti-HER2 therapy [55,56].
Also, it has been repeatedly shown that the mutation profile changes significantly during tumor development, and any stress-related conditions, especially chemo- or radiotherapy, enhance mutagenesis due to increased genomic instability and mitotic catastrophes [57]. Thus, in the course of tumor progression and subsequent therapy, additional mutations may occur providing the resistance of tumor cells to the drugs. Among chemotherapy-induced mutations, mutations in genes of the EGFR-dependent signaling pathway (EGFR, HER2, RAS) targeted by the drugs are common [58,59,60,61]. In addition to mutations that lead to a decrease in the effectiveness of targeted therapy, some mutations decrease the effects of less specific chemotherapeutic drugs through the regulation of DNA repair or inhibition of apoptosis. Some of the most common examples are mutations in the BRCA1 and BRCA2 tumor suppressor genes [62,63]. Mutations in these genes lead not only to the enhanced probability of genomic instability but also to a greater sensitivity of tumor cells to DNA-damaging agents of the platinum group [64,65] and inhibitors of poly(ADP-ribose) polymerase [63]. Thus, repeated or secondary mutations can restore the functionality of genes and lead to the emergence of resistance to the types of ongoing treatment [62,66,67]. Mutations in the TP53 gene induced by chemotherapy can lead to an acquisition of resistance to the drugs used [68,69]. In addition to genetic aberrations, epigenetic changes may occur in tumor cells in response to therapy, for example, the silencing of key tumor suppressor genes mediates by DNA hypermethylation [70,71], histone modifications, and chromatin remodeling [72].
However, current observations of drug resistance acquisition cannot be explained solely by genetic and epigenetic aberrations [73]. Many mechanisms require more detailed studies since their contribution to the development of chemoresistance of tumor cells is ambiguous. For instance, the role of EMT in the induction of drug resistance is still not obvious. Moreover, many non-specific drugs effectively act on tumor cells in a specific phase of the cell cycle. However, due to the lack of synchronization of cancer cells at the time of therapy, it is difficult to predict how a particular drug will affect the survival of tumor cells. There is also no consensus on the role of autophagy in the acquisition of drug resistance by cancer cells.

3.1. Aspects of Epithelial-to-Mesenchymal Transition

It is generally accepted the mesenchymal tumor phenotype is more aggressive and invasive than the epithelial one, and the epithelial-to-mesenchymal transition (EMT) is often associated with the emergence of resistance to anticancer therapy [74,75]. However, it is still unclear whether the epithelial-to-mesenchymal transition is itself the cause of drug resistance or merely accompanies the processes that induce therapy resistance.
Correlation between the degree of sensitivity of tumor cells to the drug and the expression of genes responsible for a particular phenotype is frequently used to assess the relationship between EMT and chemoresistance. For example, it has been shown that the sensitivity of liver cancer cell lines to cisplatin, gemcitabine, and 5-fluorouracil is associated with decreased expression of E-cadherin and increased expression of mesenchymal transcription factor ZEB1, which inhibits E-cadherin expression [76]. Increased production of a transcription factor FOXC2 is one of the major differences between cisplatin-sensitive and cisplatin-resistant A549 lung cancer cell lines [77]. Similar conclusions were made after investigation of the expression level of genes responsible for EMT in tumor tissue samples from patients and primary cell cultures of glioblastoma [78], prostate cancer [79], breast cancer [80], or non-small cell lung carcinoma [81].
ZEB1 is one of the main transcription factors responsible for the epithelial-to-mesenchymal transition. ZEB1 is activated in response to external stimuli that trigger intracellular signaling cascades PI3K/AKT/mTOR, RAS/ERK, WNT/β-catenin, and NF-κB and inhibits the expression of genes specific for the epithelial phenotype: E-cadherin, ESRP1, EpCAM, ER-α, RAB25, and ST14 [82]. Knockdown of ZEB1 in breast cancer, non-small cell lung carcinoma, and osteosarcoma cell lines leads to an increase in the sensitivity to radiotherapy while its ectopic expression in cell models of breast cancer decreases the sensitivity [83,84,85]. However, it should be noted transcription factors often regulate more than one process. For example, in addition to the induction of EMT, ZEB1 and other EMT transcription factors (SNAIL, SLUG, TWIST) are involved in the regulation of the cell cycle, proliferation, DNA repair, lipid metabolism, pumping chemotherapeutic drugs out of the cell, and activation of T lymphocytes [83,86,87,88,89,90]. Thus, EMT transcription factors participate in a wide range of processes that cause therapy resistance of tumor cells. Moreover, it has been shown that ZEB1 can induce resistance to radiotherapy without induction of EMT [83,84].
Moreover, epithelial phenotype can be reduced post-transcriptionally in several cancer cell lines through re-localization of epithelial proteins from the cell surface to endocytic complexes without activation EMT transcription factors [91]. This process may provide recycling of epithelial proteins back to the cell surface and might partially explain the existence of hybrid epithelial/mesenchymal state of cancer cells. It is noteworthy that EMT in cancer cells, both in vitro and in vivo, occurs through a range of distinct intermediate epithelial/mesenchymal state with various invasion and differentiation characteristics [92,93]. Moreover, cancer cells existing in this hybrid state were shown to be more aggressive than cells with a complete mesenchymal phenotype [92,94]. In addition, it should be borne in mind that EMT activation programs may differ depending on the type of tissue as well as the specific tumor microenvironment [92,95]. For example, the activation of transcriptional factors SNAI1 and TWIST1 induces dissemination of breast cancer cells, while both of these factors are dispensable for metastasis but induces chemoresistance in pancreatic cancer [96,97,98]. Besides transcriptional control, different regulatory networks such as epigenetic modifications, alternative splicing, and protein stability further complicate understanding of the whole picture of EMT process [75,95,99,100,101].
The EMT is directly related to metastasis and invasion of tumor cells. In the model of breast cancer, Hausser’s group shows that the main processes necessary for tumor progression, i.e., cell division, maintenance or increase in biomass and energy, lipogenesis, immune interactions, invasion, and tissue remodeling, cannot occur simultaneously [102]. Other groups of researchers also notice the difficulty of simultaneous processes of proliferation and invasion [103,104,105]. Hence, a tumor cell has to choose whether to maintain its metabolism on glucose or lipids, actively proliferate, avoid an immune response, or metastasize [102]. As will be discussed below, proliferative activity and being at a certain stage of the cell cycle can increase or decrease the sensitivity of tumor cells to the effects of therapy. For example, metastatic tumor cells can be more resistant to DNA-damaging drugs or to agents that stabilize microtubules.
Thus, signals that activate EMT often also trigger other processes associated with the emergence of therapy resistance of tumor cells. Therefore, it is often impossible to isolate the effects caused by EMT alone. Nevertheless, changes in the EMT markers in tumor cells can be used to assess the success of anticancer therapy.

3.2. Cell Cycle-Mediated Chemoresistance of Cancer Cells

Many chemotherapeutic drugs affect the cell in a certain phase of the cell cycle. Drugs that directly or indirectly damage DNA (cisplatin, doxorubicin, 5-fluorouracil, or topotecan) are active mainly in the S phase of the cell cycle [106]. Drugs leading to improper assembly of the spindle apparatus—vinca alkaloids and taxanes (docetaxel and paclitaxel)—are the most toxic to cells in the M phase [107].
The arrest of the cell cycle in the phase preceding the phase of exposure to the chemotherapeutic drug can reduce the effectiveness of treatment (Figure 3). For example, the arrest of the cell cycle in the G1-phase leads to the emergence of resistance of melanoma cells to bortezomib (protease inhibitor) and temozolomide (alkylating agent). Therefore, pretreatment of melanoma cells with drugs that cause cell cycle arrest in the G1 phase (for example, an inhibitor of the MAPK pathway) can lead to the emergence of resistance to bortezomib and temozolomide, but treatment in the reverse order does not lead to the development of resistance [108]. Resveratrol provokes cell cycle arrest in the S phase which leads to a delayed entry of the cell into mitosis. This treatment reduces the effectiveness of an M phase-specific chemotherapeutic drug paclitaxel but enhances the efficacy of cisplatin acting in the S phase [109]. Synchronization of cells in the M phase increases the sensitivity of ovarian cancer cells to paclitaxel by 2–3 times [110]. Additionally, hypoxia-induced arrest in the G1 phase increases the resistance of oral cancer to 5-fluorouracil 10-fold [111].
Furthermore, the arrest of the cell cycle at a certain phase gives the damaged cell the opportunity to recover before it enters mitosis, while premature division can trigger apoptosis. This phenomenon is most pronounced for DNA damaging agents. For example, cisplatin causes replicative stress, which activates the phosphokinases ATR, CHK1, and WEE1, thereby preventing further course of the cell cycle and leading to the arrest of tumor cells in the S phase. A reduced level of WEE1 entails a premature start of mitosis in cancer cells that carry severe DNA damage and their subsequent death [112]. Another group of authors showed that the different resistance of triple-negative breast cancer cell lines to cisplatin was associated with different mechanisms of cell cycle regulation in response to stress, rather than alteration in the DNA repair system [73]. Thus, resistant breast cancer cell lines, in contrast to sensitive ones, enter mitosis only after complete repair of DNA damaged by cisplatin. G3BP2, HMMR, and NEK2 have been proposed as participants in the regulation of the cell cycle that determine the fate of the cell in response to cisplatin.
However, there is an opposite view on the matter: the prolonged arrest of the cell cycle may as well trigger apoptosis. In the model of cisplatin-sensitive lung cancer cell line A549 and its cisplatin-resistant modification, it was shown that resistant cells tend to avoid G2/M arrest of the cell cycle [113,114]. Moreover, a resistant analog of breast cancer cell line MDA-MB-231 maintained a high level of expression of genes responsible for the progress through the cell cycle even after treatment with doxorubicin, while corresponding sensitive cells were arrested in the sub-G1 phase and undergone apoptosis [115].
Thus, the cell cycle phase of tumor cells treated with chemotherapeutic drugs can play an important role both during the direct action of chemotherapy and during cell response to it. Understanding the peculiarities of cell cycle regulation in tumor cells, as well as the effect of chemotherapeutic drugs on the cell cycle in combined chemotherapy, can be used to increase the efficiency, as well as to develop new treatment strategies.

3.3. Autophagy as a Way to Avoid Therapy-Induced Cell Death

Autophagy is present at a basal level in all cell types and serves to maintain intracellular homeostasis by recycling molecules and entire organelles within specialized membrane structures [116]. It plays a controversial role in tumor progression. At the initial stages of oncogenesis, autophagy suppresses tumor development by preventing the accumulation of damaged proteins and organelles, followed by inhibition of inflammation and oxidative stress, and a contribution to oncogene-induced senescence [117,118]. At later stages of tumorigenesis, it stimulates the survival of tumor cells by reducing stress levels during hypoxia and nutrient starvation [117,118]. However, prolonged activation of autophagy in cancer cells, as well as exposure to chemotherapeutic drugs, can lead to autophagic cell death due to gradual self-degradation [119,120,121,122].
Chemotherapy-induced stress can trigger both autophagy and apoptosis, the balance between these processes is highly dynamic, and can be controlled by the p38-MAPK, JNK, and p62 pathways [123,124]. The prevalence of autophagy over apoptosis is often associated with the resistance of tumor cells [125,126].
Activation of apoptosis and autophagy under the influence of therapy can happen simultaneously, sequentially, independently, synergistically, or antagonistically of one another [127]. In some cases, the protective autophagy pathway can be activated, and its inhibition increases therapy effectiveness. For example, activation of apoptosis induced by capsaicin or epirubicin is possible only after the blockade of protective autophagy [128,129]. Thus, successful combinations of drugs that simultaneously trigger the apoptosis of tumor cells and inhibit autophagy have been demonstrated [126,130,131,132,133,134,135,136,137,138,139,140,141,142]. However, therapeutic inhibition of autophagy may also induce the opposite effect. For example, inhibition of autophagy during the treatment with resveratrol and feroniellin A leads to the suspension of tumor cell death, demonstrating the pro-apoptotic role of autophagy in oncogenesis [143,144].
In addition to the above mentioned, multiple chemotherapy drugs that stimulate autophagy also affect PI3K/Akt/mTOR, AMPK, ERK, and JNK pathways, thereby promoting cancer cell survival [145,146,147,148,149]. Hypoxia can play a role in the activation of protective autophagy. For example, the antiangiogenic agent bevacizumab induces hypoxic stress and triggers autophagy through the HIF-1α/AMPK pathway, stimulating the survival of glioblastoma cells [150]. Treatment of cancer cells with cisplatin or taxol under hypoxic conditions induces protective autophagy, which promotes stress elimination and ensures the survival of lung cancer and breast cancer cells, respectively [146,151]. MicroRNAs may also be responsible for the activation of protective autophagy when exposed to chemotherapy. For example, cisplatin leads to a decrease in the level of miR-199a-5p in hepatocellular carcinoma cells, which, as a result, stimulates the activation of autophagy and promotes the proliferation of tumor cells [152].
Summing up, the effect of autophagy during treatment with various chemotherapeutic drugs is not predictable and depends on many factors, including the type of cancer. For example, exposure to metformin promoted the myeloma cell death by activating autophagy but led to the survival of breast cancer cells by stimulating protective autophagy [153,154]. Several studies have shown contradictory results upon autophagy inhibition in conjunction with the effect of sorafenib on human hepatocellular carcinoma cells [155,156]. The stage of autophagy is also should be taken into account. For example, in the case of treatment of glioma cells with imatinib, the cytotoxicity of the chemotherapeutic drug increased with inhibition of late stage of autophagy and decreased with inhibition of early stage of autophagy. Presumably, the increased cytotoxicity may be related to the number of sequestered mitochondria that are ineffectively eliminated and provoke additional stress [157].

4. Contribution of Intercellular Communication to Acquired Therapy Resistance

Two mechanisms of therapy resistance development in cancer were described above (clonal selection and intracellular changes induced by therapy). Both of them occur at the level of individual cells. However, there are also more complex mechanisms based on the interaction of various populations of cells with each other. These mechanisms have been first demonstrated less than 10 years ago and some of them are rather poorly understood, however, they could be described by the following general scheme. The therapy affects one cell population, and these cells secrete a variety of molecules, both in free form and encapsulated within extracellular vesicles. These secreted components, in turn, can induce the development of resistance in surrounding populations of cells. Thus, it is important to understand that tumor cells function within the tumor microenvironment: dying and surviving cells constantly exchange information with each other and with cells of the tumor stroma (Figure 1 and Figure 4). There are several ways to conduct the intercellular communication, namely directly (through cell junctions, receptor–ligand binding, or tunneling nanotubes) and indirectly (through signaling with extracellular vesicles or soluble molecules) [158]. Most often, in the literature on the acquisition of therapy resistance, the concept of intercellular communication means precisely the secretion and uptake of signaling molecules both in free form and via extracellular vesicles. Therefore, we will focus our attention on this process.

