Norepinephrine Signals Through Astrocytes To Modulate Synapses.

Locus coeruleus (LC)-derived norepinephrine (NE) drives network and behavioral adaptations to environmental saliencies by reconfiguring circuit connectivity, but the underlying synapse-level mechanisms are elusive. Here, we show that NE remodeling of synaptic function is independent from its binding on neuronal receptors. Instead, astrocytic adrenergic receptors and Ca2+ dynamics fully gate the effect of NE on synapses as the astrocyte-specific deletion of adrenergic receptors and three independent astrocyte-silencing approaches all render synapses insensitive to NE. Additionally, we find that NE suppression of synaptic strength results from an ATP-derived and adenosine A1 receptor-mediated control of presynaptic efficacy. An accompanying study from Chen et al. reveals the existence of an analogous pathway in the larval zebrafish and highlights its importance to behavioral state transitions. Together, these findings fuel a new model wherein astrocytes are a core component of neuromodulatory systems and the circuit effector through which norepinephrine produces network and behavioral adaptations, challenging an 80-year-old status quo.

Beyond conventional bounds: Surpassing system limits for stereotactic ablative (SAbR) lung radiotherapy using CBCT-based adaptive planning system.

PURPOSE: Online adaptive radiotherapy relies on a high degree of automation to enable rapid planning procedures. The Varian Ethos intelligent optimization engine (IOE) was originally designed for conventional treatments so it is crucial to provide clear guidance for lung SAbR plans. This study investigates using the Ethos IOE together with adaptive-specific optimization tuning structures we designed and templated within Ethos to mitigate inter-planner variability in meeting RTOG metrics for both online-adaptive and offline SAbR plans. METHODS: We developed a planning strategy to automate the generation of tuning structures and optimization. This was validated by retrospective analysis of 35 lung SAbR cases (total 105 fractions) treated on Ethos. The effectiveness of our planning strategy was evaluated by comparing plan quality with-and-without auto-generated tuning structures. Internal target volume (ITV) contour was compared between that drawn from CT simulation and from cone-beam CT (CBCT) at time of treatment to verify CBCT image quality and treatment effectiveness. Planning strategy robustness for lung SAbR was quantified by frequency of plans meeting reference plan RTOG constraints. RESULTS: Our planning strategy creates a gradient within the ITV with maximum dose in the core and improves intermediate dose conformality on average by 2%. ITV size showed no significant difference between those contoured from CT simulation and first fraction, and also trended towards decreasing over course of treatment. Compared to non-adaptive plans, adaptive plans better meet reference plan goals (37% vs. 100% PTV coverage compliance, for scheduled and adapted plans) while improving plan quality (improved GI (gradient index) by 3.8%, CI (conformity index) by 1.7%). CONCLUSION: We developed a robust and readily shareable planning strategy for the treatment of adaptive lung SAbR on the Ethos system. We validated that automatic online plan re-optimization along with the formulated adaptive tuning structures can ensure consistent plan quality. With the proposed planning strategy, highly ablative treatments are feasible on Ethos.

The accuracy of artificial intelligence deformed nodal structures in cervical online cone-beam-based adaptive radiotherapy.

Background and Purpose: Online cone-beam-based adaptive radiotherapy (ART) adjusts for anatomical changes during external beam radiotherapy. However, limited cone-beam image quality complicates nodal contouring. Despite this challenge, artificial-intelligence guided deformation (AID) can auto-generate nodal contours. Our study investigated the optimal use of such contours in cervical online cone-beam-based ART. Materials and Methods: From 136 adaptive fractions across 21 cervical cancer patients with nodal disease, we extracted 649 clinically-delivered and AID clinical target volume (CTV) lymph node boost structures. We assessed geometric alignment between AID and clinical CTVs via dice similarity coefficient, and 95% Hausdorff distance, and geometric coverage of clinical CTVs by AID planning target volumes by false positive dice. Coverage of clinical CTVs by AID contour-based plans was evaluated using D100, D95, V100%, and V95%. Results: Between AID and clinical CTVs, the median dice similarity coefficient was 0.66 and the median 95 % Hausdorff distance was 4.0 mm. The median false positive dice of clinical CTV coverage by AID planning target volumes was 0. The median D100 was 1.00, the median D95 was 1.01, the median V100% was 1.00, and the median V95% was 1.00. Increased nodal volume, fraction number, and daily adaptation were associated with reduced clinical CTV coverage by AID-based plans. Conclusion: In one of the first reports on pelvic nodal ART, AID-based plans could adequately cover nodal targets. However, physician review is required due to performance variation. Greater attention is needed for larger, daily-adapted nodes further into treatment.

