Synthetic and systems biology principles in the design of programmable oncolytic virus immunotherapies for glioblastoma

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  • 1 Departments of Immunology,
  • 2 Molecular Medicine,
  • 3 Neurosurgery,
  • 4 Radiation Oncology, and
  • 5 Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic;
  • 6 Mayo Clinic Alix School of Medicine;
  • 7 Mayo Clinic Graduate School of Biomedical Sciences; and Mayo Clinic College of Medicine and Science, Rochester, Minnesota
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Oncolytic viruses (OVs) are a class of immunotherapeutic agents with promising preclinical results for the treatment of glioblastoma (GBM) but have shown limited success in recent clinical trials. Advanced bioengineering principles from disciplines such as synthetic and systems biology are needed to overcome the current challenges faced in developing effective OV-based immunotherapies for GBMs, including off-target effects and poor clinical responses. Synthetic biology is an emerging field that focuses on the development of synthetic DNA constructs that encode networks of genes and proteins (synthetic genetic circuits) to perform novel functions, whereas systems biology is an analytical framework that enables the study of complex interactions between host pathways and these synthetic genetic circuits. In this review, the authors summarize synthetic and systems biology concepts for developing programmable, logic-based OVs to treat GBMs. Programmable OVs can increase selectivity for tumor cells and enhance the local immunological response using synthetic genetic circuits. The authors discuss key principles for developing programmable OV-based immunotherapies, including how to 1) select an appropriate chassis, a vector that carries a synthetic genetic circuit, and 2) design a synthetic genetic circuit that can be programmed to sense key signals in the GBM microenvironment and trigger release of a therapeutic payload. To illustrate these principles, some original laboratory data are included, highlighting the need for systems biology studies, as well as some preliminary network analyses in preparation for synthetic biology applications. Examples from the literature of state-of-the-art synthetic genetic circuits that can be packaged into leading candidate OV chassis are also surveyed and discussed.

ABBREVIATIONS Ad5 = adenovirus serotype 5; CCN1 = cellular communication network factor 1; DAMP = damage-associated molecular pattern; dsDNA = double-stranded DNA; GBM = glioblastoma; GFAP = glial fibrillary acidic protein; GM-CSF = granulocyte-macrophage colony-stimulating factor; HSV-1 = herpes simplex virus type 1; hTERT = telomerase reverse transcriptase; IL = interleukin; miRNA = micro RNA; OV = oncolytic virus; PAMP = pathogen-associated molecular pattern; pCancer = cancer-selective promoter; PD-1 = programmed death–1; PD-L1 = programmed death–ligand 1; PDX = patient-derived xenograft; PPI = protein-protein interaction; scFv = single-chain variable fragment; sTRAIL = secreted tumor necrosis factor–related apoptosis-inducing ligand; VSV = vesicular stomatitis virus.

Oncolytic viruses (OVs) are a class of immunotherapeutic agents with promising preclinical results for the treatment of glioblastoma (GBM) but have shown limited success in recent clinical trials. Advanced bioengineering principles from disciplines such as synthetic and systems biology are needed to overcome the current challenges faced in developing effective OV-based immunotherapies for GBMs, including off-target effects and poor clinical responses. Synthetic biology is an emerging field that focuses on the development of synthetic DNA constructs that encode networks of genes and proteins (synthetic genetic circuits) to perform novel functions, whereas systems biology is an analytical framework that enables the study of complex interactions between host pathways and these synthetic genetic circuits. In this review, the authors summarize synthetic and systems biology concepts for developing programmable, logic-based OVs to treat GBMs. Programmable OVs can increase selectivity for tumor cells and enhance the local immunological response using synthetic genetic circuits. The authors discuss key principles for developing programmable OV-based immunotherapies, including how to 1) select an appropriate chassis, a vector that carries a synthetic genetic circuit, and 2) design a synthetic genetic circuit that can be programmed to sense key signals in the GBM microenvironment and trigger release of a therapeutic payload. To illustrate these principles, some original laboratory data are included, highlighting the need for systems biology studies, as well as some preliminary network analyses in preparation for synthetic biology applications. Examples from the literature of state-of-the-art synthetic genetic circuits that can be packaged into leading candidate OV chassis are also surveyed and discussed.

ABBREVIATIONS Ad5 = adenovirus serotype 5; CCN1 = cellular communication network factor 1; DAMP = damage-associated molecular pattern; dsDNA = double-stranded DNA; GBM = glioblastoma; GFAP = glial fibrillary acidic protein; GM-CSF = granulocyte-macrophage colony-stimulating factor; HSV-1 = herpes simplex virus type 1; hTERT = telomerase reverse transcriptase; IL = interleukin; miRNA = micro RNA; OV = oncolytic virus; PAMP = pathogen-associated molecular pattern; pCancer = cancer-selective promoter; PD-1 = programmed death–1; PD-L1 = programmed death–ligand 1; PDX = patient-derived xenograft; PPI = protein-protein interaction; scFv = single-chain variable fragment; sTRAIL = secreted tumor necrosis factor–related apoptosis-inducing ligand; VSV = vesicular stomatitis virus.

Glioblastoma (GBM) is the most common and lethal malignancy of the CNS. This devastating disease has a median overall survival of 3 months from the time of diagnosis in untreated patients.1 Despite the significant cost and morbidity associated with the standard of care, i.e., resection combined with adjuvant chemotherapy and radiation, the median patient life expectancy is only extended to about 14 months and the disease remains almost uniformly fatal.2,3 Yet, this is the best we can do for patients after hundreds of clinical trials over several decades. Immunotherapies are breakthrough options for many cancer patients but, despite promising safety profiles, are not currently effective for those suffering from GBM.4

Oncolytic viruses (OVs) are a class of immunotherapeutic agents with an FDA-approved treatment for melanoma, a solid tumor.5 OVs work by directly lysing tumor cells, which then release tumor antigens in the context of danger signals—both damage-associated (DAMPs) and pathogen-associated (PAMPs) molecular patterns—that elicit antitumor immunity (Fig. 1A).6 Since 1991, many attempts to use OVs in GBM have had limited success. More than 20 clinical trials of 7 different OVs did not translate encouraging preclinical results to patients with GBM.7 The root causes of these failures can be traced to failed oncotropism, despite neurosurgical delivery, and oncolysis mechanisms.7 Therefore, advanced bioengineering methods are necessary to design and implement better armaments.

FIG. 1.
FIG. 1.

A: Mechanisms of OV immunotherapy for GBM. B: Sense-compute-actuate framework of programmable OV immunotherapy for GBM. C: Design-build-test-analyze development cycle of synthetic OVs for GBM blends systems and synthetic biology.

Synthetic biology involves the creation and manipulation of biological systems using rational, modular design principles from electrical engineering, computer science, and related disciplines.8 The sense-compute-actuate framework (Fig. 1B)9 is a guiding design principle in synthetic biology, in which long stretches of synthetic DNA constructs that encode networks of genes and proteins (synthetic genetic circuits) are designed to sense stimuli of interest, compute which environmental state a cell is in based on a permutation of stimuli, and actuate a response accordingly. In November 2019, the first living medicine containing a synthetic genetic circuit entered phase 1 clinical trials in the form of a bacteria-based cancer immunotherapy.10 Such engineered genetic circuits are now primed to be packaged into OVs and programmed to coordinate local tissue responses, with preclinical studies demonstrating enhanced 1) tumor cell targeting and 2) generation of antitumor immunity.

Systems biology complements synthetic biology by enabling complex design and analysis of genetic circuits. Tumor microenvironment cell states can be defined by using single-cell multiomics data sets such as RNA sequencing (RNA-seq) and mass cytometry. These high-dimensional data sets are amenable to machine learning11 and network-based classification.12 Computation identifies biological features unique to glioma cells,13 elucidates pathways orthogonal to genetic circuit designs, and helps prioritize OV-based approaches.14 After the synthetic OVs are constructed and tested, further systems analyses ensure that the OVs have predictable effects on the cells and the greater tumor microenvironment, statistically accounting for complex stochastic behavior. This knowledge is fed back into the design of improved, next-generation OV genetic circuits (Fig. 1C).

Selection and Delivery of OV Chassis

Selection of a chassis—a viral vector used to carry a synthetic genetic circuit in this case—is essential to the design of any synthetic biology system. In terms of OVs, given that we do not yet have the technology to design a virus de novo, this means identifying a naturally occurring virus that can be engineered to selectively lyse tumor cells. The essential parameters to consider are payload capacity, tropism, life cycle, immunogenicity, and tumor delivery. These considerations are just starting points because synthetic biology can be used to address some limitations inherent to the wild-type chassis.

