4.1 Introduction

Disease pathogenesis can often arise from disruptions in normal signaling pathways, which are often complex and integrated. For example, perturbations in intracellular calcium homeostasis have been implicated in the development of neurodegenerative diseases such as Parkinson's, Huntington's, and Alzheimer's disease [1–3]. Additionally, simple dysregulation of signal transduction cascades in response to alterations in the local cellular environment (e.g. oxidative stress, endoplasmic reticulum stress, mitogen‐activated protein kinase (MAPK) activity, and alterations in calcineurin, to name a few [1, 4–7]) has also been shown to contribute to disease pathogenesis. Further, perturbations in signal transduction networks can lead to altered physiological states, such as asthma and diabetes. Asthma arises from disruption of inter‐ and intracellular signaling in inflammatory cells, as well as alterations in the molecular machinery for synthesis of mucus; these changes involve lipid mediators, cytokines, T cells, and innate lymphoid cells [8–11]. Disruption of pathways signaling through Notch, MAPKs, phosphoinositide‐3‐kinase (PI3K), and protein tyrosine kinases (PTKs) has been shown to contribute to the pathogenesis of asthma [12–14]. Diabetes, which was discussed in detail in Chapter 3, is associated with alterations in Wnt and bone morphogenetic protein 4 (BMP4) signaling [15], as well as disturbance of the function of glucose transporter 4 [16, 17] and peroxisome proliferator‐activated receptors (PPARs) [18].

One of the most well‐researched examples of altered signal transduction in disease pathogenesis is the study of cancer. Large efforts in the field have been dedicated to understanding the imbalances that lead to the development of this disease. From a holistic point of view, inhibition of apoptotic pathways (that are initiated via signal transduction), which would normally eliminate mutated or otherwise damaged cells, plays a critical permissive role in carcinogenesis [19, 20]. Further, imbalances in cellular homeostasis can result in adaptations at the cellular level that facilitate disease development (e.g. loss of pathways that degrade hypoxia‐inducible factor‐1 alpha [HIF‐1α] leads to its stabilization, affording a survival advantage to oxygen‐deprived tumor cells [21]). Additionally, changes in DNA methylation, microRNAs, and disrupted Notch signaling are among the other pathways for which perturbation can contribute to the development of cancer [22–25].

From a genetic perspective, one of the most common oncogenes – or mutated genes that drives aberrant cancer growth and survival – is the small GTPase protein Ras. Mutations that prevent GTP hydrolysis and “turn‐off” of the protein are found commonly in the KRAS gene in pancreatic, colorectal, and lung cancers, as well as the NRAS gene in melanoma, both of which participate in Ras signaling. Protein kinases are another well‐studied species in the field of signal transduction and cancer, as they are often mutated or overexpressed. Kinases are affected by the activity of oncogenes as well as tumor suppressor genes. The first definitively identified oncogene was src from the Rous sarcoma virus. Both this viral protein and its mammalian cellular homolog, Src, are PTKs encoded by the src gene; other known examples of oncogenes associated with tyrosine kinases include the epidermal growth factor receptor (EGFR), the EGFR family protein HER2 in breast cancer, platelet‐derived growth factor (PDGF), vascular endothelial growth factor (VEGF), and anaplastic lymphoma kinase (ALK), which is important in lung cancer as well as lymphoma. A number of serine/threonine kinases are also mutated in melanoma, lung, and other cancers. These include the v‐Raf murine sarcoma viral oncogene homolog B (BRAF) and the kinases that activate mitogen‐activated protein kinase (MAPK) and extracellular‐regulated kinase (ERK) kinase (MEK1/2). The activity of tumor suppressor genes that code for important signaling proteins can be inactivated or lost in cancer, also resulting in dysregulation of kinase activity. For example, one of the most common tumor suppressor genes is phosphatase and tensin homolog (PTEN), which encodes phosphatidylinositol‐3,4,5‐trisphosphate 3‐phosphatase. This lipid phosphatase breaks down phosphatidylinositol (3,4,5)‐trisphosphate (PIP₃) to bisphosphate product phosphatidylinositol (4,5)‐bisphosphate (PIP₂). Since PIP₃ is the primary activator of the pro‐survival Akt kinases, loss of a key degradation pathway for PIP₃ provides an important stimulus for cancer development and progression.

