Related Experiment Video
Updated: May 4, 2026
Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies
Published on: July 3, 2025
Next generation sequencing-based expression profiling identifies signatures from benign stromal proliferations that
Introduction:
Multiple studies have shown that the tumor microenvironment (TME) of carcinomas can play an important role in the initiation, progression, and metastasis of cancer. Here we test the hypothesis that specific benign fibrous soft tissue tumor gene expression profiles may represent distinct stromal fibroblastic reaction types that occur in different breast cancers. The discovered stromal profiles could classify breast cancer based on the type of stromal reaction patterns in the TME.
Methods:
Next generation sequencing-based gene expression profiling (3SEQ) was performed on formalin fixed, paraffin embedded (FFPE) samples of 10 types of fibrous soft tissue tumors. We determined the extent to which these signatures could identify distinct subsets of breast cancers in four publicly available breast cancer datasets.
Results:
A total of 53 fibrous tumors were sequenced by 3SEQ with an average of 29 million reads per sample. Both the gene signatures derived from elastofibroma (EF) and fibroma of tendon sheath (FOTS) demonstrated robust outcome results for survival in the four breast cancer datasets. The breast cancers positive for the EF signature (20-33% of the cohort) demonstrated significantly better outcome for survival. In contrast, the FOTS signature-positive breast cancers (11-35% of the cohort) had a worse outcome.
Conclusions:
We defined and validated two new stromal signatures in breast cancer (EF and FOTS), which are significantly associated with prognosis. Our group has previously identified novel cancer stromal gene expression signatures associated with outcome differences in breast cancer by gene expression profiling of three soft tissue tumors, desmoid-type fibromatosis (DTF), solitary fibrous tumor (SFT), and tenosynovial giant cell tumor (TGCT/CSF1), as surrogates for stromal expression patterns. By combining the stromal signatures of EF and FOTS, with our previously identified DTF and TGCT/CSF1 signatures we can now characterize clinically relevant stromal expression profiles in the TME for between 74% to 90% of all breast cancers.