Pycallingcards: an integrated environment for visualizing, analyzing, and interpreting Calling Cards data.

MOTIVATION: Unraveling the transcriptional programs that control how cells divide, differentiate, and respond to their environments requires a precise understanding of transcription factors' (TFs) DNA-binding activities. Calling cards (CC) technology uses transposons to capture transient TF binding events at one instant in time and then read them out at a later time. This methodology can also be used to simultaneously measure TF binding and mRNA expression from single-cell CC and to record and integrate TF binding events across time in any cell type of interest without the need for purification. Despite these advantages, there has been a lack of dedicated bioinformatics tools for the detailed analysis of CC data. RESULTS: We introduce Pycallingcards, a comprehensive Python module specifically designed for the analysis of single-cell and bulk CC data across multiple species. Pycallingcards introduces two innovative peak callers, CCcaller and MACCs, enhancing the accuracy and speed of pinpointing TF binding sites from CC data. Pycallingcards offers a fully integrated environment for data visualization, motif finding, and comparative analysis with RNA-seq and ChIP-seq datasets. To illustrate its practical application, we have reanalyzed previously published mouse cortex and glioblastoma datasets. This analysis revealed novel cell-type-specific binding sites and potential sex-linked TF regulators, furthering our understanding of TF binding and gene expression relationships. Thus, Pycallingcards, with its user-friendly design and seamless interface with the Python data science ecosystem, stands as a critical tool for advancing the analysis of TF functions via CC data. AVAILABILITY AND IMPLEMENTATION: Pycallingcards can be accessed on the GitHub repository: https://github.com/The-Mitra-Lab/pycallingcards.

Optimizing Online Adaptation Timing in the Treatment of Locally Advanced Cervical Cancer.

PURPOSE: Online adaptive radiation therapy (ART) has emerged as a new treatment modality for cervical cancer. Daily online adapting improves target coverage and organ-at-risk (OAR) sparing compared with traditional image guided radiation therapy (IGRT); however, the required resources may not be feasible in a busy clinical setting. Less frequent adapting may still benefit cervical cancer patients due to large volume changes of the uterocervix of the treatment course. In this study, the dosimetry from different online adapt-on-demand schedules was compared. MATERIALS AND METHODS: A retrospective cohort of 10 patients with cervical cancer treated with 260 fractions of definitive daily online ART was included. Plans with different adaptation schedules were simulated with adaptations weekly, every other week, once during treatment, and no adaptations (IGRT). These plans were applied to the synthetic computed tomography (CT) images and contours generated during the patient's delivered daily adaptive workflow. The dosimetry of the weekly replan, every-other-week replan, once replan, and IGRT plans were compared using a paired t test. RESULTS: Compared with traditional IGRT plans, weekly and every-other-week ART plans had similar clinical target volume (CTV) coverage, but statistically significant improved sparing of OARs. Weekly and every-other-week ART had reduced bowel bag V40 by 1.57% and 1.41%, bladder V40 by 3.82% and 1.64%, rectum V40 by 8.49% and 7.50%, and bone marrow Dmean by 0.81% and 0.61%, respectively. Plans with a single adaptation had statistically significantly worse target coverage, and moderate improvements in OAR sparing. Of the 18-dose metrics evaluated, improvements were seen in 15 for weekly ART, 14 for every-other-week ART, and 10 for single ART plans compared with IGRT. When every-other-week ART was compared with weekly ART, both plans had similar CTV coverage and OAR sparing with only small improvements in bone marrow dosimetry with weekly ART. CONCLUSIONS: This retrospective work compares different adapt-on-demand treatment schedules using data collected from patients treated with daily online adaptive radiation therapy. Results suggest weekly or every-other-week online ART is beneficial for reduced OAR dose compared with IGRT by exploiting the gradual changes in the uterocervix target volume.

Calling Cards: A Customizable Platform to Longitudinally Record Protein-DNA Interactions Over Time in Cells and Tissues.

