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Review

Opportunities and Challenges of Small Molecule Inhibitors in Glioblastoma Treatment: Lessons Learned from Clinical Trials

by
Linde Hoosemans
1,
Marc Vooijs
1 and
Ann Hoeben
2,*
1
Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Center+, 6229 HX Maastricht, The Netherlands
2
Department of Medical Oncology, GROW School for Oncology and Reproduction, Maastricht University Medical Center+, 6229 HX Maastricht, The Netherlands
*
Author to whom correspondence should be addressed.
Cancers 2024, 16(17), 3021; https://doi.org/10.3390/cancers16173021
Submission received: 25 July 2024 / Revised: 26 August 2024 / Accepted: 29 August 2024 / Published: 29 August 2024
(This article belongs to the Special Issue Current Challenges and Opportunities in Treating Glioma)
Review Reports Versions Notes

Abstract

:

Simple Summary

Glioblastoma is the most common brain tumour, with a poor prognosis of about 15 months despite intensive treatment. Many trials have tested new drugs targeting specific genes, but none have succeeded, and treatments have not changed since 2005. This review explores why clinical trials with these drugs failed. It highlights the potential of combining different drugs to overcome resistance and suggests ways to improve future trials. The goal is to understand treatment failures and find new drug combinations to improve survival for GBM patients.

Abstract

Glioblastoma (GBM) is the most prevalent central nervous system tumour (CNS). Patients with GBM have a dismal prognosis of 15 months, despite an intensive treatment schedule consisting of surgery, chemoradiation and concurrent chemotherapy. In the last decades, many trials have been performed investigating small molecule inhibitors, which target specific genes involved in tumorigenesis. So far, these trials have been unsuccessful, and standard of care for GBM patients has remained the same since 2005. This review gives an overview of trials investigating small molecule inhibitors on their own, combined with chemotherapy or other small molecule inhibitors. We discuss possible resistance mechanisms in GBM, focussing on intra- and intertumoral heterogeneity, bypass mechanisms and the influence of the tumour microenvironment. Moreover, we emphasise how combining inhibitors can help overcome these resistance mechanisms. We also address strategies for improving trial outcomes through modifications to their design. In summary, this review aims to elucidate different resistance mechanisms against small molecule inhibitors, highlighting their significance in the search for novel therapeutic combinations to improve the overall survival of GBM patients.

1. Introduction

Glioblastoma (GBM) is the most common malignant primary brain tumour. The 2016 CNS WHO classification defines two groups: (1) isocitrate dehydrogenase (IDH)-wildtype (wt) glioblastoma and (2) IDH-mutant (mt) grade IV astrocytoma [1]. IDH-wt GBM is mainly diagnosed in adults aged 50 to 60, and patients have a median survival of 15 months [2,3], whereas IDH-mutant grade IV astrocytoma is mostly diagnosed between the ages of 35 and 45 and is the result of dedifferentiation from a low-grade astrocytoma [2,4]. Patients with IDH-mt grade IV astrocytoma have a median survival of approximately 31 months [5]. This review will focus on IDH-wt GBM.
After diagnosis, treatment consists of maximal surgical resection, followed by radiation and chemotherapy (temozolomide) [3,6]. Even with this intensive multimodality treatment schedule, patients with GBM cannot be cured, and recurrence is inevitable for all patients [3,5]. Despite multiple trials, no innovative life-prolonging treatments have been identified for patients with IDH-wt GBM to improve the current standard of care.
One of the main reasons for the failure of the clinical trials in GBM is inter- and intra-tumoral heterogeneity on the phenotypic, genotypic and transcriptional levels. The intertumoral heterogeneity of GBM tumours is marked by four subtypes Verhaak et al. identified by genomic analysis [7]. First, the classical subtype is primarily characterised by alterations in epidermal growth factor receptor (EGFR). This subtype also shows a homozygous deletion spanning the Ink4a/ARF locus. Second, the pro-neural subtype is primarily characterised by amplification or activating the mutation of platelet-derived growth factor receptor A (PDGFRA) and point mutations in IDH1 and TP53. Third, the neural subtype is characterised by the expression of neuronal markers such as neurofilament light chain (NEFL), gamma-aminobutyric acid type A receptor subunit alpha1 (GABRA1), synaptotagmin 1 (SYT1) and potassium chloride transporter member 5 (SLC12A5). Finally, the mesenchymal subtype is primarily characterised by the loss of neurofibromatosis type I (NF1) [7].
In addition to this intertumoral heterogeneity, Patel et al. found that one genotypic subtype does not characterise a GBM tumour, but rather, several subtypes occur in one tumour [8]. Additionally, intra-tumoral heterogeneity is further supported by identifying four main cellular states in which tumour cells in GBM exist: neural progenitor-like (NP-like), oligodendrocyte-progenitor-like (OPC-like), astrocyte-like (AC-like) and mesenchymal-like (MES-like) states [9]. These states are influenced by the tumour microenvironment, which has been shown to have a spatially heterogeneous immune compartment based upon the immune cell subtype and activation levels [9]. Alterations in cyclin-dependent kinase 4 (CDK4), EGFR, PDGFRA and NF1 are associated with the relative frequency of cells in each state, which varies between GBM samples [9]. This inter- and intra-tumoral heterogeneity in GBM may be the reason why “one shoe fits all” conventional treatment options do not achieve long-term responses in GBM [8,9,10].
Achieving a uniform drug distribution in GBM is complex due to the intact blood–brain barrier (BBB) in some areas. The BBB, primarily composed of endothelial cells forming tight junctions, restricts drug transport from the capillaries to the brain [11,12]. Only drugs with a molecular mass under 500 Da and high lipid solubility can pass the BBB [13]. Consequently, all large molecule agents, such as gene therapeutics and monoclonal antibodies, and over 98% of small molecule inhibitors cannot cross the BBB [13]. Efflux transporters further complicate drug delivery by expelling chemotherapy and small molecule inhibitors at the BBB [14,15].
Small molecule inhibitors might offer a potential solution to the inter- and intra-tumoral heterogeneity in GBM by targeting the drivers of tumorigenesis [16]. They can bind to various intracellular and extracellular targets [17,18]. Small molecule inhibitors can either act as enzyme inhibitors, reducing enzyme activity, or as receptor antagonists, countering receptor effects [17,18]. These compounds can function as multi-kinase inhibitors, targeting a wide range of the human kinome, or as selective inhibitors, targeting a specific component of a signalling pathway [19]. By targeting different signalling pathways and cellular processes involved in cancer, small molecule inhibitors are therefore an interesting treatment option for various types of cancer [19]. In BRAF-mutated metastatic melanoma and EGFR-mutated advanced non-small cell lung cancer (NSCLC), small molecule inhibitors targeting BRAF or EGFR, respectively, significantly increased the overall survival and have become the standard of care in these patients [20,21,22].
Nonetheless, despite different trials with small molecule inhibitors in patients with GBM, none has led to an improvement in the standard of care for these patients.
Therefore, we will review trials involving small molecule inhibitors in GBM patients and discuss possible resistance mechanisms, contributing to their lack of efficacy. Furthermore, we will propose potential combinatorial therapies based on the molecular characteristics of individual tumours.

2. Clinical Trials with Small Molecule Inhibitors in GBM

2.1. Mono-Target Small Molecule Inhibitors

Clinical trials in GBM with small molecule inhibitors specifically inhibiting one specific target are listed in Table 1.
Only a few mono-target small molecule inhibitors evaluated in phase I GBM trials have progressed to phase II trials, mostly with disappointing results (Table 1). However, most of these trials are biomarker naïve and unable to identify “on-target” effects of the specific drug, monitor treatment response or identify resistance mechanisms. This challenge arises since re-sampling of the tumour during treatment is not feasible due to its location, which could damage eloquent brain areas and result in a subsequent loss of function. Furthermore, the potential of liquid biopsy in GBM remains unexplored, as it faces several challenges, such as the extremely low concentrations of biomarkers in the blood and their short half-life, both of which complicate detection [23]. So far, no prognostic or predictive biomarkers have been identified in the blood of GBM patients [23,24]. Further studies are necessary to assess the sensitivity and specificity of liquid biopsies in GBM.
The mono-target compounds that have shown some clinical benefit, defined as an improvement in progression-free survival (PFS) and/or overall survival (OS) compared to the standard of care, in (a subset of) GBM patients included in these trials, will be discussed in more detail.
One of the most frequently altered genes in GBM is the epidermal growth factor receptor (EGFR). EGFR is a tyrosine kinase receptor amplified and constitutively activated in 57% of all GBM patients [4]. In fifty percent of the tumours with EGFR amplification, activating EGFR gene rearrangements occur, with the most common extracellular domain mutation being EGFRvIII [25,26]. This mutation leads to a deletion of exons 2–7 of the EGFR gene and renders the mutant receptor incapable of binding any known ligand. Despite this, EGFRvIII displays low-level ligand independent constitutive signalling that is amplified by reduced internalisation and downregulation.
The small molecule inhibitors erlotinib and gefitinib, both binding to the kinase domain of EGFR and inhibiting phosphorylation and activation, have been assessed in multiple trials (Table 1). Overall, the response rates were low, and overall survival did not improve significantly. Moreover, EGFRvIII/EGFR amplification did not correlate with a better outcome compared to EGFR wildtype [27,28,29,30].
However, in a small subset of patients, a stabilisation or even decrease in tumour volume was identified when treated with erlotinib. Haas-Kogan et al. showed, based upon a tissue analysis, that erlotinib has a significantly better effect on tumours expressing high levels of EGFR and low levels of phosphorylated protein kinase B (AKT) compared to tumours with low EGFR levels and high levels of phosphorylated AKT [31,32]. This indicates that activation might be a resistance mechanism of GBM to EGFR inhibitors. This mechanism was also suggested in a trial investigating gefitinib, which showed a significantly better PFS and OS in patients with de novo GBM with EGFR gene alterations combined with wildtype phosphatase and tensin homolog (PTEN) (inactivation of the AKT pathway) compared to patients with wildtype EGFR and altered PTEN (activation of the AKT pathway) [33]. Similarly, in EGFR-mutated non-small cell lung cancer (NSCLC), PTEN loss is known as a resistance mechanism to EGFR inhibitors [34,35].
A phase II trial also found that erlotinib treatment in patients with recurrent GBM resulted in improved six-month progression-free survival (PFS-6) and comparable median OS compared to historical values for patients undergoing treatment with irinotecan [36]. Nonetheless, because of the small number of responses, no conclusions could be drawn from the molecular subgroup analyses. Based on the limited efficacy of erlotinib and gefitinib in the total, unstratified GBM study population, these compounds are currently not approved for the standard of care in GBM patients.
In addition to the trials mentioned above, other trials have been conducted investigating mono-target small molecule inhibitors but were not able to show an effect on tumour size and therefore did not improve overall survival for GBM patients. These trials investigated the effects of buparlisib (phosphoinositide 3-kinase (PI3K) inhibitor), capmatinib (mesenchymal epithelial transition (c-MET) inhibitor), periforsine (AKT inhibitor), deforolimus (mechanistic target of rapamycin (mTOR) inhibitor), PF-06840003 (indoleamine 2,3-dioxygenase-1 (IDO-1) inhibitor), GSK2256098 (focal adhesion kinase (FAK-kinase) inhibitor), adavosertib (Wee-1 inhibitor), pegdinetanib (VEGFR-2 inhibitor), navtemadlin (mouse double minute 2 homolog (MDM2) inhibitor), tipifarnib (FTase subunit ß inhibitor), AXL1717 (insulin-like growth factor 1 receptor (IGF-1R) inhibitor) and selinexor (exportin 1 (XPO-1) inhibitor). More details on these trials can be found in Table 1.
In summary, mono-target small molecule inhibitors showed poor efficacy in GBM, with only a few trials demonstrating modest efficacy in a specific subgroup. An explanation for the ineffectiveness of mono-target small molecule inhibitors is the clonal selection of resistant tumour cells due to intra-tumour heterogeneity [8,9]. Investigation of primary GBM tumour samples has revealed that multiple receptor tyrosine kinases (RTKs) are activated within a single tumour sample [8,10,37]. Moreover, malignant cells in a GBM tumour exhibit plasticity, and a single cell is able to generate all four states [9]. This plasticity of the cells leads to changes in distribution of these states after targeting genetic drivers [9]. By inhibiting only one genetic driver—for example, EGFR—only cells harbouring alterations in EGFR will be targeted, while the other tumour cells remain unaffected, leading to sub-clonal selection and, eventually, to recurrence.
Additionally, most trials include unstratified GBM patient populations. Consequently, by not taking biomarkers or tumour heterogeneity into account, GBM tumours are included that are already intrinsically resistant to small molecule inhibitors because of their molecular characteristics.
Therefore, multitarget small molecule inhibitors might be more effective in tackling the inter- and intra-tumoral heterogeneity than mono-target small molecule inhibitors and prevent sub-clonal selection [9].

2.2. Multitarget Small Molecule Inhibitors

Clinical trials with small molecule inhibitors targeting multiple targets are presented in Table 2.
Remarkably, most trials that showed a promising result after treatment with a multitarget small molecule inhibitor involved small molecule inhibitors targeting vascular endothelial growth factor (VEGF/VEGFR). Since GBM exhibit extensive vascularity, VEGF/VEGFR is an important target [38].
Different small molecule inhibitors have been designed to target VEGF/VEGFR. For example, in a randomised phase II trial comparing axitinib, which, apart from VEGFR, also targets PDGFRß, and c-Kit, with physicians’ best choice of therapy in patients with recurrent GBM, axitinib monotherapy was found to increase the 6-month PFS and overall response rate but not OS [39].
Another trial compared treatment with axitinib alone to axitinib combined with lomustine in patients with recurrent GBM. Axitinib improved the response rate and progression-free survival in this population compared to historical controls, but the overall survival of these patients treated with axitinib alone did not improve [40].
A phase II trial investigating the effects of cediranib, a pan-VEGFR inhibitor also targeting FLT1/4, c-Kit and PDGFRβ, showed promising results in patients with recurrent GBM [41]. Since its results were comparable to historical controls, the inhibitor proceeded to a phase III trial. However, the primary endpoint of the trial (PFS prolongation) when comparing cediranib as a monotherapy and in combination with lomustine was not met. Subgroup analysis investigating the effect of the VEGFR baseline levels showed no significant effect after cediranib monotherapy compared to lomustine monotherapy [42]. Hence, cediranib has not been approved for GBM.
Regorafenib, which inhibits VEGFR, PDGFR, c-Kit, c-RET, Raf-1, FGFR and Abl, showed in a randomised, controlled, phase II trial a significant improvement in overall survival in patients with recurrent GBM compared to lomustine [43]. Regorafenib therefore continues to be investigated in a phase III trial, the results of which are awaited (NCT03970447). One explanation for regorafenib’s potential as an inhibitor lies in its role as a multi-kinase inhibitor, targeting not only VEGFR but also targeting kinases involved in oncogenesis and the tumour microenvironment [44].
Although fusion genes were discovered a long time ago, their significance and role in oncogenesis was not recognised until recently [45,46]. Currently, different tyrosine kinase inhibitors (TKI) are investigated in trials to target fusion genes in recurrent GBM patients [47]. In the context of GBM, fusion genes are present in approximately 30–50% of all GBM patients, and druggable fusion genes are found in approximately 4% of these patients [48,49]. Multiple fusion genes have been identified, with the most prevalent involving MET, EGFR, FGFR, NTRK, RET and ROS [47,50]. Currently, several TKIs are investigated in trials to target these fusion genes in recurrent GBM [47]. Infigratinib (FGFR 1/2/3 inhibitor) showed limited efficacy in the entire recurrent glioma patients with the FGFR alterations population but demonstrated a durable response in patients with FGFR1 or FGFR3 point mutations and those with FGFR3-TACC3 fusions [51]. More research is necessary to clarify the efficacy of TKIs targeting fusion genes in a biomarker-driven GBM patient cohort.
In addition to the trials mentioned above, many trials have been conducted investigating multitarget small molecule inhibitors, but none were able to improve OS (Table 2).
While there are no phase III trials with mono-target small molecule inhibitors, there have been two phase III trials with multitarget small molecule inhibitors. However, none have shown an improvement in overall survival in a phase III trial so far.

