Oncolytic adenovirus and tumor-targeting immune modulatory therapy improve autologous cancer vaccination

Oncolytic viruses selectively lyse tumor cells, disrupt immunosuppression within the tumor and reactivate anti-tumor immunity, but they have yet to live up to their therapeutic potential. Immune checkpoint modulation has been efficacious in a variety of cancer with an immunogenic microenvironment, but is associated with toxicity due to nonspecific T-cell activation. Therefore, combining these two strategies would likely result in both effective and specific cancer therapy. To test the hypothesis, we first constructed oncolytic adenovirus Delta-24-RGDOX expressing the immune co-stimulator OX40 ligand (OX40L). Like its predecessor Delta-24-RGD, Delta-24-RGDOX induced immunogenic cell death and recruit lymphocytes to the tumor site. Compared to Delta-24-RGD, Delta-24-RGDOX exhibited superior tumor-specific activation of lymphocytes and proliferation of CD8+ T cells specific to tumor-associated antigens, resulting in cancer-specific immunity. Delta-24-RGDOX mediated more potent anti-glioma activity in immune-competent C57BL/6 but not immune-deficient athymic mice, leading to specific immune memory against the tumor. In order to further overcome the immune suppression mediated by programmed death-ligand 1 (PD-L1) expression on cancer cells accompanied with virotherapy, intratumoral injection of Delta-24-RGDOX and an anti-PD-L1 antibody showed synergistic inhibition of gliomas and significantly increased survival in mice. Our data demonstrate that combining an oncolytic virus with tumor-targeting immune checkpoint modulators elicits potent in situ autologous cancer vaccination, resulting in an efficacious, tumor-specific and long-lasting therapeutic effect.

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Pharmacokinetics and drug-interactions determine optimum combination strategies in computational models of cancer evolution

The identification of optimal drug administration schedules to battle the emergence of resistance is a major challenge in cancer research. The existence of a multitude of resistance mechanisms necessitates administering drugs in combination, significantly complicating the endeavor of predicting the evolutionary dynamics of cancers and optimal intervention strategies. A thorough understanding of the important determinants of cancer evolution under combination therapies is therefore crucial for correctly predicting treatment outcomes. Here we developed the first computational strategy to explore pharmacokinetic and drug interaction effects in evolutionary models of cancer progression - a crucial step towards making clinically relevant predictions. We found that incorporating these phenomena into our multi-scale stochastic modeling framework significantly changes the optimum drug administration schedules identified, often predicting non-intuitive strategies for combination therapies. We applied our approach to an ongoing phase-Ib clinical trial (TATTON) administering AZD9291 and selumetinib to EGFR-mutant lung cancer patients. Our results suggest that the schedules used in the three trial arms have almost identical efficacies, but slight modifications in the dosing frequencies of the two drugs can significantly increase tumor cell eradication. Interestingly, we also predict that drug concentrations lower than the maximally tolerated dose are as efficacious, suggesting that lowering the total amount of drug administered could lower toxicities while not compromising on the effectiveness of the drugs. Our approach highlights the fact that quantitative knowledge of pharmacokinetic, drug interaction and evolutionary processes is essential for identifying best intervention strategies. Our method is applicable to diverse cancer and treatment types and allows for a rational design of clinical trials.

 

MAJOR FINDINGS

 

Drugs used in cancer treatment exhibit varied temporal dynamics of absorption and clearance, summarized by their pharmacokinetic (PK) parameters. Typically these dynamics are neglected in evolutionary models of cancer progression under combination therapy. By developing a computational framework to incorporate PK and drug interaction effects, we showed that the complex interplay of dynamics of drugs and various cancer clones significantly affect dosing strategies. Applied to an ongoing phase-Ib clinical trial, our framework predicted efficacies of different dosing schedules and suggested new strategies to significantly improve cancer cell eradication and reduce associated toxicities.

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Monitoring the vascular response and resistance to sunitinib in renal cell carcinoma in vivo with susceptibility contrast MRI

Anti-angiogenic therapy is efficacious in metastatic renal cell carcinoma (mRCC). However, the ability of anti-angiogenic drugs to delay tumor progression and extend survival is limited, due to either innate or acquired drug resistance. Furthermore, there are currently no validated biomarkers that predict which mRCC patients will benefit from anti-angiogenic therapy. Here we exploit susceptibility contrast magnetic resonance imaging (SC-MRI) using intravascular ultrasmall superparamagnetic iron oxide particles to quantify and evaluate tumor fractional blood volume (fBV) as a non-invasive imaging biomarker of response to the anti-angiogenic drug sunitinib. We also interrogate the vascular phenotype of RCC xenografts exhibiting acquired resistance to sunitinib. SC-MRI of 786-0 xenografts prior to and two weeks after daily treatment with 40mg/kg sunitinib revealed a 71% (p<0.01) reduction in fBV in the absence of any change in tumor volume. This response was associated with significantly lower microvessel density (p<0.01) and lower uptake of the perfusion marker Hoechst 33342 (p<0.05). The average pre-treatment tumor fBV was negatively correlated (R2=0.92, p<0.0001) with sunitinib-induced changes in tumor fBV across the cohort. SC-MRI also revealed suppressed fBV in tumors that acquired resistance to sunitinib. In conclusion, SC-MRI enabled monitoring of the anti-angiogenic response of 786-0 RCC xenografts to sunitinib, which revealed that pre-treatment tumor fBV was found to be a predictive biomarker of subsequent reduction in tumor blood volume in response to sunitinib, and acquired resistance to sunitinib was not associated with a parallel increase in tumor blood volume.