The chassis needs to be able to accommodate sufficient genetic material to produce a functional circuit or, in the case of a combination OV therapeutic approach, a circuit component. Ideally, it should also avoid targeting sensitive host cells at baseline while preferentially entering and replicating in tumor cells. If not, then the chassis needs to be highly amenable to engineering of its entry and metabolic tropisms. To generate antitumor immunity, the innate immune system must be activated at some point during OV replication. This awakens the adaptive immune system to precisely target all cancerous cells, both infected and uninfected.15 If the patient has been exposed to the viral chassis before, by either infection or vaccination, then this may limit the efficacy of the OV. Host genome integration (e.g., lentivirus) may be advantageous for persistent antitumor effects but comes with the risk of genetic damage and chronic latent infection.16 Ultimately, however, the immune system must be able to clear the OV infection, particularly in the CNS, to prevent chronic inflammation and its sequelae.

Neurotropic Viruses

A chassis needs to target glioma cells, which arise from healthy cells of the brain parenchyma and share many cell surface and metabolic features, so naturally occurring neurotropic viruses are the obvious starting point.

Herpes Simplex Virus Type 1

Herpes simplex virus type 1 (HSV-1) is an enveloped virus with a linear 152-kilobase (Kb) double-stranded DNA (dsDNA) genome.17 It is capable of carrying large payloads—as much as 20 Kb—making it particularly attractive for synthetic biology applications. It is the first and only OV that has garnered FDA approval with talimogene laherparepvec, which has been engineered extensively to target melanoma.18 Using HSV-1 against GBM was also the first proposed OV with experimental success in a murine model;19 therefore, HSV-1 is particularly promising as a treatment for GBM.

The high prevalence of HSV-1 infection in humans, however, means that preexisting immunity (e.g., neutralizing antibodies) poses a barrier to its application as an effective OV. Modulating this OV-host immune interaction is a possibility with synthetic gene circuits.20 Additionally, several molecular features of GBM have been identified that influence the efficacy of HSV-1 OV in vitro and in vivo, including the integrin ligand cellular communication network factor 1 (CCN1) protein found in the extracellular matrix of aggressive gliomas.21 Pathway analysis of HSV-1–resistant GBM has informed the design of improved OV, such as HSV-1 armed with a vasculostatin payload to abrogate the integrin signaling effects of CCN1 and thus enhance oncolysis.22

Integration of multiple pathways yields networks that offer a systems-level view of OV resistance mechanisms. Using our systems biology network analysis tools,12 our group has recently identified key protein-protein interaction (PPI) networks that play a role in the cellular response to HSV-1.13 In particular, we have identified a prioritized subnetwork associated with CCN1high expressing LN229 glioma cells (Fig. 2A). Network edges (Fig. 2B) and nodes are currently being evaluated for their utility in this GBM subtype. Genetic circuits23 that sense and exploit these networks could enhance the efficacy of HSV-1 OV—for example, using a simple Boolean AND gate in which two repressors sense distinct input RNAs and output a proapoptotic or proinflammatory molecule only if both RNAs are present (Fig. 2C).

FIG. 2.
FIG. 2.

A: NetDecoder prioritized network infers salient PPIs in expression microarray data from LN229 human GBM cells depending on the state of CCN1 expression, predictive of HSV-1 OV resistance. PPIs are represented at edges and nodes with higher (red) and lower (blue) flows under the CCN1high state versus the CCN1low control. Nodes consist of sources (diamonds), routers (circles), and sinks (squares). B: PPIs are prioritized based on the CCN1high state (yellow). Edge flow differences between these and the CCN1low control state (blue) are ranked in order, increasing from left to right on the histogram. Each edge ID corresponds to a known PPI. C: Repressor-based Boolean logic can be coded in a genetic circuit to sense GBM (RNA-a) and OV resistance (RNA-b) network states and actuate gene expression that triggers immunogenic cell death. A prototype AND gate with two inputs (Repressors 1 and 2) and one output (Actuator) is shown for illustration.

Poliovirus

Poliovirus, a nonenveloped 7.5-Kb positive-strand RNA virus, enters cells via the CD155 transmembrane glycoprotein. A recombinant nonneuropathogenic polio-rhinovirus chimera (PVS-RIPO) has demonstrated some clinical efficacy and safety in early clinical trials.24 It infects both glioma cells for direct oncolysis and antigen-presenting cells to stimulate adaptive antitumor immunity. Therefore, it is suitable for delivering genetic circuit components that may signal between these cell types. Whole-genome poliovirus synthesis enables significant build control despite limited payload capacity.25

Other Neurotropic Viruses

Vesicular stomatitis virus (VSV), enveloped with an 11-Kb nonsegmented, negative-sense RNA genome, has been able to destroy GBM cells with some success26 but has thus far proven prohibitively neurotoxic. Synthetic circuitry may fine-tune VSV neuroimmune interactions for better outcomes.27 Zika, a positive-sense RNA virus, enters glia via the receptor tyrosine kinase AXL and has activity against GBM stem cells.28

All of these OVs, however, run the risk of chronic neuroinflammation, potentially leading to neurodegeneration, due to their inherent neurotropism. Using attenuated, pseudotyped, chimeric, or alternate serotype strains of these OVs has not fully eliminated adverse effects or sufficiently increased efficacy against GBM.

Nonneurotropic Viruses

There is an opportunity to use nonneurotropic chassis to engineer safer OVs using synthetic biology.

Measles Virus

Measles virus is an enveloped negative-sense RNA virus that is lymphotropic. Measles enters the cell through interaction with the CD46 protein, which is overexpressed by glioma cells with stem cell–like properties.29 Numerous strains of measles demonstrate efficacy against GBM.30 This efficacy can be improved with adjuvant radiotherapy31 and immune checkpoint blockade,32 suggesting mechanistic opportunities for synthetic gene circuits to enhance synergy. Systems biology has been used to predict GBM permissiveness of measles OV, yielding a naïve Bayes machine learning–based classifier,33 which could inform improved designs using synthetic biology. Despite being nonneurotropic, there is risk of neurological sequelae due to chronic neuroinflammation, such as subacute sclerosing panencephalitis.34

Vaccinia Virus

Vaccinia virus is an enveloped virus with a linear 190-Kb dsDNA genome with approximately 220 protein-coding genes.35 It was the first safe and effective vaccine used in humans, ultimately eradicating the smallpox pandemic. The large, complex structure of vaccinia virus lends itself to extensive genetic modification. Vaccinia OVs engineered with tumor suppressor gene TP53,36 immunomodulatory granulocyte-macrophage colony-stimulating factor (GM-CSF),37 differentiation factor bone morphogenetic protein–4 (BMP-4),38 and fusion suicide gene FCU139 have shown some preclinical success against GBM.

Adenovirus

Adenovirus is a particularly strong candidate OV for GBM because of its negligible neurotropism and associated neurotoxicity. Its linear, 36-Kb dsDNA genome, which yields myriad spliced transcripts, is amenable to bioengineering using robust synthetic biology methods.40,41 The nonenveloped adenovirus serotype 5 (Ad5) enters cells natively via the coxsackievirus and adenovirus receptor. For safety, a 24-bp deletion in E1A (Δ24) ensures that Ad5-Δ24 replicates only in cells lacking the retinoblastoma tumor suppressor protein (pRb),42 common in GBM but not in other CNS cells. Arming Ad5 with RGD-4C43 or GITRL44 ligands enhances glioma entry or antiglioma immunity, while synthetic riboswitches enable external regulation for safety.45 It is a highly promising OV given its anti-GBM effects alone or in combination therapies, both in animal models46 and in recent clinical trials as DNX-2401 (tasadenoturev).47

Innate resistance to adenovirus poses a significant challenge because OV permissiveness in GBM is the essential first step to success as an immunotherapy. Elucidating complex tumor-OV interactions at the systems level can identify immunoresistance mechanisms. We have begun to survey the Mayo Clinic Brain Tumor Patient-Derived Xenograft (PDX) National Resource48 for GBM responses to Ad5-Δ24. The resource currently has 92 GBM PDX models with associated data on the clinical phenotype, as well as growth characteristics, invasiveness, molecular subtype, and tissue microarray data. We have found a wide array of dose- and time-dependent responses to the Ad5-Δ24 (Fig. 3A and B). Our previous systems biology analyses suggest that these variable responses could be due to network effects.13 Targeting network edges and nodes critical to resistance using genetic circuits (as in Fig. 2A–C for HSV-1) could convert resistant GBM to permissive.

FIG. 3.
FIG. 3.

Representative viabilities of GBM PDX neurospheres (Sp-GBMs) after treatment with Ad5-Δ24 OV at five multiplicities of infection (MOIs) after 2 (A) and 8 (B) days. Sp-GBMs were cultured ex vivo in Gibco StemPro neural stem cell serum-free medium. Cell viability was assessed using the Promega CellTiter-Blue reagent, which measures the metabolic capacity of the Sp-GBM cells to convert resazurin redox dye into resorufin. MOI is defined as 293A plaque-forming units (PFUs) per Sp-GBM cell. Error bars represent the standard error of the mean for biological triplicates.

