Next Article in Journal
Multiparametric Analysis of Longitudinal Quantitative MRI Data to Identify Distinct Tumor Habitats in Preclinical Models of Breast Cancer
Previous Article in Journal
Endometriosis-Associated Ovarian Cancer: The Origin and Targeted Therapy
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Cancers
Volume 12
Issue 6
10.3390/cancers12061677
cancers-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

Integrating the Tumor Microenvironment into Cancer Therapy

by
Sabina Sanegre
1,†,
Federico Lucantoni
1,†,
Rebeca Burgos-Panadero
1,2,
Luis de La Cruz-Merino
3,
Rosa Noguera
1,2,* and
Tomás Álvaro Naranjo
2,4,5,*
1
Department of Pathology, Medical School, University of Valencia—INCLIVA Biomedical Health Research Institute, 46010 Valencia, Spain
2
Low Prevalence Tumors, Centro de investigación biomédica en red de cáncer (CIBERONC), Instituto de Salud Carlos III, 28029 Madrid, Spain
3
Department of Oncology, Hospital Universitario Virgen Macarena, 41009 Seville, Spain
4
Department of Pathology, Hospital de Tortosa Verge de la Cinta, Catalan Institute of Health, Institut d’Investigació Sanitària Pere Virgili (IISPV), 43500 Tortosa, Spain
5
Department of Morphological Science, Medical School, Rovira i Virgili University, 43201 Reus, Spain
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Cancers 2020, 12(6), 1677; https://doi.org/10.3390/cancers12061677
Submission received: 18 May 2020 / Revised: 11 June 2020 / Accepted: 18 June 2020 / Published: 24 June 2020
(This article belongs to the Section Tumor Microenvironment)
Browse Figures
Review Reports Versions Notes

Abstract

:
Tumor progression is mediated by reciprocal interaction between tumor cells and their surrounding tumor microenvironment (TME), which among other factors encompasses the extracellular milieu, immune cells, fibroblasts, and the vascular system. However, the complexity of cancer goes beyond the local interaction of tumor cells with their microenvironment. We are on the path to understanding cancer from a systemic viewpoint where the host macroenvironment also plays a crucial role in determining tumor progression. Indeed, growing evidence is emerging on the impact of the gut microbiota, metabolism, biomechanics, and the neuroimmunological axis on cancer. Thus, external factors capable of influencing the entire body system, such as emotional stress, surgery, or psychosocial factors, must be taken into consideration for enhanced management and treatment of cancer patients. In this article, we review prognostic and predictive biomarkers, as well as their potential evaluation and quantitative analysis. Our overarching aim is to open up new fields of study and intervention possibilities, within the framework of an integral vision of cancer as a functional tissue with the capacity to respond to different non-cytotoxic factors, hormonal, immunological, and mechanical forces, and others inducing stroma and tumor reprogramming.

1. Introduction

Since the 1980s, cancer research has focused on developing new therapeutic agents targeting DNA alterations and the search for a suitable cure, rather than understanding cancer as an integrated system composed of several modules. To date, the ability of cancer cells to survive for a prolonged time is still incompletely understood. In this context, we are beginning to include the role of stem cells in the tumor evolution process and to point out cellular pathways as a physiological adaptive process like a “wound that never heals” [1]. The idea that cancer originates as a consequence of a malignant cellular development can be considered in a multi-level framework where different processes such as senescence, regeneration, wound healing and proliferation play a key role.
The majority of tumors have a heterogeneous cellular population, and the vision that cancer originates from clonogenic expansion from a single mutated cell could be classed as simplistic and inexact. Microscopic observation of tumors reveals a multicellular 3-dimensional complex tissue underlying developmental alterations and a variable degree of morphological, immunophenotypic, and genomic heterogeneity. Indeed, the strategy used until recently of targeting one gene/protein/process at a time has proved unsuccessful. The Cancer Genome Atlas (TCGA), a public repertoire of genomic, epigenomic, transcriptomic and proteomic data, available to anyone in the research community, that leads to improvements in the ability to diagnose, treat and prevent cancer, represents a remarkable feat [2]; however, every tumor shows several mutations in the genome, and with current knowledge, the search for drug candidates for each mutation seems unfeasible. Indeed, even the most clinically-promising drugs, such as tyrosine kinase inhibitors, represent a small advancement in comparison to the diversity of processes and pathway interactions regulated by these enzymes [3]. The majority of breast, colon, and pancreatic cancers encompass between 50 and 80 mutated genes [2] and thousands of mutations in single cancer cells; in addition, this single tumor might exhibit a remarkable degree of morphological and genetic heterogeneity at different stages of its development.
The tumor microenvironment (TME), which surrounds and interacts with tumor cells including the extracellular matrix (ECM) elements, stromal cells, blood and lymph vessels, nerve fibers and the signaling molecules, has been put forward to provide a more complex overview of how cancers develop and progress [4,5]. Researchers, oncologists, and pathologists must understand that the complexity and heterogeneity found at a cellular and molecular level are influenced by a pro-tumorigenic TME and systemic macroenvironment and vice versa. Thus, in this review, we discuss recent developments in cancer and its ecosystem biomarkers and technologies based on an advanced understanding of the pathophysiological nature of cancer and its environment. We analyze the TME as the modulator of the dynamic ecosystem and examine the intrinsic and extrinsic systems capable of inducing TME reprogramming.

2. Is the TME Potentially Reprogrammable?

The concept of cancer as a disease based on tissues and not only on genetic alterations at the cellular level [6] opens the door to a new understanding of cancer and its treatment. Growing evidence shows that the stroma is decisively involved in carcinogenesis [7]. An emerging strategy for cancer treatment is to revert the malignant phenotype by targeting the TME instead of or additionally to the cancer cell population [8], to modify the relationship between the cells and the stromal compartment to obtain a response on the substrate where tumors grow. Breast carcinoma cells injected into the adipose tissue of syngeneic rats not exposed to the carcinogen, reverse their malignant phenotype and acquire benign features [9]. In addition, components of the ECM such as type I collagen, basement membrane components, and the presence of normal fibroblasts show the ability to reverse the tumor phenotype through interactions with a favorable TME [10]. These observations present cancer as a functional tissue with the capacity to respond to different local and distant non-cytotoxic factors, hormonal, immunological, mechanical, and other influences, capable of reprogramming their stroma and cells.

3. Emerging Systems of TME Reprogramming

Several local factors that shape the immune response, the ECM, and the adaptive process of angiogenesis contribute to the TME and tumor evolution. Host factors such as intestinal dysbiosis, neurotransmitters/neurohormones implicated in stress response, host and tumor metabolism, infections, surgical and physico-chemical stimuli can also impact on treatment response, activate the hypothalamic–pituitary–adrenal (HPA) axis and increase the risk of metastasis [11,12].
Tumors cells become aggressive through different mechanisms, such as epithelial-mesenchymal transition (EMT), which provides cancer cells with invasion and motility properties. This suggests the utility of modulating EMT and the inverse process, the mesenchymal to epithelial transition (MET), at a clinical level [13] to revert the malignant phenotype. In fact, this reported EMT-MET cycle implies that changes in cell plasticity during tumor progression are temporary and could be reversed [14]. EMT-MET can lead to the acquisition of stem cell-like features, to modify physico-chemical immunoregulatory properties and genetic, epigenetic, and functional behavior at all levels [15]. EMT is characterized by remodeling of the ECM and factors secreted by the mesenchymal stem cells that actively modulate several oncogenic signaling pathways such as JAK/STAT, Hedgehog, Wnt, Notch, NF- κβ among others, to help stem cells maintain their properties [16]. MET induces regrowth and re-establishment of cancer cells at secondary/metastatic sites. TME factors like Runx2 expression, loss of promoter methylation, and/or miRNAs, can contribute to MET at the metastatic site [16].
Analysis and insight into these processes could support stratifying tumoral heterogeneity at the morphological, immunophenotypic, and genetic level throughout the stroma thus allowing us to identify targetable features [17]. Beyond the dynamic and progressive genetic alterations of cancer cells, other factors show multiple connections on the tumoral tissue with functional response capacities, such as TME molecules, hormones, cytokines, and neurotransmitters, as well as the macroenvironment, where the intestinal microbiota and external factors ranging from stress to medication intake play a role.

3.1. Intrinsic Systems Capable of Inducing TME Reprogramming

3.1.1. Remodeling TME to Enhance Antitumor Immune Activity

The immunological landscape of the TME plays a pivotal role in tumor progression. Cancer immunotherapy has gained growing importance in the last decade and several therapeutic strategies are based on the reactivation of the immune system. However, most immunotherapies employed to date are administered systemically, leading to toxicity [18]. Targeting the TME by intratumoral injection of immunomodulators has been studied widely as a method to overcome this limitation, as have combinatorial strategies, stimulatory cytokines administration, inhibitory cytokine blockade and inhibition of immune checkpoints to restore immunological capability at the tumor site [19]. Although immune checkpoint blockade (ICB) appears very effective in a subset of patients, non-responders still remain very high. Several mechanisms for ICB resistance have been proposed [20] but a deeper understanding of the immune landscape at the TME is required for better patient stratification so they can benefit from ICB therapy. Characterization of the immune cell subpopulations is currently assessed using methods such as fluorescence-activated cell sorting (FACS) or immunohistochemistry (IHC)-staining. Novel transcriptome-based cell-type quantification algorithms are being optimized to provide cell-type signatures for immuno-oncology [21]. However, increasing the spatio-temporal resolution of these techniques is still necessary to achieve a more comprehensive view of the immune TME milieu, predict response to ICB and encourage the discovery of new immunotherapeutics [22].
The reciprocal interaction between cancer cells and TME determines the recruitment, activation, and reprogramming of stromal, inflammatory, and immune cells [23] (Figure 1). We have found emerging evidence of the role of ECM remodeling, structural plasticity, and mechanical forces in regulating immune cell trafficking, activation, and immunological synapse formation [24].
Cancer cells can downregulate the expression of endothelial adhesion molecules secreted into the TME and required for the transendothelial migration of leucocytes to diminish exposure to cytotoxic effector cells and evade the immunological response [25]. In addition, activation and proliferation of leucocytes have been shown to be a mechanosensitive process relying on substrate stiffness [26,27,28]. Leucocytes are also able to exert a certain degree of mechanical force on the ECM and surface of interacting cells [29].
Several components of the TME have been shown to play a direct role in shaping the immune response. Cancer-associated fibroblast (CAF)-mediated ECM remodeling and fibrosis contribute towards an immunosuppressed and pro-tumorigenic TME by affecting the recruitment and function of various innate and adaptive immune cells [30]. A dense architecture barrier can be imposed by ECM elements such as collagen, hyaluronic acid (HA), and laminins. Whereas high molecular weight HA provides structural integrity and increases regulatory T (Tregs) cell activity to suppress the immune system [31,32], laminins prevent transmigration and polarize leucocytes [33]. In addition, ECM remodeling enzymes such as metalloproteinases and matrikines direct polarization and activation of immune cells, acting as cytokines and chemokines promoting IL expression and T cell chemoattractant [34,35,36].
Given these findings, we suggest adding a systematic study of the relationship between ECM remodeling and the inflamed stromal components of the TME to the current characterization of the immune TME. In an attempt to methodize evaluation of the host immune response in regarding the TME, the Immunoscore assay quantifies immune cell density to predict patient prognosis and suggest clinical treatment [37]. This takes into account tumoral/TME heterogeneity and from a spatial point of view considers the core of the tumor and its invasive margin, with the contribution of T-cell subpopulations (CD3, CD8, and CD45RO); this could be included in the anatomopathological report as a classification in five levels (0–4), giving more information regarding the treatment options for decision making in a personalized medicine approach [38]. Production of IFN-γ induces PD-L1 (programmed death-ligand 1, also termed B7–H1) tumoral expression correlating with the presence of tumor-infiltrating lymphocytes (TIL), which in turn produce IFN-γ (Figure 2a). The presence or absence of PD-L1 and TIL determines the classification into 4 subtypes listed as TIME (Tumor Immune MicroEnvironment): T1 (PD-L1−, TIL−), T2 (PD-L1+, TIL+), T3 (PD-L1−, TIL+) and T4 (PD-L1+, TI−) (Figure 2(b1–b4)) although the existence of the latter is under debate because, in the absence of TIL, PD-L1 is not expected [39]. T2 tumors have been shown to correlate with better response to anti-PD-1 therapy [40]. Although T3 tumors have TIL, they do not express PD-L1, most likely due to a cellular dysfunction where T effector cells are not able to produce IFN-γ [41]. Co-stimulation of T3 tumors with OX-40 or 4-1BB agonists could disrupt the T-cell tolerance [42] and reprogram the TME into a more treatable tumor. However, the majority of tumors are classified as T1 and T4, both lacking TILs possibly owing to an active suppression of inflammatory infiltration or failure of tumor antigen presentation. The TIME that shows abundant immune cells in the periphery, but is empty of cytotoxic lymphocytes (CTL) in the tumor core, is called TIME infiltrated-excluded (I-E). It possesses a high number of CTLs with low expression of activation markers and low CTL infiltration in the tumor core, meaning that adaptive immunity is unable to recognize malignant cells. On the other hand, infiltrated-inflamed (I-I) TIME is characterized by abundant CTLs that express programmed cell death protein 1 (PD-1) with an antitumoral cytotoxic capacity, therefore considered highly immunogenic tumors. Several strategies could induce the TME reprogramming by promoting an inflammatory infiltration, from focal radiation, locally administered oncolytic viruses, and cryotherapy to the use of anti-cytotoxic T-lymphocyte-associated protein–4 antibody, cancer vaccines, and adoptive T-cell therapy. Costimulatory targeting of 4-1BB or OX40 could also potentially increase tumor infiltration in T1 and T4 tumors [42,43]. A pan-cancer combined quantitative analysis of genomics and proteomics of the tumor matrisome (ECM and its related components) not only relates the tumor matrisome index to mutational load and tumor pathology but also predicts survival rates. Worthy of note, high tumor matrisome index tumors revealed enrichment of specific tumor-infiltrating immune cell populations, along with signatures predictive of resistance to ICB, and clinically targetable immune checkpoints [44].