The above examples merely scratch the surface of intracellular signaling pathways that contribute to disease. In this chapter, we will focus on changes that go beyond single cells to understand the complex interactions between cell types that often contribute to disease. Rather than attempt complete coverage, this chapter will focus on two examples: (i) First, diseases in which tissue fibrosis develops will illustrate the complexity and extensive interaction of cells and signaling events that participate in the development of fibrotic diseases. (ii) Second, the development of cancer resistance to the new “targeted” drugs that modulate signal transduction can involve cell–cell interactions, and it is important to note that both heterogeneity and intercellular interactions among the large population of cancer cells will contribute to the complexity of response. With these two topics as the focus of the chapter, we aim to illustrate general principles that are broadly applicable to signal transduction in disease.

4.2 Fibrosis as an Example of Complex Signaling

Fibrosis is a chronic condition characterized by excess deposition of extracellular matrix (ECM) proteins and thickening and stiffening of tissue, resulting in impairment of function and alterations in tissue architecture. It is considered a wound healing process of last resort, one that is initiated when repair processes fail. Fibrosis occurs in a variety of tissues including liver, lung, and kidney, and it is a result of persistent damage and inflammation. Fibrosis can range from a benign condition to one that is quite serious, and it can even be a precursor to neoplastic disease [26]. However, recent efforts have brought to light that even advanced changes are reversible, and resolution can be achieved [27]. This section will focus on intracellular signaling events and cellular interactions that lead to fibrosis, using hepatic fibrosis as an example. Hepatic fibrosis is the example of choice, as many of the signaling pathways involved in fibrosis of the liver are relevant to other tissues.

4.2.1 Development of Liver Fibrosis

Liver fibrosis can occur as a result of chronic injury due to viral infection, chemical insult (including chronic alcohol consumption), nonalcoholic steatohepatitis (fatty liver), autoimmune reactions, or cholestatic liver disease, among others [28]. Necrosis, accumulation of inflammatory cells, increased numbers of myofibroblasts, and excessive deposition of ECM are all evident in liver fibrosis [29]. Development of fibrosis occurs due to interactions among multiple resident liver cell types, inflammatory cells, and mediators, as well as ECM components.

Hepatocellular damage is the initiator of fibrosis. In response to cell damage, reactive oxygen species (ROS) and other signal transduction mediators are released from dying hepatocytes and Kupffer cells (KCs), the resident macrophages of the liver (Figure 4.1). These mediators attract other macrophages and inflammatory cells from the blood, leading to increased production and release of pro‐inflammatory mediators [27]. Cytokines, such as tumor necrosis factor‐α (TNF‐α), interleukin (IL)‐6, IL‐1β, and transforming growth factor‐β1 (TGF‐β1), are released by macrophages to promote inflammation and recruit other immune cells, including T lymphocytes (T cells). Growth factors, including PDGF and epidermal growth factor (EGF), are also generated during this response. TGF‐β1 released by hepatocytes is a major activator of hepatic stellate cells (HSCs), which then release more TGF‐β1 to act in an autocrine manner for further activation [30]. Consequences of HSC activation are discussed in the following sections.

4.2.6 Intracellular Signaling Pathways Critical to Liver Fibrosis

4.2.6.1 TGF‐β1

As previously mentioned, the activation of HSCs is affected primarily by TGF‐β1. TGF‐β1 is a member of the TGF‐β superfamily of proteins that includes activins, inhibins, BMPs, and other proteins. TGF‐β1 is synthesized as a propeptide. The latent form is bound to latency‐associated peptide (LAP) and latent TGF‐β binding proteins (LTBPs), forming a large complex. TGF‐β1 can be activated by integrins, MMPs, plasmin, ROS, and other substances, as well as by myofibroblast contraction [27, 84–87].