4.1. From Cancer Cells to Cancer Cells

Chemotherapy, especially preoperative chemotherapy, induces a significant decrease in tumor size, which implies massive cell death. During the process, most of the sensitive tumor cells die, but for some reason, e.g., lack of proliferative activity, uneven distribution of the drug in the tissue, etc., some sensitive tumor cells can survive and continue to exist after treatment. Thus, several patterns of tumor cell secretion after chemotherapy can be distinguished: (i) secretion by sensitive or resistant clones that were not affected by chemotherapy; (ii) secretion by dying tumor cells; (iii) secretion by tumor cells that have progressed into a state of senescence (a durable form of growth arrest) (Figure 4).
Therapy-sensitive and therapy-resistant tumor cells are known to secrete significantly different sets of signaling molecules [159,160,161,162,163,164,165]. Using cell models of breast cancer [166,167,168,169,170,171], ovarian cancer [172,173], prostate cancer [174], melanoma [175,176], acute lymphoblastic leukemia [177], acute myeloid leukemia [178], colorectal cancer [179], esophageal cancer [180], non-small cell lung cancer [181], osteosarcoma [182], and renal cell carcinoma [183] therapy-resistant tumor cells were shown to release a number of molecules into the extracellular space, which contributes to the acquisition of resistance in more sensitive tumor cells.
One of the components frequently involved in such intercellular communication is microRNAs: miR-30a and miR-100 [166]; miR-19b and miR-20a [178]; miR-155 [171]; miR-221 [167]; miR-222 [166,167,170,181]. Proteins ALKres [176], GSTP1, p-STAT3 [179], PDGFRb [175], UCH-L1 [169], Syntaxin 6 [184], and EphA2 [185] also play an important role. The exact mechanisms of action of most of these effector molecules have not yet been studied. However, many of them facilitate the avoidance of cell death, a decrease in the expression of targets of targeted therapy, either directly or through changes in the regulation of the cell cycle, EMT, autophagy.
The phenomenon of the ABC transporters transfer from resistant tumor clones to more sensitive ones was demonstrated by multiple studies. It was shown that multidrug resistance proteins, as well as their transcripts, can be exported by chemoresistant cells and absorbed by sensitive tumor cells as parts of extracellular vesicles, thereby contributing to a reduction of the chemotherapeutic drug concentration inside the recipient cell and a decrease its sensitivity to therapy [168,169,173,177,178,182].
During therapy, an abundance of resistant clones grows within the tumor, which inevitably leads to an increase in the content of the molecules secreted by them in the total tumor secretome. As a result, the remaining sensitive clones acquire resistance to therapy during communication with the increasing number of resistant tumor cells.
In addition to the transfer of individual molecules and vesicles, the possibility of intercellular transport of large structures such as mitochondria was demonstrated both during the communication of tumor cells with their microenvironment (bone marrow mesenchymal stem cells, cancer-associated fibroblasts, immune cells) and within the tumor cell population itself [186,187,188,189]. Tumor cells that have damaged mitochondria (for example, as a result of therapy) receive healthy mitochondria from donor cells, thereby restoring their mitochondrial activity. On the other hand, the release of mitochondria by tumor cells leads to the modification of the surrounding “healthy” stromal cells [190].
Apparently, the most significant contribution to intercellular communication is made by components secreted from tumor cells dying under the effect of chemotherapy, since the first stages of chemotherapy induce death of a major part of the tumor cell population. The death of a significant part of the tumor cells occurs. It turns out that dying tumor cells are capable of releasing components into the extracellular space that “prepare” intact tumor cells for subsequent therapeutic insults [31,191,192,193].
After treatment with tyrosine kinase inhibitors, dying sensitive melanoma and lung cancer cells secrete into the extracellular space components that stimulate chemoresistance and metastasis of recipient tumor cells due to activation of the PI3K/AKT/mTOR pathway [191]. Moreover, recipient cancer cells acquire therapy resistance only in the presence of a large fraction of dying sensitive tumor cells nearby. However, specific signaling molecules provoking these events in resistant clones have not been identified.
Dying tumor cells of breast cancer and bladder cancer secrete prostaglandin E2 (PGE2) and arachidonic acid, causing the repopulation of residual tumors through activation of the WNT/β-catenin pathway and stimulation of EMT [31,193]. The increased release of PGE2 and arachidonic acid was due to the activation of calcium-independent phospholipase A2 (iPLA2) by activated caspase 3 in dying tumor cells.
A significant number of glioblastoma cells dying after radiotherapy stimulates the acquisition of a more aggressive phenotype by surviving cancer cells by secreting apoptotic vesicles containing various spliceosomal proteins [192]. These vesicles induce changes in the splicing of mRNAs encoding key proteins associated with EMT and cell cycle regulation; they also upregulate glycolysis in recipient tumor cells. In particular, the splicing factor RBM11 can be transferred via apoptotic vesicles and regulate the cell cycle of recipient cells through binding to mRNAs that encode Cyclin D1 (cell cycle regulator) and MDM4 (apoptosis regulator) and switching their splicing to more oncogenic isoforms cyclin D1a and MDM4s, respectively, that were associated with a poorer prognosis for patients.
Noteworthy, various types of anticancer therapy: radiotherapy [192,194], alkylating agents [195,196], topoisomerase inhibitors [197], antimetabolites [198,199], and taxanes [200,201], as well as kinase inhibitors [191,197] trigger such communication between tumor cells. Components of the spliceosome [192]; PGE2 [31,193]; survivin [200,202]; MDR1 [203], HMGB1 [204,205]; several microRNAs (miR-21 [196], miR-155 [198,199], and miR-194-5p [194], and lincRNA-VLDLR [197] have been proposed as the main participants that trigger the acquisition of therapy resistance by tumor cells (Figure 2).
Unfortunately, there are only a few published studies that analyze global differences in the profiles of tumor cell secretion before and after chemotherapy. In this regard, it is still difficult to identify main effector molecules that can change the state of recipient tumor cells towards a more aggressive phenotype. The situation is further complicated by the fact that the secretion profiles of tumor cells that die through different mechanisms also vary significantly [206]. Thus, cells that die by necroptosis release a significant amount of lysosomal proteins, while apoptotic cells secrete histones and other components of the nuclear fraction into the extracellular space.
Also, under the effect of chemotherapy, tumor cells can transform into a state of irreversible or transient senescence. Cells with a senescence-associated secretory phenotype (SASP) secrete a special set of molecules that promotes the emergence of drug resistance in chemonaïve recipient tumor cells. This phenomenon has been demonstrated in senescent malignant pleural mesothelioma, melanoma, and breast cancer cells [207,208,209,210,211].
Thus, the components secreted by tumor cells can significantly destabilize the homeostasis present in chemonaïve tumor cells and provoke cell transition from less resistant to a more resistant state.

4.2. Communication between Cancer Cells and Surrounding Stromal Cells

Tumor microenvironment (TME) includes tumor-associated fibroblasts (CAFs), immune cells, mesenchymal stem cells, adipocytes, and endothelial cells. All of them can participate in both maintenance and inhibition of tumor growth [158,212,213]. The contribution of stromal cells to the development of therapy resistance differs from patient to patient since their abundance can vary over a very wide range, even among the same type of tumor [214]. Moreover, the proportion of stromal cells can be changed during treatment, thereby affecting the course of therapy. Thus, a number of studies have shown that a high content of stromal cells may indicate poor prognosis of patient outcome [215].
Cancer cells actively remodel the tumor microenvironment during therapy. For example, cells that die from chemotherapy release molecules that regulate the immune response, stimulate angiogenesis, alter the physicochemical parameters of the tumor microenvironment, or activate cellular invasion [216]. An immunosuppressive effect has been shown for molecules released by dying cancer cells, e.g., secreted CCL20 recruits regulatory T cells via the FOXO1/CEBPB/NF-κB signaling [217]; sphingosine-1 phosphate activates and polarizes of tumor-associated macrophages into M2 macrophages. These M2 macrophages secrete anti-inflammatory IL-10, and PGE2 supporting the migration of endothelial cells and angiogenesis [218]. In addition to promoting angiogenesis, PGE2 released from dying cells also provokes the recruitment of macrophages, CAFs, and neutrophils into TME and suppresses the antitumor functions of T cells and natural killer cells [219].
The major component of the tumor stroma is tumor-associated fibroblasts [220]. Communication between CAFs and tumor cells promotes the formation of a therapy-resistant phenotype in the latter [221]. For example, CAFs have been associated with the development of resistance to gemcitabine in pancreatic cancer through secretion of miRNA-106b [222]; to gefitinib in non-small cell lung cancer via secretion of IGF-1 and HGF [223]; to 5-fluorouracil in colorectal cancer [224]; to cisplatin in esophageal squamous cell cancer through secretion of PAI-1 [225] and TGFβ1 [226], in gastric cancer through secretion of miR-522 [227], IL-11, IL-6, and other cytokines and growth factors [228], and in lung adenocarcinoma due to secretion of IL-11 [229].
In response to therapy treatment, CAFs secrete microRNAs encapsulated into extracellular vesicles. After entering recipient tumor cells, miRNA-106b or miR-522 reduce cancer cells’ sensitivity to the chemotherapy by targeting antiproliferative and pro-apoptotic protein TP53INP1 [222] and arachidonate lipoxygenase (ALOX15), leading to a decrease in the accumulation of lipid peroxides in cells and inhibition of ferroptosis [227]. The mechanism of action of miR-21 is based on targeting APAF1, which leads to impairment in the activation of caspase-3 and apoptosis [230]. Similarly, miR-92a-3p activates the Wnt/β-catenin pathway and directly inhibits pro-apoptotic proteins FBXW7 and MOAP1 [231]. Proteins secreted by CAFs during chemotherapy also contribute to increased proliferation of recipient tumor cells and the formation of a more resistant phenotype therein. Thus, exosomal Wnts stimulate the dedifferentiation of cancer cells directly through Wnt signaling [232]. Secreted PAI-1 activates the AKT and ERK1/2 signaling pathways and inhibits caspase-3 activity [225]. Under the influence of HGF and IGF-1 secreted by CAFs, tumor cells increase the expression of Annexin A2, which may lead to the induction of EMT [223]. As a result of the secretion of IL-11 by CAFs, STAT3 is phosphorylated and increases of expression of anti-apoptotic proteins Bcl-2 and survivin in cancer cells [229]; IL-8 activates NF-κB and elevates the expression of MDR1 [233]. Also, TGFβ1 [226], IL-6 [234,235], and GDNF [236] participate in CAF-mediated chemoresistance. Special attention should be paid to the fact that some of the above-mentioned molecules, namely miR-106b [222], miR-522 [227], IL-11 [229], PAI-1 [225], and GDNF [236] are secreted by CAFs in response to therapy.
Not surprisingly that cancer therapy leads to tissue damage and, as a consequence, the attraction and accumulation of a large number of myeloid cells, mainly tumor-associated macrophages (TAM), to the damaged areas, where they participate in the restoration of tumor tissues [237]. There are various mechanisms through which the process occurs: suppression of the T-cell immune response, activation of the revascularization process, and inhibition of cell death signaling pathways in cancer cells by secretion of various growth factors, chemokines, and cytokines. For example, decreased expression of miR155-5p has been shown to increase the expression of C/EBPβ and IL6 in TAMs, which in turn leads to activation of the IL6R/STAT3/miR-204-5p pathway and induction of chemoresistance in colorectal cancer cells [238]. Similarly, in pancreatic ductal adenocarcinoma, macrophages that phagocytose apoptotic cells secrete the 14-3-3 zeta/delta (14-3-3ζ) protein, which inhibits apoptosis through 14-3-3ζ/Axl pathway, leading to phosphorylation of Akt and activation of cellular pro-survival mechanisms in the tumor cells [239]. The role of tumor-associated macrophages in the development of chemoresistance was discussed in detail in a recent review by Larionova et al. [240].
The presence of mesenchymal stem cells (MSC) in tumors increases in response to chemotherapy. In particular, it has been shown that after gemcitabine treatment of pancreatic adenocarcinoma, MSCs begin to actively secrete CXCL10 and activate the CXCR3 signaling in cancer cells and thus contribute to drug resistance and tumor regrowth [241]. A similar mechanism has been demonstrated for gastric cancer: TGF-β1 secreted by MSCs activates SMAD2/3 and thereby induces the expression of lncRNA MACC1-AS1 in tumor cells, which promotes fatty acid oxidation-dependent stemness and chemoresistance through antagonizing miR-145- 5p [242].
The considered examples of crosstalk between cancer cells and cells of the tumor microenvironment allow us to conclude that therapy leads not only to a change in the cellular composition of the tumor microenvironment, but also to a change in the secretion profiles of its cells. Together, these mechanisms contribute to the acquisition of resistance to the applied therapy.

5. Conclusions

The results of cancer research using high throughput single-cell technologies, including single-cell DNA and RNA sequencing, lead to a more comprehensive understanding of tumor heterogeneity, complexity, and high plasticity. It is important to study mutations, copy number alterations, epigenetic changes, and gene expression profiles to understand how different populations of malignant and non-malignant cells within a tumor can potentially respond to therapy (in pre-treatment samples), or how cells that survive after therapy become more resistant (in samples after treatment). Knowing which therapy-resistant phenotypes are present in a newly emerging tumor will allow physicians to choose the best strategies to avoid drug resistance or re-sensitize tumor cells.
The emergence of the first cytotoxic antineoplastic drugs (DNA alkylating agents, antimetabolites, antimitotics, topoisomerase inhibitors, cytotoxic antibiotics, etc.) marks a milestone in the history of anticancer therapy [243]. Although these drugs are not specific for cell types or targets, they are still the standard of care for many types of cancer (e.g., acute leukemias, breast cancer, ovarian cancer, colorectal cancer, lung cancer, glioblastoma). However, it quickly became clear that the stress response mechanisms existing in a cancer cell allow them to overcome the impairments that arise upon exposure to these classes of chemotherapy. Since the 1980s, more specific drugs have begun to appear, such as selective kinase inhibitors and monoclonal antibodies. These classes of therapy target specific molecules, such as EGFR, VEGF, PD1, HER2, mTOR, etc. Their use in combination with traditional chemotherapeutic drugs has allowed to achieve significant progress in the treatment of advanced and/or metastatic cancers. However, the cells still manage to adapt to targeted therapy or combined treatment regimens by activating alternative signaling pathways.
In the case of cancers with an extremely high relapse rate, for which the current standard of care demonstrates no substantial survival benefit (glioblastoma, ovarian cancer, etc.), it might be promising to use drugs that can alter intercellular communication. Thus, compounds that activate interactions between the tumor and the patient’s immune cells have shown their high efficiency [244,245]. Also, drugs that inhibit molecular mediators of intercellular communication are currently being actively investigated [246,247]. Therefore, future therapeutic developments should take into account the highly dynamic heterogeneity and the complexity of the microenvironment of tumor cells.

Author Contributions

Conceptualization, P.V.S., O.M.I., M.A.L., V.M.G., and V.O.S.; writing—original draft preparation, P.V.S., O.M.I., I.K.M., and I.A.S.; writing—review and editing, M.S.P., M.A.L., K.S.A., V.M.G., and V.O.S.; visualization, P.V.S.; supervision, V.O.S.; funding acquisition, P.V.S., O.M.I., I.K.M., I.A.S., K.S.A., M.S.P., M.A.L., and V.O.S. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by grant 075-15-2019-1669 from the Ministry of Science and Higher Education of the Russian Federation (P.V.S., O.M.I., M.A.L., K.S.A.); the Russian Science Foundation project no. 19-75-10123 (V.O.S., I.K.M., I.A.S.); the Russian Foundation for Basic Research projects nos. 17-00-00172 (M.A.L.), 20-04-00804 (M.S.P.), and 17-29-06056 (M.S.P.).