3.1.2. The Nervous System (NS), Adrenaline and Glucocorticoids, and Their Role in Metastases

In cancer both the protective mechanisms of the immune system and the protective influence of the NS are lost [45], leaving the peripheral nervous system (PNS) as a critical part of the tumor stroma, its function, and its structure [46]. While there is increased insight into the function and significance of nervous components of TME, the neurobiology of cancer is an emerging discipline in oncology. It provides growing evidence on the master regulatory effect on immunity of neurotransmitters and psychosocial factors [47] and shows that tumors are not isolated structures, but interact with different systems, directly cell to cell, through electromagnetic signals, as well as through neuronal signaling molecules [48].
TME components such as nerve fibers are important tissue elements in defining the intra- and peritumoral neural milieu [49,50,51]. Clinical and in vitro experiments have shown that tumor innervation release neurotransmitters, neuropeptides, and neurotrophins, acting directly on receptors expressed by cancer cells and modulating signaling, apoptosis, angiogenesis, metastases, and progression [52,53]. In fact, in gastric cancer patients, tumor stage has been correlated with neural density whereas vagotomy reduced the risk of gastric cancer [54]. The tumor cells emit humoral and nervous signals that not only reach the brain, which uses the information to modulate the neuroendocrine and immune system [55] but can spread via perineural invasion of surrounding nerves and related structures. Notably, perineural invasion relates to poor prognosis, correlating with decreased overall and disease-free survival time in colon, pancreatic, gastric, and head and neck carcinoma [56,57,58,59].
Most human tumors express surface adrenergic receptors, which when activated by stress catecholamines play a role in facilitating tumorigenesis and tumor progression [60]. In lung and breast in vitro cancer models, stimulation of β-adrenergic receptors resulted in the increased metastatic potential of cancer cells, among others via natural killer (NK)-cell, macrophage signaling, or osteoblast stimulation [61]. In vitro, in vivo, and clinical studies show that stress-related processes can affect the pathways involved in cancer progression, including immune regulation, angiogenesis, and invasion. It has been shown that chronic use of beta-blocking drugs (antagonizing norepinephrine and adrenaline (Table 1), is associated with lower recurrence and mortality of breast cancer and malignant melanoma and could to decrease prostate cancer risk [62]. Moreover, low-dose glucocorticoids can suppress ovarian cancer progression and metastasis, probably through upregulation of metastasis suppressor microRNAs, but also via modulation of tumor-associated macrophages and myeloid-derived suppressor cells (MDSCs) in the TME [63]. In addition, the sympathetic nervous system (SNS) regulates pathological gene expression in human tumors, leading to DNA damage repair inhibition, oncogene activation, apoptosis, and anoikis suppression [64]. Other neurotransmitters such as endorphins influence tumor proliferation and electrical stimulation of the hypothalamus, increasing the cytotoxicity of NK cells, while pinealectomy affects the course of breast cancer, an effect reversed by the administration of melatonin [48].
Understanding the interaction between cancer cells and NS is becoming indispensable for the development of new targeted therapeutic intervention.

3.1.3. Intestinal Microbiota as TME Regulator

In cancer, the intestinal microbiota (IMB), the complex and dynamic population of microorganisms that live in the digestive tracts, is of two-fold importance: on the one hand, for its etiopathogenic role [65] and on the other for its effect on cancer treatment efficacy, both through an impact on TME [66]. Several studies link the IMB to the maturation of the immune system, the structure of the TME, metabolism modulation, response to chemical and immunotherapeutic treatment, and most hallmarks of cancer [66,67,68,69]. The existence of a tumor microbiota, found in situ in the TME, could, therefore, have major physiopathological and therapeutic implications [70,71]. All these functions perhaps partly owing to the privileged bidirectional communication between IMB and NS, through the so-called neurenteric axis, HPA regulator, and the accompanying homeostatic equilibrium triangle made up by the endocrine, immune and NS [72].
Importantly, IMB modulates the immunotherapeutic response of anti-PD-1 in patients with melanoma [73] and epithelial tumors [74], showing how IMB regulation is a key factor in the equilibrium between Treg cells, antigen processing/immunoglobulin-secreting cells and the TME structure [75]. On the other hand, dysbiosis develops a pro-inflammatory environment, deregulates the immune response, and diminishes the concentration of chemotherapeutic agents by increasing desmoplasia in the TME [75]. Metagenomic analyses have shown an enrichment of Fusobacterium nucleatum in colorectal carcinoma tissue (Table 1). Fusobacterium nucleatum causes immunosuppression and recruits tumor-infiltrating immune cells, thus yielding a pro-inflammatory microenvironment, which promotes colorectal neoplasia progression [76]. Antibiotics adversely affect overall and disease-free survival in cancer, regardless of other criteria, due to the destruction of IMB [74], required for chemotherapy to be effective. Overall survival of 20 months without and 11 months with antibiotics (15 and 8 months respectively in lung cancer) show how the IBM governs immune checkpoints and opens up new approaches for a decisive intestinal ecosystem in resistance to inhibitors of immune response control and modification of TME [77].
This opens a new field of research and clinical application and puts forward the IMB as an interesting biomarker that determines the efficacy of immunotherapy treatment [78] (Table 1).

3.1.4. Metabolic Regulation and Mitochondrial Dysfunction of Cancer

Otto Warburg pioneered the study of tumor metabolism [114], which established hypoxia and acidosis as characteristics of cancer. This specific metabolic pattern is based on aerobic glycolysis of tumor cells (Warburg effect), which is a necessary source of substrates for uncontrolled tumor cell growth considering that tumor suppressor oncogenes and genes might be carriers of bioenergetic alterations [115]. Metabolic reprogramming is an essential mechanism by which cancer cells switch to different pathways to obtain the energy necessary to survive and proliferate. This metabolic and bioenergetic shift sustains high proliferation rates, as carbon sources are rapidly diverted to produce lipids, nucleic acids, and proteins [116]. This process is also essential to regulate the interaction between cancer and immune cells, as well as to recruit a variety of immune cells [117]. Cancer metabolism has led to a scientific focus on tumor cell reprogramming of glucose consumption [118] to correct the dysfunctional behavior of tumors. Indeed, it is well accepted that aberrant cancer metabolism is linked to treatment resistance [119]. Glutamine levels, which are decreased in the hypoxic core of the tumor, drive histone methylation, and tumor de-differentiation to lead drug resistance [120]. Glutamine also affects the stroma by changing it to a tumor-promoting environment through increased glutamine-induced autophagy in fibroblasts [121]. Lipid metabolism also supports TME reprogramming; indeed, it has been found that Tregs accumulate lipids and combine glycolysis and fatty acid synthesis and oxidation to survive [122]. In addition, recent studies show that the metabolic state of TME, oxygen levels, acidity, and nutrient availability affect T-cell infiltration, survival, and effector function [123,124]. Furthermore, gradients of extracellular metabolites, levels of ischemia, hypoxia, and lactate act as morphogens and increase stromal stiffness. Among other findings, metabolic alterations of the stroma impact tumor heterogeneity, affecting both morpho-immunophenotypic and genetic features [17]; they contribute to identifying sampling error as a cause of lack of correlation between biomarker research and response to immunotherapeutic treatment; shed light on the spatial structure of the TME depending on the Warburg effect [125] and finally show the ability of tumor cells to reprogram their metabolism and survive the harsh conditions of TME [126].
Furthermore, mitochondrial lesions not only affect tumor metabolism but also alter apoptosis and present cancer as mitochondrial dysfunction [127]. The cells act as if there is oxygen shortage, even if there is abundance as HIF-1a maintains the expression of normally inactive genes that make the cell immortal, unable to activate programmed cell death and keep its reproduction program activated [128] (Table 1) through the concatenation of hypoxia, lactate levels, malignancy, and metastatic capacity. While the highly glycolytic tumor cell is very aggressive and invasive, cells without mitochondrial DNA cannot form tumors [129], unless they acquire it from adjacent cells, as occurs through mitochondria donation from stromal cells to tumor-deficient tumor cells. Tumor cells recover respiratory capacity and biological aggressiveness when they take up mitochondrial DNA from TME cells [130]. A promising perspective could be based around the ability of the mitochondria to suppress the malignant phenotype [127].

3.1.5. Mechanotransduction and Biotensegrity as Properties of the TME

The mechanical properties of the tumor stroma are determinants of cell biology and clinical behavior [131]. A recurrent tumor feature is the stiffness of the stroma, through which tumors can be detected by palpation or radiological examination. The main mechanical perturbations of the TME are stiffness of the ECM, elevated interstitial fluid pressure, and/or an increase in solid tension caused by tumor growth [132]. These mechanical properties are difficult to study and evaluate with conventional histological techniques, and advances in the field now stem from a new knowledge area called mechanobiology, an interdisciplinary science branch combining biology, physics and engineering.
The tumor tissue biotensegrity mechanism is based on tension forces on cells and various elements of the ECM, which receive mechanical impact through specifically designed elements [102]. The physical stimuli at the tissue, cellular and molecular level profoundly affect the chemical signals of the tumor cell, capable of perceiving the mechanics of the substrate and transferring this information to the internal signaling pathways by a process known as mechanotransduction [133]. Therefore, matrix stiffness profoundly influences cell morphology and cellular behavior, and vice versa [131]. Intracellular signaling in response to TME mechanics primarily uses the integrin family and affects tumor suppressor genes, oncogenes, and genes for development and differentiation, relating tissue mechanics to carcinogenesis. In addition, to carry out many processes such as invasion and metastasis, cells need to invade the surrounding tissue, break down cell-cell contacts, remodel cell-matrix adhesion sites, and follow a chemoattractive path through the ECM [134]. During all these steps, tumor cells undergo dramatic morphological changes, based on cytoskeleton rearrangement related to changes in mechanical properties [135].
Currently, several strategies based on mechanotransduction are being studied, such as blocking tumor cell interaction through the glycoprotein vitronectin, which reduces the tumor’s invasive capacity [101], or the use of metformin in cancer, which acts on stromal cells by decreasing ECM rigidity and reprogramming tumor cell metabolism towards less aggressive phenotypes [136]. In addition, the increased deposition of fibrillar proteins of the ECM constitutes an independent prognostic factor [137] regarding the integrated tension system that the cell has to maintain its morphology and function. These elements, together with stromal cells and other intrinsic and extrinsic components and stimuli, determine the phenotypic diversity, gene expression, and therapeutic response of tumor cellularity [138,139]. Development of more realistic and complex model systems, along with biophysical instrumentation for study and real-time remodeling of cell-dynamics, raise hopes of future novel therapeutic strategies in oncology [140,141].

3.2. Extrinsic Stimuli with the Capacity to Induce TME Reprogramming

3.2.1. Effect of Surgery

Surgery promotes physical and emotional stress, leading to activation of neuroendocrine mediating mechanisms which in turn affects immunity and the TME, with a prominent role for catecholamines and prostaglandins in this process [142]. This observation would suggest the benefit of moderating catecholamines and prostaglandins levels which show a strong correlation with survival and cancer progression. For this reason, the use of β-blockers, which act by modulating stress levels and catecholamines, should be considered as an additional measure for TME reprogramming [60]. The systemic inflammatory response induced after surgery promotes the appearance of tumors with growth restricted by a specific T lymphocyte response, and therefore perioperative anti-inflammatory treatment has been proposed to reduce early metastatic recurrence in patients with breast cancer [143].

3.2.2. Stress, Psychosocial Factors, and Physical Exercise

Regulation of stress levels and psychosocial factors not only improves patient quality of life but has also demonstrated physiological implications in the migratory activity of carcinoma cells as regards levels of norepinephrine, dopamine, and substance P, which depend on the activation state of the SNS [144,145]. In addition, exogenous and endogenous glucocorticoids such as cortisol lead to an increase of glucocorticoid receptors and favor metastatic spread in patients with breast cancer, while inducing an immunosuppressed state that favors tumor progression [11,12]. Furthermore, the sleep-wake cycle, regional brain activity, and behavior also modify psychoneuroimmune regulatory functions and are determining factors in the pathogenesis of the tumor [48,61,146].
Physical exercise is another way to regulate emotional state in people with cancer, and as we have already seen, to contribute to the remodeling of TME through the HPA axis. The physiopathogenic mechanisms involved are currently a matter of preferential attention for the medical and research community [147], which has led to its promotion from a purely preventive consideration to a first-level therapeutic tool, especially associated with immunotherapy [148], with important physiological effects on inflammation and immunity, as well as hormonal, metabolic and IMB status. The impact of exercise on cancer in terms of mortality, recurrence, and treatment-related adverse effects has been demonstrated [149], and its histopathological bases are being studied in depth as the basis for a crucial complementary therapeutic approach in cancer.

3.2.3. Vitamin D3 and Vitamin D Receptor (VDR)

Serum vitamin D3 concentration is associated with all-cause mortality [150] and with survival in the most common cancers such as colon, breast, or lung [151,152,153]. This hypothesis was also supported for ovarian cancer, pancreas, prostate, and invasive tumors considered all together in another study [154]. The mechanism of action relies on genomic regulation of expression levels of cell signaling and differentiation pathways key regulators. Part of the process involves activating intracellular signaling pathways that allow differentiation of myeloid cells [155], and specifically, the capacity of stromal reprogramming mediated by the vitamin D receptor (VDR), with the ability to modify the TME and its response to treatment [156] (Figure 3). In addition, vitamin D3 regulates the IMB and with this the TEM, as well as the host immune response [157].

