Prognostic significance of tumour stroma ratio in inflammatory breast cancer

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.

Deep learning based tumor-stroma ratio scoring in colon cancer correlates with microscopic assessment

Background

The amount of stroma within the primary tumor is a prognostic parameter for colon cancer patients. This phenomenon can be assessed using the tumor-stroma ratio (TSR), which classifies tumors in stroma-low (≤50% stroma) and stroma-high (>50% stroma). Although the reproducibility for TSR determination is good, improvement might be expected from automation. The aim of this study was to investigate whether the scoring of the TSR in a semi- and fully automated method using deep learning algorithms is feasible.

Methods

A series of 75 colon cancer slides were selected from a trial series of the UNITED study. For the standard determination of the TSR, 3 observers scored the histological slides. Next, the slides were digitized, color normalized, and the stroma percentages were scored using semi- and fully automated deep learning algorithms. Correlations were determined using intraclass correlation coefficients (ICCs) and Spearman rank correlations.

Results

37 (49%) cases were classified as stroma-low and 38 (51%) as stroma-high by visual estimation. A high level of concordance between the 3 observers was reached, with ICCs of 0.91, 0.89, and 0.94 (all P < .001). Between visual and semi-automated assessment the ICC was 0.78 (95% CI 0.23-0.91, P-value 0.005), with a Spearman correlation of 0.88 (P < .001). Spearman correlation coefficients above 0.70 (N=3) were observed for visual estimation versus the fully automated scoring procedures.

Conclusion

Good correlations were observed between standard visual TSR determination and semi- and fully automated TSR scores. At this point, visual examination has the highest observer agreement, but semi-automated scoring could be helpful to support pathologists.

Tumour stroma ratio is a potential predictor for 5-year disease-free survival in breast cancer

Background

The tumour–stroma ratio (TSR) is identified as a promising prognostic parameter for breast cancer, but the cutoff TSR value is mostly assessed by visual assessment, which lacks objective measurement. The aims of this study were to optimize the cutoff TSR value, and evaluate its prognosis value in patients with breast cancer both as continuous and categorical variables.

Methods

Major clinicopathological and follow-up data were collected for a series of patients with breast cancer. Tissue microarray images stained with cytokeratin immunohistochemistry were evaluated by automated quantitative image analysis algorithms to assess TSR. The potential cutoff point for TSR was optimized using maximally selected rank statistics. The association between TSR and 5-year disease-free survival (5-DFS) was assessed by Cox regression analysis. Kaplan–Meier analysis and log-rank test were used to assess the significance in survival analysis.

Results

The optimal cut-off TSR value was 33.5%. Using this cut-off point, categorical variable analysis found that low TSR (i.e., high stroma, TSR ≤ 33.5%) predicts poor outcomes for 5-DFS (hazard ratio [HR] = 2.82, 95% confidence interval [CI] = 1.81–4.40, P = 0.000). When TSR was considered as a continuous parameter, results showed that increased stroma content was associated with worse 5-DFS (HR = 1.71, 95% CI = 1.34–2.18, P = 0.000). Similar results were also obtained in three molecular subtypes in continuous and categorical variable analyses. Moreover, in the Kaplan–Meier analysis, log-rank test showed that low TSR displayed a worse 5-DFS than high TSR (P = 0.000). Similar results were also obtained in patients with triple-negative breast cancer, human epidermal growth factor receptor 2 (HER2)-positive breast cancer, and luminal–HER2-negative breast cancer.

Conclusion

TSR is an independent predictor for 5-DFS in breast cancer with worse survival outcomes in low TSR. The prognostic value of TSR was also observed in other three molecular subtypes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-10183-5.

The Prognostic Role of Intratumoral Stromal Content in Lobular Breast Cancer

Simple Summary

High intratumoral stromal content is related to worse outcomes in several types of cancer. However, its prognostic role in breast cancer seems to differ between different subtypes. High intratumoral stromal content is a negative prognostic marker in triple-negative breast cancer, while the opposite is the case for estrogen-receptor-positive breast cancer, in which higher stromal content is indicative of a better prognosis. Most lobular breast cancers are estrogen-receptor-positive, and the tumor tissue has a clearly defined histological appearance, often with a high intratumoral stromal content. To date, the prognostic role of intratumoral stromal content in lobular breast cancer remains unclear. In this study, we aimed to investigate the prognostic importance of intratumoral stromal content in estrogen-receptor-positive lobular breast cancer. Our results show that high intratumoral stromal content is an easily assessed and clinically useful indicator of a good prognosis in lobular breast cancer.