Acknowledgments

We thank Veronika Boychenko and Natalia R. Onishchenko for critical reading and editing of the manuscript. The original figures were created with BioRender.com.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

14-3-3ζ14-3-3 protein zeta/delta
ADAM10Disintegrin and metalloproteinase domain-containing protein 1
AKTProtein kinase B
ALKALK tyrosine kinase receptor
ALOX15Polyunsaturated fatty acid lipoxygenase ALOX15
AMPKAMP-activated protein kinase
APAF1Apoptotic protease-activating factor 1
ATRSerine/threonine-protein kinase ATR
AxlTyrosine-protein kinase receptor UFO
B2MBeta-2-microglobulin
BclB-cell lymphoma protein
BRCA1/2Breast cancer type 1/2 susceptibility protein
CAFsCancer-associated fibroblasts
CCL20C-C motif chemokine 20
CDKN1ACyclin-dependent kinase inhibitor 1
CEBPBCCAAT/enhancer-binding protein beta
CHK1Serine/threonine-protein kinase Chk1
CXCL10C-X-C motif chemokine 10
CXCR3C-X-C chemokine receptor type 3
EGFREpidermal growth factor receptor
EMTEpithelial-to-mesenchymal transition
EpCAMEpithelial cell adhesion molecule
EphA2Ephrin type-A receptor 2
ERK1/2Mitogen-activated protein kinase 3/1
ER-αEstrogen receptor alpha
ESRP1Epithelial splicing regulatory protein 1
FBXW7F-box/WD repeat-containing protein 7
FOXC2Forkhead box protein C2
FOXO1Forkhead box protein O1
G3BP2Ras GTPase-activating protein-binding protein 2
GDNFGlial cell line-derived neurotrophic factor
GSTP1Glutathione S-transferase P
HER2Receptor tyrosine-protein kinase erbB-2
HGFHepatocyte growth factor
HIF-1αHypoxia-inducible factor 1-alpha
HMGB1High mobility group protein B1
HMMRHyaluronan mediated motility receptor
IGF-1Insulin-like growth factor I
IGFBP7Insulin-like growth factor-binding protein 7
IlsInterleukins
iPLA2Calcium independent phospholipase A2
JakTyrosine-protein kinase JAK
JAM-AJunctional adhesion molecule A
JNKc-Jun N-terminal kinase
lncRNAsLong non-coding RNAs
MAPKMitogen-activated protein kinase
MDKMidkine
MDM4Protein Mdm4
MDR1ATP-dependent translocase ABCB1
miRmicroRNA
MOAP1Modulator of apoptosis 1
MRP1Multidrug resistance-associated protein 1
MSCsMesenchymal stem cells
mTORMammalian target of rapamycin
MXRBroad substrate specificity ATP-binding cassette transporter ABCG2
MYCNN-myc proto-oncogene protein
NEK2Serine/threonine-protein kinase Nek2
NF-κBNuclear factor kappa-light-chain-enhancer of activated B cells
p38-MAPKp38 mitogen-activated protein kinase
p62Ubiquitin-binding protein p62
PAI-1Plasminogen activator inhibitor 1
PD1Programmed cell death-1
PDGFRbPlatelet-derived growth factor receptor beta
PD1L1Programmed cell death 1 ligand 1
PGE2Prostaglandin E2
PI3KPhosphoinositide 3-kinase
p-STAT3Phospho-Stat3, Signal transducer and activator of transcription 3
RAB25Ras-related protein Rab-25
RASRas GTPase
RBM11Splicing regulator RBM11
SASPSenescence-associated secretory phenotype
SLUGZinc finger protein SNAI2
SMAD2/3Mothers against decapentaplegic homolog 2/3
SNAILZinc finger protein SNAI1
ST14Suppressor of tumorigenicity 14 protein
STATSignal transducer and activator of transcription
TAMsTumor-associated macrophages
TGFβ1Transforming growth factor beta-1 proprotein
TMETumor microenvironment
TP53Cellular tumor antigen p53
TP53INP1Tumor protein p53-inducible nuclear protein 1
TWISTTwist-related protein
UCH-L1Ubiquitin carboxyl-terminal hydrolase isozyme L1
UTRUntranslated region
VEGFVascular endothelial growth factor A
WEE1Wee1-like protein kinase
WNTProto-oncogene Wnt
ZEB1Zinc finger E-box-binding homeobox 1