3.2.4. Metformin and Other Biguanides

Discovering drugs that can regulate metabolism, without cytotoxic effects but regulating TME and immunity while avoiding unwanted side effects and complications is particularly vital in tumors with insulin dependence, such as breast, prostate, ovary and endometrium cancers [158], especially as the available epidemiological data links metabolic status, overweight and obesity to cancer [159]. Since energy metabolism reprogramming is considered a new hallmark of cancer, drugs with metabolic effects acting on tissue remodeling have recently received attention [160]. Metformin and other biguanides have a marked impact on the TME and host immune response [139]. Metformin is an oral antidiabetic drug that inhibits mitochondrial complex I and oxidative phosphorylation increases CD8+ levels and modulates Treg cells in the TME, which clinically correlates with better outcomes in different tumors [160]. Interestingly, metformin not only acts at the hepatic level, mainly via the metabolism but also produces an indirect hypoglycemic effect through its impact on the IMB [161].

3.2.5. Phytochemicals Derived from Natural Sources

Various phytochemicals derived from natural sources, such as curcumin, ursolic acid, resveratrol, thymoquinone, and γ-tocotrienol, show different effects on the structure of TME, its function, and its metabolism, and remain to be studied in-depth as basic systems of stromal reprogramming and regulation of the TME [162].
Curcumin, the active component of turmeric, has been studied for its anti-inflammatory and anti-cancer effects. It suppresses the onset, progression, and metastasis of a variety of tumors in vitro and in vivo, and its effects are predominantly mediated through the negative regulation of various transcription factors, growth factors, inflammatory cytokines, protein kinases and especially their effect on the vasculature, fibroblasts, apoptosis, and metabolic regulation of TME [163]. Recently, it has been shown that alone or in combination with other substances, it affects the structure of the tumor vessels, normalizing the tumor vascular pattern and inhibiting tumor growth in an orthotopic nude mouse model of hepatocarcinoma [164]. Furthermore, curcumin shows a wide range of stromal effects, such as overcoming stromal protection of chronic lymphocytic leukemia B cells in vitro [165]. Moreover, it caused a decreased release of EMT-mediators in carcinoma-associated fibroblasts and reversal of EMT in tumor cells, which was associated with decreased invasion [166]. An in vitro inhibitory effect has been suggested for curcumin on lipogenic enzymes, and thus on cancer cell line progression, although further corroborative evidence from preclinical and clinical studies is required [167]. Its mechanism of action is not restricted to a single effect, but rather it acts through a broad range of functions and signaling pathways, as shown in human myeloid leukemia and embryonic kidney cells [168]. An advantage of using curcumin as a therapeutic agent is that there is no dose threshold linked to toxicity [169].

3.2.6. Tensional Homeostasis

The emerging field of mechanobiology aims to study the mechanical properties associated with tissue morphogenesis, cell-cell or cell-matrix interactions, cellular migration, tumorigenesis, and progression of cancer [15]. As described in the section on biotensegrity and mechanotransduction, cells can sense the mechanical forces in their surroundings and modify their molecular behavior. ECM stiffness, shear stress, increased interstitial fluid pressure, and elevated interstitial fluid flow are all sensed and processed through mechanotransduction, eliciting a biological response [15]. Although growing evidence is pointing towards the importance of the immediate TME biophysics and its impingement on tumors, little is known about the systemic biophysical effects on cancer development [132,170,171]. However, if cells have the machinery to sense physical forces, such as mechanical load or traction forces, the different tissues (especially connective tissue), organs (particularly HPA axis and bone marrow), and systems (mainly the vascular system) in the human body serve as a platform to transmit systemic biophysical cues within the organism [172,173,174]. Based on distinctive physical features of tumor cells, acoustic-based approaches have been developed representing the significant potential for circulating tumor cell (CTC) sorting and diagnosis [175]. Recently, researchers have developed a platform that combines acoustics and microfluidics to isolate rare CTCs from peripheral blood in high throughput [176]. This achievement highlights how the exploitation of differential physical properties of cancer cells can lead to the development of diagnostic tools and prognosis. In addition, differential sorting of these cells has also demonstrated therapy response, in which stiff cells are more sensitive to chemotherapy than softer cells [177]. Further research is necessary to pinpoint the mechanisms by which cells react to external mechanical cues [171], to be able not only to establish diagnostic tools but also propose emerging alternative cancer therapies based on mechanotherapy.

4. Evaluation of New Biomarkers

The evaluation of new prognostic and predictive biomarkers focuses on multiple cellular subpopulations, molecules and metabolites of the TME that can be analyzed through precise tools: IHC, DNA/RNA/miRNA sequencing, flow cytometry, and mass flow cytometry. Besides genetic lesions, novel regulators of tumoral development are emerging, such as cell-cell and cell-ECM communication, HPA axis, microbiota, and mitochondrial dysfunction (Figure 4). In this context, the search for new biomarkers with clinical and therapeutic applications can be classified into four different groups: immune escape mechanisms, immune composition and activity in tumors, tumor-intrinsic factors, and host factors [178].
The evaluation of immune composition/activity and tumor/systemic biomarkers can be employed to predict the success of a therapeutic cycle, through analysis of the TME and the immune profile of the patient before starting treatment [179,180,181]. The different grade of the stroma [136] and the strength of the host immune response at both local and systemic levels all determine the efficacy of the immune antitumoral response [182]. Other parameters, which include the study of the intestinal microbiota and tumoral metabolism, are in process of being included in an integrated evaluation to identify, design, and apply anti-tumoral treatments tailored to each specific patient in the framework of personalized oncology.
Several hypotheses in cancer physiopathology assume that lymphoid cell activity is the principal mechanism involved in tumorigenesis inhibition, through cell immunity and secretion of several cytokines such as TNF (tumor necrosis factor) or IFN-γ, the complement system, opsonization, and others. This observation implies that processes such as inhibition of immunosuppressive cells (such as macrophages) or modulation of factors affecting the immune system (such as the microbiota), ultimately affect lymphoid cell activity, and support a relationship between tumor and immunity, such as the so-called “cancer immunogram” [179]. The most advanced therapies, such as immunotherapy, without a direct cytotoxic effect, reach response rates of 20–30% [183], results could be improved by developing immunotherapy with modified T-cells. On the other hand, adoptive immunotherapy has demonstrated outstanding results in a minority of hematological tumors (relapsed acute lymphoblastic leukemia and diffuse large B cell lymphoma) and the success of this approach seems to rely in the dominant role of CD19 as a target for ex-vivo technically modified lymphocytes. Although the focus of substantial research during the last few years results in solid tumors remain unfortunately poor. The elusive efficacy of adoptive immunotherapy in this scenario can probably be explained by the intrinsic heterogeneity that most solid tumors share with several different mutations, as expressed in different sections of the manuscript. Nonetheless, some progress has recently been made. Indeed, as presented in the latest online edition of ASCO 2020 (American Society of clinical oncology), adoptive T-cell therapy with ADP-A2M4 targeting MAGE-A4 has shown early activity in patients with different advanced solid tumors [184]. Therefore, the main limitation for adoptive immunotherapy lies in identifying the proper target in every tumor, to guide immune cell attack. Whether TME components should also be targets or not remains so far unknown. To overcome the limited patient response rate, severe side effects, and elevated costs of immunotherapy, it is crucial to discover novel biomarkers to predict which patients will respond. As a general rule, the best responders are patients with higher numbers of CD4+/CD8+lymphocytes, and in some cases, myeloid cells polarized to have a response similar to T helper lymphocytes [185]. Tumor subtypes with lower host immune response and with increased levels of the TGF-β, with a high content of M2 macrophages and an immunosuppressed TME tend to have the worst outcomes [186], while the subtypes rich in IFN-γ and with an inflammatory profile were found to be much more frequent, to share a Th1-like immune response and show more favorable prognosis [186]. Several biomarkers can be used to predict treatments based on the anti-cytotoxic T lymphocyte antigen-4 (CTLA-4) and against PD-1. While memory T-cell CD4+ and CD8+ have a fundamental role in both the CTLA-4 and PD-1 response, NK cells correlate with clinical response to anti-PD-1 treatment [187] (Table 1). Independently, PD-1-negative T cells can be rescued through co-stimulation with OX-40 or 4-1BB antagonists, which has shown that TIME subtype T3 tumors can be rewired to TIME subtype T2 [42].

5. Conclusions

The enormous plasticity that enables tumor cells to modify their phenotype and function [188] combined with intratumoral heterogeneity, make the TME a complex element of study and evaluation, decisive in the effectiveness of cancer treatment, especially immunotherapy [189]. Taken together, these observations invite a comprehensive reexamination of current cancer protocols and a shift towards new, more effective and safer therapeutic and follow-up horizons. The main unwanted effects of classical therapies are caused by altering the TME and inducing a pro-inflammatory response. Systemic changes derived from bone marrow, microbiota and CNS regulate the degree of cancer resistance at the macroscopic, microscopic and mesoscopic levels, and could be starting points in the trial of new therapeutic approaches [190]. Finding a “cure for cancer” is still beyond the reach of our current knowledge and technology. The available evidence suggests that the principal treatment modalities, including radiotherapy, chemotherapy, and surgical procedures, may induce an increase in circulating tumor cells, with the consequent heightened risk of distant metastases [191,192]. The clinical significance of circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and exosomes for diagnosis and prognosis of cancer should be underlined as should liquid biomarkers documenting the real-time monitoring of tumor evolution and therapeutic responses for personalized medicine.
Host immune response composition and activity can be evaluated using predictive and prognostic tools such as the Immunoscore or TIME. The impact of immune cells such as B cells, natural killers, MDSC, macrophages, and T cells shows that tumors originating from different tissues are unique and possess a differentiated immune profile [131,193]. Due to considerable intratumoral heterogeneity, a PD-1/TILs positive or negative tumor can be mischaracterized when interpreting from a small biopsy sample; in this scenario, some PD-1 negative tumors may respond to anti-PD-1 therapy. In vitro cell-based assays are critical for probing the complex and dynamic nature of the interactions between the immune and cancer systems. Novel types of organ-on-chip models, based on a series of different human 3D-cell culture micro-chambers and immune cells, will allow us to understand mechanisms of resistance to immunotherapy and to evaluate in vitro strategies to overcome them [194].
The view of cancer as a metabolic disease points to sugar and fat consumption by the tumor, the importance of systemic support factors such as caloric restriction, entosis and autophagy, the role of the ketogenic diet, and mitochondrial metabolic therapy. PD-L1 can also regulate cancer cell metabolism through the mTOR pathway and extracellular glucose availability [195]. Metabolic TME remodeling could be achieved by exhausting tumor-promoting immune cells and promoting glycolysis in newly generated T cells, through a combination of inhibitory immune therapy against CTLA-4 (which diminish Treg cells) and metabolism inhibitors, with the consequent increase of effector T cells [126].
Rewiring different TME elements, including components of the nervous system, biotensegral structures at all body levels, and microbiota could constitute a starting point for novel therapies based on an extended oncology framework and an integral vision of cancer. Collectively, these observations invite us to rethink current oncological and pathological protocols and advance in our discovery of new biomarkers/tools to efficiently manage safe therapeutic follow-up.

Funding

This work was supported by grants from ISCIII (FIS) and FEDER (European Regional Development Fund) PI17/01558; CIBERONC (contract CB16/12/00484); Fundación Científica de la Asociación Española contra el Cáncer (FAECC2015/006) and NEN Association (Nico contra el cancer infantil 2017—PVR00157). The funders had no involvement in the research process nor in the preparation and submission of the article.

