In contrast to neurons in the central nervous system, neurons in the peripheral nervous system can regenerate axons after injury via activation of a pro‐regenerative transcriptional programme. Pathogenic mutations in leucine‐rich repeat kinase 2 (LRRK2) are the most common genetic cause of Parkinson's disease, and several small‐molecule LRRK2 kinase inhibitors have been developed, with some in clinical trials. However, the physiological role of endogenous, non‐pathogenic LRRK2 remains largely unknown.
Eun-Hae Jang, E. Yang, Gil Song et al.· British Journal of Pharmacol...· 0 citations
Deep learning models can predict cancer recurrence from H&E stained slides, but the localized molecular states underlying these predictions remain largely obscured. Here, we developed an outcome informed spatial pathology framework in TNBC that integrates AI generated recurrence risk heatmaps with mass spectrometry based spatial proteomics. In a cohort of 156 patients, distribution based aggregation of high scoring patches achieved an AUC of 0.77 and a C-index of 0.77 in an independent test cohort. Bulk proteomics associated high image derived risk with cell cycle and genome maintenance programs and low risk with immune activation. High and low risk patches coexisted within the same tumor compartment and displayed distinct nuclear and architectural features, revealing intratumoral heterogeneity beyond tissue compartment identity. We then used the heatmaps as coordinate level guides to physically isolate and profile 46 AI defined tumor regions from two recurrence patients. Spatial proteomic profiling revealed a concordant molecular contrast across both patients: mitotic programs were enriched in high risk regions and immune and antigen presentation programs in low risk regions. A 13 protein composite derived from these spatial contrasts showed a trend toward poorer recurrence-free survival with increasing scores in an expanded cohort, while the corresponding transcript based composite stratified recurrence free survival in the independent METABRIC TNBC cohort. Integrating the protein composite with the H&E derived risk score improved the out of bag C-index from 0.679 to 0.739 and enhanced time dependent discrimination at 3 and 5 years. Together, these findings define a new role for outcome trained AI models as spatially explicit experimental guides that connect prognostic morphology with localized molecular states and advance biologically grounded, multiscale biomarker discovery in TNBC.
Yesung Cho, Ji Hwan Park, Chanil Kim et al.· 0 citations
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