Abstract

Previous studies have shown that high intratumoral stromal content is associated with a worse prognosis in breast cancer, especially in the triple-negative subtype. However, contradictory results have been reported for estrogen-receptor-positive (ER+) breast cancer, indicating that the prognostic role of intratumoral stromal content may be subtype-dependent. In this study, we investigated the importance of intratumoral stromal content for breast cancer-specific mortality (BCM) in a well-defined subgroup (n = 182) of ER+/human-epidermal growth-factor-receptor-2 negative (HER2−) invasive lobular breast cancer (ILC). The intratumoral stromal content was assessed on hematoxylin–eosin-stained whole sections and graded into high stroma (>50%) or low stroma (≤50%). A total of 82 (45%) patients had high-stroma tumors, and 100 (55%) had low-stroma tumors. High-stroma tumors were associated with a lower Nottingham histological grade, low Ki67, and a luminal A-like subtype. After a 10-year follow-up, the patients with high-stroma tumors had a lower BCM (HR: 0.43, 95% CI: 0.21–0.89, p = 0.023) in univariable analysis. Essentially the same effect was found in both the multivariable analysis (10-year follow-up) and univariable analysis (25-year follow-up), but these findings were not strictly significant. In ER+/HER2− ILC, high intratumoral stromal content is an easily assessable histological indicator of a good prognosis.

Prognostic Efficacy of Tumor-Stroma Ratio in Women With Breast Cancer: A Meta-Analysis of Cohort Studies

Background

Tumor-stroma ratio (TSR) has been suggested as an emerging prognostic predictor in women with breast cancer. However, previous studies evaluating the association between TSR and survival in women with breast cancer showed inconsistent results. We performed a meta-analysis to systematically evaluate the possible prognostic role of TSR in breast cancer.

Methods

Relevant cohort studies were obtained via search of PubMed, Embase, and Web of Science databases. A random-effects model, which incorporated the potential heterogeneity, was used to pool the results.

Results

Twelve cohort studies with 6175 patients were included. Nine of the 12 studies used 50% as the cutoff to divide the patients into those with stroma-rich (low TSR) and stroma-poor (high TSR) tumors. Pooled results showed that compared women with stroma-poor tumor, those with stroma-rich tumor were associated with worse survival outcomes (disease-free survival [DFS]: hazard ratio [HR] = 1.56, 95% confidence interval [CI]: 1.32 to 1.85, P < 0.001; overall survival [OS]: HR = 1.67, 95% CI: 1.46 to 1.91, P < 0.001; and cancer-specific survival [CSS]: HR = 1.75, 95% CI: 1.40 to 2.20, P < 0.001). Analysis limited to women with triple-negative breast cancer (TNBC) showed consistent results (DFS: HR: 2.07, 95% CI: 1.59 to 2.71, P < 0.001; OS: HR: 2.04, 95% CI: 1.52 to 2.73, P < 0.001; and CSS: HR: 2.40, 95% CI: 1.52 to 3.78, P < 0.001).

Conclusions

Current evidence from retrospective studies supports that tumor TSR is a prognostic predictor or poor survival in women with breast cancer.

Organotypic 3D decellularized matrix tumor spheroids for high-throughput drug screening

Decellularized extracellular matrix (dECM) is emerging as a valuable tool for generating 3D in vitro tumor models that better recapitulate tumor-stroma interactions. However, the development of dECM-3D heterotypic microtumors exhibiting a controlled morphology is yet to be materialized. Precisely controlling microtumors morphologic features is key to avoid an inaccurate evaluation of therapeutics performance during preclinical screening. To address this, herein we employed ultra-low adhesion surfaces for bioengineering organotypic 3D metastatic breast cancer-fibroblast models enriched with dECM microfibrillar fragments, as a bottom-up strategy to include major matrix components and their associated biomolecular cues during the early stages of 3D microtissue spheroids assembly, simulating pre-existing ECM presence in the in vivo setting. This biomimetic approach enabled the self-assembly of dECM-3D tumor-stroma spheroids with tunable size and reproducible morphology. Along time, dECM enriched and stroma-rich microtumors exhibited necrotic core formation, secretion of key biomarkers and higher cancer-cell specific resistance to different chemotherapeutics in comparison to standard spheroids. Exometabolomics profiling of dECM-Spheroid in vitro models further identified important breast cancer metabolic features including glucose/pyruvate consumption and lactate excretion, which suggest an intense glycolytic activity, recapitulating major hallmarks of the native microenvironment. Such organotypic dECM-enriched microtumors overcome the morphologic variability generally associated with cell-laden dECM models, while providing a scalable testing platform that can be foreseeable leveraged for high-throughput screening of candidate therapeutics.

Toward a quantitative method for estimating tumour-stroma ratio in breast cancer using polarized light microscopy

The tumour-stroma ratio (TSR) has been explored as a useful source of prognostic information in various cancers, including colorectal, breast, and gastric. Despite research showing potential prognostic utility, its uptake into the clinic has been limited, in part due to challenges associated with subjectivity, reproducibility, and quantification. We have recently proposed a simple, robust, and quantifiable high-contrast method of imaging intra- and peri-tumoural stroma based on polarized light microscopy. Here we report on its use to quantify TSR in human breast cancer using unstained slides from 40 patient samples of invasive ductal carcinoma (IDC). Polarimetric results based on a stromal abundance metric correlated well with pathology designations, showing a statistically significant difference between high- and low-stroma samples as scored by two clinical pathologists. The described polarized light imaging methodology shows promise for use as a quantitative, automatic, and standardizable tool for quantifying TSR, potentially addressing some of the challenges associated with its current estimation.

