Gene expression studies have identified the microenvironment as a prognostic player in diffuse large @entity1892 . However, there is a lack of simple immune biomarkers that can be applied in the clinical setting and could be helpful in stratifying @entity1 . Immunohistochemistry has been used for this purpose but the results are inconsistent. We decided to reinvestigate the immune microenvironment and its impact using immunohistochemistry, with two systems of image analysis, in a large set of @entity1 with diffuse large @entity1892 . Diagnostic tissue from 309 @entity1 was arrayed onto tissue microarrays. Results from 161 chemoimmunotherapy-treated @entity1 were used for outcome prediction. Positive cells, percentage stained area and numbers of pixels/area were quantified and results were compared with the purpose of inferring consistency between the two semi-automated systems. Measurement cutpoints were assessed using a recursive partitioning algorithm classifying results according to survival. Kaplan-Meier estimators and Fisher exact tests were evaluated to check for significant differences between measurement classes, and for dependence between pairs of measurements, respectively. Results were validated by multivariate analysis incorporating the International Prognostic Index. The concordance between the two systems of image analysis was surprisingly high, supporting their applicability for immunohistochemistry studies. @entity1 with a high density of CD3 and @entity403 by both methods had a better outcome. Automated analysis should be the preferred method for immunohistochemistry studies. Following the use of two methods of semi-automated analysis we suggest that CD3 and @entity403 play a role in predicting response to chemoimmunotherapy in diffuse large @entity1892 .

Revisiting the immune microenvironment of diffuse large @entity1892 using a tissue microarray and immunohistochemistry: robust semi-automated analysis reveals CD3 and XXXX as potential predictors of response to R-CHOP.

@entity1 :: ('9606', 'Species') :: ['patients', 'Patients'] @entity1892 :: ('MESH:D016393', 'Disease') :: ['B-cell lymphoma'] @entity403 :: ('50943', 'Gene') :: ['FoxP3']
@entity403 :: (50943,Gene) :: ['FoxP3']