References

  1. Housman, G.; Byler, S.; Heerboth, S.; Lapinska, K.; Longacre, M.; Snyder, N.; Sarkar, S. Drug resistance in cancer: An overview. Cancers 2014, 6, 1769–1792. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Holohan, C.; Van Schaeybroeck, S.; Longley, D.B.; Johnston, P.G. Cancer drug resistance: An evolving paradigm. Nat. Rev. Cancer 2013, 13, 714–726. [Google Scholar] [CrossRef] [PubMed]
  3. Dillekås, H.; Rogers, M.S.; Straume, O. Are 90% of deaths from cancer caused by metastases? Cancer Med. 2019, 8, 5574–5576. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Welch, D.R.; Hurst, D.R. Defining the hallmarks of metastasis. Cancer Res. 2019, 79, 3011–3027. [Google Scholar] [CrossRef]
  5. Fares, J.; Fares, M.Y.; Khachfe, H.H.; Salhab, H.A.; Fares, Y. Molecular principles of metastasis: A hallmark of cancer revisited. Signal. Transduct. Target Ther. 2020, 5, 28. [Google Scholar] [CrossRef]
  6. Miller, K.D.; Nogueira, L.; Mariotto, A.B.; Rowland, J.H.; Yabroff, K.R.; Alfano, C.M.; Jemal, A.; Kramer, J.L.; Siegel, R.L. Cancer treatment and survivorship statistics, 2019. CA Cancer J. Clin. 2019, 69, 363–385. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Nabors, L.B.; Portnow, J.; Ammirati, M.; Baehring, J.; Brem, H.; Butowski, N.; Fenstermaker, R.A.; Forsyth, P.; Hattangadi-Gluth, J.; Holdhoff, M.; et al. NCCN guidelines insights: Central nervous system cancers, version 1.2017. J. Natl. Compr. Cancer Netw. 2017, 15, 1331–1345. [Google Scholar] [CrossRef]
  8. Weller, M.; Felsberg, J.; Hartmann, C.; Berger, H.; Steinbach, J.P.; Schramm, J.; Westphal, M.; Schackert, G.; Simon, M.; Tonn, J.C.; et al. Molecular predictors of progression-free and overall survival in patients with newly diagnosed glioblastoma: A prospective translational study of the German Glioma Network. J. Clin. Oncol. 2009, 27, 5743–5750. [Google Scholar] [CrossRef] [Green Version]
  9. Corrado, G.; Salutari, V.; Palluzzi, E.; Distefano, M.G.; Scambia, G.; Ferrandina, G. Optimizing treatment in recurrent epithelial ovarian cancer. Expert Rev. Anticancer Ther. 2017, 17, 1147–1158. [Google Scholar] [CrossRef]
  10. Sjoquist, K.M.; Lord, S.J.; Friedlander, M.L.; Simes, J.R.; Marschner, I.C.; Lee, C.K. Progression-free survival as a surrogate endpoint for overall survival in modern ovarian cancer trials: A meta-analysis. Ther. Adv. Med. Oncol. 2018, 10, 1758835918788500. [Google Scholar] [CrossRef] [Green Version]
  11. Goss, P.E.; Ingle, J.N.; Pritchard, K.I.; Robert, N.J.; Muss, H.; Gralow, J.; Gelmon, K.; Whelan, T.; Strasser-Weippl, K.; Rubin, S.; et al. Extending aromatase-inhibitor adjuvant therapy to 10 years. N. Engl. J. Med. 2016, 375, 209–219. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Brookman-May, S.D.; May, M.; Shariat, S.F.; Novara, G.; Zigeuner, R.; Cindolo, L.; De Cobelli, O.; De Nunzio, C.; Pahernik, S.; Wirth, M.P.; et al. Time to recurrence is a significant predictor of cancer-specific survival after recurrence in patients with recurrent renal cell carcinoma--results from a comprehensive multi-centre database (CORONA/SATURN-Project). BJU Int. 2013, 112, 909–916. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Baghban, R.; Roshangar, L.; Jahanban-Esfahlan, R.; Seidi, K.; Ebrahimi-Kalan, A.; Jaymand, M.; Kolahian, S.; Javaheri, T.; Zare, P. Tumor microenvironment complexity and therapeutic implications at a glance. Cell Commun. Signal. 2020, 18, 59. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Madden, E.C.; Gorman, A.M.; Logue, S.E.; Samali, A. Tumour cell secretome in chemoresistance and tumour recurrence. Trends Cancer Res. 2020, 6, 489–505. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Morrissy, A.S.; Garzia, L.; Shih, D.J.H.; Zuyderduyn, S.; Huang, X.; Skowron, P.; Remke, M.; Cavalli, F.M.G.; Ramaswamy, V.; Lindsay, P.E.; et al. Divergent clonal selection dominates medulloblastoma at recurrence. Nature 2016, 529, 351–357. [Google Scholar] [CrossRef] [Green Version]
  16. Shlush, L.I.; Mitchell, A.; Heisler, L.; Abelson, S.; Ng, S.W.K.; Trotman-Grant, A.; Medeiros, J.J.F.; Rao-Bhatia, A.; Jaciw-Zurakowsky, I.; Marke, R.; et al. Tracing the origins of relapse in acute myeloid leukaemia to stem cells. Nature 2017, 547, 104–108. [Google Scholar] [CrossRef]
  17. Gerlinger, M.; Rowan, A.J.; Horswell, S.; Math, M.; Larkin, J.; Endesfelder, D.; Gronroos, E.; Martinez, P.; Matthews, N.; Stewart, A.; et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 2012, 366, 883–892. [Google Scholar] [CrossRef] [Green Version]
  18. Salichos, L.; Meyerson, W.; Warrell, J.; Gerstein, M. Estimating growth patterns and driver effects in tumor evolution from individual samples. Nat. Commun. 2020, 11, 732. [Google Scholar] [CrossRef] [Green Version]
  19. Ben-David, U.; Beroukhim, R.; Golub, T.R. Genomic evolution of cancer models: Perils and opportunities. Nat. Rev. Cancer 2019, 19, 97–109. [Google Scholar] [CrossRef]
  20. Kim, C.; Gao, R.; Sei, E.; Brandt, R.; Hartman, J.; Hatschek, T.; Crosetto, N.; Foukakis, T.; Navin, N.E. Chemoresistance evolution in triple-negative breast cancer delineated by single-cell sequencing. Cell 2018, 173, 879–893.e13. [Google Scholar] [CrossRef] [Green Version]
  21. Maynard, A.; McCoach, C.E.; Rotow, J.K.; Harris, L.; Haderk, F.; Kerr, D.L.; Yu, E.A.; Schenk, E.L.; Tan, W.; Zee, A.; et al. Therapy-induced evolution of human lung cancer revealed by single-cell RNA sequencing. Cell 2020, 182, 1232–1251.e22. [Google Scholar] [CrossRef] [PubMed]
  22. Izar, B.; Tirosh, I.; Stover, E.H.; Wakiro, I.; Cuoco, M.S.; Alter, I.; Rodman, C.; Leeson, R.; Su, M.-J.; Shah, P.; et al. A single-cell landscape of high-grade serous ovarian cancer. Nat. Med. 2020, 26, 1271–1279. [Google Scholar] [CrossRef]
  23. Park, S.R.; Namkoong, S.; Friesen, L.; Cho, C.-S.; Zhang, Z.Z.; Chen, Y.-C.; Yoon, E.; Kim, C.H.; Kwak, H.; Kang, H.M.; et al. Single-cell transcriptome analysis of colon cancer cell response to 5-fluorouracil-induced DNA damage. Cell Rep. 2020, 32, 108077. [Google Scholar] [CrossRef] [PubMed]
  24. Lee, H.W.; Chung, W.; Lee, H.-O.; Jeong, D.E.; Jo, A.; Lim, J.E.; Hong, J.H.; Nam, D.-H.; Jeong, B.C.; Park, S.H.; et al. Single-cell RNA sequencing reveals the tumor microenvironment and facilitates strategic choices to circumvent treatment failure in a chemorefractory bladder cancer patient. Genome Med. 2020, 12, 47. [Google Scholar] [CrossRef]
  25. Roh, V.; Abramowski, P.; Hiou-Feige, A.; Cornils, K.; Rivals, J.-P.; Zougman, A.; Aranyossy, T.; Thielecke, L.; Truan, Z.; Mermod, M.; et al. Cellular barcoding identifies clonal substitution as a hallmark of local recurrence in a surgical model of head and neck squamous cell carcinoma. Cell Rep. 2018, 25, 2208–2222.e7. [Google Scholar] [CrossRef] [Green Version]
  26. Bhang, H.-E.C.; Ruddy, D.A.; Radhakrishna, K.V.; Caushi, J.X.; Zhao, R.; Hims, M.M.; Singh, A.P.; Kao, I.; Rakiec, D.; Shaw, P.; et al. Studying clonal dynamics in response to cancer therapy using high-complexity barcoding. Nat. Med. 2015, 21, 440–448. [Google Scholar] [CrossRef]
  27. Venkatesan, S.; Swanton, C.; Taylor, B.S.; Costello, J.F. Treatment-induced mutagenesis and selective pressures sculpt cancer evolution. Cold Spring Harb. Perspect. Med. 2017, 7. [Google Scholar] [CrossRef] [PubMed]
  28. Szikriszt, B.; Póti, Á.; Pipek, O.; Krzystanek, M.; Kanu, N.; Molnár, J.; Ribli, D.; Szeltner, Z.; Tusnády, G.E.; Csabai, I.; et al. A comprehensive survey of the mutagenic impact of common cancer cytotoxics. Genome Biol. 2016, 17, 99. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. Patch, A.-M.; Christie, E.L.; Etemadmoghadam, D.; Garsed, D.W.; George, J.; Fereday, S.; Nones, K.; Cowin, P.; Alsop, K.; Bailey, P.J.; et al. Whole-genome characterization of chemoresistant ovarian cancer. Nature 2015, 521, 489–494. [Google Scholar] [CrossRef] [PubMed]
  30. Kim, H.; Zheng, S.; Amini, S.S.; Virk, S.M.; Mikkelsen, T.; Brat, D.J.; Grimsby, J.; Sougnez, C.; Muller, F.; Hu, J.; et al. Whole-genome and multisector exome sequencing of primary and post-treatment glioblastoma reveals patterns of tumor evolution. Genome Res. 2015, 25, 316–327. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Kurtova, A.V.; Xiao, J.; Mo, Q.; Pazhanisamy, S.; Krasnow, R.; Lerner, S.P.; Chen, F.; Roh, T.T.; Lay, E.; Ho, P.L.; et al. Blocking PGE2-induced tumour repopulation abrogates bladder cancer chemoresistance. Nature 2015, 517, 209–213. [Google Scholar] [CrossRef] [PubMed]
  32. Liu, D.; Abbosh, P.; Keliher, D.; Reardon, B.; Miao, D.; Mouw, K.; Weiner-Taylor, A.; Wankowicz, S.; Han, G.; Teo, M.Y.; et al. Mutational patterns in chemotherapy resistant muscle-invasive bladder cancer. Nat. Commun. 2017, 8, 2193. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Stiehl, T.; Baran, N.; Ho, A.D.; Marciniak-Czochra, A. Clonal selection and therapy resistance in acute leukaemias: Mathematical modelling explains different proliferation patterns at diagnosis and relapse. J. R. Soc. Interface 2014, 11, 20140079. [Google Scholar] [CrossRef] [PubMed]
  34. Vosberg, S.; Greif, P.A. Clonal evolution of acute myeloid leukemia from diagnosis to relapse. Genes Chromosomes Cancer 2019, 58, 839–849. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Suvà, M.L.; Tirosh, I. The glioma stem cell model in the era of single-cell genomics. Cancer Cell 2020, 37, 630–636. [Google Scholar] [CrossRef]
  36. Vetrie, D.; Helgason, G.V.; Copland, M. The leukaemia stem cell: Similarities, differences and clinical prospects in CML and AML. Nat. Rev. Cancer 2020, 20, 158–173. [Google Scholar] [CrossRef]
  37. Giovannelli, P.; Di Donato, M.; Galasso, G.; Di Zazzo, E.; Medici, N.; Bilancio, A.; Migliaccio, A.; Castoria, G. Breast cancer stem cells: The role of sex steroid receptors. World J. Stem Cells 2019, 11, 594–603. [Google Scholar] [CrossRef] [PubMed]
  38. Durinikova, E.; Kozovska, Z.; Poturnajova, M.; Plava, J.; Cierna, Z.; Babelova, A.; Bohovic, R.; Schmidtova, S.; Tomas, M.; Kucerova, L.; et al. ALDH1A3 upregulation and spontaneous metastasis formation is associated with acquired chemoresistance in colorectal cancer cells. BMC Cancer 2018, 18, 848. [Google Scholar] [CrossRef] [Green Version]
  39. Wang, J.; Sakariassen, P.Ø.; Tsinkalovsky, O.; Immervoll, H.; Bøe, S.O.; Svendsen, A.; Prestegarden, L.; Røsland, G.; Thorsen, F.; Stuhr, L.; et al. CD133 negative glioma cells form tumors in nude rats and give rise to CD133 positive cells. Int. J. Cancer 2008, 122, 761–768. [Google Scholar] [CrossRef]
  40. Brescia, P.; Ortensi, B.; Fornasari, L.; Levi, D.; Broggi, G.; Pelicci, G. CD133 is essential for glioblastoma stem cell maintenance. Stem Cells 2013, 31, 857–869. [Google Scholar] [CrossRef]
  41. Dirkse, A.; Golebiewska, A.; Buder, T.; Nazarov, P.V.; Muller, A.; Poovathingal, S.; Brons, N.H.C.; Leite, S.; Sauvageot, N.; Sarkisjan, D.; et al. Stem cell-associated heterogeneity in Glioblastoma results from intrinsic tumor plasticity shaped by the microenvironment. Nat. Commun. 2019, 10, 1787. [Google Scholar] [CrossRef] [PubMed]
  42. Capp, J.-P. Cancer stem cells: From historical roots to a new perspective. J. Oncol. 2019, 2019, 5189232. [Google Scholar] [CrossRef] [PubMed]
  43. Auffinger, B.; Tobias, A.L.; Han, Y.; Lee, G.; Guo, D.; Dey, M.; Lesniak, M.S.; Ahmed, A.U. Conversion of differentiated cancer cells into cancer stem-like cells in a glioblastoma model after primary chemotherapy. Cell Death Differ. 2014, 21, 1119–1131. [Google Scholar] [CrossRef] [PubMed]
  44. Kester, L.; van Oudenaarden, A. Single-cell transcriptomics meets lineage tracing. Cell Stem Cell 2018, 23, 166–179. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Karimi Roshan, M.; Soltani, A.; Soleimani, A.; Kahkhaie, R.K.; Afshari, A.R.; Soukhtanloo, M. Role of AKT and mTOR signaling pathways in the induction of epithelial-mesenchymal transition (EMT) process. Biochimie 2019, 165, 229–234. [Google Scholar] [CrossRef] [PubMed]
  46. Jin, W. Role of JAK/STAT3 signaling in the regulation of metastasis, the transition of cancer stem cells, and chemoresistance of cancer by epithelial-mesenchymal transition. Cells 2020, 9, 217. [Google Scholar] [CrossRef] [Green Version]
  47. Hu, W.; Wang, Z.; Zhang, S.; Lu, X.; Wu, J.; Yu, K.; Ji, A.; Lu, W.; Wang, Z.; Wu, J.; et al. IQGAP1 promotes pancreatic cancer progression and epithelial-mesenchymal transition (EMT) through Wnt/β-catenin signaling. Sci. Rep. 2019, 9, 7539. [Google Scholar] [CrossRef] [Green Version]
  48. Marusyk, A.; Janiszewska, M.; Polyak, K. Intratumor heterogeneity: The rosetta stone of therapy resistance. Cancer Cell 2020, 37, 471–484. [Google Scholar] [CrossRef]
  49. Zahreddine, H.; Borden, K.L.B. Mechanisms and insights into drug resistance in cancer. Front. Pharmacol. 2013, 4, 28. [Google Scholar] [CrossRef] [Green Version]
  50. Yoshida, A.; Bu, Y.; Qie, S.; Wrangle, J.; Camp, E.R.; Hazard, E.S.; Hardiman, G.; de Leeuw, R.; Knudsen, K.E.; Diehl, J.A. SLC36A1-mTORC1 signaling drives acquired resistance to CDK4/6 inhibitors. Sci. Adv. 2019, 5, eaax6352. [Google Scholar] [CrossRef] [Green Version]
  51. Muley, H.; Fadó, R.; Rodríguez-Rodríguez, R.; Casals, N. Drug uptake-based chemoresistance in breast cancer treatment. Biochem. Pharmacol. 2020, 177, 113959. [Google Scholar] [CrossRef]
  52. Baxter, D.E.; Kim, B.; Hanby, A.M.; Verghese, E.T.; Sims, A.H.; Hughes, T.A. Neoadjuvant endocrine therapy in breast cancer upregulates the cytotoxic drug pump ABCG2/BCRP, and may lead to resistance to subsequent chemotherapy. Clin. Breast Cancer 2018, 18, 481–488. [Google Scholar] [CrossRef]
  53. Yu, T.; Cheng, H.; Ding, Z.; Wang, Z.; Zhou, L.; Zhao, P.; Tan, S.; Xu, X.; Huang, X.; Liu, M.; et al. GPER mediates decreased chemosensitivity via regulation of ABCG2 expression and localization in tamoxifen-resistant breast cancer cells. Mol. Cell. Endocrinol. 2020, 506, 110762. [Google Scholar] [CrossRef]
  54. Robey, R.W.; Pluchino, K.M.; Hall, M.D.; Fojo, A.T.; Bates, S.E.; Gottesman, M.M. Revisiting the role of ABC transporters in multidrug-resistant cancer. Nat. Rev. Cancer 2018, 18, 452–464. [Google Scholar] [CrossRef] [PubMed]
  55. Leech, A.O.; Vellanki, S.H.; Rutherford, E.J.; Keogh, A.; Jahns, H.; Hudson, L.; O’Donovan, N.; Sabri, S.; Abdulkarim, B.; Sheehan, K.M.; et al. Cleavage of the extracellular domain of junctional adhesion molecule-A is associated with resistance to anti-HER2 therapies in breast cancer settings. Breast Cancer Res. 2018, 20, 140. [Google Scholar] [CrossRef]
  56. Brennan, K.; McSherry, E.A.; Hudson, L.; Kay, E.W.; Hill, A.D.K.; Young, L.S.; Hopkins, A.M. Junctional adhesion molecule-A is co-expressed with HER2 in breast tumors and acts as a novel regulator of HER2 protein degradation and signaling. Oncogene 2013, 32, 2799–2804. [Google Scholar] [CrossRef] [Green Version]