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Venetoclax Synergizes with Radiotherapy for Treatment of B cell Lymphomas

Constitutive B cell receptor signaling leads to overexpression of the anti-apoptotic BCL-2 protein and is implicated in the pathogenesis of many types of B cell Non-Hodgkin Lymphoma (B-NHL). The BCL-2 small molecule inhibitor venetoclax shows promising clinical response rates in several lymphomas, but is not curative as monotherapy. Radiotherapy (RT) is a rational candidate for combining with BCL-2 inhibition, as DNA damage caused by RT increases the activity of pro-apoptotic BCL-2 pathway proteins, and lymphomas are exquisitely sensitive to radiation. We tested B-NHL responses to venetoclax combined with either external beam RT or radioimmunotherapy (RIT), which joins the selectivity of antibody targeting with the effectiveness of irradiation. We first tested cytotoxicity of cesium-137 irradiation plus venetoclax in 14 B-NHL cell lines representing five lymphoma sub-types. Combination treatment synergistically increased cell death in ten of 14 lines. Lack of synergy was predicted by resistance to single-agent venetoclax and high BCL-XL expression. We then assessed the efficacy of external beam RT plus venetoclax in murine xenograft models of mantle cell (MCL), germinal-center diffuse large B-cell (GCB-DLBCL), and activated B-cell (ABC-DLBCL) lymphomas. In each model, external beam RT plus venetoclax synergistically increased mouse survival time, curing up to 10%. We finally combined venetoclax treatment of MCL and ABC-DLBCL xenografts with a pretargeted RIT (PRIT) system directed against the CD20 antigen. Optimal dosing of PRIT plus venetoclax cured 100% of mice with no detectable toxicity. Venetoclax combined with RT may be a promising treatment for a wide range of lymphomas.

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Somatic mutations drive distinct imaging phenotypes in lung cancer

Tumors are characterized by somatic mutations that drive biological processes ultimately reflected in tumor phenotype. With regard to radiographic phenotypes, generally unconnected through present understanding to the presence of specific mutations, artificial intelligence (AI) methods can automatically quantify phenotypic characters by using predefined, engineered algorithms or automatic deep-learning methods, a process also known as radiomics. Here we demonstrate how imaging phenotypes can be connected to somatic mutations through an integrated analysis of independent datasets of 763 lung adenocarcinoma patients with somatic mutation testing and engineered computed tomography (CT) image analytics. We developed radiomic signatures capable of distinguishing between tumor genotypes in a discovery cohort (n=353) and verified them in an independent validation cohort (n=352). All radiomic signatures significantly outperformed conventional radiographic predictors (tumor volume and maximum diameter). We found a radiomic signature related to radiographic heterogeneity that successfuilly discriminated between EGFR+ and EGFR- cases (AUC=0.69). Combining this signature with a clinical model of EGFR status (AUC=0.70) significantly improved prediction accuracy (AUC=0.75). The highest performing signature was capable of distinguishing between EGFR+ and KRAS+ tumors (AUC=0.80) and, when combined with a clinical model (AUC=0.81), substantially improved its performance (AUC=0.86). A KRAS+/KRAS- radiomic signature also showed significant albeit lower performance (AUC=0.63) and did not improve accuracy of a clinical predictor of KRAS status. Our results argue that somatic mutations drive distinct radiographic phenotypes that can be predicted by radiomics. This work has implications for the use of imaging-based biomarkers in the clinic, as applied non-invasively, repeatedly and at low cost.

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Prevalence of obstructive sleep apnea in patients with posttraumatic stress disorder and its impact on adherence to continuous positive airway pressure therapy: A meta-analysis

Although some authors have recently investigated the co-occurrence of posttraumatic stress disorder (PTSD) and obstructive sleep apnea (OSA), the topic remains insufficiently studied. The aim of this meta-analysis was to detect the pooled prevalence of OSA in PTSD and its impact on adherence to continuous positive airway pressure (CPAP) therapy.

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Cannabis for restless legs syndrome: A report of six patients

Restless legs syndrome (RLS) is a chronic and sometimes severe sensorimotor disorder of still unclear pathophysiology [1]. Usually symptoms respond well to dopamine agonists (DA), opiates, or anticonvulsants, used either alone or in combination. However, a subset of patients remains refractory to medical therapy, and serious side effects such as augmentation and impulse control disorder have been observed with DA. We present six patients’ spontaneous reports of a remarkable and total remission of RLS symptoms after cannabis use.

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