Acknowledgments

The authors are grateful to the Spanish Society of Pediatric Hemato-Oncology (SEHOP) for their valuable support. The authors also thank Kathryn Davies for English correction. Table 1 has been adapted and published with permission. Original source: Noguera R, Burgos-Panadero R, Gamero-Sandemetrio E, de la Cruz-Merino L y Álvaro Naranjo T. Una visión integral del cáncer (II). Campos de estudio y biomarcadores emergentes. Rev Esp Patol. 2019(52):222-233. © 2019 Sociedad Española de Anatomía Patológica. Published by Elsevier España, S.L.U. All Rights reserved.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Riss, J.; Khanna, C.; Koo, S.; Chandramouli, G.V.; Yang, H.H.; Hu, Y.; Kleiner, D.E.; Rosenwald, A.; Schaefer, C.F.; Ben-Sasson, S.A.; et al. Cancers as wounds that do not heal: Differences and similarities between renal regeneration/repair and renal cell carcinoma. Cancer Res. 2006, 66, 7216–7224. [Google Scholar] [CrossRef] [Green Version]
  2. Weinstein, J.N.; Collisson, E.A.; Mills, G.B.; Shaw, K.R.; Ozenberger, B.A.; Ellrott, K.; Shmulevich, I.; Sander, C.; Stuart, J.M. The cancer genome atlas pan-cancer analysis project. Nat. Genet. 2013, 45, 1113–1120. [Google Scholar] [CrossRef] [PubMed]
  3. Ahsan, A. Mechanisms of resistance to EGFR Tyrosine kinase inhibitors and therapeutic approaches: An update. Adv. Exp. Med. Biol. 2016, 893, 137–153. [Google Scholar] [CrossRef] [PubMed]
  4. Balkwill, F.R.; Capasso, M.; Hagemann, T. The tumor microenvironment at a glance. J. Cell Sci. 2012, 125, 5591–5596. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Sun, Y. Tumor microenvironment and cancer therapy resistance. Cancer Lett. 2016, 380, 205–215. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Soto, A.M.; Sonnenschein, C. The tissue organization field theory of cancer: A testable replacement for the somatic mutation theory. Bioessays 2011, 33, 332–340. [Google Scholar] [CrossRef] [Green Version]
  7. Dang, T.T.; Prechtl, A.M.; Pearson, G.W. Breast cancer subtype-specific interactions with the microenvironment dictate mechanisms of invasion. Cancer Res. 2011, 71, 6857–6866. [Google Scholar] [CrossRef] [Green Version]
  8. Maffini, M.V.; Soto, A.M.; Calabro, J.M.; Ucci, A.A.; Sonnenschein, C. The stroma as a crucial target in rat mammary gland carcinogenesis. J. Cell Sci. 2004, 117, 1495–1502. [Google Scholar] [CrossRef] [Green Version]
  9. Maffini, M.V.; Calabro, J.M.; Soto, A.M.; Sonnenschein, C. Stromal regulation of neoplastic development: Age-dependent normalization of neoplastic mammary cells by mammary stroma. Am. J. Pathol. 2005, 167, 1405–1410. [Google Scholar] [CrossRef]
  10. Krause, S.; Maffini, M.V.; Soto, A.M.; Sonnenschein, C. The microenvironment determines the breast cancer cells’ phenotype: Organization of MCF7 cells in 3D cultures. BMC Cancer 2010, 10, 263. [Google Scholar] [CrossRef] [Green Version]
  11. Galluzzi, L.; Kroemer, G. Cancer cells thrive on stress. Trends Cell Biol. 2019, 29, 447–449. [Google Scholar] [CrossRef] [PubMed]
  12. Obradovic, M.M.S.; Hamelin, B.; Manevski, N.; Couto, J.P.; Sethi, A.; Coissieux, M.M.; Munst, S.; Okamoto, R.; Kohler, H.; Schmidt, A.; et al. Glucocorticoids promote breast cancer metastasis. Nature 2019, 567, 540–544. [Google Scholar] [CrossRef] [PubMed]
  13. Weidenfeld, K.; Barkan, D. EMT and stemness in tumor dormancy and outgrowth: Are they intertwined processes? Front. Oncol. 2018, 8, 381. [Google Scholar] [CrossRef] [PubMed]
  14. van Zijl, F.; Zulehner, G.; Petz, M.; Schneller, D.; Kornauth, C.; Hau, M.; Machat, G.; Grubinger, M.; Huber, H.; Mikulits, W. Epithelial-mesenchymal transition in hepatocellular carcinoma. Future Oncol. 2009, 5, 1169–1179. [Google Scholar] [CrossRef] [Green Version]
  15. Roy Choudhury, A.; Gupta, S.; Chaturvedi, P.K.; Kumar, N.; Pandey, D. Mechanobiology of cancer stem cells and their niche. Cancer Microenviron. 2019, 12, 17–27. [Google Scholar] [CrossRef]
  16. Plaks, V.; Kong, N.; Werb, Z. The cancer stem cell niche: How essential is the niche in regulating stemness of tumor cells? Cell Stem Cell 2015, 16, 225–238. [Google Scholar] [CrossRef] [Green Version]
  17. Carmona-Fontaine, C.; Deforet, M.; Akkari, L.; Thompson, C.B.; Joyce, J.A.; Xavier, J.B. Metabolic origins of spatial organization in the tumor microenvironment. Proc. Natl. Acad. Sci. USA 2017, 114, 2934–2939. [Google Scholar] [CrossRef] [Green Version]
  18. Puzanov, I.; Diab, A.; Abdallah, K.; Bingham, C.O., 3rd; Brogdon, C.; Dadu, R.; Hamad, L.; Kim, S.; Lacouture, M.E.; LeBoeuf, N.R.; et al. Managing toxicities associated with immune checkpoint inhibitors: Consensus recommendations from the Society for Immunotherapy of Cancer (SITC) Toxicity Management Working Group. J. Immunother. Cancer 2017, 5, 95. [Google Scholar] [CrossRef] [Green Version]
  19. Van der Jeught, K.; Bialkowski, L.; Daszkiewicz, L.; Broos, K.; Goyvaerts, C.; Renmans, D.; Van Lint, S.; Heirman, C.; Thielemans, K.; Breckpot, K. Targeting the tumor microenvironment to enhance antitumor immune responses. Oncotarget 2015, 6, 1359–1381. [Google Scholar] [CrossRef] [Green Version]
  20. Kalbasi, A.; Ribas, A. Tumour-intrinsic resistance to immune checkpoint blockade. Nat. Rev. Immunol. 2020, 20, 25–39. [Google Scholar] [CrossRef]
  21. Sturm, G.; Finotello, F.; Petitprez, F.; Zhang, J.D.; Baumbach, J.; Fridman, W.H.; List, M.; Aneichyk, T. Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology. Bioinformatics 2019, 35, i436–i445. [Google Scholar] [CrossRef] [PubMed]
  22. Binnewies, M.; Roberts, E.W.; Kersten, K.; Chan, V.; Fearon, D.F.; Merad, M.; Coussens, L.M.; Gabrilovich, D.I.; Ostrand-Rosenberg, S.; Hedrick, C.C.; et al. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat. Med. 2018, 24, 541–550. [Google Scholar] [CrossRef] [PubMed]
  23. Drak Alsibai, K.; Meseure, D. Significance of Tumor Microenvironment Scoring and Immune Biomarkers in Patient Stratification and Cancer Outcomes; ItechOpen: London, UK, 2018. [Google Scholar] [CrossRef] [Green Version]
  24. Mushtaq, M.U.; Papadas, A.; Pagenkopf, A.; Flietner, E.; Morrow, Z.; Chaudhary, S.G.; Asimakopoulos, F. Tumor matrix remodeling and novel immunotherapies: The promise of matrix-derived immune biomarkers. J. Immunother. Cancer 2018, 6, 65. [Google Scholar] [CrossRef] [PubMed]
  25. Castermans, K.; Griffioen, A.W. Tumor blood vessels, a difficult hurdle for infiltrating leukocytes. Biochim. Biophys. Acta 2007, 1776, 160–174. [Google Scholar] [CrossRef]
  26. O’Connor, R.S.; Hao, X.; Shen, K.; Bashour, K.; Akimova, T.; Hancock, W.W.; Kam, L.C.; Milone, M.C. Substrate rigidity regulates human T cell activation and proliferation. J. Immunol. 2012, 189, 1330–1339. [Google Scholar] [CrossRef] [Green Version]
  27. Wan, Z.; Zhang, S.; Fan, Y.; Liu, K.; Du, F.; Davey, A.M.; Zhang, H.; Han, W.; Xiong, C.; Liu, W. B cell activation is regulated by the stiffness properties of the substrate presenting the antigens. J. Immunol. 2013, 190, 4661–4675. [Google Scholar] [CrossRef] [Green Version]
  28. Zeng, Y.; Yi, J.; Wan, Z.; Liu, K.; Song, P.; Chau, A.; Wang, F.; Chang, Z.; Han, W.; Zheng, W.; et al. Substrate stiffness regulates B-cell activation, proliferation, class switch, and T-cell-independent antibody responses in vivo. Eur. J. Immunol. 2015, 45, 1621–1634. [Google Scholar] [CrossRef]
  29. Huse, M. Mechanical forces in the immune system. Nat. Rev. Immunol. 2017, 17, 679–690. [Google Scholar] [CrossRef]
  30. Monteran, L.; Erez, N. The dark side of fibroblasts: Cancer-associated fibroblasts as mediators of immunosuppression in the tumor microenvironment. Front. Immunol. 2019, 10, 1835. [Google Scholar] [CrossRef] [Green Version]
  31. Chanmee, T.; Ontong, P.; Itano, N. Hyaluronan: A modulator of the tumor microenvironment. Cancer Lett. 2016, 375, 20–30. [Google Scholar] [CrossRef]
  32. Bollyky, P.L.; Falk, B.A.; Wu, R.P.; Buckner, J.H.; Wight, T.N.; Nepom, G.T. Intact extracellular matrix and the maintenance of immune tolerance: High molecular weight hyaluronan promotes persistence of induced CD4+CD25+ regulatory T cells. J. Leukoc. Biol. 2009, 86, 567–572. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Simon, T.; Bromberg, J.S. Regulation of the Immune System by Laminins. Trends Immunol. 2017, 38, 858–871. [Google Scholar] [CrossRef] [PubMed]
  34. Thomas, A.H.; Edelman, E.R.; Stultz, C.M. Collagen fragments modulate innate immunity. Exp. Biol. Med. (Maywood) 2007, 232, 406–411. [Google Scholar]
  35. Horejs, C.M.; Serio, A.; Purvis, A.; Gormley, A.J.; Bertazzo, S.; Poliniewicz, A.; Wang, A.J.; DiMaggio, P.; Hohenester, E.; Stevens, M.M. Biologically-active laminin-111 fragment that modulates the epithelial-to-mesenchymal transition in embryonic stem cells. Proc. Natl. Acad. Sci. USA 2014, 111, 5908–5913. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Hope, C.; Foulcer, S.; Jagodinsky, J.; Chen, S.X.; Jensen, J.L.; Patel, S.; Leith, C.; Maroulakou, I.; Callander, N.; Miyamoto, S.; et al. Immunoregulatory roles of versican proteolysis in the myeloma microenvironment. Blood 2016, 128, 680–685. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Pages, F.; Mlecnik, B.; Marliot, F.; Bindea, G.; Ou, F.S.; Bifulco, C.; Lugli, A.; Zlobec, I.; Rau, T.T.; Berger, M.D.; et al. International validation of the consensus Immunoscore for the classification of colon cancer: A prognostic and accuracy study. Lancet 2018, 391, 2128–2139. [Google Scholar] [CrossRef]
  38. Taube, J.M.; Galon, J.; Sholl, L.M.; Rodig, S.J.; Cottrell, T.R.; Giraldo, N.A.; Baras, A.S.; Patel, S.S.; Anders, R.A.; Rimm, D.L.; et al. Implications of the tumor immune microenvironment for staging and therapeutics. Mod. Pathol. 2018, 31, 214–234. [Google Scholar] [CrossRef]
  39. Zhang, Y.; Chen, L. Classification of Advanced Human Cancers Based on Tumor Immunity in the MicroEnvironment (TIME) for Cancer Immunotherapy. JAMA Oncol. 2016, 2, 1403–1404. [Google Scholar] [CrossRef]
  40. Tumeh, P.C.; Harview, C.L.; Yearley, J.H.; Shintaku, I.P.; Taylor, E.J.; Robert, L.; Chmielowski, B.; Spasic, M.; Henry, G.; Ciobanu, V.; et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 2014, 515, 568–571. [Google Scholar] [CrossRef]
  41. Sanmamed, M.F.; Chen, L. Inducible expression of B7-H1 (PD-L1) and its selective role in tumor site immune modulation. Cancer J. 2014, 20, 256–261. [Google Scholar] [CrossRef] [Green Version]
  42. Chen, L.; Flies, D.B. Molecular mechanisms of T cell co-stimulation and co-inhibition. Nat. Rev. Immunol. 2013, 13, 227–242. [Google Scholar] [CrossRef] [PubMed]
  43. Zou, W.; Wolchok, J.D.; Chen, L. PD-L1 (B7-H1) and PD-1 pathway blockade for cancer therapy: Mechanisms, response biomarkers, and combinations. Sci. Transl. Med. 2016, 8, 328rv324. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Bin Lim, S.; Chua, M.L.K.; Yeong, J.P.S.; Tan, S.J.; Lim, W.T.; Lim, C.T. Pan-cancer analysis connects tumor matrisome to immune response. NPJ Precis Oncol. 2019, 3, 15. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Ondicova, K.; Mravec, B. Role of nervous system in cancer aetiopathogenesis. Lancet Oncol. 2010, 11, 596–601. [Google Scholar] [CrossRef]
  46. Noguera, R.; Burgos-Panadero, R.; Gamero-Sandemetrio, E.; de la Cruz-Merino, L.; Alvaro Naranjo, T. An integral view of cancer (II). Fields of investigation and emerging biomarkers. Rev. Esp. Patol. 2019, 52, 222–233. [Google Scholar] [CrossRef]
