Recent developments in computational pathology and spatial transcriptomics have seen significant advancements, particularly in the integration of multimodal data and the enhancement of molecular awareness in pathology image representation learning. Innovations such as the introduction of molecular-enhanced pathology image representation frameworks and the development of robust cell segmentation models with minimal annotation requirements are pushing the boundaries of what is possible in these fields. Additionally, the modeling of spatial transcriptomics data in a continuous and compact manner, as well as the use of hierarchical graph-based approaches for gene expression prediction, are notable contributions that improve the accuracy and efficiency of these analyses. Notably, the emergence of multimodal large language models for whole slide image analysis and the incorporation of 3D spatial imputation techniques are opening new avenues for more comprehensive and precise diagnostic tools. These advancements collectively underscore the growing sophistication and integration of computational methods in pathology and transcriptomics, promising to enhance both research and clinical applications.