ABSTRACT
Both molecular profiling of tumors and longitudinal tumor size data modeling are relevant strategies to predict cancer patients' response to treatment. Herein we propose a model of tumor growth inhibition integrating a tumor's genetic characteristics (p53 mutation and 1p/19q codeletion) that successfully describes the time course of tumor size in patients with low-grade gliomas treated with first-line temozolomide chemotherapy. The model captures potential tumor progression under chemotherapy by accounting for the emergence of tissue resistance to treatment following prolonged exposure to temozolomide. Using information on individual tumors' genetic characteristics, in addition to early tumor size measurements, the model was able to predict the duration and magnitude of response, especially in those patients in whom repeated assessment of tumor response was obtained during the first 3 months of treatment. Combining longitudinal tumor size quantitative modeling with a tumor''s genetic characterization appears as a promising strategy to personalize treatments in patients with low-grade gliomas.
ABSTRACT
Samples of goat milk with low and high alpha S1-casein content collected from animals of Alpine and Saanen breeds in the same stage of lactation were compared for coagulation properties (coagulation time, rate of curd formation, curd firmness) and chemical composition (total solids, ash, total protein, total casein, whey protein, fat, Ca, P, pH). Milk with low alpha S1-casein had a faster coagulation time, whereas milk with high levels produced the firmer curd associated with a better chemical composition. Within high alpha S1-casein milk, comparison between breeds showed milk from the Alpine breed had significantly better coagulation properties than that from the Saanen breed. Milk composition accounted for 27% of the variation in coagulation time, 21% of variation in cured formation rate, and 54% of variation in curd firmness.