  47. Green McDonald, P.; O’Connell, M.; Lutgendorf, S.K. Psychoneuroimmunology and cancer: A decade of discovery, paradigm shifts, and methodological innovations. Brain Behav. Immun. 2013, 30, S1–S9. [Google Scholar] [CrossRef] [Green Version]
  48. Saloman, J.L.; Albers, K.M.; Rhim, A.D.; Davis, B.M. Can Stopping Nerves, Stop Cancer? Trends Neurosci. 2016, 39, 880–889. [Google Scholar] [CrossRef] [Green Version]
  49. Arese, M.; Bussolino, F.; Pergolizzi, M.; Bizzozero, L.; Pascal, D. Tumor progression: The neuronal input. Ann. Transl. Med. 2018, 6, 89. [Google Scholar] [CrossRef] [Green Version]
  50. Faulkner, S.; Jobling, P.; March, B.; Jiang, C.C.; Hondermarck, H. Tumor neurobiology and the war of nerves in cancer. Cancer Discov. 2019, 9, 702–710. [Google Scholar] [CrossRef] [Green Version]
  51. Jobling, P.; Pundavela, J.; Oliveira, S.M.; Roselli, S.; Walker, M.M.; Hondermarck, H. Nerve-Cancer Cell Cross-talk: A Novel Promoter of Tumor Progression. Cancer Res. 2015, 75, 1777–1781. [Google Scholar] [CrossRef] [Green Version]
  52. Entschladen, F.; Drell, T.L.t.; Lang, K.; Joseph, J.; Zaenker, K.S. Tumour-cell migration, invasion, and metastasis: Navigation by neurotransmitters. Lancet Oncol. 2004, 5, 254–258. [Google Scholar] [CrossRef]
  53. Mancino, M.; Ametller, E.; Gascon, P.; Almendro, V. The neuronal influence on tumor progression. Biochim. Biophys. Acta 2011, 1816, 105–118. [Google Scholar] [CrossRef] [Green Version]
  54. Zhao, C.M.; Hayakawa, Y.; Kodama, Y.; Muthupalani, S.; Westphalen, C.B.; Andersen, G.T.; Flatberg, A.; Johannessen, H.; Friedman, R.A.; Renz, B.W.; et al. Denervation suppresses gastric tumorigenesis. Sci. Transl. Med. 2014, 6, 250ra115. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. Deborde, S.; Omelchenko, T.; Lyubchik, A.; Zhou, Y.; He, S.; McNamara, W.F.; Chernichenko, N.; Lee, S.Y.; Barajas, F.; Chen, C.H.; et al. Schwann cells induce cancer cell dispersion and invasion. J. Clin. Investig. 2016, 126, 1538–1554. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Lee, L.Y.; De Paz, D.; Lin, C.Y.; Fan, K.H.; Wang, H.M.; Hsieh, C.H.; Lee, L.A.; Yen, T.C.; Liao, C.T.; Yeh, C.H.; et al. Prognostic impact of extratumoral perineural invasion in patients with oral cavity squamous cell carcinoma. Cancer Med. 2019, 8, 6185–6194. [Google Scholar] [CrossRef] [PubMed]
  57. Liang, D.; Shi, S.; Xu, J.; Zhang, B.; Qin, Y.; Ji, S.; Xu, W.; Liu, J.; Liu, L.; Liu, C.; et al. New insights into perineural invasion of pancreatic cancer: More than pain. Biochim. Biophys. Acta 2016, 1865, 111–122. [Google Scholar] [CrossRef]
  58. Deng, J.; You, Q.; Gao, Y.; Yu, Q.; Zhao, P.; Zheng, Y.; Fang, W.; Xu, N.; Teng, L. Prognostic value of perineural invasion in gastric cancer: A systematic review and meta-analysis. PLoS ONE 2014, 9, e88907. [Google Scholar] [CrossRef] [Green Version]
  59. Mirkin, K.A.; Hollenbeak, C.S.; Mohamed, A.; Jia, Y.; El-Deiry, W.S.; Messaris, E. Impact of perineural invasion on survival in node negative colon cancer. Cancer Biol. Ther. 2017, 18, 740–745. [Google Scholar] [CrossRef]
  60. Eng, J.W.; Kokolus, K.M.; Reed, C.B.; Hylander, B.L.; Ma, W.W.; Repasky, E.A. A nervous tumor microenvironment: The impact of adrenergic stress on cancer cells, immunosuppression, and immunotherapeutic response. Cancer Immunol. Immunother. 2014, 63, 1115–1128. [Google Scholar] [CrossRef] [Green Version]
  61. Kuol, N.; Stojanovska, L.; Apostolopoulos, V.; Nurgali, K. Role of the nervous system in cancer metastasis. J. Exp. Clin. Cancer Res. 2018, 37, 5. [Google Scholar] [CrossRef] [Green Version]
  62. Fitzgerald, P.J. Is norepinephrine an etiological factor in some types of cancer? Int. J. Cancer 2009, 124, 257–263. [Google Scholar] [CrossRef] [PubMed]
  63. Lin, K.T.; Sun, S.P.; Wu, J.I.; Wang, L.H. Low-dose glucocorticoids suppresses ovarian tumor growth and metastasis in an immunocompetent syngeneic mouse model. PLoS ONE 2017, 12, e0178937. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Cole, S.W.; Nagaraja, A.S.; Lutgendorf, S.K.; Green, P.A.; Sood, A.K. Sympathetic nervous system regulation of the tumour microenvironment. Nat. Rev. Cancer 2015, 15, 563–572. [Google Scholar] [CrossRef] [Green Version]
  65. Lin, C.; Cai, X.; Zhang, J.; Wang, W.; Sheng, Q.; Hua, H.; Zhou, X. Role of Gut Microbiota in the Development and Treatment of Colorectal Cancer. Digestion 2019, 100, 72–78. [Google Scholar] [CrossRef] [PubMed]
  66. Iida, N.; Dzutsev, A.; Stewart, C.A.; Smith, L.; Bouladoux, N.; Weingarten, R.A.; Molina, D.A.; Salcedo, R.; Back, T.; Cramer, S.; et al. Commensal bacteria control cancer response to therapy by modulating the tumor microenvironment. Science 2013, 342, 967–970. [Google Scholar] [CrossRef]
  67. Roy, S.; Trinchieri, G. Microbiota: A key orchestrator of cancer therapy. Nat. Rev. Cancer 2017, 17, 271–285. [Google Scholar] [CrossRef]
  68. Adachi, K.; Tamada, K. Microbial biomarkers for immune checkpoint blockade therapy against cancer. J. Gastroenterol. 2018, 53, 999–1005. [Google Scholar] [CrossRef] [Green Version]
  69. Fulbright, L.E.; Ellermann, M.; Arthur, J.C. The microbiome and the hallmarks of cancer. PLoS Pathog. 2017, 13, e1006480. [Google Scholar] [CrossRef]
  70. Meng, S.; Chen, B.; Yang, J.; Wang, J.; Zhu, D.; Meng, Q.; Zhang, L. Study of microbiomes in aseptically collected samples of human breast tissue using needle biopsy and the potential role of in situ tissue microbiomes for promoting malignancy. Front. Oncol. 2018, 8, 318. [Google Scholar] [CrossRef] [Green Version]
  71. Zhou, Z.; Chen, J.; Yao, H.; Hu, H. Fusobacterium and colorectal cancer. Front. Oncol. 2018, 8, 371. [Google Scholar] [CrossRef]
  72. Martin, C.R.; Osadchiy, V.; Kalani, A.; Mayer, E.A. The brain-gut-microbiome axis. Cell Mol. Gastroenterol. Hepatol. 2018, 6, 133–148. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  73. Gopalakrishnan, V.; Spencer, C.N.; Nezi, L.; Reuben, A.; Andrews, M.C.; Karpinets, T.V.; Prieto, P.A.; Vicente, D.; Hoffman, K.; Wei, S.C.; et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 2018, 359, 97–103. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Routy, B.; Le Chatelier, E.; Derosa, L.; Duong, C.P.M.; Alou, M.T.; Daillere, R.; Fluckiger, A.; Messaoudene, M.; Rauber, C.; Roberti, M.P.; et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 2018, 359, 91–97. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  75. McQuade, J.L.; Daniel, C.R.; Helmink, B.A.; Wargo, J.A. Modulating the microbiome to improve therapeutic response in cancer. Lancet Oncol. 2019, 20, e77–e91. [Google Scholar] [CrossRef]
  76. Sun, C.H.; Li, B.B.; Wang, B.; Zhao, J.; Zhang, X.Y.; Li, T.T.; Li, W.B.; Tang, D.; Qiu, M.J.; Wang, X.C.; et al. The role of Fusobacterium nucleatum in colorectal cancer: From carcinogenesis to clinical management. Chronic Dis. Transl. Med. 2019, 5, 178–187. [Google Scholar] [CrossRef]
  77. Zitvogel, L.; Daillere, R.; Roberti, M.P.; Routy, B.; Kroemer, G. Anticancer effects of the microbiome and its products. Nat. Rev. Microbiol. 2017, 15, 465–478. [Google Scholar] [CrossRef]
  78. Yi, M.; Yu, S.; Qin, S.; Liu, Q.; Xu, H.; Zhao, W.; Chu, Q.; Wu, K. Gut microbiome modulates efficacy of immune checkpoint inhibitors. J. Hematol. Oncol. 2018, 11, 47. [Google Scholar] [CrossRef] [Green Version]
  79. Ni, C.; Yang, L.; Xu, Q.; Yuan, H.; Wang, W.; Xia, W.; Gong, D.; Zhang, W.; Yu, K. CD68- and CD163-positive tumor infiltrating macrophages in non-metastatic breast cancer: A retrospective study and meta-analysis. J. Cancer 2019, 10, 4463–4472. [Google Scholar] [CrossRef] [Green Version]
  80. Hiramatsu, S.; Tanaka, H.; Nishimura, J.; Sakimura, C.; Tamura, T.; Toyokawa, T.; Muguruma, K.; Yashiro, M.; Hirakawa, K.; Ohira, M. Neutrophils in primary gastric tumors are correlated with neutrophil infiltration in tumor-draining lymph nodes and the systemic inflammatory response. BMC Immunol. 2018, 19, 13. [Google Scholar] [CrossRef] [Green Version]
  81. Schmidt, M.; Weyer-Elberich, V.; Hengstler, J.G.; Heimes, A.S.; Almstedt, K.; Gerhold-Ay, A.; Lebrecht, A.; Battista, M.J.; Hasenburg, A.; Sahin, U.; et al. Prognostic impact of CD4-positive T cell subsets in early breast cancer: A study based on the FinHer trial patient population. Breast Cancer Res. 2018, 20, 15. [Google Scholar] [CrossRef]
  82. Ostroumov, D.; Fekete-Drimusz, N.; Saborowski, M.; Kühnel, F.; Woller, N. CD4 and CD8 T lymphocyte interplay in controlling tumor growth. Cell Mol. Life Sci. 2018, 75, 689–713. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  83. Hannani, D.; Vétizou, M.; Enot, D.; Rusakiewicz, S.; Chaput, N.; Klatzmann, D.; Desbois, M.; Jacquelot, N.; Vimond, N.; Chouaib, S.; et al. Anticancer immunotherapy by CTLA-4 blockade: Obligatory contribution of IL-2 receptors and negative prognostic impact of soluble CD25. Cell Res. 2015, 25, 208–224. [Google Scholar] [CrossRef]
  84. deLeeuw, R.J.; Kost, S.E.; Kakal, J.A.; Nelson, B.H. The prognostic value of FoxP3+ tumor-infiltrating lymphocytes in cancer: A critical review of the literature. Clin. Cancer Res. 2012, 18, 3022–3029. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  85. Chiffoleau, E. C-Type Lectin-Like Receptors As emerging orchestrators of sterile inflammation represent potential therapeutic targets. Front. Immunol. 2018, 9. [Google Scholar] [CrossRef] [Green Version]
  86. Wculek, S.K.; Cueto, F.J.; Mujal, A.M.; Melero, I.; Krummel, M.F.; Sancho, D. Dendritic cells in cancer immunology and immunotherapy. Nat. Rev. Immunol. 2020, 20, 7–24. [Google Scholar] [CrossRef] [PubMed]
  87. Poli, A.; Michel, T.; Thérésine, M.; Andrès, E.; Hentges, F.; Zimmer, J. CD56bright natural killer (NK) cells: An important NK cell subset. Immunology 2009, 126, 458–465. [Google Scholar] [CrossRef]
  88. Edin, S.; Kaprio, T.; Hagström, J.; Larsson, P.; Mustonen, H.; Böckelman, C.; Strigård, K.; Gunnarsson, U.; Haglund, C.; Palmqvist, R. The prognostic importance of CD20+ B lymphocytes in Colorectal cancer and the relation to other immune cell subsets. Sci. Rep. 2019, 9, 19997. [Google Scholar] [CrossRef]
  89. Su, S.; Chen, J.; Yao, H.; Liu, J.; Yu, S.; Lao, L.; Wang, M.; Luo, M.; Xing, Y.; Chen, F.; et al. CD10(+)GPR77(+) cancer-associated fibroblasts promote cancer formation and chemoresistance by sustaining cancer stemness. Cell 2018, 172, 841–856.e16. [Google Scholar] [CrossRef]
  90. Park, C.K.; Jung, W.H.; Koo, J.S. Expression of cancer-associated fibroblast-related proteins differs between invasive lobular carcinoma and invasive ductal carcinoma. Breast Cancer Res. Treat. 2016, 159, 55–69. [Google Scholar] [CrossRef]
  91. Iwamoto, S.; Burrows, R.C.; Agoff, S.N.; Piepkorn, M.; Bothwell, M.; Schmidt, R. The p75 neurotrophin receptor, relative to other Schwann cell and melanoma markers, is abundantly expressed in spindled melanomas. Am. J. Dermatopathol. 2001, 23, 288–294. [Google Scholar] [CrossRef]
  92. Zhao, W.; Li, Y.; Zhang, X. Stemness-related markers in cancer. Cancer Transl. Med. 2017, 3, 87–95. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  93. Nissen, N.I.; Karsdal, M.; Willumsen, N. Collagens and cancer associated fibroblasts in the reactive stroma and its relation to cancer biology. J. Exp. Clin. Cancer Res. 2019, 38, 115. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  94. Tadeo, I.; Berbegall, A.P.; Navarro, S.; Castel, V.; Noguera, R. A stiff extracellular matrix is associated with malignancy in peripheral neuroblastic tumors. Pediatr. Blood Cancer 2017, 64. [Google Scholar] [CrossRef] [PubMed]