  57. Gerstung, M.; Jolly, C.; Leshchiner, I.; Dentro, S.C.; Gonzalez, S.; Rosebrock, D.; Mitchell, T.J.; Rubanova, Y.; Anur, P.; Yu, K.; et al. The evolutionary history of 2,658 cancers. Nature 2020, 578, 122–128. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Harrison, P.T.; Vyse, S.; Huang, P.H. Rare epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer. Semin. Cancer Biol. 2020, 61, 167–179. [Google Scholar] [CrossRef]
  59. Sampera, A.; Sánchez-Martín, F.J.; Arpí, O.; Visa, L.; Iglesias, M.; Menéndez, S.; Gaye, É.; Dalmases, A.; Clavé, S.; Gelabert-Baldrich, M.; et al. HER-family ligands promote acquired resistance to trastuzumab in gastric cancer. Mol. Cancer Ther. 2019, 18, 2135–2145. [Google Scholar] [CrossRef] [Green Version]
  60. Bray, S.M.; Lee, J.; Kim, S.T.; Hur, J.Y.; Ebert, P.J.; Calley, J.N.; Wulur, I.H.; Gopalappa, T.; Wong, S.S.; Qian, H.-R.; et al. Genomic characterization of intrinsic and acquired resistance to cetuximab in colorectal cancer patients. Sci. Rep. 2019, 9, 15365. [Google Scholar] [CrossRef] [Green Version]
  61. Del Re, M.; Rofi, E.; Restante, G.; Crucitta, S.; Arrigoni, E.; Fogli, S.; Di Maio, M.; Petrini, I.; Danesi, R. Implications of KRAS mutations in acquired resistance to treatment in NSCLC. Oncotarget 2018, 9, 6630–6643. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Gornstein, E.L.; Sandefur, S.; Chung, J.H.; Gay, L.M.; Holmes, O.; Erlich, R.L.; Soman, S.; Martin, L.K.; Rose, A.V.; Stephens, P.J.; et al. BRCA2 reversion mutation associated with acquired resistance to olaparib in estrogen receptor-positive breast cancer detected by genomic profiling of tissue and liquid biopsy. Clin. Breast Cancer 2018, 18, 184–188. [Google Scholar] [CrossRef]
  63. Faraoni, I.; Graziani, G. Role of BRCA mutations in cancer treatment with poly(ADP-ribose) polymerase (PARP) inhibitors. Cancers 2018, 10, 487. [Google Scholar] [CrossRef] [Green Version]
  64. Tassone, P.; Tagliaferri, P.; Perricelli, A.; Blotta, S.; Quaresima, B.; Martelli, M.L.; Goel, A.; Barbieri, V.; Costanzo, F.; Boland, C.R.; et al. BRCA1 expression modulates chemosensitivity of BRCA1-defective HCC1937 human breast cancer cells. Br. J. Cancer 2003, 88, 1285–1291. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Kwa, M.; Edwards, S.; Downey, A.; Reich, E.; Wallach, R.; Curtin, J.; Muggia, F. Ovarian cancer in BRCA mutation carriers: Improved outcome after intraperitoneal (IP) cisplatin. Ann. Surg. Oncol. 2014, 21, 1468–1473. [Google Scholar] [CrossRef]
  66. Sakai, W.; Swisher, E.M.; Karlan, B.Y.; Agarwal, M.K.; Higgins, J.; Friedman, C.; Villegas, E.; Jacquemont, C.; Farrugia, D.J.; Couch, F.J.; et al. Secondary mutations as a mechanism of cisplatin resistance in BRCA2-mutated cancers. Nature 2008, 451, 1116–1120. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  67. Pishvaian, M.J.; Biankin, A.V.; Bailey, P.; Chang, D.K.; Laheru, D.; Wolfgang, C.L.; Brody, J.R. BRCA2 secondary mutation-mediated resistance to platinum and PARP inhibitor-based therapy in pancreatic cancer. Br. J. Cancer 2017, 116, 1021–1026. [Google Scholar] [CrossRef] [Green Version]
  68. Lorenzon, I.; Pellarin, I.; Pellizzari, I.; D’Andrea, S.; Belletti, B.; Sonego, M.; Baldassarre, G.; Schiappacassi, M. Identification and characterization of a new platinum-induced TP53 mutation in MDAH ovarian cancer cells. Cells 2019, 9, 36. [Google Scholar] [CrossRef] [Green Version]
  69. Williams, D.S.; Mouradov, D.; Browne, C.; Palmieri, M.; Elliott, M.J.; Nightingale, R.; Fang, C.G.; Li, R.; Mariadason, J.M.; Faragher, I.; et al. Overexpression of TP53 protein is associated with the lack of adjuvant chemotherapy benefit in patients with stage III colorectal cancer. Mod. Pathol. 2020, 33, 483–495. [Google Scholar] [CrossRef]
  70. Brown, R.; Curry, E.; Magnani, L.; Wilhelm-Benartzi, C.S.; Borley, J. Poised epigenetic states and acquired drug resistance in cancer. Nat. Rev. Cancer 2014, 14, 747–753. [Google Scholar] [CrossRef]
  71. Romero-Garcia, S.; Prado-Garcia, H.; Carlos-Reyes, A. Role of DNA methylation in the resistance to therapy in solid tumors. Front. Oncol. 2020, 10, 1152. [Google Scholar] [CrossRef]
  72. Ponnusamy, L.; Mahalingaiah, P.K.S.; Chang, Y.-W.; Singh, K.P. Role of cellular reprogramming and epigenetic dysregulation in acquired chemoresistance in breast cancer. CDR 2019. [Google Scholar] [CrossRef] [Green Version]
  73. Heijink, A.M.; Everts, M.; Honeywell, M.E.; Richards, R.; Kok, Y.P.; de Vries, E.G.E.; Lee, M.J.; van Vugt, M.A.T.M. Modeling of cisplatin-induced signaling dynamics in triple-negative breast cancer cells reveals mediators of sensitivity. Cell Rep. 2019, 28, 2345–2357.e5. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Dongre, A.; Weinberg, R.A. New insights into the mechanisms of epithelial-mesenchymal transition and implications for cancer. Nat. Rev. Mol. Cell Biol. 2019, 20, 69–84. [Google Scholar] [CrossRef] [PubMed]
  75. Pradella, D.; Naro, C.; Sette, C.; Ghigna, C. EMT and stemness: Flexible processes tuned by alternative splicing in development and cancer progression. Mol. Cancer 2017, 16, 8. [Google Scholar] [CrossRef] [Green Version]
  76. Arumugam, T.; Ramachandran, V.; Fournier, K.F.; Wang, H.; Marquis, L.; Abbruzzese, J.L.; Gallick, G.E.; Logsdon, C.D.; McConkey, D.J.; Choi, W. Epithelial to mesenchymal transition contributes to drug resistance in pancreatic cancer. Cancer Res. 2009, 69, 5820–5828. [Google Scholar] [CrossRef] [Green Version]
  77. He, Y.; Xie, H.; Yu, P.; Jiang, S.; Wei, L. FOXC2 promotes epithelial-mesenchymal transition and cisplatin resistance of non-small cell lung cancer cells. Cancer Chemother. Pharmacol. 2018, 82, 1049–1059. [Google Scholar] [CrossRef] [PubMed]
  78. Chandra, A.; Jahangiri, A.; Chen, W.; Nguyen, A.T.; Yagnik, G.; Pereira, M.P.; Jain, S.; Garcia, J.H.; Shah, S.S.; Wadhwa, H.; et al. Clonal ZEB1-driven mesenchymal transition promotes targetable oncologic antiangiogenic therapy resistance. Cancer Res. 2020, 80, 1498–1511. [Google Scholar] [CrossRef]
  79. Marín-Aguilera, M.; Codony-Servat, J.; Reig, Ò.; Lozano, J.J.; Fernández, P.L.; Pereira, M.V.; Jiménez, N.; Donovan, M.; Puig, P.; Mengual, L.; et al. Epithelial-to-mesenchymal transition mediates docetaxel resistance and high risk of relapse in prostate cancer. Mol. Cancer Ther. 2014, 13, 1270–1284. [Google Scholar] [CrossRef] [Green Version]
  80. Creighton, C.J.; Li, X.; Landis, M.; Dixon, J.M.; Neumeister, V.M.; Sjolund, A.; Rimm, D.L.; Wong, H.; Rodriguez, A.; Herschkowitz, J.I.; et al. Residual breast cancers after conventional therapy display mesenchymal as well as tumor-initiating features. Proc. Natl. Acad. Sci. USA 2009, 106, 13820–13825. [Google Scholar] [CrossRef] [Green Version]
  81. Weng, C.-H.; Chen, L.-Y.; Lin, Y.-C.; Shih, J.-Y.; Lin, Y.-C.; Tseng, R.-Y.; Chiu, A.-C.; Yeh, Y.-H.; Liu, C.; Lin, Y.-T.; et al. Epithelial-mesenchymal transition (EMT) beyond EGFR mutations per se is a common mechanism for acquired resistance to EGFR TKI. Oncogene 2019, 38, 455–468. [Google Scholar] [CrossRef] [PubMed]
  82. Zhang, Y.; Xu, L.; Li, A.; Han, X. The roles of ZEB1 in tumorigenic progression and epigenetic modifications. Biomed. Pharmacother. 2019, 110, 400–408. [Google Scholar] [CrossRef] [PubMed]
  83. Zhang, P.; Sun, Y.; Ma, L. ZEB1: At the crossroads of epithelial-mesenchymal transition, metastasis and therapy resistance. Cell Cycle 2015, 14, 481–487. [Google Scholar] [CrossRef] [Green Version]
  84. Zhang, P.; Wei, Y.; Wang, L.; Debeb, B.G.; Yuan, Y.; Zhang, J.; Yuan, J.; Wang, M.; Chen, D.; Sun, Y.; et al. ATM-mediated stabilization of ZEB1 promotes DNA damage response and radioresistance through CHK1. Nat. Cell Biol. 2014, 16, 864–875. [Google Scholar] [CrossRef]
  85. Cortez, M.A.; Valdecanas, D.; Zhang, X.; Zhan, Y.; Bhardwaj, V.; Calin, G.A.; Komaki, R.; Giri, D.K.; Quini, C.C.; Wolfe, T.; et al. Therapeutic delivery of miR-200c enhances radiosensitivity in lung cancer. Mol. Ther. 2014, 22, 1494–1503. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  86. Jiang, Z.-S.; Sun, Y.-Z.; Wang, S.-M.; Ruan, J.-S. Epithelial-mesenchymal transition: Potential regulator of ABC transporters in tumor progression. J. Cancer 2017, 8, 2319–2327. [Google Scholar] [CrossRef] [Green Version]
  87. Wu, Y.; Jin, D.; Wang, X.; Du, J.; Di, W.; An, J.; Shao, C.; Guo, J. UBE2C induces cisplatin resistance via ZEB1/2-dependent upregulation of ABCG2 and ERCC1 in NSCLC cells. J. Oncol. 2019, 2019, 8607859. [Google Scholar] [CrossRef] [Green Version]
  88. Zhang, X.; Zhang, Z.; Zhang, Q.; Zhang, Q.; Sun, P.; Xiang, R.; Ren, G.; Yang, S. ZEB1 confers chemotherapeutic resistance to breast cancer by activating ATM. Cell Death Dis. 2018, 9, 57. [Google Scholar] [CrossRef] [Green Version]
  89. Viswanathan, V.S.; Ryan, M.J.; Dhruv, H.D.; Gill, S.; Eichhoff, O.M.; Seashore-Ludlow, B.; Kaffenberger, S.D.; Eaton, J.K.; Shimada, K.; Aguirre, A.J.; et al. Dependency of a therapy-resistant state of cancer cells on a lipid peroxidase pathway. Nature 2017, 547, 453–457. [Google Scholar] [CrossRef]
  90. Gubelmann, C.; Schwalie, P.C.; Raghav, S.K.; Röder, E.; Delessa, T.; Kiehlmann, E.; Waszak, S.M.; Corsinotti, A.; Udin, G.; Holcombe, W.; et al. Identification of the transcription factor ZEB1 as a central component of the adipogenic gene regulatory network. Elife 2014, 3, e03346. [Google Scholar] [CrossRef]
  91. Aiello, N.M.; Maddipati, R.; Norgard, R.J.; Balli, D.; Li, J.; Yuan, S.; Yamazoe, T.; Black, T.; Sahmoud, A.; Furth, E.E.; et al. EMT subtype influences epithelial plasticity and mode of cell migration. Dev. Cell 2018, 45, 681–695.e4. [Google Scholar] [CrossRef] [Green Version]
  92. Pastushenko, I.; Brisebarre, A.; Sifrim, A.; Fioramonti, M.; Revenco, T.; Boumahdi, S.; Van Keymeulen, A.; Brown, D.; Moers, V.; Lemaire, S.; et al. Identification of the tumour transition states occurring during EMT. Nature 2018, 556, 463–468. [Google Scholar] [CrossRef]
  93. Nieto, M.A.; Huang, R.Y.-J.; Jackson, R.A.; Thiery, J.P. EMT: 2016. Cell 2016, 166, 21–45. [Google Scholar] [CrossRef] [Green Version]
  94. Loret, N.; Denys, H.; Tummers, P.; Berx, G. The role of epithelial-to-mesenchymal plasticity in ovarian cancer progression and therapy resistance. Cancers 2019, 11, 838. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  95. Yang, J.; Antin, P.; Berx, G.; Blanpain, C.; Brabletz, T.; Bronner, M.; Campbell, K.; Cano, A.; Casanova, J.; Christofori, G.; et al. Guidelines and definitions for research on epithelial-mesenchymal transition. Nat. Rev. Mol. Cell Biol. 2020, 21, 341–352. [Google Scholar] [CrossRef] [Green Version]
  96. Zheng, X.; Carstens, J.L.; Kim, J.; Scheible, M.; Kaye, J.; Sugimoto, H.; Wu, C.-C.; LeBleu, V.S.; Kalluri, R. Epithelial-to-mesenchymal transition is dispensable for metastasis but induces chemoresistance in pancreatic cancer. Nature 2015, 527, 525–530. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  97. Xu, Y.; Lee, D.-K.; Feng, Z.; Xu, Y.; Bu, W.; Li, Y.; Liao, L.; Xu, J. Breast tumor cell-specific knockout of Twist1 inhibits cancer cell plasticity, dissemination, and lung metastasis in mice. Proc. Natl. Acad. Sci. USA 2017, 114, 11494–11499. [Google Scholar] [CrossRef] [Green Version]
  98. Tran, H.D.; Luitel, K.; Kim, M.; Zhang, K.; Longmore, G.D.; Tran, D.D. Transient SNAIL1 expression is necessary for metastatic competence in breast cancer. Cancer Res. 2014, 74, 6330–6340. [Google Scholar] [CrossRef] [Green Version]
  99. Serrano-Gomez, S.J.; Maziveyi, M.; Alahari, S.K. Regulation of epithelial-mesenchymal transition through epigenetic and post-translational modifications. Mol. Cancer 2016, 15, 18. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  100. Díaz, V.M.; de Herreros, A.G. F-box proteins: Keeping the epithelial-to-mesenchymal transition (EMT) in check. Semin. Cancer Biol. 2016, 36, 71–79. [Google Scholar] [CrossRef] [PubMed]
  101. Di Zazzo, E.; Galasso, G.; Giovannelli, P.; Di Donato, M.; Bilancio, A.; Perillo, B.; Sinisi, A.A.; Migliaccio, A.; Castoria, G. Estrogen receptors in epithelial-mesenchymal transition of prostate cancer. Cancers 2019, 11, 1418. [Google Scholar] [CrossRef] [Green Version]
  102. Hausser, J.; Szekely, P.; Bar, N.; Zimmer, A.; Sheftel, H.; Caldas, C.; Alon, U. Tumor diversity and the trade-off between universal cancer tasks. Nat. Commun. 2019, 10, 5423. [Google Scholar] [CrossRef] [Green Version]
  103. Celià-Terrassa, T.; Meca-Cortés, O.; Mateo, F.; Martínez de Paz, A.; Rubio, N.; Arnal-Estapé, A.; Ell, B.J.; Bermudo, R.; Díaz, A.; Guerra-Rebollo, M.; et al. Epithelial-mesenchymal transition can suppress major attributes of human epithelial tumor-initiating cells. J. Clin. Investig. 2012, 122, 1849–1868. [Google Scholar] [CrossRef] [Green Version]
  104. Tsuji, T.; Ibaragi, S.; Shima, K.; Hu, M.G.; Katsurano, M.; Sasaki, A.; Hu, G.-F. Epithelial-mesenchymal transition induced by growth suppressor p12CDK2-AP1 promotes tumor cell local invasion but suppresses distant colony growth. Cancer Res. 2008, 68, 10377–10386. [Google Scholar] [CrossRef] [Green Version]
  105. Hoek, K.S.; Eichhoff, O.M.; Schlegel, N.C.; Döbbeling, U.; Kobert, N.; Schaerer, L.; Hemmi, S.; Dummer, R. In vivo switching of human melanoma cells between proliferative and invasive states. Cancer Res. 2008, 68, 650–656. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  106. Hoffman, R.M.; Yano, S. Tumor-specific S/G2-phase cell cycle arrest of cancer cells by methionine restriction. Methods Mol. Biol. 2019, 1866, 49–60. [Google Scholar] [CrossRef]
  107. Zhou, J.; Giannakakou, P. Targeting microtubules for cancer chemotherapy. Curr. Med. Chem. Anticancer Agents 2005, 5, 65–71. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  108. Beaumont, K.A.; Hill, D.S.; Daignault, S.M.; Lui, G.Y.L.; Sharp, D.M.; Gabrielli, B.; Weninger, W.; Haass, N.K. Cell cycle phase-specific drug resistance as an escape mechanism of melanoma cells. J. Investig. Dermatol. 2016, 136, 1479–1489. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  109. Mao, Q.-Q.; Bai, Y.; Lin, Y.-W.; Zheng, X.-Y.; Qin, J.; Yang, K.; Xie, L.-P. Resveratrol confers resistance against taxol via induction of cell cycle arrest in human cancer cell lines. Mol. Nutr. Food Res. 2010, 54, 1574–1584. [Google Scholar] [CrossRef]
  110. Wang, X.; Pan, L.; Mao, N.; Sun, L.; Qin, X.; Yin, J. Cell-cycle synchronization reverses Taxol resistance of human ovarian cancer cell lines. Cancer Cell Int. 2013, 13, 77. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  111. Yoshiba, S.; Ito, D.; Nagumo, T.; Shirota, T.; Hatori, M.; Shintani, S. Hypoxia induces resistance to 5-fluorouracil in oral cancer cells via G(1) phase cell cycle arrest. Oral Oncol. 2009, 45, 109–115. [Google Scholar] [CrossRef] [PubMed]
