Oncology/Hematology

Comparative Analysis of Pathology Foundation Models for Automated Detection of Tertiary Lymphoid Structures in H&E-Stained Digital Pathology Images

Tertiary lymphoid structures (TLS) have been observed in solid tumors and have been associated with better outcomes in patients treated with immunotherapy, but their dynamic nature makes identifying TLS in clinical samples challenging. Recently, pathology foundation models have emerged as powerful tools in computational pathology. In this study, we aimed to develop a computational tool capable of ...

Metappuccino: Large Language Model-driven Reconstruction of Sequence Read Archive Metadata for Cancer Research

High-throughput RNA-sequencing has significantly advanced transcriptomic profiling in on-cology. Millions of RNA-seq datasets have accumulated in public databases such as the Sequence Read Archive-SRA. However, fragmented, ambiguous or missing metadata can severely limit accurate cohort selection, introduce bias and delay discoveries. To address these issues, we introduce Metappuccino : a metadata...

Predictive power of different Akkermansia phylogroups in clinical response to PD-1 blockade against non-small cell lung cancer

Immune checkpoint blockade has emerged as a promising form of cancer therapy. However, only some patients respond to checkpoint inhibitors, while a si...

SHEST: Single-cell-level artificial intelligence from haematoxylin and eosin morphology for cell type prediction and spatial transcriptomics reconstruction

A comprehensive understanding of cancer progression requires integrating tissue morphol-ogy with spatial molecular profiles. We present SHEST, a multi...

A unified language model bridging de novo and fragment-based 3D molecule design delivers potent CBL-B inhibitors for cancer treatment

The rational design of small molecules is central to drug discovery, yet current artificial intelligence (AI) methodologies for generating three-dimen...

Language may be all omics needs: Harmonizing multimodal data for omics understanding with CellHermes

Decoding cellular systems requires integrating diverse omics data, yet most models are trained from scratch on a single modality, restricting generali...

Inferring virtual cell environments using multi-agent reinforcement learning

Single cells interact continuously to form a cell environment that drives key biological processes. Cells and cell environments are highly dynamic acr...

Integrative transcriptomic analysis identifies miR-642a-5p as a regulator of POFUT1 expression in colon cancer

Colorectal cancer (CRC) is one of the most common and deadly cancers worldwide, underscoring the urgent need for novel biomarkers and therapeutic targ...

IMMUNIA: A Multi-LLM Reasoning Agent for Immunoregulatory Surfaceome Discovery

Biomarker discovery for immunotherapy often requires reasoning across complex immune contexts. We present IMMUNIA, a multi-large-language-model (multi...

Star-Motifs: Revealing Single-Cell Spatiotypes from Routine Histology Using Star-Convex Neighborhoods

The tumor microenvironment (TME) is a dynamic interplay among cancer, immune, and stromal cells that profoundly influences tumor growth, progression, ...

An integrated platform for high-throughput phenospace learning of 3D multilineage organoid systems

Complex multilineage organoid systems lack quantitative phenotyping methods preserving spatial architecture at high throughput. Current approaches com...

ATHENA: A deep learning–based AI for functional prediction of genomic mutations and synergistic vulnerabilities in prostate cancer

Identifying functional mutations that drive therapy resistance remains a major challenge in prostate cancer. Large-scale sequencing often produces ext...

A memory-driven reinforcement learning model of phenotypic adaptation for anticipating therapeutic resistance in prostate cancer

While contemporary cancer treatment strategies have significantly prolonged the lives of patients, therapeutic resistance remains a predominant cause ...

Targeting peptide–MHC complexes with designed T cell receptors and antibodies

Class I major histocompatibility complexes (MHCs), expressed on the surface of all nucleated cells, present peptides derived from intracellular protei...

cfOncoXpress: Tumor gene expression prediction from cell-free DNA whole-genome sequences

Cell-free DNA (cfDNA) fragments in the plasma capture cellular nucleosomal profiles since nucleosome-protected regions escape enzymatic degradation wh...

The Spatial Atlas of Human Anatomy (SAHA): A Multimodal Subcellular-Resolution Reference Across Human Organs

The Spatial Atlas of Human Anatomy (SAHA) represents the first multimodal, subcellular- resolution reference of healthy adult human tissues across mul...

Self-supervised AI reveals a lethal discohesive phenotype in lung adenocarcinoma

Applications of artificial intelligence (AI) to histopathology are now common, but most require supervision which inherently limits their scope. By us...

A generalizable cross-continent prediction of esophageal squamous cell carcinoma using the oral microbiome

Esophageal squamous cell carcinoma (ESCC) is a disease with limited tools for early screening and a poor prognosis. Symptoms typically appear late, an...

Empirical Evaluation of Single-Cell Foundation Models for Predicting Cancer Outcomes

Foundation models pretrained on large-scale single-cell RNA sequencing data present a promising opportunity to advance translational cancer research. ...

RegFormer: A Single-Cell Foundation Model Powered by Gene Regulatory Hierarchies

Single-cell RNA sequencing (scRNA-seq) enables high-resolution profiling of cellular diversity, but current computational models often fail to incorpo...