Introduction Several gene expression signatures have been proposed and demonstrated to be predictive of outcome in breast cancer. In the present article we address the following issues: Do these signatures perform similarly? Are there (common) molecular processes reported by these signatures? Can better prognostic predictors be constructed based on these identified molecular processes? Methods We performed a comprehensive analysis of the performance of nine gene expression signatures on seven different breast cancer datasets. To better characterize the functional processes associated with these signatures, we enlarged each signature by including all probes with a significant correlation to at least one of the genes in the original signature. The enrichment of functional groups was assessed using four ontology databases. Results The classification performance of the nine gene expression signatures is very similar in terms of assigning a sample to either a poor outcome group or a good outcome group. Nevertheless the concordance in classification at the sample level is low, with only 50% of the breast cancer samples classified in the same outcome group by all classifiers. The predictive accuracy decreases with the number of poor outcome assignments given to a sample. The best classification performance was obtained for the group of patients with only good outcome assignments. Enrichment analysis of the enlarged signatures revealed 11 functional modules with prognostic ability. The combination of the RNA-splicing and immune modules resulted in a classifier with high prognostic performance on an independent validation set. Conclusions The study revealed that the nine signatures perform similarly but exhibit a large degree of discordance in prognostic group assignment. Functional analyses indicate that proliferation is a common cellular process, but that other functional categories are also enriched and show independent prognostic ability. We provide new evidence of the potentially promising prognostic impact of immunity and RNA-splicing processes in breast cancer.

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In estrogen-receptor-positive, HER2-negative (ER+HER2−) 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 cells in breast cancer and harbor immunomodulatory capabilities. Here, we evaluate the molecular and clinical impact of the spatial patterns of TILs and fibroblast in ER+HER2− breast cancer. We used a deep neural network to locate and identify tumor, TILs, and fibroblasts on hematoxylin and eosin-stained slides from 179 ER+HER2− breast tumors (ICGC cohort) together with a new density estimation analysis to measure the spatial patterns. We clustered tumors based on their spatial patterns and gene set enrichment analysis was performed to study their molecular characteristics. We independently assessed the spatial patterns in a second cohort of ER+HER2− breast cancer (N = 630, METABRIC) and studied their prognostic value. The spatial integration of fibroblasts, TILs, and tumor cells leads to a new reproducible spatial classification of ER+HER2− breast cancer and is linked to inflammation, fibroblast meddling, or immunosuppression. ER+HER2− patients with high TIL did not have a significant improved overall survival (HR = 0.76, P = 0.212), except when they had received chemotherapy (HR = 0.447). A poorer survival was observed for patients with high fibroblasts that did not show a high level of TILs (HR = 1.661, P = 0.0303). Especially spatial mixing of fibroblasts and TILs was associated with a good prognosis (HR = 0.464, P = 0.013). Our findings demonstrate a reproducible pipeline for the spatial profiling of TILs and fibroblasts in ER+HER2− breast cancer and suggest that this spatial interplay holds a decisive role in their cancer-immune interactions.

Single cell technologies and spatial analyses are emerging as powerful tools to investigate tumor heterogeneity, cell state of key effector or suppressing immune cells and detailed distribution of cells within the tumor microenvironment. Their application will undoubtedly provide further understanding of the effects of cancer immunotherapy on the tumor microenvironment, which is particularly important for the largely non-immunogenic breast carcinomas. However, analyses of clinical samples require well-coordinated, multidisciplinary teams and specific expertise. Moreover, large scale analyses have major technical challenges and is still relatively costly. We will discuss the promise and hurdles of single cell and spatial analyses on the road to find biomarker for breast cancer immunotherapy and avenues for novel immunomodulatory treatments for breast cancer patients. Citation Format: M Kok. Optimizing immunotherapy efficacy in the clinic through biomarkers: Advances in single cell and spatial histology analyse [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr ES11-3.

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#2026 To determine the prognosis of breast cancer patients, clinical and pathological factors are currently employed. Gene expression micro-arrays offer new opportunities to determine individual prognosis. Publications have raised concerns about micro-arrays studies who have the potential to preclude their use in clinical routine. To improve the understanding of gene-expression classifiers we addressed the following issues: 1) Is the performance similar between independent classifiers? 2) Is proliferation a common biological theme that represents various signatures? 3) Are there other enriched pathways among signatures with prognostic ability?
 Methods:
 On 6 public datasets we applied the 76-gene signature; the Molecular subtypes; the Chromosomal Instability Signature; the Wound Signature; the Invasiveness Gene Signature; the Molecular Prognosis Index; and the Genomic Grade Index. Survival, predictive accuracy and overlap analyses were performed. We created enlarged signatures by including all probes with significant correlation to at least one of the genes in the original signatures. We gathered a collection of gene sets from four databases (GO, KEGG, Reactome, MSDB). For each signature, we evaluated whether specific gene sets (modules) are overrepresented. We tested the prognosis ability of each of them.
 Results:
 The survival and predictive accuracy analyses gave similar results for each of the 9 signatures. They all added significant information to a multivariate model including standard pathological and clinical criteria. Nevertheless, we showed that none of these signatures were able to identify good and poor prognosis patients when applied to samples with intrinsically poor prognosis features (Positive Lymph Node, Negative Estrogen Receptor, High Grade). Conversely they identified good and poor prognosis patients when applied to samples with intrinsically good prognosis features (Negative LN, Positive ER Low Grade). The overlap analysis showed a low agreement between the signatures. 50% of the samples had almost one discordant classification result out of the 9 classifiers tested. The intersection of the signatures revealed a set of proliferation genes. The signatures were build on 10 different gene ontology modules with prognostic ability.
 Conclusion:
 This study underlines the need of large prospective validation studies of gene expression signatures. Further computational intelligence and system biology studies would be held to determine the best way to use these classifiers in clinical routine. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 2026.