CAR T cell trogocytosis and cooperative killing regulate tumour antigen escape.

Chimeric antigen receptors (CARs) are synthetic antigen receptors that reprogram T cell specificity, function and persistence1. Patient-derived CAR T cells have demonstrated remarkable efficacy against a range of B-cell malignancies1-3, and the results of early clinical trials suggest activity in multiple myeloma4. Despite high complete response rates, relapses occur in a large fraction of patients; some of these are antigen-negative and others are antigen-low1,2,4-9. Unlike the mechanisms that result in complete and permanent antigen loss6,8,9, those that lead to escape of antigen-low tumours remain unclear. Here, using mouse models of leukaemia, we show that CARs provoke reversible antigen loss through trogocytosis, an active process in which the target antigen is transferred to T cells, thereby decreasing target density on tumour cells and abating T cell activity by promoting fratricide T cell killing and T cell exhaustion. These mechanisms affect both CD28- and 4-1BB-based CARs, albeit differentially, depending on antigen density. These dynamic features can be offset by cooperative killing and combinatorial targeting to augment tumour responses to immunotherapy.

Targeting a CAR to the TRAC locus with CRISPR/Cas9 enhances tumour rejection.

Chimeric antigen receptors (CARs) are synthetic receptors that redirect and reprogram T cells to mediate tumour rejection. The most successful CARs used to date are those targeting CD19 (ref. 2), which offer the prospect of complete remission in patients with chemorefractory or relapsed B-cell malignancies. CARs are typically transduced into the T cells of a patient using γ-retroviral vectors or other randomly integrating vectors, which may result in clonal expansion, oncogenic transformation, variegated transgene expression and transcriptional silencing. Recent advances in genome editing enable efficient sequence-specific interventions in human cells, including targeted gene delivery to the CCR5 and AAVS1 loci. Here we show that directing a CD19-specific CAR to the T-cell receptor α constant (TRAC) locus not only results in uniform CAR expression in human peripheral blood T cells, but also enhances T-cell potency, with edited cells vastly outperforming conventionally generated CAR T cells in a mouse model of acute lymphoblastic leukaemia. We further demonstrate that targeting the CAR to the TRAC locus averts tonic CAR signalling and establishes effective internalization and re-expression of the CAR following single or repeated exposure to antigen, delaying effector T-cell differentiation and exhaustion. These findings uncover facets of CAR immunobiology and underscore the potential of CRISPR/Cas9 genome editing to advance immunotherapies.

Differences in Liver Parenchyma are Measurable with CT Radiomics at Initial Colon Resection in Patients that Develop Hepatic Metastases from Stage II/III Colon Cancer.

BACKGROUND: Currently, there are no methods to identify patients with an increased risk of liver metastases to guide patient selection for liver-directed therapies. We tried to determine whether quantitative image features (radiomics) of the liver obtained from preoperative staging CT scans at the time of initial colon resection differ in patients that subsequently develop liver metastases, extrahepatic metastases, or demonstrate prolonged disease-free survival. METHODS: Patients who underwent resection of stage II/III colon cancer from 2004 to 2012 with available preoperative CT scans were included in this single-institution, retrospective case-control study. Patients were grouped by initial recurrence patterns: liver recurrence, extrahepatic recurrence, or no evidence of disease at 5 years. Radiomic features of the liver parenchyma extracted from CT images were compared across groups. RESULTS: The cohort consisted of 120 patients divided evenly between three recurrence groups, with an equal number of stage II and III patients in each group. After adjusting for multiple comparisons, 44 of 254 (17%) imaging features displayed different distributions across the three patient groups (p < 0.05), with the clearest distinction between those with liver recurrence and no evidence of disease. Increased heterogeneity in the liver parenchyma by radiomic analysis was protective of liver metastases. CONCLUSIONS: CT radiomics is a promising tool to identify patients at high risk of developing liver metastases and is worthy of further investigation and validation.

Dynamic contrast-enhanced MRI model selection for predicting tumor aggressiveness in papillary thyroid cancers.

