Generating annotated pairs of realistic tissue images along with their annotations is a challenging task in computational histopathology. Such synthetic images and their annotations can be useful in training and evaluation of algorithms in the domain of digital pathology. To address this, we present a framework to generate pairs of realistic colon cancer histology images with corresponding tissue component masks from the input glandular structure layout. The framework shows the ability to generate realistic qualitative tissue images preserving morphological characteristics including stroma, goblet cells and glandular lumen. We also validate the quality of generated annotated pair with the help of a gland segmentation algorithm.