I. Nederlof (Amsterdam, Netherlands)
Author Of 1 Presentation
3O - Spatial analysis of lymphocytes and fibroblasts identifies biological relevant patterns in estrogen receptor positive breast cancer (ID 249)
Abstract
Background
In estrogen receptor positive (ER+) breast cancer, higher levels of tumor infiltrating lymphocytes (TILs) are often associated with a poor prognosis and this phenomenon is still poorly understood. Fibroblasts represent one of the most frequent and plastic cell types in breast cancer and harbor intriguing immunomodulatory and architectural capabilities. However, the clinical significance of the spatial patterns of tumor cells, TILs and fibroblasts is largely unknown. Here we evaluate the patterns and clinical impact of the proximity between cancer cells, TILs and fibroblast in ER+ breast cancer.
Methods
A deep neural network was used to locate and identify tumor cells, TILs and fibroblasts on hematoxylin and eosin (H&E) stained whole slides from 179 ER+ breast tumors (included in ICGC series) together with a new empirical density estimation analysis to measure the spatial patterns. To quantify the proximity of the pairs of cell distributions, we measured the Kullback-Leibler divergence between the cell types. Next, we hierarchically clustered the tumors based on their spatial profiles. Gene set enrichment analysis was performed for each spatial cluster to study specific molecular characteristic per cluster. We validated the spatial clusters in an independent cohort of ER+ breast cancer (n=756, included in METABRIC series) and in addition studied their prognostic value.
Results
The spatial integration of fibroblasts, TILs and tumor cells leads to a new reproducible classification of ER+ breast cancer, where the spatial composition of tumors was linked to inflammation, fibroblast meddling or immunosuppression. ER+ patients with high TIL and low fibroblasts had improved survival (HR=0.429, p=0.0024), whilst the patients with intermediate TIL, high fibroblasts and epithelial to mesenchymal transition did not (HR=1.5, p=0.052). Especially a high spatial overlap of fibroblasts and TILs was associated with a good prognosis (HR=0.668, p=0.027).
Conclusions
Our findings demonstrate a reproducible pipeline for the spatial profiling of TILs and fibroblasts in ER+ breast cancer from H&E slides and suggest that the spatial interplay of fibroblasts and TILs potentially holds a decisive role in the ER+ breast cancer-immune response.
Legal entity responsible for the study
The authors.
Funding
Dutch Cancer Foundation (grant KWF-10510).
Disclosure
C. Desmedt: Research grant/Funding (institution): Belgian Cancer Foundation; Research grant/Funding (institution): Luxemburg Cancer Foundation. R.F. Salgado: Research grant/Funding (institution): Merck; Research grant/Funding (institution): Puma Biotechnology; Advisory/Consultancy, Research grant/Funding (institution): Roche; Advisory/Consultancy: Bristol-Myers Squibb. M. Kok: Advisory/Consultancy, Research grant/Funding (institution): Bristol-Myers Squibb; Advisory/Consultancy, Research grant/Funding (self): Roche; Advisory/Consultancy: MSD; Advisory/Consultancy: Daiichi Sankyo. Y. Yuan: Honoraria (institution): Roche; Advisory/Consultancy: Merck. H. Horlings: Advisory/Consultancy: slidescore.com; Advisory/Consultancy: ellogon.ai; Research grant/Funding (institution): Roche. All other authors have declared no conflicts of interest.
Presenter Of 1 Presentation
3O - Spatial analysis of lymphocytes and fibroblasts identifies biological relevant patterns in estrogen receptor positive breast cancer (ID 249)
Abstract
Background
In estrogen receptor positive (ER+) breast cancer, higher levels of tumor infiltrating lymphocytes (TILs) are often associated with a poor prognosis and this phenomenon is still poorly understood. Fibroblasts represent one of the most frequent and plastic cell types in breast cancer and harbor intriguing immunomodulatory and architectural capabilities. However, the clinical significance of the spatial patterns of tumor cells, TILs and fibroblasts is largely unknown. Here we evaluate the patterns and clinical impact of the proximity between cancer cells, TILs and fibroblast in ER+ breast cancer.
Methods
A deep neural network was used to locate and identify tumor cells, TILs and fibroblasts on hematoxylin and eosin (H&E) stained whole slides from 179 ER+ breast tumors (included in ICGC series) together with a new empirical density estimation analysis to measure the spatial patterns. To quantify the proximity of the pairs of cell distributions, we measured the Kullback-Leibler divergence between the cell types. Next, we hierarchically clustered the tumors based on their spatial profiles. Gene set enrichment analysis was performed for each spatial cluster to study specific molecular characteristic per cluster. We validated the spatial clusters in an independent cohort of ER+ breast cancer (n=756, included in METABRIC series) and in addition studied their prognostic value.
Results
The spatial integration of fibroblasts, TILs and tumor cells leads to a new reproducible classification of ER+ breast cancer, where the spatial composition of tumors was linked to inflammation, fibroblast meddling or immunosuppression. ER+ patients with high TIL and low fibroblasts had improved survival (HR=0.429, p=0.0024), whilst the patients with intermediate TIL, high fibroblasts and epithelial to mesenchymal transition did not (HR=1.5, p=0.052). Especially a high spatial overlap of fibroblasts and TILs was associated with a good prognosis (HR=0.668, p=0.027).
Conclusions
Our findings demonstrate a reproducible pipeline for the spatial profiling of TILs and fibroblasts in ER+ breast cancer from H&E slides and suggest that the spatial interplay of fibroblasts and TILs potentially holds a decisive role in the ER+ breast cancer-immune response.
Legal entity responsible for the study
The authors.
Funding
Dutch Cancer Foundation (grant KWF-10510).
Disclosure
C. Desmedt: Research grant/Funding (institution): Belgian Cancer Foundation; Research grant/Funding (institution): Luxemburg Cancer Foundation. R.F. Salgado: Research grant/Funding (institution): Merck; Research grant/Funding (institution): Puma Biotechnology; Advisory/Consultancy, Research grant/Funding (institution): Roche; Advisory/Consultancy: Bristol-Myers Squibb. M. Kok: Advisory/Consultancy, Research grant/Funding (institution): Bristol-Myers Squibb; Advisory/Consultancy, Research grant/Funding (self): Roche; Advisory/Consultancy: MSD; Advisory/Consultancy: Daiichi Sankyo. Y. Yuan: Honoraria (institution): Roche; Advisory/Consultancy: Merck. H. Horlings: Advisory/Consultancy: slidescore.com; Advisory/Consultancy: ellogon.ai; Research grant/Funding (institution): Roche. All other authors have declared no conflicts of interest.