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Identification and transfer of spatial transcriptomics signatures for cancer diagnosis
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, 141 83, Sweden.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-4035-5258
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences), University Medical Center Utrecht, Cancer Genomics Netherlands, Utrecht, Netherlands.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-2207-7370
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2020 (English)In: Breast Cancer Research, ISSN 1465-5411, E-ISSN 1465-542X, Vol. 22, no 1, article id 6Article in journal (Refereed) Published
Abstract [en]

Background: Distinguishing ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) regions in clinical biopsies constitutes a diagnostic challenge. Spatial transcriptomics (ST) is an in situ capturing method, which allows quantification and visualization of transcriptomes in individual tissue sections. In the past, studies have shown that breast cancer samples can be used to study their transcriptomes with spatial resolution in individual tissue sections. Previously, supervised machine learning methods were used in clinical studies to predict the clinical outcomes for cancer types. Methods: We used four publicly available ST breast cancer datasets from breast tissue sections annotated by pathologists as non-malignant, DCIS, or IDC. We trained and tested a machine learning method (support vector machine) based on the expert annotation as well as based on automatic selection of cell types by their transcriptome profiles. Results: We identified expression signatures for expert annotated regions (non-malignant, DCIS, and IDC) and build machine learning models. Classification results for 798 expression signature transcripts showed high coincidence with the expert pathologist annotation for DCIS (100%) and IDC (96%). Extending our analysis to include all 25,179 expressed transcripts resulted in an accuracy of 99% for DCIS and 98% for IDC. Further, classification based on an automatically identified expression signature covering all ST spots of tissue sections resulted in prediction accuracy of 95% for DCIS and 91% for IDC. Conclusions: This concept study suggest that the ST signatures learned from expert selected breast cancer tissue sections can be used to identify breast cancer regions in whole tissue sections including regions not trained on. Furthermore, the identified expression signatures can classify cancer regions in tissue sections not used for training with high accuracy. Expert-generated but even automatically generated cancer signatures from ST data might be able to classify breast cancer regions and provide clinical decision support for pathologists in the future.

Place, publisher, year, edition, pages
BioMed Central, 2020. Vol. 22, no 1, article id 6
Keywords [en]
Breast cancer, Cancer diagnosis, Expression signature, Machine learning, Spatial transcriptomics
National Category
Clinical Medicine
Identifiers
URN: urn:nbn:se:kth:diva-267833DOI: 10.1186/s13058-019-1242-9ISI: 000519924300001PubMedID: 31931856Scopus ID: 2-s2.0-85077786085OAI: oai:DiVA.org:kth-267833DiVA, id: diva2:1394634
Note

QC 20200427

Available from: 2020-02-19 Created: 2020-02-19 Last updated: 2020-05-11Bibliographically approved

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Fernandez Navarro, JoseSalmén, FredrikStåhl, Patrik

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Yoosuf, NiyazFernandez Navarro, JoseSalmén, FredrikStåhl, Patrik
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Gene TechnologyScience for Life Laboratory, SciLifeLab
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