Sensitisation of cancer cells to radiotherapy by serine and glycine starvation

Background: Cellular metabolism is an integral component of cellular adaptation to stress, playing a pivotal role in the resistance of cancer cells to various treatment modalities, including radiotherapy. In response to radiotherapy, cancer cells engage antioxidant and DNA repair mechanisms which mitigate and remove DNA damage, facilitating cancer cell survival. Given the reliance of these resistance mechanisms on amino acid metabolism, we hypothesised that controlling the exogenous availability of the non-essential amino acids serine and glycine would radiosensitise cancer cells. Methods: We exposed colorectal, breast and pancreatic cancer cell lines/organoids to radiation in vitro and in vivo in the presence and absence of exogenous serine and glycine. We performed phenotypic assays for DNA damage, cell cycle, ROS levels and cell death, combined with a high-resolution untargeted LCMS metabolomics and RNA-Seq. Results: Serine and glycine restriction sensitised a range of cancer cell lines, patient-derived organoids and syngeneic mouse tumour models to radiotherapy. Comprehensive metabolomic and transcriptomic analysis of central carbon metabolism revealed that amino acid restriction impacted not only antioxidant response and nucleotide synthesis but had a marked inhibitory effect on the TCA cycle. Conclusion: Dietary restriction of serine and glycine is a viable radio-sensitisation strategy in cancer

Magnetic Resonance Imaging-Guided Adaptive Radiotherapy for Colorectal Liver Metastases

Technological advances have enabled well tolerated and effective radiation treatment for small liver metastases. Stereotactic ablative radiation therapy (SABR) refers to ablative dose delivery (>100 Gy BED) in five fractions or fewer. For larger tumors, the safe delivery of SABR can be challenging due to a more limited volume of healthy normal liver parenchyma and the proximity of the tumor to radiosensitive organs such as the stomach, duodenum, and large intestine. In addition to stereotactic treatment delivery, controlling respiratory motion, the use of image guidance, adaptive planning and increasing the number of radiation fractions are sometimes necessary for the safe delivery of SABR in these situations. Magnetic Resonance (MR) image-guided adaptive radiation therapy (MRgART) is a new and rapidly evolving treatment paradigm. MR imaging before, during and after treatment delivery facilitates direct visualization of both the tumor target and the adjacent normal healthy organs as well as potential intrafraction motion. Real time MR imaging facilitates non-invasive tumor tracking and treatment gating. While daily adaptive re-planning permits treatment plans to be adjusted based on the anatomy of the day. MRgART therapy is a promising radiation technology advance that can overcome many of the challenges of liver SABR and may facilitate the safe tumor dose escalation of colorectal liver metastases

Radiomics artificial intelligence modelling for prediction of local control for colorectal liver metastases treated with radiotherapy

Background and Purpose: Prognostic assessment of local therapies for colorectal liver metastases (CLM) is essential for guiding management in radiation oncology. Computed tomography (CT) contains liver texture information which may be predictive of metastatic environments. To investigate the feasibility of analyzing CT texture, we sought to build an automated model to predict progression-free survival using CT radiomics and artificial intelligence (AI). Materials and Methods: Liver CT scans and outcomes for N = 97 CLM patients treated with radiotherapy were retrospectively obtained. A survival model was built by extracting 108 radiomic features from liver and tumor CT volumes for a random survival forest (RSF) to predict local progression. Accuracies were measured by concordance indices (C-index) and integrated Brier scores (IBS) with 4-fold cross-validation. This was repeated with different liver segmentations and radiotherapy clinical variables as inputs to the RSF. Predictive features were identified by perturbation importances. Results: The AI radiomics model achieved a C-index of 0.68 (CI: 0.62–0.74) and IBS below 0.25 and the most predictive radiomic feature was gray tone difference matrix strength (importance: 1.90 CI: 0.93–2.86) and most predictive treatment feature was maximum dose (importance: 3.83, CI: 1.05–6.62). The clinical data only model achieved a similar C-index of 0.62 (CI: 0.56–0.69), suggesting that predictive signals exist in radiomics and clinical data. Conclusions: The AI model achieved good prediction accuracy for progression-free survival of CLM, providing support that radiomics or clinical data combined with machine learning may aid prognostic assessment and management

Correlating Computed Tomography Perfusion Changes in the Pharyngeal Constrictor Muscles During Head-and-Neck Radiotherapy to Dysphagia Outcome

Translation corpora of original texts with translations and comparable texts from the genre external business communication.CLARIN Metadata summary for Covert translation: Business Communication (old) (CMDI-based) Title: Covert translation: Business Communication (old) Description: Translation corpora of original texts with translations and comparable texts from the genre external business communication Publication date: 2011-01-01 Data owner: Juliane House Contributors: Juliane House (compiler) Project: K4 "Covert Translation" Keywords: translated texts, business communication, parallel corpus, comparable corpus Languages: German (deu), English (eng) Size: 53 texts, 64980 words Segmentation units: orthographic sentence Annotation types: Morphological analysis Temporal Coverage: 1978/1999 Spatial Coverage: DE Genre: discourse Modality: writte