  95. Nikitovic, D.; Berdiaki, A.; Spyridaki, I.; Krasanakis, T.; Tsatsakis, A.; Tzanakakis, G.N. Proteoglycans-biomarkers and targets in cancer therapy. Front. Endocrinol. (Lausanne) 2018, 9, 69. [Google Scholar] [CrossRef] [Green Version]
  96. Kondisetty, S.; Menon, K.N.; Pooleri, G.K. Fibronectin protein expression in renal cell carcinoma in correlation with clinical stage of tumour. Biomark. Res. 2018, 6, 23. [Google Scholar] [CrossRef] [Green Version]
  97. Rousselle, P.; Scoazec, J.Y. Laminin 332 in cancer: When the extracellular matrix turns signals from cell anchorage to cell movement. Semin. Cancer Biol. 2020, 62, 149–165. [Google Scholar] [CrossRef]
  98. Burgos-Panadero, R.; Noguera, I.; Cañete, A.; Navarro, S.; Noguera, R. Vitronectin as a molecular player of the tumor microenvironment in neuroblastoma. BMC Cancer 2019, 19, 479. [Google Scholar] [CrossRef]
  99. Xu, Y.; Pasche, B. TGF-β signaling alterations and susceptibility to colorectal cancer. Hum. Mol. Genet. 2007, 16, R14–R20. [Google Scholar] [CrossRef] [Green Version]
  100. Chung, A.S.; Wu, X.; Zhuang, G.; Ngu, H.; Kasman, I.; Zhang, J.; Vernes, J.M.; Jiang, Z.; Meng, Y.G.; Peale, F.V.; et al. An interleukin-17-mediated paracrine network promotes tumor resistance to anti-angiogenic therapy. Nat. Med. 2013, 19, 1114–1123. [Google Scholar] [CrossRef]
  101. Vicente-Munuera, P.; Burgos-Panadero, R.; Noguera, I.; Navarro, S.; Noguera, R.; Escudero, L.M. The topology of vitronectin: A complementary feature for neuroblastoma risk classification based on computer-aided detection. Int. J. Cancer 2020, 146, 553–565. [Google Scholar] [CrossRef]
  102. Tadeo, I.; Berbegall, A.P.; Escudero, L.M.; Alvaro, T.; Noguera, R. Biotensegrity of the extracellular matrix: Physiology, dynamic mechanical balance, and implications in oncology and mechanotherapy. Front. Oncol. 2014, 4, 39. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  103. Martino, F.; Perestrelo, A.R.; Vinarský, V.; Pagliari, S.; Forte, G. Cellular mechanotransduction: From tension to function. Front. Physiol. 2018, 9, 824. [Google Scholar] [CrossRef]
  104. Croteau, E.; Renaud, J.M.; Richard, M.A.; Ruddy, T.D.; Bénard, F.; deKemp, R.A. PET Metabolic biomarkers for cancer. Biomark. Cancer 2016, 8, 61–69. [Google Scholar] [CrossRef]
  105. Chen, M.; Chen, C.; Shen, Z.; Zhang, X.; Chen, Y.; Lin, F.; Ma, X.; Zhuang, C.; Mao, Y.; Gan, H.; et al. Extracellular pH is a biomarker enabling detection of breast cancer and liver cancer using CEST MRI. Oncotarget 2017, 8, 45759–45767. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  106. Taylor, S.E.; Bagnall, J.; Mason, D.; Levy, R.; Fernig, D.G.; See, V. Differential sub-nuclear distribution of hypoxia-inducible factors (HIF)-1 and -2 alpha impacts on their stability and mobility. Open Biol. 2016, 6. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  107. Faes, S.; Uldry, E.; Planche, A.; Santoro, T.; Pythoud, C.; Demartines, N.; Dormond, O. Acidic pH reduces VEGF-mediated endothelial cell responses by downregulation of VEGFR-2; relevance for anti-angiogenic therapies. Oncotarget 2016, 7, 86026–86038. [Google Scholar] [CrossRef] [Green Version]
  108. Liu, F.; Li, J.; Guan, Y.; Lou, Y.; Chen, H.; Xu, M.; Deng, D.; Chen, J.; Ni, B.; Zhao, L.; et al. Dysbiosis of the Gut microbiome is associated with tumor biomarkers in lung cancer. Int. J. Biol. Sci. 2019, 15, 2381–2392. [Google Scholar] [CrossRef]
  109. Leinwand, J.C.; Miller, G. Microbes as biomarkers and targets in pancreatic cancer. Nat. Rev. Clin. Oncol. 2019, 16, 665–666. [Google Scholar] [CrossRef]
  110. Shirazi, M.S.R.; Al-Alo, K.Z.K.; Al-Yasiri, M.H.; Lateef, Z.M.; Ghasemian, A. Microbiome dysbiosis and predominant bacterial species as human cancer biomarkers. J. Gastrointest. Cancer 2019. [Google Scholar] [CrossRef]
  111. Hoeppner, L.H.; Wang, Y.; Sharma, A.; Javeed, N.; Van Keulen, V.P.; Wang, E.; Yang, P.; Roden, A.C.; Peikert, T.; Molina, J.R.; et al. Dopamine D2 receptor agonists inhibit lung cancer progression by reducing angiogenesis and tumor infiltrating myeloid derived suppressor cells. Mol. Oncol. 2015, 9, 270–281. [Google Scholar] [CrossRef]
  112. Chen, X.Y.; Ru, G.Q.; Ma, Y.Y.; Xie, J.; Chen, W.Y.; Wang, H.J.; Wang, S.B.; Li, L.; Jin, K.T.; He, X.L.; et al. High expression of substance P and its receptor neurokinin-1 receptor in colorectal cancer is associated with tumor progression and prognosis. Onco. Targets Ther. 2016, 9, 3595–3602. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  113. Zhang, C.; Murugan, S.; Boyadjieva, N.; Jabbar, S.; Shrivastava, P.; Sarkar, D.K. Beta-endorphin cell therapy for cancer prevention. Cancer Prev. Res. (Phila.) 2015, 8, 56–67. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  114. Warburg, O. On the origin of cancer cells. Science 1956, 123, 309–314. [Google Scholar] [CrossRef] [PubMed]
  115. Ferreira, L.M.; Hebrant, A.; Dumont, J.E. Metabolic reprogramming of the tumor. Oncogene 2012, 31, 3999–4011. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  116. Vander Heiden, M.G.; Cantley, L.C.; Thompson, C.B. Understanding the Warburg effect: The metabolic requirements of cell proliferation. Science 2009, 324, 1029–1033. [Google Scholar] [CrossRef] [Green Version]
  117. Wegiel, B.; Vuerich, M.; Daneshmandi, S.; Seth, P. Metabolic switch in the tumor microenvironment determines immune responses to anti-cancer therapy. Front. Oncol. 2018, 8, 284. [Google Scholar] [CrossRef] [Green Version]
  118. Schwartsburd, P. Cancer-induced reprogramming of host glucose metabolism: “vicious cycle” supporting cancer progression. Front. Oncol. 2019, 9, 218. [Google Scholar] [CrossRef] [Green Version]
  119. Morandi, A.; Indraccolo, S. Linking metabolic reprogramming to therapy resistance in cancer. Biochim. Biophys. Acta Rev. Cancer 2017, 1868, 1–6. [Google Scholar] [CrossRef]
  120. Pan, M.; Reid, M.A.; Lowman, X.H.; Kulkarni, R.P.; Tran, T.Q.; Liu, X.; Yang, Y.; Hernandez-Davies, J.E.; Rosales, K.K.; Li, H.; et al. Regional glutamine deficiency in tumours promotes dedifferentiation through inhibition of histone demethylation. Nat. Cell Biol. 2016, 18, 1090–1101. [Google Scholar] [CrossRef]
  121. Ko, Y.H.; Lin, Z.; Flomenberg, N.; Pestell, R.G.; Howell, A.; Sotgia, F.; Lisanti, M.P.; Martinez-Outschoorn, U.E. Glutamine fuels a vicious cycle of autophagy in the tumor stroma and oxidative mitochondrial metabolism in epithelial cancer cells: Implications for preventing chemotherapy resistance. Cancer Biol. Ther. 2011, 12, 1085–1097. [Google Scholar] [CrossRef] [Green Version]
  122. Pacella, I.; Procaccini, C.; Focaccetti, C.; Miacci, S.; Timperi, E.; Faicchia, D.; Severa, M.; Rizzo, F.; Coccia, E.M.; Bonacina, F.; et al. Fatty acid metabolism complements glycolysis in the selective regulatory T cell expansion during tumor growth. Proc. Natl. Acad. Sci. USA 2018, 115, E6546–E6555. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  123. Rivadeneira, D.B.; Delgoffe, G.M. Antitumor T-cell reconditioning: Improving metabolic fitness for optimal cancer immunotherapy. Clin. Cancer Res. 2018, 24, 2473–2481. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  124. Yin, Z.; Bai, L.; Li, W.; Zeng, T.; Tian, H.; Cui, J. Targeting T cell metabolism in the tumor microenvironment: An anti-cancer therapeutic strategy. J. Exp. Clin. Cancer Res. 2019, 38, 403. [Google Scholar] [CrossRef]
  125. Lyssiotis, C.A.; Kimmelman, A.C. Metabolic Interactions in the tumor microenvironment. Trends Cell Biol. 2017, 27, 863–875. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  126. Chang, C.H.; Qiu, J.; O’Sullivan, D.; Buck, M.D.; Noguchi, T.; Curtis, J.D.; Chen, Q.; Gindin, M.; Gubin, M.M.; van der Windt, G.J.; et al. Metabolic competition in the tumor microenvironment is a driver of cancer progression. Cell 2015, 162, 1229–1241. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  127. Kaipparettu, B.A.; Ma, Y.; Park, J.H.; Lee, T.L.; Zhang, Y.; Yotnda, P.; Creighton, C.J.; Chan, W.Y.; Wong, L.C. Correction: Crosstalk from non-cancerous mitochondria can inhibit tumor properties of metastatic cells by suppressing oncogenic pathways. PLoS ONE 2019, 14, e0221671. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  128. Lu, J.; Sharma, L.K.; Bai, Y. Implications of mitochondrial DNA mutations and mitochondrial dysfunction in tumorigenesis. Cell Res. 2009, 19, 802–815. [Google Scholar] [CrossRef]
  129. Wallace, D.C. Mitochondria and cancer: Warburg addressed. Cold Spring Harb. Symp. Quant. Biol. 2005, 70, 363–374. [Google Scholar] [CrossRef] [Green Version]
  130. Dong, L.F.; Kovarova, J.; Bajzikova, M.; Bezawork-Geleta, A.; Svec, D.; Endaya, B.; Sachaphibulkij, K.; Coelho, A.R.; Sebkova, N.; Ruzickova, A.; et al. Horizontal transfer of whole mitochondria restores tumorigenic potential in mitochondrial DNA-deficient cancer cells. Elife 2017, 6. [Google Scholar] [CrossRef] [Green Version]
  131. Alvaro, T.; de la Cruz-Merino, L.; Henao-Carrasco, F.; Villar Rodriguez, J.L.; Vicente Baz, D.; Codes Manuel de Villena, M.; Provencio, M. Tumor microenvironment and immune effects of antineoplastic therapy in lymphoproliferative syndromes. J. Biomed. Biotechnol. 2010, 2010. [Google Scholar] [CrossRef]
  132. Zanotelli, M.R.; Reinhart-King, C.A. Mechanical forces in tumor angiogenesis. Adv. Exp. Med. Biol. 2018, 1092, 91–112. [Google Scholar] [CrossRef] [PubMed]
  133. Graham, D.M.; Burridge, K. Mechanotransduction and nuclear function. Curr. Opin. Cell Biol. 2016, 40, 98–105. [Google Scholar] [CrossRef] [Green Version]
  134. Yilmaz, M.; Christofori, G. EMT, the cytoskeleton, and cancer cell invasion. Cancer Metastasis Rev. 2009, 28, 15–33. [Google Scholar] [CrossRef] [Green Version]
  135. Rianna, C.; Kumar, P.; Radmacher, M. The role of the microenvironment in the biophysics of cancer. Semin. Cell Dev. Biol. 2018, 73, 107–114. [Google Scholar] [CrossRef] [PubMed]
  136. Burgos-Panadero, R.; Lucantoni, F.; Gamero-Sandemetrio, E.; Cruz-Merino, L.; Alvaro, T.; Noguera, R. The tumour microenvironment as an integrated framework to understand cancer biology. Cancer Lett. 2019, 461, 112–122. [Google Scholar] [CrossRef] [PubMed]
  137. Aerts, H.J.; Velazquez, E.R.; Leijenaar, R.T.; Parmar, C.; Grossmann, P.; Carvalho, S.; Bussink, J.; Monshouwer, R.; Haibe-Kains, B.; Rietveld, D.; et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 2014, 5, 4006. [Google Scholar] [CrossRef] [PubMed]
  138. Campanella, G.; Hanna, M.G.; Geneslaw, L.; Miraflor, A.; Werneck Krauss Silva, V.; Busam, K.J.; Brogi, E.; Reuter, V.E.; Klimstra, D.S.; Fuchs, T.J. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 2019, 25, 1301–1309. [Google Scholar] [CrossRef] [PubMed]
  139. Curry, J.M.; Johnson, J.; Mollaee, M.; Tassone, P.; Amin, D.; Knops, A.; Whitaker-Menezes, D.; Mahoney, M.G.; South, A.; Rodeck, U.; et al. Metformin clinical trial in HPV+ and HPV- head and neck squamous cell carcinoma: Impact on cancer cell apoptosis and immune infiltrate. Front. Oncol. 2018, 8, 436. [Google Scholar] [CrossRef] [PubMed]
  140. Wall, M.; Butler, D.; El Haj, A.; Bodle, J.C.; Loboa, E.G.; Banes, A.J. Key developments that impacted the field of mechanobiology and mechanotransduction. J. Orthop. Res. 2018, 36, 605–619. [Google Scholar] [CrossRef]
  141. Li, L.; Eyckmans, J.; Chen, C.S. Designer biomaterials for mechanobiology. Nat. Mater. 2017, 16, 1164–1168. [Google Scholar] [CrossRef]
  142. Neeman, E.; Ben-Eliyahu, S. Surgery and stress promote cancer metastasis: New outlooks on perioperative mediating mechanisms and immune involvement. Brain Behav. Immun. 2013, 30, S32–S40. [Google Scholar] [CrossRef] [Green Version]