The purpose of this study was to identify the optimal tracer kinetic model from T1 -weighted dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data and evaluate whether parameters estimated from the optimal model predict tumor aggressiveness determined from histopathology in patients with papillary thyroid carcinoma (PTC) prior to surgery. In this prospective study, 18 PTC patients underwent pretreatment DCE-MRI on a 3 T MR scanner prior to thyroidectomy. This study was approved by the institutional review board and informed consent was obtained from all patients. The two-compartment exchange model, compartmental tissue uptake model, extended Tofts model (ETM) and standard Tofts model were compared on a voxel-wise basis to determine the optimal model using the corrected Akaike information criterion (AICc) for PTC. The optimal model is the one with the lowest AICc. Statistical analysis included paired and unpaired t-tests and a one-way analysis of variance. Bonferroni correction was applied for multiple comparisons. Receiver operating characteristic (ROC) curves were generated from the optimal model parameters to differentiate PTC with and without aggressive features, and AUCs were compared. ETM performed best with the lowest AICc and the highest Akaike weight (0.44) among the four models. ETM was preferred in 44% of all 3419 voxels. The ETM estimates of Ktrans in PTCs with the aggressive feature extrathyroidal extension (ETE) were significantly higher than those without ETE (0.78 ± 0.29 vs. 0.34 ± 0.18 min-1 , P = 0.005). From ROC analysis, cut-off values of Ktrans , ve and vp , which discriminated between PTCs with and without ETE, were determined at 0.45 min-1 , 0.28 and 0.014 respectively. The sensitivities and specificities were 86 and 82% (Ktrans ), 71 and 82% (ve ), and 86 and 55% (vp ), respectively. Their respective AUCs were 0.90, 0.71 and 0.71. We conclude that ETM Ktrans has shown potential to classify tumors with and without aggressive ETE in patients with PTC.

Variance prior specification for a basket trial design using Bayesian hierarchical modeling.

BACKGROUND: In the era of targeted therapies, clinical trials in oncology are rapidly evolving, wherein patients from multiple diseases are now enrolled and treated according to their genomic mutation(s). In such trials, known as basket trials, the different disease cohorts form the different baskets for inference. Several approaches have been proposed in the literature to efficiently use information from all baskets while simultaneously screening to find individual baskets where the drug works. Most proposed methods are developed in a Bayesian paradigm that requires specifying a prior distribution for a variance parameter, which controls the degree to which information is shared across baskets. METHODS: A common approach used to capture the correlated binary endpoints across baskets is Bayesian hierarchical modeling. We evaluate a Bayesian adaptive design in the context of a non-randomized basket trial and investigate three popular prior specifications: an inverse-gamma prior on the basket-level variance, a uniform prior and half-t prior on the basket-level standard deviation. RESULTS: From our simulation study, we can see that the inverse-gamma prior is highly sensitive to the input hyperparameters. When the prior mean value of the variance parameter is set to be near zero (≤0.5) , this can lead to unacceptably high false-positive rates (≥40%) in some scenarios. Thus, use of this prior requires a fully comprehensive sensitivity analysis before implementation. Alternatively, we see that a prior that places sufficient mass in the tail, such as the uniform or half-t prior, displays desirable and robust operating characteristics over a wide range of prior specifications, with the caveat that the upper bound of the uniform prior and the scale parameter of the half-t prior must be larger than 1. CONCLUSION: Based on the simulation results, we recommend that those involved in designing basket trials that implement hierarchical modeling avoid using a prior distribution that places a majority of the density mass near zero for the variance parameter. Priors with this property force the model to share information regardless of the true efficacy configuration of the baskets. Many commonly used inverse-gamma prior specifications have this undesirable property. We recommend to instead consider the more robust uniform prior or half-t prior on the standard deviation.

A Bayesian adaptive phase I-II trial design for optimizing the schedule of therapeutic cancer vaccines.

Phase I-II clinical trials refer to the class of designs that evaluate both the safety and efficacy of a novel therapeutic agent in a single trial. Typically, Phase I-II oncology trials take the form of dose-escalation studies, where initial subjects are treated at the lowest dose level and subsequent subjects are treated at progressively higher doses until the optimal dose is identified. While dose-escalation designs are well-motivated in the case of traditional chemotherapeutic agents, an alternate approach may be considered for therapeutic cancer vaccines, where an investigator's main objective is to evaluate the safety and efficacy of a set of dosing schedules or adjuvant combinations rather than to compare the safety and efficacy of progressively higher dose levels. We present a two-stage, Bayesian adaptive Phase I-II trial design to evaluate the safety and efficacy of therapeutic cancer vaccines. In the first stage, we determine whether a vaccination schedule achieves a minimum level of performance by comparing the toxicity and immune response rates to historical benchmarks. Vaccination schedules that achieve a minimum level of performance are compared using their magnitudes of immune response. If the superiority of a single schedule cannot be established after the first stage, Bayesian posterior predictive probabilities are used to determine the additional sample size required to identify the optimal vaccination schedule in a second stage. Copyright © 2016 John Wiley & Sons, Ltd.