2.3. Small Molecule Inhibitors Combined with Chemotherapy/Bevacizumab

An overview of all the trials involving small molecule inhibitors combined with chemotherapy or bevacizumab can be found in Table 3.
Of all these trials, only two showed an increase in PFS and OS [52,53]. In the first trial, recurrent GBM patients with VEGF overexpression and EGFRvIII mutation showed an increase in the response rate and PFS after treatment with erlotinib combined with bevacizumab. However, since the trial only included four patients, no conclusions could be drawn [52]. In addition, other trials involving erlotinib combined with bevacizumab have also not shown an increase in PFS or OS [54,55]. However, these other trials were not biomarker-driven.
In the second trial, anlotinib, a multitarget small molecule inhibitor with similar targets as regorafenib, showed promising results in a phase II single-arm trial in twenty-one recurrent GBM patients in combinations with temozolomide with an increase in the response rate, PFS (7.3 months (95% CI 4.9–9.7)) and median OS (16.9 months (95% CI 7.8–26.0)) compared to historical controls [53].
Additionally, different trials have investigated the effects of different small molecule inhibitors combined with chemotherapy or bevacizumab. Lomustine has been investigated together with buparlisib (PI3K inhibitor), but this combination did not improve OS in 18 recurrent patients [56]. The combination of hydroxyurea with imatinib and vatalanib (VEGFR/FLT inhibitors) also did not show a positive effect on the OS of these patients [57,58]. Trials investigating the combination of irinotecan with CT-322 (VEGFR-2 inhibitor) or sunitinib (VEGFR/PDGFR inhibitor) were unsuccessful as well [59,60]. Other small molecule inhibitors have been investigated in combination with temozolomide or bevacizumab, but almost all of these trials could not show an increase in OS (Table 3).

2.4. Combinations of Small Molecule Inhibitors

A method to target intra-tumoral heterogeneity is to target multiple kinases in the same tumour. Only a limited number of trials have investigated the combinatorial treatments of different small molecule inhibitors. These trials are listed in Table 4.
A trial combining gefitinib and cediranib, targeting EGFR and VEGFR, showed a trend towards an improved response rate in recurrent GBM patients in a phase II trial [61]. Unfortunately, this trial was discontinued prematurely, and no statistically significant analysis of the PFS and OS was performed.
The BRAF V600E mutation is found in approximately 3% of GBM patients [62]. A single-arm, phase II basket trial that was focused on BRAF V600E-mutated glioma showed an increase in the response rate ((32% (95% CI 17–51)) and OS (13.7 months (95% CI 8.4–25.6)) in 31 patients with recurrent BRAF V600E-mutated GBM after they were treated with dabrafenib, a BRAF V600E inhibitor, combined with trametinib, a MEK inhibitor [63]. This combination therefore makes an interesting treatment option for this specific GBM subgroup.
Most of the trials combined different small molecule inhibitors with a mTOR inhibitor. The PI3K/Akt/mTOR pathway is frequently altered in GBM, making it an interesting target [64]. As discussed above, targeting this pathway is not effective as a monotherapy (Table 1 and Table 2). Combinations with erlotinib or gefitinib and sirolimus or temsirolimus (mTOR inhibitor) and temsirolimus with perifosine (AKT inhibitor) or sorafenib (Raf/VEGFR/PDGFR inhibitor) have been investigated. None of these trials, however, showed an increase in the PFS, OS or response rate (Table 4). This is most likely due to the fact that the majority of PI3K/mTOR inhibitors are unable to pass the BBB and achieve adequate concentrations at the tumour site [65,66].
In addition, researchers have investigated combinations of different (multitarget) small molecule inhibitors, targeting various pathways, none of which showed an increase in OS in GBM patients (Table 4).

3. Discussion

3.1. Intrinsic Versus Acquired Resistance

The discouraging results of trials with small molecule inhibitors in patients with GBM underscore the complexity of developing a treatment schedule for these patients. Multi-omics analysis has unravelled the complexity of proteomic, phosphorylation, metabolic, lipidic and immunogenic alterations and their influence on GBM subtypes and survival; yet, despite this progress, a uniformly present and druggable driver oncogene across all GBM tumour remains elusive, making a “one show fits all” treatment targeting all GBM subclones unavailable for these patients [4,7,8,9,67,68].
Extensive research has been conducted investigating the effects of mono-target small molecule inhibitors (Table 1), but most of these trials did not show an improvement in PFS and/or OS. One of the possible reasons for failure of these trials is the lack of patient stratification. In two studies, subgroup analyses demonstrated that patients with amplified EGFR and inactivation of the AKT pathway responded better to EGFR inhibitors than those without [31,32,33]. Additionally, the investigation of primary GBM tumour samples has revealed that multiple RTKs are activated within a single tumour sample [8,10,37]. The inhibition of only one genetic driver will lead to the clonal selection of resistant tumour cells and, eventually, recurrence.
In addition to inter- and intra-tumoral heterogeneity and the clonal selection of resistant tumour cells, other mechanisms may contribute to the primary resistance to small molecule inhibitors in GBM. First, while the majority of small molecule inhibitors primarily target the kinase domain of a receptor, the situation differs in GBM, where mutations may not always be confined to this domain. An illustration of this variability is observed in the case of the EGFR receptor, which most prevalent mutation, the EGFRvIII mutation, is situated in the extracellular domain of the EGFR receptor, resulting in the constitutive activation of non-mutated kinase domains [25,26]. Third-generation EGFR inhibitors, like Osimertinib, used in EGFR-mutated non-small cell lung cancer, specifically targeting the mutated kinase domains, are ineffective in EGFR-amplified or EGFRvIII-mutated GBM [25,26].
Lastly, GBM tumours can bypass the effect of a single small molecule inhibitor by activating other pathways. These bypass signalling pathways could be another explanation for treatment resistance, which supports the need for combination therapies.
In GBM, ERBB3, IGFR1R and TGFBR2 were positively correlated with PDGFR, and combining a PDGFR inhibitor with either an ERBB3 inhibitor or an IGF1R inhibitor resulted in a more significant reduction in tumour growth than each inhibitor alone [69].
Moreover, the upregulation of c-MET has been found after EGFR monotherapy in glioblastoma stem cells (GSCs) in vitro, causing long-term self-renewal ability in these cells [70]. The inhibition of both MET and EGFR resulted in overcoming the resistance to an EGFR inhibitor (gefitinib) in a preclinical mouse model [71]. Further research, however, found that, after EGFR and c-MET inhibition, ERK was reactivated via a NF-kB-dependent feedback loop that led to the activation of FGFRs. This bypass resistance mechanism could be overcome in vivo through simultaneously inhibiting FGFR [72]. More research has demonstrated that the expression of EGFRvIII induces the transactivation of c-Jun N-terminal kinase isoform 2 (JNK2) in GBM cells, which, in turn, activates the hepatocyte growth factor (HGF)/c-MET signalling pathway [73].
These preclinical results demonstrate crosstalk between different RTKs but also highlight that the inhibition of three or more RTKs is essential to overcome this resistance mechanism. While this approach is feasible in preclinical settings, using a combination of three or more mono-target small molecule inhibitors in patients could lead to severe toxicity, hindering clinical application. Consequently, targeting downstream feedback loops may circumvent the need for multiple inhibitors, thereby reducing multi-drug toxicity while maintaining a high efficacy in overcoming treatment resistance in GBM.
Crosstalk between the PI3K/mTOR pathway and MEK/ERK pathway is one of these feedback loops. Inactivation of the PI3K/mTOR pathway activates the MEK/ERK pathway and vice versa, thereby bypassing the effect of either PI3K/mTOR inhibitors or MEK/ERK inhibitors [74,75]. The combinatorial treatment of a MEK inhibitor with a dual PI3K/mTOR inhibitor was therefore found as a synergistic combination treatment in vitro [74]. However, the synergistic efficacy in vivo was minimal, most likely due to subtherapeutic dosing due to dose-limiting toxicity and poor tumour penetrance [76]. Trials exploring combinations of the MAPK pathway and PI3K/mTOR pathway have proven unsuccessful due to their low efficacy because of low tumour penetrance and high toxicity (Table 4) [77,78,79]. Therefore, novel brain-penetrant PI3K and mTOR inhibitors, such as paxalisib, are of interest and are currently being evaluated.
Even though most trials involving multitarget small molecule inhibitors have been ineffective (Table 2), a few inhibitors tend to show some promising results, all targeting VEGFR. Regorafenib and anlotinib both showed an improvement in OS, but in both trials, the treatment group exhibited more favourable prognostic factors, potentially leading to an overestimation of the observed improvement in OS [39,40,43,53].
An explanation why regorafenib and anlotinib showed better results than axitinib might be that axitinib only targets VEGFR, while regorafenib and anlotinib target VEGFR, PDGFR and FGFR [44,53]. PDGFR and FGFR are also involved in angiogenesis and upregulated upon VEGFR inhibition, thereby bypassing the inhibitory effect of the VEGFR inhibitor [80,81,82,83]. The inhibition of all three pathways is therefore more effective than inhibiting VEGFR alone [39,40,44,53].
Moreover, anlotinib was more effective than regorafenib, which could be explained by the fact that anlotinib was combined with TMZ in GBM. Preclinical research has shown that anlotinib combined with TMZ decreased GBM growth more than anlotinib alone via inhibition of the JAK2/STAT3/VEGFA pathway [84]. This illustrates how the concurrent administration of chemotherapy with, in particular, an angiogenesis inhibitor can enhance the efficacy. However, a combination of bevacizumab with lomustine in recurrent GBM failed to show a survival advantage in a phase III trial [85].
In other types of solid cancer, such as colorectal carcinoma, the inhibition of bevacizumab/VEGFR also failed to show an improvement in OS [86]. This indicates that targeting angiogenesis alone merely slows tumour growth and the tumour can circumvent the inhibition, which results in an increase in PFS but not in OS.
Different combinatorial treatments with small molecule inhibitors have been investigated, most of them with disappointing results (Table 4). One trial investigating the combination of dabrafenib with trametinib in recurrent BRAF V600E-mutated GBM patients showed an increase in OS [63]. Dabrafenib combined with trametinib is currently considered the standard of care in progressive BRAF V600E-mutated CNS tumours [87]. Most likely, this combination is successful, since, by adding a MEK inhibitor, the MAPK pathway is inhibited at two positions.

3.2. Influence of the Tumour Microenvironment

The likelihood of a combination treatment exclusively targeting tumour cells curing GBM is low, since the tumour microenvironment (TME) also plays a significant role in therapy resistance within GBM.
The TME in a GBM tumour contains not only tumour cells but also non-neoplastic cells, including immune cells, vascular cells and other glial cells [88]. GBMs are considered as immunogenic cold tumours due to low numbers of tumour-infiltrating lymphocytes (TILs) and other immune effector cell types [89].
One of the critical immunosuppressive factors in the GBM microenvironment is hypoxia, which suppresses the antitumour functions of immune cells that are able to infiltrate into the GBM microenvironment. Vice versa, functions of immunosuppressive cells, such M2 macrophages, are enhanced by hypoxia [90,91].
Trials investigating the currently available immune checkpoint inhibitors (ICIs) in newly diagnosed and recurrent GBM have not demonstrated an improvement in overall survival [92,93,94]. A key factor limiting the effectiveness of these inhibitors is the very low number of TILs in the GBM microenvironment [89,95].
Consequently, future research must focus on targeting the GBM-specific immune environment, which is characterised by immunosuppressive tumour-associated macrophages (TAM). To achieve this, it is imperative to elucidate and understand the interplay between the GBM tumour and TAMs. Additionally, more information is required on how inhibitors might be able to shift the TME to become immune-permissive. The combination of Lenvatinib with anti-PD-1 antibody and cabozantinib with a poxviral-based cancer vaccine caused a more permissive TME with a decrease in TAMs and activation of the type-I interferon response when investigated in other types of cancer [96,97].
Preclinical research in GBM has revealed that combining an EGFR inhibitor with a mTOR inhibitor can modulate the TME by downregulating immunosuppressive chemokines and inhibiting tumour-promoting macrophage infiltration [98]. Thus, small molecule inhibitors could possibly be used with a novel intent to create a shift towards an immune-permissive TME. This approach might lead towards innovative combinations of small molecule inhibitors with immunotherapy, potentially preventing the clonal expansion of resistant tumour clones. Further research is needed to explore whether such combinations of immunotherapy with small molecule inhibitors will be effective and overcome treatment resistance in GBM.