Other Nonneurotropic Viruses

Reovirus, a replication-competent dsRNA virus that exploits Ras signaling, has shown clinical safety and efficacy in patients with glioma49 and synergizes with checkpoint blockade.50 Preclinical data support the use of Sindbis virus in GBM, but clinical development has languished.51

These and other potentially useful chassis (summarized in Fig. 4), along with the current state of clinical trials for gliomas, are presented in detail by Martikainen and Essand52 and Eissa et al.53 While many show some promise, no candidates presently offer more than incrementally better outcomes for our patients. Major hurdles include susceptibility to innate antiviral immunity,54 tumor escape from adaptive immunity,55 and inflammatory neurodegeneration.56 Arming OVs with synthetic genetic circuits could offer better control over the treatment and may lead to drastically better results.

FIG. 4.
FIG. 4.

Timeline of key candidate OV chassis by year, first described in GBM preclinical studies. First OV strain described in the literature is shown in parentheses.

Delivery Approaches

Most OV clinical trials for gliomas have relied on neurosurgical delivery using direct intratumoral or intraparenchymal bolus injection. This effectively bypasses the blood-brain barrier, overcomes interstitial hypertension, and reduces off-target effects, but increases risks of stroke, infection, and mass effect, as well as costs. Minimally invasive procedures, including local convection-enhanced delivery through a transcranial cannula57 and regional intraarterial delivery via carotid or vertebral catheterization,58 may be advantageous.

Systemic intravenous delivery would be ideal in terms of patient experience and neurosurgical utilization. Pseudotyping and polymer coating have had limited success in achieving this goal, largely because the OVs are rapidly cleared from circulation.59 Loading OVs into cellular carriers—reovirus into monocytes in situ60 or measles into mesenchymal stem cells ex vivo,61 for example—appears most promising. This approach expands the chassis to include the cell-based delivery vehicle for purposes of genetic circuit design.

Genetic Circuit Design for Programmable OV-Based Immunotherapy

Synthetic genetic circuits can be packaged into OVs and used to increase the selectivity of viruses to tumor cells, as well as enhance the local immunotherapeutic response. Here, we illustrate several genetic circuit design principles based on a programmable OV recently reported in the literature and apply these principles to designing OV-based immunotherapies for GBMs.

General Genetic Circuit Design Principles

Results from Huang et al.62 on the efficacy of a programmable OV-based immunotherapy in mice effectively demonstrate several principles for designing synthetic genetic circuits. The investigators developed an oncolytic adenovirus programmed by a 6.5-Kb synthetic gene circuit to selectively replicate and release immune mediators in hepatocellular carcinoma cells. Using synthetic biology principles, they created a genetic circuit with interchangeable cancer-selective promoters (pCancer), micro RNA (miRNA) target sites to detect distinct miRNA profiles of cancer cells, and genes that encode immune mediators such as interleukin (IL)–2, GM-CSF, or single-chain variable fragments (scFvs) against either programmed death–1 (PD-1) or programmed death–ligand 1 (PD-L1).

The overall design of the circuit involves a promoter selectively activated in hepatocellular carcinoma cells (pCancer) that drives expression of a potent transcriptional activator (Gal4-VP16), which, in turn, drives expression of two mutually inhibitory repressors, Rep-a and Rep-b. The expression of these repressors is inhibited by miRNAs (miR-a and miR-b, respectively) for added selectivity. miR-a represents any miRNA that is high in normal cells and low in cancer cells, whereas miR-b is an miRNA that is high in cancer cells and low in normal cells. The expression of Rep-a is coupled using autocatalytic linker proteins (L) to protein E1A, which increases adenoviral replication and an immune effector of choice (IL-2, GM-CSF, scFvs against PD-1 or PD-L1) that stimulates the host immune response. Autocatalytic linker proteins effectively reduce the amount of DNA required to transcribe three separate proteins by combining them into one therapeutic payload.

The complex logic encoded by their genetic circuit achieves increased selectivity, as well an enhanced host immune response. In normal cells, where miR-a is high, miR-b is low, and pCancer has basal activity, expression of the genetic circuit payload of E1A linked to an immune effector linked to Rep-a is low. Additionally, the activity of Rep-b and miR-a further reduces payload expression below basal levels, limiting replication of OV in normal cells. In cancer cells, where miR-a is low, miR-b is high, and pCancer has high activity, there will be high expression of the genetic circuit payload. This effectively limits expression of the therapeutic payload to tumor cells.

Oncolytic adenoviruses were packaged with the synthetic genetic circuit and injected intratumorally into immune-competent mice that were xenografted with hepatocellular carcinoma cells. All mice (n = 10) were treated twice in 1 week with 1 × 109 viral particles containing synthetic genetic circuits expressing GM-CSF and scFvs against PD-1 eliminated tumors within 33 days. These are promising results of a programmable OV-based immunotherapy in a validated preclinical model for hepatocellular carcinoma. Their modular circuit design enables rapid translation to other cancers including GBM (Fig. 5A–C).

FIG. 5.
FIG. 5.

Development of an oncolytic adenoviral therapy programmed with a synthetic genetic circuit to selectively replicate and express immune mediators in tumor cells. The binary truth table in panel A indicates the target level of each circuit element in host cells, with 0 = low and 1 = high. The design of circuit logic is schematized in panel B and the build strategy is shown in panel C. Activator = Gal4-VP16 transcriptional activator; Actuator = immune effector protein (IL-2, GM-CSF, scFvs against PD-1 or PD-L1); E1A = adenovirus early region 1A replication protein; L = linker protein; pBasal = basal promoter; Rep-a/Rep-b = Repressor a/b.

Genetic Circuit Design Principles Specific for Treating GBMs

Several design principles can be used to guide the creation of other advanced synthetic genetic circuits to program precision OV-based immunotherapies for GBMs.

Genetic Circuit Design Elements

Promoters that drive expression of the genetic circuit can be chosen so that they are selectively activated in brain tissue or tumor cells. This serves as a design feature that provides an initial protection against off-target effects of the viral therapy. For example, the neuron-specific enolase promoter can restrict OV expression to the brain.63 Other promoters such as the telomerase reverse transcriptase (hTERT), glial fibrillary acidic protein (GFAP), and nestin have been shown to have selective, increased expression in glioma cell lines.64,65 Radiosensitive promoters such as the survivin promoter, which have increased expression in cells that are exposed to radiation, can be used to selectively express the genetic circuit in irradiated GBMs.66

Binding Sites for Differentially Expressed miRNA

Binding sites for miRNAs that are differentially expressed in GBMs can be used to enhance the selectivity of OV-based immunotherapy. Adding these binding sites to genes encoded by the genetic circuits can allow for selective expression of the therapeutic payload in cells with a particular miRNA profile. Several miRNAs have been identified to be differentially expressed in GBM, including miR-21, miR-93, miR-196, and miR-335, which have been shown to be upregulated, and miR-7, miR-34a, and miR-124a, which have been shown to be downregulated.67–69 An example of how RNA binding sites can be used to create genetic circuits that perform simple logic operations like the AND gate is shown in Fig. 2C.

Proapoptotic and Proinflammatory Therapeutic Payloads

Several proapoptotic and proinflammatory genes can be selected as the therapeutic payload expressed by the genetic circuit to enhance the local response to the OV-based immunotherapy. Proapoptotic genes such as secreted tumor necrosis factor–related apoptosis-inducing ligand (sTRAIL)63 and hBax70 can enhance tumor killing. Similarly, proinflammatory genes such as IL-2 and GM-CSF, as well as scFvs against PD-1 or PD-L1,62 can promote local immune responses to GBM (Tables 1 and 2).

TABLE 1.

Genetic circuit sensor elements for creating programmable OV therapies for GBMs

Design ElementCharacterization Notes
Selectively activated promoters
 NSEActivity restricted to brain, mostly neurons
 hTERTActivity in U251 and T98G glioma cell lines
 GFAPActivity in U251 and T98G glioma cell lines
 SurvivinActivity increases after irradiation
 NestinActivity restricted to glioma cell lines
Binding sites for differentially expressed miRNA
 miR-21Upregulated in GBM cells, associated w/ increased proliferation
 miR-93Upregulated in GBM cells, associated w/ tumor invasion
 miR-196Upregulated in GBM cells, associated w/ increased proliferation
 miR-335Upregulated in GBM cells, associated w/ increased proliferation
 miR-7Downregulated in GBM cells, associated w/ greater EGFR production
 miR-34aDownregulated in GBM cells, associated w/ decreased p53
 miR-124aDownregulated in GBM cells, normal in neurons;68 limited off-target effects in oncolytic HSV

EGFR = epidermal growth factor receptor; NSE = neuron-specific enolase.

TABLE 2.

Genetic circuit actuator elements for creating programmable OV therapies for GBMs

Design ElementCharacterization Notes
Proapoptotic payloads
 sTRAILPromotes apoptosis via TNF family receptor signaling
 hBaxPromotes apoptosis via release of cytochrome C from mitochondria
Proinflammatory payloads
 IL-2Promotes inflammatory response by stimulating T-cell proliferation
 GM-CSFPromotes inflammatory response by stimulating immune cell proliferation
 scFvs against PD-1 or PD-L1Promotes inflammatory response by inhibiting PD-1 or PD-L1

TNF = tumor necrosis factor.

