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Spatially Resolved Transcriptomics Enables Dissection of Genetic Heterogeneity in Stage III Cutaneous Malignant Melanoma
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
Karolinska Inst, Dept Oncol Pathol, SE-17176 Stockholm, Sweden.;Karolinska Univ Hosp, Dept Oncol, SE-17176 Stockholm, Sweden..
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
Karolinska Inst, Dept Oncol Pathol, SE-17176 Stockholm, Sweden.;Karolinska Univ Hosp, Dept Oncol, SE-17176 Stockholm, Sweden..
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2018 (English)In: Cancer Research, ISSN 0008-5472, E-ISSN 1538-7445, Vol. 78, no 20, p. 5970-5979Article in journal (Refereed) Published
Abstract [en]

Cutaneous malignant melanoma (melanoma) is characterized by a high mutational load, extensive intertumoral and intratumoral genetic heterogeneity, and complex tumor microenvironment (TME) interactions. Further insights into the mechanisms underlying melanoma are crucial for understanding tumor progression and responses to treatment. Here we adapted the technology of spatial transcriptomics (ST) to melanoma lymph node biopsies and successfully sequenced the transcriptomes of over 2,200 tissue domains. Deconvolution combined with traditional approaches for dimensional reduction of transcriptome-wide data enabled us to both visualize the transcriptional landscape within the tissue and identify gene expression profiles linked to specific histologic entities. Our unsupervised analysis revealed a complex spatial intratumoral composition of melanoma metastases that was not evident through morphologic annotation. Each biopsy showed distinct gene expression profiles and included examples of the coexistence of multiple melanoma signatures within a single tumor region as well as shared profiles for lymphoid tissue characterized according to their spatial location and gene expression profiles. The lymphoid area in close proximity to the tumor region displayed a specific expression pattern, which may reflect the TME, a key component to fully understanding tumor progression. In conclusion, using the ST technology to generate gene expression profiles reveals a detailed landscape of melanoma metastases. This should inspire researchers to integrate spatial information into analyses aiming to identify the factors underlying tumor progression and therapy outcome. Significance: Applying ST technology to gene expression profiling in melanoma lymph node metastases reveals a complex transcriptional landscape in a spatial context, which is essential for understanding the multiple components of tumor progression and therapy outcome. (C) 2018 AACR.

Place, publisher, year, edition, pages
AMER ASSOC CANCER RESEARCH , 2018. Vol. 78, no 20, p. 5970-5979
National Category
Medical Biotechnology (with a focus on Cell Biology (including Stem Cell Biology), Molecular Biology, Microbiology, Biochemistry or Biopharmacy)
Identifiers
URN: urn:nbn:se:kth:diva-238120DOI: 10.1158/0008-5472.CAN-18-0747ISI: 000447552500022PubMedID: 30154148Scopus ID: 2-s2.0-85054897805OAI: oai:DiVA.org:kth-238120DiVA, id: diva2:1264570
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscience
Note

QC 20181120

Available from: 2018-11-20 Created: 2018-11-20 Last updated: 2018-11-20Bibliographically approved

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Thrane, KimMaaskola, JonasLundeberg, Joakim

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