Cancer Research and Treatment

Original Article

Breast cancer

Spatial Transcriptomic Landscape of Brain Metastases from Triple-Negative Breast Cancer: Comparison of Primary Tumor and Brain Metastases Using Spatial Analysis: Jihwan Yoo, Inho Park, Hyun Jung Kim, Hun Ho Park, Sora Lee, Jee Hung Kim, Yoon Jin Cha; Cancer Res Treat. 2026;58(1):182-197. Published online April 15, 2025; DOI: https://doi.org/10.4143/crt.2025.033

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Purpose
Triple-negative breast cancer (TNBC) is a particularly aggressive subtype of breast cancer, with approximately 30% of patients eventually developing brain metastases (BM), which result in poor outcomes. An understanding of the tumor microenvironment (TME) at both primary and metastatic sites offers insights into the mechanisms underlying BM and potential therapeutic targets.
Materials and Methods
Spatial RNA sequencing (spRNA-seq) was performed on primary TNBC and paired BM tissues from three patients, one of whom had previously received immune checkpoint inhibitors before BM diagnosis. Specimen regions were categorized into tumor, proximal, and distal TME based on their spatial locations. Gene expression differences across these zones were analyzed, and immune cell infiltration was estimated using TIMER. A gene module analysis was conducted to identify key gene clusters associated with BM.
Results
Distinct gene expression profiles were noted in the proximal and distal TMEs. In BM, the proximal TME exhibited neuronal gene expression, suggesting neuron-tumor interactions compared to tumor, and upregulation of epithelial genes compared to the distal TME. Immune cell analysis revealed dynamic changes in CD8+ T cells and macrophages across the tumor and TME zones. Gene module analysis identified five key modules, including one related to glycolysis, which correlated with patient survival. Drug repurposing analysis identified potential therapeutic targets, including VEGFA, RAC1, EGLN3, and CAMK1D.
Conclusion
This study provides novel insights into the transcriptional landscapes in TNBC BM using spRNA-seq, emphasizing the role of neuron-tumor interactions and immune dynamics. These findings suggest new therapeutic strategies and underscore the importance of further research.

Citations

Citations to this article as recorded by

Rewiring Glycolysis in Cancer: From Tumor Initiation to Therapeutic Vulnerabilities
Shicai Sun, Lulu Jia, Ying Yu, Seung-Jun Jeong, Yan Zhang, Dongryeol Ryu, Guang Ta
Cells.2026; 15(9): 771. CrossRef
The RAC1 tripartite hub: coupling metabolic plasticity and immune evasion to dictate breast cancer cell fate
Hung-Yu Lin, Yi-Heng Chen, Pei-Yi Chu
Biochemical Pharmacology.2026; 253: 118269. CrossRef
Drug resistance in breast cancer brain metastasis: mechanisms and therapeutic strategies
Paromita Sarker, Shreyas S. Rao
Biochimica et Biophysica Acta (BBA) - Reviews on Cancer.2026; 1881(5): 189684. CrossRef

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Review Article

Applicability of Spatial Technology in Cancer Research: Sangjeong Ahn, Hye Seung Lee; Cancer Res Treat. 2024;56(2):343-356. Published online January 30, 2024; DOI: https://doi.org/10.4143/crt.2023.1302

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This review explores spatial mapping technologies in cancer research, highlighting their crucial role in understanding the complexities of the tumor microenvironment (TME). The TME, which is an intricate ecosystem of diverse cell types, has a significant impact on tumor dynamics and treatment outcomes. This review closely examines cutting-edge spatial mapping technologies, categorizing them into capture-, imaging-, and antibody-based approaches. Each technology was scrutinized for its advantages and disadvantages, factoring in aspects such as spatial profiling area, multiplexing capabilities, and resolution. Additionally, we draw attention to the nuanced choices researchers face, with capture-based methods lending themselves to hypothesis generation, and imaging/antibody-based methods that fit neatly into hypothesis testing. Looking ahead, we anticipate a scenario in which multi-omics data are seamlessly integrated, artificial intelligence enhances data analysis, and spatiotemporal profiling opens up new dimensions.

