Multi-omic analysis of gallbladder cancer identifies distinct tumor microenvironments associated with disease progression – Nature

Report on Multi-Omic Analysis of Gallbladder Cancer and Its Implications for Sustainable Development Goals

Introduction

Gallbladder carcinoma (GBC) represents the most aggressive form of biliary tract cancer, characterized by a high mortality rate. The therapeutic management of GBC is challenged by the complex and elusive drivers within the tumor microenvironment that facilitate disease progression. Understanding these drivers is critical for improving patient outcomes and aligns with the Sustainable Development Goal (SDG) 3: Good Health and Well-being, which aims to reduce premature mortality from non-communicable diseases through prevention and treatment.

Methodology

A comprehensive single-cell atlas comprising 1,117,245 cells from 102 patients was developed, alongside a detailed mutational landscape analysis. This multi-omic approach enabled the spatial and temporal characterization of cellular compositions, intercellular interactions, and molecular functions within the GBC microenvironment.

Key Findings

1. Identification of Local Ecosystems

   Five distinct local ecosystems within the tumor microenvironment were identified, which stratify clinical outcomes. This stratification supports personalized medicine approaches, contributing to SDG 3 by promoting effective and targeted therapies.

2. Integrated Epithelial Program (AI-EPI)

   The AI-EPI program, integrated with spatial transcriptome analysis, revealed the co-localization of a highly malignant tumor subtype (GM16) with AREG⁺ immune cell subtypes, including T cells, B cells, dendritic cells, and macrophages, within the pro-metastatic niche of primary adenocarcinomas.

3. Mechanisms Promoting Metastasis and Immunotherapy Resistance

   Experimental evidence from in vitro and in vivo studies demonstrated that AREG promotes metastasis and induces CXCL5 expression in tumor cells via the EGFR–pERK–EGR1 signaling pathway. This leads to increased neutrophil infiltration, which impairs the efficacy of immunotherapy treatments.

Implications for Sustainable Development Goals

• SDG 3 (Good Health and Well-being): The study advances understanding of GBC pathogenesis and resistance mechanisms, facilitating the development of novel therapeutic strategies to improve survival rates and quality of life for patients.
• SDG 9 (Industry, Innovation, and Infrastructure): The application of cutting-edge single-cell and spatial transcriptomics technologies exemplifies innovation in biomedical research infrastructure.
• SDG 17 (Partnerships for the Goals): The collaborative research involving multiple institutions and data sharing through public repositories fosters global partnerships essential for addressing complex health challenges.

Data and Code Availability

In alignment with SDG 17, the raw sequencing data and processed datasets have been deposited in publicly accessible repositories such as the CNGB Sequence Archive (CNSA), Genome Sequence Archive (GSA), and Zenodo. Additionally, the software and analysis codes are available on GitHub, promoting transparency and reproducibility in research.

Conclusion

This multi-omic analysis provides an unprecedented spatial-temporal landscape of the gallbladder cancer microenvironment, uncovering critical cellular interactions and molecular pathways that drive disease progression and immunotherapy resistance. The findings offer potential strategies to overcome therapeutic resistance, contributing significantly to SDG 3 by advancing health outcomes. Furthermore, the study exemplifies the integration of innovative technologies and collaborative efforts, supporting broader sustainable development objectives.

1. Sustainable Development Goals (SDGs) Addressed or Connected

1. SDG 3: Good Health and Well-being
   • The article focuses on gallbladder carcinoma (GBC), a highly aggressive cancer with high mortality, addressing health and well-being challenges.
   • It discusses therapeutic challenges and immunotherapy resistance, which relate to improving health outcomes.
2. SDG 9: Industry, Innovation and Infrastructure
   • The use of advanced technologies such as single-cell atlas creation, mutational landscape analysis, and AI-based epithelial program identification (AI-EPI) reflects innovation in medical research.
3. SDG 17: Partnerships for the Goals
   • The article mentions data sharing through multiple public repositories and collaborative research efforts, supporting partnerships and data accessibility.

2. Specific Targets Under the Identified SDGs

1. SDG 3: Good Health and Well-being
   • Target 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment.
   • Target 3.b: Support the research and development of vaccines and medicines for communicable and non-communicable diseases that primarily affect developing countries.
2. SDG 9: Industry, Innovation and Infrastructure
   • Target 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors, including encouraging innovation and increasing the number of research and development workers.
3. SDG 17: Partnerships for the Goals
   • Target 17.6: Enhance North-South, South-South and triangular regional and international cooperation on and access to science, technology and innovation.
   • Target 17.8: Fully operationalize the technology bank and science, technology and innovation capacity-building mechanism for least developed countries.

3. Indicators Mentioned or Implied to Measure Progress

1. SDG 3 Indicators
   • Mortality rate attributed to cancer (Indicator 3.4.1) is implied through the discussion of high mortality in GBC.
   • Proportion of patients receiving immunotherapy and response rates, implied by the article’s focus on immunotherapy resistance.
2. SDG 9 Indicators
   • Research and development expenditure as a proportion of GDP (Indicator 9.5.1), implied by the advanced research methods and data sharing.
   • Number of researchers per million inhabitants (Indicator 9.5.2), implied by the extensive collaborative research team and data analysis.
3. SDG 17 Indicators
   • Number of science and technology cooperation agreements (Indicator 17.6.1), implied by data sharing in multiple international repositories.
   • Access to technology and innovation data repositories, implied by the availability of datasets and code on public platforms.

4. Table: SDGs, Targets and Indicators

Source: nature.com