3.3. Clinical Trial Design

In addition to the reasons mentioned above, the disappointing results observed in trials involving small molecule inhibitors for glioblastoma patients could be attributed to several other factors related to the trial design.
The BBB makes it difficult for different drugs to arrive in effective concentrations at the tumour site in brain cancer [12,13]. The aim of phase 0 trials is to investigate the biological effect of a study drug in human patients by using a pharmacologically active but non-therapeutic dosage [99]. However, in brain tumours, a higher systemic dose will be necessary to detect study drug levels in the CNS with greater risks of related side effects [100]. Different techniques to open the BBB, such as focused ultrasound (FUS), are being investigated. These techniques might be a way to enhance drug delivery at the tumour site without the need to increase the systemic dose [101,102,103].
The advantage of the study design of phase 0 trials is that it allows for the examination of different patient samples over time, such as blood, cerebrospinal fluid and tumour tissue, hereby giving information on pharmacokinetics and -dynamics, BBB passage and target inhibition [99,100,104]. On the one hand, this information will be crucial to eliminate drugs with low tumour penetration or target inhibition and prevent unnecessary burdens on patients in phase I and II trials. On the other hand, newly developed drugs that are able to pass the BBB and inhibit the desired target effectively can be taken into phase I and II trials to investigate their effect on tumour growth, PFS and OS.
Additionally, the absence or limited use of molecular markers in patient selection might contribute to the failure of these trials, given the considerable intertumoral heterogeneity. Therefore, to find an effective drug in GBM patients, it may be necessary to select patients based on the molecular characteristics of the tumour. However, to date, only a few biomarker-driven trials have been performed using specific molecular markers of the parental tumour as an inclusion criterion. Moreover, in CNS tumours, the predictive significance of these mutations remains insufficiently studied [87].
Whole genome sequencing (WGS) is gaining prominence in oncological care. Compared to commonly used DNA diagnostic approaches, WGS offers a high sensitivity and accuracy, and all important mutation types may be consistently identified in a single assay [105]. The importance of WGS is highlighted in a study wherein 62% of the patients with different metastatic cancers had mutations that could be used for further treatment or inclusion in trials after using WGS [106].
The first biomarker-driven trial was the IMPACT study, where patients with advanced metastatic cancer that received tumour genetic profiling and were evaluated for investigational therapy were included. In total, the overall response rate (ORR) was 27% for the patients treated with matched targeted therapy and 5% for patients with non-matched therapy, emphasising the potential of biomarker-driven studies [107]. Hereafter, other biomarker-driven trials followed, such as the SHIVA trial, IMPACT-II and NCI-MATCH, to investigate the benefits of using genetic alterations to guide matched targeted therapies, aiming to enhance the ORR [108,109,110].
Given the absence of FDA approval for the use of all targeted drugs in all types of cancers, the TAPUR trial investigated the effects of FDA-approved drugs outside their approved indications in different cancer types based on pre-specified genomic targets [111]. Encouragingly, most treatment arms have shown success, underscoring the potential of this approach [111]. Similarly, the ongoing DRUP (Drug Rediscovery Protocol, NCT02925234) study, an ongoing biomarker-driven, multi-basket trial in the Netherlands, allocates patients to different treatments based on the mutational status of their tumours, achieving an overall clinical benefit rate of 33% for both rare and non-rare cancers [112].
These results highlight the importance of performing molecular tests to guarantee that all cancer patients have an equal chance of receiving the right treatment.
By identifying actionable mutations and enabling access to a broader spectrum of treatment options, studies like DRUP aim to offer renewed hope and extended survival prospects for patients with refractory GBM.

4. Conclusions and Future Perspectives

Although small molecule inhibitors have become part of the standard of care in various types of cancer, the overall results of clinical trials in GBM patients have been disappointing. The main factor contributing to this poor performance is the pervasive inter- and intra-tumoral heterogeneity characteristics of GBM. This tumour heterogeneity undermines the feasibility of a universal therapeutic approach, emphasising the inefficacy of a “one-size fits all” drug for these patients. Moreover, various aspects of the TME can counteract the effectiveness of small molecule inhibitors. Consequently, a need arises for further research directed towards identifying drug combinations that can target tumour clones that drive tumour relapse and are capable of modulating the complex dynamics of the TME.
In addition, more phase 0 trials will be needed to investigate drug penetration and target inhibition at the tumour site of GBM of small molecule inhibitors. Furthermore, future trials should focus on patient stratification based on molecular tumour markers to achieve the best possible results and identify drugs and drug combinations for certain patient subpopulations.

Author Contributions

Writing—original draft preparation, L.H.; writing—review and editing, A.H. and M.V.; supervision, A.H. All authors have read and agreed to the published version of the manuscript.

Funding

LH was supported by a grant from the KWF Dutch Cancer Society (grant INTO-PROT 12092).

