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Renal cell carcinomas (RCC) are classified according to their histological features. Accurate classification of RCC and comprehensive understanding of their metabolic dysregulation are of critical importance. Here we investigate the use of metabolomic analyses to classify the main RCC subtypes and to describe the metabolic variation for each subtype. To this end, we performed metabolomic profiling of 65 RCC frozen samples (40 clear cell, 14 papillary and 11 chromophobe) using liquid chromatography-mass spectrometry. OPLS-DA multivariate analysis based on metabolomic data showed clear discrimination of all three main subtypes of RCC (R2 = 75.0%, Q2 = 59.7%). The prognostic performance was evaluated using an independent cohort and showed an AUROC of 0.924, 0.991 and 1 for clear cell, papillary and chromophobe RCC, respectively. Further pathway analysis using the 21 top metabolites showed significant differences in amino acid and fatty acid metabolism between three RCC subtypes. In conclusion, this study shows that metabolomic profiling could serve as a tool that is complementary to histology for RCC subtype classification. An overview of metabolic dysregulation in RCC subtypes was established giving new insights into the understanding of their clinical behaviour and for the development of targeted therapeutic strategies.

Hyperprogressive disease (HPD), an unexpected acceleration of tumor growth kinetics, is described in cancer patients treated with anti-PD-1/anti-PD-L1 agents. Here, our aim was to take into consideration the host and explore whether single nucleotide polymorphisms (SNPs) in key genes involved in immune response might predispose to HPD. DNA was extracted from blood-samples from 98 patients treated under CPI monotherapy. Four candidate genes (PD-1, PD-L1, IDO1 and VEGFR2) and 15 potential SNPs were selected. The TGKR (ratio of the slope of tumor growth before treatment and the slope of tumor growth on treatment) was calculated. Hyperprogression was defined as a TGKR≥2. TGKR calculation was feasible for 80 patients (82%). HPD was observed for 11 patients (14%) and was associated with shorter overall survival (P = 0.003). In univariate analysis, HPD was significantly associated with age ≥70 y (P = 0.025), immune-related toxicity (P = 0.016), VEGFR2 rs1870377 A/T or A/A (P = 0.005), PD-L1 rs2282055 G/T or G/G (P = 0.024) and PD-L1 rs2227981 G/A or A/A (P = 0.024). Multivariate analysis confirmed the correlation between HPD and age ≥70 y (P = 0.006), VEGFR2 rs1870377 A/T or A/A (P = 0.007) and PD-L1 rs2282055 G/T or G/G (P = 0.018). Immunogenetics could become integral predictive factors for CPI-based immunotherapy.

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The aim of this work was to evaluate differences in energy flows between normal and immortalized cells when these distinct biological systems are exposed to environmental stimulation. These differences were considered using a constructal thermodynamic approach and were subsequently verified experimentally. The application of constructal law to cell analysis led to the conclusion that temperature differences between cells with distinct behaviour can be amplified by interaction between cells and external fields. Experimental validation of the principle was carried out on two cellular models exposed to electromagnetic fields. By infrared thermography we were able to assess small changes in heat dissipation measured as a variation in cell internal energy. The experimental data thus obtained are in agreement with the theoretical calculation, because they show a different thermal dispersion pattern when normal and immortalized cells are exposed to electromagnetic fields. By using two methods that support and validate each other, we have demonstrated that the cell/environment interaction can be exploited to enhance cell behavior differences, in particular heat dissipation. We propose infrared thermography as a technique effective in discriminating distinct patterns of thermal dispersion and therefore able to distinguish a normal phenotype from a transformed one.