Targeting transcription factors in cancer — from undruggable to reality

Mutated or dysregulated transcription factors represent a unique class of drug targets that mediate aberrant gene expression, including blockade of differentiation and cell death gene expression programmes, hallmark properties of cancers. Transcription factor activity is altered in numerous cancer types via various direct mechanisms including chromosomal translocations, gene amplification or deletion, point mutations and alteration of expression, as well as indirectly through non-coding DNA mutations that affect transcription factor binding. Multiple approaches to target transcription factor activity have been demonstrated, preclinically and, in some cases, clinically, including inhibition of transcription factor–cofactor protein–protein interactions, inhibition of transcription factor–DNA binding and modulation of levels of transcription factor activity by altering levels of ubiquitylation and subsequent proteasome degradation or by inhibition of regulators of transcription factor expression. In addition, several new approaches to targeting transcription factors have recently emerged including modulation of auto-inhibition, proteolysis targeting chimaeras (PROTACs), use of cysteine reactive inhibitors, targeting intrinsically disordered regions of transcription factors and combinations of transcription factor inhibitors with kinase inhibitors to block the development of resistance. These innovations in drug development hold great promise to yield agents with unique properties that are likely to impact future cancer treatment.

More than 15 years ago, James E. Darnell authored a Review in this journal titled ‘Transcription factors as targets for cancer therapy’ with a summary that presciently stated “A limited list of transcription factors are overactive in most human cancer cells, which makes them targets for the development of anticancer drugs. That they are the most direct and hopeful targets for treating cancer is proposed, and this is supported by the fact that there are many more human oncogenes in signalling pathways than there are oncogenic transcription factors. But how could specific transcription-factor activity be inhibited?” 1 . In the intervening years, a wealth of literature has validated numerous transcription factor targets in cancer, confirming Darnell’s hypothesis ( TABLE 1 ). Indeed, a review article by Lee and Young 2 identified 33 transcription factors whose dysregulation plays a key role in various types of cancer. It is clear that small-molecule modulation of transcription factor activity has substantial potential, not only for cancer but in other disease settings as well 2 . Importantly, the increased validation of transcription factor targets has been accompanied by a substantial expansion of approaches being explored to modulate transcription factor activity with small molecules and definitive successes have been achieved on this front.

Table 1 |

Examples of transcription factors driving the hallmarks of cancer

AML, acute myeloid leukaemia; APL, acute promyelocytic leukaemia; CBFβ, core binding factor β; DUSP6, dual-specificity phosphatase 6; EMT, epithelial-to-mesenchymal transition; FLT3, FMS-like tyrosine kinase 3; FOXO, forkhead box O; GABP, CA binding protein; GATA3, GATA binding factor 3; HCC, hepatocellular cancer; ITD, internal tandem duplication; KLF8, Krüppel-like factor 8; MLL, mixed lineage leukaemia; NK, natural killer; PDL1, programmed cell death protein 1 ligand 1; PML, promyelocytic leukaemia; RARα, retinoic acid receptor α; RUNX, runt-related transcription factor; SIX1, sine oculis homeobox homologue 1; SMMHC, smooth muscle myosin heavy chain; STAT1, signal transducer and activator of transcription 1; TAL1, T cell acute lymphocytic leukaemia 1; T-ALL, T cell acute lymphoblastic leukaemia; TERT, telomerase reverse transcriptase.

Although the view has shifted substantially, transcription factors were historically viewed as ‘undruggable’. This arose from the challenges associated with targeting either the protein–DNA or protein–protein interactions that mediate their function, as opposed to more tractable active sites of kinases or other enzymes. Indeed, there is a profound paucity of inhibitors of protein–DNA binding, in particular, likely owing to the typically convex and highly positively charged DNA binding interfaces being difficult targets for development of small-molecule inhibitors with drug-like properties. Protein–protein interaction surfaces are typically flatter and do not present the deep pockets present in enzyme active sites, making the development of small-molecule inhibitors more challenging 3 . The recognition that there are a limited number of so-called hotspot residues that contribute the majority of the interaction energy and are typically spatially localized has strongly suggested that the development of protein–protein interaction inhibitors of transcription factors can be tractable 3 . In addition, the demonstration of allosteric modulation of protein–protein interactions 4,5 opens an alternative approach to modulation of these interactions.

Numerous recent successes in targeting of protein–protein interactions, including those involving transcription factors, suggest that such an approach is feasible 3 . For example, inhibitors of the protein–protein interaction between p53 and its negative regulator MDM2 result in reduced proteasome degradation of p53. These inhibitors have shown in vivo activity against numerous cancers. The first inhibitors of this class from Roche were based on the Nutlin scaffold 6–8 , but subsequent compounds from Sanofi, Amgen, Merck, Novartis and Diachi Sankyo have utilized alternative scaffolds 9–11 . Numerous clinical trials are underway with these agents both in solid tumours as well as in haematological cancers 12 (see TABLE 2 for examples). In addition, the recent emergence of drugs targeting proteins involved in epigenetic signalling also supports the notion that alteration of the gene expression programme has the potential to be an effective approach. For example, inhibitors of the bromodomain, an epigenetic reader of histone acetylation, have shown promising results in mouse models of various cancers. Agents from AbbVie, Bayer, Boehringer Ingelheim, Bristol-Myers Squibb, Constellation, Forma, Gilead, GlaxoSmithKline, Incyte and Merck have advanced to clinical trials 13–16 (see TABLE 2 for examples). Inhibitors of the histone H3 lysine 27 (H3K27) methyltransferase enhancer of zeste homologue 2 (EZH2), an epigenetic writer of histone methylation, have also entered clinical trials 17 . However, these agents all affect the expression of large numbers of genes, likely with limited functional commonality, potentially restricting the selectivity of such an approach and possibly increasing the likelihood of dose-limiting toxicity. Directly targeting well-validated transcription factor drivers of disease has the potential to be much more specific in its effects. Therefore, this Review will focus on describing the approaches being explored to develop small-molecule modulators of transcription factors and some of the successes that have been achieved in order to demonstrate feasibility as well as provide paradigms for targeting other transcription factors.

Table 2 |

Examples of transcription factor inhibitors in clinical development or in clinical trials

AML, acute myeloid leukaemia; AR, androgen receptor; BET, bromodomain and extra-terminal; CBFβ, core binding factor β; CDK7, cyclin-dependent kinase 7; ER, oestrogen receptor; IND, investigational new drug; MLL, mixed lineage leukaemia; PROTAC, proteolysis targeting chimaera; RUNX1, runt-related transcription factor 1; SMMHC, smooth muscle myosin heavy chain.

Transcription factors in cancer

The first transcription factors to be identified as drivers of cancer were fusion proteins that arise in various subtypes of leukaemia, including promyelocytic leukaemia protein (PML)–retinoic acid receptor α (RARα), acute myeloid leukaemia 1 (AML1)–ETO (also known as RUNX1–MTG8), TEL–AML1 (also known as ETV6–RUNX1), core binding factor β (CBFβ)–smooth muscle myosin heavy chain (SMMHC; also known as myosin 11) and mixed lineage leukaemia (MLL) fusions 18 . Work over many years established these transcription factor fusions as drivers of disease and early events in the development of the disease, highlighting their potential as targets for therapeutic development. It is well established that these fusion proteins block differentiation, leaving cells in a more stem cell-like state 19–21 . Furthermore, it has been shown that some of these transcription factor fusions alter DNA repair genes 19 , creating a fertile environment for the acquisition of additional mutations that can drive the proliferation as well as the clonal heterogeneity of the disease. As retention of the transcription factor fusion with altered secondary mutations has been observed for transcription factor fusion-positive leukaemia upon relapse 22–24 , it is likely that transcription factor fusion proteins are the initiating genomic alterations in the clonal evolution of leukaemia, making them attractive targets for drug development.

Identification of transcription factor drivers in solid tumours has expanded considerably in recent years. Overexpression of ETS-related gene (ERG) and ETS translocation variant 1 (ETV1), members of the ETS family of transcription factors, occurs via chromosomal translocation events and has clearly been shown to be a driving event in prostate cancer 25–28 . Overexpression of ETV1 is also implicated in gastrointestinal stromal tumours (GIST) 29 as well as in melanoma 30 . In addition, the runt-related transcription factors (RUNX1–RUNX3) and their heterodimerization partner CBFβ are now strongly implicated in numerous epithelial cancers 31–33 .

FIGURE 1 highlights hallmark properties of cancer identified by Hanahan and Weinberg 34,35 that could be modulated by drugs targeting transcription factors. TABLE 1 presents illustrative examples of specific transcription factors that modulate each of the properties highlighted in FIG. 1 . The potential utility of transcription factor targeted drugs to modulate this range of properties, in contrast to the typically limited range of effects of various kinase inhibitors (mainly antiproliferative), strongly suggests these drugs would have unique effects that will be clinically relevant.

Schematic showing possible beneficial outcomes of inhibiting the activity of transcription factor drivers in cancer. EMT, epithelial-to-mesenchymal transition.

Successful targeting

Several previous reviews have described small-molecule modulators of transcription factors 3,36–40 . This Review focuses on the various different approaches that have been used to target transcription factors, with illustrative examples of each.

Targeting nuclear hormone receptor ligand binding domains.

By far the most successful targeting of transcription factors in cancer to date has been by means of small molecules that bind to specific nuclear hormone receptors 41 . Indeed, drugs that modulate the activity of the oestrogen receptor (ER), androgen receptor (AR), RAR and glucocorticoid receptor (GR) are currently used for treatment of breast cancer, prostate cancer, acute promyelocytic leukaemia (APL) and acute lymphoblastic leukaemia (ALL), respectively 41,42 . Nuclear hormone receptors have a DNA binding domain and a ligand binding domain (LBD), of which the latter binds to a small-molecule modulator (hormone) that alters its activity to regulate gene expression 43–45 . Agents targeting nuclear hormone receptors have definitive advantages in terms of binding to a site on the protein where a small molecule already binds, making the development of such agents more tractable.

Approximately 75% of breast cancers are ER-positive, that is, they express the ER 46 . In ER-positive breast cancers, ER signalling has clearly been established as a driver of proliferation, making the ER an important therapeutic target 47 . As illustrated in FIG. 2 , drugs which bind to the ER to modulate its activity come in two forms, selective oestrogen receptor modulators (SERMs) and selective oestrogen receptor degraders (SERDs) 47 . SERMs such as tamoxifen bind to the LBD and block the conformational changes necessary for recruitment of co-activators, leaving the LBD in a conformation that recruits co-repressors instead, resulting in reduced target gene expression 48 . Interestingly, the effects of these drugs can be quite tissue specific, likely owing to the differing repertoire of co-activators and co-repressors expressed in different tissues, thus affording enhanced specificity of action. For example, the SERM tamoxifen is an antagonist in breast cancer cells but shows partial agonist activity in endometrial cancer cells 49,50 . SERDs such as fulvestrant bind to the LBD and facilitate proteasome-mediated degradation of the ER, thus leading to reduced target gene expression. The poor bioavailability of fulvestrant has led to further efforts in SERD development, including the agents AZD9496 and elacestrant 47 .

Schematic showing the regulation of gene expression by the nuclear hormone receptor oestrogen receptor (ER) and by small-molecule modulators of ER function. a | The oestrogen steroid hormone oestradiol binds to the ligand binding domain (LBD) of the ER to induce a conformational change that facilitates co-activator recruitment and activation of gene expression. b | Binding of a selective oestrogen receptor modulator (SERM) to the LBD blocks co-activator recruitment and thereby blocks gene activation. c | Binding of a selective oestrogen receptor degrader (SERD) promotes proteasome-mediated degradation of the ER, thereby blocking gene activation. DBD, DNA binding domain.