Data Availability Statement

No new data were created or analysed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Louis, D.N.; Perry, A.; Reifenberger, G.; von Deimling, A.; Figarella-Branger, D.; Cavenee, W.K.; Ohgaki, H.; Wiestler, O.D.; Kleihues, P.; Ellison, D.W. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: A summary. Acta Neuropathol. 2016, 131, 803–820. [Google Scholar] [CrossRef]
  2. Ohgaki, H.; Kleihues, P. The definition of primary and secondary glioblastoma. Clin. Cancer Res. 2013, 19, 764–772. [Google Scholar] [CrossRef]
  3. Stupp, R.; Mason, W.P.; van den Bent, M.J.; Weller, M.; Fisher, B.; Taphoorn, M.J.; Belanger, K.; Brandes, A.A.; Marosi, C.; Bogdahn, U.; et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N. Engl. J. Med. 2005, 352, 987–996. [Google Scholar] [CrossRef] [PubMed]
  4. Brennan, C.W.; Verhaak, R.G.; McKenna, A.; Campos, B.; Noushmehr, H.; Salama, S.R.; Zheng, S.; Chakravarty, D.; Sanborn, J.Z.; Berman, S.H.; et al. The somatic genomic landscape of glioblastoma. Cell 2013, 155, 462–477. [Google Scholar] [CrossRef]
  5. Yan, H.; Parsons, D.W.; Jin, G.; McLendon, R.; Rasheed, B.A.; Yuan, W.; Kos, I.; Batinic-Haberle, I.; Jones, S.; Riggins, G.J.; et al. IDH1 and IDH2 mutations in gliomas. N. Engl. J. Med. 2009, 360, 765–773. [Google Scholar] [CrossRef] [PubMed]
  6. Perry, J.R.; Laperriere, N.; O’Callaghan, C.J.; Brandes, A.A.; Menten, J.; Phillips, C.; Fay, M.; Nishikawa, R.; Cairncross, J.G.; Roa, W.; et al. Short-Course Radiation plus Temozolomide in Elderly Patients with Glioblastoma. N. Engl. J. Med. 2017, 376, 1027–1037. [Google Scholar] [CrossRef] [PubMed]
  7. Verhaak, R.G.; Hoadley, K.A.; Purdom, E.; Wang, V.; Qi, Y.; Wilkerson, M.D.; Miller, C.R.; Ding, L.; Golub, T.; Mesirov, J.P.; et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 2010, 17, 98–110. [Google Scholar] [CrossRef] [PubMed]
  8. Patel, A.P.; Tirosh, I.; Trombetta, J.J.; Shalek, A.K.; Gillespie, S.M.; Wakimoto, H.; Cahill, D.P.; Nahed, B.V.; Curry, W.T.; Martuza, R.L.; et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 2014, 344, 1396–1401. [Google Scholar] [CrossRef]
  9. Neftel, C.; Laffy, J.; Filbin, M.G.; Hara, T.; Shore, M.E.; Rahme, G.J.; Richman, A.R.; Silverbush, D.; Shaw, M.L.; Hebert, C.M.; et al. An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma. Cell 2019, 178, 835–849.e821. [Google Scholar] [CrossRef]
  10. Stommel, J.M.; Kimmelman, A.C.; Ying, H.; Nabioullin, R.; Ponugoti, A.H.; Wiedemeyer, R.; Stegh, A.H.; Bradner, J.E.; Ligon, K.L.; Brennan, C.; et al. Coactivation of receptor tyrosine kinases affects the response of tumor cells to targeted therapies. Science 2007, 318, 287–290. [Google Scholar] [CrossRef]
  11. Daneman, R.; Prat, A. The blood-brain barrier. Cold Spring Harb. Perspect. Biol. 2015, 7, a020412. [Google Scholar] [CrossRef]
  12. Greene, C.; Campbell, M. Tight junction modulation of the blood brain barrier: CNS delivery of small molecules. Tissue Barriers 2016, 4, e1138017. [Google Scholar] [CrossRef] [PubMed]
  13. Pardridge, W.M. The blood-brain barrier: Bottleneck in brain drug development. NeuroRx 2005, 2, 3–14. [Google Scholar] [CrossRef] [PubMed]
  14. Lin, F.; de Gooijer, M.C.; Roig, E.M.; Buil, L.C.; Christner, S.M.; Beumer, J.H.; Wurdinger, T.; Beijnen, J.H.; van Tellingen, O. ABCB1, ABCG2, and PTEN determine the response of glioblastoma to temozolomide and ABT-888 therapy. Clin. Cancer Res. 2014, 20, 2703–2713. [Google Scholar] [CrossRef]
  15. Szakacs, G.; Paterson, J.K.; Ludwig, J.A.; Booth-Genthe, C.; Gottesman, M.M. Targeting multidrug resistance in cancer. Nat. Rev. Drug Discov. 2006, 5, 219–234. [Google Scholar] [CrossRef]
  16. Sun, G.; Rong, D.; Li, Z.; Sun, G.; Wu, F.; Li, X.; Cao, H.; Cheng, Y.; Tang, W.; Sun, Y. Role of small molecule targeted compounds in cancer: Progress, opportunities, and challenges. Front. Cell Dev. Biol. 2021, 9, 694363. [Google Scholar] [CrossRef] [PubMed]
  17. Wu, P.; Nielsen, T.E.; Clausen, M.H. Small-molecule kinase inhibitors: An analysis of FDA-approved drugs. Drug Discov. Today 2016, 21, 5–10. [Google Scholar] [CrossRef] [PubMed]
  18. Roskoski Jr, R. Properties of FDA-approved small molecule protein kinase inhibitors: A 2021 update. Pharmacol. Res. 2021, 165, 105463. [Google Scholar] [CrossRef]
  19. Bedard, P.L.; Hyman, D.M.; Davids, M.S.; Siu, L.L. Small molecules, big impact: 20 years of targeted therapy in oncology. Lancet 2020, 395, 1078–1088. [Google Scholar] [CrossRef]
  20. Greenhalgh, J.; Boland, A.; Bates, V.; Vecchio, F.; Dundar, Y.; Chaplin, M.; Green, J.A. First-line treatment of advanced epidermal growth factor receptor (EGFR) mutation positive non-squamous non-small cell lung cancer. Cochrane Database Syst. Rev. 2021, 3, CD010383. [Google Scholar] [CrossRef]
  21. Rosell, R.; Carcereny, E.; Gervais, R.; Vergnenegre, A.; Massuti, B.; Felip, E.; Palmero, R.; Garcia-Gomez, R.; Pallares, C.; Sanchez, J.M.; et al. Erlotinib versus standard chemotherapy as first-line treatment for European patients with advanced EGFR mutation-positive non-small-cell lung cancer (EURTAC): A multicentre, open-label, randomised phase 3 trial. Lancet Oncol. 2012, 13, 239–246. [Google Scholar] [CrossRef]
  22. Mok, T.S.; Wu, Y.L.; Thongprasert, S.; Yang, C.H.; Chu, D.T.; Saijo, N.; Sunpaweravong, P.; Han, B.; Margono, B.; Ichinose, Y.; et al. Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N. Engl. J. Med. 2009, 361, 947–957. [Google Scholar] [CrossRef] [PubMed]
  23. Muller Bark, J.; Kulasinghe, A.; Chua, B.; Day, B.W.; Punyadeera, C. Circulating biomarkers in patients with glioblastoma. Br. J. Cancer 2020, 122, 295–305. [Google Scholar] [CrossRef]
  24. Ronvaux, L.; Riva, M.; Coosemans, A.; Herzog, M.; Rommelaere, G.; Donis, N.; D’Hondt, L.; Douxfils, J. Liquid Biopsy in Glioblastoma. Cancers 2022, 14, 3394. [Google Scholar] [CrossRef] [PubMed]
  25. Gan, H.K.; Cvrljevic, A.N.; Johns, T.G. The epidermal growth factor receptor variant III (EGFRvIII): Where wild things are altered. FEBS J. 2013, 280, 5350–5370. [Google Scholar] [CrossRef] [PubMed]
  26. Hoogstrate, Y.; Ghisai, S.A.; de Wit, M.; de Heer, I.; Draaisma, K.; van Riet, J.; van de Werken, H.J.G.; Bours, V.; Buter, J.; Vanden Bempt, I.; et al. The EGFRvIII transcriptome in glioblastoma: A meta-omics analysis. Neuro Oncol. 2022, 24, 429–441. [Google Scholar] [CrossRef]
  27. Hegi, M.E.; Diserens, A.C.; Bady, P.; Kamoshima, Y.; Kouwenhoven, M.C.; Delorenzi, M.; Lambiv, W.L.; Hamou, M.F.; Matter, M.S.; Koch, A.; et al. Pathway analysis of glioblastoma tissue after preoperative treatment with the EGFR tyrosine kinase inhibitor gefitinib—A phase II trial. Mol. Cancer Ther. 2011, 10, 1102–1112. [Google Scholar] [CrossRef]
  28. Uhm, J.H.; Ballman, K.V.; Wu, W.; Giannini, C.; Krauss, J.C.; Buckner, J.C.; James, C.D.; Scheithauer, B.W.; Behrens, R.J.; Flynn, P.J.; et al. Phase II evaluation of gefitinib in patients with newly diagnosed Grade 4 astrocytoma: Mayo/North Central Cancer Treatment Group Study N0074. Int. J. Radiat. Oncol. Biol. Phys. 2011, 80, 347–353. [Google Scholar] [CrossRef]
  29. Franceschi, E.; Cavallo, G.; Lonardi, S.; Magrini, E.; Tosoni, A.; Grosso, D.; Scopece, L.; Blatt, V.; Urbini, B.; Pession, A.; et al. Gefitinib in patients with progressive high-grade gliomas: A multicentre phase II study by Gruppo Italiano Cooperativo di Neuro-Oncologia (GICNO). Br. J. Cancer 2007, 96, 1047–1051. [Google Scholar] [CrossRef]
  30. Rich, J.N.; Reardon, D.A.; Peery, T.; Dowell, J.M.; Quinn, J.A.; Penne, K.L.; Wikstrand, C.J.; Van Duyn, L.B.; Dancey, J.E.; McLendon, R.E.; et al. Phase II trial of gefitinib in recurrent glioblastoma. J. Clin. Oncol. 2004, 22, 133–142. [Google Scholar] [CrossRef]
  31. Haas-Kogan, D.A.; Prados, M.D.; Tihan, T.; Eberhard, D.A.; Jelluma, N.; Arvold, N.D.; Baumber, R.; Lamborn, K.R.; Kapadia, A.; Malec, M.; et al. Epidermal growth factor receptor, protein kinase B/Akt, and glioma response to erlotinib. J. Natl. Cancer Inst. 2005, 97, 880–887. [Google Scholar] [CrossRef] [PubMed]
  32. de Vries, N.A.; Buckle, T.; Zhao, J.; Beijnen, J.H.; Schellens, J.H.; van Tellingen, O. Restricted brain penetration of the tyrosine kinase inhibitor erlotinib due to the drug transporters P-gp and BCRP. Investig. New Drugs 2012, 30, 443–449. [Google Scholar] [CrossRef] [PubMed]
  33. Arif, S.H.; Pandith, A.A.; Tabasum, R.; Ramzan, A.U.; Singh, S.; Siddiqi, M.A.; Bhat, A.R. Significant Effect of Anti-tyrosine Kinase Inhibitor (Gefitinib) on Overall Survival of the Glioblastoma Multiforme Patients in the Backdrop of Mutational Status of Epidermal Growth Factor Receptor and PTEN Genes. Asian J. Neurosurg. 2018, 13, 46–52. [Google Scholar] [CrossRef]
  34. Sos, M.L.; Koker, M.; Weir, B.A.; Heynck, S.; Rabinovsky, R.; Zander, T.; Seeger, J.M.; Weiss, J.; Fischer, F.; Frommolt, P.; et al. PTEN loss contributes to erlotinib resistance in EGFR-mutant lung cancer by activation of Akt and EGFR. Cancer Res. 2009, 69, 3256–3261. [Google Scholar] [CrossRef]
  35. Yamamoto, C.; Basaki, Y.; Kawahara, A.; Nakashima, K.; Kage, M.; Izumi, H.; Kohno, K.; Uramoto, H.; Yasumoto, K.; Kuwano, M.; et al. Loss of PTEN expression by blocking nuclear translocation of EGR1 in gefitinib-resistant lung cancer cells harboring epidermal growth factor receptor-activating mutations. Cancer Res. 2010, 70, 8715–8725. [Google Scholar] [CrossRef] [PubMed]
  36. Yung, W.K.; Vredenburgh, J.J.; Cloughesy, T.F.; Nghiemphu, P.; Klencke, B.; Gilbert, M.R.; Reardon, D.A.; Prados, M.D. Safety and efficacy of erlotinib in first-relapse glioblastoma: A phase II open-label study. Neuro Oncol. 2010, 12, 1061–1070. [Google Scholar] [CrossRef] [PubMed]
  37. Little, S.E.; Popov, S.; Jury, A.; Bax, D.A.; Doey, L.; Al-Sarraj, S.; Jurgensmeier, J.M.; Jones, C. Receptor tyrosine kinase genes amplified in glioblastoma exhibit a mutual exclusivity in variable proportions reflective of individual tumor heterogeneity. Cancer Res. 2012, 72, 1614–1620. [Google Scholar] [CrossRef]
  38. Taal, W.; Oosterkamp, H.M.; Walenkamp, A.M.; Dubbink, H.J.; Beerepoot, L.V.; Hanse, M.C.; Buter, J.; Honkoop, A.H.; Boerman, D.; de Vos, F.Y.; et al. Single-agent bevacizumab or lomustine versus a combination of bevacizumab plus lomustine in patients with recurrent glioblastoma (BELOB trial): A randomised controlled phase 2 trial. Lancet Oncol. 2014, 15, 943–953. [Google Scholar] [CrossRef]
  39. Duerinck, J.; Du Four, S.; Vandervorst, F.; D’Haene, N.; Le Mercier, M.; Michotte, A.; Van Binst, A.M.; Everaert, H.; Salmon, I.; Bouttens, F.; et al. Randomized phase II study of axitinib versus physicians best alternative choice of therapy in patients with recurrent glioblastoma. J. Neurooncol. 2016, 128, 147–155. [Google Scholar] [CrossRef]
  40. Duerinck, J.; Du Four, S.; Bouttens, F.; Andre, C.; Verschaeve, V.; Van Fraeyenhove, F.; Chaskis, C.; D’Haene, N.; Le Mercier, M.; Rogiers, A.; et al. Randomized phase II trial comparing axitinib with the combination of axitinib and lomustine in patients with recurrent glioblastoma. J. Neurooncol. 2018, 136, 115–125. [Google Scholar] [CrossRef]
  41. Batchelor, T.T.; Duda, D.G.; di Tomaso, E.; Ancukiewicz, M.; Plotkin, S.R.; Gerstner, E.; Eichler, A.F.; Drappatz, J.; Hochberg, F.H.; Benner, T.; et al. Phase II study of cediranib, an oral pan-vascular endothelial growth factor receptor tyrosine kinase inhibitor, in patients with recurrent glioblastoma. J. Clin. Oncol. 2010, 28, 2817–2823. [Google Scholar] [CrossRef] [PubMed]
  42. Batchelor, T.T.; Mulholland, P.; Neyns, B.; Nabors, L.B.; Campone, M.; Wick, A.; Mason, W.; Mikkelsen, T.; Phuphanich, S.; Ashby, L.S.; et al. Phase III randomized trial comparing the efficacy of cediranib as monotherapy, and in combination with lomustine, versus lomustine alone in patients with recurrent glioblastoma. J. Clin. Oncol. 2013, 31, 3212–3218. [Google Scholar] [CrossRef]
  43. Lombardi, G.; De Salvo, G.L.; Brandes, A.A.; Eoli, M.; Ruda, R.; Faedi, M.; Lolli, I.; Pace, A.; Daniele, B.; Pasqualetti, F.; et al. Regorafenib compared with lomustine in patients with relapsed glioblastoma (REGOMA): A multicentre, open-label, randomised, controlled, phase 2 trial. Lancet Oncol. 2019, 20, 110–119. [Google Scholar] [CrossRef] [PubMed]
  44. Wilhelm, S.M.; Dumas, J.; Adnane, L.; Lynch, M.; Carter, C.A.; Schutz, G.; Thierauch, K.H.; Zopf, D. Regorafenib (BAY 73-4506): A new oral multikinase inhibitor of angiogenic, stromal and oncogenic receptor tyrosine kinases with potent preclinical antitumor activity. Int. J. Cancer 2011, 129, 245–255. [Google Scholar] [CrossRef] [PubMed]
  45. Gao, Q.; Liang, W.W.; Foltz, S.M.; Mutharasu, G.; Jayasinghe, R.G.; Cao, S.; Liao, W.W.; Reynolds, S.M.; Wyczalkowski, M.A.; Yao, L.; et al. Driver Fusions and Their Implications in the Development and Treatment of Human Cancers. Cell Rep. 2018, 23, 227–238.e223. [Google Scholar] [CrossRef]
  46. Mitelman, F.; Johansson, B.; Mertens, F. The impact of translocations and gene fusions on cancer causation. Nat. Rev. Cancer 2007, 7, 233–245. [Google Scholar] [CrossRef]
  47. You, G.; Fan, X.; Hu, H.; Jiang, T.; Chen, C.C. Fusion Genes Altered in Adult Malignant Gliomas. Front. Neurol. 2021, 12, 715206. [Google Scholar] [CrossRef] [PubMed]
  48. Shah, N.; Lankerovich, M.; Lee, H.; Yoon, J.G.; Schroeder, B.; Foltz, G. Exploration of the gene fusion landscape of glioblastoma using transcriptome sequencing and copy number data. BMC Genom. 2013, 14, 818. [Google Scholar] [CrossRef] [PubMed]
  49. Yoshihara, K.; Wang, Q.; Torres-Garcia, W.; Zheng, S.; Vegesna, R.; Kim, H.; Verhaak, R.G. The landscape and therapeutic relevance of cancer-associated transcript fusions. Oncogene 2015, 34, 4845–4854. [Google Scholar] [CrossRef]
  50. Woo, H.Y.; Na, K.; Yoo, J.; Chang, J.H.; Park, Y.N.; Shim, H.S.; Kim, S.H. Glioblastomas harboring gene fusions detected by next-generation sequencing. Brain Tumor Pathol. 2020, 37, 136–144. [Google Scholar] [CrossRef]
  51. Lassman, A.B.; Sepulveda-Sanchez, J.M.; Cloughesy, T.F.; Gil-Gil, M.J.; Puduvalli, V.K.; Raizer, J.J.; De Vos, F.Y.F.; Wen, P.Y.; Butowski, N.A.; Clement, P.M.J.; et al. Infigratinib in Patients with Recurrent Gliomas and FGFR Alterations: A Multicenter Phase II Study. Clin. Cancer Res. 2022, 28, 2270–2277. [Google Scholar] [CrossRef] [PubMed]
  52. D’Alessandris, Q.G.; Montano, N.; Cenci, T.; Martini, M.; Lauretti, L.; Bianchi, F.; Larocca, L.M.; Maira, G.; Fernandez, E.; Pallini, R. Targeted therapy with bevacizumab and erlotinib tailored to the molecular profile of patients with recurrent glioblastoma. Preliminary experience. Acta Neurochir. 2013, 155, 33–40. [Google Scholar] [CrossRef] [PubMed]
  53. Xu, Q.; Huang, K.; Meng, X.; Weng, Y.; Zhang, L.; Bu, L.; Zheng, X.; Cai, J.; Zhan, R.; Chen, Q. Safety and Efficacy of Anlotinib Hydrochloride Plus Temozolomide in Patients with Recurrent Glioblastoma. Clin. Cancer Res. 2023, 29, 3859–3866. [Google Scholar] [CrossRef] [PubMed]
  54. Sathornsumetee, S.; Desjardins, A.; Vredenburgh, J.J.; McLendon, R.E.; Marcello, J.; Herndon, J.E.; Mathe, A.; Hamilton, M.; Rich, J.N.; Norfleet, J.A.; et al. Phase II trial of bevacizumab and erlotinib in patients with recurrent malignant glioma. Neuro Oncol. 2010, 12, 1300–1310. [Google Scholar] [CrossRef]
  55. Raizer, J.J.; Giglio, P.; Hu, J.; Groves, M.; Merrell, R.; Conrad, C.; Phuphanich, S.; Puduvalli, V.K.; Loghin, M.; Paleologos, N.; et al. A phase II study of bevacizumab and erlotinib after radiation and temozolomide in MGMT unmethylated GBM patients. J. Neurooncol. 2016, 126, 185–192. [Google Scholar] [CrossRef]
  56. Rosenthal, M.; Clement, P.M.; Campone, M.; Gil-Gil, M.J.; DeGroot, J.; Chinot, O.; Idbaih, A.; Gan, H.; Raizer, J.; Wen, P.Y.; et al. Buparlisib plus carboplatin or lomustine in patients with recurrent glioblastoma: A phase Ib/II, open-label, multicentre, randomised study. ESMO Open 2020, 5, e000672. [Google Scholar] [CrossRef]
  57. Reardon, D.A.; Egorin, M.J.; Quinn, J.A.; Rich, J.N.; Gururangan, S.; Vredenburgh, J.J.; Desjardins, A.; Sathornsumetee, S.; Provenzale, J.M.; Herndon, J.E., 2nd; et al. Phase II study of imatinib mesylate plus hydroxyurea in adults with recurrent glioblastoma multiforme. J. Clin. Oncol. 2005, 23, 9359–9368. [Google Scholar] [CrossRef]
  58. Reardon, D.A.; Egorin, M.J.; Desjardins, A.; Vredenburgh, J.J.; Beumer, J.H.; Lagattuta, T.F.; Gururangan, S.; Herndon, J.E., 2nd; Salvado, A.J.; Friedman, H.S. Phase I pharmacokinetic study of the vascular endothelial growth factor receptor tyrosine kinase inhibitor vatalanib (PTK787) plus imatinib and hydroxyurea for malignant glioma. Cancer 2009, 115, 2188–2198. [Google Scholar] [CrossRef]
  59. Schiff, D.; Kesari, S.; de Groot, J.; Mikkelsen, T.; Drappatz, J.; Coyle, T.; Fichtel, L.; Silver, B.; Walters, I.; Reardon, D. Phase 2 study of CT-322, a targeted biologic inhibitor of VEGFR-2 based on a domain of human fibronectin, in recurrent glioblastoma. Investig. New Drugs 2015, 33, 247–253. [Google Scholar] [CrossRef]
  60. Reardon, D.A.; Vredenburgh, J.J.; Coan, A.; Desjardins, A.; Peters, K.B.; Gururangan, S.; Sathornsumetee, S.; Rich, J.N.; Herndon, J.E.; Friedman, H.S. Phase I study of sunitinib and irinotecan for patients with recurrent malignant glioma. J. Neurooncol. 2011, 105, 621–627. [Google Scholar] [CrossRef]
  61. Brown, N.; McBain, C.; Nash, S.; Hopkins, K.; Sanghera, P.; Saran, F.; Phillips, M.; Dungey, F.; Clifton-Hadley, L.; Wanek, K.; et al. Multi-Center Randomized Phase II Study Comparing Cediranib plus Gefitinib with Cediranib plus Placebo in Subjects with Recurrent/Progressive Glioblastoma. PLoS ONE 2016, 11, e0156369. [Google Scholar] [CrossRef] [PubMed]
  62. Ballester, L.Y.; Fuller, G.N.; Powell, S.Z.; Sulman, E.P.; Patel, K.P.; Luthra, R.; Routbort, M.J. Retrospective Analysis of Molecular and Immunohistochemical Characterization of 381 Primary Brain Tumors. J. Neuropathol. Exp. Neurol. 2017, 76, 179–188. [Google Scholar] [CrossRef] [PubMed]
  63. Wen, P.Y.; Stein, A.; van den Bent, M.; De Greve, J.; Wick, A.; de Vos, F.; von Bubnoff, N.; van Linde, M.E.; Lai, A.; Prager, G.W.; et al. Dabrafenib plus trametinib in patients with BRAF(V600E)-mutant low-grade and high-grade glioma (ROAR): A multicentre, open-label, single-arm, phase 2, basket trial. Lancet Oncol. 2022, 23, 53–64. [Google Scholar] [CrossRef]
  64. Cancer Genome Atlas Research, N. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 2008, 455, 1061–1068. [Google Scholar] [CrossRef] [PubMed]
  65. Mason, W.P. Blood-brain barrier-associated efflux transporters: A significant but underappreciated obstacle to drug development in glioblastoma. Neuro Oncol. 2015, 17, 1181–1182. [Google Scholar] [CrossRef]
  66. Tang, S.C.; Sparidans, R.W.; Cheung, K.L.; Fukami, T.; Durmus, S.; Wagenaar, E.; Yokoi, T.; van Vlijmen, B.J.; Beijnen, J.H.; Schinkel, A.H. P-glycoprotein, CYP3A, and plasma carboxylesterase determine brain and blood disposition of the mTOR Inhibitor everolimus (Afinitor) in mice. Clin. Cancer Res. 2014, 20, 3133–3145. [Google Scholar] [CrossRef]
  67. Wang, L.B.; Karpova, A.; Gritsenko, M.A.; Kyle, J.E.; Cao, S.; Li, Y.; Rykunov, D.; Colaprico, A.; Rothstein, J.H.; Hong, R.; et al. Proteogenomic and metabolomic characterization of human glioblastoma. Cancer Cell 2021, 39, 509–528.e520. [Google Scholar] [CrossRef]
  68. Yanovich-Arad, G.; Ofek, P.; Yeini, E.; Mardamshina, M.; Danilevsky, A.; Shomron, N.; Grossman, R.; Satchi-Fainaro, R.; Geiger, T. Proteogenomics of glioblastoma associates molecular patterns with survival. Cell Rep. 2021, 34, 108787. [Google Scholar] [CrossRef]
  69. Song, K.; Yuan, Y.; Lin, Y.; Wang, Y.X.; Zhou, J.; Gai, Q.J.; Zhang, L.; Mao, M.; Yao, X.X.; Qin, Y.; et al. ERBB3, IGF1R, and TGFBR2 expression correlate with PDGFR expression in glioblastoma and participate in PDGFR inhibitor resistance of glioblastoma cells. Am. J. Cancer Res. 2018, 8, 792–809. [Google Scholar]
  70. Jun, H.J.; Bronson, R.T.; Charest, A. Inhibition of EGFR induces a c-MET-driven stem cell population in glioblastoma. Stem Cells 2014, 32, 338–348. [Google Scholar] [CrossRef]
  71. Jun, H.J.; Acquaviva, J.; Chi, D.; Lessard, J.; Zhu, H.; Woolfenden, S.; Bronson, R.T.; Pfannl, R.; White, F.; Housman, D.E.; et al. Acquired MET expression confers resistance to EGFR inhibition in a mouse model of glioblastoma multiforme. Oncogene 2012, 31, 3039–3050. [Google Scholar] [CrossRef] [PubMed]
  72. Day, E.K.; Sosale, N.G.; Xiao, A.; Zhong, Q.; Purow, B.; Lazzara, M.J. Glioblastoma Cell Resistance to EGFR and MET Inhibition Can Be Overcome via Blockade of FGFR-SPRY2 Bypass Signaling. Cell Rep. 2020, 30, 3383–3396.e3387. [Google Scholar] [CrossRef] [PubMed]
  73. Saunders, V.C.; Lafitte, M.; Adrados, I.; Quereda, V.; Feurstein, D.; Ling, Y.; Fallahi, M.; Rosenberg, L.H.; Duckett, D.R. Identification of an EGFRvIII-JNK2-HGF/c-Met-Signaling Axis Required for Intercellular Crosstalk and Glioblastoma Multiforme Cell Invasion. Mol. Pharmacol. 2015, 88, 962–969. [Google Scholar] [CrossRef]