Calling Cards is a platform technology to record a cumulative history of transient protein-DNA interactions in the genome of genetically targeted cell types. The record of these interactions is recovered by next-generation sequencing. Compared with other genomic assays, readouts of which provide a snapshot at the time of harvest, Calling Cards enables correlation of historical molecular states to eventual outcomes or phenotypes. To achieve this, Calling Cards uses the piggyBac transposase to insert self-reporting transposon "Calling Cards" into the genome, leaving permanent marks at interaction sites. Calling Cards can be deployed in a variety of in vitro and in vivo biological systems to study gene regulatory networks involved in development, aging, and disease. Out of the box, it assesses enhancer usage but can be adapted to profile-specific transcription factor (TF) binding with custom TF-piggyBac fusion proteins. The Calling Cards workflow has five main stages: delivery of Calling Cards reagents, sample preparation, library preparation, sequencing, and data analysis. Here, we first present a comprehensive guide for experimental design, reagent selection, and optional customization of the platform to study additional TFs. Then, we provide an updated protocol for the five steps, using reagents that improve throughput and decrease costs, including an overview of a newly deployed computational pipeline. This protocol is designed for users with basic molecular biology experience to process samples into sequencing libraries in 2 days. Familiarity with bioinformatic analysis and command line tools is required to set up the pipeline in a high-performance computing environment and to conduct downstream analyses. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Preparation and delivery of Calling Cards reagents Support Protocol 1: Next-generation sequencing quantification of barcode distribution within self-reporting transposon plasmid pool and adeno-associated virus genome Basic Protocol 2: Sample collection and RNA purification Support Protocol 2: Library density quantitative PCR Basic Protocol 3: Sequencing library preparation Basic Protocol 4: Library pooling and sequencing Basic Protocol 5: Data analysis.

Bony structure enhanced synthetic CT generation using Dixon sequences for pelvis MR-only radiotherapy.

BACKGROUND: MRI-only radiotherapy planning (MROP) is beneficial to patients by avoiding MRI/CT registration errors, simplifying the radiation treatment simulation workflow and reducing exposure to ionizing radiation. MRI is the primary imaging modality for soft tissue delineation. Treatment planning CTs (i.e., CT simulation scan) are redundant if a synthetic CT (sCT) can be generated from the MRI to provide the patient positioning and electron density information. Unsupervised deep learning (DL) models like CycleGAN are widely used in MR-to-sCT conversion, when paired patient CT and MR image datasets are not available for model training. However, compared to supervised DL models, they cannot guarantee anatomic consistency, especially around bone. PURPOSE: The purpose of this work was to improve the sCT accuracy generated from MRI around bone for MROP. METHODS: To generate more reliable bony structures on sCT images, we proposed to add bony structure constraints in the unsupervised CycleGAN model's loss function and leverage Dixon constructed fat and in-phase (IP) MR images. Dixon images provide better bone contrast than T2-weighted images as inputs to a modified multi-channel CycleGAN. A private dataset with a total of 31 prostate cancer patients were used for training (20) and testing (11). RESULTS: We compared model performance with and without bony structure constraints using single- and multi-channel inputs. Among all the models, multi-channel CycleGAN with bony structure constraints had the lowest mean absolute error, both inside the bone and whole body (50.7 and 145.2 HU). This approach also resulted in the highest Dice similarity coefficient (0.88) of all bony structures compared with the planning CT. CONCLUSION: Modified multi-channel CycleGAN with bony structure constraints, taking Dixon-constructed fat and IP images as inputs, can generate clinically suitable sCT images in both bone and soft tissue. The generated sCT images have the potential to be used for accurate dose calculation and patient positioning in MROP radiation therapy.

Artificial intelligence guided physician directive improves head and neck planning quality and practice Uniformity: A prospective study.

•AI dose predictor was fully integrated with treatment planning system and used as a physicain decision support tool to improve uniformity of practice.•Model was trained based on our standard of practice, but implemented at the time of expansion with 3 new physicians join the practice.•Phase 1 retrospective evaluation demonstrated the non-uniform practice among 3 MDs and only 52.9% frequency planner can achieve physicians' directives.•Significant improvement in practice uniformity of practice was observed after utilizing AI as DST and 80.4% frequency clinical plan can achieve AI-guided physician directives.

Aspirin Use Is Associated With Improved Outcomes in Inflammatory Breast Cancer Patients.