Activated TGF‐β1 binds a heterotetrameric complex containing two types of cell surface serine/threonine‐specific protein kinase receptors, the TGF‐β receptors type I and type II (TBRI and TBRII). Binding of TGF‐β1 induces phosphorylation of TBRI by TBRII, which initiates intracellular signaling through phosphorylation of intracellular regulators (Figure 4.2).

The TGF‐β1 signaling response involves Smad proteins, which are the main signal transducers for the TGF‐β family. Smad proteins are either receptor‐regulated (R‐Smads; Smad1, Smad2, Smad3, Smad5, and Smad8), inhibitory (Smad6 and Smad7), or common (Smad4). They contain two Mad Homology (MH) domains separated by a linker region. MH1 binds DNA, whereas MH2 binds to TGF receptors or other MH2 domains in coactivators and corepressors. When TGF‐β1 binds to its receptor, phosphorylation of TBRI reveals its Smad binding site, allowing association of R‐Smads and their phosphorylation at C‐terminal serine domains. Phosphorylation creates a motif capable of binding MH2 domains on R‐Smads or on Smad4 [88, 89]. Once phosphorylated, Smad2 and Smad3 associate with Smad4 and translocate to the nucleus where the complex functions as a transcription factor [88–91]. Although free Smads move easily between the cytoplasm and the nucleus using nuclear porins, phosphorylated Smads use a variety of nuclear transport factors [92, 93].

A conserved domain known as MH1 facilitates sequence‐specific Smad binding to DNA. Phosphorylation of the MH1 domain of Smad2 or Smad3 inhibits DNA binding [94], illustrating one method by which signal transduction can regulate the activity of Smads. Smad‐regulated genes include those encoding collagen, αSMA, E‐cadherin, and Timp1 [40, 41]. The gene for inhibitory Smad7 is also regulated through this pathway; its transcription and translation lead to downregulation of Smad signaling.

Interaction among Smad proteins determines the outcome of signaling. For example, Smad2 and Smad3 might not contribute equally to fibrosis. In human fibrotic tissue and cells, both Smad2 and Smad3 are phosphorylated [29]. In Smad3 knockout mice, treatment with CCl₄ elicited a smaller increase in expression of collagen mRNA, although αSMA expression was increased relative to wild‐type (WT) mice [95]. Results were similar in HSCs isolated from Smad3‐deficient mice. Levels of collagen and Timp1 were decreased in TGF‐β1‐treated human HSCs deficient in Smad3. Conversely, overexpression of Smad3 increased the expression of collagen and Timp1 and decreased expression of MMP‐2 in vitro[29]. Manipulation of Smad2 in vitro had the opposite effect of that described for Smad3 [29, 95]. In extrahepatic tissues, deficiency of Smad3 blocked EMT and fibrosis, while Smad2 deletion increased fibrosis [96, 97]. Both functioning Smad2 and Smad3 are required for transcriptional activity, as evidenced by a lack of nuclear translocation in the absence of either [29, 95]. These results suggest a degree of complexity in Smad signaling at the level of interaction of Smads. The essential role of Smad4 in fibrosis is illustrated by the observation that deletion of Smad4 inhibits TGF‐β1‐induced ECM deposition by mesangial cells [98].

Smad7 downregulates Smad signaling by regulating binding of Smads to TBRI, thereby preventing their phosphorylation. In addition, Smad7 facilitates the degradation of Smad2 and TBRI by recruiting ubiquitin‐specific processing protease 15 and Smad ubiquitination regulatory factors (SMURFs) [99, 100], which target the proteins for degradation. In CCl₄‐induced liver fibrosis, loss of Smad7 enhanced HSC activation and development of fibrosis, but overexpression of Smad7 diminished fibrosis [101, 102].

