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Metabolic reprogramming is known to critically contribute to therapy response in cancer, including the resistance of triple-negative breast cancer (TNBC) patients to conventional radio/(chemo)therapy. Our study, therefore, characterized the metabolic response of TNBC cell models to irradiation using untargeted metabolomics. We identified radiation-induced metabolic changes by harnessing a metabolite-gene interaction network comprising 44 genes. Nine genes with significant radiosensitizing potential in TNBC cells were identified by RNAi-screening. Among the druggable genes, high expression of pyruvate kinase M (PKM) was associated with worse overall survival in the METABRIC-TNBC cohort. Pharmacological PKM inhibition led to radiosensitization in TNBC cell models, which was attributed to S-phase related metabolic changes. Selective disruption of the S-phase by PKM inhibition led to both reduced DNA synthesis and increased replication stress, as evidenced by increased DNA damage at active replication forks. This resulted in prolonged cell cycle arrest after irradiation. Metabolic profiling upon PKM depletion and irradiation revealed two positively PKM-associated gene clusters, predominantly involved in the glycolysis pathway. Each cluster was functionally distinct, one shared the PKM-associated effects in survival reduction, while the other correlated with its radiosensitization. High expression of the first cluster was significantly correlated with worse overall survival, whereas high expression of the second cluster, consisting of PKM, ENO1, GAPDH and GPI, predicted poor response to radiotherapy in the METABRIC-TNBC cohort. Thus, our study suggests that glycolytic-associated metabolic reprogramming upon irradiation plays a major role in radioresistance of TNBC. Analysis of our identified glycolysis-related gene cluster in patients could predict the response to radiotherapy, and targeting PKM and the related glycolytic pathway holds promise for radiosensitization in TNBC patients.

Proton therapy (PT) provides dose delivery with high spatial precision, allowing more conformal treatments and the minimization of adverse exposure to adjacent healthy tissues. However, PT is susceptible to uncertainties in the proton range which currently reduces treatment efficacy. To mitigate this, the NOVO collaboration is developing a real-time, multi-particle imaging system capable of simultaneous imaging of prompt gamma rays (PGs) and fast neutrons (FNs) emitted during treatment. This study presents the commissioning experiment of a prototype NOVO detector aimed at imaging a point-like source of accelerator-based neutrons. The array is composed of 14 interwoven organic scintillator bars using two novel scintillator materials - OGS and M600. Experiments were conducted at the Physikalisch-Technische Bundesanstalt in Braunschweig, Germany, where monoenergetic neutron fields from 1.2 MeV to 19 MeV were generated via accelerator-driven reactions. Images of the neutron source were reconstructed through the simple back-projection of double-scattered events, with the detector array position iterated using a two-axis motor table. The source position could be determined with a resolution of less than 5 mm for neutron fields above 14.8 MeV with 10,000 detected events. Further improvements to the array geometry and implementation of advanced reconstruction algorithms suggest detection of range shifts down to 1 mm is possible.

Tumor asphericity in 18F-FDG PET is a prognostic marker that has been investigated in small pilot studies of patients with head and neck squamous cell carcinoma (HNSCC). Here, we investigated the prognostic role of asphericity in a large multicenter database of patients with HNSCC treated with primary radiotherapy or chemoradiation and assessed its independent prognostic value. Methods: In total, 1,104 patients were included in this analysis. All received pretreatment 18F-FDG PET scans. Clinical risk factors were evaluated, and quantitative PET parameters SUVmax, metabolic tumor volume (MTV), total lesion glycolysis, and asphericity were calculated. Primary study endpoints were overall survival (OS) and locoregional control (LRC). Uni- and multivariate Cox regression analyses were performed. Additionally, asphericity was combined with the best-established quantitative PET parameter of MTV, and the combinatory approach of using asphericity and MTV was compared with the use of only asphericity or MTV by bootstrap analyses. Results: Asphericity showed only a modest correlation with the established PET parameters of MTV, SUVmax, and total lesion glycolysis. On univariate testing asphericity was strongly associated with the outcome of patients (LRC and OS with P < 0.001). In multivariate testing of all imaging parameters that were not highly correlated, both MTV and asphericity showed a significant association with LRC (P < 0.001 for MTV and P = 0.021 for asphericity) and OS (P < 0.001 for MTV and asphericity). Asphericity and MTV were binarized and combined for risk stratification, and the prognostic value of the combination was compared with the prognostic value of individual parameters. Bootstrapping revealed significantly better performance by the combinatory approach when compared with MTV (P = 0.012 for LRC and P < 0.001 for OS) and asphericity with regard to OS (P < 0.001) but not for LRC (P = 0.53). Conclusion: We were able to show that asphericity bears independent prognostic value and significantly improves risk stratification when combined with MTV in a comprehensive retrospective cohort of HNSCC patients.