PURPOSE: Inflammatory breast cancer (IBC) is the most aggressive form of breast cancer and has a high propensity for distant metastases. Our previous data suggested that aspirin (acetylsalicylic acid, ASA) use may be associated with reduced risk of distant metastases in aggressive breast cancer; however, there are no reported studies on the potential benefit of ASA use in patients with IBC. METHODS: Data from patients with non-metastatic IBC treated between 2000-2017 at two institutions, were reviewed. Overall survival (OS), disease-free survival (DFS), and distant metastasis-free survival (DMFS) were performed using Kaplan-Meier analysis. Univariate and multivariable logistic regression models were used to identify significant associated factors. RESULTS: Of 59 patients meeting the criteria for analysis and available for review, 14 ASA users were identified. ASA users demonstrated increased OS (p = 0.03) and DMFS (p = 0.02), with 5-year OS and DMFS of 92% (p = 0.01) and 85% (p = 0.01) compared to 51% and 43%, respectively, for non-ASA users. In univariate analysis, pT stage, pN stage, and ASA use were significantly correlated (p < 0.05) with OS and DFS. On multivariable analysis, ASA use (hazard ratio [HR], 0.11; 95% confidence interval [CI], 0.01-0.8) and lymph node stage (HR, 5.9; 95% CI, 1.4-25.9) remained significant for OS and DFS ASA use (HR, 0.13; 95% CI, 0.03-0.56) and lymph node stage (HR, 5.6; 95% CI, 1.9-16.4). CONCLUSION: ASA use during remission was associated with significantly improved OS and DMFS in patients with IBC. These results suggest that ASA may provide survival benefits to patients with IBC. Prospective clinical trials of ASA use in patients with high-risk IBC in remission should be considered.

The New Kid on the Block: Online Adaptive Radiotherapy in the Treatment of Gynecologic Cancers.

Online adaptive radiation is a new and exciting modality of treatment for gynecologic cancers. Traditional radiation treatments deliver the same radiation plan to cancers with large margins. Improvements in imaging, technology, and artificial intelligence have made it possible to account for changes between treatments and improve the delivery of radiation. These advances can potentially lead to significant benefits in tumor coverage and normal tissue sparing. Gynecologic cancers can uniquely benefit from this technology due to the significant changes in bladder, bowel, and rectum between treatments as well as the changes in tumors commonly seen between treatments. Preliminary studies have shown that online adaptive radiation can maintain coverage of the tumor while sparing nearby organs. Given these potential benefits, numerous clinical trials are ongoing to investigate the clinical benefits of online adaptive radiotherapy. Despite the benefits, implementation of online adaptive radiotherapy requires significant clinical resources. Additionally, the timing and workflow for online adaptive radiotherapy is being optimized. In this review, we discuss the history and evolution of radiation techniques, the logistics and implementation of online adaptive radiation, and the potential benefits of online adaptive radiotherapy for gynecologic cancers.

Spare the Bowel, Don't Spoil the Target: Optimal Margin Assessment for Online Cone Beam Adaptive Radiation Therapy (OnC-ART) of the Cervix.

PURPOSE: The standard treatment for locally advanced cervical cancer involves pelvic chemoradiation. Intensity modulated radiation therapy planning requires expansion of the cervix and uterus clinical target volume (CTV) by 1.5 to 2 cm to account for motion. With online cone beam adaptive radiation therapy (OnC-ART), interfractional movement is accounted for, which can potentially lead to smaller CTV to planned target volume (PTV) margins. In this study, we attempted to determine the optimal CTV-to-PTV margin for adequate coverage with OnC-ART and factors that can affect CTV coverage. METHODS AND MATERIALS: A retrospective cohort of 21 patients with cervical cancer treated with definitive chemoradiation was included. Nine patients treated with nonadaptive radiation had the uterocervix contoured on pretreatment cone beam computed tomography (CBCT) and end-treatment CBCTs. Anterior-posterior, lateral, and superior-inferior shifts and the average shift in all directions were calculated. A CTV-to-PTV expansion was determined and verified on a validation cohort of 12 patients treated with OnC-ART. RESULTS: The average anterior-posterior, lateral, and superior-inferior shifts with standard deviation were 0.32 ± 1.55 cm, 0.12 ± 2.31 cm, and 1.67 ± 3.41 cm, respectively. A uniform 5-mm expansion around the pretreatment CTV covered 98.85% ± 1.23% of the end-treatment CTV. This 5-mm expansion was applied to our validation cohort treated with OnC-ART, and 98.39% ± 3.0% of the end-treatment CTV was covered. Time between CBCTs >30 minutes and change in bladder volume were significantly correlated to CTV coverage. CONCLUSIONS: Based on our analysis, a CTV-to-PTV margin of 5 mm is adequate to encompass 98% of the CTV. A significantly reduced margin could potentially decrease the toxicities associated with radiation for patients with cervical cancer and lead to improved patient reported toxicity outcomes. We recommend physicians begin with a 5-mm margin and assess adequate coverage with image guidance during daily adaptation.

