Comparative Oncology: Cross-Sectional Single-Cell Transcriptomic Profiling of the Tumor Microenvironment Across Seven Human Cancers

TL;DR

This study compares the cellular makeup and interactions in the tumor environment across seven human cancers to explain differences in cancer behavior and treatment response.

Contribution

The study provides a cross-cancer comparison of single-cell transcriptomic profiles, revealing distinct tumor microenvironment features and signaling patterns.

Findings

Pancreatic cancer is dominated by neutrophils that interact mainly with immune cells.

Liver cancer lacks fibroblast support cells, while esophageal and breast cancers are rich in fibroblasts that promote tumor growth.

Thyroid cancer retains tumor-suppressor genes that may slow tumor progression.

Abstract

Cancer is not composed of a single cell type but rather a complex community of cancer cells, immune cells, and supporting stromal cells that communicate with each other. These cellular conversations shape how each cancer grows, spreads, and responds to treatment. In this study, we compared single-cell sequencing data from seven different human cancers to explore how these cell interactions differ among tumor types. We found that pancreatic cancer contains many neutrophils, a type of immune cell that interacts mainly with other immune cells, while liver cancer lacks the usual fibroblast support cells. In contrast, esophageal and breast cancers were rich in fibroblasts that send growth signals to tumor cells, and thyroid cancer retained genes that may slow tumor progression. These differences help explain why some cancers behave more aggressively than others. Our findings provide a clearer picture of how the tumor environment varies among cancers and may guide the development of new strategies to treat solid tumors by targeting their surrounding cells.

Background/Objectives: To elucidate the differential transcriptional and intercellular signaling features of tumor components across various cancers, we conducted a comparative analysis using single-cell RNA sequencing (scRNA-seq). This technology enables detailed characterization of tumor ecosystems and may explain variations in tumor behavior among distinct cancer types. Methods: We analyzed publicly available scRNA-seq datasets (GEO) from seven cancer types—pancreatic ductal adenocarcinoma (PDAC), hepatocellular carcinoma (HCC), esophageal squamous cell carcinoma (ESCC), breast cancer (BC), thyroid cancer (TC), gastric cancer (GC), and colorectal cancer (CRC)—to define their unique molecular characteristics and intercellular interactions. Results: PDAC displayed a distinct tumor microenvironment (TME) dominated by myeloid cells (~42%), including abundant CXCR1/CXCR2-expressing tumor-associated neutrophils (TANs) that preferentially interacted with immune rather than cancer cells. The competitive receptor ACKR1 was minimally expressed on endothelial cells, consistent with PDAC hypo-vascularity. In HCC, tumor cells lacked EPCAM and expressed complement and stem cell markers; cancer-associated fibroblasts (CAFs) were scarce, and stellate cells expressed the pericyte marker RGS5. CAFs were abundant in ESCC and BC, with IGF1/2 expression, while in GC, these markers were uniquely found in plasma cells. Since BC and GC subtypes exhibit distinct TME patterns, it is necessary to perform subtype-specific analyses for these cancers. TC showed high expression of tumor-suppressor genes, including HOPX, in tumor cells. Differential interactions and the presence of “dominant signaling cell populations “ with dominant outgoing signals may underlie the heterogeneity in tumor aggressiveness across these cancers. Conclusions: Comparative scRNA-seq analysis of multiple cancers reveals distinct tumor phenotypes and cell–cell communication patterns, offering insights into the molecular architecture of human solid tumors.

Figures