An efficient basket trial design.

The landscape for early phase cancer clinical trials is changing dramatically because of the advent of targeted therapy. Increasingly, new drugs are designed to work against a target such as the presence of a specific tumor mutation. Because typically only a small proportion of cancer patients will possess the mutational target, but the mutation is present in many different cancers, a new class of basket trials is emerging, whereby the drug is tested simultaneously in different baskets, that is, subgroups of different tumor types. Investigators desire not only to test whether the drug works but also to determine which types of tumors are sensitive to the drug. A natural strategy is to conduct parallel trials, with the drug 's effectiveness being tested separately, using for example, the popular Simon two-stage design independently in each basket. The work presented is motivated by the premise that the efficiency of this strategy can be improved by assessing the homogeneity of the baskets ' response rates at an interim analysis and aggregating the baskets in the second stage if the results suggest the drug might be effective in all or most baskets. Via simulations, we assess the relative efficiencies of the two strategies. Because the operating characteristics depend on how many tumor types are sensitive to the drug, there is no uniformly efficient strategy. However, our investigation demonstrates that substantial efficiencies are possible if the drug works in most or all baskets, at the cost of modest losses of power if the drug works in only a single basket. Copyright © 2017 John Wiley & Sons, Ltd.

A practical Bayesian stepped wedge design for community-based cluster-randomized clinical trials: The British Columbia Telehealth Trial.

BACKGROUND: Many clinical trial designs are impractical for community-based clinical intervention trials. Stepped wedge trial designs provide practical advantages, but few descriptions exist of their clinical implementational features, statistical design efficiencies, and limitations. OBJECTIVES: Enhance efficiency of stepped wedge trial designs by evaluating the impact of design characteristics on statistical power for the British Columbia Telehealth Trial. METHODS: The British Columbia Telehealth Trial is a community-based, cluster-randomized, controlled clinical trial in rural and urban British Columbia. To determine the effect of an Internet-based telehealth intervention on healthcare utilization, 1000 subjects with an existing diagnosis of congestive heart failure or type 2 diabetes will be enrolled from 50 clinical practices. Hospital utilization is measured using a composite of disease-specific hospital admissions and emergency visits. The intervention comprises online telehealth data collection and counseling provided to support a disease-specific action plan developed by the primary care provider. The planned intervention is sequentially introduced across all participating practices. We adopt a fully Bayesian, Markov chain Monte Carlo-driven statistical approach, wherein we use simulation to determine the effect of cluster size, sample size, and crossover interval choice on type I error and power to evaluate differences in hospital utilization. RESULTS: For our Bayesian stepped wedge trial design, simulations suggest moderate decreases in power when crossover intervals from control to intervention are reduced from every 3 to 2 weeks, and dramatic decreases in power as the numbers of clusters decrease. Power and type I error performance were not notably affected by the addition of nonzero cluster effects or a temporal trend in hospitalization intensity. CONCLUSION/LIMITATIONS: Stepped wedge trial designs that intervene in small clusters across longer periods can provide enhanced power to evaluate comparative effectiveness, while offering practical implementation advantages in geographic stratification, temporal change, use of existing data, and resource distribution. Current population estimates were used; however, models may not reflect actual event rates during the trial. In addition, temporal or spatial heterogeneity can bias treatment effect estimates.

Switching diets after 6-months does not result in renewed weight loss: a secondary analysis of a 12-month crossover randomized trial.

Weight change trajectory from diet and lifestyle interventions typically involves rapid weight loss followed by a weight plateau after approximately 6 months. Changing from one weight-loss diet to another at the time of the plateau could instigate renewed weight loss. Therefore, our secondary analysis aimed to assess trajectory of weight loss in a 12-month, randomized, cross-over study. Forty-two adults were randomized to eat a healthy low-fat or healthy low-carbohydrate diet for 6 months then switched to the opposite diet for an additional 6 months. Regardless of diet assignment, participants experienced rapid initial weight loss, which slowed between 3 to 6 months. After switching diets at 6 months, weight modestly decreased until 9 months, but at a rate slower than the initial 3 months and slower than the rate from 3 to 6 months. This suggests that the weight loss plateau typically seen at 6 months is physiological and cannot be overcome by simply switching to a different weight-loss diet.

