Targeting serine/glycine metabolism improves radiotherapy response in non-small cell lung cancer.

BACKGROUND: Lung cancer is the most lethal cancer, and 85% of cases are classified as non-small cell lung cancer (NSCLC). Metabolic rewiring is a cancer hallmark that causes treatment resistance, and lacks insights into serine/glycine pathway adaptations upon radiotherapy. METHODS: We analyzed radiotherapy responses using mass-spectrometry-based metabolomics in NSCLC patient's plasma and cell lines. Efficacy of serine/glycine conversion inhibitor sertraline with radiotherapy was investigated by proliferation, clonogenic and spheroid assays, and in vivo using a serine/glycine dependent NSCLC mouse model by assessment of tumor growth, metabolite and cytokine levels, and immune signatures. RESULTS: Serine/glycine pathway metabolites were significantly consumed in response to radiotherapy in NSCLC patients and cell models. Combining sertraline with radiotherapy impaired NSCLC proliferation, clonogenicity and stem cell self-renewal capacity. In vivo, NSCLC tumor growth was reduced solely in the sertraline plus radiotherapy combination treatment group. Tumor weights linked to systemic serine/glycine pathway metabolite levels, and were inhibited in the combination therapy group. Interestingly, combination therapy reshaped the tumor microenvironment via cytokines associated with natural killer cells, supported by eradication of immune checkpoint galectin-1 and elevated granzyme B levels. CONCLUSION: Our findings highlight that targeting serine/glycine metabolism using sertraline restricts cancer cell recovery from radiotherapy and provides tumor control through immunomodulation in NSCLC.

A deep-learning assisted bioluminescence tomography method to enable radiation targeting in rat glioblastoma.

Objective. A novel solution is required for accurate 3D bioluminescence tomography (BLT) based glioblastoma (GBM) targeting. The provided solution should be computationally efficient to support real-time treatment planning, thus reducing the x-ray imaging dose imposed by high-resolution micro cone-beam CT.Approach. A novel deep-learning approach is developed to enable BLT-based tumor targeting and treatment planning for orthotopic rat GBM models. The proposed framework is trained and validated on a set of realistic Monte Carlo simulations. Finally, the trained deep learning model is tested on a limited set of BLI measurements of real rat GBM models.Significance. Bioluminescence imaging (BLI) is a 2D non-invasive optical imaging modality geared toward preclinical cancer research. It can be used to monitor tumor growth in small animal tumor models effectively and without radiation burden. However, the current state-of-the-art does not allow accurate radiation treatment planning using BLI, hence limiting BLI's value in preclinical radiobiology research.Results. The proposed solution can achieve sub-millimeter targeting accuracy on the simulated dataset, with a median dice similarity coefficient (DSC) of 61%. The provided BLT-based planning volume achieves a median encapsulation of more than 97% of the tumor while keeping the median geometrical brain coverage below 4.2%. For the real BLI measurements, the proposed solution provided median geometrical tumor coverage of 95% and a median DSC of 42%. Dose planning using a dedicated small animal treatment planning system indicated good BLT-based treatment planning accuracy compared to ground-truth CT-based planning, where dose-volume metrics for the tumor fall within the limit of agreement for more than 95% of cases.Conclusion. The combination of flexibility, accuracy, and speed of the deep learning solutions make them a viable option for the BLT reconstruction problem and can provide BLT-based tumor targeting for the rat GBM models.

Treating colorectal peritoneal metastases with an injectable cytostatic loaded supramolecular hydrogel in a rodent animal model.