Case Report: Resolution of radiation pneumonitis with androgens and growth hormone.

Radiation pneumonitis (RP) occurs in some patients treated with thoracic radiation therapy. RP often self-resolves, but when severe it is most commonly treated with corticosteroids because of their anti-inflammatory properties. Androgens and human growth hormone (HGH) also have anti-inflammatory and healing properties in the lung, but have not been studied as a remedy for RP. Here we present a case of corticosteroid-refractory RP that resolved with androgen and HGH-based therapy. Case Presentation: A 62 year old male body builder with excellent performance status presented with locally advanced non-small cell lung cancer characterized by a 7 cm mass in the right lower lobe and associated right hilar and subcarinal lymph node involvement. He was treated with chemoradiation and an excellent tumor response was observed. However, 2 months post-treatment he developed severe shortness of breath and imaging was consistent with RP. His RP was refractory to prednisone and antibiotic therapy, despite various regimens over a 9 month period. The patient self-treated with an androgen and HGH-based regimen and the RP promptly resolved. Conclusion: The anti-inflammatory properties of androgens and HGH have prompted an exploration of their potential role in therapeutic strategies to treat pro-inflammatory conditions such as sepsis, infections and interstitial lung disease. This case study suggests a potential role for the use of androgens for the treatment of steroid-refractory RP after radiation therapy. However, the applicability of this strategy to general populations should be weighed carefully against secondary effects of these agents, especially in the setting of cancer survivorship.

A viral toolkit for recording transcription factor-DNA interactions in live mouse tissues.

Transcription factors (TFs) enact precise regulation of gene expression through site-specific, genome-wide binding. Common methods for TF-occupancy profiling, such as chromatin immunoprecipitation, are limited by requirement of TF-specific antibodies and provide only end-point snapshots of TF binding. Alternatively, TF-tagging techniques, in which a TF is fused to a DNA-modifying enzyme that marks TF-binding events across the genome as they occur, do not require TF-specific antibodies and offer the potential for unique applications, such as recording of TF occupancy over time and cell type specificity through conditional expression of the TF-enzyme fusion. Here, we create a viral toolkit for one such method, calling cards, and demonstrate that these reagents can be delivered to the live mouse brain and used to report TF occupancy. Further, we establish a Cre-dependent calling cards system and, in proof-of-principle experiments, show utility in defining cell type-specific TF profiles and recording and integrating TF-binding events across time. This versatile approach will enable unique studies of TF-mediated gene regulation in live animal models.

A machine-learning-based prediction model of fistula formation after interstitial brachytherapy for locally advanced gynecological malignancies.

PURPOSE: External beam radiotherapy combined with interstitial brachytherapy is commonly used to treat patients with bulky, advanced gynecologic cancer. However, the high radiation dose needed to control the tumor may result in fistula development. There is a clinical need to identify patients at high risk for fistula formation such that treatment may be managed to prevent this toxic side effect. This work aims to develop a fistula prediction model framework using machine learning based on patient, tumor, and treatment features. METHODS AND MATERIALS: This retrospective study included 35 patients treated at our institution using interstitial brachytherapy for various gynecological malignancies. Five patients developed rectovaginal fistula and two developed both rectovaginal and vesicovaginal fistula. For each patient, 31 clinical features of multiple data types were collected to develop a fistula prediction framework. A nonlinear support vector machine was used to build the prediction model. Sequential backward feature selection and sequential floating backward feature selection methods were used to determine optimal feature sets. To overcome data imbalance issues, the synthetic minority oversampling technique was used to generate synthetic fistula cases for model training. RESULTS: Seven mixed data features were selected by both sequential backward selection and sequential floating backward selection methods. Our prediction model using these features achieved a high prediction accuracy, that is, 0.904 area under the curve, 97.1% sensitivity, and 88.5% specificity. CONCLUSIONS: A machine-learning-based prediction model of fistula formation has been developed for patients with advanced gynecological malignancies treated using interstitial brachytherapy. This model may be clinically impactful pending refinement and validation in a larger series.