Cardiometabolic Effects of Omnivorous vs Vegan Diets in Identical Twins: A Randomized Clinical Trial.

Importance: Increasing evidence suggests that, compared with an omnivorous diet, a vegan diet confers potential cardiovascular benefits from improved diet quality (ie, higher consumption of vegetables, legumes, fruits, whole grains, nuts, and seeds). Objective: To compare the effects of a healthy vegan vs healthy omnivorous diet on cardiometabolic measures during an 8-week intervention. Design, Setting, and Participants: This single-center, population-based randomized clinical trial of 22 pairs of twins (N = 44) randomized participants to a vegan or omnivorous diet (1 twin per diet). Participant enrollment began March 28, 2022, and continued through May 5, 2022. The date of final follow-up data collection was July 20, 2022. This 8-week, open-label, parallel, dietary randomized clinical trial compared the health impact of a vegan diet vs an omnivorous diet in identical twins. Primary analysis included all available data. Intervention: Twin pairs were randomized to follow a healthy vegan diet or a healthy omnivorous diet for 8 weeks. Diet-specific meals were provided via a meal delivery service from baseline through week 4, and from weeks 5 to 8 participants prepared their own diet-appropriate meals and snacks. Main Outcomes and Measures: The primary outcome was difference in low-density lipoprotein cholesterol concentration from baseline to end point (week 8). Secondary outcome measures were changes in cardiometabolic factors (plasma lipids, glucose, and insulin levels and serum trimethylamine N-oxide level), plasma vitamin B12 level, and body weight. Exploratory measures were adherence to study diets, ease or difficulty in following the diets, participant energy levels, and sense of well-being. Results: A total of 22 pairs (N = 44) of twins (34 [77.3%] female; mean [SD] age, 39.6 [12.7] years; mean [SD] body mass index, 25.9 [4.7]) were enrolled in the study. After 8 weeks, compared with twins randomized to an omnivorous diet, the twins randomized to the vegan diet experienced significant mean (SD) decreases in low-density lipoprotein cholesterol concentration (-13.9 [5.8] mg/dL; 95% CI, -25.3 to -2.4 mg/dL), fasting insulin level (-2.9 [1.3] µIU/mL; 95% CI, -5.3 to -0.4 µIU/mL), and body weight (-1.9 [0.7] kg; 95% CI, -3.3 to -0.6 kg). Conclusions and Relevance: In this randomized clinical trial of the cardiometabolic effects of omnivorous vs vegan diets in identical twins, the healthy vegan diet led to improved cardiometabolic outcomes compared with a healthy omnivorous diet. Clinicians can consider this dietary approach as a healthy alternative for their patients. Trial Registration: ClinicalTrials.gov Identifier: NCT05297825.

Effect of a ketogenic diet versus Mediterranean diet on glycated hemoglobin in individuals with prediabetes and type 2 diabetes mellitus: The interventional Keto-Med randomized crossover trial.

