Highlights d HMGCS2 enriches for Lgr5 + ISCs to generate the ketone body bOHB d bOHB depletion reduces stemness, alters differentiation, and hampers regeneration d bOHB, through class I HDAC inhibition, reinforces the NOTCH program in ISCs d Dietary fat and glucose counter-regulate ketone body signaling to instruct ISCs

Objective: Using CT texture analysis and machine learning methods, this study aims to distinguish the lesions imaged via 68Ga-prostate-specific membrane antigen (PSMA) positron emission tomography (PET)/CT as metastatic and completely responded in patients with known bone metastasis and who were previously treated. Methods: We retrospectively reviewed the 68Ga-PSMA PET/CT images of 75 patients after treatment, who were previously diagnosed with prostate cancer and had known bone metastasis. A texture analysis was performed on the metastatic lesions showing PSMA expression and completely responded sclerotic lesions without PSMA expression through CT images. Textural features were compared in two groups. Thus, the distinction of metastasis/completely responded lesions and the most effective parameters in this issue were determined by using various methods [decision tree, discriminant analysis, support vector machine (SVM), k-nearest neighbor (KNN), ensemble classifier] in machine learning. Results: In 28 of the 35 texture analysis findings, there was a statistically significant difference between the two groups. The Weighted KNN method had the highest accuracy and area under the curve, has been chosen as the best model. The weighted KNN algorithm was succeeded to differentiate sclerotic lesion from metastasis or completely responded lesions with 0.76 area under the curve. GLZLM_SZHGE and histogram-based kurtosis were found to be the most important parameters in differentiating metastatic and completely responded sclerotic lesions. Conclusions: Metastatic lesions and completely responded sclerosis areas in CT images, as determined by 68Ga-PSMA PET, could be distinguished with good accuracy using texture analysis and machine learning (Weighted KNN algorithm) in prostate cancer. Advances in knowledge: Our findings suggest that, with the use of newly emerging software, CT imaging can contribute to identifying the metastatic lesions in prostate cancer.

Epidermal growth factor receptor (EGFR) mutations are potential markers driving carcinogenesis, and may alter the response to EGFR tyrosine kinase inhibitors in patients with non-small cell lung cancer (NSCLC). The frequency of EGFR mutations in patients with NSCLC differs according to sex, smoking habits and regional-based ethnicity differences. The aim of the present study was to determine the frequency of EGFR mutations in Turkish patients with NSCLC to highlight the importance of regional differences, and their associations with patient characteristics. Genomic DNA was extracted from formalin-fixed and paraffin-embedded tumor tissue sections of 409 NSCLC patients. The most common EGFR mutations in exons 18, 19, 20 and 21 were detected using BioFilmChip-based microarray assay. The overall EGFR mutation frequency was 16.6%, and the highest mutation frequencies were observed in exon 19 (6.4%) and exon 21 (7.3%). There was a higher frequency of EGFR mutations in females compared with males and in never-smokers compared with smokers (both P≤0.05). These results were similar to other European population-based studies, but not consistent Middle-Eastern based studies. The present study may contribute to understanding the gradient frequency of EGFR mutation across different ethnicities, and in designing genome wide-based collaborations that may reveal novel decision making and susceptibility mutations in EGFR in patients with NSCLC.

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Fluoropyrimidine-based chemotherapy is extensively used for the treatment of solid cancers, including colorectal cancer. However, fluoropyrimidine-driven toxicities are a major problem in the management of the disease. The grade and type of the toxicities depend on demographic factors, but substantial inter-individual variation in fluoropyrimidine-related toxicity is partly explained by genetic factors. The aim of this study was to investigate the effect of dihydropyrimidine dehydrogenase (DPYD), thymidylate synthase (TYMS), and methylenetetrahydrofolate reductase (MTHFR) polymorphisms in colorectal cancer patients. Eighty-five patients who were administered fluoropyrimidine-based treatment were included in the study. The DPYD, TYMS and MTHFR polymorphisms were scanned by a next generation Sequenom MassARRAY. Fluoropyrimidine toxicities were observed in 92% of all patients. The following polymorphisms were detected: DPYD 85T>C (29.4% heterozygote mutants, 7.1% homozygote mutants), DPYD IVS 14+1G>A (1.2% heterozygote mutants), TYMS 1494del TTAAAG (38.4% heterozygote mutants, 24.7% homozygote mutants), MTHFR 677C>T (43.5% heterozygote mutants, 9.4% homozygote mutants) and MTHFR 1298A>C (8.2% heterozygote mutants, 2.4% homozygote mutants). A statistically significant association was demonstrated between MTHFR 677C>T and fluoropyrimidine-related toxicity (p value = 0.007). Furthermore, MTHFR 1298A>C was associated with hematopoietic toxicity (p value = 0.008). MTHFR polymorphisms may be considered as related factors of fluoropyrimidine toxicity and may be useful as predictive biomarkers for the determination of the colorectal cancer patients who can receive the greatest benefit from fluoropyrimidine-based treatments.