Few attempts have been made to include the oxygen enhancement ratio (OER) in treatment planning for ion beam therapy, and systematic studies to evaluate the impact of hypoxia in treatment with the beam of different ion species are sorely needed. The radiobiological models used to quantify the OER in such studies are mainly based on the dose-averaged LET estimates, and do not explicitly distinguish between the ion species and fractionation schemes. In this study, a new type of OER modelling, based on the microdosimetric kinetic model, taking into account the specificity of the different ions, LET spectra, tissues and fractionation schemes, has been developed. The model has been benchmarked with published in vitro data, HSG, V79 and CHO cells in aerobic and hypoxic conditions, for different ion irradiation. The model has been included in the simulation of treatments for a clinical case (brain tumour) using proton, lithium, helium, carbon and oxygen ion beams. A study of the tumour control probability (TCP) as a function of oxygen partial pressure, dose per fraction and primary ion type has been performed. The modelled OER depends on both the LET and ion type, also showing a decrease for an increased dose per fraction with a slope that depends on the LET and ion type, in good agreement with the experimental data. In the investigated clinical case, a significant increase in TCP has been found upon increasing the ion charge. Higher OER variations as a function of dose per fraction have also been found for low-LET ions (up to 15% varying from 2 to 8 Gy(RBE) for protons). This model could be exploited in the identification of treatment condition optimality in the presence of hypoxia, including fractionation and primary particle selection.

Recent advances in machine learning research, combined with the reduced sequencing costs enabled by modern next-generation sequencing, paved the way to the implementation of precision medicine through routine multi-omics molecular profiling of tumours. Thus, there is an emerging need of reliable models exploiting such data to retrieve clinically useful information. Here, we introduce an original consensus clustering approach, overcoming the intrinsic instability of common clustering methods based on molecular data. This approach is applied to the case of non-small cell lung cancer (NSCLC), integrating data of an ongoing clinical study (PROMOLE) with those made available by The Cancer Genome Atlas, to define a molecular-based stratification of the patients beyond, but still preserving, histological subtyping. The resulting subgroups are biologically characterized by well-defined mutational and gene-expression profiles and are significantly related to disease-free survival (DFS). Interestingly, it was observed that (1) cluster B, characterized by a short DFS, is enriched in KEAP1 and SKP2 mutations, that makes it an ideal candidate for further studies with inhibitors, and (2) over- and under-representation of inflammation and immune systems pathways in squamous-cell carcinomas subgroups could be potentially exploited to stratify patients treated with immunotherapy.