Similar to the case of the ER in breast cancer, the AR binds androgens such as testosterone or dihydrotestosterone to drive gene expression 51 . Binding of androgens to the AR releases it from the chaperone heat shock protein 90 (HSP90) so that it can translocate to the nucleus and bind to target genes to regulate expression 51 . Prostate cancer cells are highly dependent on androgens, an effect mediated by binding of androgens to the LBD of the AR to alter gene expression 51 , thus driving their proliferation and survival. Comparable to the action of drugs targeting the ER, drugs targeting the AR LBD to block androgen binding and thereby reduce AR-driven gene expression have been developed. The first generation of such agents included bicalutamide, flutamide and nilutamide 41 . In the case of patients whose disease has progressed to castration-resistant prostate cancer (CRPC), these agents have proven to have limited benefit, so second-generation compounds have been developed 52 . Enzalutamide is a more potent antagonist of androgen binding to the AR and also blocks the ability of the AR to translocate to the nucleus, with the latter effect having been demonstrated to be the primary driver of the therapeutic benefit 53 . Clinical trials of enzalutamide demonstrated robust activity in patients with CRPC and was approved as a first-line treatment option for CRPC in 2014 (REF. 54 ). As described below, proteolysis targeting chimaera (PROTAC) approaches show considerable promise for generating compounds which can mediate proteasomal degradation of the ER as well as the AR.

Targeting essential protein–protein interactions.

Protein–protein interactions of transcription factors with co-activators and co-repressors result in effects on gene expression at specific target sites in the genome. In addition, protein–protein interactions with other transcription factors can lead to cooperative binding and specific localization in the genome. For example, the transcription factors RUNX1 and ETS1 physically interact, leading to cooperative binding to DNA 55–57 and localization to sites of neighbouring RUNX and ETS DNA binding motifs in the genome 58 . There has been an increasing level of success in the development of small-molecule inhibitors of protein–protein interactions 3 .

The transcription factor MLL is modified by chromosomal translocations that fuse it in frame to one of over 90 partners, leading to acute myeloid leukaemia (AML) and ALL, both of which are characterized by poor prognoses 59–62 . Two regions in the MLL portion of these fusions are essential for their ability to induce leukaemia: an N-terminal motif that binds to the co-activators menin and LEDGF (also known as PSIP1) 63,64 , and the CXXC domain that binds specifically to non-methylated CpG motifs in the genome 65 . Based on functional data showing that the menin–MLL interaction is essential for MLL fusion-positive leukaemia 63,64 , Grembecka, Cierpicki and co-workers developed small-molecule protein–protein interaction inhibitors of the menin–MLL fusion protein interaction, including MI-538 and MI-1481 (REFS 66–70 ) ( FIG. 3 ). Importantly, these inhibitors were clearly demonstrated to reduce the occupancy of MLL fusions at target genes as well as reduce expression of key genes that are drivers of MLL fusion-positive leukaemia, including the genes encoding the homeobox transcription factors HOXA9 and MEIS1, establishing a well-validated mechanism of action for the compounds 66,69 . These inhibitors were also shown to increase both differentiation and apoptosis of MLL fusion-positive leukaemia cells 66,69 . Subsequently, the group of Grembecka and Cierpicki further optimized this class of compounds for increased potency as well as for absorption, distribution, metabolism, excretion, toxicity (ADMET) properties to develop orally bioavailable derivatives with efficacy in mouse models of MLL fusion protein-driven leukaemia 68 . Because hotspots on a binding epitope may cover a substantial area on the protein, it is frequently the case that small-molecule inhibitors of protein–protein interactions are of higher molecular weight than the average values observed for other orally bioavailable drugs. This can lead to ADMET challenges for these inhibitors. Grembecka and Cierpicki pointed out these challenges associated with optimization of the typically larger molecules, which are necessary to disrupt protein–protein interactions but also provided a demonstrable example of success in this regard. In this case, substantial medicinal chemistry optimization that focused on the ADMET properties led to considerable improvement. This class of compounds has been licensed to Kura Oncology and the investigational new drug (IND) application has been approved by the US Food and Drug Administration (FDA) ( TABLE 2 ). Phase I trials are planned for 2019. In addition, Syndax Pharmaceuticals has developed a menin–MLL inhibitor which is progressing towards clinical testing ( TABLE 2 ).

a | Core binding factor β (CBFβ) is a component of the CBF family of transcription factors that heterodimerizes with the runt-related transcription factor (RUNX) family of DNA binding proteins. CBFβ binding to RUNX1 enhances its binding to DNA and protects it from degradation. The CBFβ–smooth muscle myosin heavy chain (SMMHC) fusion protein associated with acute myeloid leukaemia retains the ability to bind RUNX1 through the CBFβ containing N-terminal portion of the fusion protein. The protein–protein interaction inhibitor AI-10-49 binds selectively to the CBFβ–SMMHC fusion to release RUNX1 and allow it to bind to its cognate sites in the genome and alter the gene expression programme. The inhibitor has two CBFβ binding moieties to make a bivalent interaction with the oligomeric CBFβ–SMMHC and achieve specificity for CBFβ–SMMHC versus wild-type CBFβ, which is monomeric in solution. b | MI-538 and subsequent derivatives bind to menin, a transcriptional co-activator that retains the ability to bind to the mixed lineage leukaemia (MLL) portion of MLL fusion proteins that arise in leukaemia (such as MLL–AF9, MLL–ENL and MLL–AF4). As a consequence, these protein–protein interaction inhibitors disrupt the binding of MLL fusions to specific sites in the genome, reducing expression of key MLL fusion target genes. CEBPA, CCAAT/enhancer binding protein α; CSF1R, colony-stimulating factor 1 receptor.

Two of the genes encoding the heterodimeric transcription factor CBF, composed of RUNX (RUNX1, RUNX2 or RUNX3) and CBFβ subunits, are frequent targets of mutations in human leukaemia. The RUNX1 gene is disrupted by various chromosomal translocations and point mutations 18 . The CBFB gene is disrupted in ~10% of patients with AML by the inversion of chromosome 16 (inv(16)(p13q22)), and less frequently by the variant t(16;16)(p13q22), with both translocations always observed in the M4Eo subtype of AML 71 . This inversion breaks and joins the CBFB and myosin 11 (MYH11) genes, encoding the fusion protein CBFβ–SMMHC 18 . Heterozygous knock-in mice for Cbfb–Myh11 lack definitive haematopoiesis, a similar phenotype to that seen for the complete knockout of Runx1 or Cbfb 72 . The CBFβ–SMMHC fusion protein acts as a dominant repressor of CBF function, binding RUNX1 through the CBFβ and SMMHC portions of the fusion protein and dysregulating the expression of multiple genes required for normal haematopoiesis 73 . My colleagues and I developed an inhibitor (AI-10-49) that disrupts the protein–protein interaction between CBFβ–SMMHC and RUNX1 via binding to the CBFβ portion of the fusion protein 74 ( FIG. 3 ). This compound is potent and shows excellent specificity in inhibiting the binding of CBFβ–SMMHC to RUNX1 but not inhibiting the binding of wild-type CBFβ to RUNX1. Furthermore, we have shown that this compound restores expression of RUNX1 target genes by restoring RUNX1 occupancy on these genes, thus establishing a clear mechanism of action ( FIG. 3 ). This inhibitor increases survival in a genetically engineered mouse model of inv(16)-positive leukaemia, which combines Cbfb–Myh11 and Nras G12D alleles 74 . In addition, we have shown that AI-10-49 increases apoptosis of human primary inv(16)-positive leukaemia cells as well as decreasing their colony-forming ability 74 . More recently, in collaborative work with Castilla and co-workers, we have shown that treatment of inv(16)-positive AML cells (an ME-1 leukaemia cell line) with this inhibitor increases RUNX1 occupancy across the genome and decreases MYC expression 10-fold via RUNX1 binding to a specific set of enhancer elements and mediating a switch from activating (SWI/SNF complex) to repressive (polycomb repressive complex (PRC)) chromatin complexes at these enhancers 75 . As expected based on the context-dependent functions of RUNX1, we observed 591 genes upregulated greater than two fold and 696 genes downregulated less than two fold after 6 h of treatment with AI-10-49. Included among these are the well-validated targets of repression by CBFβ–SMMHC, such as RUNX3, colony-stimulating factor 1 receptor (CSF1R) and CCAAT/enhancer binding protein α (CEBPA), all of which are upregulated by AI-10-49 treatment. This compound has been licensed to Systems Oncology for clinical development.

As mentioned above, the wild-type CBFβ and RUNX transcription factor family has recently been implicated in an expanding array of epithelial cancers. The CBFβ subunit modulates the activity of RUNX proteins (RUNX1, RUNX2 and RUNX3) by relieving autoinhibition and thereby increasing binding to DNA 55 . My colleagues and I have also developed and characterized small-molecule inhibitors of the protein–protein interaction between wild-type CBFβ and RUNX proteins 5 . These compounds bind to CBFβ and are derived from the AI-10-49 scaffold described above but are monovalent, rather than bivalent, that is, there is only one CBFβ binding scaffold rather than two. Using human cell lines and engineered mouse cells, we have shown that these inhibitors disrupt the protein–protein interaction between wild-type CBFβ and RUNX proteins 5,76,77 , reduce occupancy of RUNX1 on target genes and reduce expression of those genes that are activated by RUNX1 binding 5 . These inhibitors act via an allosteric mechanism to alter the conformational dynamics of CBFβ and thereby reduce binding. Specifically, we showed using nuclear magnetic resonance (NMR) spectroscopy that the mobility of amino acids on the RUNX binding interface on CBFβ, which are in a spatially distinct location from where the inhibitor binds, was altered in a manner that would lead to decreased binding of RUNX to CBFβ. Such allosteric effectors of protein–protein interactions have a distinct advantage in that they do not need to compete for binding with the endogenous partner protein. My colleagues and I propose that such allosteric inhibition of protein–protein interactions of transcription factors may be applicable to many other proteins. The CBFβ–RUNX inhibitors show potent inhibition of the in vitro growth of T cell acute lymphoblastic leukaemia (T-ALL) cells, in which RUNX1 is part of a critical transcription factor autoregulatory loop ( TABLE 1 ) that drives the disease 76 . The transcription factors TAL1, RUNX1 and GATA2 co-occupy regulatory elements for their own and each other’s genes, forming a positive autoregulatory loop that is essential for T-ALL initiation and maintenance. The efficacy of the CBFβ inhibitor in this context highlights the potential for disruption of these circuits in cancer. Busino and co-workers have shown these inhibitors to be synergistic with lenalidomide (an immunomodulatory imide drug and a derivative of thalidomide used in the treatment of multiple myeloma, amongst other haematological malignancies) in inhibiting the growth of multiple myeloma cells, an effect driven by disruption of binding between the Ikaros transcription factors IKZF1 and IKZF3 and RUNX, which enhanced lenalidomide-mediated proteasomal destruction of IKZFs 77 . Furthermore, Bhalla and co-workers have shown that these inhibitors preferentially inhibit the in vitro growth of mutant RUNX1-expressing leukaemic cells, another subtype of AML lacking an effective targeted therapy 78 . In the context of epithelial cancers, my colleagues and I have shown that these inhibitors completely block colony formation of basal-like triple-negative breast cancer cells 5 , in which there is overexpression of RUNX2 (REFS 79,80 ). Similarly, in vitro treatment of ovarian cancer cell lines, which have considerable gene expression similarity to basal-like breast cancer 81,82 , showed complete inhibition of colony formation and alteration of expression of genes related to the epithelial-to-mesenchymal transition (EMT) 83 .

Modulating proteasomal degradation of transcription factors.