  74. Sunayama, J.; Matsuda, K.; Sato, A.; Tachibana, K.; Suzuki, K.; Narita, Y.; Shibui, S.; Sakurada, K.; Kayama, T.; Tomiyama, A.; et al. Crosstalk between the PI3K/mTOR and MEK/ERK pathways involved in the maintenance of self-renewal and tumorigenicity of glioblastoma stem-like cells. Stem Cells 2010, 28, 1930–1939. [Google Scholar] [CrossRef] [PubMed]
  75. Turke, A.B.; Song, Y.; Costa, C.; Cook, R.; Arteaga, C.L.; Asara, J.M.; Engelman, J.A. MEK inhibition leads to PI3K/AKT activation by relieving a negative feedback on ERBB receptors. Cancer Res. 2012, 72, 3228–3237. [Google Scholar] [CrossRef]
  76. McNeill, R.S.; Canoutas, D.A.; Stuhlmiller, T.J.; Dhruv, H.D.; Irvin, D.M.; Bash, R.E.; Angus, S.P.; Herring, L.E.; Simon, J.M.; Skinner, K.R.; et al. Combination therapy with potent PI3K and MAPK inhibitors overcomes adaptive kinome resistance to single agents in preclinical models of glioblastoma. Neuro Oncol. 2017, 19, 1469–1480. [Google Scholar] [CrossRef]
  77. Wen, P.Y.; Chang, S.M.; Lamborn, K.R.; Kuhn, J.G.; Norden, A.D.; Cloughesy, T.F.; Robins, H.I.; Lieberman, F.S.; Gilbert, M.R.; Mehta, M.P.; et al. Phase I/II study of erlotinib and temsirolimus for patients with recurrent malignant gliomas: North American Brain Tumor Consortium trial 04-02. Neuro Oncol. 2014, 16, 567–578. [Google Scholar] [CrossRef]
  78. Reardon, D.A.; Quinn, J.A.; Vredenburgh, J.J.; Gururangan, S.; Friedman, A.H.; Desjardins, A.; Sathornsumetee, S.; Herndon, J.E., 2nd; Dowell, J.M.; McLendon, R.E.; et al. Phase 1 trial of gefitinib plus sirolimus in adults with recurrent malignant glioma. Clin. Cancer Res. 2006, 12, 860–868. [Google Scholar] [CrossRef]
  79. Reardon, D.A.; Desjardins, A.; Vredenburgh, J.J.; Gururangan, S.; Friedman, A.H.; Herndon, J.E., 2nd; Marcello, J.; Norfleet, J.A.; McLendon, R.E.; Sampson, J.H.; et al. Phase 2 trial of erlotinib plus sirolimus in adults with recurrent glioblastoma. J. Neurooncol. 2010, 96, 219–230. [Google Scholar] [CrossRef]
  80. Carmeliet, P.; Jain, R.K. Angiogenesis in cancer and other diseases. Nature 2000, 407, 249–257. [Google Scholar] [CrossRef]
  81. Yancopoulos, G.D.; Davis, S.; Gale, N.W.; Rudge, J.S.; Wiegand, S.J.; Holash, J. Vascular-specific growth factors and blood vessel formation. Nature 2000, 407, 242–248. [Google Scholar] [CrossRef]
  82. Bergers, G.; Hanahan, D. Modes of resistance to anti-angiogenic therapy. Nat. Rev. Cancer 2008, 8, 592–603. [Google Scholar] [CrossRef]
  83. Liu, T.; Ma, W.; Xu, H.; Huang, M.; Zhang, D.; He, Z.; Zhang, L.; Brem, S.; O’Rourke, D.M.; Gong, Y.; et al. PDGF-mediated mesenchymal transformation renders endothelial resistance to anti-VEGF treatment in glioblastoma. Nat. Commun. 2018, 9, 3439. [Google Scholar] [CrossRef] [PubMed]
  84. Xu, P.; Wang, H.; Pan, H.; Chen, J.; Deng, C. Anlotinib combined with temozolomide suppresses glioblastoma growth via mediation of JAK2/STAT3 signaling pathway. Cancer Chemother. Pharmacol. 2022, 89, 183–196. [Google Scholar] [CrossRef] [PubMed]
  85. Wick, W.; Gorlia, T.; Bendszus, M.; Taphoorn, M.; Sahm, F.; Harting, I.; Brandes, A.A.; Taal, W.; Domont, J.; Idbaih, A.; et al. Lomustine and Bevacizumab in Progressive Glioblastoma. N. Engl. J. Med. 2017, 377, 1954–1963. [Google Scholar] [CrossRef] [PubMed]
  86. Salvatore, L.; Bria, E.; Sperduti, I.; Hinke, A.; Hegewisch-Becker, S.; Aparicio, T.; Le Malicot, K.; Boige, V.; Koeberle, D.; Baertschi, D.; et al. Bevacizumab as maintenance therapy in patients with metastatic colorectal cancer: A meta-analysis of individual patients’ data from 3 phase III studies. Cancer Treat. Rev. 2021, 97, 102202. [Google Scholar] [CrossRef] [PubMed]
  87. Capper, D.; Reifenberger, G.; French, P.J.; Schweizer, L.; Weller, M.; Touat, M.; Niclou, S.P.; Euskirchen, P.; Haberler, C.; Hegi, M.E.; et al. EANO guideline on rational molecular testing of gliomas, glioneuronal, and neuronal tumors in adults for targeted therapy selection. Neuro Oncol. 2023, 25, 813–826. [Google Scholar] [CrossRef]
  88. Gieryng, A.; Pszczolkowska, D.; Walentynowicz, K.A.; Rajan, W.D.; Kaminska, B. Immune microenvironment of gliomas. Lab. Investig. 2017, 97, 498–518. [Google Scholar] [CrossRef]
  89. Thorsson, V.; Gibbs, D.L.; Brown, S.D.; Wolf, D.; Bortone, D.S.; Ou Yang, T.H.; Porta-Pardo, E.; Gao, G.F.; Plaisier, C.L.; Eddy, J.A.; et al. The Immune Landscape of Cancer. Immunity 2019, 51, 411–412. [Google Scholar] [CrossRef]
  90. Vito, A.; El-Sayes, N.; Mossman, K. Hypoxia-Driven Immune Escape in the Tumor Microenvironment. Cells 2020, 9, 992. [Google Scholar] [CrossRef]
  91. Park, J.H.; Lee, H.K. Current Understanding of Hypoxia in Glioblastoma Multiforme and Its Response to Immunotherapy. Cancers 2022, 14, 1176. [Google Scholar] [CrossRef] [PubMed]
  92. Lim, M.; Weller, M.; Idbaih, A.; Steinbach, J.; Finocchiaro, G.; Raval, R.R.; Ansstas, G.; Baehring, J.; Taylor, J.W.; Honnorat, J.; et al. Phase III trial of chemoradiotherapy with temozolomide plus nivolumab or placebo for newly diagnosed glioblastoma with methylated MGMT promoter. Neuro Oncol. 2022, 24, 1935–1949. [Google Scholar] [CrossRef] [PubMed]
  93. Omuro, A.; Brandes, A.A.; Carpentier, A.F.; Idbaih, A.; Reardon, D.A.; Cloughesy, T.; Sumrall, A.; Baehring, J.; van den Bent, M.; Bahr, O.; et al. Radiotherapy combined with nivolumab or temozolomide for newly diagnosed glioblastoma with unmethylated MGMT promoter: An international randomized phase III trial. Neuro Oncol. 2023, 25, 123–134. [Google Scholar] [CrossRef]
  94. Reardon, D.A.; Brandes, A.A.; Omuro, A.; Mulholland, P.; Lim, M.; Wick, A.; Baehring, J.; Ahluwalia, M.S.; Roth, P.; Bahr, O.; et al. Effect of Nivolumab vs Bevacizumab in Patients with Recurrent Glioblastoma: The CheckMate 143 Phase 3 Randomized Clinical Trial. JAMA Oncol. 2020, 6, 1003–1010. [Google Scholar] [CrossRef] [PubMed]
  95. Yu, M.W.; Quail, D.F. Immunotherapy for Glioblastoma: Current Progress and Challenges. Front. Immunol. 2021, 12, 676301. [Google Scholar] [CrossRef] [PubMed]
  96. Kwilas, A.R.; Ardiani, A.; Donahue, R.N.; Aftab, D.T.; Hodge, J.W. Dual effects of a targeted small-molecule inhibitor (cabozantinib) on immune-mediated killing of tumor cells and immune tumor microenvironment permissiveness when combined with a cancer vaccine. J. Transl. Med. 2014, 12, 294. [Google Scholar] [CrossRef]
  97. Kato, Y.; Tabata, K.; Kimura, T.; Yachie-Kinoshita, A.; Ozawa, Y.; Yamada, K.; Ito, J.; Tachino, S.; Hori, Y.; Matsuki, M.; et al. Lenvatinib plus anti-PD-1 antibody combination treatment activates CD8+ T cells through reduction of tumor-associated macrophage and activation of the interferon pathway. PLoS ONE 2019, 14, e0212513. [Google Scholar] [CrossRef]
  98. Sidorov, M.; Dighe, P.; Woo, R.W.L.; Rodriguez-Brotons, A.; Chen, M.; Ice, R.J.; Vaquero, E.; Jian, D.; Desprez, P.Y.; Nosrati, M.; et al. Dual Targeting of EGFR and MTOR Pathways Inhibits Glioblastoma Growth by Modulating the Tumor Microenvironment. Cells 2023, 12, 547. [Google Scholar] [CrossRef]
  99. Kummar, S.; Kinders, R.; Rubinstein, L.; Parchment, R.E.; Murgo, A.J.; Collins, J.; Pickeral, O.; Low, J.; Steinberg, S.M.; Gutierrez, M.; et al. Compressing drug development timelines in oncology using phase ‘0′ trials. Nat. Rev. Cancer 2007, 7, 131–139. [Google Scholar] [CrossRef]
  100. Tien, A.C.; Li, J.; Bao, X.; Derogatis, A.; Kim, S.; Mehta, S.; Sanai, N. A Phase 0 Trial of Ribociclib in Recurrent Glioblastoma Patients Incorporating a Tumor Pharmacodynamic- and Pharmacokinetic-Guided Expansion Cohort. Clin. Cancer Res. 2019, 25, 5777–5786. [Google Scholar] [CrossRef]
  101. Wei, H.J.; Upadhyayula, P.S.; Pouliopoulos, A.N.; Englander, Z.K.; Zhang, X.; Jan, C.I.; Guo, J.; Mela, A.; Zhang, Z.; Wang, T.J.C.; et al. Focused Ultrasound-Mediated Blood-Brain Barrier Opening Increases Delivery and Efficacy of Etoposide for Glioblastoma Treatment. Int. J. Radiat. Oncol. Biol. Phys. 2021, 110, 539–550. [Google Scholar] [CrossRef]
  102. Hersh, A.M.; Bhimreddy, M.; Weber-Levine, C.; Jiang, K.; Alomari, S.; Theodore, N.; Manbachi, A.; Tyler, B.M. Applications of Focused Ultrasound for the Treatment of Glioblastoma: A New Frontier. Cancers 2022, 14, 4920. [Google Scholar] [CrossRef]
  103. Narsinh, K.H.; Perez, E.; Haddad, A.F.; Young, J.S.; Savastano, L.; Villanueva-Meyer, J.E.; Winkler, E.; de Groot, J. Strategies to Improve Drug Delivery Across the Blood-Brain Barrier for Glioblastoma. Curr. Neurol. Neurosci. Rep. 2024, 24, 123–139. [Google Scholar] [CrossRef] [PubMed]
  104. Burt, T.; Young, G.; Lee, W.; Kusuhara, H.; Langer, O.; Rowland, M.; Sugiyama, Y. Phase 0/microdosing approaches: Time for mainstream application in drug development? Nat. Rev. Drug Discov. 2020, 19, 801–818. [Google Scholar] [CrossRef]
  105. Roepman, P.; de Bruijn, E.; van Lieshout, S.; Schoenmaker, L.; Boelens, M.C.; Dubbink, H.J.; Geurts-Giele, W.R.R.; Groenendijk, F.H.; Huibers, M.M.H.; Kranendonk, M.E.G.; et al. Clinical Validation of Whole Genome Sequencing for Cancer Diagnostics. J. Mol. Diagn. 2021, 23, 816–833. [Google Scholar] [CrossRef] [PubMed]
  106. Priestley, P.; Baber, J.; Lolkema, M.P.; Steeghs, N.; de Bruijn, E.; Shale, C.; Duyvesteyn, K.; Haidari, S.; van Hoeck, A.; Onstenk, W.; et al. Pan-cancer whole-genome analyses of metastatic solid tumours. Nature 2019, 575, 210–216. [Google Scholar] [CrossRef]
  107. Tsimberidou, A.M.; Iskander, N.G.; Hong, D.S.; Wheler, J.J.; Falchook, G.S.; Fu, S.; Piha-Paul, S.; Naing, A.; Janku, F.; Luthra, R.; et al. Personalized medicine in a phase I clinical trials program: The MD Anderson Cancer Center initiative. Clin. Cancer Res. 2012, 18, 6373–6383. [Google Scholar] [CrossRef] [PubMed]
  108. Le Tourneau, C.; Delord, J.P.; Goncalves, A.; Gavoille, C.; Dubot, C.; Isambert, N.; Campone, M.; Tredan, O.; Massiani, M.A.; Mauborgne, C.; et al. Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer (SHIVA): A multicentre, open-label, proof-of-concept, randomised, controlled phase 2 trial. Lancet Oncol. 2015, 16, 1324–1334. [Google Scholar] [CrossRef]
  109. Vo, H.H.; Fu, S.; Hong, D.S.; Karp, D.D.; Piha-Paul, S.; Subbiah, V.; Janku, F.; Naing, A.; Yap, T.A.; Rodon, J.; et al. Challenges and opportunities associated with the MD Anderson IMPACT2 randomized study in precision oncology. NPJ Precis. Oncol. 2022, 6, 78. [Google Scholar] [CrossRef]
  110. Flaherty, K.T.; Gray, R.J.; Chen, A.P.; Li, S.; McShane, L.M.; Patton, D.; Hamilton, S.R.; Williams, P.M.; Iafrate, A.J.; Sklar, J.; et al. Molecular Landscape and Actionable Alterations in a Genomically Guided Cancer Clinical Trial: National Cancer Institute Molecular Analysis for Therapy Choice (NCI-MATCH). J. Clin. Oncol. 2020, 38, 3883–3894. [Google Scholar] [CrossRef]
  111. Mangat, P.K.; Halabi, S.; Bruinooge, S.S.; Garrett-Mayer, E.; Alva, A.; Janeway, K.A.; Stella, P.J.; Voest, E.; Yost, K.J.; Perlmutter, J.; et al. Rationale and Design of the Targeted Agent and Profiling Utilization Registry (TAPUR) Study. JCO Precis. Oncol. 2018, 2, 1–14. [Google Scholar] [CrossRef]
  112. Hoes, L.R.; van Berge Henegouwen, J.M.; van der Wijngaart, H.; Zeverijn, L.J.; van der Velden, D.L.; van de Haar, J.; Roepman, P.; de Leng, W.J.; Jansen, A.M.L.; van Werkhoven, E.; et al. Patients with Rare Cancers in the Drug Rediscovery Protocol (DRUP) Benefit from Genomics-Guided Treatment. Clin. Cancer Res. 2022, 28, 1402–1411. [Google Scholar] [CrossRef]
Table 1. Clinical trials involving mono-target small molecule inhibitors.
Table 1. Clinical trials involving mono-target small molecule inhibitors.
CompoundTarget PMIDTrialDe Novo/Recurrent Study PopulationPFS/OSResponse RateBiomarker Analysis
Adavosertib (AZD1775)Wee129798906Phase 0RecurrentGBM n = 20NANANA
BuparlisibPI3K30715997Phase IIRecurrentGBM n = 65PFS 1.7m (95% CI, 1.4–1.8).
OS 9.8m (95% CI, 8.4–12.1)		CR 0%, PR 0%, SD 42%, PD 54%
DRC 43.8% (95% CI 31–58%)		No statistically significant association was found between PTEN, PIK3CA, PIK3R1, EGFR, PDGFRA, IDH1/2 and TP53, and PFS6 or OS.
No statistically significant association in PFS between PIK3CA/PIK3R1-mutant or PTEN mutant PIK3CA/PIK3R1-wildtype or PTEN wildtype.
Capmatinibc-MET31776899Phase IIRecurrent GBM n = 10, altered PTEN status PFS not assessed due to insufficient sample sizeCR 0%, PR 0%, SD 30% NA
DeforolimusmTOR22037923Phase IRecurrentGrade IV malignant glioma n = 3NASD 33% as best responseNA
ErlotinibEGFR20150372Phase IIRecurrentGBM n = 42PFS 2m
OS 6m		CR 0%, PR 0%, SD 7.1%, PD 62%NA
22946346Phase I + IIRecurrentGBM n = 8PFS 1.9m
OS 6.9m		NANA
24352766Phase IIRecurrentGBM n = 40, EGFR or PTEN-mutatedPFS 3.9m (95% CI 1.6–6.1)
OS 7m (95% CI 1.41–4.7)		CR 0%, PR 7%, SD 21%, PD 72%NA
20615922Phase IIRecurrentGBM n = 48PFS6 20% (95% CI 10.0–32.4)
OS 9.7m (95% CI 5.9–11.6)		CR 2.1%, PR 6.3%, SD 33.3%, PD 54.2% No conclusion could be draw from molecular subgroup analyses due to low response rate.
EGFR amplified:
OS 8.3m (95% CI 4.1–10.7)
CR 4.3%, PR 4.3%, SD 43.5%, PD 47.8%
Non-EGFR amplified:
OS 10.6m (95% CI 4.7–14.1)
CR 0%, PR 8.0%, SD 43.5%, PD 60.0%
19204207Phase IIRecurrentGBM n = 110PFS 1.8m
OS 7.7m		NAEGFRvIII was correlated with poor PFS in the erlotinib arm (p = 0.003).
EGFR amplification was significant for poor outcome in the entire study population (p = 0.048).
GefitinibEGFR29492119Phase IIDe novoGBM n = 40PFS 6m
OS 14m		NAPFS and OS were significantly (p = 0.005) higher in EGFR +ve/PTEN-ve compared to EGFR-ve/PTEN+ve with 9 months versus 6 months, and 20 months versus 13 months, respectively.
14638850Phase IIRecurrentGBM n = 53EFS 8.1w (95% CI, 7.9–9.1)
OS 39.4w(95% CI, 24.3–59.4)		CR 0%, PR 0%, SD 42%, PD 58.4% within 2 months. PD 96.2% at end of follow-upEpidermal growth factor receptor expression did not correlate with either EFS or OS.
17353924Phase IIRecurrentGBM n = 16
AO n = 3
AA n = 9		PFS 8.4w
OS 24.6w		DCR 12.5% (95% CI 1.6–38.4%).
SD 12.5%		EGFR expression or gene status, and p-Akt expression predict activity of gefitinib.
20510539Phase IIDe novoGBM n = 96PFS-1year 16.7%, OS-1year 54.2%NAClinical outcome was not affected by EGFR amplification or EGFRvIII mutation.
21471286Phase IIRecurrentGBM n = 22OS 8.8mNANo difference between patients with an amplified or a normal EGFR status.
GSK2256098FAK kinase29788497Phase IRecurrentGBM n = 13PFS 5.7w (95% CI 3.1–8.3)SD 27%, PD 73%NA
Navtemadlin (AMG-232)MDM231359240Phase IDe novo and recurrentGBM n = 10,
p53wt		NASD 60%NA
Pegdinetanib
(CT-322)		VEGFR-225388940Phase IIRecurrentGBM n = 63PFS 1.8m1mg/kg: ORR 14.3%
2mg/kg: ORR 3.8%		NA
PerifosineAkt31325145Phase IIRecurrentGBM n = 16PFS 1.58m (95% CI 1.08–1.84)
OS 3.68m (95% CI 2.50–7.79)		SD 12.5%, PD 75%NA
PF-06840003IDO-132436060Phase IRecurrent GBM n = 14
AA gr III n = 2
AO gr III n = 1		PFS 1.9–2.8mDCR 47%NA
Picropodophyllin (AXL1717)IGF-1R29113409Phase I/IIRecurrentGBM n = 8
Gliosarcoma n = 1		PFS 8w
OS 15w		CR 0%, PR 11.1%, SD 44.4%, PD 44.4%NA
Rapamycin mTOR18215105Phase IRecurrent GBM n = 14, PTEN-deficientNo PFS or OS reportedNANA
Selinexor XPO-134728525 Phase IIRecurrentGBM n = 76Arm B: PFS6 10% (95% CI 2.67–35.4)
OS 10.5m (95% CI, 4.9–17.0)
Arm C: PFS6 7.7% (95% CI 1.2–50.6)
OS 8.5m (95% CI, 7.3–not evaluable)
Arm D: 17.2% (95% CI, 7.78–38.3)
OS 10.2m (95% CI, 7.0–15.4)		Arm B: ORR 8.3%, SD 25%, PD 62.5%
Arm C: ORR 7.7%, SD 30.8%, PD 61.5%
Arm D: ORR 10%, SD 23.3%, PD 56.7%		Patients with mutations in pancreatic and duodenal homeobox 1 (PDX1), E1A Binding Protein P400 (EP400) or Dedicator of Cytokinesis 8 (DOCK8) survived longer than patients with wildtype tumours
TipifarnibFTase subunit ß16877733Phase IIRecurrent GBM n = 67Non-EIAED: PFS 9w (95% CI 7–14)
EAIED: PFS 6w (95% CI 4–8w)		CR 0%, PR 7.5%
Non-EIAED: PR 11%
EAIED: PR 3%		NA
TrotabresibBET36455228Phase IRecurrentGBM n = 19
AA n = 1		PFS 1.9m (95% CI 1.4–3.4)
IDH-wt:
PFS 3.0m (95% CI 1.4–3.6)		SD 41%, PD 59%NA
Vismodegib
(GDC-0449)		SMO36581779Phase 0/IIRecurrentGBM n = 41PFS 2.3m (95% CI 1.9–2.6)
OS 7.8m (95% CI 5.4–10.1)		CR 0%, PR 0%, SD 25.8%, PD 74.2%NA
GBM: glioblastoma, AO: anaplastic oligodendroglioma, AA: anaplastic astrocytoma, MG: malignant glioma, PFS: progression-free survival, OS: overall survival, EFS: event free survival, CR: complete response, PR: partial response, SD: stable disease, PD: progressive disease, DCR: disease control rate, ORR: objective response rate. EIAED: enzyme-inducing antiepileptic drug. m: months and w: weeks.
Table 2. Clinical trials involving multitarget small molecule inhibitors.
Table 2. Clinical trials involving multitarget small molecule inhibitors.
TargetsCompoundTargets SpecifiedPMIDTrialDe Novo/
Recurrent GBM		Study PopulationPFS/OS Response RateBiomarker Analysis
Tumourcell AbemaciclibCDK 4 & 627217383Phase IRecurrentGBM n = 17NASD 17.6%NA
AfatinibEGFR, ERBB2, ERBB425140039Phase I + IIRecurrentGBM n = 119PFS 0.99m (p = 0.032)
OS 9.8m (p = 0.386)		DCR 36.6% (95% CI 22.1–53.1).
CR 0%, PR 2.4%, SD 34.1%, PD 34.1%		EGFR vIII+ tumours showed higher PFS versus EGFRvIII- tumours.
Bortezomib20S proteasome20213332Phase IRecurrentGBM n = 51
AA n = 8
AO n = 3
Other n = 4		PFS 2.1m (95% CI 1.7–2.8)
OS 6.0m (95% CI 3.9–7.4) 		ORR 3%, CR 0%, PR 3%, SD 23%NA
Cilengitide Integrins ανβ3 and ανβ517470857Phase IRecurrent GBM n = 37
AA n = 11
AO n = 1
Mixed AG n = 2 		OS 5.6m (95% CI 4.3–8.4)ORR 9.8%, CR 3.9%, PR 5.9%, SD 31.4%NA
18981465Phase IIRecurrentGBM n=81500 mg/d:
TTP 7.9w (95% CI 7.7–15.6)
OS 6.5m (95% CI 5.2–9.3)
2000 mg/d:
TTP 8.1w (95% CI 7.9–15.0)
OS 9.9m (95% CI 6.4–15.7)		500 mg/d: ORR 5%2000 mg/d:
ORR 13%		NA
21739168Phase IIRecurrentGBM n = 26PFS 8w (95% CI 4–16)NANA
DacomitinibEGFR, ERBB2 and
ERBB4 		28575464Phase IIRecurrent GBM n = 30, EGFR amplification
GBM n = 19, EGFR amplification and EGFRvIII mutation		PFS 2.7m (95% CI 2.3–3.1)
OS 7.4m (95% CI 5.6–9.2)		CR 2%, PR 4.1%, SD 24.5%, PD 61.2%EGFR amplification without EGFRvIII mutation: PFS 2.7m, OS 7.8m.
CR 3.3%, PR 3.3%, SD 26.7%, PD 56.7%
EGFR amplification with EGFRvIII mutation: PFS 2.6m, OS 6.7m.
CR 0%, PR 5.3%, SD 21.1%, PD 68.4%
Dasatinib
(BMS-354825)		Abl1, Src, c-Kit, Lck, Yes, induces autophagy25758746Phase IIRecurrentGBM n = 50PFS 1.7m (95% CI 1.3–1.9)
OS 7.9m (95% CI 5.6–10.2)		CR 0%, PR 0%, SD 24%, PD 72%NA
EnzastaurinPKCβ, PKCα, PKCγ and PKCε
Chk1/Chk2		20150385Phase I/IIRecurrentGBM n = 57PFS 1.3m
OS 4.6m		ORR 30%, PR 3.5%NA
20124186Phase IIIRecurrentGBM n = 174PFS 1.51m
OS 6.60m		ORR 2.9%, SD 38.5%, PD 41.4%NA
LapatinibEGFR, ERBB219499221Phase I + IIRecurrentGBM n = 17NASD 23.5%No relation between PTEN loss or EGFRvIII and outcome
MarimastatMMP-9, MMP-1,
MMP-2, MMP-14,
MMP-7		16636750Phase II De novo GBM n = 154
Gliosarcoma n = 8		PFS 17.1w,
OS 42.9w		NANA
PalbociclibCDK4 & 630151703Phase IIRecurrentGBM n = 22, Rb1-positivePFS 5.14 weeks, OS 15.4 weeks PD 95%NA
Paxalisib
(GDC-0084)		PI3K, mTOR31937616Phase IRecurrent WHO grade III n = 14
WHO grade IV n = 33		NAORR 0%, SD 40%, PD 55%No correlation between PTEN loss or PI3K mutations and response to GDC-0084.
NCT03522298Phase IIDe novoGBM n = 30PFS 8.6m
OS 15.9m		NANA
RibociclibCDK4 & 631399936Phase IbRecurrentGBM n = 3, Rb+ Patient 1: PFS 2 m
OS 10m
Patient 2: PFS 5m
OS 19m
Patient 3: PFS 2m
OS 12m		NANA
31285369Phase 0RecurrentGBM n = 6PFS 9.7w
OS 7.8m		NANA
RomidepsinHDAC 1, 2 (4 and 6)21377994Phase I/IIRecurrent Phase I: GBM n = 8
Phase II: GBM n = 35		PFS 8w (95% CI 5–8)
OS 34w (95% CI 21–47)		CR 0%, PR 0%, SD 28%, PD 72%NA
Sonolisib
(PX-866)		PI3K (p110α),
(p120γ), (p110δ)		25605819Phase IIRecurrentGBM n = 33PFS-6 17% (95% CI 5–32%)CR 0%, PR 3%, SD 24%, PD 73%No statistically significant association between stable disease and PTEN, EGFRvIII, PIK3CA mutation or PIK3R1 mutation
TandutinibFLT3, c-Kit, PDGFR27663390Phase I + IIRecurrentPhase I: GBM n = 19
Phase II: GBM n = 30		First stage: PFS 1.9m (95% CI: 1.5–3.7)
OS 8.8m (95% CI: 5.9–15.4)
Time of analysis: PFS6 16%		CR 3%NA
VistusertibmTOR,
PI3K isoforms α/β/γ/δ		31707687Phase IRecurrentGBM n = 14PFS6 26.6%ORR 8%, PR8%, SD 38%No correlation between pS6 status and response.
VorinostatHDAC 1, 2, 3, 6, 8 19307505Phase IIRecurrent GBM n = 66PFS 1.9m
OS 5.7m		ORR 3%NA
WP1066JAK2, STAT335575067Phase IRecurrentGBM n = 8PFS 2.3m (95% CI 1.7–NA)
OS form initial diagnosis 25m (95% CI 22.5–NA)		PD 100%NA
Tumourcell + angiogenesis AxitinibVEGFR1, VEGFR2, VEGFR3
PDGFRß, c-Kit		28988341Phase IIRecurrentGBM n = 50PFS 12.4w (95% CI 11–13)
OS 29w (95% CI 20–38)		NAMGMT-promoter hypermethylation is significantly correlated with PFS and OS
26935577Phase IIRecurrentGBM n = 22PFS 13w (95% CI 11–14)
OS 29w (95% CI, 17–40)		CR 9%, PR 18%, SD 14%No difference in PFS or OS for tested mutations.
33067319Phase IIRecurrent GBM n = 27PFS6 18.5%
OS 18w		ORR 22.2%, CR 3.7%, PR 18.5%, SD 25.9%, PD 51.9%
NA
CabozantinibVEGFR1-3, c-Met, Ret,
Kit, Flt-1/3/4, Tie2, AXL		29016998Phase IIRecurrentGBM n = 152PFS 3.7m
OS 7.7–10.4m (depending on dose)		140 mg/day:
ORR 17.6%, PR 17.6%, SD 58.8%, PD 11.8%

