A method for determining the presence or absence of at least one cancer type in an animal comprising - determining lipids amounts of lipids in a lipid set in a sample from the animal, and - determining the presence or absence of at least one cancer type in the animal with a predictive model; wherein - the lipid amounts of lipids in the lipid set comprise an input of the predictive model, and - the sample comprises a bodily fluid or treatment thereof, - the at least one cancer type is selected from the group consisting of lung cancer, breast cancer, and tumors associated with any of the aforementioned cancer types, - the lipid set comprises FA (16:3), and - the predictive model comprises one or more of dimension reduction method, clustering method, machine learning method, principle component analysis, soft independent modeling of class analogy, partial least squares regression, orthogonal least squares regression, partial least squares discriminant analysis, orthogonal partial least squares discriminant analysis, mean centering, median centering, Pareto scaling, unit variance scaling, orthogonal signal correction, integration, differentiation, cross validation, or receiver operating characteristic curves. - determining lipids amounts of lipids in a lipid set in a sample from the animal, and - determining the presence or absence of at least one cancer type in the animal with a predictive model; wherein - the lipid amounts of lipids in the lipid set comprise an input of the predictive model, and - the sample comprises a bodily fluid or treatment thereof, - the at least one cancer type is selected from the group consisting of lung cancer, breast cancer, and tumors associated with any of the aforementioned cancer types, - the lipid set comprises FA (16:3), and - the predictive model comprises one or more of dimension reduction method, clustering method, machine learning method, principle component analysis, soft independent modeling of class analogy, partial least squares regression, orthogonal least squares regression, partial least squares discriminant analysis, orthogonal partial least squares discriminant analysis, mean centering, median centering, Pareto scaling, unit variance scaling, orthogonal signal correction, integration, differentiation, cross validation, or receiver operating characteristic curves. - the lipid amounts of lipids in the lipid set comprise an input of the predictive model, and - the sample comprises a bodily fluid or treatment thereof, - the at least one cancer type is selected from the group consisting of lung cancer, breast cancer, and tumors associated with any of the aforementioned cancer types, - the lipid set comprises FA (16:3), and - the predictive model comprises one or more of dimension reduction method, clustering method, machine learning method, principle component analysis, soft independent modeling of class analogy, partial least squares regression, orthogonal least squares regression, partial least squares discriminant analysis, orthogonal partial least squares discriminant analysis, mean centering, median centering, Pareto scaling, unit variance scaling, orthogonal signal correction, integration, differentiation, cross validation, or receiver operating characteristic curves. <EOS>