4.2.6.2 Kinase Pathways Involved in Fibrotic Responses

Activation of protein kinases also plays a role in development of liver fibrosis. TGF‐β1 increases phosphorylation of ERK, c‐Jun N‐terminal kinase (JNK), and p38 MAPKs [103–105]. Thioacetamide‐ or CCl₄‐induced fibrosis in mice was associated with activation of ERK [106], and interference with ERK signaling reduced expression of collagen [107]. Furthermore, transcriptomic analysis of human keratinocytes revealed a role for ERK‐regulated pathways in cell adhesion and motility; inhibition of MAPK signaling inhibited EMT [103, 108]. Conversely, activation of ERK can lead to phosphorylation of Smad proteins in the linker region that causes their retention in the cytoplasm [109], thereby inhibiting their transcriptional activity.

PI3 kinase/Akt signaling is also involved in fibrosis, especially through effects on EMT. Loss of Akt is associated with loss of EMT phenotype and increased expression of E‐cadherin [110]. Snail1 and Snail2 are transcription factors that are upregulated through TGF‐β1 signaling (Figure 4.3). Their overexpression induces EMT [111, 112]. They complex with corepressors and histone deacetylase to reduce expression of E‐cadherin. Akt signals through nuclear factor κB (NF‐κB) to induce transcription of SNAI1, the gene for Snail1 [113]. Akt also inhibits glycogen synthase kinase‐3β (GSK‐3β), which targets Snail1 protein for degradation [114]. Accordingly, Akt both increases Snail1 protein synthesis and inhibits its degradation, permitting Snail‐mediated EMT. In addition, Akt inactivates proteins (e.g. heterogeneous nuclear ribonucleoprotein E1 [hnRNP E1]) that suppress EMT [115]. Furthermore, in tumor cells, Akt activates mammalian target of rapamycin (mTOR) complexes 1 and 2, both of which contribute to EMT [116, 117].

4.2.6.3 HIF‐1α

As mentioned previously, tissue hypoxia occurs during fibrosis, likely from diminished hepatic blood flow as well as deposition of fibrin in sinusoids [118, 119]. Hypoxia leads to activation of HIF‐1α. HIF‐1α was increased in livers of mice subjected to BDL [120]. This was accompanied by an increase in mRNA for collagen, αSMA, and PDGF. These responses were reduced in livers of mice deficient in HIF. In patients with fibrotic liver disease, HIF‐1α protein was detected in macrophages, hepatocytes, and fibroblasts [121], and experimental evidence suggests that activation of HIF‐1α in KCs contributes to hepatic fibrosis. In mice with myeloid cell‐specific deficiency in HIF‐1α or HIF‐1β, BDL led to smaller increases in expression of collagen and αSMA [122]. Furthermore, exposure of isolated KCs to hypoxia led to increased mRNA for PDGF and VEGF; this response was absent in KCs isolated from HIF‐1β‐deficient mice [122].

4.2.6.4 miRNA

MicroRNAs (miRNAs) are small noncoding RNAs that regulate the translation and transcription of genes. The role of miRNAs in disease has been a relatively recent subject of research exploration. Several miRNAs have been suggested to play different roles in the pathogenesis of hepatic fibrosis, although no causality has been demonstrated directly. In human tissue, expression of miR‐199a, miR‐199a*, miR‐200a, and miR‐200b tracked positively with severity of fibrosis [123]. Similarly, miR‐21 has a positive effect on activation of HSCs as well as EMT [124, 125].

On the other hand, some miRNAs correlate negatively with fibrosis. Microarray analysis of livers from CCl₄‐treated mice detected 31 differentially expressed miRNAs, of which the majority were decreased in expression compared to control mice [126]. Among these, decreased expression of miR‐29b correlated with decreased liver fibrosis. Expression of miR‐29 family members was also decreased in livers and plasma from patients with fibrosis/cirrhosis [126]. miR‐29 family members were highly expressed in HSCs isolated from livers of control mice, and expression was decreased by exposure to TGF‐β1 in vitro. Overexpression of miR29b in HSC led to decreased expression of Col1a1 but did not affect Timp1 or αSMA expression. Similar results were observed with miR‐122 [127].

