Yoonyoung Lee | Searcharxiv

arXiv2026

Spatial proteomics guided by H&E-based AI reveals recurrence-risk niches in triple-negative breast cancer

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.

cs.AI↗

arXiv2026

Signatures of Massive Neutrinos in the Cosmic Web via Persistent Homology

We present the second paper in our program characterizing the impact of massive neutrinos on the multiscale cosmic web using global topology and persistent homology. Building on the methodology established in Paper I, based on discrete Morse theory, we analyze a subset of the Quijote simulations to compute persistent diagrams, Betti curves, and additional topological statistics for both dark matter and halo density fields, across redshifts z=0,1,2. A central result of our study is the first clear demonstration that apex points in persistent diagrams are especially sensitive to neutrino mass, with enhanced sensitivity for specific pairs of saddle points at high redshift. In addition, Betti curves from dark matter density fields broaden and flatten with increasing neutrino masses, exhibiting two characteristic density thresholds where Betti numbers remain invariant. These mass-dependent signatures are detectable at the few-percent level, even for $M_ν \sim 0.1$ eV, providing a robust, physically grounded probe of massive neutrinos in the cosmic web. While traditional two-point statistics encode only pairwise correlations and cannot fully break parameter degeneracies, persistent homology captures higher-order, multiscale information that can lift these degeneracies. Moreover, its high sensitivity to the sum of neutrino masses makes it a promising complement to conventional analyses. Our results thus establish a solid foundation for forward-modeling or emulator-based approaches using persistent homology and environment-based statistics to constrain neutrino mass - potentially enabling direct detection - and additional cosmological parameters, with immediate relevance for ongoing and upcoming galaxy surveys, including DESI, Euclid, and Rubin-LSST.

astro-ph.CO↗