Because transcription factors regulate large numbers of genes and thereby play key roles in cellular decisions about differentiation, senescence or death, their protein levels are carefully regulated in the cell via ubiquitylation and proteasomal degradation 84 . In this regard, it is important to note that heterozygous knockouts of numerous transcription factors in mice display clear developmental and/or cancer-promoting phenotypes (for example, RUNX1, p53, ERG and SMAD4 (REFS 85–88 )), so even a two fold reduction in the protein level can have substantial effects. The proteasome pathway has provided a unique approach to modulate the activity of transcription factors, namely, via modulation of the interactions of transcription factors with the proteins that mediate ubiquitylation and deubiquitylation ( FIG. 4 ).

a | Enhanced transcription factor (TF) degradation by small-molecule-promoted E3 ubiquitin ligase binding, leading to ubiquitylation and proteasome-dependent destruction of the protein of interest. For example, thalidomide and its derivatives that inhibit the growth of multiple myeloma cells do so by binding to a specific pocket in the E3 ubiquitin ligase cereblon (CRBN), enhancing CRBN interaction with the Ikaros family proteins IKZF1 and IKZF3, key drivers of multiple myeloma, and enhancing their ubiquitylation. b | Enhanced TF degradation by small-molecule inhibition of a deubiquitinase (DUB) specific for that TF, leading to higher levels of ubiquitylation and enhanced proteasomal degradation. For example, the DUB ubiquitin-specific-processing protease 9X (USP9X) deubiquitylates the transcription factor ETS-related gene (ERG), a critical driver of prostate cancer. Treatment with the USP9X inhibitor WP1130 leads to enhanced ubiquitylation and proteasomal destruction of ERG. c | Reduced TF degradation by small-molecule disruption of E3 ubiquitin ligase binding, leading to reduced ubiquitylation and reduced proteasomal degradation. For transcription factors that act as tumour suppressors, increasing their protein level via this approach could have therapeutic value. Ub, ubiquitin; VHL, von-Hippel Lindau.

E3 ubiquitin ligases (shortened to E3 ligases hereafter) interact with substrate proteins to drive ubiquitylation and proteasomal degradation 89–91 . Modulation of these interactions via small molecules has the potential to provide an approach to change protein levels in a cell. Very few E3 ligases have been successfully targeted, but one successful example involves the E3 ligase von-Hippel Lindau (VHL) and the transcription factor hypoxia-inducible factor 1α (HIF1α) 92,93 . Under normoxic conditions, HIF1α is hydroxylated on proline, leading to binding to VHL, which mediates ubiquitylation and subsequent degradation of HIF1α by the proteasome. This keeps the level of HIF1α low under normoxic conditions. Under hypoxic conditions, the levels of HIF1α go up due to lower levels of hydroxylation, leading to reduced ubiquitylation and proteasomal degradation 94 . Crews and co-workers developed small-molecule inhibitors of the VHL–HIF1α interaction that bind to VHL 92,93 and have found utility in the PROTAC approach to mediating degradation of target proteins described below.

Another example of manipulation of the interaction of an E3 ligase and a transcription factor involves the use of thalidomide and its derivatives in the treatment of multiple myeloma. The mechanism of action of thalidomide in this disease was unclear until it was shown to enhance the binding of the E3 ligase cereblon (CRBN) to the Ikaros transcription factors IKZF1 and IKZF3, key drivers in multiple myeloma 95 . This results in proteasomal degradation of the IKZF proteins. This mechanism of action can have distinct pharmacodynamic advantages as the proteasomal degradation of the target protein requires the cell to synthesize new protein to recover activity, which takes time and therefore can lead to more durable inhibition and reduced dosing needs. This suggests the possibility that activators of binding of substrate proteins to one of the ~400 E3 ligases in humans may provide an attractive approach for pharmacological manipulation of transcription factors. These studies have also led to the use of thalidomide and its derivatives in the PROTAC approach to target proteins for proteasomal degradation described below.

In addition to enhancement of E3 ligase activity, inhibition of deubiquitinases (DUBs) can result in enhanced polyubiquitylation and degradation of transcription factors. The DUB ubiquitin-specific-processing protease 10 (USP10) protects SLUG and SNAI2, key transcription factor drivers of EMT, from degradation 96 . DUB3 binds, deubiquitylates and increases cellular levels of the EMT transcription factor SNAIL1 (REF. 97 ) and also protects SLUG and TWIST 98 . Levels of MYC, a critical transcription factor driver in many cancers, are enhanced by USP22 (REF. 99 ). Thus, pharmacological agents to inhibit DUBs have the potential to reduce the levels of transcription factors that are required for cancer growth and metastasis. The ETS family transcription factors ERG and ETV1 have been shown to be the targets of chromosomal translocations with transmembrane protease serine 2 (TMPRSS2), which are observed in 80% of primary prostate cancer samples from patients 27 . The expression of TMPRSS2 is androgen-regulated, resulting in overexpression of ERG or ETV1 in these prostate cancers 27,100–102 . The protein level of ERG is regulated by ubiquitylation, with TRIM25 acting as the E3 ligase for ERG and USP9X acting as the DUB 103,104 . One report on the use of the small-molecule USP9X inhibitor WP1130 showed decreased levels of ERG in the human prostate cancer cell line VCaP in vitro and reduced tumour volume in a mouse xenograft using VCaP cells 103 , providing support for this approach to targeting transcription factor activity. Although quite encouraging, the molecule WP1130 should give some pause. This compound has two reactive moieties, a Michael acceptor as well as a 2-bromo-pyridine moiety, both of which can covalently react with proteins. This reactivity could make WP1130 a promiscuous molecule, which clouds the interpretation of the observed effects. Consistent with this, WP1130 is known to be active against several DUBs 105 . More importantly, as this is potentially a quite reactive molecule, the lack of a proteome-wide evaluation of WP1130 targets makes it difficult to assign these observed activities to a particular target.

The transcription factor p53, the most frequently altered protein in human cancer 106 , is a critical tumour suppressor that regulates pathways involved in cell-cycle control, apoptosis, DNA repair and senescence 107–109 . Under normal conditions, the level of p53 is kept low via binding to MDM2, which acts as an E3 ligase to mediate ubiquitylation and subsequent proteasomal degradation of p53 (REFS 110,111 ). MDM2 and p53 are involved in an autoregulatory circuit in which increased p53 increases expression of MDM2 (REF. 112 ). Amplification or overexpression of MDM2 has been observed in many cancers 113 . Vassilev and co-workers at Roche first reported the development of inhibitors of the protein–protein interaction between MDM2 and p53, termed Nutlins as described above 8 . It was shown that these inhibitors do indeed increase the protein level of p53 as well as induce expression of target genes such as p21 (REFS 6,8 ). They were also shown to reduce the viability of wild-type p53-expressing cancer cell lines but not mutant p53-expressing cancer cell lines, as the mutant forms of p53 cannot bind DNA to drive the necessary changes in gene expression to induce cell death 6 . Nutlins were also shown to have in vivo efficacy in mouse xenograft models of wild-type p53-expressing osteosarcoma and prostate cancer 6 . Second-generation and third-generation derivatives of these were then developed with improved potency and ADMET properties 6,7 . Currently, several inhibitors of the MDM2–p53 protein–protein interaction are in clinical trials 9–11,114 ( TABLE 2 ).

Degrading transcription factors with PROTACs.

The elegant work by Crews and co-workers 115,116 and Bradner and co-workers 117 showing that bifunctional molecules, in which a ligand that binds to an E3 ligase is covalently attached to a ligand that binds to a specific protein, can drive ubiquitylation and subsequent proteasome-mediated destruction of the bound protein provides a chemical biology-based approach to achieve knockdown of specific proteins in cells ( FIG. 5 ). These molecules have been termed PROTACs. The knockdown mediated by PROTACs has the advantage of abrogating all functions of the target protein rather than just the activity targeted by a specific small-molecule inhibitor. Strikingly, this approach is also catalytic, allowing a single molecule to mediate destruction of multiple target proteins via repeated cycles of binding and degradation 116 , unlike traditional pharmacological approaches. In addition, as mentioned above, such a knockdown approach has the advantage that recovery from these agents will require the cell to synthesize new protein, which takes time. Based on these properties of PROTACs, it is likely that less frequent dosing will be required for efficacy and that compounds with shorter half-lives in vivo may still be efficacious. Indeed, Bradner and co-workers showed with short exposure times that their thalidomide-coupled bromodomain-containing protein 4 (BRD4) inhibitor was much more effective than the parent BRD4 inhibitor 117 . Similarly, Crews and co-workers have also created PROTACs based on existing inhibitors and demonstrated enhanced activity of PROTAC derivatives of receptor tyrosine kinase (RTK) inhibitors targeting epidermal growth factor receptor (EGFR; lapatinib, gefitinib), human epidermal growth factor receptor 2 (HER2; lapatinib) and MET (foretinib) relative to the parent inhibitors 118 . PROTAC development thus far has focused on the use of VHL ligands and thalidomide derivatives as the E3 ligase recruiting ligands. The development of additional E3 ligase recruiting ligands, including those that bind to MDM2 and cellular inhibitor of apoptosis (cIAP), has expanded the repertoire of possibilities for PROTAC development as well as improving the potential for achieving a high degree of specificity of action 119 . Demonstration of this approach for transcription factor targets has recently been made with reports by Arvinas of orally bioavailable PROTAC degraders of the AR and the ER, which have now entered clinical trials ( TABLE 2 ). This approach requires the covalent linking of a target protein binding ligand and an E3 ligase recruiting ligand, resulting in larger molecules with more challenging ADMET properties. The recently initiated clinical trials of the Arvinas PROTAC-based ER and AR drugs will be highly informative regarding the potential clinical applicability of this novel and exciting approach. With other transcription factor targets, it will be necessary to identify ligands that bind to the transcription factor with sufficient affinity to apply this approach, which will represent an important area for future efforts. Importantly, these ligands need not act on any specific function of the transcription factor. They only need to bind to the protein, possibly making it easier to identify and develop such binder ligands.

This schematic illustrates the mode of action of a proteolysis targeting chimaera (PROTAC) targeting the epigenetic reader bromodomain-containing protein 4 (BRD4). PROTACs are bifunctional small molecules that interact with the protein of interest while simultaneously engaging an E3 ubiquitin ligase, effectively hijacking the cellular protein quality control machinery to selectively degrade the target protein. The PROTAC shown contains a ligand derived from thalidomide (blue square) for recruiting the E3 ubiquitin ligase cereblon (CRBN), a linker and another ligand (green circle) to bind to the bromodomain of BRD4. Once the BRD4–PROTAC–E3 ubiquitin ligase complex is formed, E2 ubiquitin-conjugating enzymes transfer ubiquitin (Ub) to lysine residues on the surface of BRD4. Consequently, the recognition of the lysine polyubiquitylation signal by the proteasome facilitates the degradation of BRD4. As the PROTAC can bind to one BRD4 protein and mediate its ubiquitylation, and then dissociate and bind to another BRD4 protein, the small molecule acts as a catalyst for ubiquitylation of BRD4 (indicated by the dashed arrow).

Modulating expression of transcription factor drivers.

Targeting regulators of the expression of transcription factor drivers provides another potential avenue for modulation of their activity. Cyclin-dependent kinase 7 (CDK7), a component of the general transcription factor II H (TFIIH), phosphorylates the C-terminal domain (CTD) of RNA polymerase II (RNAPII) to regulate RNAPII initiation and pausing 120–122 . Gray and co-workers developed a Cys reactive covalent inhibitor of CDK7 (and also of CDK12) named THZ1 that is potent and effective at reducing CDK7-regulated gene expression 123 . This inhibitor potently inhibited the proliferation of T-ALL cell lines in culture as well as KOPTK1 T-ALL cells in a mouse xenograft model 123 . Gene expression analysis showed, as expected, a global reduction in overall transcription. However, very specific reductions in certain genes at lower doses were observed, indicating differential sensitivity of genes to CDK7 inhibition. In particular, RUNX1 was among the most downregulated genes in T-ALL cells upon low-dose THZ1 treatment. As noted above ( TABLE 1 ), T-ALL cells form an autoregulatory circuit involving the transcription factors RUNX1, TAL1 and GATA3, which drives the disease. Notably, the expression of all three of these genes was reduced in T-ALL cells after THZ1 treatment at lower doses, lending support to the concept that targeting individual transcription factors in autoregulatory circuits will disrupt the entire circuit. It was suggested that the preferential sensitivity of RUNX1 expression to low-dose THZ1 may result from the very large super-enhancer regulating its expression in T-ALL cells, which is likely to be particularly susceptible to disruption 123 . THZ1 has also been shown to be effective in preclinical models for various other cancers including neuroblastoma, Ewing sarcoma, lymphoma and small-cell lung cancer 124,125 . Syros has subsequently developed an orally bioavailable CDK7 inhibitor, which is currently in clinical trials ( TABLE 2 ).