100 mg/day: ORR 14.5%, PR 14.5%, SD 67.5%, PD 12.0%		NA
29036345Phase IIRecurrentGBM n = 70PFS 2.3m
OS 4.6m		140 mg/day:
ORR 8.3%, PR 8.3%, SD 50.0%, PD 16.7%
100 mg/day:
ORR 3.4%, PR 3.4%, SD 46.6%, PD 27.6%		NA
DovitinibFLT3/c-Kit, FGFR1/3,
VEGFR1-4, PDGF		31292802Phase IIRecurrentGBM n = 19TTP 1.8m (95% CI 1.4–1.8)
OS 5.6m (95% CI 4.2–8.1)		NANo impact on OS.
Higher BMP 9, CD73, endoglin and VEGF D, and lower TSP 2 were associated with poorer PFS
27100354Phase IRecurrentGBM n = 12PFS 1.8m (95% CI 1.7–1.9)
OS 9.5m (95% CI 2.6–16.4)		CR 0%, PR 0%, SD 36.4%, PD 63.4%Presence of FGFR-TACC gene fusion did not affect PFS-6
InfigratinibFGFR 1/2/335344029Phase IIRecurrentGBM n = 19
AA n = 5
Other n = 2		PFS 1.7m (95% CI 1.1–2.8)
OS 6.7m (95% CI 4.2–11.7)		ORR 4.8%, PR 4.8%, SD 28.6%, PD 61.9%Tumours harbouring FGFR1 or FGFR3 point mutations or FGFR3-TACC3 fusions showed
durable disease control for more than 1 year
NintedanibVEGFR1/2/3, FGFR1/2/3 and PDGFRα/β23184145Phase IIRecurrentGBM n = 25PFS-6 4.0% (95% CI 0.1–20.4)
OS 6m (95% CI 3.6–8.4)		CR 0%, PR 0%, SD 12.0%, PD 88.0%NA
25338318Phase IIRecurrent GBM n = 22Bevacizumab-naive: PFS 28d (95% CI 27–83)
OS 6.9m (95% CI 3.7–8.1).
bevacizumab-treated: PFS 28d (95% CI 22–28)
OS 2.6m (95% CI 1.0–6.9)		Bevacizumab-naive: CR 0%, PR 0%, SD 33%, PD 67%
Bevacizumab-treated: CR 0%, PR 0%, SD 10%, PD 90%		NA
RegorafenibVEGFR1, VEGFR2, VEGFR3, PDGFRa, PDGFRβ, Kit (c-Kit), RET (c-RET) and Raf-1,
FGFR1, FGFR2, Abl		30522967Phase IIRecurrentGBM n = 59PFS 2.0m (95% CI 1.9–3.6)
OS 7.4m (95% CI 5.8–12.0)		CR 2%, PR 3%, SD 39%, PD 56%
NA
SunitinibVEGFR1, VEGFR2 VEGFR3, PDGFRa, PDGFRß, c-Kit,
FLT3, CSF-1R, RET 		22832897Phase IIRecurrentGBM n = 16PFS 1.4m (95% CI 1.2–4.8)
OS 12.6m (95% CI 3.9–18.1)		CR 0%, PR 0%, SD 31.3%NA
23086433Phase IIRecurrentGBM n = 31PFS 1.08m (95% CI 0.92–2.47)
OS 9.4m (95% CI 6.15–21.90)		Rate of radiographic response 10%, Levin 23%NA
24311637Phase IIRecurrentGBM n = 40PFS 2.2m (95% CI 1.8–3.3)
OS 9.2m (95% CI 11.9–49.2) 		ORR 0%, SD 12.5%, PD 82.5%c-KIT expression in vascular endothelial cells was associated with improved PFS (2.3m) versus c-KIT negative vascular endothelial cells (1.7m) (p = 0.025).
No or low expression of PDGFR-α in tumour cells was associated with improved PFS (p = 0.043) but not with OS.
24424564Phase IIDe novoGBM n = 12PFS 7.7w (95% CI 7.2–8.2)
OS 12.8w (95% CI 0.5–23.8)		ORR 0%, SD 8.3%, PD 91.7%NA
SYHA1813VEGFR, CSF1R36884148Phase IRecurrentGBM n = 4NAPR 25% NA
TivozanibVEGFR1/2/3, PDGFR, c-Kit
Low activity against FGFR-1, Flt3, c-Met, EGFR and IGF-1R		27853960Phase IIRecurrentGBM n = 10PFS 2.3m (95% CI 1.5–4.0)
OS 8.1m (95% CI 5.2–12.5)		CR 10%, PR 10%, SD 40%, PD 40%None of the investigated blood biomarkers were associated with OS or PFS.
Tumourcell + micro-environment AEE788EGFR, HER2/ErbB2, VEGFR2/KDR, c-Abl, c-Src, Flt-122392572Phase IRecurrentGBM n = 64PFS 2.7m (90% CI 1.9–2.8) CR 0%, PR 0% SD 17%p-KDR was a significant predictor of PFS (p = 0.01)
BosutinibSrc/Abl, PI3K/AKT/mTOR, MAPK/ERK, JAK/STAT3. Lyn, HCK.
Promotes autophagy		25411098Phase IIRecurrentGBM n = 9PFS 7.71w (95% CI 2.6–7.9)
OS 50w (95% CI 2.9–NA) 		PD 100%NA
Cediranib
VEGFR(KDR), Flt1/4, c-Kit, PDGFRβ, induces autophagic vacuole accumulation.20458050Phase IIRecurrentGBM n = 30PFS 117d (95% CI 82–145)
OS 227d (95% CI 177–293)		2D measurements: PR 26.6%
3D measurements: PR 56.7%		At baseline, no biomarkers showed correlations with PFS or OS
23940216Phase IIIRecurrentGBM n = 131PFS 92d
OS 8m		CR 0,8%, PR 14.4%, SD 64.4%, PD 8.5%Baseline VEGF levels did not have a significant effect on PFS or OS
Diazepinomicin
(TLN-4601)		RAS, peripheral benzodiazepine receptor (PBR)22048878Phase IIRecurrentGBM n = 17PFS-6 0%
OS 150d		CR + PR + SD 21.4%NA
Erdafitinib
(JNJ-42756493)		FGFR1/2/3/4, RET (c-RET), CSF-1R, PDGFR-α/PDGFR-β, FLT4, Kit (c-Kit), VEGFR-226324363Phase I RecurrentGBM n = 3NAPR 66.7%NA
Imatinibv-Abl, c-Kit, SCF, and PDGFR, induces autophagy18824712Phase IIRecurrentGBM n = 51PFS 1.8m (95% CI 1.7–2.3)
OS 5.9m (95% CI 4.2–7.8)		PR 6%, SD 26%PFS was not correlated with PDGFRα SNPs.
16914578Phase I+IIRecurrentPhase I: GBM n = 35
Phase II: GBM n = 24		PFS6 3%Phase I: CR 0%, PR 2.9%, SD 34.3%
Phase II: CR 0%, PR 5,9%, SD 17.6%		NA
31514200Phase IIDe novo and recurrentDe novo: GBM n = 19
Recurrent: GBM n = 32		De novo: PFS 2.8m (95% CI 0.3–8)
OS 5.0m (95% CI 0.8–30)
Recurrent: PFS 2.1m (95% CI 0.3–19.3)
OS 6.5m (95% CI 0.3–51.5) 		NANA
19789313Phase IIDe novo GBM n = 20OS 6.2mCR 0%, PR 0%, SD 90%, PD 5%, nonevaluable 5%NA
ONC201Akt and ERK to induce TNF-related apoptosis-inducing ligand (TRAIL)31702782Phase IIRecurrentGBM n = 20PFS 1.8m
OS 7.5m		CR 0%NA
PazopanibVEGFR1, VEGFR2,
VEGFR3, PDGFRa/β,c-Kit, FGFR1, c-Fms
Induces autophagy		20200024Phase IIRecurrentGBM n = 35PFS 12w (95% CI 8–14)
OS 35w (95% CI 24–47)		ORR 5.9% (95% CI: 0.7–21%). SD 59%, PD 32%NA
Pexidartinib (PLX3397)CSF-1R, Kit (c-Kit), FLT3, PDGFRβ26449250Phase IIRecurrentGBM n = 37PFS-6 8.8% (90% CI 3.5%, 21.6%)
OS 9.4m (90% CI 6.67–NA)		CR 0%, PR 0%PDGFRA amplification and gains did not correlate significantly with PFS6 or other parameters.
PonatinibAbl, PDGFRα, VEGFR2, FGFR1, Src31444999Phase IIRecurrentGBM n = 15, Bevacizumab-refractoryPFS 28d (95% CI 27–30)
OS 98d (95% CI 56–257) 		SD 13.7%, PD 66.7%NA
TemsirolimusmTOR, induces autophagy16012795Phase IIRecurrentGBM n = 43PFS 9wPR 4.7%, SD 46.5%NA
15998902Phase IIRecurrentGBM n = 65TTP 2.3m (95% CI 1.9–3.2)
OS 4.4m (95% CI 3.6–4.8)		ORR 0%Significant association between neuroimaging response and p70s6 kinase phosphorylation in baseline tumour samples (p = 0.04)
VandetanibVEGFR2, VEGFR3, EGFR, RET, induces autophagy by increasing the level of reactive oxygen species (ROS) 23099652Phase I/IIRecurrent GBM n = 32PFS 1.3m (95% CI 0.9–1.9)
OS 6.3m (95% CI 3.8–8.5)		ORR 15%, CR 3.7% NA
VemurafenibB-RafV600E, induces cell autophagy30351999Phase IIRecurrentGBM n = 6, BRAFV600mt
AA n = 5, BRAFV600mt		PFS 5.3m (95% CI 1.8–12.9), OS 11.9m (95% CI 8.3–40.1)CR 0%, PR 9.1%, SD 45.5%, PD 27.3%NA
StemcellRO4929097y-secretase, Aβ40 and Notch 33027815Phase IIRecurrent GBM n = 47PFS 1.7m (95% CI 1.2–1.8)
OS 7.0m (95% CI 5.4–9.1)		CR 2%, PR 0%, SD 6%, PD 81%NA
NCT01122901Phase IIRecurrentGBM n = 40PFS 1.7m (95% CI 1.1–1.8)
OS 6.6m (95% CI 5.3–10.5)		CR 2.5%, PR 0%, SD 7.5%, PD 82.5%NA
GBM: glioblastoma, AO: anaplastic oligodendroglioma, AA: anaplastic astrocytoma, AG: anaplastic glioma, MG: malignant glioma, PFS: progression-free survival, TTP: time until progression, OS: overall survival, CR: complete response, PR: partial response, SD: stable disease, PD: progressive disease, DCR: disease control rate, ORR: objective response rate. m: months, w: weeks and d: days.
Table 3. Clinical trials investigating the combination of small molecule inhibitors with the standard of care.
Table 3. Clinical trials investigating the combination of small molecule inhibitors with the standard of care.
CompoundsTargets SpecifiedPMIDTrialDe Novo/
Recurrent 		Study PopulationPFS/OSResponse RateBiomarker Analysis
Afatinib + TMZAfatinib:
EGFR, HER2, HER4		25140039Phase IIRecurrentGBM n = 39PFS 1.53m
OS 8.0m		CR 2.6%, PR 5.1%, SD 35.9%, PD 43.6%No statistically significant relation between EGFRvIII and treatment outcome
Anlotinib + TMZVEGFR2/3, FGFR1-4, PDGFR α/β, c-Kit, and Ret37477938Phase IIRecurrentGBM n = 21PFS 7.3m (95% CI 4.9–9.7)
OS 16.9m (95% CI 7.8–26.0)		ORR 81% (95% CI 62.6–99.3), CR 43%, PR 38%NA
Bortezomib + bevacizumabBortezomib: 20S proteasome, inhibits NF-κB and induces ERK phosphorylation to suppress cathepsin B and inhibit the catalytic process of autophagyNCT00611325Phase IIRecurrentGBM n = 56EAIED:
PFS 2m (95% CI 2–4)
OS 8m (95% CI 5–11)
Non-EAIED:
PFS 2.5m (95% CI 1–4)
OS 6m (95% CI 4–10) 		EAIED: RRR 7.1% (95% CI 0–16.6)