Patients with peritoneal metastases (PM) of colorectal cancer have a very poor outcome. Intraperitoneal delivery of chemotherapy is the preferred route for PM treatment. The main limitation of the treatment options is the short residence time of the cytostatic, with subsequent short exposure of the cancer cells. To address this, a supramolecular hydrogel has been developed that allows both local and slow release of its encapsulated drug, mitomycin C (MMC) or cholesterol-conjugated MMC (cMMC), respectively. This experimental study investigates if drug delivery using this hydrogel improves the therapeutic efficacy against PM. PM was induced in WAG/Rij rats (n = 72) by intraperitoneally injecting syngeneic colon carcinoma cells (CC531) expressing luciferase. After seven days, animals received a single intraperitoneal injection with saline (n = 8), unloaded hydrogel (n = 12), free MMC (n = 13), free cMMC (n = 13), MMC-loaded hydrogel (n = 13), or cMMC-loaded hydrogel (n = 13). Primary outcome was overall survival with a maximum follow-up of 120 days. Intraperitoneal tumor development was non-invasive monitored via bioluminescence imaging. Sixty-one rats successfully underwent all study procedures and were included to assess therapeutic efficacy. After 120 days, the overall survival in the MMC-loaded hydrogel and free MMC group was 78% and 38%, respectively. A trend toward significance was found when comparing the survival curves of the MMC-loaded hydrogel and free MMC (p = 0.087). No survival benefit was found for the cMMC-loaded hydrogel compared to free cMMC. Treating PM with our MMC-loaded hydrogel, exhibiting prolonged MMC exposure, seems effective in improving survival compared to treatment with free MMC.

A deep learning and Monte Carlo based framework for bioluminescence imaging center of mass-guided glioblastoma targeting.

Objective.Bioluminescence imaging (BLI) is a valuable tool for non-invasive monitoring of glioblastoma multiforme (GBM) tumor-bearing small animals without incurring x-ray radiation burden. However, the use of this imaging modality is limited due to photon scattering and lack of spatial information. Attempts at reconstructing bioluminescence tomography (BLT) using mathematical models of light propagation show limited progress.Approach.This paper employed a different approach by using a deep convolutional neural network (CNN) to predict the tumor's center of mass (CoM). Transfer-learning with a sizeable artificial database is employed to facilitate the training process for, the much smaller, target database including Monte Carlo (MC) simulations of real orthotopic glioblastoma models. Predicted CoM was then used to estimate a BLI-based planning target volume (bPTV), by using the CoM as the center of a sphere, encompassing the tumor. The volume of the encompassing target sphere was estimated based on the total number of photons reaching the skin surface.Main results.Results show sub-millimeter accuracy for CoM prediction with a median error of 0.59 mm. The proposed method also provides promising performance for BLI-based tumor targeting with on average 94% of the tumor inside the bPTV while keeping the average healthy tissue coverage below 10%.Significance.This work introduced a framework for developing and using a CNN for targeted radiation studies for GBM based on BLI. The framework will enable biologists to use BLI as their main image-guidance tool to target GBM tumors in rat models, avoiding delivery of high x-ray imaging dose to the animals.

'On the Spot' Digital Pathology of Breast Cancer Based on Single-Cell Mass Spectrometry Imaging.

The molecular pathology of breast cancer is challenging due to the complex heterogeneity of cellular subtypes. The ability to directly identify and visualize cell subtype distribution at the single-cell level within a tissue section enables precise and rapid diagnosis and prognosis. Here, we applied mass spectrometry imaging (MSI) to acquire and visualize the molecular profiles at the single-cell and subcellular levels of 14 different breast cancer cell lines. We built a molecular library of genetically well-characterized cell lines. Multistep processing, including deep learning, resulted in a breast cancer subtype, the cancer's hormone status, and a genotypic recognition model based on metabolic phenotypes with cross-validation rates of up to 97%. Moreover, we applied our single-cell-based recognition models to complex tissue samples, identifying cell subtypes in tissue context within seconds during measurement. These data demonstrate "on the spot" digital pathology at the single-cell level using MSI, and they provide a framework for fast and accurate high spatial resolution diagnostics and prognostics.

Inter-observer variability of organ contouring for preclinical studies with cone beam Computed Tomography imaging.

