The paper introduces Conserved Immune Topology (CIT), a lightweight spatial representation that enhances cross‑cancer MSI‑H prediction by augmenting pathology foundation‑model embeddings with immune‑related descriptors. CIT identifies immune‑associated tiles via unsupervised clustering and encodes features such as tertiary lymphoid structures, peritumoral immune reactions, tumor‑infiltrating lymphocyte density, and immune‑tumor mixing, all without requiring annotations or target‑domain data. In cross‑site and cross‑cancer experiments on CPTAC‑COAD and TCGA‑STAD cohorts, CIT improved zero‑shot TransMIL AUC from 0.6627 to 0.7161, demonstrating that spatial immune topology can provide an organ‑invariant representation for MSI‑H prediction.
arXiv:2606. 29949v1 Announce Type: cross Abstract: H&E-stained whole-slide images offer cohort-scale availability and rich spatial context but lack molecular specificity, whereas bulk RNA-seq provides transcriptome-wide resolution at high cost with limited archival availability.
arXiv:2608. 03145v1 Announce Type: new Abstract: Deep learning models can predict cancer recurrence from H&E stained slides, but the localized molecular states underlying these predictions remain largely obscured.
arXiv:2606. 28676v1 Announce Type: cross Abstract: Predicting the risk of distant metastasis from primary tumor tissue histology is a critical yet challenging task in computational pathology.
arXiv:2607. 18218v1 Announce Type: cross Abstract: Foundation models have emerged as a driving force in computational pathology, with the potential to transform cancer diagnosis, prognosis, and treatment selection by learning transferable representations from large-scale histopathology data.
The study evaluated nine transcriptomic models—five bulk RNA‑seq and four single‑cell RNA‑seq—designed to predict response to immune checkpoint inhibitors. Across independent datasets, bulk models performed near chance while single‑cell models offered only modest gains, and pathway analyses revealed inconsistent biomarker signals. The results highlight the limited cross‑cohort robustness and biological consistency of current transcriptomic ICI predictors.
arXiv:2606.28676v2 Announce Type: replace-cross Abstract: Predicting distant metastasis from the digital H & E slides of the primary tumor is a critical yet challenging task in computational patholog...
arXiv:2602. 17330v5 Announce Type: replace-cross Abstract: Comparative analysis of adaptive immune repertoires at population scale is hampered by two practical bottlenecks: the near-quadratic cost of pairwise affinity evaluations and dataset imbalances that obscure clinically important minority clonotypes.
arXiv:2512. 22262v2 Announce Type: replace-cross Abstract: Routine histology contains rich prognostic information in stage II/III colorectal cancer, much of which is embedded in complex spatial tissue organisation.
arXiv:2606. 12346v1 Announce Type: cross Abstract: Hematoxylin and eosin (H&E) staining is the cornerstone of histopathology, yet scalable, quantitative analysis of H&E whole-slide images (WSIs) remains a central challenge in computational pathology.
arXiv:2608. 08148v1 Announce Type: cross Abstract: Attention mechanisms have been widely utilized in modern deep learning, and many existing multi-omics models inherit their conventional use to allow unrestricted bidirectional interactions.
arXiv:2606. 04154v1 Announce Type: cross Abstract: Antibodies neutralize foreign antigens by binding to specific surface regions called epitopes.
arXiv:2608.22785v1 Announce Type: new Abstract: Spatial multi-omics technologies jointly profile gene expression, surface proteins, and histology at each tissue spot, yet most spatial domain discover...