Background: Projection of future cancer incidence is an important task in cancer epidemiology. The results are of interest also for biomedical research and public health policy. Age-Period-Cohort (APC) models, usually based on long-term cancer registry data (> 20 yrs), are established for such projections. In many countries (including Germany), however, nationwide long-term data are not yet available. General guidance on statistical approaches for projections using rather short-term data is challenging and software to enable researchers to easily compare approaches is lacking. Methods: To enable a comparative analysis of the performance of statistical approaches to cancer incidence projection, we developed an R package (incAnalysis), supporting in particular Bayesian models fitted by Integrated Nested Laplace Approximations (INLA). Its use is demonstrated by an extensive empirical evaluation of operating characteristics (bias, coverage and precision) of potentially applicable models differing by complexity. Observed long-term data from three cancer registries (SEER-9, NORDCAN, Saarland) was used for benchmarking. Results: Overall, coverage was high (mostly > 90%) for Bayesian APC models (BAPC), whereas less complex models showed differences in coverage dependent on projection-period. Intercept-only models yielded values below 20% for coverage. Bias increased and precision decreased for longer projection periods (> 15 years) for all except intercept-only models. Precision was lowest for complex models such as BAPC models, generalized additive models with multivariate smoothers and generalized linear models with age x period interaction effects. Conclusion: The incAnalysis R package allows a straightforward comparison of cancer incidence rate projection approaches. Further detailed and targeted investigations into model performance in addition to the presented empirical results are recommended to derive guidance on appropriate statistical projection methods in a given setting.

Purpose: Targeting immunosuppressive and pro-tumorigenic glioblastoma (GBM)-associated macrophages and microglial cells (GAM) has great potential to improve patient outcomes. Colony-stimulating factor-1 receptor (CSF1R) has emerged as a promising target for reprograming anti-inflammatory M2-like GAMs. However, treatment data on patient-derived, tumor-educated GAMs and their influence on the adaptive immunity are lacking. Experimental Design: CD11b+-GAMs freshly isolated from patient tumors were treated with CSF1R-targeting drugs PLX3397, BLZ945, and GW2580. Phenotypical changes upon treatment were assessed using RNA sequencing, flow cytometry, and cytokine quantification. Functional analyses included inducible nitric oxide synthase activity, phagocytosis, transmigration, and autologous tumor cell killing assays. Antitumor effects and changes in GAM activation were confirmed in a complex patient-derived 3D tumor organoid model serving as a tumor avatar. Results: The most effective reprogramming of GAMs was observed upon GW2580 treatment, which led to the downregulation of M2-related markers, IL6, IL10, ERK1/2, and MAPK signaling pathways, while M1-like markers, gene set enrichment indicating activated MHC-II presentation, phagocytosis, and T-cell killing were substantially increased. Moreover, treatment of patient-derived GBM organoids with GW2580 confirmed successful reprogramming, resulting in impaired tumor cell proliferation. In line with its failure in clinical trials, PLX3397 was ineffective in our analysis. Conclusions: This comparative analysis of CSF1R-targeting drugs on patient-derived GAMs and human GBM avatars identified GW2580 as the most powerful inhibitor with the ability to polarize immunosuppressive GAMs to a proinflammatory phenotype, supporting antitumor T-cell responses while also exerting a direct antitumor effect. These data indicate that GW2580 could be an important pillar in future therapies for GBM.

High-precision radiotherapy (HPR) of recurrent high grade glioma (HGG) requires accurate spatial allocation of these infiltrative tumors. We investigated the impact of ¹⁸F-FET PET on tumor delineation and progression of recurrent HGG after HPR with carbon ions. T₁ contrast enhanced MRI and ¹⁸F-FET-PET scans of 26 HGG patients were fused with radiotherapy planning volumes. PET-positive (PET+) tumor volumes using different isocontours (I%) were systematically investigated and compared with MRI-derived gross tumor volumes (GTV). Standardized uptake ratios (SUR) were further correlated with GTV and tumor progression patterns. In grade IV glioma, SUR > 2.92 significantly correlated with poor median overall survival (6.5 vs 13.1 months, p = 0.00016). We found no reliable SUR cut-off criteria for definition of PET+ volumes. Overall conformity between PET and MRI-based contours was low, with maximum conformities between 0.42–0.51 at I40%. The maximum sensitivity and specificity for PET+ volumes outside of GTV predicting tumor progression were 0.16 (I40%) and 0.52 (I50%), respectively. In 75% of cases, FLAIR hyperintense area covered over 80% of PET+ volumes. ¹⁸F-FET-PET derived SUR has a prognostic impact in grade IV glioma. The value of substantial mismatches between MRI-based GTV and PET+ volumes to improve tumor delineation in radiotherapy awaits further validation in randomized prospective trials.

