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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.

Comparison between average CT planned and CBCT recalculated dose metrics (Gy) for the target structures. All doses are reported as means [standard deviation] in units of Gy

Purpose: Conventional anticancer treatments are often impaired by the presence of hypoxia. TH-302 selectively targets hypoxic tumor regions, where it is converted into a cytotoxic agent. This study assessed the efficacy of the combination treatment of TH-302 and radiotherapy in two preclinical tumor models. The effect of oxygen modification on the combination treatment was evaluated and the effect of TH-302 on the hypoxic fraction (HF) was monitored using [18F]HX4-PET imaging and pimonidazole IHC stainings.Experimental Design: Rhabdomyosarcoma R1 and H460 NSCLC tumor-bearing animals were treated with TH-302 and radiotherapy (8 Gy, single dose). The tumor oxygenation status was altered by exposing animals to carbogen (95% oxygen) and nicotinamide, 21% or 7% oxygen breathing during the course of the treatment. Tumor growth and treatment toxicity were monitored until the tumor reached four times its start volume (T4×SV).Results: Both tumor models showed a growth delay after TH-302 treatment, which further increased when combined with radiotherapy (enhancement ratio rhabdomyosarcoma 1.23; H460 1.49). TH-302 decreases the HF in both models, consistent with its hypoxia-targeting mechanism of action. Treatment efficacy was dependent on tumor oxygenation; increasing the tumor oxygen status abolished the effect of TH-302, whereas enhancing the HF enlarged TH-302′s therapeutic effect. An association was observed in rhabdomyosarcoma tumors between the pretreatment HF as measured by [18F]HX4-PET imaging and the T4×SV.Conclusions: The combination of TH-302 and radiotherapy is promising and warrants clinical testing, preferably guided by the companion biomarker [18F]HX4 hypoxia PET imaging for patient selection. Clin Cancer Res; 21(13); 2984–92. ©2015 AACR.

Supplementary figure 4. Body weight was monitored during and after treatment for both rhabdomyosarcoma (A) and H460 (B) tumor-bearing animals. The change in body weight is represented in percentage. Data represent mean {plus minus} SEM.

Time to reach 2-times starting tumor volume (TGTV2) increases linearly with increasing uniform radiation dose.

Supplementary Table 2. Time to reach 4 times start volume per treatment group for H460 bearing animals. Data represent mean {plus minus} SD, p-value and sensitization enhancement ratio.

An example of distribution of hypoxia tracer HX4 (3 hours post injection) and FDG (2 hours post injection) in the same rhabdomyosarcoma bearing rat 20 hours apart, demonstrating large overlap between the tracers. White contours correspond to 30% of the GTV (magenta contours) with the highest SUV. Dice similarity coefficient is 0.79 for this example.

Experimental design. Hot Boost (Cold Boost ) 40% (60%) indicates 40% (60%) higher radiation dose to tumor sub-volume with the highest (lowest) FDG uptake than to the rest of the tumor.

Purpose: We tested therapeutic efficacy of two dose painting strategies of applying higher radiation dose to tumor subvolumes with high FDG uptake (biologic target volume, BTV): dose escalation and dose redistribution. We also investigated whether tumor response was determined by the highest dose in BTV or the lowest dose in gross tumor volume (GTV).Experimental Design: FDG uptake was evaluated in rat rhabdomyosarcomas prior to irradiation. BTV was defined as 30% of GTV with the highest (BTVhot) or lowest (BTVcold) uptake. To test efficacy of dose escalation, tumor response (time to reach two times starting tumor volume, TGTV2) to Hot Boost irradiation (40% higher dose to BTVhot) was compared with Cold Boost (40% higher dose to BTVcold), while mean dose to GTV remained 12 Gy. To test efficacy of dose redistribution, TGTV2 after Hot Boost was compared with uniform irradiation with the same mean dose (8 or 12 Gy).Results: TGTV2 after 12 Gy delivered heterogeneously (Hot and Cold Boost) or uniformly were not significantly different: 20.2, 19.5, and 20.6 days, respectively. Dose redistribution (Hot Boost) with 8 Gy resulted in faster tumor regrowth as compared with uniform irradiation (13.3 vs. 17.1 days; P = 0.026). Further increase in dose gradient to 60% led to a more pronounced decrease in TGTV2 (10.9 days; P < 0.0001).Conclusions: Dose escalation effect was independent of FDG uptake in target tumor volume, while dose redistribution was detrimental in this tumor model for dose levels applied here. Our data are consistent with the hypothesis that tumor response depends on the minimum intratumoral dose. Clin Cancer Res; 21(24); 5511–8. ©2015 AACR.

Supplementary figure 3. A) Tumor growth of rhabdomyosarcoma bearing animals (n=8) treated with TH-302 (25mg/kg) in combination with 4, 8 or 12Gy of RT. Data represent mean {plus minus} SEM. B) T4xSV for rhabdomyosarcoma bearing animals treated with TH-302 in combination with RT 0, 4, 8 or 12Gy. C) T4xSV for rhabdomyosarcoma bearing animals exposed to different oxygen concentrations in combination with TH-302 and RT. D) T4xSV for H460 bearing animals exposed to different oxygen concentrations in combination with TH-302 and RT.

Supplementary figure 1. A) Treatment schedule for the rhabdomyosarcoma and H460 models. All animals were randomized over either the treatment or the control group. These groups were both treated with either NaCl or TH-302 (rhabdomysarcoma 25mg/kg, H460 50mg/kg). Rhabdomyosarcoma bearing animals started with oxygen modification at day 0 and a [18F]HX4 hypoxia PET scan. For histological controls only, this scan was repeated on day 4. Animals from the treatment group were exposed to radiotherapy (RT) at the end of treatment day 3 and their tumor volume was monitored until 4 times start volume was reached. H460 tumor bearing animals from the histological control group were injected with pimonidazole and Hoechst on the last treatment day and sacrificed. Animals from the treatment group were exposed to radiotherapy at day 4 and monitored until a tumor volume of 4 times start treatment volume was reached.

Supplementary Table 1. Time to reach 4 times start volume per treatment group for rhabdomyosarcoma bearing animals. Data represent mean {plus minus} SD, p-value and sensitization enhancement ratio.

Supplementary figure 5. Quantification of H460 histological staining for A) necrotic fraction (NF), B) relative vessel area (RVA), C) perfusion fraction (PF) and D) relative perfusion area (RPA). Data represent mean {plus minus} SEM.

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.

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. The authors would like to thank Dr Brent van der Heyden for fruitful discussion. This work was partially supported by special research fund for double doctorate degree projects in the framework of the cooperation between Hasselt university and Maastricht UMC + (Grant No. BOF17DOCMA13).

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 the μCBCT 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 95p 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.