Other Genetic Circuit Design Considerations

Optimizing Genetic Circuit Size. The size of genetic circuits used to program OVs is fundamentally limited by the quantity of DNA that can be packaged, which ranges from 4 to 15 Kb depending on the viral vector chosen.71 Recently developed modular DNA assembly techniques that take advantage of Golden Gate and Gibson assembly reactions have been shown to efficiently construct multigene expression vectors of reasonable construct size that can be packaged into a lentivirus and integrated into the genome.72 Moreover, there are several design strategies that may be used to optimize genetic circuit size. For example, autocatalytic linker proteins may be used to produce multiple proteins from one RNA transcript.62 Minimal promoter elements containing only the essential promoter sequences required for transcription to take place can be used instead of natural mammalian promoter sequences, which are often large.73

Orthogonality With Host Pathways. Orthogonality is an important design principle that entails using components of a genetic circuit that operate predominantly at specified sites in the genetic circuit with minimal off-target effects.74 For example, Huang et al.62 had used an activator (Gal4-VP16) that binds to Gal4 sites from the yeast genome and has minimal interactions with the host genome (Fig. 5C). Therefore, their activator primarily interacted with their synthetic genetic circuit and was orthogonal to the host genome. If the activator was not orthogonal, an oncogene might have been activated instead, thus defeating the purpose of the genetic circuit.

Mitigating Transcriptional Noise. Expression of the therapeutic payload by the genetic circuit must be resistant to transcriptional noise so as to minimize off-target effects of the therapy. Transcriptional noise is a reality of biological systems and can lead to low-level basal expression of the therapeutic payload in healthy cells. Network motifs such as mutual repression can mitigate the effect of transcriptional noise and enhance the steepness between ON and OFF states of the genetic circuit.67,75 Incorporating these network motifs into any genetic circuits designed for OV-based immunotherapy is essential for ensuring safety in humans.

Discussion

Synthetic and systems biology are ushering the way for novel rationally designed therapeutics that respect biological complexity. In this review, we have summarized these concepts as applied to developing programmable OV-based immunotherapies for GBMs. This approach may be limited by genetic circuit size, incomplete host network orthogonality, OV replication fidelity, and an inability of quasispecies to coevolve with GBM cells over time. OVs successfully programmed with a synthetic genetic circuit, however, have the benefits of increased selectivity to tumor cells, enhanced local response due to the release of customizable therapeutic payloads, and a modular design that allows for rapid prototyping of different miRNA binding sites and therapeutic payloads.

As we have described above, any programmable OV-based immunotherapy requires design of 1) a chassis vector with delivery vehicle, and 2) a synthetic genetic circuit that encodes a network of molecular effectors that sense the tumor microenvironment and trigger release of a therapeutic payload. Chassis include both neurotropic and nonneurotropic viruses with different genome sizes and natural selectivity for tumor cells. Synthetic genetic circuits can be designed in a modular fashion with selectively activated promoters only active in GBM cells, RNA binding sites that differentiate GBM cells from healthy tissue, and customizable therapeutic payloads that release proapoptotic and proinflammatory factors.

Programmable circuits represent the natural next step in the evolution of OV immunotherapies for GBM. Promising preclinical results have been described for programmable OVs designed using synthetic biology principles. We hope that the principles outlined here will help guide the additional preclinical research development required to bring this exciting new technology to patients with GBM in the near future.

Acknowledgments

D.D.M. is supported by the National Institute of General Medical Sciences (grant no. T32 GM 65841) and the Mayo Clinic Medical Scientist Training Program. H.L. is supported by the NIH (grant nos. AG056318 and CA208517), Glenn Foundation for Medical Research, Mayo Clinic Center for Biomedical Discovery, Center for Individualized Medicine, Mayo Clinic Cancer Center, and David F. and Margaret T. Grohne Cancer Immunology and Immunotherapy Program. Figures created with GraphPad Prism, NetDecoder, and BioRender.com.

Disclosures

Dr. Sarkaria reports support of non–study-related clinical or research effort from Novartis, Basilea, Genentech, Sanofi, Beigene, Lilly, GlaxoSmithKline, Peloton, Glionova, Bristol-Myers Squibb Pharmaceuticals, Cavion, Curtana, Forma, AbbVie, Actuate, Boehringer Ingelheim, Bayer, Celgene, Cible, Mitochon, Wayshine, and Nerviano Medical Sciences.

Author Contributions

Conception and design: Li, Monie, Bhandarkar. Acquisition of data: Monie. Analysis and interpretation of data: Monie, Vile. Drafting the article: Monie, Bhandarkar. Critically revising the article: all authors. Reviewed submitted version of manuscript: all authors. Approved the final version of the manuscript on behalf of all authors: Li. Study supervision: Li.

References

  • 1

    Coffey RJ, Lunsford LD, Taylor FH. Survival after stereotactic biopsy of malignant gliomas. Neurosurgery. 1988;22(3):465473.

  • 2

    Malmström A, Grønberg BH, Marosi C, Temozolomide versus standard 6-week radiotherapy versus hypofractionated radiotherapy in patients older than 60 years with glioblastoma: the Nordic randomised, phase 3 trial. Lancet Oncol. 2012;13(9):916926.

    • Search Google Scholar
    • Export Citation
  • 3

    Stupp R, Mason WP, van den Bent MJ, Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med. 2005;352(10):987996.

    • Search Google Scholar
    • Export Citation
  • 4

    Farber SH, Elsamadicy AA, Atik AF, The safety of available immunotherapy for the treatment of glioblastoma. Expert Opin Drug Saf. 2017;16(3):277287.

    • Search Google Scholar
    • Export Citation
  • 5

    Corrigan PA, Beaulieu C, Patel RB, Lowe DK. Talimogene laherparepvec: an oncolytic virus therapy for melanoma. Ann Pharmacother. 2017;51(8):675681.

    • Search Google Scholar
    • Export Citation
  • 6

    Lemos de Matos A, Franco LS, McFadden G. Oncolytic viruses and the immune system: the dynamic duo. Mol Ther Methods Clin Dev. 2020;17:349358.

    • Search Google Scholar
    • Export Citation
  • 7

    Wollmann G, Ozduman K, van den Pol AN. Oncolytic virus therapy for glioblastoma multiforme: concepts and candidates. Cancer J. 2012;18(1):6981.

    • Search Google Scholar
    • Export Citation
  • 8

    Serrano L. Synthetic biology: promises and challenges. Mol Syst Biol. 2007;3:158.

  • 9

    Beal J, Weiss R, Densmore D, An end-to-end workflow for engineering of biological networks from high-level specifications. ACS Synth Biol. 2012;1(8):317331.

    • Search Google Scholar
    • Export Citation
  • 10

    Leventhal DS, Sokolovska A, Li N, Immunotherapy with engineered bacteria by targeting the STING pathway for anti-tumor immunity. Nat Commun. 2020;11(1):2739.

    • Search Google Scholar
    • Export Citation
  • 11

    Ghanat Bari M, Ung CY, Zhang C, Machine learning-assisted network inference approach to identify a new class of genes that coordinate the functionality of cancer networks. Sci Rep. 2017;7(1):6993.

    • Search Google Scholar
    • Export Citation
  • 12

    da Rocha EL, Ung CY, McGehee CD, NetDecoder: a network biology platform that decodes context-specific biological networks and gene activities. Nucleic Acids Res. 2016;44(10):e100.

    • Search Google Scholar
    • Export Citation
  • 13

    Monie DD, Zhang C, Correia C, Ung C, Vile RG, Li H. Network-guided bioengineering of oncolytic immunovirotherapies for glioblastoma. J Immunol. 2020;204(1 Supplement):169.17.

    • Search Google Scholar
    • Export Citation
  • 14

    Swift SL, Stojdl DF. Big data offers novel insights for oncolytic virus immunotherapy. Viruses. 2016;8(2):E45.

  • 15

    Alayo QA, Ito H, Passaro C, Glioblastoma infiltration of both tumor- and virus-antigen specific cytotoxic T cells correlates with experimental virotherapy responses. Sci Rep. 2020;10(1):5095.

    • Search Google Scholar
    • Export Citation
  • 16

    Logg CR, Robbins JM, Jolly DJ, Retroviral replicating vectors in cancer. Methods Enzymol. 2012;507:199228.

  • 17

    Miyagawa Y, Marino P, Verlengia G, Herpes simplex viral-vector design for efficient transduction of nonneuronal cells without cytotoxicity. Proc Natl Acad Sci U S A. 2015;112(13):E1632E1641.

    • Search Google Scholar
    • Export Citation
  • 18

    Conry RM, Westbrook B, McKee S, Norwood TG. Talimogene laherparepvec: First in class oncolytic virotherapy. Hum Vaccin Immunother. 2018;14(4):839846.