4.2.6.5 Toll‐Like Receptors (TLRs)

Toll‐like receptors (TLRs) are receptors for pathogen‐associated and damage‐associated molecular pattern molecules. They mediate responses to inflammagens, such as bacterial products (e.g. lipopolysaccharide) and molecules released by dying cells (e.g. high‐mobility group box 1 protein). Both TLR2 and TLR4 have been implicated in fibrogenesis. Activation of TLR4 signaling pathways leads to decreased expression of the BMP and activin membrane‐bound inhibitor homolog (Bambi), which is a pseudoreceptor for TGF‐β1 that lacks intracellular kinase activity. Reductions in Bambi play a permissive role for profibrogenic actions of TGF‐β1 on HSCs [128]. In TLR2‐deficient mice treated with CCl₄, hepatic fibrosis, HSC activation, and collagen deposition were reduced, as were inflammation and necrosis [129]. Similar results were observed in TLR5‐deficient mice [130]. These latter two studies demonstrate the role of inflammation in the development of fibrosis. Taken together, these results suggest that inflammatory signaling through TLRs can contribute to the pathogenesis of liver fibrosis.

4.3 Cancer Drug Resistance: Complex Cellular and Population Changes

Modern cancer research is based on a deep understanding of signal transduction mechanisms. Many of the genetic mutations, amplifications, or chromosomal translocations that cause cancer (i.e. oncogenes or tumor suppressors) are in signal transduction proteins as described in Chapter 2. Oncogenic mutations occur in a broad array of receptors (e.g. receptor tyrosine kinases [RTKs]), many kinase pathways such as the mitogen‐activated kinase (MAPK) and cell cycle‐dependent kinase (CDK) mechanisms, small GTPases (e.g. KRAS), and transcription factors (e.g. MYC). Tumor suppressors include phosphatases (e.g. PTEN) and GTPase deactivators (e.g. neurofibromatosis 1 [NF1]). As might be expected, the mutated oncoproteins are excellent drug targets when inhibitors can be found. EGFR is commonly mutated in non‐small cell lung cancer (NSCLC), and EGFR tyrosine kinase inhibitors (TKIs) have become a valuable treatment strategy for these tumors [131–133]. In melanoma, the MAPK pathway is frequently mutated with nearly 60% of melanomas having mutations in the serine/threonine kinase BRAF. Inhibitors of BRAF (BRAFi) produce excellent clinical responses in BRAF‐mutant melanomas [134].

Drug resistance, however, remains a major hurdle in achieving durable therapeutic responses for EGFR and BRAFi and for virtually every “targeted” cancer therapy that is based on signal transduction mechanisms. Drug resistance can be divided into two broad classes: (i) intrinsic resistance and (ii) acquired resistance. The exact definition of intrinsic versus acquired resistance is still debated and depends on whether the cancer is being discussed on a tumor level or on a cellular level. For example, a point mutation found in 0.01% of tumor cells may promote intrinsic resistance to a drug on the cellular level for those rare cells with a resistance‐causing mutation. For the tumor, resistance to the drug would be acquired as the mutant cell clonally expands. Due to this confusion, another way to stratify resistance mechanisms is to consider genomic resistance and non‐genomic resistance mechanisms.

4.3.1 Genomic Resistance Mechanisms

Genomic resistance mechanisms are typically caused by mutations, copy number alterations (CNAs), or chromosomal translocations. Cells that harbor genomic resistance mechanisms may only comprise a small subpopulation of cells in drug‐naïve tumors (Figure 4.4). For lung cancers treated with EGFR TKIs, one frequently observed resistance mechanism is the so‐called “gatekeeper” mutation in EGFR, in which a single amino acid is altered from threonine to methionine (i.e. EGFR^T790M) [135–137]. This prevents drug binding and causes loss of effectiveness to first‐generation EGFR inhibitors. Finally, activation of parallel signaling pathways can promote drug resistance, since activation of the parallel pathway may phenocopy the role of the drug target pathway. An example of this is deletion or mutation of PTEN, which promotes resistance to a wide range of targeted therapies via activation of the PI3K/Akt pathway [138–140].

