The affections from genomics to morphology can prompt the genomic profile interpretation from inexpensive pathological image data rather than highly cost genomic sequencing data. Due to the extremely large size of Whole Slide Image (WSI), directly processing the complete Lung Adenocarcinoma (LUAD) WSI with traditional deep learning methods, will lead to memory overflow. In comparison to complete WSI, the smaller size patches can be easier processed by the existing deep learning based methods. Nevertheless, the split patches severely break the potential relationships between genomic abnormalities and morphological features, and the traditional deep learning methods may be difficult to capture the information of the broken relationship. Fortunately, a recent study has shown that the graph-structure representation can feasibly demonstrate the relationships among both local and remote regions. In consideration of the obstacles of both the break of remote area relationships and the lack of tissue-level contour for genomics-to-morphology associations, we propose Spatial and Morphological Features Co-action Graph Neural Network model (SMCGNN) to achieve the pathological tissue-level contour genomic profile interpretation of LUAD. Our SMCGNN achieves better performance on the genomic profile interpretation task than those of previous researches, yielding a relative performance increment of 9.3%. To the best of our knowledge, our SMCGNN is the first model to interpret the biological tissue-level contour of the genomic abnormality-related morphological regions. In summary, our method can provide pathologists with more fine-grained hints to molecular profile. The interpreted tissue-level regions can be accessed via the link: https://github.com/xianyvxxx/tissue-level-contour-regions-associated-with-genomic-profile.