    • Search Google Scholar
    • Export Citation
  • 19

    Martuza RL, Malick A, Markert JM, Experimental therapy of human glioma by means of a genetically engineered virus mutant. Science. 1991;252(5007):854856.

    • Search Google Scholar
    • Export Citation
  • 20

    Nissim L, Wu MR, Pery E, Synthetic RNA-based immunomodulatory gene circuits for cancer immunotherapy. Cell. 2017;171(5):11381150.e15.

    • Search Google Scholar
    • Export Citation
  • 21

    Haseley A, Boone S, Wojton J, Extracellular matrix protein CCN1 limits oncolytic efficacy in glioma. Cancer Res. 2012;72(6):13531362.

    • Search Google Scholar
    • Export Citation
  • 22

    Tomita Y, Kurozumi K, Yoo JY, Oncolytic herpes virus armed with vasculostatin in combination with bevacizumab abrogates glioma invasion via the CCN1 and AKT signaling pathways. Mol Cancer Ther. 2019;18(8):14181429.

    • Search Google Scholar
    • Export Citation
  • 23

    Kitada T, DiAndreth B, Teague B, Weiss R. Programming gene and engineered-cell therapies with synthetic biology. Science. 2018;359(6376):eaad1067.

    • Search Google Scholar
    • Export Citation
  • 24

    Desjardins A, Gromeier M, Herndon JE II, Recurrent glioblastoma treated with recombinant poliovirus. N Engl J Med. 2018;379(2):150161.

    • Search Google Scholar
    • Export Citation
  • 25

    Wimmer E, Paul AV. Synthetic poliovirus and other designer viruses: what have we learned from them? Annu Rev Microbiol. 2011;65:583609.

    • Search Google Scholar
    • Export Citation
  • 26

    Wollmann G, Rogulin V, Simon I, Some attenuated variants of vesicular stomatitis virus show enhanced oncolytic activity against human glioblastoma cells relative to normal brain cells. J Virol. 2010;84(3):15631573.

    • Search Google Scholar
    • Export Citation
  • 27

    Le Bœuf F, Batenchuk C, Vähä-Koskela M, Model-based rational design of an oncolytic virus with improved therapeutic potential. Nat Commun. 2013;4:1974.

    • Search Google Scholar
    • Export Citation
  • 28

    Zhu Z, Mesci P, Bernatchez JA, Zika virus targets glioblastoma stem cells through a SOX2-integrin αvβ5 axis. Cell Stem Cell. 2020;26(2):187204.e10.

    • Search Google Scholar
    • Export Citation
  • 29

    Allen C, Opyrchal M, Aderca I, Oncolytic measles virus strains have significant antitumor activity against glioma stem cells. Gene Ther. 2013;20(4):444449.

    • Search Google Scholar
    • Export Citation
  • 30

    Allen C, Paraskevakou G, Liu C, Oncolytic measles virus strains in the treatment of gliomas. Expert Opin Biol Ther. 2008;8(2):213220.

    • Search Google Scholar
    • Export Citation
  • 31

    Liu C, Sarkaria JN, Petell CA, Combination of measles virus virotherapy and radiation therapy has synergistic activity in the treatment of glioblastoma multiforme. Clin Cancer Res. 2007;13(23):71557165.

    • Search Google Scholar
    • Export Citation
  • 32

    Hardcastle J, Mills L, Malo CS, Immunovirotherapy with measles virus strains in combination with anti-PD-1 antibody blockade enhances antitumor activity in glioblastoma treatment. Neuro Oncol. 2017;19(4):493502.

    • Search Google Scholar
    • Export Citation
  • 33

    Kurokawa C, Iankov ID, Anderson SK, Constitutive interferon pathway activation in tumors as an efficacy determinant following oncolytic virotherapy. J Natl Cancer Inst. 2018;110(10):11231132.

    • Search Google Scholar
    • Export Citation
  • 34

    Garg RK. Subacute sclerosing panencephalitis. Postgrad Med J. 2002;78(916):6370.

  • 35

    Prazsák I, Tombácz D, Szűcs A, Full genome sequence of the western reserve strain of vaccinia virus determined by third-generation sequencing. Genome Announc. 2018;6(11):e01570-17.

    • Search Google Scholar
    • Export Citation
  • 36

    Timiryasova TM, Li J, Chen B, Antitumor effect of vaccinia virus in glioma model. Oncol Res. 1999;11(3):133144.

  • 37

    Lun X, Chan J, Zhou H, Efficacy and safety/toxicity study of recombinant vaccinia virus JX-594 in two immunocompetent animal models of glioma. Mol Ther. 2010;18(11):19271936.

    • Search Google Scholar
    • Export Citation
  • 38

    Duggal R, Geissinger U, Zhang Q, Vaccinia virus expressing bone morphogenetic protein-4 in novel glioblastoma orthotopic models facilitates enhanced tumor regression and long-term survival. J Transl Med. 2013;11:155.

    • Search Google Scholar
    • Export Citation
  • 39

    Foloppe J, Kempf J, Futin N, The enhanced tumor specificity of TG6002, an armed oncolytic vaccinia virus deleted in two genes involved in nucleotide metabolism. Mol Ther Oncolytics. 2019;14:114.

    • Search Google Scholar
    • Export Citation
  • 40

    Miciak JJ, Hirshberg J, Bunz F. Seamless assembly of recombinant adenoviral genomes from high-copy plasmids. PLoS One. 2018;13(6):e0199563.

    • Search Google Scholar
    • Export Citation
  • 41

    Hagedorn C, Kreppel F. Capsid engineering of adenovirus vectors: overcoming early vector-host interactions for therapy. Hum Gene Ther. 2017;28(10):820832.

    • Search Google Scholar
    • Export Citation
  • 42

    Fueyo J, Gomez-Manzano C, Alemany R, A mutant oncolytic adenovirus targeting the Rb pathway produces anti-glioma effect in vivo. Oncogene. 2000;19(1):212.

    • Search Google Scholar
    • Export Citation
  • 43

    Fueyo J, Alemany R, Gomez-Manzano C, Preclinical characterization of the antiglioma activity of a tropism-enhanced adenovirus targeted to the retinoblastoma pathway. J Natl Cancer Inst. 2003;95(9):652660.

    • Search Google Scholar
    • Export Citation
  • 44

    Rivera-Molina Y, Jiang H, Fueyo J, GITRL-armed Delta-24-RGD oncolytic adenovirus prolongs survival and induces anti-glioma immune memory. Neurooncol Adv. 2019;1(1):vdz009.

    • Search Google Scholar
    • Export Citation
  • 45

    Ketzer P, Haas SF, Engelhardt S, Synthetic riboswitches for external regulation of genes transferred by replication-deficient and oncolytic adenoviruses. Nucleic Acids Res. 2012;40(21):e167.

    • Search Google Scholar
    • Export Citation
  • 46

    Jiang H, Clise-Dwyer K, Ruisaard KE, Delta-24-RGD oncolytic adenovirus elicits anti-glioma immunity in an immunocompetent mouse model. PLoS One. 2014;9(5):e97407.

    • Search Google Scholar
    • Export Citation
  • 47

    Lang FF, Conrad C, Gomez-Manzano C, Phase I study of DNX-2401 (Delta-24-RGD) oncolytic adenovirus: replication and immunotherapeutic effects in recurrent malignant glioma. J Clin Oncol. 2018;36(14):14191427.

    • Search Google Scholar
    • Export Citation
  • 48

    Carlson BL, Pokorny JL, Schroeder MA, Sarkaria JN. Establishment, maintenance and in vitro and in vivo applications of primary human glioblastoma multiforme (GBM) xenograft models for translational biology studies and drug discovery. Curr Protoc Pharmacol. 2011;Chapter 14:Unit 14.16.

    • Search Google Scholar
    • Export Citation
  • 49

    Kicielinski KP, Chiocca EA, Yu JS, Phase 1 clinical trial of intratumoral reovirus infusion for the treatment of recurrent malignant gliomas in adults. Mol Ther. 2014;22(5):10561062.

    • Search Google Scholar
    • Export Citation
  • 50

    Samson A, Scott KJ, Taggart D, Intravenous delivery of oncolytic reovirus to brain tumor patients immunologically primes for subsequent checkpoint blockade. Sci Transl Med. 2018;10(422):eaam7577.

    • Search Google Scholar
    • Export Citation
  • 51

    Zhang J, Frolov I, Russell SJ. Gene therapy for malignant glioma using Sindbis vectors expressing a fusogenic membrane glycoprotein. J Gene Med. 2004;6(10):10821091.

    • Search Google Scholar
    • Export Citation
  • 52

    Martikainen M, Essand M. Virus-based immunotherapy of glioblastoma. Cancers (Basel). 2019;11(2):E186.

