RESUMEN
Traditional feature dimension reduction methods have been widely used to uncover biological patterns or structures within individual spatial transcriptomics data. However, these methods are designed to yield feature representations that emphasize patterns or structures with dominant high variance, such as the normal tissue spatial pattern in a precancer setting. Consequently, they may inadvertently overlook patterns of interest that are potentially masked by these high-variance structures. Herein we present our graph contrastive feature representation method called CoCo-ST (Comparing and Contrasting Spatial Transcriptomics) to overcome this limitation. By incorporating a background data set representing normal tissue, this approach enhances the identification of interesting patterns in a target data set representing precancerous tissue. Simultaneously, it mitigates the influence of dominant common patterns shared by the background and target data sets. This enables discerning biologically relevant features crucial for capturing tissue-specific patterns, a capability we showcased through the analysis of serial mouse precancerous lung tissue samples.
RESUMEN
Hyperactivation of the PI 3-kinase/AKT pathway is a driving force of many cancers. Here we identify the AKT-inactivating phosphatase PHLPP1 as a prostate tumor suppressor. We show that Phlpp1-loss causes neoplasia and, on partial Pten-loss, carcinoma in mouse prostate. This genetic setting initially triggers a growth suppressive response via p53 and the Phlpp2 ortholog, and reveals spontaneous Trp53 inactivation as a condition for full-blown disease. Surprisingly, the codeletion of PTEN and PHLPP1 in patient samples is highly restricted to metastatic disease and tightly correlated to deletion of TP53 and PHLPP2. These data establish a conceptual framework for progression of PTEN mutant prostate cancer to life-threatening disease.