BACKGROUND AND PURPOSE: In preclinical radiation studies, there is great interest in quantifying the radiation response of healthy tissues. Manual contouring has significant impact on the treatment-planning because of variation introduced by human interpretation. This results in inconsistencies when assessing normal tissue volumes. Evaluation of these discrepancies can provide a better understanding on the limitations of the current preclinical radiation workflow. In the present work, interobserver variability (IOV) in manual contouring of rodent normal tissues on cone-beam Computed Tomography, in head and thorax regions was evaluated. MATERIALS AND METHODS: Two animal technicians performed manually (assisted) contouring of normal tissues located within the thorax and head regions of rodents, 20 cases per body site. Mean surface distance (MSD), displacement of center of mass (ΔCoM), DICE similarity coefficient (DSC) and the 95th percentile Hausdorff distance (HD95) were calculated between the contours of the two observers to evaluate the IOV. RESULTS: For the thorax organs, right lung had the lowest IOV (ΔCoM: 0.08 ±â€¯0.04 mm, DSC: 0.96 ±â€¯0.01, MSD:0.07 ±â€¯0.01 mm, HD95:0.20 ±â€¯0.03 mm) while spinal cord, the highest IOV (ΔCoM:0.5 ±â€¯0.3 mm, DSC:0.81 ±â€¯0.05, MSD:0.14 ±â€¯0.03 mm, HD95:0.8 ±â€¯0.2 mm). Regarding head organs, right eye demonstrated the lowest IOV (ΔCoM:0.12 ±â€¯0.08 mm, DSC: 0.93 ±â€¯0.02, MSD: 0.15 ±â€¯0.04 mm, HD95: 0.29 ±â€¯0.07 mm) while complete brain, the highest IOV (ΔCoM: 0.2 ±â€¯0.1 mm, DSC: 0.94 ±â€¯0.02, MSD: 0.3 ±â€¯0.1 mm, HD95: 0.5 ±â€¯0.1 mm). CONCLUSIONS: Our findings reveal small IOV, within the sub-mm range, for thorax and head normal tissues in rodents. The set of contours can serve as a basis for developing an automated delineation method for e.g., treatment planning.

Automatic contouring of normal tissues with deep learning for preclinical radiation studies.

Objective.Delineation of relevant normal tissues is a bottleneck in image-guided precision radiotherapy workflows for small animals. A deep learning (DL) model for automatic contouring using standardized 3D micro cone-beam CT (µCBCT) volumes as input is proposed, to provide a fully automatic, generalizable method for normal tissue contouring in preclinical studies.Approach.A 3D U-net was trained to contour organs in the head (whole brain, left/right brain hemisphere, left/right eye) and thorax (complete lungs, left/right lung, heart, spinal cord, thorax bone) regions. As an important preprocessing step, Hounsfield units (HUs) were converted to mass density (MD) values, to remove the energy dependency of theµCBCT scanner and improve generalizability of the DL model. Model performance was evaluated quantitatively by Dice similarity coefficient (DSC), mean surface distance (MSD), 95th percentile Hausdorff distance (HD95p), and center of mass displacement (ΔCoM). For qualitative assessment, DL-generated contours (for 40 and 80 kV images) were scored (0: unacceptable, manual re-contouring needed - 5: no adjustments needed). An uncertainty analysis using Monte Carlo dropout uncertainty was performed for delineation of the heart.Main results.The proposed DL model and accompanying preprocessing method provide high quality contours, with in general median DSC > 0.85, MSD < 0.25 mm, HD95p < 1 mm and ΔCoM < 0.5 mm. The qualitative assessment showed very few contours needed manual adaptations (40 kV: 20/155 contours, 80 kV: 3/155 contours). The uncertainty of the DL model is small (within 2%).Significance.A DL-based model dedicated to preclinical studies has been developed for multi-organ segmentation in two body sites. For the first time, a method independent of image acquisition parameters has been quantitatively evaluated, resulting in sub-millimeter performance, while qualitative assessment demonstrated the high quality of the DL-generated contours. The uncertainty analysis additionally showed that inherent model variability is low.

Selectively Targeting Tumor Hypoxia With the Hypoxia-Activated Prodrug CP-506.