  • 53

    Eissa IR, Bustos-Villalobos I, Ichinose T, The current status and future prospects of oncolytic viruses in clinical trials against melanoma, glioma, pancreatic, and breast cancers. Cancers (Basel). 2018;10(10):E356.

    • Search Google Scholar
    • Export Citation
  • 54

    Power AT, Wang J, Falls TJ, Carrier cell-based delivery of an oncolytic virus circumvents antiviral immunity. Mol Ther. 2007;15(1):123130.

    • Search Google Scholar
    • Export Citation
  • 55

    Workenhe ST, Verschoor ML, Mossman KL. The role of oncolytic virus immunotherapies to subvert cancer immune evasion. Future Oncol. 2015;11(4):675689.

    • Search Google Scholar
    • Export Citation
  • 56

    Klein RS, Garber C, Howard N. Infectious immunity in the central nervous system and brain function. Nat Immunol. 2017;18(2):132141.

  • 57

    Szerlip NJ, Walbridge S, Yang L, Real-time imaging of convection-enhanced delivery of viruses and virus-sized particles. J Neurosurg. 2007;107(3):560567.

    • Search Google Scholar
    • Export Citation
  • 58

    Huang R, Boltze J, Li S. Strategies for improved intra-arterial treatments targeting brain tumors: a systematic review. Front Oncol. 2020;10:1443.

    • Search Google Scholar
    • Export Citation
  • 59

    Hill C, Carlisle R. Achieving systemic delivery of oncolytic viruses. Expert Opin Drug Deliv. 2019;16(6):607620.

  • 60

    Berkeley RA, Steele LP, Mulder AA, Antibody-neutralized reovirus is effective in oncolytic virotherapy. Cancer Immunol Res. 2018;6(10):11611173.

    • Search Google Scholar
    • Export Citation
  • 61

    Mader EK, Butler G, Dowdy SC, Optimizing patient derived mesenchymal stem cells as virus carriers for a phase I clinical trial in ovarian cancer. J Transl Med. 2013;11:20.

    • Search Google Scholar
    • Export Citation
  • 62

    Huang H, Liu Y, Liao W, Oncolytic adenovirus programmed by synthetic gene circuit for cancer immunotherapy. Nat Commun. 2019;10(1):4801.

    • Search Google Scholar
    • Export Citation
  • 63

    Crommentuijn MH, Kantar R, Noske DP, Systemically administered AAV9-sTRAIL combats invasive glioblastoma in a patient-derived orthotopic xenograft model. Mol Ther Oncolytics. 2016;3:16017.

    • Search Google Scholar
    • Export Citation
  • 64

    Liu F, Xu K, Yang H, A novel approach to glioma therapy using an oncolytic adenovirus with two specific promoters. Oncol Lett. 2018;15(3):33623368.

    • Search Google Scholar
    • Export Citation
  • 65

    Kurihara H, Zama A, Tamura M, Glioma/glioblastoma-specific adenoviral gene expression using the nestin gene regulator. Gene Ther. 2000;7(8):686693.

    • Search Google Scholar
    • Export Citation
  • 66

    Naoum GE, Zhu ZB, Buchsbaum DJ, Survivin a radiogenetic promoter for glioblastoma viral gene therapy independently from CArG motifs. Clin Transl Med. 2017;6(1):11.

    • Search Google Scholar
    • Export Citation
  • 67

    Huang SW, Ali ND, Zhong L, Shi J. MicroRNAs as biomarkers for human glioblastoma: progress and potential. Acta Pharmacol Sin. 2018;39(9):14051413.

    • Search Google Scholar
    • Export Citation
  • 68

    Mazzacurati L, Marzulli M, Reinhart B, Use of miRNA response sequences to block off-target replication and increase the safety of an unattenuated, glioblastoma-targeted oncolytic HSV. Mol Ther. 2015;23(1):99107.

    • Search Google Scholar
    • Export Citation
  • 69

    Mucaj V, Lee SS, Skuli N, MicroRNA-124 expression counteracts pro-survival stress responses in glioblastoma. Oncogene. 2015;34(17):22042214.

    • Search Google Scholar
    • Export Citation
  • 70

    Xie Z, Wroblewska L, Prochazka L, Multi-input RNAi-based logic circuit for identification of specific cancer cells. Science. 2011;333(6047):13071311.

    • Search Google Scholar
    • Export Citation
  • 71

    Zainutdinov SS, Kochneva GV, Netesov SV, Directed evolution as a tool for the selection of oncolytic RNA viruses with desired phenotypes. Oncolytic Virother. 2019;8:926.

    • Search Google Scholar
    • Export Citation
  • 72

    Duportet X, Wroblewska L, Guye P, A platform for rapid prototyping of synthetic gene networks in mammalian cells. Nucleic Acids Res. 2014;42(21):1344013451.

    • Search Google Scholar
    • Export Citation
  • 73

    Schlabach MR, Hu JK, Li M, Elledge SJ. Synthetic design of strong promoters. Proc Natl Acad Sci U S A. 2010;107(6):25382543.

  • 74

    Brophy JA, Voigt CA. Principles of genetic circuit design. Nat Methods. 2014;11(5):508520.

  • 75

    Sokolowski TR, Erdmann T, ten Wolde PR. Mutual repression enhances the steepness and precision of gene expression boundaries. PLOS Comput Biol. 2012;8(8):e1002654.

    • Search Google Scholar
    • Export Citation

If the inline PDF is not rendering correctly, you can download the PDF file here.

Contributor Notes

Correspondence Hu Li: Mayo Clinic College of Medicine and Science, Rochester, MN. li.hu@mayo.edu.

INCLUDE WHEN CITING DOI: 10.3171/2020.12.FOCUS20855.

D.D.M. and A.R.B. contributed equally to this work.

Disclosures Dr. Sarkaria reports support of non–study-related clinical or research effort from Novartis, Basilea, Genentech, Sanofi, Beigene, Lilly, GlaxoSmithKline, Peloton, Glionova, Bristol-Myers Squibb Pharmaceuticals, Cavion, Curtana, Forma, AbbVie, Actuate, Boehringer Ingelheim, Bayer, Celgene, Cible, Mitochon, Wayshine, and Nerviano Medical Sciences.

  • View in gallery

    A: Mechanisms of OV immunotherapy for GBM. B: Sense-compute-actuate framework of programmable OV immunotherapy for GBM. C: Design-build-test-analyze development cycle of synthetic OVs for GBM blends systems and synthetic biology.

  • View in gallery

    A: NetDecoder prioritized network infers salient PPIs in expression microarray data from LN229 human GBM cells depending on the state of CCN1 expression, predictive of HSV-1 OV resistance. PPIs are represented at edges and nodes with higher (red) and lower (blue) flows under the CCN1high state versus the CCN1low control. Nodes consist of sources (diamonds), routers (circles), and sinks (squares). B: PPIs are prioritized based on the CCN1high state (yellow). Edge flow differences between these and the CCN1low control state (blue) are ranked in order, increasing from left to right on the histogram. Each edge ID corresponds to a known PPI. C: Repressor-based Boolean logic can be coded in a genetic circuit to sense GBM (RNA-a) and OV resistance (RNA-b) network states and actuate gene expression that triggers immunogenic cell death. A prototype AND gate with two inputs (Repressors 1 and 2) and one output (Actuator) is shown for illustration.

  • View in gallery

    Representative viabilities of GBM PDX neurospheres (Sp-GBMs) after treatment with Ad5-Δ24 OV at five multiplicities of infection (MOIs) after 2 (A) and 8 (B) days. Sp-GBMs were cultured ex vivo in Gibco StemPro neural stem cell serum-free medium. Cell viability was assessed using the Promega CellTiter-Blue reagent, which measures the metabolic capacity of the Sp-GBM cells to convert resazurin redox dye into resorufin. MOI is defined as 293A plaque-forming units (PFUs) per Sp-GBM cell. Error bars represent the standard error of the mean for biological triplicates.

  • View in gallery

    Timeline of key candidate OV chassis by year, first described in GBM preclinical studies. First OV strain described in the literature is shown in parentheses.

  • View in gallery

    Development of an oncolytic adenoviral therapy programmed with a synthetic genetic circuit to selectively replicate and express immune mediators in tumor cells. The binary truth table in panel A indicates the target level of each circuit element in host cells, with 0 = low and 1 = high. The design of circuit logic is schematized in panel B and the build strategy is shown in panel C. Activator = Gal4-VP16 transcriptional activator; Actuator = immune effector protein (IL-2, GM-CSF, scFvs against PD-1 or PD-L1); E1A = adenovirus early region 1A replication protein; L = linker protein; pBasal = basal promoter; Rep-a/Rep-b = Repressor a/b.

  • 1

    Coffey RJ, Lunsford LD, Taylor FH. Survival after stereotactic biopsy of malignant gliomas. Neurosurgery. 1988;22(3):465473.

  • 2

    Malmström A, Grønberg BH, Marosi C, Temozolomide versus standard 6-week radiotherapy versus hypofractionated radiotherapy in patients older than 60 years with glioblastoma: the Nordic randomised, phase 3 trial. Lancet Oncol. 2012;13(9):916926.