Risk factors for fistula formation after interstitial brachytherapy for locally advanced gynecological cancers involving vagina.

PURPOSE: To determine risk factors for fistula formation after interstitial brachytherapy (ISBT) in patients with advanced gynecologic cancers. MATERIAL AND METHODS: We performed an Institutional Review Board (IRB) approved retrospective review of 44 patients treated with transperineal template-based ISBT from 2011 to 2017 at a major metropolitan county and university health system. All patients were treated with image-guided high-dose-rate ISBT. Statistical analysis was performed using the χ2 test to identify factors correlated with fistula formation. Survival and tumor control outcomes were calculated using Kaplan Meier analyses. RESULTS: Patients had a mean age of 53 years (range, 28-81 years), a mean external beam dose of 43.1 Gy (range, 42.5-51.3 Gy), and a mean brachytherapy dose of 22.8 Gy (range, 21.3-30 Gy). Two of 44 patients had fistulas that could be definitively attributed to therapy for a fistula rate of 4.5%. Six additional patients (13.6%) developed fistula after treatment with associated recurrent disease but were included in the causality analysis. We analyzed patient tumor and treatment factors, and on univariate analyses we found that age ≥ 60 years, Hispanic ethnicity, bladder involvement, rectal D2 cc ≥ 70 Gy, and whether patients had post-radiation biopsies were predictors for fistula formation. The 1-year overall survival (OS), progression-free survival (PFS), and local control (LC) were 85%, 58.5%, and 76.9%, respectively, with a mean follow-up time 23 months (range, 4.0-68.8 months). CONCLUSIONS: We identified factors that predict fistula formation in patients with advanced gynecologic tumors treated with ISBT. These factors can be used to stratify patients into a high-risk group, with potential for modification of brachytherapy planning to reduce their risk of fistula formation.

PI3K/Akt/mTOR signaling pathway and targeted therapy for glioblastoma.

Glioblastoma multiform (GBM) is the most common malignant glioma of all the brain tumors and currently effective treatment options are still lacking. GBM is frequently accompanied with overexpression and/or mutation of epidermal growth factor receptor (EGFR), which subsequently leads to activation of many downstream signal pathways such as phosphatidylinositol 3-kinase (PI3K)/Akt/rapamycin-sensitive mTOR-complex (mTOR) pathway. Here we explored the reason why inhibition of the pathway may serve as a compelling therapeutic target for the disease, and provided an update data of EFGR and PI3K/Akt/mTOR inhibitors in clinical trials.

Reactive oxygen species responsive nanoprodrug to treat intracranial glioblastoma.

Chemotherapy for intracranial gliomas is hampered by limited delivery of therapeutic agents through the blood brain barrier (BBB). An optimal therapeutic agent for brain tumors would selectively cross the BBB, accumulates in the tumor tissue and be activated from an innocuous prodrug within the tumor. Here we show brain tumor-targeted delivery and therapeutic efficacy of a nanometer-sized prodrug (nanoprodrug) of camptothecin (CPT) to treat experimental glioblastoma multiforme (GBM). The CPT nanoprodrug was prepared using spontaneous nanoemulsification of a biodegradable, antioxidant CPT prodrug and α-tocopherol. The oxidized nanoprodrug was activated more efficiently than nonoxidized nanoprodrug, suggesting enhanced therapeutic efficacy in the oxidative tumor microenvironment. The in vitro imaging of U-87 MG glioma cells revealed an efficient intracellular uptake of the nanoprodrug via direct cell membrane penetration rather than via endocytosis. The in vivo study in mice demonstrated that the CPT nanoprodrug passed through the BBB and specifically accumulated in brain tumor tissue, but not in healthy brain tissue and other organs. The accumulation preferably occurred at the periphery of the tumor where cancer cells are most actively proliferating, suggesting optimal therapeutic efficacy of the nanoprodrug. The nanoprodrug was effective in treating subcutaneous and intracranial tumors. The nanoprodrug inhibited subcutaneous tumor growth more than 80% compared with control. The median survival time of mice implanted with an intracranial tumor increased from 40.5 days for control to 72.5 days for CPT nanoprodrug. This nanoprodrug approach is a versatile method for developing therapeutic nanoparticles enabling tumor-specific targeting and treatment. The nontoxic, tumor-specific targeting properties of the nanoprodrug system make it a safe, low cost, and versatile nanocarrier for pharmaceuticals, imaging agents, and diagnostic agents.