  112. Zheng, H.; Shao, F.; Martin, S.; Xu, X.; Deng, C.-X. WEE1 inhibition targets cell cycle checkpoints for triple negative breast cancers to overcome cisplatin resistance. Sci. Rep. 2017, 7, 43517. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  113. Sarin, N.; Engel, F.; Kalayda, G.V.; Mannewitz, M.; Cinatl, J., Jr.; Rothweiler, F.; Michaelis, M.; Saafan, H.; Ritter, C.A.; Jaehde, U.; et al. Cisplatin resistance in non-small cell lung cancer cells is associated with an abrogation of cisplatin-induced G2/M cell cycle arrest. PLoS ONE 2017, 12, e0181081. [Google Scholar] [CrossRef] [PubMed]
  114. Horibe, S.; Matsuda, A.; Tanahashi, T.; Inoue, J.; Kawauchi, S.; Mizuno, S.; Ueno, M.; Takahashi, K.; Maeda, Y.; Maegouchi, T.; et al. Cisplatin resistance in human lung cancer cells is linked with dysregulation of cell cycle associated proteins. Life Sci. 2015, 124, 31–40. [Google Scholar] [CrossRef]
  115. Tormo, E.; Ballester, S.; Adam-Artigues, A.; Burgués, O.; Alonso, E.; Bermejo, B.; Menéndez, S.; Zazo, S.; Madoz-Gúrpide, J.; Rovira, A.; et al. The miRNA-449 family mediates doxorubicin resistance in triple-negative breast cancer by regulating cell cycle factors. Sci. Rep. 2019, 9, 5316. [Google Scholar] [CrossRef] [Green Version]
  116. Levine, B.; Kroemer, G. Autophagy in the pathogenesis of disease. Cell 2008, 132, 27–42. [Google Scholar] [CrossRef] [Green Version]
  117. White, E. Deconvoluting the context-dependent role for autophagy in cancer. Nat. Rev. Cancer 2012, 12, 401–410. [Google Scholar] [CrossRef] [Green Version]
  118. White, E. The role for autophagy in cancer. J. Clin. Investig. 2015, 125, 42–46. [Google Scholar] [CrossRef] [Green Version]
  119. Kim, T.W.; Lee, S.Y.; Kim, M.; Cheon, C.; Ko, S.-G. Kaempferol induces autophagic cell death via IRE1-JNK-CHOP pathway and inhibition of G9a in gastric cancer cells. Cell Death Dis. 2018, 9, 875. [Google Scholar] [CrossRef]
  120. Sun, D.; Zhu, L.; Zhao, Y.; Jiang, Y.; Chen, L.; Yu, Y.; Ouyang, L. Fluoxetine induces autophagic cell death via eEF2K-AMPK-mTOR-ULK complex axis in triple negative breast cancer. Cell Prolif. 2018, 51, e12402. [Google Scholar] [CrossRef] [Green Version]
  121. Lee, Y.J.; Won, A.J.; Lee, J.; Jung, J.H.; Yoon, S.; Lee, B.M.; Kim, H.S. Molecular mechanism of SAHA on regulation of autophagic cell death in tamoxifen-resistant MCF-7 breast cancer cells. Int. J. Med. Sci. 2012, 9, 881–893. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  122. Fulda, S.; Kögel, D. Cell death by autophagy: Emerging molecular mechanisms and implications for cancer therapy. Oncogene 2015, 34, 5105–5113. [Google Scholar] [CrossRef]
  123. Sui, X.; Kong, N.; Ye, L.; Han, W.; Zhou, J.; Zhang, Q.; He, C.; Pan, H. p38 and JNK MAPK pathways control the balance of apoptosis and autophagy in response to chemotherapeutic agents. Cancer Lett. 2014, 344, 174–179. [Google Scholar] [CrossRef]
  124. Li, X.; Zhou, Y.; Li, Y.; Yang, L.; Ma, Y.; Peng, X.; Yang, S.; Liu, J.; Li, H. Autophagy: A novel mechanism of chemoresistance in cancers. Biomed. Pharmacother. 2019, 119, 109415. [Google Scholar] [CrossRef] [PubMed]
  125. Yu, L.; Gu, C.; Zhong, D.; Shi, L.; Kong, Y.; Zhou, Z.; Liu, S. Induction of autophagy counteracts the anticancer effect of cisplatin in human esophageal cancer cells with acquired drug resistance. Cancer Lett. 2014, 355, 34–45. [Google Scholar] [CrossRef]
  126. O’Donovan, T.R.; O’Sullivan, G.C.; McKenna, S.L. Induction of autophagy by drug-resistant esophageal cancer cells promotes their survival and recovery following treatment with chemotherapeutics. Autophagy 2011, 7, 509–524. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  127. Panda, P.K.; Mukhopadhyay, S.; Das, D.N.; Sinha, N.; Naik, P.P.; Bhutia, S.K. Mechanism of autophagic regulation in carcinogenesis and cancer therapeutics. Semin. Cell Dev. Biol. 2015, 39, 43–55. [Google Scholar] [CrossRef]
  128. Choi, C.-H.; Jung, Y.-K.; Oh, S.-H. Autophagy induction by capsaicin in malignant human breast cells is modulated by p38 and extracellular signal-regulated mitogen-activated protein kinases and retards cell death by suppressing endoplasmic reticulum stress-mediated apoptosis. Mol. Pharmacol. 2010, 78, 114–125. [Google Scholar] [CrossRef] [Green Version]
  129. Sun, W.-L.; Chen, J.; Wang, Y.-P.; Zheng, H. Autophagy protects breast cancer cells from epirubicin-induced apoptosis and facilitates epirubicin-resistance development. Autophagy 2011, 7, 1035–1044. [Google Scholar] [CrossRef] [Green Version]
  130. Xu, L.; Liu, J.-H.; Zhang, J.; Zhang, N.; Wang, Z.-H. Blockade of autophagy aggravates endoplasmic reticulum stress and improves Paclitaxel cytotoxicity in human cervical cancer cells. Cancer Res. Treat. 2015, 47, 313–321. [Google Scholar] [CrossRef]
  131. Yoon, J.-H.; Ahn, S.-G.; Lee, B.-H.; Jung, S.-H.; Oh, S.-H. Role of autophagy in chemoresistance: Regulation of the ATM-mediated DNA-damage signaling pathway through activation of DNA-PKcs and PARP-1. Biochem. Pharmacol. 2012, 83, 747–757. [Google Scholar] [CrossRef] [PubMed]
  132. Han, M.W.; Lee, J.C.; Choi, J.-Y.; Kim, G.C.; Chang, H.W.; Nam, H.Y.; Kim, S.W.; Kim, S.Y. Autophagy inhibition can overcome radioresistance in breast cancer cells through suppression of TAK1 activation. Anticancer Res. 2014, 34, 1449–1455. [Google Scholar] [PubMed]
  133. Min, H.; Xu, M.; Chen, Z.-R.; Zhou, J.-D.; Huang, M.; Zheng, K.; Zou, X.-P. Bortezomib induces protective autophagy through AMP-activated protein kinase activation in cultured pancreatic and colorectal cancer cells. Cancer Chemother. Pharmacol. 2014, 74, 167–176. [Google Scholar] [CrossRef] [PubMed]
  134. Shen, J.; Zheng, H.; Ruan, J.; Fang, W.; Li, A.; Tian, G.; Niu, X.; Luo, S.; Zhao, P. Autophagy inhibition induces enhanced proapoptotic effects of ZD6474 in glioblastoma. Br. J. Cancer 2013, 109, 164–171. [Google Scholar] [CrossRef] [PubMed]
  135. Wang, J.; Wu, G.S. Role of autophagy in cisplatin resistance in ovarian cancer cells. J. Biol. Chem. 2014, 289, 17163–17173. [Google Scholar] [CrossRef] [Green Version]
  136. Chen, X.; Tan, M.; Xie, Z.; Feng, B.; Zhao, Z.; Yang, K.; Hu, C.; Liao, N.; Wang, T.; Chen, D.; et al. Inhibiting ROS-STAT3-dependent autophagy enhanced capsaicin-induced apoptosis in human hepatocellular carcinoma cells. Free Radic. Res. 2016, 50, 744–755. [Google Scholar] [CrossRef]
  137. Masui, A.; Hamada, M.; Kameyama, H.; Wakabayashi, K.; Takasu, A.; Imai, T.; Iwai, S.; Yura, Y. Autophagy as a survival mechanism for squamous cell carcinoma cells in endonuclease g-mediated apoptosis. PLoS ONE 2016, 11, e0162786. [Google Scholar] [CrossRef] [Green Version]
  138. Song, P.; Ye, L.; Fan, J.; Li, Y.; Zeng, X.; Wang, Z.; Wang, S.; Zhang, G.; Yang, P.; Cao, Z.; et al. Asparaginase induces apoptosis and cytoprotective autophagy in chronic myeloid leukemia cells. Oncotarget 2015, 6, 3861–3873. [Google Scholar] [CrossRef] [Green Version]
  139. Chen, Y.S.; Song, H.X.; Lu, Y.; Li, X.; Chen, T.; Zhang, Y.; Xue, J.X.; Liu, H.; Kan, B.; Yang, G.; et al. Autophagy inhibition contributes to radiation sensitization of esophageal squamous carcinoma cells. Dis. Esophagus 2011, 24, 437–443. [Google Scholar] [CrossRef]
  140. Lin, J.-F.; Tsai, T.-F.; Liao, P.-C.; Lin, Y.-H.; Lin, Y.-C.; Chen, H.-E.; Chou, K.-Y.; Hwang, T.I.-S. Benzyl isothiocyanate induces protective autophagy in human prostate cancer cells via inhibition of mTOR signaling. Carcinogenesis 2013, 34, 406–414. [Google Scholar] [CrossRef] [Green Version]
  141. Lin, C.-J.; Lee, C.-C.; Shih, Y.-L.; Lin, T.-Y.; Wang, S.-H.; Lin, Y.-F.; Shih, C.-M. Resveratrol enhances the therapeutic effect of temozolomide against malignant glioma in vitro and in vivo by inhibiting autophagy. Free Radic. Biol. Med. 2012, 52, 377–391. [Google Scholar] [CrossRef] [PubMed]
  142. Sui, X.; Kong, N.; Wang, X.; Fang, Y.; Hu, X.; Xu, Y.; Chen, W.; Wang, K.; Li, D.; Jin, W.; et al. JNK confers 5-fluorouracil resistance in p53-deficient and mutant p53-expressing colon cancer cells by inducing survival autophagy. Sci. Rep. 2014, 4, 4694. [Google Scholar] [CrossRef] [Green Version]
  143. Lang, F.; Qin, Z.; Li, F.; Zhang, H.; Fang, Z.; Hao, E. Apoptotic cell death induced by resveratrol is partially mediated by the autophagy pathway in human ovarian cancer cells. PLoS ONE 2015, 10, e0129196. [Google Scholar] [CrossRef] [PubMed]
  144. Kaewpiboon, C.; Surapinit, S.; Malilas, W.; Moon, J.; Phuwapraisirisan, P.; Tip-Pyang, S.; Johnston, R.N.; Koh, S.S.; Assavalapsakul, W.; Chung, Y.-H. Feroniellin A-induced autophagy causes apoptosis in multidrug-resistant human A549 lung cancer cells. Int. J. Oncol. 2014, 44, 1233–1242. [Google Scholar] [CrossRef] [Green Version]
  145. Han, W.; Pan, H.; Chen, Y.; Sun, J.; Wang, Y.; Li, J.; Ge, W.; Feng, L.; Lin, X.; Wang, X.; et al. EGFR tyrosine kinase inhibitors activate autophagy as a cytoprotective response in human lung cancer cells. PLoS ONE 2011, 6, e18691. [Google Scholar] [CrossRef]
  146. Notte, A.; Ninane, N.; Arnould, T.; Michiels, C. Hypoxia counteracts taxol-induced apoptosis in MDA-MB-231 breast cancer cells: Role of autophagy and JNK activation. Cell Death Dis. 2013, 4, e638. [Google Scholar] [CrossRef] [PubMed]
  147. Kim, M.; Jung, J.-Y.; Choi, S.; Lee, H.; Morales, L.D.; Koh, J.-T.; Kim, S.H.; Choi, Y.-D.; Choi, C.; Slaga, T.J.; et al. GFRA1 promotes cisplatin-induced chemoresistance in osteosarcoma by inducing autophagy. Autophagy 2017, 13, 149–168. [Google Scholar] [CrossRef] [Green Version]
  148. Cheng, X.; Liu, H.; Jiang, C.-C.; Fang, L.; Chen, C.; Zhang, X.-D.; Jiang, Z.-W. Connecting endoplasmic reticulum stress to autophagy through IRE1/JNK/beclin-1 in breast cancer cells. Int. J. Mol. Med. 2014, 34, 772–781. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  149. Pan, B.; Chen, D.; Huang, J.; Wang, R.; Feng, B.; Song, H.; Chen, L. HMGB1-mediated autophagy promotes docetaxel resistance in human lung adenocarcinoma. Mol. Cancer 2014, 13, 165. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  150. Hu, Y.-L.; DeLay, M.; Jahangiri, A.; Molinaro, A.M.; Rose, S.D.; Carbonell, W.S.; Aghi, M.K. Hypoxia-induced autophagy promotes tumor cell survival and adaptation to antiangiogenic treatment in glioblastoma. Cancer Res. 2012, 72, 1773–1783. [Google Scholar] [CrossRef] [Green Version]
  151. Wu, H.-M.; Jiang, Z.-F.; Ding, P.-S.; Shao, L.-J.; Liu, R.-Y. Hypoxia-induced autophagy mediates cisplatin resistance in lung cancer cells. Sci. Rep. 2015, 5, 12291. [Google Scholar] [CrossRef]
  152. Xu, N.; Zhang, J.; Shen, C.; Luo, Y.; Xia, L.; Xue, F.; Xia, Q. Cisplatin-induced downregulation of miR-199a-5p increases drug resistance by activating autophagy in HCC cell. Biochem. Biophys. Res. Commun. 2012, 423, 826–831. [Google Scholar] [CrossRef]
  153. Wang, Y.; Xu, W.; Yan, Z.; Zhao, W.; Mi, J.; Li, J.; Yan, H. Metformin induces autophagy and G0/G1 phase cell cycle arrest in myeloma by targeting the AMPK/mTORC1 and mTORC2 pathways. J. Exp. Clin. Cancer Res. 2018, 37, 63. [Google Scholar] [CrossRef]
  154. Tan, M.; Wu, A.; Liao, N.; Liu, M.; Guo, Q.; Yi, J.; Wang, T.; Huang, Y.; Qiu, B.; Zhou, W. Inhibiting ROS-TFE3-dependent autophagy enhances the therapeutic response to metformin in breast cancer. Free Radic. Res. 2018, 52, 872–886. [Google Scholar] [CrossRef]
  155. Tai, W.-T.; Shiau, C.-W.; Chen, H.-L.; Liu, C.-Y.; Lin, C.-S.; Cheng, A.-L.; Chen, P.-J.; Chen, K.-F. Mcl-1-dependent activation of Beclin 1 mediates autophagic cell death induced by sorafenib and SC-59 in hepatocellular carcinoma cells. Cell Death Dis. 2013, 4, e485. [Google Scholar] [CrossRef]
  156. Shi, Y.-H.; Ding, Z.-B.; Zhou, J.; Hui, B.; Shi, G.-M.; Ke, A.-W.; Wang, X.-Y.; Dai, Z.; Peng, Y.-F.; Gu, C.-Y.; et al. Targeting autophagy enhances sorafenib lethality for hepatocellular carcinoma via ER stress-related apoptosis. Autophagy 2011, 7, 1159–1172. [Google Scholar] [CrossRef] [PubMed]
  157. Shingu, T.; Fujiwara, K.; Bögler, O.; Akiyama, Y.; Moritake, K.; Shinojima, N.; Tamada, Y.; Yokoyama, T.; Kondo, S. Inhibition of autophagy at a late stage enhances imatinib-induced cytotoxicity in human malignant glioma cells. Int. J. Cancer 2009, 124, 1060–1071. [Google Scholar] [CrossRef] [PubMed]
  158. Dominiak, A.; Chełstowska, B.; Olejarz, W.; Nowicka, G. Communication in the cancer microenvironment as a target for therapeutic interventions. Cancers 2020, 12, 1232. [Google Scholar] [CrossRef]
  159. Xiang, Y.; Liu, Y.; Yang, Y.; Hu, H.; Hu, P.; Ren, H.; Zhang, D. A secretomic study on human hepatocellular carcinoma multiple drug-resistant cell lines. Oncol. Rep. 2015, 34, 1249–1260. [Google Scholar] [CrossRef] [PubMed]
  160. Bosse, K.; Haneder, S.; Arlt, C.; Ihling, C.H.; Seufferlein, T.; Sinz, A. Mass spectrometry-based secretome analysis of non-small cell lung cancer cell lines. Proteomics 2016, 16, 2801–2814. [Google Scholar] [CrossRef]
  161. Böttger, F.; Schaaij-Visser, T.B.; de Reus, I.; Piersma, S.R.; Pham, T.V.; Nagel, R.; Brakenhoff, R.H.; Thunnissen, E.; Smit, E.F.; Jimenez, C.R. Proteome analysis of non-small cell lung cancer cell line secretomes and patient sputum reveals biofluid biomarker candidates for cisplatin response prediction. J. Proteomics 2019, 196, 106–119. [Google Scholar] [CrossRef] [PubMed]
  162. Bateman, N.W.; Jaworski, E.; Ao, W.; Wang, G.; Litzi, T.; Dubil, E.; Marcus, C.; Conrads, K.A.; Teng, P.-N.; Hood, B.L.; et al. Elevated AKAP12 in paclitaxel-resistant serous ovarian cancer cells is prognostic and predictive of poor survival in patients. J. Proteome Res. 2015, 14, 1900–1910. [Google Scholar] [CrossRef] [Green Version]
  163. Teng, P.-N.; Wang, G.; Hood, B.L.; Conrads, K.A.; Hamilton, C.A.; Maxwell, G.L.; Darcy, K.M.; Conrads, T.P. Identification of candidate circulating cisplatin-resistant biomarkers from epithelial ovarian carcinoma cell secretomes. Br. J. Cancer 2014, 110, 123–132. [Google Scholar] [CrossRef] [Green Version]
  164. Yao, L.; Zhang, Y.; Chen, K.; Hu, X.; Xu, L.X. Discovery of IL-18 as a novel secreted protein contributing to doxorubicin resistance by comparative secretome analysis of MCF-7 and MCF-7/Dox. PLoS ONE 2011, 6, e24684. [Google Scholar] [CrossRef]
  165. Wojtuszkiewicz, A.; Schuurhuis, G.J.; Kessler, F.L.; Piersma, S.R.; Knol, J.C.; Pham, T.V.; Jansen, G.; Musters, R.J.P.; van Meerloo, J.; Assaraf, Y.G.; et al. Exosomes secreted by apoptosis-resistant Acute Myeloid Leukemia (AML) blasts harbor regulatory network proteins potentially involved in antagonism of apoptosis. Mol. Cell. Proteomics 2016, 15, 1281–1298. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  166. Chen, W.-X.; Liu, X.-M.; Lv, M.-M.; Chen, L.; Zhao, J.-H.; Zhong, S.-L.; Ji, M.-H.; Hu, Q.; Luo, Z.; Wu, J.-Z.; et al. Exosomes from drug-resistant breast cancer cells transmit chemoresistance by a horizontal transfer of microRNAs. PLoS ONE 2014, 9, e95240. [Google Scholar] [CrossRef] [PubMed]
  167. Wei, Y.; Lai, X.; Yu, S.; Chen, S.; Ma, Y.; Zhang, Y.; Li, H.; Zhu, X.; Yao, L.; Zhang, J. Exosomal miR-221/222 enhances tamoxifen resistance in recipient ER-positive breast cancer cells. Breast Cancer Res. Treat. 2014, 147, 423–431. [Google Scholar] [CrossRef] [PubMed]