    • Search Google Scholar
    • Export Citation
  • 3

    Stupp R, Mason WP, van den Bent MJ, Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med. 2005;352(10):987996.

    • Search Google Scholar
    • Export Citation
  • 4

    Farber SH, Elsamadicy AA, Atik AF, The safety of available immunotherapy for the treatment of glioblastoma. Expert Opin Drug Saf. 2017;16(3):277287.

    • Search Google Scholar
    • Export Citation
  • 5

    Corrigan PA, Beaulieu C, Patel RB, Lowe DK. Talimogene laherparepvec: an oncolytic virus therapy for melanoma. Ann Pharmacother. 2017;51(8):675681.

    • Search Google Scholar
    • Export Citation
  • 6

    Lemos de Matos A, Franco LS, McFadden G. Oncolytic viruses and the immune system: the dynamic duo. Mol Ther Methods Clin Dev. 2020;17:349358.

    • Search Google Scholar
    • Export Citation
  • 7

    Wollmann G, Ozduman K, van den Pol AN. Oncolytic virus therapy for glioblastoma multiforme: concepts and candidates. Cancer J. 2012;18(1):6981.

    • Search Google Scholar
    • Export Citation
  • 8

    Serrano L. Synthetic biology: promises and challenges. Mol Syst Biol. 2007;3:158.

  • 9

    Beal J, Weiss R, Densmore D, An end-to-end workflow for engineering of biological networks from high-level specifications. ACS Synth Biol. 2012;1(8):317331.

    • Search Google Scholar
    • Export Citation
  • 10

    Leventhal DS, Sokolovska A, Li N, Immunotherapy with engineered bacteria by targeting the STING pathway for anti-tumor immunity. Nat Commun. 2020;11(1):2739.

    • Search Google Scholar
    • Export Citation
  • 11

    Ghanat Bari M, Ung CY, Zhang C, Machine learning-assisted network inference approach to identify a new class of genes that coordinate the functionality of cancer networks. Sci Rep. 2017;7(1):6993.

    • Search Google Scholar
    • Export Citation
  • 12

    da Rocha EL, Ung CY, McGehee CD, NetDecoder: a network biology platform that decodes context-specific biological networks and gene activities. Nucleic Acids Res. 2016;44(10):e100.

    • Search Google Scholar
    • Export Citation
  • 13

    Monie DD, Zhang C, Correia C, Ung C, Vile RG, Li H. Network-guided bioengineering of oncolytic immunovirotherapies for glioblastoma. J Immunol. 2020;204(1 Supplement):169.17.

    • Search Google Scholar
    • Export Citation
  • 14

    Swift SL, Stojdl DF. Big data offers novel insights for oncolytic virus immunotherapy. Viruses. 2016;8(2):E45.

  • 15

    Alayo QA, Ito H, Passaro C, Glioblastoma infiltration of both tumor- and virus-antigen specific cytotoxic T cells correlates with experimental virotherapy responses. Sci Rep. 2020;10(1):5095.

    • Search Google Scholar
    • Export Citation
  • 16

    Logg CR, Robbins JM, Jolly DJ, Retroviral replicating vectors in cancer. Methods Enzymol. 2012;507:199228.

  • 17

    Miyagawa Y, Marino P, Verlengia G, Herpes simplex viral-vector design for efficient transduction of nonneuronal cells without cytotoxicity. Proc Natl Acad Sci U S A. 2015;112(13):E1632E1641.

    • Search Google Scholar
    • Export Citation
  • 18

    Conry RM, Westbrook B, McKee S, Norwood TG. Talimogene laherparepvec: First in class oncolytic virotherapy. Hum Vaccin Immunother. 2018;14(4):839846.

    • Search Google Scholar
    • Export Citation
  • 19

    Martuza RL, Malick A, Markert JM, Experimental therapy of human glioma by means of a genetically engineered virus mutant. Science. 1991;252(5007):854856.

    • Search Google Scholar
    • Export Citation
  • 20

    Nissim L, Wu MR, Pery E, Synthetic RNA-based immunomodulatory gene circuits for cancer immunotherapy. Cell. 2017;171(5):11381150.e15.

    • Search Google Scholar
    • Export Citation
  • 21

    Haseley A, Boone S, Wojton J, Extracellular matrix protein CCN1 limits oncolytic efficacy in glioma. Cancer Res. 2012;72(6):13531362.

    • Search Google Scholar
    • Export Citation
  • 22

    Tomita Y, Kurozumi K, Yoo JY, Oncolytic herpes virus armed with vasculostatin in combination with bevacizumab abrogates glioma invasion via the CCN1 and AKT signaling pathways. Mol Cancer Ther. 2019;18(8):14181429.

    • Search Google Scholar
    • Export Citation
  • 23

    Kitada T, DiAndreth B, Teague B, Weiss R. Programming gene and engineered-cell therapies with synthetic biology. Science. 2018;359(6376):eaad1067.

    • Search Google Scholar
    • Export Citation
  • 24

    Desjardins A, Gromeier M, Herndon JE II, Recurrent glioblastoma treated with recombinant poliovirus. N Engl J Med. 2018;379(2):150161.

    • Search Google Scholar
    • Export Citation
  • 25

    Wimmer E, Paul AV. Synthetic poliovirus and other designer viruses: what have we learned from them? Annu Rev Microbiol. 2011;65:583609.

    • Search Google Scholar
    • Export Citation
  • 26

    Wollmann G, Rogulin V, Simon I, Some attenuated variants of vesicular stomatitis virus show enhanced oncolytic activity against human glioblastoma cells relative to normal brain cells. J Virol. 2010;84(3):15631573.

    • Search Google Scholar
    • Export Citation
  • 27

    Le Bœuf F, Batenchuk C, Vähä-Koskela M, Model-based rational design of an oncolytic virus with improved therapeutic potential. Nat Commun. 2013;4:1974.

    • Search Google Scholar
    • Export Citation
  • 28

    Zhu Z, Mesci P, Bernatchez JA, Zika virus targets glioblastoma stem cells through a SOX2-integrin αvβ5 axis. Cell Stem Cell. 2020;26(2):187204.e10.

    • Search Google Scholar
    • Export Citation
  • 29

    Allen C, Opyrchal M, Aderca I, Oncolytic measles virus strains have significant antitumor activity against glioma stem cells. Gene Ther. 2013;20(4):444449.

    • Search Google Scholar
    • Export Citation
  • 30

    Allen C, Paraskevakou G, Liu C, Oncolytic measles virus strains in the treatment of gliomas. Expert Opin Biol Ther. 2008;8(2):213220.

    • Search Google Scholar
    • Export Citation
  • 31

    Liu C, Sarkaria JN, Petell CA, Combination of measles virus virotherapy and radiation therapy has synergistic activity in the treatment of glioblastoma multiforme. Clin Cancer Res. 2007;13(23):71557165.

    • Search Google Scholar
    • Export Citation
  • 32

    Hardcastle J, Mills L, Malo CS, Immunovirotherapy with measles virus strains in combination with anti-PD-1 antibody blockade enhances antitumor activity in glioblastoma treatment. Neuro Oncol. 2017;19(4):493502.

    • Search Google Scholar
    • Export Citation
  • 33

    Kurokawa C, Iankov ID, Anderson SK, Constitutive interferon pathway activation in tumors as an efficacy determinant following oncolytic virotherapy. J Natl Cancer Inst. 2018;110(10):11231132.

    • Search Google Scholar
    • Export Citation
  • 34

    Garg RK. Subacute sclerosing panencephalitis. Postgrad Med J. 2002;78(916):6370.

  • 35

    Prazsák I, Tombácz D, Szűcs A, Full genome sequence of the western reserve strain of vaccinia virus determined by third-generation sequencing. Genome Announc. 2018;6(11):e01570-17.

    • Search Google Scholar
    • Export Citation
  • 36

    Timiryasova TM, Li J, Chen B, Antitumor effect of vaccinia virus in glioma model. Oncol Res. 1999;11(3):133144.

  • 37

    Lun X, Chan J, Zhou H, Efficacy and safety/toxicity study of recombinant vaccinia virus JX-594 in two immunocompetent animal models of glioma. Mol Ther. 2010;18(11):19271936.

    • Search Google Scholar
    • Export Citation
  • 38

    Duggal R, Geissinger U, Zhang Q, Vaccinia virus expressing bone morphogenetic protein-4 in novel glioblastoma orthotopic models facilitates enhanced tumor regression and long-term survival. J Transl Med. 2013;11:155.

    • Search Google Scholar
    • Export Citation
  • 39

    Foloppe J, Kempf J, Futin N, The enhanced tumor specificity of TG6002, an armed oncolytic vaccinia virus deleted in two genes involved in nucleotide metabolism. Mol Ther Oncolytics. 2019;14:114.