Initial efforts to target proteins involved in epigenetic signalling focused on the enzymes involved in writing or erasing specific epigenetic marks (that is, acetyl transferases, deacetylases, methyltransferases and demethylases) 126–128 . Being enzymes with active sites, these were deemed likely to be druggable targets. The successful development of small-molecule inhibitors of various histone deacetylases, the histone methylation writers EZH2 and DOT1L, and the histone methylation eraser lysine-specific demethylase 1 (LSD1) has shown that these are indeed druggable targets 128 . More recently, efforts to target the epigenetic readers of such marks (binding proteins without enzymatic activity) have also been successful. The group of Bradner, as well as others, targeted the bromodomain and extra-terminal (BET) protein family of epigenetic readers whose bromodomains bind to acetylated lysine residues on histones and transcription factors 129 . Specific transcription factors, acting both directly and indirectly, regulate the recruitment of BET family proteins to sites in the genome 130–133 . Bradner and co-workers developed a potent (K_d = 50 nM), selective (inhibits 7 of 36 bromodomains tested) and effective small-molecule inhibitor of the bromodomains of BRD4 (and other BET subfamily members) 13 . This was tested in vivo in a mouse model of NUT-midline carcinoma, which is driven by a fusion protein between NUT and BRD4, and was shown to increase survival 13 . Subsequent studies of this inhibitor in several other preclinical cancer contexts have shown a wide array of possible applications 134 . This likely results from the demonstrated ability of this inhibitor to lower levels of MYC expression 135,136, which has been shown to be regulated by BET subfamily members. As MYC is a key transcription factor that is upregulated in numerous cancers, this provides a mechanism to alter the MYC-driven gene expression programme without directly targeting MYC, which has proven challenging 137,138 but has presented some encouraging recent results 139,140 . Based on these studies, multiple companies have developed clinically effective BET protein bromodomain inhibitors, which have entered clinical trials 128,134,141 (two examples provided in TABLE 2 ).

Disrupting transcription factor–DNA binding with DNA binding compounds.

There is a long history of efforts to develop small molecules that bind specifically to DNA to inhibit transcription factor activity 142–144 . Much of this work derives from early efforts by Dervan and co-workers to develop polyamide-based DNA minor groove binding compounds 145–147 . By making interactions with a series of bases in the minor groove, these compounds achieve specificity for specific DNA sequences 32,145–148 . As no molecule of this class has progressed to clinical trials for cancer, their clinical utility remains unclear at this time. Recent examples of this approach include efforts to target RUNX–DNA binding and binding of the ETS family member PU. 1 to DNA 32,148 . In the case of the agents targeting RUNX binding, a polyamide coupled to a reactive nitrogen mustard chlorambucil moiety was used for the studies 32 . With the use of such a DNA alkylating agent as part of the compound, it is challenging to assess how much of the observed effects are driven by inhibition of RUNX binding and how much are based on DNA damage effects. However, effects on expression of RUNX-regulated genes are observed that are consistent with the mechanism of action 32 . Encouragingly, significant increases in survival were observed with this agent in mouse xenograft models of AML (using the MV4-11 cell line), ALL (using the SU/SR cell line) and non-small-cell lung cancer (NSCLC) (using the A549 cell line) 32 . In addition, a significant decrease in tumour volume was observed with a mouse xenograft model of gastric cancer (using the MKN45 cell line) 32 .

Future directions

All of the above illustrative examples highlight the considerable progress that has been made in targeting transcription factor activity in cancer. Furthermore, these successful efforts have largely debunked the previous widely held view that transcription factors are undruggable. With this in mind, it is worthwhile looking forward to additional approaches that could be implemented in the future to target transcription factors in cancer. To that end, several potential approaches are highlighted below.

Targeting the auto-inhibited state of transcription factors.

There is a profound paucity of small-molecule inhibitors that bind to a protein to inhibit protein–DNA binding. This almost certainly derives from the substantial challenges associated with developing drug-like molecules that can bind with specificity and potency to the highly positively charged and typically convex DNA binding interfaces found on transcription factors. Auto-inhibition is a common property of many proteins, in which regions outside a functional domain (catalytic domain, DNA binding domain, protein binding domain and so forth) bind to the functional domain to inhibit its activity. This process is often regulated by post-translational modifications (PTMs) or protein–protein interactions. A recent successful effort to target a member of another class of ‘undruggable’ targets, phosphatases, may provide an approach for targeting transcription factors as well. Efforts to develop small-molecule inhibitors targeting phosphatase active sites have yielded relatively little progress, largely due to the positively charged nature of the active site that is complementary to the negatively charged (phosphorylated) substrates. To target the phosphatase SHP2, a group at Novartis instead screened for compounds that could stabilize the auto-inhibited state of the protein, optimized the activity of the initial hit and showed the structural basis for the stabilization of the auto-inhibited state 149 . As auto-inhibition is a common property of many transcription factors 150–152 , this concept of stabilizing the auto-inhibited state has the potential to have broad utility. In the context of families of transcription factors, which typically possess a highly conserved DNA binding domain present in all family members, this approach has a distinct advantage in terms of specificity. Namely, the sequences of the elements mediating auto-inhibition typically differ among family members, so targeting small molecules to these sites has the potential to achieve specificity for a specific transcription factor within a family of closely related proteins. Furthermore, these regions comprise a distribution of amino acids that more closely resembles that seen on the surfaces of other proteins, unlike the highly positively charged DNA binding interfaces, so the likelihood of finding drug-like molecules that can bind to these regions is much higher.

Applying Cys reactive strategies with transcription factor targets.

Covalent reaction with their targets, particularly with Cys residues, is a known mechanism for many drugs; however, there is some risk associated with this approach due to the potential for off-target effects. The recent development of chemoproteomics approaches to profile the reactivity of such molecules across the entire proteome provides a powerful approach to assess the specificity of covalent drugs (see, for example, REF. 153 ). The recent FDA approval of the Cys targeted irreversible kinase inhibitors afatinib (targeting EGFR) and ibrutinib (targeting Bruton tyrosine kinase (BTK)) for NSCLC and chronic lymphocytic leukaemia, respectively, highlights the potential for this approach 154–156 . In addition, the potent and selective inhibitor of CDK7 mentioned above used the same Cys reactive targeting approach 123 . Covalent addition to the protein increases the potency of the inhibitor and, perhaps more importantly, the duration of inhibition, as recovery of activity requires synthesis of new protein. Cys residues located at DNA binding interfaces are critical targets of redox signalling by way of reactive oxygen species (ROS) and reactive nitrogen species (RNS) 157 . Indeed, there are numerous transcription factors whose DNA binding, as well as other cofactor interactions, has been shown to be regulated in this manner, including AP-1, nuclear factor-κB (NF-κB), HIF, nuclear factor erythroid 2-related factor 2 (NRF2), p53 and RUNX1 (REFS 157,158 ). Cys residues such as these, which often display a reduced pK_a to increase their reactivity and therefore sensitivity to redox signalling, represent potential targets for development of molecules which can covalently react. In addition, other Cys residues on transcription factor targets could present opportunities as they can be used as anchor points enabling a small-molecule inhibitor to have a longer duration of action due to covalent attachment. The challenge in both cases will be to ensure the development of agents with a high degree of specificity in their covalent reactivity to avoid off-target and/or toxicity effects.

Targeting activity by modulation of post-translational modifications.

PTMs of transcription factors, which modulate activity, is an area which we have only just begun to investigate. Modifications which can affect activity include phosphorylation of serine (Ser) and/or threonine (Thr) or tyrosine (Tyr), methylation of lysine (Lys) or arginine (Arg) and acetylation of Lys, in addition to ubiquitylation, sumoylation and ADP ribosylation. Indeed, a recent proteomics study of p53 identified 222 such PTMs involving 99 residues on p53 (REF 159 ). Similarly, RUNX1 has been shown to be regulated by phosphorylation at Ser, Thr and Tyr, methylation on Arg and acetylation on Lys 160 . These modifications can alter DNA binding activity as well as cofactor interactions. As enzymes mediate the ‘writing’ and ‘erasing’ of these marks, these modifications represent highly tractable targets for modulation of transcription factor activity.

Targeting of intrinsically disordered regions of transcription factors.

Transcription factors have been shown to frequently contain large regions of intrinsic disorder, that is, regions that do not form a stable 3D structure 161–163 . Indeed, 79% of cancer-associated proteins contain an intrinsically disordered region of 30 amino acids or more 164 . These intrinsically disordered regions are unstructured on their own, but often become structured upon interaction with binding partners, a process referred to as coupled folding and binding (see, for example, REFS 165–167 ). These intrinsically disordered regions frequently mediate interactions of transcription factors with cofactors. For example, the AF9 portion of the leukaemia-inducing transcription factor fusion MLL–AF9 is an intrinsically disordered region that mediates binding to AF4, DOT1L, BCL6 co-repressor (BCOR) and chromobox protein homologue 8 (CBX8), with the interactions with AF4 and DOT1L shown to be critical for MLL–AF9-driven leukaemia 166–168 . Traditional views of pharmacology and protein–small molecule interaction would tend to rule out such intrinsically disordered regions as targets due to their lack of structure and the concomitant lack of binding pockets for small molecules to interact. However, a recent analysis based on the structures of intrinsically disordered proteins when folded and bound to their partners suggests they actually have a higher proportion of potential cavities where a small molecule could bind than their folded counterparts 169 . In addition, their inherent flexibility may allow them to adapt conformation in a manner which can more easily adjust to complement a small molecule. This suggests that exploration of the druggability of these regions, although likely to have inherent challenges, is highly worthwhile. Furthermore, success in this regard would open up a large proportion of the proteome to small-molecule modulation, thereby offering a substantial increase in the number of potential targets. Indeed, there have been some efforts in this area including inhibitors targeting the interaction of MYC with its cofactor MAX 170,171 , EWS–FLI1 transcription factor fusion 172 , TFIID 173 and the AF9–AF4 protein–protein interaction within the super elongation complex (SEC) 174 . Among the substantial challenges that arise with these types of targets are definitive verification of binding to the target as well as the approach to employ for compound optimization. Validation of specific binding to a target by NMR or X-ray crystallography is a critical component of any inhibitor development programme. X-ray crystallography is not an option for intrinsically disordered proteins due to the inability to crystallize such disordered regions. NMR data, although more challenging with these systems, will be an important criterion to evaluate inhibitors of this class. As these regions are dynamic and there is unlikely to be detailed information on the binding mode, the optimization of compounds of this class will likely rely on more traditional medicinal chemistry approaches, with the confounding effects of the dynamic nature of the target perhaps making it more challenging to delineate structure–activity relationships. Indeed, a recent molecular dynamics study suggested small molecules that interact with such regions may be viewed as a ‘ligand cloud binding to a protein cloud’ 175 . Nevertheless, the opportunity to open up a large additional landscape for inhibitor development clearly justifies additional efforts in this area.

Concluding remarks

It is clear from the success stories targeting transcription factors described in this Review that Darnell’s prescient enthusiasm with regard to the targeting of transcription factors is now being reduced to practice. The results thus far are highly encouraging and we have only just begun the exploration in terms of targets. Perhaps more importantly, there are numerous novel approaches currently being tested that could substantially improve our ability to modulate this important class of proteins. There is also considerable potential to utilize transcription factor targeted agents in combination with kinase inhibitors to overcome the frequently observed development of resistance with kinase inhibitors as single agents 176–179 . Finally, it is certainly plausible that molecules that can activate transcription factor activity could be developed, making it possible to increase the activity of tumour-suppressor transcription factors that are reduced in expression and/or activity in specific cancers.