Objective.A four-dimensional robust optimisation (4DRO) is usually employed when the tumour respiratory motion needs to be addressed. However, it is computationally demanding, and an automated method is preferable for adaptive planning to avoid manual trial-and-error. This study proposes a 4DRO technique based on dose mimicking for automated adaptive planning.Approach.Initial plans for 4DRO intensity modulated proton therapy were created on an average CT for four patients with clinical target volume (CTV) in the lung, oesophagus, or pancreas, respectively. These plans were robustly optimised using three phases of four-dimensional computed tomography (4DCT) and accounting for setup and density uncertainties. Weekly 4DCTs were used for adaptive replanning, using a constant relative biological effectiveness (cRBE) of 1.1. Two methods were used: (1) template-based adaptive (TA) planning and (2) dose-mimicking-based adaptive (MA) planning. The plans were evaluated using variable RBE (vRBE) weighted doses and biologically consistent dose accumulation (BCDA).Main results.MA and TA plans had comparable CTV coverage except for one patient where the MA plan had a higher D98 and lower D2 but with an increased D2 in few organs at risk (OARs). CTV D98 deviations in non-adaptive plans from the initial plans were up to -7.2 percentage points (p.p.) in individual cases and -1.8 p.p. when using BCDA. For the OARs, MA plans showed a reduced mean dose and D2 compared to the TA plans, with few exceptions. The vRBE-weighted accumulated doses had a mean dose and D2 difference of up to 0.3 Gy and 0.5 Gy, respectively, in the OARs with respect to cRBE-weighted doses.Significance.MA plans indicate better performance in target coverage and OAR dose sparing compared to the TA plans in 4DRO adaptive planning. Moreover, MA method is capable of handling both forms of anatomical variation, namely, changes in density and relative shifts in the position of OARs.

The Y-box binding protein-1 (YBX1) gene codes for a multifunctional oncoprotein that is increasingly being linked to the regulations of many aspects of cancer cell biology. Disparities in treatment outcomes between male and female cancer patients are increasingly reported. This study aimed to examine the relationship between YBX1 expression and overall survival in male and female patients with solid tumours. Overall survival and YBX1 expression data for cohorts of male and female cancer patients obtained from freely available databases were analysed with a cox proportional hazard model with covariates of biological sex and YBX1 expression. Kaplan–Meier curves and Violin plots were constructed for segregated male and female cohorts. High YBX1 expression was significantly associated with poor survival in 2 female-only and 4 mixed-sex cancer sites. In female lung cancer patients, better survival and lower YBX1 expression were identified. The clinical importance of YBX1 expression in cancer ought to be evaluated in a sex-specific manner, especially in lung cancer.

Personalized treatment strategies based on non-invasive biomarkers have potential to improve patient management in patients with newly diagnosed glioblastoma (GBM). The residual tumour burden after surgery in GBM patients is a prognostic imaging biomarker. However, in clinical patient management, its assessment is a manual and time-consuming process that is at risk of inter-rater variability. Furthermore, the prediction of patient outcome prior to radiotherapy may identify patient subgroups that could benefit from escalated radiotherapy doses. Therefore, in this study, we investigate the capabilities of traditional radiomics and 3D convolutional neural networks for automatic detection of the residual tumour status and to prognosticate time-to-recurrence (TTR) and overall survival (OS) in GBM using postoperative [11C] methionine positron emission tomography (MET-PET) and gadolinium-enhanced T1-w magnetic resonance imaging (MRI). On the independent test data, the 3D-DenseNet model based on MET-PET achieved the best performance for residual tumour detection, while the logistic regression model with conventional radiomics features performed best for T1c-w MRI (AUC: MET-PET 0.95, T1c-w MRI 0.78). For the prognosis of TTR and OS, the 3D-DenseNet model based on MET-PET integrated with age and MGMT status achieved the best performance (Concordance-Index: TTR 0.68, OS 0.65). In conclusion, we showed that both deep-learning and conventional radiomics have potential value for supporting image-based assessment and prognosis in GBM. After prospective validation, these models may be considered for treatment personalization.

Background and purposeMany 4D particle therapy research concepts have been recently translated into clinics, however, remaining substantial differences depend on the indication and institute-related aspects. This work aims to summarise current state-of-the-art 4D particle therapy technology and outline a roadmap for future research and developments. Material and methodsThis review focused on the clinical implementation of 4D approaches for imaging, treatment planning, delivery and evaluation based on the 2021 and 2022 4D Treatment Workshops for Particle Therapy as well as a review of the most recent surveys, guidelines and scientific papers dedicated to this topic. ResultsAvailable technological capabilities for motion surveillance and compensation determined the course of each 4D particle treatment. 4D motion management, delivery techniques and strategies including imaging were diverse and depended on many factors. These included aspects of motion amplitude, tumour location, as well as accelerator technology driving the necessity of centre-specific dosimetric validation. Novel methodologies for X-ray based image processing and MRI for real-time tumour tracking and motion management were shown to have a large potential for online and offline adaptation schemes compensating for potential anatomical changes over the treatment course. The latest research developments were dominated by particle imaging, artificial intelligence methods and FLASH adding another level of complexity but also opportunities in the context of 4D treatments. ConclusionThis review showed that the rapid technological advances in radiation oncology together with the available intrafractional motion management and adaptive strategies paved the way towards clinical implementation.

Robust and fast in vivo treatment verification is expected to increase the clinical efficacy of proton therapy. The combined detection of prompt gamma rays and neutrons has recently been proposed for this purpose and shown to increase the monitoring accuracy. However, the potential of this technique is not fully exploited yet since the proton range reconstruction relies only on a simple landmark of the particle production distributions. Here, we apply machine learning based feature selection and multivariate modelling to improve the range reconstruction accuracy of the system in an exemplary lung cancer case in silico. We show that the mean reconstruction error of this technique is reduced by 30%–50% to a root mean squared error per spot of 0.4, 1.0, and 1.9 mm for pencil beam scanning spot intensities of 108, 107, and 106 initial protons, respectively. The best model performance is reached when combining distribution features of both gamma rays and neutrons. This confirms the advantage of hybrid gamma/neutron imaging over a single-particle approach in the presented setup and increases the potential of this system to be applied clinically for proton therapy treatment verification.