  143. Krall, J.A.; Reinhardt, F.; Mercury, O.A.; Pattabiraman, D.R.; Brooks, M.W.; Dougan, M.; Lambert, A.W.; Bierie, B.; Ploegh, H.L.; Dougan, S.K.; et al. The systemic response to surgery triggers the outgrowth of distant immune-controlled tumors in mouse models of dormancy. Sci. Transl. Med. 2018, 10. [Google Scholar] [CrossRef] [Green Version]
  144. Kuwahara, J.; Yamada, T.; Egashira, N.; Ueda, M.; Zukeyama, N.; Ushio, S.; Masuda, S. Comparison of the anti-tumor effects of selective serotonin reuptake inhibitors as well as serotonin and norepinephrine reuptake inhibitors in human hepatocellular carcinoma Cells. Biol. Pharm. Bull. 2015, 38, 1410–1414. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  145. Kannen, V.; Garcia, S.B.; Silva, W.A., Jr.; Gasser, M.; Monch, R.; Alho, E.J.; Heinsen, H.; Scholz, C.J.; Friedrich, M.; Heinze, K.G.; et al. Oncostatic effects of fluoxetine in experimental colon cancer models. Cell Signal. 2015, 27, 1781–1788. [Google Scholar] [CrossRef]
  146. Saloman, J.L.; Albers, K.M.; Li, D.; Hartman, D.J.; Crawford, H.C.; Muha, E.A.; Rhim, A.D.; Davis, B.M. Ablation of sensory neurons in a genetic model of pancreatic ductal adenocarcinoma slows initiation and progression of cancer. Proc. Natl. Acad. Sci. USA 2016, 113, 3078–3083. [Google Scholar] [CrossRef] [Green Version]
  147. Ruiz-Casado, A.; Martin-Ruiz, A.; Perez, L.M.; Provencio, M.; Fiuza-Luces, C.; Lucia, A. Exercise and the hallmarks of cancer. Trends Cancer 2017, 3, 423–441. [Google Scholar] [CrossRef]
  148. Idorn, M.; Thor Straten, P. Exercise and cancer: From “healthy” to “therapeutic”? Cancer Immunol. Immunother. 2017, 66, 667–671. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  149. Cormie, P.; Zopf, E.M.; Zhang, X.; Schmitz, K.H. The Impact of exercise on cancer mortality, recurrence, and treatment-related adverse effects. Epidemiol. Rev. 2017, 39, 71–92. [Google Scholar] [CrossRef] [PubMed]
  150. Garland, C.F.; Kim, J.J.; Mohr, S.B.; Gorham, E.D.; Grant, W.B.; Giovannucci, E.L.; Baggerly, L.; Hofflich, H.; Ramsdell, J.W.; Zeng, K.; et al. Meta-analysis of all-cause mortality according to serum 25-hydroxyvitamin D. Am. J. Public Health 2014, 104, e43–e50. [Google Scholar] [CrossRef] [PubMed]
  151. Maalmi, H.; Walter, V.; Jansen, L.; Boakye, D.; Schottker, B.; Hoffmeister, M.; Brenner, H. Association between blood 25-Hydroxyvitamin D levels and survival in colorectal cancer patients: An updated systematic review and meta-analysis. Nutrients 2018, 10, 896. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  152. McDonnell, S.L.; Baggerly, C.A.; French, C.B.; Baggerly, L.L.; Garland, C.F.; Gorham, E.D.; Hollis, B.W.; Trump, D.L.; Lappe, J.M. Breast cancer risk markedly lower with serum 25-hydroxyvitamin D concentrations >/=60 vs. <20 ng/ml (150 vs. 50 nmol/L): Pooled analysis of two randomized trials and a prospective cohort. PLoS ONE 2018, 13, e0199265. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  153. Haznadar, M.; Krausz, K.W.; Margono, E.; Diehl, C.M.; Bowman, E.D.; Manna, S.K.; Robles, A.I.; Ryan, B.M.; Gonzalez, F.J.; Harris, C.C. Inverse association of vitamin D3 levels with lung cancer mediated by genetic variation. Cancer Med. 2018, 7, 2764–2775. [Google Scholar] [CrossRef] [PubMed]
  154. McDonnell, S.L.; Baggerly, C.; French, C.B.; Baggerly, L.L.; Garland, C.F.; Gorham, E.D.; Lappe, J.M.; Heaney, R.P. Serum 25-Hydroxyvitamin D concentrations >/=40 ng/ml are associated with >65% lower cancer Risk: Pooled analysis of randomized trial and prospective cohort Study. PLoS ONE 2016, 11, e0152441. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  155. Hughes, P.J.; Marcinkowska, E.; Gocek, E.; Studzinski, G.P.; Brown, G. Vitamin D3-driven signals for myeloid cell differentiation--implications for differentiation therapy. Leuk. Res. 2010, 34, 553–565. [Google Scholar] [CrossRef] [Green Version]
  156. Sherman, M.H.; Yu, R.T.; Engle, D.D.; Ding, N.; Atkins, A.R.; Tiriac, H.; Collisson, E.A.; Connor, F.; Van Dyke, T.; Kozlov, S.; et al. Vitamin D receptor-mediated stromal reprogramming suppresses pancreatitis and enhances pancreatic cancer therapy. Cell 2014, 159, 80–93. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  157. Ooi, J.H.; Li, Y.; Rogers, C.J.; Cantorna, M.T. Vitamin D regulates the gut microbiome and protects mice from dextran sodium sulfate-induced colitis. J. Nutr. 2013, 143, 1679–1686. [Google Scholar] [CrossRef]
  158. Lee, T.Y.; Martinez-Outschoorn, U.E.; Schilder, R.J.; Kim, C.H.; Richard, S.D.; Rosenblum, N.G.; Johnson, J.M. Metformin as a therapeutic target in endometrial cancers. Front. Oncol. 2018, 8, 341. [Google Scholar] [CrossRef]
  159. Ackerman, S.E.; Blackburn, O.A.; Marchildon, F.; Cohen, P. Insights into the link between obesity and cancer. Curr. Obes. Rep. 2017, 6, 195–203. [Google Scholar] [CrossRef]
  160. Kunisada, Y.; Eikawa, S.; Tomonobu, N.; Domae, S.; Uehara, T.; Hori, S.; Furusawa, Y.; Hase, K.; Sasaki, A.; Udono, H. Attenuation of CD4(+)CD25(+) regulatory T Cells in the tumor microenvironment by metformin, a Type 2 Diabetes drug. EBioMedicine 2017, 25, 154–164. [Google Scholar] [CrossRef] [Green Version]
  161. Sun, L.; Xie, C.; Wang, G.; Wu, Y.; Wu, Q.; Wang, X.; Liu, J.; Deng, Y.; Xia, J.; Chen, B.; et al. Gut microbiota and intestinal FXR mediate the clinical benefits of metformin. Nat. Med. 2018, 24, 1919–1929. [Google Scholar] [CrossRef]
  162. Deng, S.; Shanmugam, M.K.; Kumar, A.P.; Yap, C.T.; Sethi, G.; Bishayee, A. Targeting autophagy using natural compounds for cancer prevention and therapy. Cancer 2019, 125, 1228–1246. [Google Scholar] [CrossRef]
  163. Shanmugam, M.K.; Rane, G.; Kanchi, M.M.; Arfuso, F.; Chinnathambi, A.; Zayed, M.E.; Alharbi, S.A.; Tan, B.K.; Kumar, A.P.; Sethi, G. The multifaceted role of curcumin in cancer prevention and treatment. Molecules 2015, 20, 2728–2769. [Google Scholar] [CrossRef]
  164. Tang, D.; Zhang, S.; Shi, X.; Wu, J.; Yin, G.; Tan, X.; Liu, F.; Wu, X.; Du, X. Combination of Astragali Polysaccharide and curcumin improves the morphological structure of tumor vessels and induces tumor vascular normalization to inhibit the growth of hepatocellular carcinoma. Integr. Cancer Ther. 2019, 18. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  165. Ghosh, A.K.; Kay, N.E.; Secreto, C.R.; Shanafelt, T.D. Curcumin inhibits prosurvival pathways in chronic lymphocytic leukemia B cells and may overcome their stromal protection in combination with EGCG. Clin. Cancer Res. 2009, 15, 1250–1258. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  166. Dudas, J.; Fullar, A.; Romani, A.; Pritz, C.; Kovalszky, I.; Hans Schartinger, V.; Mathias Sprinzl, G.; Riechelmann, H. Curcumin targets fibroblast-tumor cell interactions in oral squamous cell carcinoma. Exp. Cell Res. 2013, 319, 800–809. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  167. Naeini, M.B.; Momtazi, A.A.; Jaafari, M.R.; Johnston, T.P.; Barreto, G.; Banach, M.; Sahebkar, A. Antitumor effects of curcumin: A lipid perspective. J. Cell Physiol. 2019. [Google Scholar] [CrossRef] [PubMed]
  168. Aggarwal, S.; Ichikawa, H.; Takada, Y.; Sandur, S.K.; Shishodia, S.; Aggarwal, B.B. Curcumin (diferuloylmethane) down-regulates expression of cell proliferation and antiapoptotic and metastatic gene products through suppression of IkappaBalpha kinase and Akt activation. Mol. Pharmacol. 2006, 69, 195–206. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  169. Cheng, A.L.; Hsu, C.H.; Lin, J.K.; Hsu, M.M.; Ho, Y.F.; Shen, T.S.; Ko, J.Y.; Lin, J.T.; Lin, B.R.; Ming-Shiang, W.; et al. Phase I clinical trial of curcumin, a chemopreventive agent, in patients with high-risk or pre-malignant lesions. Anticancer Res. 2001, 21, 2895–2900. [Google Scholar]
  170. Wong, S.W.; Lenzini, S.; Shin, J.W. Perspective: Biophysical regulation of cancerous and normal blood cell lineages in hematopoietic malignancies. APL Bioeng. 2018, 2, 031802. [Google Scholar] [CrossRef] [Green Version]
  171. Darling, A.L.; Yalavarthy, P.K.; Doyley, M.M.; Dehghani, H.; Pogue, B.W. Interstitial fluid pressure in soft tissue as a result of an externally applied contact pressure. Phys. Med. Biol. 2007, 52, 4121–4136. [Google Scholar] [CrossRef] [Green Version]
  172. Li, J.; Barbone, P.E.; Smith, M.L.; Stamenovic, D. Effect of correlation between traction forces on tensional homeostasis in clusters of endothelial cells and fibroblasts. J. Biomech. 2020, 100, 109588. [Google Scholar] [CrossRef] [PubMed]
  173. Shukla, A.; Dunn, A.R.; Moses, M.A.; Van Vliet, K.J. Endothelial cells as mechanical transducers: Enzymatic activity and network formation under cyclic strain. Mech. Chem. Biosyst. 2004, 1, 279–290. [Google Scholar] [PubMed]
  174. Bisson, M.A.; Beckett, K.S.; McGrouther, D.A.; Grobbelaar, A.O.; Mudera, V. Transforming growth factor-beta1 stimulation enhances Dupuytren’s fibroblast contraction in response to uniaxial mechanical load within a 3-dimensional collagen gel. J. Hand Surg. Am. 2009, 34, 1102–1110. [Google Scholar] [CrossRef] [PubMed]
  175. Li, P.; Mao, Z.; Peng, Z.; Zhou, L.; Chen, Y.; Huang, P.H.; Truica, C.I.; Drabick, J.J.; El-Deiry, W.S.; Dao, M.; et al. Acoustic separation of circulating tumor cells. Proc. Natl. Acad. Sci. USA 2015, 112, 4970–4975. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  176. Wu, M.; Huang, P.H.; Zhang, R.; Mao, Z.; Chen, C.; Kemeny, G.; Li, P.; Lee, A.V.; Gyanchandani, R.; Armstrong, A.J.; et al. Circulating Tumor Cell Phenotyping via High-Throughput Acoustic Separation. Small 2018, 14, e1801131. [Google Scholar] [CrossRef]
  177. Islam, M.; Mezencev, R.; McFarland, B.; Brink, H.; Campbell, B.; Tasadduq, B.; Waller, E.K.; Lam, W.; Alexeev, A.; Sulchek, T. Microfluidic cell sorting by stiffness to examine heterogenic responses of cancer cells to chemotherapy. Cell Death Dis. 2018, 9, 239. [Google Scholar] [CrossRef] [Green Version]
  178. Zhu, Y.; Zhao, F.; Li, Z.; Yu, J. Current landscape and future directions of biomarkers for predicting responses to immune checkpoint inhibitors. Cancer Manag. Res. 2018, 10, 2475–2488. [Google Scholar] [CrossRef] [Green Version]
  179. Blank, C.U.; Haanen, J.B.; Ribas, A.; Schumacher, T.N. Cancer immunology. The “cancer immunogram”. Science 2016, 352, 658–660. [Google Scholar] [CrossRef]
  180. Galluzzi, L.; Chan, T.A.; Kroemer, G.; Wolchok, J.D.; Lopez-Soto, A. The hallmarks of successful anticancer immunotherapy. Sci. Transl. Med. 2018, 10. [Google Scholar] [CrossRef]
  181. Senovilla, L.; Vacchelli, E.; Galon, J.; Adjemian, S.; Eggermont, A.; Fridman, W.H.; Sautes-Fridman, C.; Ma, Y.; Tartour, E.; Zitvogel, L.; et al. Trial watch: Prognostic and predictive value of the immune infiltrate in cancer. Oncoimmunology 2012, 1, 1323–1343. [Google Scholar] [CrossRef] [Green Version]
  182. Whiteside, T.L. Immune responses to cancer: Are they potential biomarkers of prognosis? Front. Oncol. 2013, 3, 107. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  183. Sharma, P.; Hu-Lieskovan, S.; Wargo, J.A.; Ribas, A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 2017, 168, 707–723. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  184. Sanderson, J.P.; Crowley, D.J.; Wiedermann, G.E.; Quinn, L.L.; Crossland, K.L.; Tunbridge, H.M.; Cornforth, T.V.; Barnes, C.S.; Ahmed, T.; Howe, K.; et al. Preclinical evaluation of an affinity-enhanced MAGE-A4-specific T-cell receptor for adoptive T-cell therapy. Oncoimmunology 2020, 9, 1682381. [Google Scholar] [CrossRef] [Green Version]