Acknowledgements

The author thanks the many outstanding trainees in the laboratory over the years who facilitated the work and whose many stimulating discussions guided the composition of this Review, and D. Brautigan at University of Virginia, USA, for helping produce a readable meaningful scientific review. Apologies to those whose important contributions have not been highlighted owing to space limitations.

Glossary

Footnotes

J.H.B. has a licensing agreement with Systems Oncology for the CBFβ–SMMHC inhibitor AI-10-49 (LeuSO).

References

1. Darnell JE Jr. Transcription factors as targets for cancer therapy . Nat. Rev Cancer 2 , 740–749 (2002). [PubMed] [Google Scholar]

2. Lee TI & Young RA Transcriptional regulation and its misregulation in disease . Cell 152 , 1237–1251 (2013). [PMC free article] [PubMed] [Google Scholar]

3. Arkin MR, Tang Y & Wells JA Small-molecule inhibitors of protein–protein interactions: progressing toward the reality . Chem. Biol 21 , 1102–1114 (2014). [PMC free article] [PubMed] [Google Scholar] This comprehensive review illustrates the extraordinary progress that has been made in the development of protein–protein interaction inhibitors for use in a wide variety of disease settings.

4. Silvian LF et al. Small molecule inhibition of the TNF family cytokine CD40 ligand through a subunit fracture mechanism . ACS Chem. Biol 6 , 636–647 (2011). [PMC free article] [PubMed] [Google Scholar]

5. Illendula A et al. Small molecule inhibitor of CBFβ–RUNX binding for RUNX transcription factor driven cancers . EBioMedicine 8 , 117–131 (2016). [PMC free article] [PubMed] [Google Scholar]

6. Tovar C et al. MDM2 small-molecule antagonist RG7112 activates p53 signaling and regresses human tumors in preclinical cancer models . Cancer Res . 73 , 2587–2597 (2013). [PubMed] [Google Scholar]

7. Ding Q et al. Discovery of RG7388, a potent and selective p53–MDM2 inhibitor in clinical development . J. Med. Chem 56 , 5979–5983 (2013). [PubMed] [Google Scholar]

8. Vassilev LT et al. In vivo activation of the p53 pathway by small-molecule antagonists of MDM2 . Science 303 , 844–848 (2004). [PubMed] [Google Scholar] This paper describes the development and evaluation of the first MDM2–p53 inhibitor of the Nutlin class.

9. Zhang Z et al. Discovery of potent and selective spiroindolinone MDM2 inhibitor, RO8994, for cancer therapy . Bioorg. Med. Chem 22 , 4001–4009 (2014). [PubMed] [Google Scholar]

10. Zhao Y et al. A potent small-molecule inhibitor of the MDM2–p53 interaction (MI-888) achieved complete and durable tumor regression in mice . J. Med. Chem 56 , 5553–5561 (2013). [PMC free article] [PubMed] [Google Scholar]

11. Miyazaki M et al. Lead optimization of novel p53–MDM2 interaction inhibitors possessing dihydroimidazothiazole scaffold . Bioorg. Med. Chem. Lett 23 , 728–732 (2013). [PubMed] [Google Scholar]

12. Tisato V, Voltan R, Gonelli A, Secchiero P & Zauli G MDM2/X inhibitors under clinical evaluation: perspectives for the management of hematological malignancies and pediatric cancer . J. Hematol. Oncol 10 , 133 (2017). [PMC free article] [PubMed] [Google Scholar]

13. Filippakopoulos P et al. Selective inhibition of BET bromodomains . Nature 468 , 1067–1073 (2010). [PMC free article] [PubMed] [Google Scholar]

14. Gehling VS et al. Discovery, design, ond Optimization of isoxazole azepine BET inhibitors . ACS Med. Chem. Lett 4 , 835–840 (2013). [PMC free article] [PubMed] [Google Scholar]

15. Mirguet O et al. Discovery of epigenetic regulator I-BET762: lead optimization to afford a clinical candidate inhibitor of the BET bromodomains . J. Med. Chem 56 , 7501–7515 (2013). [PubMed] [Google Scholar]

16. Xu Y & Vakoc CR Targeting cancer cells with BET bromodomain inhibitors . Cold Spring Harb. Perspect. Med 7 , a026674 (2017). [PMC free article] [PubMed] [Google Scholar]

17. Helin K & Dhanak D Chromatin proteins and modifications as drug targets . Nature 502 , 480–488 (2013). [PubMed] [Google Scholar]

18. Look AT Oncogenic transcription factors in the human acute leukemias . Science 278 , 1059–1064 (1997). [PubMed] [Google Scholar]

19. Alcalay M et al. Acute myeloid leukemia fusion proteins deregulate genes involved in stem cell maintenance and DNA repair . J. Clin. Invest 112 , 1751–1761 (2003). [PMC free article] [PubMed] [Google Scholar]

20. Martens JH & Stunnenberg HG The molecular signature of oncofusion proteins in acute myeloid leukemia . FEBS Lett 584 , 2662–2669 (2010). [PubMed] [Google Scholar]

21. Di Croce L Chromatin modifying activity of leukaemia associated fusion proteins . Hum. Mol. Genet 14 , Spec No 1 , R77–R84 (2005). [PubMed] [Google Scholar]

22. Kottaridis PD et al. Studies of FLT3 mutations in paired presentation and relapse samples from patients with acute myeloid leukemia: implications for the role of FLT3 mutations in leukemogenesis, minimal residual disease detection, and possible therapy with FLT3 inhibitors . Blood 100 , 2393–2398 (2002). [PubMed] [Google Scholar]

23. Shih LY et al. Cooperating mutations of receptor tyrosine kinases and Ras genes in childhood core-binding factor acute myeloid leukemia and a comparative analysis on paired diagnosis and relapse samples . Leukemia 22 , 303–307 (2008). [PubMed] [Google Scholar]

24. Nakano Y et al. Molecular evolution of acute myeloid leukaemia in relapse: unstable N-ras and FLT3 genes compared with p53 gene . Br. J. Haematol 104 , 659–664 (1999). [PubMed] [Google Scholar]

25. Tomlins SA et al. ETS gene fusions in prostate cancer: from discovery to daily clinical practice . Eur. Urol 56 , 275–286 (2009). [PubMed] [Google Scholar]

26. Clark JP & Cooper CS ETS gene fusions in prostate cancer . Nat. Rev Urol 6 , 429–439 (2009). [PubMed] [Google Scholar]

27. Tomlins SA et al. Recurrent fusion of TMPRSS2 and ETS transcription factor genes in prostate cancer . Science 310 , 644–648 (2005). [PubMed] [Google Scholar]

28. Chen Y et al. ETS factors reprogram the androgen receptor cistrome and prime prostate tumorigenesis in response to PTEN loss . Nat. Med 19 , 1023–1029 (2013). [PMC free article] [PubMed] [Google Scholar]

29. Chi P et al. ETV1 is a lineage survival factor that cooperates with KIT in gastrointestinal stromal tumours . Nature 467 , 849–853 (2010). [PMC free article] [PubMed] [Google Scholar]

30. Jane-Valbuena J et al. An oncogenic role for ETV1 in melanoma . Cancer Res . 70 , 2075–2084 (2010). [PMC free article] [PubMed] [Google Scholar]

31. Scheitz CJ, Lee TS, McDermitt DJ & Tumbar T Defining a tissue stem cell-driven Runx1/Stat3 signalling axis in epithelial cancer . EMBO J . 31 , 4124–4139 (2012). [PMC free article] [PubMed] [Google Scholar]

32. Morita K et al. Genetic regulation of the RUNX transcription factor family has antitumor effects . J. Clin. Invest 127 , 2815–2828 (2017). [PMC free article] [PubMed] [Google Scholar]

33. Chuang LS, Ito K & Ito Y Roles of RUNX in solid tumors . Adv. Exp. Med. Biol 962 , 299–320 (2017). [PubMed] [Google Scholar]

34. Hanahan D & Weinberg RA Hallmarks of cancer: the next generation . Cell 144 , 646–674 (2011). [PubMed] [Google Scholar]

35. Hanahan D & Weinberg RA The hallmarks of cancer . Cell 100 , 57–70 (2000). [PubMed] [Google Scholar]

36. Koehler AN A complex task? Direct modulation of transcription factors with small molecules . Curr. Opin. Chem. Biol 14 , 331–340 (2010). [PMC free article] [PubMed] [Google Scholar]

37. Majmudar CY & Mapp AK Chemical approaches to transcriptional regulation . Curr. Opin. Chem. Biol 9 , 467–474 (2005). [PubMed] [Google Scholar]

38. Arndt HD Small molecule modulators of transcription . Angew. Chem 45 , 4552–4560 (2006). [PubMed] [Google Scholar]

39. Berg T Inhibition of transcription factors with small organic molecules . Curr. Opin. Chem. Biol 12 , 464–471 (2008). [PubMed] [Google Scholar]

40. Bhagwat AS & Vakoc CR Targeting transcription factors in cancer . Trends Cancer 1 , 53–65 (2015). [PMC free article] [PubMed] [Google Scholar]

41. Burris TP et al. Nuclear receptors and their selective pharmacologic modulators . Pharmacol. Rev 65 , 710–778 (2013). [PMC free article] [PubMed] [Google Scholar]

42. de The H Differentiation therapy revisited . Nat. Rev. Cancer 18 , 117–127 (2018). [PubMed] [Google Scholar]

43. Mangelsdorf DJ et al. The nuclear receptor superfamily: the second decade . Cell 83 , 835–839 (1995). [PMC free article] [PubMed] [Google Scholar]

44. Robinson-Rechavi M, Escriva Garcia H & Laudet V The nuclear receptor superfamily . J. Cell Sci 116 , 585–586 (2003). [PubMed] [Google Scholar]

45. Pawlak M, Lefebvre P & Staels B General molecular biology and architecture of nuclear receptors . Curr. Top. Med. Chem 12 , 486–504 (2012). [PMC free article] [PubMed] [Google Scholar]

46. Perou CM et al. Molecular portraits of human breast tumours . Nature 406 , 747–752 (2000). [PubMed] [Google Scholar]

47. Patel HK & Bihani T Selective estrogen receptor modulators (SERMs) and selective estrogen receptor degraders (SERDs) in cancer treatment . Pharmacol. Ther 186 , 1–24 (2018). [PubMed] [Google Scholar]

48. Shiau AK et al. The structural basis of estrogen receptor/coactivator recognition and the antagonism of this interaction by tamoxifen . Cell 95 , 927–937 (1998). [PubMed] [Google Scholar]

49. Romano A et al. Identification of novel ER-α target genes in breast cancer cells: gene- and cell-selective co-regulator recruitment at target promoters determines the response to 17β-estradiol and tamoxifen . Mol. Cell Endocrinol 314 , 90–100 (2010). [PubMed] [Google Scholar]

50. Shang Y & Brown M Molecular determinants for the tissue specificity of SERMs . Science 295 , 2465–2468 (2002). [PubMed] [Google Scholar]

51. Dai C, Heemers H & Sharifi N Androgen signaling in prostate cancer . Cold Spring Harb. Perspect. Med 7 , a030452 (2017). [PMC free article] [PubMed] [Google Scholar]

52. Crona DJ, Milowsky MI & Whang YE Androgen receptor targeting drugs in castration-resistant prostate cancer and mechanisms of resistance . Clin. Pharmacol. Ther 98 , 582–589 (2015). [PMC free article] [PubMed] [Google Scholar]

53. Efstathiou E et al. Molecular characterization of enzalutamide-treated bone metastatic castration-resistant prostate cancer . Eur. Urol 67 , 53–60 (2015). [PMC free article] [PubMed] [Google Scholar]

54. Nevedomskaya E, Baumgart SJ & Haendler B Recent advances in prostate cancer treatment and drug discovery . Int. J. Mol. Sci 19 (2018). [PMC free article] [PubMed] [Google Scholar]