BACKGROUND: Consensus has not been reached on what constitutes an optimal diet in individuals with prediabetes and type 2 diabetes mellitus (T2DM), especially between low-carbohydrate options. OBJECTIVES: We compared 2 low-carbohydrate diets with 3 key similarities (incorporating nonstarchy vegetables and avoiding added sugars and refined grains) and 3 key differences (incorporating compared with avoiding legumes, fruits, and whole, intact grains) for their effects on glucose control and cardiometabolic risk factors in individuals with prediabetes and T2DM. METHODS: Keto-Med was a randomized, crossover, interventional trial. Forty participants aged ≥18 years with prediabetes or T2DM followed the well-formulated ketogenic diet (WFKD) and the Mediterranean-plus diet (Med-Plus) for 12 weeks each, in random order. The diets shared the 3 key similarities noted above. The Med-Plus incorporated legumes, fruits, and whole, intact grains, while the WFKD avoided them. The primary outcome was the percentage change in glycated hemoglobin (HbA1c) after 12 weeks on each diet. Secondary and exploratory outcomes included percentage changes in body weight, fasting insulin, glucose, and blood lipids; average glucose from continuous glucose monitor (CGM), and nutrient intake. RESULTS: The primary analysis was of 33 participants with complete data. The HbA1c values did not differ between diets at 12 weeks. Triglycerides decreased more for the WFKD [percentage changes, -16% (SEM, 4%) compared with -5% (SEM, 6%) for the Med-Plus; P = 0.02] and LDL cholesterol was higher for the WFKD [percentage changes, +10% (SEM, 4%) compared with -5% (SEM, 5%) for the Med-Plus; P = 0.01]. Weight decreased 8% (SEM, 1%) compared with 7% (SEM, 1%) and HDL cholesterol increased 11% (SEM, 2%) compared with 7% (SEM, 3%) for the WFKD compared with the Med-Plus, respectively; however, there was a significant interaction of diet × order for both. Participants had lower intakes of fiber and 3 nutrients on the WFKD compared with the Med-Plus. Twelve-week follow-up data suggest the Med-Plus is more sustainable. CONCLUSIONS: HbA1c values were not different between diet phases after 12 weeks, but improved from baseline on both diets, likely due to several shared dietary aspects. The WFKD led to a greater decrease in triglycerides, but also had potential untoward risks from elevated LDL cholesterol and lower nutrient intakes from avoiding legumes, fruits, and whole, intact grains, as well as being less sustainable. This trial was registered at clinicaltrials.gov as NCT03810378.

Changes in Ultra-Processed Food Consumption and Lifestyle Behaviors Following COVID-19 Shelter-in-Place: A Retrospective Study.

Ultra-processed food (UPF) consumption poses a potential risk to public health and may be related to shelter-in-place orders. This study utilized the level of food processing as a lens by which to examine the relationships between diet, weight change, and lifestyle changes (including cooking, snacking, and sedentary activity) that occurred during regional shelter-in-place orders. This study used a cross-sectional, retrospective survey (n = 589) to assess baseline demographics, changes in lifestyle behaviors using a Likert scale, and changes in dietary behaviors using a modified food frequency questionnaire from mid-March to May 2020; data were collected in the California Bay Area from August to October 2020. Foods were categorized by level of processing (minimally processed, processed, and ultra-processed) using the NOVA scale. Stepwise multiple linear regression and univariate linear regression models were used to determine the associations between these factors. Increased snacking was positively associated with a change in the percent of the calories derived from UPF and weight gain (ß = 1.0, p < 0.001; ß = 0.8 kg, p < 0.001) and negatively associated with the share of MPF calories consumed (ß = -0.9, p < 0.001). These relationships have public health implications as interventions designed around decreased snacking may positively impact diet and weight management and thereby mitigate negative health outcomes.

Phase II trial of single agent amrubicin in patients with previously treated advanced thymic malignancies.

OBJECTIVES: There are limited treatment options for patients with thymic malignancies. Here we present data supporting treatment with single agent amrubicin, a third generation anthracycline and topoisomerase II inhibitor. MATERIALS AND METHODS: This was a phase 2 open-label, single arm trial of amrubicin in patients with thymoma (T) or thymic carcinoma (TC), conducted at two academic institutions. Patients were included if they had received at least one prior chemotherapy regimen. The first 18 patients received amrubicin at 40 mg/m2 IV days 1-3 repeated every 3-weeks. Due to the high incidence of febrile neutropenia, dosing was subsequently amended to 35 mg/m2 for the final 15 patients. RESULTS: A total of 33 patients (14 T/19 TC) were enrolled from 2011 to 2014. Median number of prior therapies was 2. Best response included 6 partial responses, 21 stable disease, and 6 progressive disease (all TC). Objective response rate was 18% (90% exact binomial CI 8.2%-32.8%; T = 4/14 (29%), TC = 2/19 (11%)). Median progression-free survival was 7.7 months (T: 8.3 months; TC: 7.3) and median overall survival was 29.7 months (T: 54.1 months; TC: 18 months). There was a high rate of febrile neutropenia (7 patients) that occurred despite a reduction in amrubicin dose and one related death. Five patients had reduction in LVEF below 50% during the course of treatment resulting in treatment discontinuation in one patient. CONCLUSION: Amrubicin shows promise as a single agent in heavily pre-treated patients with thymic malignancies. Notable side effects include febrile neutropenia and the use of growth factor support is essential. Further investigation of this agent is warranted.