Purpose: This study investigates the impact of different intensity normalization (IN) methods on the overall survival (OS) radiomics models’ performance of MR sequences in primary (pHGG) and recurrent high-grade glioma (rHGG). Methods: MR scans acquired before radiotherapy were retrieved from two independent cohorts (rHGG C1: 197, pHGG C2: 141) from multiple scanners (15, 14). The sequences are T1 weighted (w), contrast-enhanced T1w (T1wce), T2w, and T2w-FLAIR. Sequence-specific significant features (SF) associated with OS, extracted from the tumour volume, were derived after applying 15 different IN methods. Survival analyses were conducted using Cox proportional hazard (CPH) and Poisson regression (POI) models. A ranking score was assigned based on the 10-fold cross-validated (CV) concordance index (C-I), mean square error (MSE), and the Akaike information criterion (AICs), to evaluate the methods’ performance. Results: Scatter plots of the 10-CV C-I and MSE against the AIC showed an impact on the survival predictions between the IN methods and MR sequences (C1/C2 C-I range: 0.62–0.71/0.61–0.72, MSE range: 0.20–0.42/0.13–0.22). White stripe showed stable results for T1wce (C1/C2 C-I: 0.71/0.65, MSE: 0.21/0.14). Combat (0.68/0.62, 0.22/0.15) and histogram matching (HM, 0.67/0.64, 0.22/0.15) showed consistent prediction results for T2w models. They were also the top-performing methods for T1w in C2 (Combat: 0.67, 0.13; HM: 0.67, 0.13); however, only HM achieved high predictions in C1 (0.66, 0.22). After eliminating IN impacted SF using Spearman’s rank-order correlation coefficient, a mean decrease in the C-I and MSE of 0.05 and 0.03 was observed in all four sequences. Conclusion: The IN method impacted the predictive power of survival models; thus, performance is sequence-dependent.

Radiation-induced normal tissue toxicity often limits the curative treatment of cancer. Moreover, normal tissue relative biological effectiveness data for high-linear energy transfer particles are urgently needed. We propose a strategy based on transcriptome analysis of patient-derived human intestinal organoids (HIO) to determine molecular surrogates for radioresponse of gastrointestinal (GI) organs at risk in a personalized manner. HIO were generated from induced pluripotent stem cells (iPSC), which were derived from skin biopsies of three patients, including two patients with FANCA deficiency as a paradigm for enhanced radiosensitivity. For the two Fanconi anemia (FA) patients (HIO-104 and 106, previously published as FA-A#1 IND-iPS1 and FA-A#2 IND-iPS3), FANCA expression was reconstituted as a prerequisite for generation of HIO via lentiviral expression of a doxycycline inducible construct. For radiosensitivity analysis, FANCA deficient and FANCA rescued as well as wtHIO were sham treated or irradiated with 4Gy photon, proton or carbon ions at HIT, respectively. Immunofluorescence staining of HIO for 53BP1-foci was performed 1 h post IR and gene expression analyses was performed 12 and 48 h post IR. 53BP1-foci numbers and size correlated with the higher RBE of carbon ions. A FANCA dependent differential gene expression in response to radiation was found (p < 0.01, ANOVA; n = 1071 12 h; n = 1100 48 h). Pathways associated with FA and DNA-damage repair i.e., transcriptional coupled nucleotide excision repair, homology-directed repair and translational synthesis were found to be differentially regulated in FANCA deficient HIO. Next, differential regulated genes were investigated as a function of radiation quality (RQ, p < 0.05, ANOVA; n = 742 12 h; n = 553 48 h). Interestingly, a gradual increase or decrease of gene expression was found to correlate with the three main qualities, from photon to proton and carbon irradiation. Clustering separated high-linear energy transfer irradiation with carbons from proton and photon irradiation. Genes associated with dual incision steps of TC-NER were differentially regulated in photon vs. proton and carbon irradiation. Consequently, SUMO3, ALC1, POLE4, PCBP4, MUTYH expression correlated with the higher RBE of carbon ions. An interaction between the two studied parameters FA and RQ was identified (p < 0.01, 2-way ANOVA n = 476). A comparison of genes regulated as a function of FA, RQ and RBE suggest a role for p53 interacting genes BRD7, EWSR1, FBXO11, FBXW8, HMGB1, MAGED2, PCBP4, and RPS27 as modulators of FA in response to radiation. This proof of concept study demonstrates that patient tailored evaluation of GI response to radiation is feasible via generation of HIO and comparative transcriptome profiling. This methodology can now be further explored for a personalized assessment of GI radiosensitivity and RBE estimation.

Purpose: The Ethos (Varian Medical Systems) radiotherapy device combines semi-automated anatomy detection and plan generation for cone beam computer tomography (CBCT)-based daily online adaptive radiotherapy (oART). However, CBCT offers less soft tissue contrast than magnetic resonance imaging (MRI). This work aims to present the clinical workflow of CBCT-based oART with shuttle-based offline MR guidance. Methods: From February to November 2023, 31 patients underwent radiotherapy on the Ethos (Varian, Palo Alto, CA, USA) system with machine learning (ML)-supported daily oART. Moreover, patients received weekly MRI in treatment position, which was utilized for daily plan adaptation, via a shuttle-based system. Initial and adapted treatment plans were generated using the Ethos treatment planning system. Patient clinical data, fractional session times (MRI + shuttle transport + positioning, adaptation, QA, RT delivery) and plan selection were assessed for all fractions in all patients. Results: In total, 737 oART fractions were applied and 118 MRIs for offline MR guidance were acquired. Primary sites of tumors were prostate (n = 16), lung (n = 7), cervix (n = 5), bladder (n = 1) and endometrium (n = 2). The treatment was completed in all patients. The median MRI acquisition time including shuttle transport and positioning to initiation of the Ethos adaptive session was 53.6 min (IQR 46.5–63.4). The median total treatment time without MRI was 30.7 min (IQR 24.7–39.2). Separately, median adaptation, plan QA and RT times were 24.3 min (IQR 18.6–32.2), 0.4 min (IQR 0.3–1,0) and 5.3 min (IQR 4.5–6.7), respectively. The adapted plan was chosen over the scheduled plan in 97.7% of cases. Conclusion: This study describes the first workflow to date of a CBCT-based oART combined with a shuttle-based offline approach for MR guidance. The oART duration times reported resemble the range shown by previous publications for first clinical experiences with the Ethos system.