Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types

Sep 21, 2026

The complexity of the tumor microenvironment challenges effective cancer therapy. An integrative analysis combines 4.9 million single-cell transcriptomes from 1070 tumor and 493 normal samples with pan-cancer spatial transcriptomics, TCGA, and checkpoint-inhibitor-treated bulk tumors to map tumor-normal ecosystems. The study defines numerous cell states and identifies hallmark gene signatures across cell types and organs, including spatially co-localized inflammatory fibroblasts and interferon-enriched community states with tertiary lymphoid structure components. Immunotherapy outcomes and spatial deconvolution validate TLS-enriched cell types and reveal inter- and intra-tumoral heterogeneity.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages17

Reading Time43 min

AccessFree to download

Overview

Integrative multi-omics strategy

Defining cell states and gene signatures

Spatial localization and cellular interactions

Interferon-enriched communities and TLS components

Immunotherapy validation and spatial deconvolution

Implications for tumor heterogeneity

| | | | | | --- | --- | --- | --- | | Article [https://doi.org/10.1038/s41467-024-48310-4](https://doi.org/10.1038/s41467-024-48310-4) | | | | | Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types | | | | | Received: 26 May 2023
Accepted: 26 April 2024
Published online: 14 May 2024 Check for updates | Junho Kang1,9, Jun Hyeong Lee2,9, Hongui Cha3, Jinhyeon An 2,
Joonha Kwon 2,4, Seongwoo Lee1, Seongryong Kim1, Mert Yakup Baykan 1, So Yeon Kim2, Dohyeon An2, Ah-Young Kwon 5, Hee Jung An5,
Se-Hoon Lee 3,6 , Jung Kyoon Choi 2,7 & Jong-Eun Park 1,8
The complexity of the tumor microenvironment poses signiﬁcant challenges in cancer therapy. Here, to comprehensively investigate the tumor-normal ecosystems, we perform an integrative analysis of 4.9 million single-cell transcriptomes from 1070 tumor and 493 normal samples in combination with pan-cancer 137 spatial transcriptomics, 8887 TCGA, and 1261 checkpoint inhibitor-treated bulk tumors. We deﬁne a myriad of cell states constituting the tumor-normal ecosystems and also identify hallmark gene signatures across different cell types and organs. Our atlas characterizes distinctions between inﬂammatory ﬁbroblasts marked by AKR1C1 or WNT5A in terms of cellular interactions and spatial co-localization patterns. Co-occurrence analysis reveals interferon-enriched community states including tertiary lymphoid structure (TLS) components, which exhibit differential rewiring between tumor, adjacent normal, and healthy normal tissues. The favorable response of interferon-enriched community states to immunotherapy is validated using immunotherapy-treated cancers (n = 1261) including our lung cancer cohort (n = 497). Deconvolution of spatial transcriptomes discriminates TLS-enriched from non-enriched cell types among immunotherapy-favorable components. Our systematic dissection of tumor-normal ecosystems provides a deeper understanding of inter-and intra-tumoral heterogeneity. | | | | Tumors are highly heterogeneous entities composed of malignant cells and diverse tissue-inﬁltrating stromal and immune cells that form the tumor microenvironment (TME)1. The advent of single-cell RNA sequencing (scRNA-seq) technologies has provided unbiased and | | systematic molecular proﬁling for high-resolution characterization of extensive heterogeneity embedded in the TME2–4.
Molecular and cellular heterogeneity within the TME collectively inﬂuences various aspects of tumors, including progression, metastasis, | | 1Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea. 2Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea. 3Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. 4Division of Cancer Data Science, National Cancer Center, Bioinformatics Branch, Goyang, Republic of Korea. 5Department of Pathology, CHA Bundang Medical Center, CHA University, Seongnamsi, Republic of Korea. 6Department of Health Sciences and Technology, Samsung Advanced Institute of Health Science and Technology, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. 7PentaMedix Co., Ltd., Seongnam-si, Gyeonggi-do, Republic of Korea. 8Biomedical Research Center, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea. 9These authors contributed equally: Junho Kang, Jun Hyeong Lee. e-mail: [shlee119@skku.edu](shlee119@skku.edu); [jungkyoon@kaist.ac.kr](jungkyoon@kaist.ac.kr); [jp24@kaist.ac.kr](jp24@kaist.ac.kr) and treatment response1. As evidence of intra-tumoral and inter-tumoral heterogeneity mounts, various attempts are being made to compile consensus gene signatures at the pan-cancer level. For example, a pancancer atlas of T, myeloid, and malignant cells has recently bee

FAQs

What data sources were integrated to study tumor-normal ecosystems?

The analysis integrates 4.9 million single-cell transcriptomes from tumor and normal samples with pan-cancer spatial transcriptomics, TCGA cohorts, and bulk tumors treated with checkpoint inhibitors.

What cell populations and gene programs are highlighted in the study?

The work identifies diverse tumor-normal cell states and hallmark gene signatures, including inflammatory fibroblasts marked by AKR1C1 or WNT5A and interferon-enriched community states related to TLS components.

How does the study connect TLS-related states to immunotherapy response?

Interferon-enriched community states show favorable response to immunotherapy in checkpoint inhibitor-treated cancers, and spatial deconvolution distinguishes TLS-enriched from non-enriched cell types among immunotherapy-favorable components.

Sep 21, 2026

Uploaded by Emma Wilson