Hierarchical models for sharing information across populations in phase I dose-escalation studies.

The primary goal of a phase I clinical trial in oncology is to evaluate the safety of a novel treatment and identify the maximum tolerated dose, defined as the maximum dose with a toxicity rate below some pre-specified threshold. Researchers are often interested in evaluating the performance of a novel treatment in multiple patient populations, which may require multiple phase I trials if the treatment is to be used with background standard-of-care that varies by population. An alternate approach is to run parallel trials but combine the data through a hierarchical model that allows for a different maximum tolerated dose in each population but shares information across populations to achieve a more accurate estimate of the maximum tolerated dose. In this manuscript, we discuss hierarchical extensions of three commonly used models for the dose-toxicity relationship in phase I oncology trials. We then propose three dose-finding guidelines for phase I oncology trials using hierarchical modeling. The proposed guidelines allow us to fully realize the benefits of hierarchical modeling while achieving a similar toxicity profile to standard phase I designs. Finally, we evaluate the operating characteristics of a phase I clinical trial using the proposed hierarchical models and dose-finding guidelines by simulation. Our simulation results suggest that incorporating hierarchical modeling in phase I dose-escalation studies will increase the probability of correctly identifying the maximum tolerated dose and the number of patients treated at the maximum tolerated dose, while decreasing the rate of dose-limiting toxicities and number of patients treated above the maximum tolerated dose, in most cases.

Efficacy/toxicity dose-finding using hierarchical modeling for multiple populations.

Traditionally, Phase I oncology trials evaluate the safety profile of a novel agent and identify a maximum tolerable dose based on toxicity alone. With the development of biologically targeted agents, investigators believe the efficacy of a novel agent may plateau or diminish before reaching the maximum tolerable dose while toxicity continues to increase. This motivates dose-finding based on the simultaneous evaluation of toxicity and efficacy. Previously, we investigated hierarchical modeling in the context of Phase I dose-escalation studies for multiple populations and found borrowing strength across populations improved operating characteristics. In this article, we discuss three hierarchical extensions to commonly used probability models for efficacy and toxicity in Phase I-II trials and adapt our previously proposed dose-finding algorithm for multiple populations to this setting. First, we consider both parametric and non-parametric bivariate models for binary outcomes and, in addition, we consider an under-parameterized model that combines toxicity and efficacy into a single trinary outcome. Our simulation results indicate hierarchical modeling increases the probability of correctly identifying the optimal dose and increases the average number of patients treated at the optimal dose, with the under-parameterized hierarchical model displaying desirable and robust operating characteristics.

PARP-1-Targeted Radiotherapy in Mouse Models of Glioblastoma.

The DNA repair enzyme poly(ADP-ribose) polymerase 1 (PARP-1) is overexpressed in glioblastoma, with overall low expression in healthy brain tissue. Paired with the availability of specific small molecule inhibitors, PARP-1 is a near-ideal target to develop novel radiotherapeutics to induce DNA damage and apoptosis in cancer cells, while sparing healthy brain tissue. Methods: We synthesized an 131I-labeled PARP-1 therapeutic and investigated its pharmacology in vitro and in vivo. A subcutaneous tumor model was used to quantify retention times and therapeutic efficacy. A potential clinical scenario, intratumoral convection-enhanced delivery, was mimicked using an orthotopic glioblastoma model combined with an implanted osmotic pump system to study local administration of 131I-PARPi (PARPi is PARP inhibitor). Results:131I-PARPi is a 1(2H)-phthalazinone, similar in structure to the Food and Drug Administration-approved PARP inhibitor AZD-2281. In vitro studies have shown that 131I-PARPi and AZD-2281 share similar pharmacologic profiles. 131I-PARPi delivered 134.1 cGy/MBq intratumoral injected activity. Doses to nontarget tissues, including liver and kidney, were significantly lower. Radiation damage and cell death in treated tumors were shown by p53 activation in U87-MG cells transfected with a p53-bioluminescent reporter. Treated mice showed significantly longer survival than mice receiving vehicle (29 vs. 22 d, P < 0.005) in a subcutaneous model. Convection-enhanced delivery demonstrated efficient retention of 131I-PARPi in orthotopic brain tumors, while quickly clearing from healthy brain tissue. Conclusion: Our results demonstrate 131I-PARPi's high potential as a therapeutic and highlight PARP's relevance as a target for radionuclide therapy. Radiation plays an integral role in brain tumor therapy, and radiolabeled PARP therapeutics could ultimately lead to improvements in the standard of care.