While drug‐naïve tumors may have only a few cells with this mutation, drug treatment kills the cells with WT EGFR, providing selective pressure that gives a growth advantage to the EGFR^T790M cells, allowing for their clonal expansion. Similarly, various estrogen receptor (ER) mutations in breast cancer can promote resistance to ER antagonists [141]. In both cases, drug resistance results from direct genomic modification of the drug target to result in signaling pathway reactivation.

Pathway reactivation can also result from mutation or CNAs in genes which encode proteins upstream or downstream of the drug target (Figure 4.5). BRAFi initially produce excellent responses in BRAF‐mutant melanomas [134], but resistance develops rapidly. For example, one common mechanism of resistance to BRAFi is mutations or CNAs in NRAS or MEK, which flank BRAF in the pathway [142, 143]. Because of this, MEK inhibitors (MEKi) have also been used (see below) with significant benefit.

4.3.6 Drug‐Induced Rewiring of Signaling Networks as a Mechanism of Drug Resistance

Until now, we have discussed mutationally driven resistance mechanisms. On a cellular level, these mechanisms are mostly static, in the sense that drug treatment does not specifically induce the mutations; rather the drug simply selects for resistant cells. In many cases, drug treatment can specifically and dynamically induce changes in signaling pathway activation and can rewire the signaling networks within a cell without inducing genomic alterations. The time scales of these alterations can vary greatly from seconds to days or weeks. This may involve epigenetic mechanisms or stable alterations in gene transcription programs. Since this broad class of drug resistance mechanisms is induced by drug treatment, these mechanisms fall under the umbrella of acquired resistance mechanisms.

Most BRAF‐mutant melanoma tumors respond to the BRAFi vemurafenib, but this response is not observed in BRAF‐mutant colorectal cancer (CRC) patients even though both tumor types harbor the same mutation. One reason for this lack of response is because CRC tumors adaptively activate EGFR in response to BRAF inhibition [179]. This compensatory EGFR activation results in activation of the PI3K/Akt pathway and reactivation of the MAPK pathway, which subsequently promotes vemurafenib resistance (Figure 4.6). One might then assume that simultaneous inhibition of BRAF and EGFR may be enough to prevent drug resistance. This strategy works initially, but, ultimately, patients still relapse on these therapies. In some cases, relapse on anti‐EGFR + BRAF therapy results from compensatory activation of other RTKs such as c‐Met.

Resistance to targeted therapies through activation of RTKs appears to be a recurrent theme in cancer biology. In addition to CRC, melanoma tumors can also develop resistance to BRAFi through activation of RTKs such as EGFR, PDGFR, IGF‐1R, and AXL. Ultimately, this results in compensatory reactivation of the MAPK pathway through a BRAF‐independent mechanism or via activation of parallel pathways such as the PI3K/Akt pathway [180]. Due to the degree of similarity in RTK effector pathways, compensatory activation of other RTKs is also a common bypass mechanism of resistance to anti‐RTK targeted therapies [181]. Earlier we discussed how selection for the rare NSCLC cell that harbors an EGFR^T790M mutation promotes resistance to EGFR inhibitors. However, this resistance mechanism is not present in all tumors, and some cancers develop resistance through compensatory activation of other signaling pathways. For example, in lung cancer, activation of the RTK AXL results in resistance to the EGFR inhibitor erlotinib [182]. Also, signaling by fibroblast growth factor 2 (FGF2) acting through the fibroblast growth factor receptor 1 (FGFR1) promotes resistance to the EGFR inhibitor gefitinib through an autocrine/paracrine loop [183]. There are other mechanisms of compensatory RTK activation that are not necessarily drug induced, such as selection for NSCLC cells that harbor MET amplification [184]. Ultimately, activation of any of these RTKs results in activation of signaling pathways involved in cell proliferation such as the MAPK pathway or the PI3K pathway.

