Results 201 to 210 of about 320,395 (368)

Systematic profiling of cancer‐fibroblast interactions reveals drug combinations in ovarian cancer

Molecular Oncology, EarlyView.
Fibroblasts, cells in the tumor environment, support ovarian cancer cell growth and alter morphology and drug response. We used fibroblast and cancer cell co‐culture models to test 528 drugs and discovered new drugs for combination treatment. We showed that adding Vorinostat or Birinapant to standard chemotherapy may improve drug response, suggesting ...
Greta Gudoityte, Osheen Sharma, Laura Leuenberger, Emelie Wallin, Josefin Fernebro, Päivi Östling, Rebecka Bergström, Johan Lindberg, Ulrika Joneborg, Olli Kallioniemi, Brinton Seashore‐Ludlow   +10 more
wiley   +1 more source

Comprehensive omics‐based classification system in adult patients with B‐cell acute lymphoblastic leukemia

Molecular Oncology, EarlyView.
The COMBAT classification system, developed through multi‐omics integration, stratifies adult patients with B‐cell acute lymphoblastic leukemia(B‐ALL) into three molecular subtypes with distinct surface antigen patterns, immune landscape, methylation patterns, biological pathways and prognosis.
Yang Song, Ting Liu, Qishan Hao, Qiuyun Fang, Xiaoyuan Gong, Yan Li, Zheng Tian, Hui Wei, Min Wang, Jianxiang Wang, Tao Cheng, Yingchang Mi   +11 more
wiley   +1 more source

Understanding and measuring mechanical signals in the tumor stroma

FEBS Open Bio, EarlyView.
This review discusses cancer‐associated fibroblast subtypes and their functions, particularly in relation to extracellular matrix production, as well as the development of 3D models to study tumor stroma mechanics in vitro. Several quantitative techniques to measure tissue mechanical properties are also described, to emphasize the diagnostic and ...
Fàtima de la Jara Ortiz, Chiara Cimmino, Maurizio Ventre, Alessandra Cambi   +3 more
wiley   +1 more source

SpaRED benchmark: Enhancing Gene Expression Prediction from Histology Images with Spatial Transcriptomics Completion [PDF]

arXiv
Spatial Transcriptomics is a novel technology that aligns histology images with spatially resolved gene expression profiles. Although groundbreaking, it struggles with gene capture yielding high corruption in acquired data. Given potential applications, recent efforts have focused on predicting transcriptomic profiles solely from histology images ...
arxiv  

Beyond digital twins: the role of foundation models in enhancing the interpretability of multiomics modalities in precision medicine

FEBS Open Bio, EarlyView.
This review highlights how foundation models enhance predictive healthcare by integrating advanced digital twin modeling with multiomics and biomedical data. This approach supports disease management, risk assessment, and personalized medicine, with the goal of optimizing health outcomes through adaptive, interpretable digital simulations, accessible ...
Sakhaa Alsaedi, Xin Gao, Takashi Gojobori   +2 more
wiley   +1 more source

Spatial Transcriptomics Iterative Hierarchical Clustering (stIHC): A Novel Method for Identifying Spatial Gene Co-Expression Modules [PDF]

arXiv
Recent advancements in spatial transcriptomics technologies allow researchers to simultaneously measure RNA expression levels for hundreds to thousands of genes while preserving spatial information within tissues, providing critical insights into spatial gene expression patterns, tissue organization, and gene functionality.
arxiv  

Bioengineering facets of the tumor microenvironment in 3D tumor models: insights into cellular, biophysical and biochemical interactions

FEBS Open Bio, EarlyView.
The tumor microenvironment is a dynamic, multifaceted complex system of interdependent cellular, biochemical, and biophysical components. Three‐dimensional in vitro models of the tumor microenvironment enable a better understanding of these interactions and their impact on cancer progression and therapeutic resistance.
Salma T. Rafik, Deniz Bakkalci, Alexander J. MacRobert, Umber Cheema   +3 more
wiley   +1 more source

MOSAIK: Multi-Origin Spatial Transcriptomics Analysis and Integration Kit [PDF]

arXiv
Spatial transcriptomics (ST) has revolutionised transcriptomics analysis by preserving tissue architecture, allowing researchers to study gene expression in its native spatial context. However, despite its potential, ST still faces significant technical challenges. Two major issues include: (1) the integration of raw data into coherent and reproducible
arxiv  

Comparative single‐cell transcriptomic profiling of patient‐derived renal carcinoma cells in cellular and animal models of kidney cancer

FEBS Open Bio, EarlyView.
We generated and characterized clear cell renal cell carcinoma models using the patient‐derived RCC243 cell line—including cell culture, orthotopic, and metastatic tumors—via single‐cell RNA‐sequencing for comparisons between models and patient tumor datasets.
Richard Huang, Lynn Kee, Alexander Gont, Jalna Meens, Fraser G. Ferens, Meredith S. Irwin, Laurie Ailles, Scott A. Yuzwa, Claire M. Robinson, Michael Ohh   +9 more
wiley   +1 more source

A Cross Modal Knowledge Distillation & Data Augmentation Recipe for Improving Transcriptomics Representations through Morphological Features [PDF]

arXiv
Understanding cellular responses to stimuli is crucial for biological discovery and drug development. Transcriptomics provides interpretable, gene-level insights, while microscopy imaging offers rich predictive features but is harder to interpret. Weakly paired datasets, where samples share biological states, enable multimodal learning but are scarce ...
arxiv