55. Gu TL, Goetz TL, Graves BJ & Speck NA Auto-inhibition and partner proteins, core-binding factor beta (CBFβ) and Ets-1, modulate DNA binding by CBFα2 (AML1) . Mol. Cell. Biol 20 , 91–103 (2000). [PMC free article] [PubMed] [Google Scholar]

56. Goetz TL, Gu TL, Speck NA & Graves BJ Auto-inhibition of Ets-1 is counteracted by DNA binding cooperativity with core-binding factor α2 . Mol. Cell. Biol 20 , 81–α90 (2000). [PMC free article] [PubMed] [Google Scholar]

57. Shrivastava T et al. Structural basis of Ets1 activation by Runx1 . Leukemia 28 , 2040–2048 (2014). [PMC free article] [PubMed] [Google Scholar]

58. Hollenhorst PC, Shah AA, Hopkins C & Graves BJ Genome-wide analyses reveal properties of redundant and specific promoter occupancy within the ETS gene family . Genes Dev . 21 , 1882–1894 (2007). [PMC free article] [PubMed] [Google Scholar]

59. Slany RK When epigenetics kills: MLL fusion proteins in leukemia . Hematol. Oncol 23 , 1–9 (2005). [PubMed] [Google Scholar]

60. Cox MC et al. Chromosomal aberration of the 11q23 locus in acute leukemia and frequency of MLL gene translocation: results in 378 adult patients . Am. J. Clin. Pathol 122 , 298–306 (2004). [PubMed] [Google Scholar]

61. Popovic R & Zeleznik-Le NJ MLL: how complex does it get? J. Cell. Biochem 95 , 234–242 (2005). [PubMed] [Google Scholar]

62. Dimartino JF & Cleary ML MLL rearrangements in haematological malignancies: lessons from clinical and biological studies . Br. J. Haematol 106 , 614–626 (1999). [PubMed] [Google Scholar]

63. Yokoyama A et al. The menin tumor suppressor protein is an essential oncogenic cofactor for MLL-associated leukemogenesis . Cell 123 , 207–218 (2005). [PubMed] [Google Scholar]

64. Caslini C et al. Interaction of MLL amino terminal sequences with menin is required for transformation . Cancer Res . 67 , 7275–7283 (2007). [PMC free article] [PubMed] [Google Scholar]

65. Cierpicki T et al. Structure of the MLL CXXC domain–DNA complex and its functional role in MLL–AF9 leukemia . Nat. Struct. Mol. Biol 17 , 62–68 (2010). [PMC free article] [PubMed] [Google Scholar]

66. Grembecka J et al. Menin–MLL inhibitors reverse oncogenic activity of MLL fusion proteins in leukemia . Nat. Chem. Biol 8 , 277–284 (2012). [PMC free article] [PubMed] [Google Scholar] This paper describes the development and demonstration of on-target activity of the first inhibitors of the menin–MLL fusion protein interaction.

67. He S et al. High-affinity small-molecule inhibitors of the menin–mixed lineage leukemia (MLL) interaction closely mimic a natural protein–protein interaction . J. Med. Chem 57 , 1543–1556 (2014). [PMC free article] [PubMed] [Google Scholar]

68. Borkin D et al. Property focused structure-based optimization of small molecule inhibitors of the protein–protein interaction between menin and mixed lineage leukemia (MLL) . J. Med. Chem 59 , 892–913 (2016). [PMC free article] [PubMed] [Google Scholar]

69. Borkin D et al. Pharmacologic inhibition of the menin–MLL interaction blocks progression of MLL leukemia in vivo . Cancer Cell 27 , 589–602 (2015). [PMC free article] [PubMed] [Google Scholar] This paper demonstrates efficacy of menin–MLL inhibitors in a mouse model of MLL fusion-positive leukaemia as well as with primary MLL fusion-positive leukaemia patient samples.

70. Borkin D et al. Complexity of blocking bivalent protein–protein interactions: development of a highly potent inhibitor of the menin–mixed-lineage leukemia interaction . J. Med. Chem 61 ,4832–4850 (2018). [PMC free article] [PubMed] [Google Scholar]

71. Liu P et al. Fusion between transcription factor CBFβ/PEBP2β and a myosin heavy chain in acute myeloid leukemia . Science 261 , 1041–1044 (1993). [PubMed] [Google Scholar]

72. Castilla LH et al. Failure of embryonic hematopoiesis and lethal hemorrhages in mouse embryos heterozygous for a knocked-in leukemia gene CBFB–MYH11 . Cell 87 , 687–696 (1996). [PubMed] [Google Scholar]

73. Mandoli A et al. CBFB–MYH11/RUNX1 together with a compendium of hematopoietic regulators, chromatin modifiers and basal transcription factors occupies self-renewal genes in inv(16) acute myeloid leukemia . Leukemia 28 , 770–778 (2014). [PubMed] [Google Scholar]

74. Illendula A et al. Chemical biology. A small-molecule inhibitor of the aberrant transcription factor CBFβ–SMMHC delays leukemia in mice . Science 347 , 779–784 (2015). [PMC free article] [PubMed] [Google Scholar] This study describes the development of the first inhibitor of the interaction between CBFβ–SMMHC and RUNX, and also demonstrates efficacy of the inhibitor in a mouse model of CBFβ–SMMHC-positive leukaemia and with primary inv(16) leukaemia patient samples.

75. Pulikkan JA et al. CBFβ–SMMHC inhibition triggers apoptosis by disrupting MYC chromatin dynamics in acute myeloid leukemia . Cell 174 , 172–186 (2018). [PMC free article] [PubMed] [Google Scholar] This study describes the mechanism leading to reduced MYC expression seen with the CBFβ–SMMHC inhibitor, which occurs via altered occupancy of BAF and PRC complexes at specific enhancers of MYC.

76. Choi A et al. RUNX1 is required for oncogenic Myb and Myc enhancer activity in T-cell acute lymphoblastic leukemia . Blood 130 , 1722–1733 (2017). [PMC free article] [PubMed] [Google Scholar]

77. Zhou N et al. RUNX proteins desensitize multiple myeloma to lenalidomide via protecting IKZFs from degradation . Leukemia 33 , 2006–2021 (2019). [PMC free article] [PubMed] [Google Scholar]

78. Mill CP et al. RUNX1 targeted therapy for AML expressing somatic or germline mutation in RUNX1 . Blood 134 , 59–73 (2019). [PMC free article] [PubMed] [Google Scholar]

79. Chimge NO & Frenkel B The RUNX family in breast cancer: relationships with estrogen signaling . Oncogene 32 , 2121–2130 (2013). [PMC free article] [PubMed] [Google Scholar]

80. McDonald L et al. RUNX2 correlates with subtype-specific breast cancer in a human tissue microarray, and ectopic expression of Runx2 perturbs differentiation in the mouse mammary gland . Dis. Model. Mechanisms 7 , 525–534 (2014). [PMC free article] [PubMed] [Google Scholar]

81. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours . Nature 490 , 61–70 (2012). [PMC free article] [PubMed] [Google Scholar]

82. Ciriello G, Cerami E, Sander C & Schultz N Mutual exclusivity analysis identifies oncogenic network modules . Genome Res . 22 , 398–406 (2012). [PMC free article] [PubMed] [Google Scholar]

83. Carlton AL et al. Small molecule inhibition of the CBFβ/RUNX interaction decreases ovarian cancer growth and migration through alterations in genes related to epithelial-to-mesenchymal transition . Gynecol. Oncol 149 , 350–360 (2018). [PMC free article] [PubMed] [Google Scholar]

84. Geng F, Wenzel S & Tansey WP Ubiquitin and proteasomes in transcription . Annu. Rev. Biochem 81 , 177–201 (2012). [PMC free article] [PubMed] [Google Scholar]

85. Mukouyama Y et al. The AML1 transcription factor functions to develop and maintain hematogenic precursor cells in the embryonic aorta–gonad–mesonephros region . Dev. Biol 220 , 27–36 (2000). [PubMed] [Google Scholar]

86. Venkatachalam S et al. Retention of wild-type p53 in tumors from p53 heterozygous mice: reduction of p53 dosage can promote cancer formation . EMBO J . 17 , 4657–4667 (1998). [PMC free article] [PubMed] [Google Scholar]

87. Xie Y et al. Reduced Erg dosage impairs survival of hematopoietic stem and progenitor cells . Stem Cells 35 , 1773–1785 (2017). [PMC free article] [PubMed] [Google Scholar]

88. Xu X et al. Haploid loss of the tumor suppressor Smad4/Dpc4 initiates gastric polyposis and cancer in mice . Oncogene 19 , 1868–1874 (2000). [PubMed] [Google Scholar]

89. Senft D, Qi J & Ronai ZA Ubiquitin ligases in oncogenic transformation and cancer therapy . Nat. Rev Cancer 18 , 69–88 (2018). [PMC free article] [PubMed] [Google Scholar]

90. Buetow L & Huang DT Structural insights into the catalysis and regulation of E3 ubiquitin ligases . Nat. Rev. Mol. Cell Biol 17 , 626–642 (2016). [PMC free article] [PubMed] [Google Scholar]

91. Zheng N & Shabek N Ubiquitin ligases: structure, function, and regulation . Annu. Rev. Biochem 86 , 129–157 (2017). [PubMed] [Google Scholar]

92. Buckley DL et al. Targeting the von Hippel-Lindau E3 ubiquitin ligase using small molecules to disrupt the VHL/HIF-1α interaction . J. Am. Chem. Soc 134 , 4465–4468 (2012). [PMC free article] [PubMed] [Google Scholar]

93. Buckley DL et al. Small-molecule inhibitors of the interaction between the E3 ligase VHL and HIF1a . Angew. Chem. Int. Ed 51 , 11463–11467 (2012). [PMC free article] [PubMed] [Google Scholar]

94. Kaelin WG Jr. The von Hippel-Lindau tumour suppressor protein: O2 sensing and cancer . Nat. Rev. Cancer 8 , 865–873 (2008). [PubMed] [Google Scholar]

95. Lu G et al. The myeloma drug lenalidomide promotes the cereblon-dependent destruction of Ikaros proteins . Science 343 , 305–309 (2014). [PMC free article] [PubMed] [Google Scholar] This paper describes the elucidation of the mechanism of action of the drug lenalidomide, which enhances binding of cereblon to IKZF to increase ubiquitination and proteasome destruction of IKZF.