  168. Lv, M.-M.; Zhu, X.-Y.; Chen, W.-X.; Zhong, S.-L.; Hu, Q.; Ma, T.-F.; Zhang, J.; Chen, L.; Tang, J.-H.; Zhao, J.-H. Exosomes mediate drug resistance transfer in MCF-7 breast cancer cells and a probable mechanism is delivery of P-glycoprotein. Tumour Biol. 2014, 35, 10773–10779. [Google Scholar] [CrossRef]
  169. Ning, K.; Wang, T.; Sun, X.; Zhang, P.; Chen, Y.; Jin, J.; Hua, D. UCH-L1-containing exosomes mediate chemotherapeutic resistance transfer in breast cancer. J. Surg. Oncol. 2017, 115, 932–940. [Google Scholar] [CrossRef]
  170. Yu, D.-D.; Wu, Y.; Zhang, X.-H.; Lv, M.-M.; Chen, W.-X.; Chen, X.; Yang, S.-J.; Shen, H.; Zhong, S.-L.; Tang, J.-H.; et al. Exosomes from adriamycin-resistant breast cancer cells transmit drug resistance partly by delivering miR-222. Tumour Biol. 2016, 37, 3227–3235. [Google Scholar] [CrossRef]
  171. Santos, J.C.; da Silva Lima, N.; Sarian, L.O.; Matheu, A.; Ribeiro, M.L.; Derchain, S.F.M. Exosome-mediated breast cancer chemoresistance via miR-155 transfer. Sci. Rep. 2018, 8, 829. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  172. Crow, J.; Atay, S.; Banskota, S.; Artale, B.; Schmitt, S.; Godwin, A.K. Exosomes as mediators of platinum resistance in ovarian cancer. Oncotarget 2017, 8, 11917–11936. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  173. Zhang, F.-F.; Zhu, Y.-F.; Zhao, Q.-N.; Yang, D.-T.; Dong, Y.-P.; Jiang, L.; Xing, W.-X.; Li, X.-Y.; Xing, H.; Shi, M.; et al. Microvesicles mediate transfer of P-glycoprotein to paclitaxel-sensitive A2780 human ovarian cancer cells, conferring paclitaxel-resistance. Eur. J. Pharmacol. 2014, 738, 83–90. [Google Scholar] [CrossRef]
  174. Corcoran, C.; Rani, S.; O’Brien, K.; O’Neill, A.; Prencipe, M.; Sheikh, R.; Webb, G.; McDermott, R.; Watson, W.; Crown, J.; et al. Docetaxel-resistance in prostate cancer: Evaluating associated phenotypic changes and potential for resistance transfer via exosomes. PLoS ONE 2012, 7, e50999. [Google Scholar] [CrossRef]
  175. Vella, L.J.; Behren, A.; Coleman, B.; Greening, D.W.; Hill, A.F.; Cebon, J. Intercellular resistance to BRAF inhibition can be mediated by extracellular vesicle-associated PDGFRβ. Neoplasia 2017, 19, 932–940. [Google Scholar] [CrossRef]
  176. Cesi, G.; Philippidou, D.; Kozar, I.; Kim, Y.J.; Bernardin, F.; Van Niel, G.; Wienecke-Baldacchino, A.; Felten, P.; Letellier, E.; Dengler, S.; et al. A new ALK isoform transported by extracellular vesicles confers drug resistance to melanoma cells. Mol. Cancer 2018, 17, 145. [Google Scholar] [CrossRef]
  177. Bebawy, M.; Combes, V.; Lee, E.; Jaiswal, R.; Gong, J.; Bonhoure, A.; Grau, G.E.R. Membrane microparticles mediate transfer of P-glycoprotein to drug sensitive cancer cells. Leukemia 2009, 23, 1643–1649. [Google Scholar] [CrossRef] [Green Version]
  178. Bouvy, C.; Wannez, A.; Laloy, J.; Chatelain, C.; Dogné, J.-M. Transfer of multidrug resistance among acute myeloid leukemia cells via extracellular vesicles and their microRNA cargo. Leuk. Res. 2017, 62, 70–76. [Google Scholar] [CrossRef]
  179. Zhang, Q.; Liu, R.-X.; Chan, K.-W.; Hu, J.; Zhang, J.; Wei, L.; Tan, H.; Yang, X.; Liu, H. Exosomal transfer of p-STAT3 promotes acquired 5-FU resistance in colorectal cancer cells. J. Exp. Clin. Cancer Res. 2019, 38, 320. [Google Scholar] [CrossRef] [PubMed]
  180. Shi, S.; Huang, X.; Ma, X.; Zhu, X.; Zhang, Q. Research of the mechanism on miRNA193 in exosomes promotes cisplatin resistance in esophageal cancer cells. PLoS ONE 2020, 15, e0225290. [Google Scholar] [CrossRef] [PubMed]
  181. Wei, F.; Ma, C.; Zhou, T.; Dong, X.; Luo, Q.; Geng, L.; Ding, L.; Zhang, Y.; Zhang, L.; Li, N.; et al. Exosomes derived from gemcitabine-resistant cells transfer malignant phenotypic traits via delivery of miRNA-222-3p. Mol. Cancer 2017, 16, 132. [Google Scholar] [CrossRef] [Green Version]
  182. Torreggiani, E.; Roncuzzi, L.; Perut, F.; Zini, N.; Baldini, N. Multimodal transfer of MDR by exosomes in human osteosarcoma. Int. J. Oncol. 2016, 49, 189–196. [Google Scholar] [CrossRef] [Green Version]
  183. Qu, L.; Ding, J.; Chen, C.; Wu, Z.-J.; Liu, B.; Gao, Y.; Chen, W.; Liu, F.; Sun, W.; Li, X.-F.; et al. Exosome-transmitted lncARSR promotes sunitinib resistance in renal cancer by acting as a competing endogenous RNA. Cancer Cell 2016, 29, 653–668. [Google Scholar] [CrossRef] [PubMed]
  184. Peak, T.C.; Panigrahi, G.K.; Praharaj, P.P.; Su, Y.; Shi, L.; Chyr, J.; Rivera-Chávez, J.; Flores-Bocanegra, L.; Singh, R.; Vander Griend, D.J.; et al. Syntaxin 6-mediated exosome secretion regulates enzalutamide resistance in prostate cancer. Mol. Carcinog. 2020, 59, 62–72. [Google Scholar] [CrossRef]
  185. Fan, J.; Wei, Q.; Koay, E.J.; Liu, Y.; Ning, B.; Bernard, P.W.; Zhang, N.; Han, H.; Katz, M.H.; Zhao, Z.; et al. Chemoresistance transmission via exosome-mediated EphA2 transfer in pancreatic cancer. Theranostics 2018, 8, 5986–5994. [Google Scholar] [CrossRef]
  186. Burt, R.; Dey, A.; Aref, S.; Aguiar, M.; Akarca, A.; Bailey, K.; Day, W.; Hooper, S.; Kirkwood, A.; Kirschner, K.; et al. Activated stromal cells transfer mitochondria to rescue acute lymphoblastic leukemia cells from oxidative stress. Blood 2019, 134, 1415–1429. [Google Scholar] [CrossRef]
  187. Sahinbegovic, H.; Jelinek, T.; Hrdinka, M.; Bago, J.R.; Turi, M.; Sevcikova, T.; Kurtovic-Kozaric, A.; Hajek, R.; Simicek, M. Intercellular mitochondrial transfer in the tumor microenvironment. Cancers 2020, 12, 1787. [Google Scholar] [CrossRef]
  188. Desir, S.; Dickson, E.L.; Vogel, R.I.; Thayanithy, V.; Wong, P.; Teoh, D.; Geller, M.A.; Steer, C.J.; Subramanian, S.; Lou, E. Tunneling nanotube formation is stimulated by hypoxia in ovarian cancer cells. Oncotarget 2016, 7, 43150–43161. [Google Scholar] [CrossRef]
  189. Lu, J.; Zheng, X.; Li, F.; Yu, Y.; Chen, Z.; Liu, Z.; Wang, Z.; Xu, H.; Yang, W. Tunneling nanotubes promote intercellular mitochondria transfer followed by increased invasiveness in bladder cancer cells. Oncotarget 2017, 8, 15539–15552. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  190. Hekmatshoar, Y.; Nakhle, J.; Galloni, M.; Vignais, M.-L. The role of metabolism and tunneling nanotube-mediated intercellular mitochondria exchange in cancer drug resistance. Biochem. J. 2018, 475, 2305–2328. [Google Scholar] [CrossRef] [PubMed]
  191. Obenauf, A.C.; Zou, Y.; Ji, A.L.; Vanharanta, S.; Shu, W.; Shi, H.; Kong, X.; Bosenberg, M.C.; Wiesner, T.; Rosen, N.; et al. Therapy-induced tumour secretomes promote resistance and tumour progression. Nature 2015, 520, 368–372. [Google Scholar] [CrossRef] [PubMed]
  192. Pavlyukov, M.S.; Yu, H.; Bastola, S.; Minata, M.; Shender, V.O.; Lee, Y.; Zhang, S.; Wang, J.; Komarova, S.; Wang, J.; et al. Apoptotic cell-derived extracellular vesicles promote malignancy of glioblastoma via intercellular transfer of splicing factors. Cancer Cell 2018, 34, 119–135.e10. [Google Scholar] [CrossRef] [Green Version]
  193. Huang, Q.; Li, F.; Liu, X.; Li, W.; Shi, W.; Liu, F.-F.; O’Sullivan, B.; He, Z.; Peng, Y.; Tan, A.-C.; et al. Caspase 3-mediated stimulation of tumor cell repopulation during cancer radiotherapy. Nat. Med. 2011, 17, 860–866. [Google Scholar] [CrossRef] [PubMed]
  194. Jiang, M.-J.; Chen, Y.-Y.; Dai, J.-J.; Gu, D.-N.; Mei, Z.; Liu, F.-R.; Huang, Q.; Tian, L. Dying tumor cell-derived exosomal miR-194-5p potentiates survival and repopulation of tumor repopulating cells upon radiotherapy in pancreatic cancer. Mol. Cancer 2020, 19, 68. [Google Scholar] [CrossRef] [PubMed]
  195. Samuel, P.; Mulcahy, L.A.; Furlong, F.; McCarthy, H.O.; Brooks, S.A.; Fabbri, M.; Pink, R.C.; Carter, D.R.F. Cisplatin induces the release of extracellular vesicles from ovarian cancer cells that can induce invasiveness and drug resistance in bystander cells. Philos. Trans. R. Soc. Lond. B Biol. Sci. 2018, 373. [Google Scholar] [CrossRef] [PubMed]
  196. Xiao, X.; Yu, S.; Li, S.; Wu, J.; Ma, R.; Cao, H.; Zhu, Y.; Feng, J. Exosomes: Decreased sensitivity of lung cancer A549 cells to cisplatin. PLoS ONE 2014, 9, e89534. [Google Scholar] [CrossRef] [Green Version]
  197. Takahashi, K.; Yan, I.K.; Wood, J.; Haga, H.; Patel, T. Involvement of extracellular vesicle long noncoding RNA (linc-VLDLR) in tumor cell responses to chemotherapy. Mol. Cancer Res. 2014, 12, 1377–1387. [Google Scholar] [CrossRef] [Green Version]
  198. Patel, G.K.; Khan, M.A.; Bhardwaj, A.; Srivastava, S.K.; Zubair, H.; Patton, M.C.; Singh, S.; Khushman, M.D.; Singh, A.P. Exosomes confer chemoresistance to pancreatic cancer cells by promoting ROS detoxification and miR-155-mediated suppression of key gemcitabine-metabolising enzyme, DCK. Br. J. Cancer 2017, 116, 609–619. [Google Scholar] [CrossRef] [Green Version]
  199. Mikamori, M.; Yamada, D.; Eguchi, H.; Hasegawa, S.; Kishimoto, T.; Tomimaru, Y.; Asaoka, T.; Noda, T.; Wada, H.; Kawamoto, K.; et al. MicroRNA-155 controls exosome synthesis and promotes gemcitabine resistance in pancreatic ductal adenocarcinoma. Sci. Rep. 2017, 7, 42339. [Google Scholar] [CrossRef]
  200. Kreger, B.T.; Johansen, E.R.; Cerione, R.A.; Antonyak, M.A. The enrichment of survivin in exosomes from breast cancer cells treated with paclitaxel promotes cell survival and chemoresistance. Cancers 2016, 8, 111. [Google Scholar] [CrossRef] [Green Version]
  201. Aldonza, M.B.D.; Hong, J.-Y.; Lee, S.K. Paclitaxel-resistant cancer cell-derived secretomes elicit ABCB1-associated docetaxel cross-resistance and escape from apoptosis through FOXO3a-driven glycolytic regulation. Exp. Mol. Med. 2017, 49, e286. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  202. Khan, S.; Jutzy, J.M.S.; Aspe, J.R.; McGregor, D.W.; Neidigh, J.W.; Wall, N.R. Survivin is released from cancer cells via exosomes. Apoptosis 2011, 16, 1–12. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  203. Wang, X.; Qiao, D.; Chen, L.; Xu, M.; Chen, S.; Huang, L.; Wang, F.; Chen, Z.; Cai, J.; Fu, L. Chemotherapeutic drugs stimulate the release and recycling of extracellular vesicles to assist cancer cells in developing an urgent chemoresistance. Mol. Cancer 2019, 18, 182. [Google Scholar] [CrossRef] [Green Version]
  204. Huang, C.-Y.; Chiang, S.-F.; Chen, W.T.-L.; Ke, T.-W.; Chen, T.-W.; You, Y.-S.; Lin, C.-Y.; Chao, K.S.C.; Huang, C.-Y. HMGB1 promotes ERK-mediated mitochondrial Drp1 phosphorylation for chemoresistance through RAGE in colorectal cancer. Cell Death Dis. 2018, 9, 1004. [Google Scholar] [CrossRef] [Green Version]
  205. He, S.; Cheng, J.; Sun, L.; Wang, Y.; Wang, C.; Liu, X.; Zhang, Z.; Zhao, M.; Luo, Y.; Tian, L.; et al. HMGB1 released by irradiated tumor cells promotes living tumor cell proliferation via paracrine effect. Cell Death Dis. 2018, 9, 648. [Google Scholar] [CrossRef]
  206. Tanzer, M.C.; Frauenstein, A.; Stafford, C.A.; Phulphagar, K.; Mann, M.; Meissner, F. Quantitative and dynamic catalogs of proteins released during apoptotic and necroptotic cell death. Cell Rep. 2020, 30, 1260–1270.e5. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  207. Mastri, M.; Tracz, A.; Lee, C.R.; Dolan, M.; Attwood, K.; Christensen, J.G.; Liu, S.; Ebos, J.M.L. A transient pseudosenescent secretome promotes tumor growth after antiangiogenic therapy withdrawal. Cell Rep. 2018, 25, 3706–3720.e8. [Google Scholar] [CrossRef] [Green Version]
  208. Ohanna, M.; Cheli, Y.; Bonet, C.; Bonazzi, V.F.; Allegra, M.; Giuliano, S.; Bille, K.; Bahadoran, P.; Giacchero, D.; Lacour, J.P.; et al. Secretome from senescent melanoma engages the STAT3 pathway to favor reprogramming of naive melanoma towards a tumor-initiating cell phenotype. Oncotarget 2013, 4, 2212–2224. [Google Scholar] [CrossRef] [Green Version]
  209. Ohanna, M.; Giuliano, S.; Bonet, C.; Imbert, V.; Hofman, V.; Zangari, J.; Bille, K.; Robert, C.; Bressac-de Paillerets, B.; Hofman, P.; et al. Senescent cells develop a PARP-1 and nuclear factor-{kappa}B-associated secretome (PNAS). Genes Dev. 2011, 25, 1245–1261. [Google Scholar] [CrossRef] [Green Version]
  210. Sun, X.; Shi, B.; Zheng, H.; Min, L.; Yang, J.; Li, X.; Liao, X.; Huang, W.; Zhang, M.; Xu, S.; et al. Senescence-associated secretory factors induced by cisplatin in melanoma cells promote non-senescent melanoma cell growth through activation of the ERK1/2-RSK1 pathway. Cell Death Dis. 2018, 9, 260. [Google Scholar] [CrossRef]
  211. Canino, C.; Mori, F.; Cambria, A.; Diamantini, A.; Germoni, S.; Alessandrini, G.; Borsellino, G.; Galati, R.; Battistini, L.; Blandino, R.; et al. SASP mediates chemoresistance and tumor-initiating-activity of mesothelioma cells. Oncogene 2012, 31, 3148–3163. [Google Scholar] [CrossRef] [Green Version]
  212. Barker, H.E.; Paget, J.T.E.; Khan, A.A.; Harrington, K.J. The tumour microenvironment after radiotherapy: Mechanisms of resistance and recurrence. Nat. Rev. Cancer 2015, 15, 409–425. [Google Scholar] [CrossRef] [PubMed]
  213. Saleh, R.; Elkord, E. Acquired resistance to cancer immunotherapy: Role of tumor-mediated immunosuppression. Semin. Cancer Biol. 2020, 65, 13–27. [Google Scholar] [CrossRef]
  214. Wu, J.; Liang, C.; Chen, M.; Su, W. Association between tumor-stroma ratio and prognosis in solid tumor patients: A systematic review and meta-analysis. Oncotarget 2016, 7, 68954–68965. [Google Scholar] [CrossRef] [Green Version]
  215. Kramer, C.J.H.; Vangangelt, K.M.H.; van Pelt, G.W.; Dekker, T.J.A.; Tollenaar, R.A.E.M.; Mesker, W.E. The prognostic value of tumour-stroma ratio in primary breast cancer with special attention to triple-negative tumours: A review. Breast Cancer Res. Treat. 2019, 173, 55–64. [Google Scholar] [CrossRef] [Green Version]
  216. Jiang, M.-J.; Gu, D.-N.; Dai, J.-J.; Huang, Q.; Tian, L. Dark side of cytotoxic therapy: Chemoradiation-induced cell death and tumor repopulation. Trends Cancer Res. 2020, 6, 419–431. [Google Scholar] [CrossRef] [PubMed]
  217. Wang, D.; Yang, L.; Yu, W.; Wu, Q.; Lian, J.; Li, F.; Liu, S.; Li, A.; He, Z.; Liu, J.; et al. Colorectal cancer cell-derived CCL20 recruits regulatory T cells to promote chemoresistance via FOXO1/CEBPB/NF-κB signaling. J. Immunother. Cancer 2019, 7, 215. [Google Scholar] [CrossRef] [Green Version]
  218. Rodriguez, Y.I.; Campos, L.E.; Castro, M.G.; Aladhami, A.; Oskeritzian, C.A.; Alvarez, S.E. Sphingosine-1 phosphate: A new modulator of immune plasticity in the tumor microenvironment. Front. Oncol. 2016, 6, 218. [Google Scholar] [CrossRef] [Green Version]
  219. Mizuno, R.; Kawada, K.; Sakai, Y. Prostaglandin E2/EP signaling in the tumor microenvironment of colorectal cancer. Int. J. Mol. Sci. 2019, 20, 6254. [Google Scholar] [CrossRef] [Green Version]
  220. Sahai, E.; Astsaturov, I.; Cukierman, E.; DeNardo, D.G.; Egeblad, M.; Evans, R.M.; Fearon, D.; Greten, F.R.; Hingorani, S.R.; Hunter, T.; et al. A framework for advancing our understanding of cancer-associated fibroblasts. Nat. Rev. Cancer 2020, 20, 174–186. [Google Scholar] [CrossRef] [Green Version]