Hypoxia-activated prodrugs (HAP) are a promising class of antineoplastic agents that can selectively eliminate hypoxic tumor cells. This study evaluates the hypoxia-selectivity and antitumor activity of CP-506, a DNA alkylating HAP with favorable pharmacologic properties. Stoichiometry of reduction, one-electron affinity, and back-oxidation rate of CP-506 were characterized by fast-reaction radiolytic methods with observed parameters fulfilling requirements for oxygen-sensitive bioactivation. Net reduction, metabolism, and cytotoxicity of CP-506 were maximally inhibited at oxygen concentrations above 1 µmol/L (0.1% O2). CP-506 demonstrated cytotoxicity selectively in hypoxic 2D and 3D cell cultures with normoxic/anoxic IC50 ratios up to 203. Complete resistance to aerobic (two-electron) metabolism by aldo-keto reductase 1C3 was confirmed through gain-of-function studies while retention of hypoxic (one-electron) bioactivation by various diflavin oxidoreductases was also demonstrated. In vivo, the antitumor effects of CP-506 were selective for hypoxic tumor cells and causally related to tumor oxygenation. CP-506 effectively decreased the hypoxic fraction and inhibited growth of a wide range of hypoxic xenografts. A multivariate regression analysis revealed baseline tumor hypoxia and in vitro sensitivity to CP-506 were significantly correlated with treatment response. Our results demonstrate that CP-506 selectively targets hypoxic tumor cells and has broad antitumor activity. Our data indicate that tumor hypoxia and cellular sensitivity to CP-506 are strong determinants of the antitumor effects of CP-506.

Releasing the brakes of tumor immunity with anti-PD-L1 and pushing its accelerator with L19-IL2 cures poorly immunogenic tumors when combined with radiotherapy.

BACKGROUND: Poorly immunogenic tumors are hardly responsive to immunotherapies such as immune checkpoint blockade (ICB) and are, therefore, a therapeutic challenge. Combination with other immunotherapies and/or immunogenic therapies, such as radiotherapy (RT), could make these tumors more immune responsive. We have previously shown that the immunocytokine L19-IL2 combined with single-dose RT resulted in 75% tumor remission and a 20% curative abscopal effect in the T cell-inflamed C51 colon carcinoma model. This treatment schedule was associated with the upregulation of inhibitory immune checkpoint (IC) molecules on tumor-infiltrating T cells, leading to only tumor growth delay in the poorly immunogenic Lewis lung carcinoma (LLC) model. METHODS: We aimed to trigger curative therapeutic responses in three tumor models (LLC, C51 and CT26) by "pushing the accelerator" of tumor immunity with L19-IL2 and/or "releasing the brakes" with ICB, such as antibodies directed against cytotoxic T lymphocyte associated protein 4 (CTLA-4), programmed death 1 (PD-1) or its ligand (PD-L1), combined with single-dose RT (10 Gy or 5 Gy). Primary tumor endpoint was defined as time to reach four times the size of tumor volume at start of treatment (4T×SV). Multivariate analysis of 4T×SV was performed using the Cox proportional hazards model comparing each treatment group with controls. Causal involvement of T and natural killer (NK) cells in the anti-tumor effect was assessed by in vivo depletion of T, NK or both cell populations. Immune profiling was performed using flow cytometry on single cell suspensions from spleens, bone marrow, tumors and blood. RESULTS: Combining RT, anti-PD-L1 and L19-IL2 cured 38% of LLC tumors, which was both CD8+ T and NK cell dependent. LLC tumors were resistant to RT +anti-PD-L1 likely explained by the upregulation of other IC molecules and increased T regulatory cell tumor infiltration. RT+L19-IL2 outperformed RT+ICB in C51 tumors; effects were comparable in CT26 tumors. Triple combinations were not superior to RT+L19-IL2 in both these models. CONCLUSIONS: This study demonstrated that combinatorial strategies rationally designed on biological effects can turn immunotherapy-resistant tumors into immunologically responsive tumors. This hypothesis is currently being tested in the international multicentric randomized phase 2 trial: ImmunoSABR (NCT03705403).