Exploring Structural Parameters for Pretargeting Radioligand Optimization.

Pretargeting offers a way to enhance target specificity while reducing off-target radiation dose to healthy tissue during payload delivery. We recently reported the development of an 18F-based pretargeting strategy predicated on the inverse electron demand Diels-Alder reaction as well as the use of this approach to visualize pancreatic tumor tissue in vivo as early as 1 h postinjection. Herein, we report a comprehensive structure: pharmacokinetic relationship study of a library of 25 novel radioligands that aims to identify radiotracers with optimal pharmacokinetic and dosimetric properties. This investigation revealed key relationships between molecular structure and in vivo behavior and produced two lead candidates exhibiting rapid tumor targeting with high target-to-background activity concentration ratios at early time points. We believe this knowledge to be of high value for the design and clinical translation of next-generation pretargeting agents for the diagnosis and treatment of disease.

Establishment of the In Vivo Efficacy of Pretargeted Radioimmunotherapy Utilizing Inverse Electron Demand Diels-Alder Click Chemistry.

The pretargeting system based on the inverse electron demand Diels-Alder reaction (IEDDA) between trans-cyclooctene (TCO) and tetrazine (Tz) combines the favorable pharmacokinetic properties of radiolabeled small molecules with the affinity and specificity of antibodies. This strategy has proven to be an efficient method for the molecularly targeted delivery of pharmaceuticals, including isotopes for radiological imaging. Despite encouraging results from in vivo PET imaging studies, this promising system has yet to be thoroughly evaluated for pretargeted radioimmunotherapy (PRIT). Toward that end, we synthesized two novel 177Lu-labeled tetrazine-bearing radioligands. Next, we compared the usefulness of our ligands for PRIT when paired with TCO-modified 5B1-a human, anti-CA19.9 mAb-in preclinical murine models of pancreatic cancer. The exemplary ligand, 177Lu-DOTA-PEG7-Tz, showed rapid (4.6 ± 0.8% ID/g at 4 hours) and persistent (16.8 ± 3.9% ID/g at 120 hours) uptake in tumors while concurrently clearing from blood and nontarget tissues. Single-dose therapy studies using 5B1-TCO and varying amounts of 177Lu-DOTA-PEG7-Tz (400, 800, and 1,200 µCi) showed that our system elicits a dose-dependent therapeutic response in mice bearing human xenografts. Furthermore, dosimetry calculations suggest that our approach is amenable to clinical applications with its excellent dosimetric profile in organs of clearance (i.e., liver and kidneys) as well as in dose-limiting tissues, such as red marrow. This study established that a pretargeted methodology utilizing the IEDDA reaction can rapidly and specifically deliver a radiotherapeutic payload to tumor tissue, thus illustrating its excellent potential for clinical translation. Mol Cancer Ther; 16(1); 124-33. ©2016 AACR.

Preliminary study of tumor heterogeneity in imaging predicts two year survival in pancreatic cancer patients.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers in the United States with a five-year survival rate of 7.2% for all stages. Although surgical resection is the only curative treatment, currently we are unable to differentiate between resectable patients with occult metastatic disease from those with potentially curable disease. Identification of patients with poor prognosis via early classification would help in initial management including the use of neoadjuvant chemotherapy or radiation, or in the choice of postoperative adjuvant therapy. PDAC ranges in appearance from homogeneously isoattenuating masses to heterogeneously hypovascular tumors on CT images; hence, we hypothesize that heterogeneity reflects underlying differences at the histologic or genetic level and will therefore correlate with patient outcome. We quantify heterogeneity of PDAC with texture analysis to predict 2-year survival. Using fuzzy minimum-redundancy maximum-relevance feature selection and a naive Bayes classifier, the proposed features achieve an area under receiver operating characteristic curve (AUC) of 0.90 and accuracy (Ac) of 82.86% with the leave-one-image-out technique and an AUC of 0.80 and Ac of 75.0% with three-fold cross-validation. We conclude that texture analysis can be used to quantify heterogeneity in CT images to accurately predict 2-year survival in patients with pancreatic cancer. From these data, we infer differences in the biological evolution of pancreatic cancer subtypes measurable in imaging and identify opportunities for optimized patient selection for therapy.