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.

Purpose Desmoid tumor, also known as aggressive fibromatosis, is well-characterized by abnormal Wnt/β-catenin signaling. Various therapeutic options, including imatinib, are available to treat desmoid tumor. However, the molecular mechanism of why imatinib works remains unclear. Here, we describe potential roles of NOTCH2 and HES1 in clinical response to imatinib at genome and transcriptome levels.Materials and Methods We identified somatic mutations in coding and noncoding regions via whole-genome sequencing. To validate the genetic interaction with expression level in desmoid-tumor condition, we utilized large-scale whole-genome sequencing and transcriptome datasets from the Pan-Cancer Analysis of Whole Genomes project. RNA-sequencing was performed using prospective and retrospective cohort samples to evaluate the expressional relevance with clinical response.Results Among 20 patients, four (20%) had a partial response and 14 (66.7%) had stable disease, 11 of which continued for ≥ 1 year. With gene-wise functional analyses, we detected a significant correlation between recurrent NOTCH2 noncoding mutations and clinical response to imatinib. Based on Pan-Cancer Analysis of Whole Genomes data analyses, NOTCH2 mutations affect expression levels particularly in the presence of CTNNB1 missense mutations. By analyzing RNA-sequencing with additional desmoid tumor samples, we found that NOTCH2 expression was significantly correlated with HES1 expression. Interestingly, NOTCH2 had no statistical power to discriminate between responders and non-responders. Instead, HES1 was differentially expressed with statistical significance between responders and non-responders.Conclusion Imatinib was effective and well tolerated for advanced desmoid tumor treatment. Our results show that HES1, regulated by NOTCH2, as an indicator of sensitivity to imatinib, and an important therapeutic consideration for desmoid tumor.

Understanding the interactions between specific cell types within tissue environments is essential for elucidating key biological processes, such as immune responses, cancer progression, inflammation, and development, in both physiological and pathological studies. The predominant methods for analyzing cell–cell interactions (CCI) rely primarily on statistical inference using mapping or network-based techniques. However, these approaches often struggle to prioritize meaningful interactions owing to the high sparsity and heterogeneity inherent in single-cell RNA sequencing (scRNA-seq) data, where small but biologically important differences can be easily overlooked. To overcome these limitations, we developed PriorCCI, a deep-learning framework that leverages a convolutional neural network (CNN) alongside Grad-CAM++, an explainable artificial intelligence algorithm. This study aims to provide a scalable, interpretable, and biologically meaningful framework for systematically identifying and prioritizing key ligand–receptor interactions between defined cell-type pairs from single-cell RNA-seq data, particularly in complex environments such as tumors. PriorCCI effectively prioritizes interactions between cancer and other cell types within the tumor microenvironment and accurately identifies biologically significant interactions related to angiogenesis. By providing a visual interpretation of gene-pair contributions, our approach enables robust inference of gene–gene interactions across distinct cell types from scRNA-seq data.

Undifferentiated sarcomas (USs), including undifferentiated pleomorphic sarcoma (UPS), are aggressive mesenchymal malignancies with limited molecular biomarkers for prognostic assessment and therapeutic stratification. Expression-based markers may provide insight into tumor aggressiveness and clinical outcomes. Here, we performed integrative transcriptomic and spatial analyses to identify differentially expressed genes (DEGs). By comparing normal tissues with sarcoma tumors and sarcoma tumors with cell lines. Intersection and clustering analyses were conducted to define shared expression programs, which revealed a subset of DEGs enriched in epithelial-mesenchymal transition (EMT)-related pathways. CosMx spatial transcriptomics was applied to xenograft tumors derived from two UPS cell lines to resolve tumor-intrinsic signatures. The National Cancer Center Cohort samples were used for validation, and immunohistochemistry confirmed the expression in thirty US tissues. Spatial transcriptomic profiling identified mesenchymal tissue–driven gene expression programs in UPS xenografts. Across bulk RNA-seq and spatial data, epithelial membrane protein 3 (EMP3) consistently emerged as highly expressed in US tissues and cell lines. EMP3 is a robust mesenchymal-associated biomarker linked to EMT, tumor progression, and clinical outcomes in USs, supporting its potential utility as a prognostic indicator and therapeutic target.

Germline PALB2 variants increase the risks of various cancers. However, these have not been comprehensively investigated in Korean patients with cancer. Our study aimed to evaluate the prevalence and clinical characteristics of PALB2 germline variants in Korean patients with cancer and compare these findings with existing data. We analyzed the clinicopathological and germline next-generation sequencing data of 3987 patients with cancer from the National Cancer Center in Korea. Additionally, we carried out functional analysis of the PALB2 splicing variant, c.3350+5G>A. A total of 104 patients presented PALB2 germline variants with eight pathogenic variants (PVs), 14 likely pathogenic variants (LPVs), and 82 variants of uncertain significance (VUS). PALB2 PV/LPVs were detected at an overall frequency of 0.6% (22/3987) across all patients. Among patients with PV/LPVs, 95.5% were women, and 19 and 3 carriers were diagnosed with breast and ovarian cancer, respectively. Further, we reclassified c.3350+5G>A as a PV rather than VUS, according to the American College of Medical Genetics and Genomics guidelines. Patients with PALB2 PV/LPVs had a younger age at first cancer diagnosis (44.6 ± 10.1 years versus 50.2 ± 12.0 years, P = 0.019) and were more likely to have multiple primary organ cancer diagnoses (22.7% versus 8.3%, P = 0.032) compared with those without these variants. Age at first cancer diagnosis and the presence of multiple primary organ cancers are key risk factors for suspected germline PALB2 PV. Hence, strategies are required to improve adherence to the National Comprehensive Cancer Network guidelines for cancer screening and family genetic testing among Korean patients with cancer.