4.3.7 Parallel Pathways and Combination Treatments

As one looks at the problem of cancer drug resistance, patterns begin to emerge. Non‐genomic drug resistance does not always occur simply by stochastic activation of other signaling pathways. Rather, drug treatment can induce specific alterations in the cellular signaling circuitry. Some of these are found repeatedly in different types of tumors. Cancer is a complex disease, and, to simplify it, we often think of each signaling pathway in isolation; the reality is that signaling pathways within a cell are generally linked to each other in complex networks. By learning how these signaling pathways interact, we can begin to understand how cancer cells adaptively respond to drug treatments. This information can be exploited to develop new therapeutic approaches to prevent or reverse drug resistance. For example, inhibition of one signaling pathway may render cells exquisitely sensitive to inhibition of another, a phenomenon known as “synthetic lethality.” This can be due to effects of two different drugs on the same pathway or different pathways. The latter is frequently used in antiviral or antibacterial therapies where drug combinations target multiple pathways with mutually exclusive resistance mechanisms.

Alternatively, consider a situation in which two distinct signaling pathways can promote proliferation of a cancer cell (Figure 4.7). These pathways function as a logical OR gate where either one being “on” results in cancer. Specifically, we could have a situation in which Pathway 1 includes proteins “A,” “B,” “C,” and “D” and Pathway 2 includes proteins “E,” “F,” “G,” and “H.” Pathway 1 could be the oncogenic driver in the cancer cell through an activating mutation in any of the four proteins, A–D. Activation of this pathway results in uncontrolled proliferation and tumor growth. Inhibiting signal transduction by this pathway with a small molecule inhibitor prevents the cell from proliferating. Activation of a second “parallel” signaling pathway may also be able to promote proliferation without the need for activation of Pathway 1. If this occurs when a cancer is treated with an inhibitor of Pathway 1, this would represent drug resistance. Such resistance mechanisms could occur through an adaptive response, wherein drug treatment actively induces the compensatory activation of the other signaling pathway. Alternatively, cells could have activation of Pathway 2 through a mutational mechanism. In this case, rare cells with a mutation in protein “E” in Pathway 2 would also be resistant to Pathway 1 inhibitors.

If there are multiple signaling pathways that control cellular proliferation, then why do cancer cells not simply acquire activation of all these pathways during the clonal evolution of a tumor? Certainly, one would assume that maximizing proliferative signaling is something cancer cells would want. Counterintuitively, excessive activation of pro‐proliferative signaling pathways results in oncogene‐induced senescence [185, 186]. In this case, an overload of pro‐proliferative signaling causes the cell to exit from the cell cycle and prevents proliferation. In some cases, concurrent activation of two pro‐proliferative signaling pathways results in suppressed proliferation, but when one of the pathways is inhibited (for example, by drug treatment), it would allow for that cell to proliferate. In this case, this signaling circuitry functions as an XOR gate rather than an OR gate (Figure 4.7B). These signaling mechanisms may explain why many tumors relapse during drug treatment with no apparent genomic resistance mechanisms. One example of this phenomenon is in melanoma where EGFR expression or TGF‐β treatment suppresses proliferation of BRAF^V600E‐mutant melanoma cells. However, in the presence of a BRAFi, TGF‐β‐treated cells or cells with EGFR expression are conferred a growth advantage.

4.3.8 Epigenetic Mechanisms of Drug Resistance

It is becoming better appreciated that stable changes in the function of a cell do not require alterations in DNA sequence. Epigenetics refers to the creation of stable functional alterations by long‐lasting, but potentially reversible, modification of DNA, RNA, or proteins. This often involves methylation of DNA or methylation, acetylation, or phosphorylation of histones or other proteins [187, 188]. Epigenetic changes in cancer cells clearly contribute to drug resistance. We discussed above how EGFR activation in melanoma cells promotes resistance to BRAFi; however, the mechanism was not described. As melanoma cells develop resistance to BRAFi, the landscape of DNA methylation marks in the genome, which largely serve to suppress gene expression, is dynamically altered [189]. Methylation of adjacent cytosine and guanine bases (i.e. CpG regions) can reduce gene expression. Methylation of CpG in the upstream and downstream enhancer regions of the EGFR gene is inversely correlated with EGFR expression. Methylation at these sites is lost in drug‐resistant cells, which should enhance EGFR expression. This prediction is validated by the observation that treatment with the demethylating agent 5‐azacytidine results in EGFR upregulation [190]. Of course, this is only one mechanism by which EGFR signaling can be increased, so there are likely other mechanisms at play. Given the importance of epigenetic changes, both as cancer drivers themselves and as mechanisms of resistance, substantial efforts are underway to target epigenetic mechanisms pharmacologically [191].