Non-EAIED: RRR 39.3% (95% CI 21.2–57.4)		NA
Bortezomib + TMZBortezomib: 20S proteasome, inhibits NF-κB and induces ERK phosphorylation to suppress cathepsin B and inhibit the catalytic process of autophagy27300524Phase IIRecurrent GBM n = 9
AO grade III n = 1		PFS 2.6m
OS 8.9m		NANA
32578964Phase IbRecurrentGBM n = 10OS 21.4mNANA
Buparlisib + bevacizumabBuparlisib:
PI3K		31392595Phase IIRecurrentGBM n = 76PFS 4.0m (95% CI 3.4–5.4)
Bevacizumab-naïve: OS 10.8m (95% CI 9.2–13.5)
Bevacizumab treated: OS
6.6m (95% CI 4.0–14.6)		ORR 26%. CR 11%, PR 16%, SD 33%, PD 29%PTEN and PIK3CA did not affect the treatment response.
Buparlisib + lomustineBuparlisib:
PI3K		32665311Phase Ib/IIRecurrentGBM n = 18NACR 0%, PR 0%, SD 11.1%, PD 77.8%NA
CT-322 + irinotectanCT-322:
VEGFR-2		25388940Phase IIRecurrentGBM n = 63PFS 8.8mORR 0%NA
Dasatinib + bevacizumab Dasatinib:
Abl, Src, c-Kit, induces autophagy		31290996Phase IIRecurrentGBM n = 83PFS 3.2m
OS 7.3m		ORR 15.7%, SD 57.8%No associations between VEGFR2, Y416.SRC (pSRC), CD31, LYN and YES and PFS or OS.
Enzastaurin + bevacizumabEnzastaurin: PKCβ, PKCα, PKCγ and PKCε26643807Phase IIDe novoGBM n = 37PFS 2.0m
OS = 7.5m		ORR 22%, SD 54%No correlation with treatment response and p-GSK-3 levels.
Erlotinib + bevacizumab Erlotinib:
EGFR		23132371NARecurrent GBM n = 4PFS 10.5m
OS 17.0m		Response rate 100%NA
20716591Phase IIRecurrent GBM n = 25PFS 18w (95% CI 12.0–23.9)
OS 44.6w (95% CI28.4–68.7)		CR 4%, PR 46%, SD 42%, PD 8%Patients with positive pS6 had a 3.4 times greater risk of progression compared with patients with negative pS6 (p = 0.05). Patients with lower values for VEGFR-2 were more likely to survive more than 1 year than those with high values of VEGFR-2 (p = 0.0079)
26476729Phase IIDe novo after treatment RT and TMZ, no progressionGBM n = 46PFS 9.2m (95% CI 6.4–11.3)
OS 13.2m (95% CI 10.8–19.6)		CR 8.7%, PR 26.1%, SD 60.9%, PD 0%, 4.3% unknownNA
Erlotinib + TMZErlotinib: EGFR16443950Phase IStable or recurrentGBM n=39NAPR 2.6%NA
Everolimus + TMZEverolimus: mTOR inhibitor of FKBP12, autophagy22160854Phase IDe novoGBM n = 17, non-EAIED
GBM n = 11, EAIED		NANon-EIAED: ORR 17.6% (95% CI: 3.8–43.4%). CR 0%, PR 3/17, SD 9/17, PD 5/17
EIAED: CR 0%, PR 0%, SD 7/11, PD 4/11, 		No differences in response and survival between patients with PTEN intact and deleted tumours.
Imatinib + hydroxyureaImatinib:
v-Abl, c-Kit and PDGFR, induces autophagy.		16361636Phase IIRecurrentGBM n = 33PFS 14.4w (95% CI 8.3–16.6)
OS 48.9w (95% CI 25.7–71.1)		CR 3%, PR 6%, SD 42%, PD 48%NA
19904263Phase IIRecurrentGBM n = 231PFS 5.6w (95% CI 4.1–7.9)
OS 26w (95% CI 21.3–31.3)		CR 0.4%, PR 3.0%, SD 19.5%, PD 61.5%Patients with increased c-KIT had significant longer PFS.
19688297Phase IIIRecurrentGBM n = 120PFS 6w(95% CI 6–7) OS 21wPD 40% NA
Imatinib mesylaat + TMZImatinib:
v-Abl, c-Kit and PDGFR, induces autophagy.		18359865Phase IStable & recurrent GBM n = 52PFS 26.6w (95% CI 9.9–36.4)
OS 45.1w (95% CI 36.1–59.1)		CR 0%, PR 12%, SD 42%NA
Lapatinib + TMZLapatinib:
EGFR, ERBB2		23292205Phase IRecurrent GBM n = 14
AA n = 2		PFS 2.4m
OS 5.9m		CR 0%, PR 6.3%, SD 31.3%NA
Lonafarnib + TMZFPTase inhibitor for H-ras, K-ras-4B, N-ras23633392Phase I/IbRecurrent GBM n = 35PFS 3.9m (95% CI 2.5–8.4)
OS 13.7m (95% CI 8.9–22.1)		CR 5.9%, PR 17.6%, SD 47.1%, PD 29.4%NA
Olaparib + TMZOlaparib:
PARP1, PARP2		32347934Phase IRecurrentGBM n = 36PFS6 39% (95% CI: 23.1–56.5%)NANA
Panobinostat + bevacizumabPanobinostat: HDAC, autophagy 25572329Phase IIRecurrentGBM n = 24PFS 5m (95% CI 3–9) OS 9m (95% CI 6–19)CR 0%, PR 29.2%, SD 58.3%, PD 12.5%NA
Sorafenib + bevacizumabSorafenib:
Raf-1, B-Raf, VEGFR-2,
VEGFR-3,
PDGFR-β, Flt-3, c-KIT		23833308Phase IIRecurrentGBM n = 54PFS 2.9m (95% CI 2.3–3.6)
OS 5.6m (95% CI 4.7–8.2)		ORR 18.5%, SD 63%PFS6 success was increased for VEGFR promoter mutant rs699947 and rs833061 and PFS6 success decreased for mutant rs1005230 and rs1570360.
PFS6 success was increased for VEGFR2 promoter heterozygous rs2071559.
Sorafenib + TMZSorafenib:
Raf-1, B-Raf, VEGFR-2,
VEGFR-3,
PDGFR-β, Flt-3, c-KIT		20443129Phase IIRecurrentGBM n = 32PFS 6.4w (95% CI 3.9–11.7)
OS 41.5w (95% CI 24.1–55.1)		CR 0%, PR 3%, SD 47%, PD 50%NA
23898124Phase IIRecurrentGBM n = 43TTP 3.2m (95% CI 1.8–4.8)
OS 7.4m (95% CI 5.6–9.0)		CR 0%, PR 12%, SD 43%, PD 48%NA
Sunitinib + irinotecanSunitinib: VEGFR2, PDGFRß, c-Kit, IRE1α21744079Phase IRecurrent, GBM n = 15
MG grade III n = 10		PFS 6.9w (95% CI 5.7–17.7),
OS 53.1w (95% CI 30.3–87.9)		CR 0%, PR 4%, SD 36%, PD 60%NA
27680966Phase IIRecurrentGBM n = 6PFS-6 not reachedORR 17%NA
Tandutinib + bevacizumabTandutinib: FLT3, c-Kit, PDGFR26860632Phase IIRecurrent GBM n = 37PFS 4.1m
OS 11m		PR 24%NA
Temsirolimus + bevacizumabTemsirolimus: mTOR, induces autophagy23564811Phase IIRecurrentGBM n = 10PFS 8w
OS 15w		CR 0%, PR 0%, SD 20% NA
Trebananib + bevacizumabTrebananib: Angiopoietin 1 and angiopoietin 2 blocking peptibody29266174Phase IIRecurrentGBM n = 37PFS 3.6m (95% CI 1.9–5.5)
OS 9.5m (95% CI 7.5–14.7)		ORR 27%,
CR 0%, PR 27%, SD 41%		High plasma VEGF was associated with poor PFS and OS. High plasma IL-8 was associated with shorter OS (p  <  0.05)
32154928Phase IIRecurrentGBM n = 57PFS 4.2m (95% CI 3.7–5.6)CR 0%, PR 4.2%, SD 18.8% PD, 77.1%NA
NCT01290263Phase I/IIRecurrent GBM n = 37PFS 108d
OS 285d 		CR 0%, PR 10.8%, SD 54%, PD 27%NA
Velparib + TMZVelparib:
PARP1, PARP2, autophagy 		26508094Phase I/IIRecurrent GBM n = 146, bevacizumab naïve
GBM n = 69, bevacizumab failure		BEV-naïve:
OS 10.3m low TMZ dose
OS 10.7m high TMZ dose
BEV failure:
OS 4.7m low TMZ dose
OS 4.7m high TMZ dose 		BEV-naïve
CR 1.9%, PR 1.9%, SD 1.9%
BEV failure: CR 5.3%, PR 0%		NA
Vorinostat + bevacizumab Vorinostat: HDAC29133513Phase IIRecurrentGBM n = 40PFS 3.7m (95% CI 2.9–4.8)
OS 10.4m (95% CI 7.6–12.8)		RRR 22.5%, CR 0%, PR 22.5%NA
32166308Phase IIRecurrentGBM n = 44PFS 3.68m (95% CI 2.33–3.94)
OS 7.79m (95% CI 5.06–9.63)		NANA
Vornistat + bevacizumab + TMZ29264836Phase I/IIRecurrentGBM n = 39PFS 6.7m (95% CI 4.8–9.4)
OS 12.5m (95% CI 8.8–14.3)		RRR 56% (95% CI 41–71)NA
GBM: glioblastoma, AO: anaplastic oligodendroglioma, AA: anaplastic astrocytoma, AG: anaplastic glioma, MG: malignant glioma, PFS: progression-free survival, TTP: time until progression, OS: overall survival, CR: complete response, PR: partial response, SD: stable disease, PD: progressive disease, DCR: disease control rate, ORR: objective response rate, RRR: radiographic response rate. EIAED: enzyme-inducing antiepileptic drug. m: months, w: weeks and d: days.
Table 4. Clinical trials investigating combinations of small molecule inhibitors.
Table 4. Clinical trials investigating combinations of small molecule inhibitors.
CompoundsTargets SpecifiedPMIDTrialDe Novo/
Recurrent 		Study PopulationPFS/OS Response RateBiomarker Analysis
Cediranib + cilengitideCediranib: VEGFR(KDR), Flt1/4, c-Kit, PDGFRβ, induces autophagic vacuole accumulation.

