The conflicting prognostic role of the stroma-ratio in breast cancer molecular subtypes.

The tumor microenvironment plays a key role in tumor progression. The proportion of the stroma to tumor cells (stroma-tumor ratio (STR)) has a variable prognostic significance in breast cancer (BC) molecular classes. In this study, we evaluated the mechanisms of stroma formation and composition in different molecular subtypes which could explain the different prognostic values. This study interrogated two large well-characterized BC cohorts. Firstly, an in-house BC cohort (n=822) encompassing all BC molecular subtypes from the Nottingham series was used. In each subtype, stromal assessment was carried out and tumors were assigned to two groups: high and low STR, and further correlation with tumor characteristics and patient outcomes was investigated. The contribution of tumor-infiltrating lymphocytes (TILs) to the stroma has also been studied. Secondly, the public domain dataset (The Cancer Genome Atlas data (TCGA), n=978) was used as a validation cohort and for differential gene expression (DGE) analysis. DGE was performed to identify a set of genes associated with high STR in the three main molecular subtypes. High STR was associated with favorable patient outcomes in the whole cohort and in the luminal subtype, whereas high STR showed an association with poor outcome in TNBC. No association with outcome was found in the HER2 enriched BC. DGE analysis identified various pathways in luminal and TNBC subtypes, with immune upregulation and hypoxia pathways enriched in TNBC, and pathways related to fibrosis and stromal remodeling enriched in the luminal group instead. Low STR accompanied by high TILs was shown to carry the most favorable prognosis in TNBC. In line with the DGE results, TILs played a major prognostic role in the stroma of TNBC, but not in the luminal or HER2-enriched subtypes. The underlying molecular mechanisms and composition of the stroma in BC are variable in the molecular subtypes and explain the difference in its prognostic significance.

Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence.

BACKGROUND: Tumour infiltrating lymphocytes (TILs) are a prognostic parameter in triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). However, their role in luminal (oestrogen receptor positive and HER2 negative (ER + /HER2-)) BC remains unclear. In this study, we used artificial intelligence (AI) to assess the prognostic significance of TILs in a large well-characterised cohort of luminal BC. METHODS: Supervised deep learning model analysis of Haematoxylin and Eosin (H&E)-stained whole slide images (WSI) was applied to a cohort of 2231 luminal early-stage BC patients with long-term follow-up. Stromal TILs (sTILs) and intratumoural TILs (tTILs) were quantified and their spatial distribution within tumour tissue, as well as the proportion of stroma involved by sTILs were assessed. The association of TILs with clinicopathological parameters and patient outcome was determined. RESULTS: A strong positive linear correlation was observed between sTILs and tTILs. High sTILs and tTILs counts, as well as their proximity to stromal and tumour cells (co-occurrence) were associated with poor clinical outcomes and unfavourable clinicopathological parameters including high tumour grade, lymph node metastasis, large tumour size, and young age. AI-based assessment of the proportion of stroma composed of sTILs (as assessed visually in routine practice) was not predictive of patient outcome. tTILs was an independent predictor of worse patient outcome in multivariate Cox Regression analysis. CONCLUSION: AI-based detection of TILs counts, and their spatial distribution provides prognostic value in luminal early-stage BC patients. The utilisation of AI algorithms could provide a comprehensive assessment of TILs as a morphological variable in WSIs beyond eyeballing assessment.

Deciphering the Morphology of Tumor-Stromal Features in Invasive Breast Cancer Using Artificial Intelligence.

Tumor-associated stroma in breast cancer (BC) is complex and exhibits a high degree of heterogeneity. To date, no standardized assessment method has been established. Artificial intelligence (AI) could provide an objective morphologic assessment of tumors and stroma, with the potential to identify new features not discernible by visual microscopy. In this study, we used AI to assess the clinical significance of (1) stroma-to-tumor ratio (S:TR) and (2) the spatial arrangement of stromal cells, tumor cell density, and tumor burden in BC. Whole-slide images of a large cohort (n = 1968) of well-characterized luminal BC cases were examined. Region and cell-level annotation was performed, and supervised deep learning models were applied for automated quantification of tumor and stromal features. S:TR was calculated in terms of surface area and cell count ratio, and the S:TR heterogeneity and spatial distribution were also assessed. Tumor cell density and tumor size were used to estimate tumor burden. Cases were divided into discovery (n = 1027) and test (n = 941) sets for validation of the findings. In the whole cohort, the stroma-to-tumor mean surface area ratio was 0.74, and stromal cell density heterogeneity score was high (0.7/1). BC with high S:TR showed features characteristic of good prognosis and longer patient survival in both the discovery and test sets. Heterogeneous spatial distribution of S:TR areas was predictive of worse outcome. Higher tumor burden was associated with aggressive tumor behavior and shorter survival and was an independent predictor of worse outcome (BC-specific survival; hazard ratio: 1.7, P = .03, 95% CI, 1.04-2.83 and distant metastasis-free survival; hazard ratio: 1.64, P = .04, 95% CI, 1.01-2.62) superior to absolute tumor size. The study concludes that AI provides a tool to assess major and subtle morphologic stromal features in BC with prognostic implications. Tumor burden is more prognostically informative than tumor size.