96. Ouchida AT et al. USP10 regulates the stability of the EMT-transcription factor Slug/SNAI2 . Biochem. Biophys. Res. Commun 502 , 429–434 (2018). [PubMed] [Google Scholar]

97. Wu Y et al. Dub3 inhibition suppresses breast cancer invasion and metastasis by promoting Snail1 degradation . Nat. Commun 8 , 14228 (2017). [PMC free article] [PubMed] [Google Scholar]

98. Lin Y et al. Stabilization of the transcription factors slug and twist by the deubiquitinase dub3 is a key requirement for tumor metastasis . Oncotarget 8 , 75127–75140 (2017). [PMC free article] [PubMed] [Google Scholar]

99. Kim D et al. Deubiquitinating enzyme USP22 positively regulates c-Myc stability and tumorigenic activity in mammalian and breast cancer cells . J. Cell. Physiol 232 , 3664–3676 (2017). [PubMed] [Google Scholar]

100. Tomlins SA et al. Role of the TMPRSS2-ERG gene fusion in prostate cancer . Neoplasia 10 , 177–188 (2008). [PMC free article] [PubMed] [Google Scholar]

101. Carver BS et al. Aberrant ERG expression cooperates with loss of PTEN to promote cancer progression in the prostate . Nat. Genet 41 , 619–624 (2009). [PMC free article] [PubMed] [Google Scholar]

102. Carver BS et al. ETS rearrangements and prostate cancer initiation . Nature 457 , E1; discussion E2–E3 (2009). [PMC free article] [PubMed] [Google Scholar]

103. Wang S et al. Ablation of the oncogenic transcription factor ERG by deubiquitinase inhibition in prostate cancer . Proc. Natl Acad. Sci. USA 111 ,4251–4256 (2014). [PMC free article] [PubMed] [Google Scholar]

104. Wang S et al. The ubiquitin ligase TRIM25 targets ERG for degradation in prostate cancer . Oncotarget 7 , 64921–64931 (2016). [PMC free article] [PubMed] [Google Scholar]

105. Kapuria V et al. Deubiquitinase inhibition by small-molecule WP1130 triggers aggresome formation and tumor cell apoptosis . Cancer Res . 70 , 9265–9276 (2010). [PubMed] [Google Scholar]

106. Hainaut P & Hollstein M p53 and human cancer: the first ten thousand mutations . Adv. Cancer Res 77 , 81–137 (2000). [PubMed] [Google Scholar]

107. Vogelstein B, Lane D & Levine AJ Surfing the p53 network . Nature 408 , 307–310 (2000). [PubMed] [Google Scholar]

108. Harris SL & Levine AJ The p53 pathway: positive and negative feedback loops . Oncogene 24 , 2899–2908 (2005). [PubMed] [Google Scholar]

109. Vousden KH & Lane DP p53 in health and disease . Nat. Rev. Mol. Cell Biol 8 , 275–283 (2007). [PubMed] [Google Scholar]

110. Freedman DA, Wu L & Levine AJ Functions of the MDM2 oncoprotein . Cell. Mol. Life Sci 55 , 96–107 (1999). [PMC free article] [PubMed] [Google Scholar]

111. Bond GL, Hu W & Levine AJ MDM2 is a central node in the p53 pathway: 12 years and counting . Curr. Cancer Drug Targets 5 , 3–8 (2005). [PubMed] [Google Scholar]

112. Wu X, Bayle JH, Olson D & Levine AJ The p53–mdm-2 autoregulatory feedback loop . Genes Dev . 7 , 1126–1132 (1993). [PubMed] [Google Scholar]

113. Momand J, Jung D, Wilczynski S & Niland J The MDM2 gene amplification database . Nucleic Acids Res . 26 , 3453–3459 (1998). [PMC free article] [PubMed] [Google Scholar]

114. Fotouhi N & Graves B Small molecule inhibitors of p53/MDM2 interaction . Curr. Top. Med. Chem 5 , 159–165 (2005). [PubMed] [Google Scholar]

115. Lai AC et al. Modular PROTAC design for the degradation of oncogenic BCR–ABL . Angew. Chem 55 , 807–810 (2016). [PMC free article] [PubMed] [Google Scholar]

116. Bondeson DP et al. Catalytic in vivo protein knockdown by small-molecule PROTACs . Nat. Chem. Biol 11 , 611–617 (2015). [PMC free article] [PubMed] [Google Scholar] This study demonstrates the catalytic behaviour of small-molecule PROTACs.

117. Winter GE et al. DRUG DEVELOPMENT. Phthalimide conjugation as a strategy for in vivo target protein degradation . Science 348 , 1376–1381 (2015). [PMC free article] [PubMed] [Google Scholar] This paper describes the development of a PROTAC targeting BRD4, its mechanism of action via proteasome-mediated reduction in the BRD4 level and its in vivo efficacy in a mouse model.

118. Burslem GM et al. The advantages of targeted protein degradation over inhibition: an RTK case study . Cell Chem. Biol 25 , 67–77 e63 (2018). [PMC free article] [PubMed] [Google Scholar]

119. Lai AC & Crews CM Induced protein degradation: an emerging drug discovery paradigm . Nat. Rev. Drug Discov 16 , 101–114 (2017). [PMC free article] [PubMed] [Google Scholar] This work is a comprehensive review of PROTACs by one of the original developers of this approach.

120. Akhtar MS et al. TFIIH kinase places bivalent marks on the carboxy-terminal domain of RNA polymerase II . Mol. Cell 34 , 387–393 (2009). [PMC free article] [PubMed] [Google Scholar]

121. Drapkin R, Le Roy G, Cho H, Akoulitchev S & Reinberg D Human cyclin-dependent kinase-activating kinase exists in three distinct complexes . Proc. Natl Acad. Sci. USA 93 , 6488–6493 (1996). [PMC free article] [PubMed] [Google Scholar]

122. Glover-Cutter K et al. TFIIH-associated Cdk7 kinase functions in phosphorylation of C-terminal domain Ser7 residues, promoter-proximal pausing, and termination by RNA polymerase II . Mol. Cell. Biol 29 , 5455–5464 (2009). [PMC free article] [PubMed] [Google Scholar]

123. Kwiatkowski N et al. Targeting transcription regulation in cancer with a covalent CDK7 inhibitor . Nature 511 ,616–620 (2014). [PMC free article] [PubMed] [Google Scholar] This paper describes the development of a CDK7 inhibitor, its mechanism of action via changes in the level of the transcription factor RUNX1 and its in vivo efficacy in a mouse model of T-ALL.

124. Chipumuro E et al. CDK7 inhibition suppresses super-enhancer-linked oncogenic transcription in MYCN-driven cancer . Cell 159 , 1126–1139 (2014). [PMC free article] [PubMed] [Google Scholar]

125. Christensen CL et al. Targeting transcriptional addictions in small cell lung cancer with a covalent CDK7 inhibitor . Cancer Cell 26 , 909–922 (2014). [PMC free article] [PubMed] [Google Scholar]

126. Ahuja N, Sharma AR & Baylin SB Epigenetic therapeutics: a new weapon in the war against cancer . Annu. Rev Med 67 , 73–89 (2016). [PMC free article] [PubMed] [Google Scholar]

127. Bennett RL & Licht JD Targeting epigenetics in cancer . Annu. Rev. Pharmacol. Toxicol 58 , 187–207 (2018). [PMC free article] [PubMed] [Google Scholar]

128. Mohammad HP, Barbash O & Creasy CL Targeting epigenetic modifications in cancer therapy: erasing the roadmap to cancer . Nat. Med 25 , 403–418 (2019). [PubMed] [Google Scholar]

129. Stathis A & Bertoni F BET proteins as targets for anticancer treatment . Cancer Discov . 8 , 24–36 (2018). [PubMed] [Google Scholar]

130. Stonestrom AJ et al. Functions of BET proteins in erythroid gene expression . Blood 125 , 2825–2834 (2015). [PMC free article] [PubMed] [Google Scholar]

131. Roe JS, Mercan F, Rivera K, Pappin DJ & Vakoc CR BET bromodomain inhibition suppresses the function of hematopoietic transcription factors in acute myeloid leukemia . Mol. Cell 58 , 1028–1039 (2015). [PMC free article] [PubMed] [Google Scholar] This paper describes the effects of a BRD4 bromodomain inhibitor on binding of BRD4 to specific haematopoietic transcription factors as well as the inhibition of the activity of these transcription factors.

132. Lamonica JM et al. Bromodomain protein Brd3 associates with acetylated GATA1 to promote its chromatin occupancy at erythroid target genes . Proc. Natl Acad. Sci. USA 108 , E159–E168 (2011). [PMC free article] [PubMed] [Google Scholar]

133. Shi J et al. Disrupting the interaction of BRD4 with diacetylated Twist suppresses tumorigenesis in basal-like breast cancer . Cancer Cell 25 , 210–225 (2014). [PMC free article] [PubMed] [Google Scholar]

134. French CA Small-molecule targeting of BET proteins in cancer . Adv. Cancer Res 131 ,21–58 (2016). [PubMed] [Google Scholar]

135. Delmore JE et al. BET bromodomain inhibition as a therapeutic strategy to target c-Myc . Cell 146 , 904–917 (2011). [PMC free article] [PubMed] [Google Scholar]

136. Loven J et al. Selective inhibition of tumor oncogenes by disruption of super-enhancers . Cell 153 , 320–334 (2013). [PMC free article] [PubMed] [Google Scholar]

137. Horiuchi D, Anderton B & Goga A Taking on challenging targets: making MYC druggable . Am. Soc. Clin. Oncol. Educ. Book , e497–e502, 10.14694/EdBook_AM.2014.34.e497 (2014). [PMC free article] [PubMed] [CrossRef] [Google Scholar]

138. Carabet LA, Rennie PS & Cherkasov A Therapeutic inhibition of MYC in cancer. Structural bases and computer-aided drug discovery approaches . Int. J. Mol. Sci 20 , E120 (2018). [PMC free article] [PubMed] [Google Scholar]

139. Castell A et al. A selective high affinity MYC-binding compound inhibits MYC:MAX interaction and MYC-dependent tumor cell proliferation . Sci. Rep 8 , (10064 (2018). [PMC free article] [PubMed] [Google Scholar]

140. Struntz NB et al. Stabilization of the Max homodimer with a small molecule attenuates Myc-driven transcription . Cell Chem. Biol 26 , 711–723 e714 (2019). [PubMed] [Google Scholar]

141. Doroshow DB, Eder JP & LoRusso PM BET inhibitors: a novel epigenetic approach . Ann. Oncol 28 , 1776–1787 (2017). [PubMed] [Google Scholar]

142. Leung CH, Chan DS, Ma VP & Ma DL DNA-binding small molecules as inhibitors of transcription factors . Med. Res. Rev 33 , 823–846 (2013). [PubMed] [Google Scholar]

143. Gniazdowski M, Denny WA, Nelson SM & Czyz M Transcription factors as targets for DNA-interacting drugs . Curr. Med. Chem 10 , 909–924 (2003). [PubMed] [Google Scholar]

144. Gniazdowski M, Denny WA, Nelson SM & Czyz M Effects of anticancer drugs on transcription factor–DNA interactions . Expert Opin. Ther. Targets 9 , 471–489 (2005). [PubMed] [Google Scholar]

145. Dervan PB Molecular recognition of DNA by small molecules . Bioorg. Med. Chem 9 , 2215–2235 (2001). [PubMed] [Google Scholar]

146. Trauger JW, Baird EE & Dervan PB Recognition of DNA by designed ligands at subnanomolar concentrations . Nature 382 , 559–561 (1996). [PubMed] [Google Scholar]

147. Best TP, Edelson BS, Nickols NG & Dervan PB Nuclear localization of pyrrole–imidazole polyamide–fluorescein conjugates in cell culture . Proc. Natl Acad. Sci. USA 100 , 12063–12068 (2003). [PMC free article] [PubMed] [Google Scholar]

148. Antony-Debre I et al. Pharmacological inhibition of the transcription factor PU.1 in leukemia . J. Clin. Invest 127 , 4297–4313 (2017). [PMC free article] [PubMed] [Google Scholar]

149. Chen YN et al. Allosteric inhibition of SHP2 phosphatase inhibits cancers driven by receptor tyrosine kinases . Nature 535 , 148–152 (2016). [PubMed] [Google Scholar] This study describes the development of an inhibitor of SHP2 based on stabilization of the auto-inhibited state of SHP2.