It is not necessary for epigenetic reprogramming to occur in every cell within a tumor. Melanoma cells can stochastically switch between a “bulk tumor cell” state and a slow cycling “pre‐resistant” state; the latter state is very rare before drug treatment due to overgrowth of the faster proliferating cells [192]. Upon treatment with vemurafenib, which is a BRAFi, cells in the “bulk tumor cell state” are unable to proliferate. However, the cells in the “pre‐resistant” state continue to proliferate and also undergo epigenetic reprogramming, which results in “burn‐in” of a drug‐resistant transcriptional state (Figure 4.8). A factor in this transcriptional new state is Sox10, a transcription factor that controls melanocyte differentiation. High Sox10 activity induces melanocyte‐specific genes such as another transcription factor, MITF, and an enzyme, tyrosinase. Tyrosinase synthesizes melanin and makes melanocytes dark. The “burn‐in” process begins with loss of accessible Sox10 binding sites through epigenetic changes, ultimately resulting in increased active chromatin and more accessible binding sites for the growth‐promoting transcription factors activator protein 1 (AP‐1) and transcriptional enhancer factor domain family member 1 (TEAD1). Interestingly, Sox10 was previously identified as a mediator of vemurafenib resistance in an shRNA screen of chromatin regulators [193].

Subpopulations of pre‐resistant cells are not unique to melanoma. Work from Jeffrey Settelman's group identified a similar subpopulation of resistant cells, or “drug‐tolerant persisters (DTPs),” in NSCLC cells treated with EGFR inhibitors [194]. Fitting with the recurring theme of compensatory RTK activation, these DTPs require signaling by the insulin‐like growth factor receptor (IGF‐1R) for sustained growth and maintenance of an altered chromatin state in the resistant cells. This altered chromatin state is associated with global changes in methylation and acetylation of histones, including changes in di‐ and tri‐methylated histone 3 at lysine 4 (H3K4me² and H3K4me³) and histone 3 acetylation at lysine 14 (H3K14^Ac) [194]. Interestingly, this study also demonstrated that cell surface antigens CD133 and CD44 are upregulated in the DTPs. Both CD133 and CD44 are putative cancer stem cell (CSC) markers. CSCs have been implicated in drug resistance in multiple cancer types including pancreatic cancer, glioblastoma, and breast cancer. DTP cells have also been identified in T‐cell‐derived acute lymphocytic leukemia (T‐ALL). These cells are also driven by an epigenetic mechanism [195]. The take‐home point from these observations is that the presence of small subpopulations of cells that are primed to become drug resistant is a recurring theme in multiple cancer types. These cells, while genetically similar, differ in their epigenetic and transcriptional landscapes. These differences profoundly alter the signaling circuitry within these cells and represent a major challenge to targeted cancer therapies.

4.4 Summary

Determining the mechanisms that may lead to morbidity and mortality requires a comprehensive theoretical understanding of cell signaling as well as experimental studies in models that appropriately match the organism (or organ or cell type) of interest. The role of many different interacting cell types and the tremendous heterogeneity even within a single (e.g. cancer) cell type make it challenging to dissect mechanisms of disease. A proper analogy would be trying to decipher the culprit in a murder mystery with no knowledge of who, when, where, how, or why the crime took place. Dissecting both the players and the roles of the multiple cellular constituents that may lead to disease often requires substantial corroborative results that provide independent identification of involvement. To accomplish this, there are many pharmacological, toxicological, and genetic techniques that can provide discriminatory power to aid in building a case (i.e. testing a hypothesis). The remainder of this book will focus on methods and techniques for collecting data, analyzing data, and drawing conclusions, with a final chapter focused on current needs in the field.

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