With individualized gene therapy, robotic surgery, sophisticated radiotherapy imaging and other advances, certain cancers are now for the first time becoming treatable.
Nevertheless, results such as these suggest that individualized longitudinal gene expression profiling has considerable potential to contribute to personalized medicine assessments.
We developed an individualized immune-based gene expression signature that predicts survival of patients with non-small cell lung cancer and validated it in over 2,000 patients (Li et al. JAMA Oncology, 2017).
We have developed a local minimax kernel learning algorithm that is capable of making individualized prediction in several microarray cancer gene expression data sets.
Gene expression profiles from DNA microarrays are individualized and useful in the diagnosis and prognosis of diseases [1].
These individualized cellular morphologies, characteristics, and functions result from the unique gene expression profiles of the different cell types.
Because we clearly observed a phenotypic heterogeneity, we individualized three distinct phenotypes on the basis of the morphology and gene expression (arbitrarily classified as categories 1, 2 and 3) (Fig. 1A).
Gene expression was normalized against expression of the GhEF1α gene.
Since the clinical course varies among patients with HER2-positive tumors, as does their response to targeted therapy, differences in global gene expression among HER2-overexpressing tumors could be important in distinguishing patients for the design and delivery of individualized targeted therapies.
Finally, we confirmed by quantitative RT- PCR that the gene expression of known adipogenic marker genes were increased in adipocytes, generated not only from primary layers, but also from strictly individualized clones.