  221. Valcz, G.; Buzás, E.I.; Sebestyén, A.; Krenács, T.; Szállási, Z.; Igaz, P.; Molnár, B. Extracellular vesicle-based communication may contribute to the co-evolution of cancer stem cells and cancer-associated fibroblasts in anti-cancer therapy. Cancers 2020, 12, 2324. [Google Scholar] [CrossRef] [PubMed]
  222. Fang, Y.; Zhou, W.; Rong, Y.; Kuang, T.; Xu, X.; Wu, W.; Wang, D.; Lou, W. Exosomal miRNA-106b from cancer-associated fibroblast promotes gemcitabine resistance in pancreatic cancer. Exp. Cell Res. 2019, 383, 111543. [Google Scholar] [CrossRef]
  223. Yi, Y.; Zeng, S.; Wang, Z.; Wu, M.; Ma, Y.; Ye, X.; Zhang, B.; Liu, H. Cancer-associated fibroblasts promote epithelial-mesenchymal transition and EGFR-TKI resistance of non-small cell lung cancers via HGF/IGF-1/ANXA2 signaling. Biochim. Biophys. Acta Mol. Basis Dis. 2018, 1864, 793–803. [Google Scholar] [CrossRef] [PubMed]
  224. Hu, Y.; Yan, C.; Mu, L.; Huang, K.; Li, X.; Tao, D.; Wu, Y.; Qin, J. Fibroblast-derived exosomes contribute to chemoresistance through priming cancer stem cells in colorectal cancer. PLoS ONE 2015, 10, e0125625. [Google Scholar] [CrossRef] [Green Version]
  225. Che, Y.; Wang, J.; Li, Y.; Lu, Z.; Huang, J.; Sun, S.; Mao, S.; Lei, Y.; Zang, R.; Sun, N.; et al. Cisplatin-activated PAI-1 secretion in the cancer-associated fibroblasts with paracrine effects promoting esophageal squamous cell carcinoma progression and causing chemoresistance. Cell Death Dis. 2018, 9, 759. [Google Scholar] [CrossRef] [Green Version]
  226. Zhang, H.; Xie, C.; Yue, J.; Jiang, Z.; Zhou, R.; Xie, R.; Wang, Y.; Wu, S. Cancer-associated fibroblasts mediated chemoresistance by a FOXO1/TGFβ1 signaling loop in esophageal squamous cell carcinoma. Mol. Carcinog. 2017, 56, 1150–1163. [Google Scholar] [CrossRef] [PubMed]
  227. Zhang, H.; Deng, T.; Liu, R.; Ning, T.; Yang, H.; Liu, D.; Zhang, Q.; Lin, D.; Ge, S.; Bai, M.; et al. CAF secreted miR-522 suppresses ferroptosis and promotes acquired chemo-resistance in gastric cancer. Mol. Cancer 2020, 19, 43. [Google Scholar] [CrossRef] [Green Version]
  228. Ham, I.-H.; Lee, D.; Hur, H. Role of cancer-associated fibroblast in gastric cancer progression and resistance to treatments. J. Oncol. 2019, 2019, 6270784. [Google Scholar] [CrossRef]
  229. Tao, L.; Huang, G.; Wang, R.; Pan, Y.; He, Z.; Chu, X.; Song, H.; Chen, L. Cancer-associated fibroblasts treated with cisplatin facilitates chemoresistance of lung adenocarcinoma through IL-11/IL-11R/STAT3 signaling pathway. Sci. Rep. 2016, 6, 38408. [Google Scholar] [CrossRef]
  230. Au Yeung, C.L.; Co, N.-N.; Tsuruga, T.; Yeung, T.-L.; Kwan, S.-Y.; Leung, C.S.; Li, Y.; Lu, E.S.; Kwan, K.; Wong, K.-K.; et al. Exosomal transfer of stroma-derived miR21 confers paclitaxel resistance in ovarian cancer cells through targeting APAF1. Nat. Commun. 2016, 7, 11150. [Google Scholar] [CrossRef] [Green Version]
  231. Hu, J.L.; Wang, W.; Lan, X.L.; Zeng, Z.C.; Liang, Y.S.; Yan, Y.R.; Song, F.Y.; Wang, F.F.; Zhu, X.H.; Liao, W.J.; et al. CAFs secreted exosomes promote metastasis and chemotherapy resistance by enhancing cell stemness and epithelial-mesenchymal transition in colorectal cancer. Mol. Cancer 2019, 18, 91. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  232. Hu, Y.-B.; Yan, C.; Mu, L.; Mi, Y.-L.; Zhao, H.; Hu, H.; Li, X.-L.; Tao, D.-D.; Wu, Y.-Q.; Gong, J.-P.; et al. Exosomal Wnt-induced dedifferentiation of colorectal cancer cells contributes to chemotherapy resistance. Oncogene 2019, 38, 1951–1965. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  233. Zhai, J.; Shen, J.; Xie, G.; Wu, J.; He, M.; Gao, L.; Zhang, Y.; Yao, X.; Shen, L. Cancer-associated fibroblasts-derived IL-8 mediates resistance to cisplatin in human gastric cancer. Cancer Lett. 2019, 454, 37–43. [Google Scholar] [CrossRef]
  234. Shintani, Y.; Fujiwara, A.; Kimura, T.; Kawamura, T.; Funaki, S.; Minami, M.; Okumura, M. IL-6 secreted from cancer-associated fibroblasts mediates chemoresistance in NSCLC by increasing epithelial-mesenchymal transition signaling. J. Thorac. Oncol. 2016, 11, 1482–1492. [Google Scholar] [CrossRef] [Green Version]
  235. Qiao, Y.; Zhang, C.; Li, A.; Wang, D.; Luo, Z.; Ping, Y.; Zhou, B.; Liu, S.; Li, H.; Yue, D.; et al. IL6 derived from cancer-associated fibroblasts promotes chemoresistance via CXCR7 in esophageal squamous cell carcinoma. Oncogene 2018, 37, 873–883. [Google Scholar] [CrossRef]
  236. Huber, R.M.; Lucas, J.M.; Gomez-Sarosi, L.A.; Coleman, I.; Zhao, S.; Coleman, R.; Nelson, P.S. DNA damage induces GDNF secretion in the tumor microenvironment with paracrine effects promoting prostate cancer treatment resistance. Oncotarget 2015, 6, 2134–2147. [Google Scholar] [CrossRef] [Green Version]
  237. Hughes, R.; Qian, B.-Z.; Rowan, C.; Muthana, M.; Keklikoglou, I.; Olson, O.C.; Tazzyman, S.; Danson, S.; Addison, C.; Clemons, M.; et al. Perivascular M2 macrophages stimulate tumor relapse after chemotherapy. Cancer Res. 2015, 75, 3479–3491. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  238. Yin, Y.; Yao, S.; Hu, Y.; Feng, Y.; Li, M.; Bian, Z.; Zhang, J.; Qin, Y.; Qi, X.; Zhou, L.; et al. The immune-microenvironment confers chemoresistance of colorectal cancer through macrophage-derived IL6. Clin. Cancer Res. 2017, 23, 7375–7387. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  239. D’Errico, G.; Alonso-Nocelo, M.; Vallespinos, M.; Hermann, P.C.; Alcalá, S.; García, C.P.; Martin-Hijano, L.; Valle, S.; Earl, J.; Cassiano, C.; et al. Tumor-associated macrophage-secreted 14-3-3ζ signals via AXL to promote pancreatic cancer chemoresistance. Oncogene 2019, 38, 5469–5485. [Google Scholar] [CrossRef] [PubMed]
  240. Larionova, I.; Cherdyntseva, N.; Liu, T.; Patysheva, M.; Rakina, M.; Kzhyshkowska, J. Interaction of tumor-associated macrophages and cancer chemotherapy. Oncoimmunology 2019, 8, 1596004. [Google Scholar] [CrossRef] [Green Version]
  241. Timaner, M.; Letko-Khait, N.; Kotsofruk, R.; Benguigui, M.; Beyar-Katz, O.; Rachman-Tzemah, C.; Raviv, Z.; Bronshtein, T.; Machluf, M.; Shaked, Y. Therapy-educated mesenchymal stem cells enrich for tumor-initiating cells. Cancer Res. 2018, 78, 1253–1265. [Google Scholar] [CrossRef] [Green Version]
  242. He, W.; Liang, B.; Wang, C.; Li, S.; Zhao, Y.; Huang, Q.; Liu, Z.; Yao, Z.; Wu, Q.; Liao, W.; et al. MSC-regulated lncRNA MACC1-AS1 promotes stemness and chemoresistance through fatty acid oxidation in gastric cancer. Oncogene 2019, 38, 4637–4654. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  243. Falzone, L.; Salomone, S.; Libra, M. Evolution of cancer pharmacological treatments at the turn of the third millennium. Front. Pharmacol. 2018, 9, 1300. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  244. Sun, L.; Zhang, L.; Yu, J.; Zhang, Y.; Pang, X.; Ma, C.; Shen, M.; Ruan, S.; Wasan, H.S.; Qiu, S. Clinical efficacy and safety of anti-PD-1/PD-L1 inhibitors for the treatment of advanced or metastatic cancer: A systematic review and meta-analysis. Sci. Rep. 2020, 10, 2083. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  245. Zhang, J.-Y.; Yan, Y.-Y.; Li, J.-J.; Adhikari, R.; Fu, L.-W. PD-1/PD-L1 based combinational cancer therapy: Icing on the cake. Front. Pharmacol. 2020, 11, 722. [Google Scholar] [CrossRef]
  246. Bertagnolli, M.M.; Eagle, C.J.; Zauber, A.G.; Redston, M.; Breazna, A.; Kim, K.; Tang, J.; Rosenstein, R.B.; Umar, A.; Bagheri, D.; et al. Five-year efficacy and safety analysis of the Adenoma Prevention with Celecoxib Trial. Cancer Prev. Res. 2009, 2, 310–321. [Google Scholar] [CrossRef] [Green Version]
  247. Li, F.; Aljahdali, I.; Ling, X. Cancer therapeutics using survivin BIRC5 as a target: What can we do after over two decades of study? J. Exp. Clin. Cancer Res. 2019, 38, 368. [Google Scholar] [CrossRef] [Green Version]
Figure 1. Acquisition of cancer cell resistance from the population point of view. The tumor is heterogeneous and consists of both therapy-sensitive and resistant cancer cells. Also, tumor-associated immune and stromal cells surround the tumor, creating a unique tumor microenvironment. Therapy simultaneously triggers many events: cell death, the transition to a state of senescence, the survival of pre-existing resistant clones, and the acquisition of new genetic and epigenetic features by cells, as well as activation of stress response cascades therein. All these processes lead not only to a change in the cellular composition of the tumor and tumor stroma but also to the transformation of the secretion profiles of all participants in intercellular communication. Such communication enhances the efficiency of the cellular response to stress, which, together with genomic instability and clonal selection, ensures the adaptation of cells to therapy, expansion of the most resistant tumor populations, and tumor recurrence.
Figure 1. Acquisition of cancer cell resistance from the population point of view. The tumor is heterogeneous and consists of both therapy-sensitive and resistant cancer cells. Also, tumor-associated immune and stromal cells surround the tumor, creating a unique tumor microenvironment. Therapy simultaneously triggers many events: cell death, the transition to a state of senescence, the survival of pre-existing resistant clones, and the acquisition of new genetic and epigenetic features by cells, as well as activation of stress response cascades therein. All these processes lead not only to a change in the cellular composition of the tumor and tumor stroma but also to the transformation of the secretion profiles of all participants in intercellular communication. Such communication enhances the efficiency of the cellular response to stress, which, together with genomic instability and clonal selection, ensures the adaptation of cells to therapy, expansion of the most resistant tumor populations, and tumor recurrence.
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Figure 2. The role of the therapy-induced intra- and intercellular events in the acquisition of cancer cell resistance. Stress caused by anticancer therapy triggers many events (acquisition of new mutations, changes in the regulation of the cell cycle, EMT, autophagy) which then leads to avoidance of cell death and a change in the efficiency of absorption/activation of the chemotherapy drug. In addition to intracellular signals, molecules from stromal cells, as well as other tumor cells, entering the cell from outside are involved in the induction of these events. Molecules of therapy-induced secretomes are marked by asterisks; molecules secreted from cancer cells are marked in black; molecules secreted from stromal cells are marked in blue.
Figure 2. The role of the therapy-induced intra- and intercellular events in the acquisition of cancer cell resistance. Stress caused by anticancer therapy triggers many events (acquisition of new mutations, changes in the regulation of the cell cycle, EMT, autophagy) which then leads to avoidance of cell death and a change in the efficiency of absorption/activation of the chemotherapy drug. In addition to intracellular signals, molecules from stromal cells, as well as other tumor cells, entering the cell from outside are involved in the induction of these events. Molecules of therapy-induced secretomes are marked by asterisks; molecules secreted from cancer cells are marked in black; molecules secreted from stromal cells are marked in blue.
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Figure 3. The success of combined therapy depends on the cell cycle. The use of chemotherapeutic drugs that depend on the phase of the cell cycle and cause its arrest may lead to both higher and lower effectiveness of the combined therapy. Arresting the cell cycle in a phase corresponding to the maximum effectiveness of the second drug prolongs its time of action. Arresting the cell cycle in a phase preceding the action of the second drug impairs its effects. A, B, C, and D represent 4 phases of the cell cycle.
Figure 3. The success of combined therapy depends on the cell cycle. The use of chemotherapeutic drugs that depend on the phase of the cell cycle and cause its arrest may lead to both higher and lower effectiveness of the combined therapy. Arresting the cell cycle in a phase corresponding to the maximum effectiveness of the second drug prolongs its time of action. Arresting the cell cycle in a phase preceding the action of the second drug impairs its effects. A, B, C, and D represent 4 phases of the cell cycle.
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Figure 4. Features of tumor intercellular communication in response to therapy. During therapy, most of the sensitive tumor cells die, which leads to a decrease of their fraction in the tumor population and an increase in the proportion of resistant clones. Similarly, the composition of the total cell secretome and, consequently, its ability to participate in the process of acquiring therapy resistance change. Moreover, tumor cells dying under the effect of therapy release a number of molecules into the extracellular space which can induce the acquisition of resistance in untreated tumor cells.
Figure 4. Features of tumor intercellular communication in response to therapy. During therapy, most of the sensitive tumor cells die, which leads to a decrease of their fraction in the tumor population and an increase in the proportion of resistant clones. Similarly, the composition of the total cell secretome and, consequently, its ability to participate in the process of acquiring therapy resistance change. Moreover, tumor cells dying under the effect of therapy release a number of molecules into the extracellular space which can induce the acquisition of resistance in untreated tumor cells.
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Shnaider, P.V.; Ivanova, O.M.; Malyants, I.K.; Anufrieva, K.S.; Semenov, I.A.; Pavlyukov, M.S.; Lagarkova, M.A.; Govorun, V.M.; Shender, V.O. New Insights into Therapy-Induced Progression of Cancer. Int. J. Mol. Sci. 2020, 21, 7872. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21217872

AMA Style

Shnaider PV, Ivanova OM, Malyants IK, Anufrieva KS, Semenov IA, Pavlyukov MS, Lagarkova MA, Govorun VM, Shender VO. New Insights into Therapy-Induced Progression of Cancer. International Journal of Molecular Sciences. 2020; 21(21):7872. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21217872

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Shnaider, Polina V., Olga M. Ivanova, Irina K. Malyants, Ksenia S. Anufrieva, Ilya A. Semenov, Marat S. Pavlyukov, Maria A. Lagarkova, Vadim M. Govorun, and Victoria O. Shender. 2020. "New Insights into Therapy-Induced Progression of Cancer" International Journal of Molecular Sciences 21, no. 21: 7872. https://0-doi-org.brum.beds.ac.uk/10.3390/ijms21217872

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