Encapsulated papillary carcinoma of the breast: does it have a native basement membrane?

BACKGROUND: Encapsulated papillary carcinoma (EPC) is surrounded by a thick fibrous capsule-like structure, which is interpreted as a thickened basement membrane (BM). This study aimed to describe the geometric characteristics of the EPC capsule and to refine whether it is an expansion of the BM or a stromal reactive process. MATERIAL AND METHODS: In all, 100 cases were divided into four groups: EPC, ductal carcinoma in situ (DCIS), normal breast tissue and invasive tumours, with an additional encapsulated papillary thyroid carcinoma (EPTC) control group. Representative slides from each case were stained with picrosirius red (PSR) stain and examined using polarised microscopy. Images were analysed using ImageJ, CT-FIRE, and Curve align image analysis programmes. RESULTS: Compared to the normal and DCIS BM, the EPC group showed a significant increase of collagen fibre width, straightness, and density, and a decrease of fibre length. The EPC capsule showed less alignment of fibres with a more perpendicular arrangement, and it was enriched with disorganised collagen type I (stromal collagen) fibres. Compared to other groups, the EPC capsule showed significant variation in the thickness, evenness, distribution of collagen fibres, and significant intracapsular heterogeneity. Compared to BM-like material in the invasive group, the EPC capsule showed a higher density of collagen fibres with longer, straighter, and more aligned fibres, but there was no difference in the distribution of both collagen types I and III. Conversely, compared to EPTC, there were no differences between both EPC and EPTC capsules except that the fibres in the EPC capsule were straighter. Although differences between normal ducts and lobules and DCIS BM collagen fibre density, straightness, orientation, and alignment were detected, both were significantly different from EPC capsule. CONCLUSION: This study provided evidence that the EPC capsule is a reactive process rather than a thickened native BM characteristic of normal and in situ lesions, which provides further evidence that EPC is an indolent invasive carcinoma based on capsule characteristics.

Defining invasion in breast cancer: the role of basement membrane.

Basement membrane (BM) is an amorphous, sheet-like structure separating the epithelium from the stroma. BM is characterised by a complex structure comprising collagenous and non-collagenous proteoglycans and glycoproteins. In the breast, the thickness, density and composition of the BM around the ductal lobular system vary during differing development stages. In pathological conditions, the BM provides a physical barrier that separates proliferating intraductal epithelial cells from the surrounding stroma, and its absence or breach in malignant lesions is a hallmark of invasion and metastases. Currently, diagnostic services often use special stains and immunohistochemistry (IHC) to identify the BM in order to distinguish in situ from invasive lesions. However, distinguishing BM on stained sections, and differentiating the native BM from the reactive capsule or BM-like material surrounding some invasive malignant breast tumours is challenging. Although diagnostic use of the BM is being replaced by myoepithelial cell IHC markers, BM is considered by many to be a useful marker to distinguish in situ from invasive lesions in ambiguous cases. In this review, the structure, function and biological and clinical significance of the BM are discussed in relation to the various breast lesions with emphasis on how to distinguish the native BM from alternative pathological tissue mimicking its histology.

Expression, assessment and significance of Ki67 expression in breast cancer: an update.

Ki67 expression is one of the most important and cost-effective surrogate markers to assess for tumour cell proliferation in breast cancer (BC). The Ki67 labelling index has prognostic and predictive value in patients with early-stage BC, particularly in the hormone receptor-positive, HER2 (human epidermal growth factor receptor 2)-negative (luminal) tumours. However, many challenges exist in using Ki67 in routine clinical practice and it is still not universally used in the clinical setting. Addressing these challenges can potentially improve the clinical utility of Ki67 in BC. In this article, we review the function, immunohistochemical (IHC) expression, methods for scoring and interpretation of results as well as address several challenges of Ki67 assessment in BC. The prodigious attention associated with use of Ki67 IHC as a prognostic marker in BC resulted in high expectation and overestimation of its performance. However, the realisation of some pitfalls and disadvantages, which are expected with any similar markers, resulted in an increasing criticism of its clinical use. It is time to consider a pragmatic approach and weigh the benefits against the weaknesses and identify factors to achieve the best clinical utility. Here we highlight the strengths of its performance and provide some insights to overcome the existing challenges.