Cilengitide: Integrins ανβ3 and ανβ5		26008604Phase IRecurrentGBM n = 45PFS 1.9m (95% CI 1.5–2.8)
OS 6.5m (95% CI 5.2–7.6)		CR 4.4%, PR 4.4%, SD 28.9%, PD 46.7%NA
Cediranib + gefitinibCediranib: VEGFR(KDR), Flt1/4, c-Kit, PDGFRβ, induces autophagic vacuole accumulation.

Gefitinib:
EGFR		27232884Phase IIRecurrentGBM n = 19PFS 3.6m
OS 7.2m		CR 0%, PR 42%NA
Erlotinib + sirolimusErlotinib:
EGFR

Sirolimus:
mTOR		19562254Phase IIRecurrentGBM n = 32PFS 6.9w (95% CI 3.9–11)
OS 33.8w (95% CI 21.9–53.6)		SD 27%, CR 0%, PR 0%No association between EGFR, PTEN, EGFRvIII, pS6 and pMAPK PFS6, borderline significance with p-AKT (p = 0.045).
Erlotinib + sorafenibErlotinib:
EGFR

Sorafenib: Raf-1, B-Raf, VEGFR-2, VEGFR-3, PDGFR-β, Flt-3, c-KIT		23328813Phase IIRecurrent GBM n = 56PFS 2.5m (95% CI 1.8–3.7)
OS 5.7m (95% CI 4.5–7.9)		CR 0%, PR 5% (all unconfirmed), SD 41%, PD 45% NA
33235994Phase I/IIRecurrentGBM n = 19PFS 1.8m
OS 5m		CR 0%, PR 0%NA
Erlotinib + temsirolimusErlotinib:
EGFR

Temsirolimus:
mTOR, induces autophagy		24470557Phase I/IIRecurrent GBM n = 42PFS 8w (95% CI 8–10)CR 0%, PR 0%, SD 29%No significant correlation between EGFRvIII, EGFR amplification PTEN, p-AKT or pS6S235/236 and PFS.
Gefitinib + sirolimusSirolimus:
mTOR

Gefitinib:
EGFR
16467100Phase IRecurrent GBM n = 29
AG n = 5		PFS 8.2w (95% CI 7.5–18.6) PR 5.9%, SD 41%NA
Pazopanib + lapatinibPazopanib:
EGFR1, VEGFR2, VEGFR3, PDGFR, FGFR, c-Kit, c-Fms/CSF1R, cathepsin B activation, autophagy.

Lapatinib:
EGFR, ERBB2		23363814Phase I/IIRecurrentGBM n = 19, biomarker positive (PTEN and/or EGFRvIII)

GBM n = 22, biomarker negative		Biomarker positive: PFS 56d (95% CI 45–113)

Biomarker negative: PFS 62d (95% CI 56–90)		Overall: CR 0%, PR 5%, SD 34%, PD 61%

Biomarker positive: CR 0%, PR 5%, SD 37%, PD 58%

Biomarker negative: CR 0%, PR 5%, SD 32%, PD 64%		NA
Temsirolimus + perifosineTemsirolimus: mTOR, induces autophagy

Perifosine:
Akt		32293798
Phase IRecurrent GBM n = 17
Other MG n = 19
PFS 2.7m (95%CI 1.8–9.2)
OS 10.4m (95% CI 7.2–16.7)		PR 3.4%, SD 44.8%, PD 51.7%NA
Temsirolimus + sorafenibTemsirolimus: mTOR, induces autophagy

Sorafenib:
Raf-1, B-Raf, VEGFR-2,
VEGFR-3,
PDGFR-β, Flt-3, c-KIT		29313954Phase I + IIRecurrentArm B (anti-VEGF therapy naïve): GBM n = 49

Arm D (prior anti-VEGF therapy): GBM n = 44		Arm B: PFS 2.7m
OS 6.6m

Arm D: PFS 1.9m
OS 3.9m		Arm B: PR 2.4%, SD 64%, PD 27%


Arm D: SD 46%, PD 51%		NA
23099651Phase I/IIRecurrent GBM n = 18PFS 8w (95% CI 5–9)CR 0%, PR 11.8%NA
Tipifarnib + sorafenibTipifarnib:
FTase

Sorafenib:
Raf-1, B-Raf, VEGFR-2,
VEGFR-3,
PDGFR-β, Flt-3, c-KIT
28988377Phase IRecurrentGBM n = 24PFS 55d
OS 4.38m		NANA
Trametinib + dabrafenibTrametinib:
MEK 1/2, activates autophagy
Dabrafenib:
BRAFV600 		PMC6217670
Phase IIRecurrent HGG n = 45, BRAFV600-mutant
PFS 1.9m (95% CI 1.7–18.5)
OS 11.7m (95% CI 6.4–not reached)		NANA
34838156Phase IIRecurrentGBM n = 31, BRAFV600E-mutantPFS 2.8Mm
OS 13.7m		ORR 32%, PD 45%, CR 6%, PR 26%
NA
Vandetanib + sirolimusVandetanib:
VEGFR2, VEGFR3, EGFR, induces autophagy by increasing the level of reactive oxygen species (ROS)

Sirolimus:
mTOR
25503302Phase IRecurrentGBM n = 19PFS 2.1m (95% CI 0.9–3.1)
OS 7.7m (95% CI 4.7–9.3)		PR 10.5% NA
Vatalanib + imatinib + hydroxyureaImatinib:
v-Abl, c-Kit and PDGFR, induces autophagy.

Vatalanib: VEGFR2/KDR, VEGFR1/Flt-1, VEGFR3/Flt-4. 		19248046Phase IRecurrentGBM n = 34
MG grade III n = 3		PFS 12w
OS 48w
PR 24%, SD 49%, PD 27%NA
Vorinostat + bortezomib Vorinostat: HDAC

Bortezomib:
20S proteasome, inhibits NF-κB and induces ERK phosphorylation to suppress cathepsin B and inhibit the catalytic process of autophagy		22090453Phase IIRecurrentGBM n = 34TTP 1.5m
OS 3.2m		Partial objective response 2.7%NA
GBM: glioblastoma, AO: anaplastic oligodendroglioma, AA: anaplastic astrocytoma, AG: anaplastic glioma, HGG: high grade glioma, MG: malignant glioma. PFS: progression-free survival, TTP: time until progression, OS: overall survival, CR: complete response, PR: partial response, SD: stable disease, PD: progressive disease, DCR: disease control rate, ORR: objective response rate, RRR: radiographic response rate. m: months, w: weeks and d: days.
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Hoosemans, L.; Vooijs, M.; Hoeben, A. Opportunities and Challenges of Small Molecule Inhibitors in Glioblastoma Treatment: Lessons Learned from Clinical Trials. Cancers 2024, 16, 3021. https://doi.org/10.3390/cancers16173021

AMA Style

Hoosemans L, Vooijs M, Hoeben A. Opportunities and Challenges of Small Molecule Inhibitors in Glioblastoma Treatment: Lessons Learned from Clinical Trials. Cancers. 2024; 16(17):3021. https://doi.org/10.3390/cancers16173021

Chicago/Turabian Style

Hoosemans, Linde, Marc Vooijs, and Ann Hoeben. 2024. "Opportunities and Challenges of Small Molecule Inhibitors in Glioblastoma Treatment: Lessons Learned from Clinical Trials" Cancers 16, no. 17: 3021. https://doi.org/10.3390/cancers16173021

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