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Sökning: onr:"swepub:oai:DiVA.org:kth-370404" > Computational patho...

Computational pathology annotation enhances the resolution and interpretation of breast cancer spatial transcriptomics data

Li, T (författare)
Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden,Department of Cell and Molecular Biology (CMB)
Yang, Q (författare)
Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden,Department of Cell and Molecular Biology (CMB)
Acs, B (författare)
Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden,Department of Cell and Molecular Biology (CMB)
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Sifakis, Emmanouil G. (författare)
Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
Toosi, Hosein (författare)
KTH,Beräkningsvetenskap och beräkningsteknik (CST),Science for Life Laboratory, SciLifeLab
Engblom, C (författare)
Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden; SciLifeLab, Division of Immunology and Respiratory Medicine, Department of Medicine Solna, Karolinska Institutet, Center for Molecular Medicine, Karolinska University Hospital, Stockholm, Sweden,Department of Cell and Molecular Biology (CMB)
Thrane, Kim (författare)
KTH,Genteknologi,Science for Life Laboratory, SciLifeLab
Lin, Qirong (författare)
Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
Mold, Jeff E. (författare)
Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
Sun, W (författare)
Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden,Department of Cell and Molecular Biology (CMB)
Boyaci, Ceren (författare)
Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden
Steen, Sanna (författare)
Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden
Frisén, Jonas (författare)
Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
Lagergren, Jens (författare)
KTH,Beräkningsvetenskap och beräkningsteknik (CST),Science for Life Laboratory, SciLifeLab
Lundeberg, Joakim (författare)
KTH,Science for Life Laboratory, SciLifeLab,Genteknologi
Chen, Xinsong (författare)
Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
Hartman, Johan (författare)
Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden; Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Stockholm, Sweden
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 (creator_code:org_t)
Springer Nature, 2025
2025
Engelska.
Ingår i: npj Precision Oncology. - : Springer Nature. - 2397-768X. ; 9:1
Relaterad länk:
https://doi.org/10.1...
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https://urn.kb.se/re...
https://doi.org/10.1...
https://ki.se/en/res...
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  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • Breast cancer is a highly heterogeneous disease with diverse outcomes, and intra-tumoral heterogeneity plays a significant role in both diagnosis and treatment. Despite its importance, the spatial distribution of intra-tumoral heterogeneity is not fully elucidated. Spatial transcriptomics has emerged as a promising tool to study the molecular mechanisms behind many diseases. It offers accurate measurements of RNA abundance, providing powerful tools to correlate the morphologies of cellular neighborhoods with localized gene expression patterns. However, the spot-based spatial transcriptomic tools, including the most widely used platform, Visium, do not achieve single-cell resolution readouts, which hinders data interpretability. In this study, we present a computational pathology image analysis pipeline (i.e., computational tissue annotation, CTA) that utilizes machine learning algorithms to accurately map tumor, stroma, and immune compartments within Visium-assayed tumor sections. Using a cohort of 23 breast tumor sections from four patients, we demonstrate that CTA can provide high-resolution annotations on the hematoxylin-and-eosin-stained images alongside the paired sequencing data, support the evaluation of deconvolution methods, deepen insights into intra-tumoral heterogeneity by increasing data analysis resolution, assist with spatially resolved intrinsic subtyping, and enhance the visualization of lymphocyte clones at single-cell resolution. The proposed pipeline provides valuable insights into the complex spatial architecture of breast cancer, contributing to more personalized diagnostics and treatment strategies.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Medicinska och farmaceutiska grundvetenskaper -- Cell- och molekylärbiologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Basic Medicine -- Cell and Molecular Biology (hsv//eng)

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