Citations

Citations to this article as recorded by

Comprehensive Overview of Gastric Cancer Immunohistochemistry: Key Biomarkers, Advanced Detection Methods, and Perspectives
Bogdan Oprea
Medicina.2026; 62(4): 683. CrossRef
The good, the bad, and the ugly: opportunities, challenges, and pitfalls in spatial proteomics modeling
Shahil Yasar Haque, Swakkhar Shatabda, Salekul Islam, Shoaib Ahmed Dipu, Riasat Azim
Briefings in Bioinformatics.2026;[Epub] CrossRef
Spatial proteogenomics in oncology: linking molecular positivity to tissue context
Haiyue You, Yida Wang, Xinfeng Yang, Feng Zhang, Yan Zhang
Academia Oncology.2026;[Epub] CrossRef
Unsupervised 20-feature immunohistochemical immune profiling identifies exploratory cervical cancer phenotypes with unfavorable survival signals
Angel Danchev Yordanov, Eva Tsoneva, Polina Damqnova Dimitrova, Stoyan Georgiev Kostov, Ihsan Hasan, Velizar Stefanov Shivarov
Frontiers in Oncology.2026;[Epub] CrossRef
Navigating the landscape of plant proteomics
Tian Sang, Zhen Zhang, Guting Liu, Pengcheng Wang
Journal of Integrative Plant Biology.2025; 67(3): 740. CrossRef
Benchmarking computational methods for detecting spatial domains and domain-specific spatially variable genes from spatial transcriptomics data
Liping Kang, Qinglong Zhang, Fan Qian, Junyao Liang, Xiaohui Wu
Nucleic Acids Research.2025;[Epub] CrossRef
The role of tumor microenvironment and immune cell crosstalk in triple-negative breast cancer (TNBC): Emerging therapeutic opportunities
Hussein Sabit, Amro Adel, Mariam M. Abdelfattah, Rehab M. Ramadan, Mahmoud Nazih, Shaimaa Abdel-Ghany, Ahmed El-hashash, Borros Arneth
Cancer Letters.2025; 628: 217865. CrossRef
Molecular pathobiology of breast fibroepithelial tumours
R.M.H. Lim, S. Haghani, H.Y. Tay, B.Y. Lim, B. Kannan, T.K. Ko, N.D. Md Nasir, B.T. Teh, P.H. Tan, J.Y. Chan
ESMO Rare Cancers.2025; 3: 100028. CrossRef
Distinctive Phenotypic and Microenvironmental Characteristics of Neuroendocrine Carcinoma and Adenocarcinoma Components in Gastric Mixed Adenoneuroendocrine Carcinoma
Yoonjin Kwak, Soo Kyung Nam, Yujun Park, Yun-Suhk Suh, Sang-Hoon Ahn, Seong-Ho Kong, Do Joong Park, Hyuk-Joon Lee, Hyung-Ho Kim, Han-Kwang Yang, Hye Seung Lee
Modern Pathology.2024; 37(10): 100568. CrossRef
Effector Function Characteristics of Exhausted CD8+ T-Cell in Microsatellite Stable and Unstable Gastric Cancer
Dong-Seok Han, Yoonjin Kwak, Seungho Lee, Soo Kyung Nam, Seong-Ho Kong, Do Joong Park, Hyuk-Joon Lee, Nak-Jung Kwon, Hye Seung Lee, Han-Kwang Yang
Cancer Research and Treatment.2024; 56(4): 1146. CrossRef
Prognostic significance of CD8 and TCF1 double positive T cell subset in microsatellite unstable gastric cancer
Juhyeong Park, Soo Kyung Nam, Yoonjin Kwak, Hyeon Jeong Oh, Seong-Ho Kong, Do Joong Park, Hyuk-Joon Lee, Han-Kwang Yang, Hye Seung Lee
Scientific Reports.2024;[Epub] CrossRef

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319 Download
11 Web of Science
11 Crossref

Original Article

Sarcoma

Case Series of Soft Tissue Sarcoma Patients with Brain Metastasis with Implications from Genomic and Transcriptomic Analysis: Changhee Park, Rokhyun Kim, Jaeyong Choi, Miso Kim, Tae Min Kim, Ilkyu Han, Jong-Il Kim, Han-Soo Kim; Cancer Res Treat. 2024;56(2):665-674. Published online September 27, 2023; DOI: https://doi.org/10.4143/crt.2023.864

Abstract PDF Supplementary Material PubReader ePub: Purpose
Brain metastasis rarely occurs in soft tissue sarcoma (STS). Here, we present five cases of STS with brain metastases with genetic profiles.
Materials and Methods
We included five patients from Seoul National University Hospital who were diagnosed with STS with metastasis to the brain. Tissue from the brain metastasis along with that from the primary site or other metastases were used for DNA and RNA sequencing to identify genetic profiles. Gene expression profiles were compared with sarcoma samples from The Cancer Genome Atlas.
Results
The overall survival after diagnosis of brain metastasis ranged from 2.2 to 34.3 months. Comparison of mutational profiles between brain metastases and matched primary or other metastatic samples showed similar profiles. In two patients, copy number variation profiles between brain metastasis and other tumors showed several differences including MYCL, JUN, MYC, and DDR2 amplification. Gene ontology analysis showed that the group of genes significantly highly expressed in the brain metastasis samples was enriched in the G-protein coupled receptor activity, structural constituent of chromatin, protein heterodimerization activity, and binding of DNA, RNA, and protein. Gene set enrichment analysis showed enrichment in the pathway of neuroactive ligand-receptor interaction and systemic lupus erythematosus.
Conclusion
The five patients had variable ranges of clinical courses and outcomes. Genomic and transcriptomic analysis of STS with brain metastasis implicates possible involvement of complex expression modification and epigenetic changes rather than the addition of single driver gene alteration.

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