150. Graves BJ et al. Autoinhibition as a transcriptional regulatory mechanism . Cold Spring Harb. Symp. Quant. Biol 63 , 621–629 (1998). [PubMed] [Google Scholar]

151. Pufall MA & Graves BJ Autoinhibitory domains: modular effectors of cellular regulation . Annu. Rev. Cell Dev. Biol 18 , 421–462 (2002). [PubMed] [Google Scholar]

152. Hollenhorst PC, McIntosh LP & Graves BJ Genomic and biochemical insights into the specificity of ETS transcription factors . Annu. Rev. Biochem 80 , 437–471 (2011). [PMC free article] [PubMed] [Google Scholar]

153. Lanning BR et al. A road map to evaluate the proteome-wide selectivity of covalent kinase inhibitors . Nat. Chem. Biol 10 , 760–767 (2014). [PMC free article] [PubMed] [Google Scholar]

154. Keating GM Afatinib: a review of its use in the treatment of advanced non-small cell lung cancer . Drugs 74 , 207–221 (2014). [PubMed] [Google Scholar]

155. Dungo RT & Keating GM Afatinib: first global approval . Drugs 73 , 1503–1515 (2013). [PubMed] [Google Scholar]

156. Cramer P, Hallek M & Eichhorst B State-of-the-art treatment and novel agents in chronic lymphocytic leukemia . Oncol. Res. Treat 39 , 25–32 (2016). [PubMed] [Google Scholar]

157. Shlomai J Redox control of protein–DNA interactions: from molecular mechanisms to significance in signal transduction, gene expression, and DNA replication . Antioxid. Redox Signal 13 , 1429–1476 (2010). [PubMed] [Google Scholar]

158. Akamatsu Y et al. Redox regulation of the DNA binding activity in transcription factor PEBP2. The roles of two conserved cysteine residues . J. Biol. Chem 272 , 14497–14500 (1997). [PubMed] [Google Scholar]

159. DeHart CJ, Chahal JS, Flint SJ & Perlman DH Extensive post-translational modification of active and inactivated forms of endogenous p53 . Mol. Cell. Proteom 13 , 1–17 (2014). [PMC free article] [PubMed] [Google Scholar]

160. Blumenthal E et al. Covalent modifications of RUNX proteins: structure affects function . Adv. Exp. Med. Biol 962 , 33–44 (2017). [PubMed] [Google Scholar]

161. Uversky VN Intrinsic disorder, protein–protein interactions, and disease . Adv. Protein Chem. Struct. Biol 110 , 85–121 (2018). [PubMed] [Google Scholar]

162. Dunker AK, Brown CJ, Lawson JD, Iakoucheva LM & Obradovic Z Intrinsic disorder and protein function . Biochemistry 41 ,6573–6582 (2002). [PubMed] [Google Scholar]

163. Dyson HJ & Wright PE Intrinsically unstructured proteins and their functions . Nat. Rev. Mol. Cell Biol 6 , 197–208 (2005). [PubMed] [Google Scholar]

164. Iakoucheva LM, Brown CJ, Lawson JD, Obradovic Z & Dunker AK Intrinsic disorder in cell-signaling and cancer-associated proteins . J. Mol. Biol 323 , 573–584 (2002). [PubMed] [Google Scholar]

165. Dyson HJ & Wright PE Role of intrinsic protein disorder in the function and interactions of the transcriptional coactivators CREB-binding protein (CBP) and p300 . J. Biol. Chem 291 , 6714–p6722 (2016). [PMC free article] [PubMed] [Google Scholar]

166. Leach BI et al. Leukemia fusion target AF9 is an intrinsically disordered transcriptional regulator that recruits multiple partners via coupled folding and binding . Structure 21 , 176–183 (2013). [PMC free article] [PubMed] [Google Scholar] This paper provides an example of an intrinsically disordered region that mediates specific cofactor binding to the leukaemia fusion protein MLL–AF9.

167. Kuntimaddi A et al. Degree of recruitment of DOT1L to MLL–AF9 defines level of H3K79 di- and tri-methylation on target genes and transformation potential . Cell Rep . 11 , 808–820 (2015). [PMC free article] [PubMed] [Google Scholar] This paper provides an example of the binding of a cofactor (DOT1L) to an intrinsically disordered region (the AF9 portion of the MLL–AF9 fusion protein) that is essential for leukaemia development.

168. Lokken AA et al. Importance of a specific amino acid pairing for murine MLL leukemias driven by MLLT1/3 or AFF1/4 . Leukemia Res . 38 , 1309–1315 (2014). [PMC free article] [PubMed] [Google Scholar]

169. Zhang Y, Cao H & Liu Z Binding cavities and druggability of intrinsically disordered proteins . Protein Sci . 24 , 688–705 (2015). [PMC free article] [PubMed] [Google Scholar]

170. Berg T et al. Small-molecule antagonists of Myc/Max dimerization inhibit Myc-induced transformation of chicken embryo fibroblasts . Proc. Natl Acad. Sci. USA 99 , 3830–3835 (2002). [PMC free article] [PubMed] [Google Scholar]

171. Shi J, Stover JS, Whitby LR, Vogt PK & Boger DL Small molecule inhibitors of Myc/Max dimerization and Myc-induced cell transformation . Bioorg. Med. Chem. Lett 19 , 6038–6041 (2009). [PMC free article] [PubMed] [Google Scholar]

172. Erkizan HV et al. A small molecule blocking oncogenic protein EWS–FLI1 interaction with RNA helicase A inhibits growth of Ewing’s sarcoma . Nat. Med 15 , 750–756 (2009). [PMC free article] [PubMed] [Google Scholar]

173. Zhang Z et al. Chemical perturbation of an intrinsically disordered region of TFIID distinguishes two modes of transcription initiation . eLife 4 , e07777 (2015). [PMC free article] [PubMed] [Google Scholar]

174. Srinivasan RS et al. The synthetic peptide PFWT disrupts AF4–AF9 protein complexes and induces apoptosis in t(4;11) leukemia cells . Leukemia 18 , 1364–1372 (2004). [PubMed] [Google Scholar]

175. Jin F, Yu C, Lai L & Liu Z Ligand clouds around protein clouds: a scenario of ligand binding with intrinsically disordered proteins . PLOS Comp. Biol 9 , e1003249 (2013). [PMC free article] [PubMed] [Google Scholar]

176. Cohen-Solal KA, Kaufman HL & Lasfar A Transcription factors as critical players in melanoma invasiveness, drug resistance, and opportunities for therapeutic drug development . Pigment. Cell Melanoma Res 31 , 241–252 (2018). [PubMed] [Google Scholar]

177. Garcia-Alonso L et al. Transcription factor activities enhance markers of drug sensitivity in cancer . Cancer Res . 78 , 769–780 (2018). [PMC free article] [PubMed] [Google Scholar]

178. Zecena H et al. Systems biology analysis of mitogen activated protein kinase inhibitor resistance in malignant melanoma . BMC Syst. Biol 12 , 33 (2018). [PMC free article] [PubMed] [Google Scholar]

179. Yao S, Fan LY & Lam EW The FOXO3–FOXM1 axis: a key cancer drug target and a modulator of cancer drug resistance . Semin. Cancer Biol 50 , 77–89 (2018). [PMC free article] [PubMed] [Google Scholar]

180. Barabe F, Kennedy JA, Hope KJ & Dick JE Modeling the initiation and progression of human acute leukemia in mice . Science 316 , 600–604 (2007). [PubMed] [Google Scholar]

181. Krivtsov AV et al. Transformation from committed progenitor to leukaemia stem cell initiated by MLL–AF9 . Nature 442 , 818–822 (2006). [PubMed] [Google Scholar]

182. Kuo YH et al. Cbfβ–SMMHC induces distinct abnormal myeloid progenitors able to develop acute myeloid leukemia . Cancer Cell 9 , 57–68 (2006). [PubMed] [Google Scholar]

183. Bell RJ et al. Cancer. The transcription factor GABP selectively binds and activates the mutant TERT promoter in cancer . Science 348 , 1036–1039 (2015). [PMC free article] [PubMed] [Google Scholar]

184. Makowski MM et al. An interaction proteomics survey of transcription factor binding at recurrent TERT promoter mutations . Proteomics 16 , 417–426 (2016). [PubMed] [Google Scholar]

185. Ptasinska A et al. Depletion of RUNX1/ETO in t(8;21) AML cells leads to genome-wide changes in chromatin structure and transcription factor binding . Leukemia 26 , 1829–1841 (2012). [PMC free article] [PubMed] [Google Scholar]

186. Zhang H et al. KLF8 involves in TGF-β-induced EMT and promotes invasion and migration in gastric cancer cells . J. Cancer Res. Clin. Oncol 139 , 1033–1042 (2013). [PubMed] [Google Scholar]

187. Micalizzi DS et al. The Six1 homeoprotein induces human mammary carcinoma cells to undergo epithelial–mesenchymal transition and metastasis in mice through increasing TGF-β signaling . J. Clin. Invest 119 , 2678–2690 (2009). [PMC free article] [PubMed] [Google Scholar]

188. Pratap J et al. The Runx2 osteogenic transcription factor regulates matrix metalloproteinase 9 in bone metastatic cancer cells and controls cell invasion . Mol. Cell. Biol 25 , 8581–8591 (2005). [PMC free article] [PubMed] [Google Scholar]

189. Chimge NO et al. Regulation of breast cancer metastasis by Runx2 and estrogen signaling: the role of SNAI2 . Breast Cancer Res . 13 , R127 (2011). [PMC free article] [PubMed] [Google Scholar]

190. Mendoza-Villanueva D, Deng W, Lopez-Camacho C & Shore P The Runx transcriptional co-activator, CBFβ, is essential for invasion of breast cancer cells . Mol. Cancer 9 , 171 (2010). [PMC free article] [PubMed] [Google Scholar]

191. Baniwal SK et al. Runx2 transcriptome of prostate cancer cells: insights into invasiveness and bone metastasis . Mol. Cancer 9 , 258 (2010). [PMC free article] [PubMed] [Google Scholar]

192. Little GH et al. Differential effects of RUNX2 on the androgen receptor in prostate cancer: synergistic stimulation of a gene set exemplified by SNAI2 and subsequent invasiveness . Cancer Res . 74 , 2857–2868 (2014). [PMC free article] [PubMed] [Google Scholar]

193. Little GH et al. Genome-wide Runx2 occupancy in prostate cancer cells suggests a role in regulating secretion . Nucleic Acids Res . 40 , 3538–3547 (2012). [PMC free article] [PubMed] [Google Scholar]

194. de The H, Pandolfi PP & Chen Z Acute promyelocytic leukemia: a paradigm for oncoprotein-targeted cure . Cancer Cell 32 , 552–560 (2017). [PubMed] [Google Scholar]

195. Matkar S et al. An epigenetic pathway regulates sensitivity of breast cancer cells to HER2 inhibition via FOXO/c-Myc axis . Cancer Cell 28 , 472–485 (2015). [PMC free article] [PubMed] [Google Scholar]

196. Hirade T et al. Internal tandem duplication of FLT3 deregulates proliferation and differentiation and confers resistance to the FLT3 inhibitor AC220 by up-regulating RUNX1 expression in hematopoietic cells . Int. J. Hematol 103 , 95–106 (2016). [PubMed] [Google Scholar]

197. Cauchy P et al. Chronic FLT3–ITD signaling in acute myeloid leukemia is connected to a specific chromatin signature . Cell Rep . 12 , 821–836 (2015). [PMC free article] [PubMed] [Google Scholar]

198. Boregowda RK et al. The transcription factor RUNX2 regulates receptor tyrosine kinase expression in melanoma . Oncotarget 7 , 29689–29707 (2016). [PMC free article] [PubMed] [Google Scholar]

199. Sanda T et al. Core transcriptional regulatory circuit controlled by the TAL1 complex in human T cell acute lymphoblastic leukemia . Cancer Cell 22 , 209–221 (2012). [PMC free article] [PubMed] [Google Scholar]

200. Morita K et al. Autonomous feedback loop of RUNX1–p53–CBFB in acute myeloid leukemia cells . Sci. Rep 7 , 16604 (2017). [PMC free article] [PubMed] [Google Scholar]

201. Tetsu O & McCormick F ETS-targeted therapy: can it substitute for MEK inhibitors? Clin. Transl. Med 6 , 16 (2017). [PMC free article] [PubMed] [Google Scholar]

202. Rakhra K et al. CD4(+) T cells contribute to the remodeling of the microenvironment required for sustained tumor regression upon oncogene inactivation . Cancer Cell 18 , 485–498 (2010). [PMC free article] [PubMed] [Google Scholar]

203. Xu Y et al. Translation control of the immune checkpoint in cancer and its therapeutic targeting . Nat. Med 25 , 301–311 (2019). [PMC free article] [PubMed] [Google Scholar]

204. Cerezo M et al. Translational control of tumor immune escape via the eIF4F–STAT1–PD-L1 axis in melanoma . Nat. Med 24 , 1877–1886 (2018). [PubMed] [Google Scholar]

205. Elias S et al. Immune evasion by oncogenic proteins of acute myeloid leukemia . Blood 123 , 1535–1543 (2014). [PMC free article] [PubMed] [Google Scholar]