  185. Krieg, C.; Nowicka, M.; Guglietta, S.; Schindler, S.; Hartmann, F.J.; Weber, L.M.; Dummer, R.; Robinson, M.D.; Levesque, M.P.; Becher, B. Author correction: High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy. Nat. Med. 2018, 24, 1773–1775. [Google Scholar] [CrossRef] [PubMed]
  186. Thorsson, V.; Gibbs, D.L.; Brown, S.D.; Wolf, D.; Bortone, D.S.; Ou Yang, T.H.; Porta-Pardo, E.; Gao, G.F.; Plaisier, C.L.; Eddy, J.A.; et al. The immune landscape of cancer. Immunity 2018, 48, 812–830.e14. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  187. Subrahmanyam, P.B.; Dong, Z.; Gusenleitner, D.; Giobbie-Hurder, A.; Severgnini, M.; Zhou, J.; Manos, M.; Eastman, L.M.; Maecker, H.T.; Hodi, F.S. Distinct predictive biomarker candidates for response to anti-CTLA-4 and anti-PD-1 immunotherapy in melanoma patients. J. Immunother. Cancer 2018, 6, 18. [Google Scholar] [CrossRef]
  188. Brabletz, T.; Kalluri, R.; Nieto, M.A.; Weinberg, R.A. EMT in cancer. Nat. Rev. Cancer 2018, 18, 128–134. [Google Scholar] [CrossRef]
  189. Behren, A.; Thompson, E.W.; Anderson, R.L.; Ferrao, P.T. Editorial: Cancer plasticity and the microenvironment: Implications for immunity and therapy response. Front. Oncol. 2019, 9, 276. [Google Scholar] [CrossRef] [Green Version]
  190. Hulsen, T.; Jamuar, S.S.; Moody, A.R.; Karnes, J.H.; Varga, O.; Hedensted, S.; Spreafico, R.; Hafler, D.A.; McKinney, E.F. From big data to precision medicine. Front. Med. (Lausanne) 2019, 6, 34. [Google Scholar] [CrossRef] [Green Version]
  191. D’Alterio, C.; Scala, S.; Sozzi, G.; Roz, L.; Bertolini, G. Paradoxical effects of chemotherapy on tumor relapse and metastasis promotion. Semin. Cancer Biol. 2020, 60, 351–361. [Google Scholar] [CrossRef]
  192. Tohme, S.; Simmons, R.L.; Tsung, A. Surgery for cancer: A trigger for metastases. Cancer Res. 2017, 77, 1548–1552. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  193. Fridman, W.H.; Zitvogel, L.; Sautes-Fridman, C.; Kroemer, G. The immune contexture in cancer prognosis and treatment. Nat. Rev. Clin. Oncol. 2017, 14, 717–734. [Google Scholar] [CrossRef] [PubMed]
  194. Mittal, R.; Woo, F.W.; Castro, C.S.; Cohen, M.A.; Karanxha, J.; Mittal, J.; Chhibber, T.; Jhaveri, V.M. Organ-on-chip models: Implications in drug discovery and clinical applications. J. Cell Physiol. 2019, 234, 8352–8380. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  195. Escors, D.; Gato-Canas, M.; Zuazo, M.; Arasanz, H.; Garcia-Granda, M.J.; Vera, R.; Kochan, G. The intracellular signalosome of PD-L1 in cancer cells. Signal. Transduct. Target. Ther. 2018, 3, 26. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Cancers 12 01677 g001 550
Figure 1. Hodgkin lymphoma is a clear example of a neoplasm where the components of the TME largely exceed the number of tumor cells. The differential staining of the histological sections corresponds to the same field of the tumor, showing overlapping layers that reveal a heterogeneous composition of the TME. (a) H&E staining of Hodgkin lymphoma. (b) little proportion of CD30 positive lymphoma tumoral cells. (c) Masson’s trichrome stain of abundant type I collagen (blue) and (d) representation of the host innate immune response via macrophage infiltrate (CD68) and the adaptive cellular response mediated by T cells (CD3) and B cells secreting antibodies against the tumor (CD138). Among others, this example constitutes the complex immune and stromal response that determines the histology, response to treatment, and tumor prognosis.
Figure 1. Hodgkin lymphoma is a clear example of a neoplasm where the components of the TME largely exceed the number of tumor cells. The differential staining of the histological sections corresponds to the same field of the tumor, showing overlapping layers that reveal a heterogeneous composition of the TME. (a) H&E staining of Hodgkin lymphoma. (b) little proportion of CD30 positive lymphoma tumoral cells. (c) Masson’s trichrome stain of abundant type I collagen (blue) and (d) representation of the host innate immune response via macrophage infiltrate (CD68) and the adaptive cellular response mediated by T cells (CD3) and B cells secreting antibodies against the tumor (CD138). Among others, this example constitutes the complex immune and stromal response that determines the histology, response to treatment, and tumor prognosis.
Cancers 12 01677 g001
Cancers 12 01677 g002 550
Figure 2. Schematic representation of TIME (Tumor Immune MicroEnvironment) classification. (a) The PD-1/PD-L1 pathway represents an adaptive immune resistance mechanism exerted by tumor cells in response to endogenous immune anti-tumor activity. Engagement of PD-L1 expressed on the tumor cells to PD-1 receptors on the activated T cells leads to inhibition of cytotoxic T cells. (b1–b4) Classification into 4 subtypes listed as TIME. (b1) PD-L1-, TIL− is classified into type 1 (T1). (b2) PD-L1+, TIL+, belongs to type 2 (T2). (b3) PD-L1−, TIL+ belongs to type 3 (T3) and (b4) PD-L1+, TIL−, classified as type 4 (T4) although its existence is under debate. MHC: major histocompatibility complex. TCR: T cell receptor. TAAs: tumor-associated antigens. TSAs: tumor-specific antigens. Legends at the top right.
Figure 2. Schematic representation of TIME (Tumor Immune MicroEnvironment) classification. (a) The PD-1/PD-L1 pathway represents an adaptive immune resistance mechanism exerted by tumor cells in response to endogenous immune anti-tumor activity. Engagement of PD-L1 expressed on the tumor cells to PD-1 receptors on the activated T cells leads to inhibition of cytotoxic T cells. (b1–b4) Classification into 4 subtypes listed as TIME. (b1) PD-L1-, TIL− is classified into type 1 (T1). (b2) PD-L1+, TIL+, belongs to type 2 (T2). (b3) PD-L1−, TIL+ belongs to type 3 (T3) and (b4) PD-L1+, TIL−, classified as type 4 (T4) although its existence is under debate. MHC: major histocompatibility complex. TCR: T cell receptor. TAAs: tumor-associated antigens. TSAs: tumor-specific antigens. Legends at the top right.
Cancers 12 01677 g002
Cancers 12 01677 g003 550
Figure 3. The reprogramming capacity of the tumor stroma is largely due both to the indirect effect of vitamin D on the microbiota and the presence of vitamin D receptor (VDR) on the components of the TME (**). Carcinoma cells (*). Image acquired at 20×.
Figure 3. The reprogramming capacity of the tumor stroma is largely due both to the indirect effect of vitamin D on the microbiota and the presence of vitamin D receptor (VDR) on the components of the TME (**). Carcinoma cells (*). Image acquired at 20×.
Cancers 12 01677 g003
Cancers 12 01677 g004 550
Figure 4. An integral vision of cancer biomarkers. TME elements are ultimately affected by systemic tumor macroenvironment (TMaE). The balance between them is a key determinant in tumor progression and aggressiveness. Therefore, analysis of this multi-level interaction could be beneficial for patient stratification and cancer therapy advancement and is also crucial for researchers in the field to improve current cancer models. Finally, emerging biomarkers need to be further explored and integrated to better understand the delicate information exchange occurring at the molecular/cellular/extracellular levels between the surrounding milieus. VDR: vitamin D3 receptor.
Figure 4. An integral vision of cancer biomarkers. TME elements are ultimately affected by systemic tumor macroenvironment (TMaE). The balance between them is a key determinant in tumor progression and aggressiveness. Therefore, analysis of this multi-level interaction could be beneficial for patient stratification and cancer therapy advancement and is also crucial for researchers in the field to improve current cancer models. Finally, emerging biomarkers need to be further explored and integrated to better understand the delicate information exchange occurring at the molecular/cellular/extracellular levels between the surrounding milieus. VDR: vitamin D3 receptor.
Cancers 12 01677 g004
Table
Table 1. Evaluation methods for emerging biomarkers.
Table 1. Evaluation methods for emerging biomarkers.
Evaluation GroupsParametersIndicatorsDetectionMethodReferences
TMEInflammatory infiltrated cellsTAMCD68, CD163IHC[79]
TANCD15, CD32, CD35 [80]
T helperCD4 [81]
Cytotoxic T cellsCD8 [82]
Memory T cellsCD8, CD4, CTLA-4 [83]
Tregs FOXP3, CD4, CD25 [84]
DCCD141, CLEC9, CD11c [85,86]
NKCD16, CD56, PD-L1, PD-L2 [87]
B lymphocytesCD20 [88]
Stromal cellsCAFαSAM, CD10, FSP1, AEBP1 [89,90]
Schwann cellsS100, GFAP, p75NTR [91]
MSCSCSox2, Oct4, CD133, Nestin, c-kit [92]
FibersCollagen type IT. Masson, van GiesonHC[93,94]
Collagen type III [94]
Elastic fibersOrcein, Gomori, Snook, Wilder, Verhoeff [94]
Interstitial fluidProteoglycansAlcian blue [95]
FibronectinAntifibronectinIHC[96]
LamininAntilaminin [97]
VitronectinAntivitronectin [98]
Growth factorTGF-βAS[99]
CytokinesLymphokinesELISA[100]
ProteasesMetalloproteinases [100]
Oxygen (ROS)GSH/GSSG [100]
3D structureFibres and Cellular elementsTopologyGraph theory[101,102]
Mechanical forcesFocal adhesions(F-actin, myosin II, α-actin, fascin)IF[103]
Stress fibres [103]
MechanotransductionMechano-actuated shuttling proteins(β-catenin, zyxin) [103]
LINC complex(SUN and nesprins) [103]
Systemic factorsGlycolic index↑metabolic index18FDGPET[104]
pHAcidosisElectrolytes serum concentrationEnzymatic[105]
Oxygen saturationHypoxia↑HIF-1, ↑lactateIHC[106]
MetabolismInflammatory response↓VEGFAS, ELISA[107]
Intestinal microbiotaDysbiosis↑BacteroidsMALDI-TOF MS[76,108,109,110]
↑FusobacteriumNGS[76,108,109,110]
↑Porphyromonas16S rRNA[76,108,109,110]
↑Enterobacter [76,108,109,110]
↑Cybrobacter [76,108,109,110]
Nervous systemDeregulation↑Norepinephrine,HPLC[62]
↑Dopamine [111]
↑substance P [112]
↓β-endorphins [113]
AS: absorption spectrometry; DC: dendritic cells; CAF: cancer-associated fibroblasts; 18FDG: fluorodeoxyglucose; GSH: glutathione; GSSG: glutathione disulfide; HC: histochemistry; HPLC: high-performance liquid chromatography; IF: immunofluorescence; IHC: immunohistochemistry; LINC: linker of nucleoskeleton and cytoskeleton; MALDI-TOF MS: matrix-assisted laser desorption/ionization time-of-flight mass spectrometry; MSC: mesenchymal stem cells; NGS: next-generation sequencing; NK: natural killer cells; PET: positron emission tomography; ROS: reactive oxygen species; 16S rRNA: ribosomal RNA 16S; SC: stem cells; TAM: tumor-associated macrophage; TAN: tumor-associated neutrophils; TME: tumor microenvironment; Tregs: regulatory T cells. Adapted from Noguera et at., 2019 [46].

Share and Cite

MDPI and ACS Style

Sanegre, S.; Lucantoni, F.; Burgos-Panadero, R.; de La Cruz-Merino, L.; Noguera, R.; Álvaro Naranjo, T. Integrating the Tumor Microenvironment into Cancer Therapy. Cancers 2020, 12, 1677. https://doi.org/10.3390/cancers12061677

AMA Style

Sanegre S, Lucantoni F, Burgos-Panadero R, de La Cruz-Merino L, Noguera R, Álvaro Naranjo T. Integrating the Tumor Microenvironment into Cancer Therapy. Cancers. 2020; 12(6):1677. https://doi.org/10.3390/cancers12061677

Chicago/Turabian Style

Sanegre, Sabina, Federico Lucantoni, Rebeca Burgos-Panadero, Luis de La Cruz-Merino, Rosa Noguera, and Tomás Álvaro Naranjo. 2020. "Integrating the Tumor Microenvironment into Cancer Therapy" Cancers 12, no. 6: 1677. https://doi.org/10.3390/cancers12061677

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop