Intra-Tumoral Heterogeneity of @entity1722 , @entity7991 , cMET and XXXX in @entity356 : Optimizing Personalized Healthcare through Innovative Pathological and Statistical Analysis.

@entity1 @entity5 @entity14050 @entity7991 @entity356 @entity1722
UNASSIGNED: Current drug development efforts on @entity356 are directed against several molecular targets driving the growth of this @entity5 . Intra-tumoral biomarker heterogeneity however, commonly observed in @entity356 , could lead to biased selection of @entity1 . MET, @entity14050 , @entity7991 , and @entity1722 were profiled on @entity356 biopsy samples. An innovative pathological assessment was performed through scoring of individual biopsies against whole biopsies from a single @entity1 to enable heterogeneity evaluation. Following this, false negative risks for each biomarker were estimated in silico. 166 @entity356 cases with multiple biopsies from single @entity1 were collected from Shanghai Renji Hospital. Following pre-set criteria, 56 ~ 78% cases showed low, 15 ~ 35% showed medium and 0 ~ 11% showed high heterogeneity within the biomarkers profiled. If 3 biopsies were collected from a single @entity1 , the false negative risk for detection of the biomarkers was close to 5% (exception for @entity7991 : 12.2%). When 6 biopsies were collected, the false negative risk approached 0%. Our study demonstrates the benefit of multiple biopsy sampling when considering personalized healthcare biomarker strategy, and provides an example to address the challenge of intra-tumoral biomarker heterogeneity using alternative pathological assessment and statistical methods.