    • Search Google Scholar
    • Export Citation
  • 40

    Miciak JJ, Hirshberg J, Bunz F. Seamless assembly of recombinant adenoviral genomes from high-copy plasmids. PLoS One. 2018;13(6):e0199563.

    • Search Google Scholar
    • Export Citation
  • 41

    Hagedorn C, Kreppel F. Capsid engineering of adenovirus vectors: overcoming early vector-host interactions for therapy. Hum Gene Ther. 2017;28(10):820832.

    • Search Google Scholar
    • Export Citation
  • 42

    Fueyo J, Gomez-Manzano C, Alemany R, A mutant oncolytic adenovirus targeting the Rb pathway produces anti-glioma effect in vivo. Oncogene. 2000;19(1):212.

    • Search Google Scholar
    • Export Citation
  • 43

    Fueyo J, Alemany R, Gomez-Manzano C, Preclinical characterization of the antiglioma activity of a tropism-enhanced adenovirus targeted to the retinoblastoma pathway. J Natl Cancer Inst. 2003;95(9):652660.

    • Search Google Scholar
    • Export Citation
  • 44

    Rivera-Molina Y, Jiang H, Fueyo J, GITRL-armed Delta-24-RGD oncolytic adenovirus prolongs survival and induces anti-glioma immune memory. Neurooncol Adv. 2019;1(1):vdz009.

    • Search Google Scholar
    • Export Citation
  • 45

    Ketzer P, Haas SF, Engelhardt S, Synthetic riboswitches for external regulation of genes transferred by replication-deficient and oncolytic adenoviruses. Nucleic Acids Res. 2012;40(21):e167.

    • Search Google Scholar
    • Export Citation
  • 46

    Jiang H, Clise-Dwyer K, Ruisaard KE, Delta-24-RGD oncolytic adenovirus elicits anti-glioma immunity in an immunocompetent mouse model. PLoS One. 2014;9(5):e97407.

    • Search Google Scholar
    • Export Citation
  • 47

    Lang FF, Conrad C, Gomez-Manzano C, Phase I study of DNX-2401 (Delta-24-RGD) oncolytic adenovirus: replication and immunotherapeutic effects in recurrent malignant glioma. J Clin Oncol. 2018;36(14):14191427.

    • Search Google Scholar
    • Export Citation
  • 48

    Carlson BL, Pokorny JL, Schroeder MA, Sarkaria JN. Establishment, maintenance and in vitro and in vivo applications of primary human glioblastoma multiforme (GBM) xenograft models for translational biology studies and drug discovery. Curr Protoc Pharmacol. 2011;Chapter 14:Unit 14.16.

    • Search Google Scholar
    • Export Citation
  • 49

    Kicielinski KP, Chiocca EA, Yu JS, Phase 1 clinical trial of intratumoral reovirus infusion for the treatment of recurrent malignant gliomas in adults. Mol Ther. 2014;22(5):10561062.

    • Search Google Scholar
    • Export Citation
  • 50

    Samson A, Scott KJ, Taggart D, Intravenous delivery of oncolytic reovirus to brain tumor patients immunologically primes for subsequent checkpoint blockade. Sci Transl Med. 2018;10(422):eaam7577.

    • Search Google Scholar
    • Export Citation
  • 51

    Zhang J, Frolov I, Russell SJ. Gene therapy for malignant glioma using Sindbis vectors expressing a fusogenic membrane glycoprotein. J Gene Med. 2004;6(10):10821091.

    • Search Google Scholar
    • Export Citation
  • 52

    Martikainen M, Essand M. Virus-based immunotherapy of glioblastoma. Cancers (Basel). 2019;11(2):E186.

  • 53

    Eissa IR, Bustos-Villalobos I, Ichinose T, The current status and future prospects of oncolytic viruses in clinical trials against melanoma, glioma, pancreatic, and breast cancers. Cancers (Basel). 2018;10(10):E356.

    • Search Google Scholar
    • Export Citation
  • 54

    Power AT, Wang J, Falls TJ, Carrier cell-based delivery of an oncolytic virus circumvents antiviral immunity. Mol Ther. 2007;15(1):123130.

    • Search Google Scholar
    • Export Citation
  • 55

    Workenhe ST, Verschoor ML, Mossman KL. The role of oncolytic virus immunotherapies to subvert cancer immune evasion. Future Oncol. 2015;11(4):675689.

    • Search Google Scholar
    • Export Citation
  • 56

    Klein RS, Garber C, Howard N. Infectious immunity in the central nervous system and brain function. Nat Immunol. 2017;18(2):132141.

  • 57

    Szerlip NJ, Walbridge S, Yang L, Real-time imaging of convection-enhanced delivery of viruses and virus-sized particles. J Neurosurg. 2007;107(3):560567.

    • Search Google Scholar
    • Export Citation
  • 58

    Huang R, Boltze J, Li S. Strategies for improved intra-arterial treatments targeting brain tumors: a systematic review. Front Oncol. 2020;10:1443.

    • Search Google Scholar
    • Export Citation
  • 59

    Hill C, Carlisle R. Achieving systemic delivery of oncolytic viruses. Expert Opin Drug Deliv. 2019;16(6):607620.

  • 60

    Berkeley RA, Steele LP, Mulder AA, Antibody-neutralized reovirus is effective in oncolytic virotherapy. Cancer Immunol Res. 2018;6(10):11611173.

    • Search Google Scholar
    • Export Citation
  • 61

    Mader EK, Butler G, Dowdy SC, Optimizing patient derived mesenchymal stem cells as virus carriers for a phase I clinical trial in ovarian cancer. J Transl Med. 2013;11:20.

    • Search Google Scholar
    • Export Citation
  • 62

    Huang H, Liu Y, Liao W, Oncolytic adenovirus programmed by synthetic gene circuit for cancer immunotherapy. Nat Commun. 2019;10(1):4801.

    • Search Google Scholar
    • Export Citation
  • 63

    Crommentuijn MH, Kantar R, Noske DP, Systemically administered AAV9-sTRAIL combats invasive glioblastoma in a patient-derived orthotopic xenograft model. Mol Ther Oncolytics. 2016;3:16017.

    • Search Google Scholar
    • Export Citation
  • 64

    Liu F, Xu K, Yang H, A novel approach to glioma therapy using an oncolytic adenovirus with two specific promoters. Oncol Lett. 2018;15(3):33623368.

    • Search Google Scholar
    • Export Citation
  • 65

    Kurihara H, Zama A, Tamura M, Glioma/glioblastoma-specific adenoviral gene expression using the nestin gene regulator. Gene Ther. 2000;7(8):686693.

    • Search Google Scholar
    • Export Citation
  • 66

    Naoum GE, Zhu ZB, Buchsbaum DJ, Survivin a radiogenetic promoter for glioblastoma viral gene therapy independently from CArG motifs. Clin Transl Med. 2017;6(1):11.

    • Search Google Scholar
    • Export Citation
  • 67

    Huang SW, Ali ND, Zhong L, Shi J. MicroRNAs as biomarkers for human glioblastoma: progress and potential. Acta Pharmacol Sin. 2018;39(9):14051413.

    • Search Google Scholar
    • Export Citation
  • 68

    Mazzacurati L, Marzulli M, Reinhart B, Use of miRNA response sequences to block off-target replication and increase the safety of an unattenuated, glioblastoma-targeted oncolytic HSV. Mol Ther. 2015;23(1):99107.

    • Search Google Scholar
    • Export Citation
  • 69

    Mucaj V, Lee SS, Skuli N, MicroRNA-124 expression counteracts pro-survival stress responses in glioblastoma. Oncogene. 2015;34(17):22042214.

    • Search Google Scholar
    • Export Citation
  • 70

    Xie Z, Wroblewska L, Prochazka L, Multi-input RNAi-based logic circuit for identification of specific cancer cells. Science. 2011;333(6047):13071311.

    • Search Google Scholar
    • Export Citation
  • 71

    Zainutdinov SS, Kochneva GV, Netesov SV, Directed evolution as a tool for the selection of oncolytic RNA viruses with desired phenotypes. Oncolytic Virother. 2019;8:926.

    • Search Google Scholar
    • Export Citation
  • 72

    Duportet X, Wroblewska L, Guye P, A platform for rapid prototyping of synthetic gene networks in mammalian cells. Nucleic Acids Res. 2014;42(21):1344013451.

    • Search Google Scholar
    • Export Citation
  • 73

    Schlabach MR, Hu JK, Li M, Elledge SJ. Synthetic design of strong promoters. Proc Natl Acad Sci U S A. 2010;107(6):25382543.

  • 74

    Brophy JA, Voigt CA. Principles of genetic circuit design. Nat Methods. 2014;11(5):508520.

  • 75

    Sokolowski TR, Erdmann T, ten Wolde PR. Mutual repression enhances the steepness and precision of gene expression boundaries. PLOS Comput Biol. 2012;8(8):e1002654.

    • Search Google Scholar
    • Export Citation

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