Computerized Assessment of the Tumor-stromal Ratio and Proposal of a Novel Nomogram for Predicting Survival in Invasive Breast Cancer

Background: Various studies have verified the prognostic significance of the tumor-stromal ratio (TSR) in several types of carcinomas using manually assessed H&E stained histologic sections. This study aimed to establish a computerized method to assess the TSR in invasive breast cancer (BC) using immunohistochemistry (IHC)-stained tissue microarrays (TMAs), and integrate the TSR into a novel nomogram for predicting survival.

Methods: IHC-staining of cytokeratin (CK) was performed in 7 prepared TMAs containing 240 patients with 480 invasive BC specimens. The ratio of tumor areas and stromal areas was determined by the computerized method, and categorized as stroma-low and stroma-high groups using the X-tile software. The prognostic value of the TSR at 5-year disease free survival (5-DFS) in each subgroup was analyzed. Univariate and multivariate analyses were performed and a novel nomogram for predicting survival in invasive breast cancer was established and assessed.

Results: The newly developed computerized method could accurately recognize CK-labeled tumor areas and non-labeled stromal areas, and automatically calculate the TSR. Stroma-low and stroma-high accounted for 38.8% (n = 93) and 61.2% (n = 147) of the cases, according to the cut-off value of 55.5% for stroma ratio. The Kaplan-Meier analysis showed that patients in the stroma-high group had a worse 5-DFS compared to patients in the stroma-low group (P = 0.031). Multivariable analysis indicated that the T stage, N status, histological grade, ER status, HER-2 gene, and the TSR were potential risk factors of invasive BC patients, which were included into the nomogram (P < 0.10 for all). The nomogram was well calibrated to predict the probability of 5-DFS and the C-index was 0.817, which was higher than any single predictor. A dynamic nomogram was built for convenient use. The area under the curve (AUC) of the nomogram was 0.870, while that of the TNM staging system was 0.723. The Kaplan-Meier analysis showed that the nomogram had a better risk stratification for invasive BC patients than the TNM staging system.

Conclusions: Based on IHC staining of CK on TMAs, this study successfully developed a computerized method for TSR assessment and established a novel nomogram for predicting survival in invasive BC patients.

Tumour Stroma Ratio Assessment Using Digital Image Analysis Predicts Survival in Triple Negative and Luminal Breast Cancer

We aimed to determine the clinical significance of tumour stroma ratio (TSR) in luminal and triple negative breast cancer (TNBC) using digital image analysis and machine learning algorithms. Automated image analysis using QuPath software was applied to a cohort of 647 breast cancer patients (403 luminal and 244 TNBC) using digital H&E images of tissue microarrays (TMAs). Kaplan-Meier and Cox proportional hazards were used to ascertain relationships with overall survival (OS) and breast cancer specific survival (BCSS). For TNBC, low TSR (high stroma) was associated with poor prognosis for both OS (HR 1.9, CI 1.1-3.3, p = 0.021) and BCSS (HR 2.6, HR 1.3-5.4, p = 0.007) in multivariate models, independent of age, size, grade, sTILs, lymph nodal status and chemotherapy. However, for luminal tumours, low TSR (high stroma) was associated with a favourable prognosis in MVA for OS (HR 0.6, CI 0.4-0.8, p = 0.001) but not for BCSS. TSR is a prognostic factor of most significance in TNBC, but also in luminal breast cancer, and can be reliably assessed using quantitative image analysis of TMAs. Further investigation into the contribution of tumour subtype stromal phenotype may further refine these findings.

Peri-tumoural stroma collagen organization of invasive ductal carcinoma assessed by polarized light microscopy differs between OncotypeDX risk group

A commercially available genomic test, OncotypeDX has emerged as a useful postsurgical treatment guide for early stage breast cancer. Despite widespread clinical adoption, there remain logistical issues with its implementation. Collagenous stromal architecture has been shown to hold prognostic value that may complement OncotypeDX. Polarimetric analysis of breast cancer surgical samples allows for the quantification of collagenous stroma abundance and organization. We examine intratumoural collagen abundance and alignment along the tumor-host interface for 45 human samples of invasive ductal carcinoma categorized as low or higher risk by OncotypeDX. Furthermore, we probe the separatory power of collagen alignment patterns to classify unlabeled samples as low or higher OncotypeDX risk group using a linear discriminant (LD) model. No significant difference in mean collagen abundance was found between the two risk groups. However, collagen alignment along the tumor boundary was found to be significantly lower in higher risk samples. The LD model achieved a 71% total accuracy and 81% sensitivity to higher risk samples. Prognostic information extracted from the stromal morphology has potential to complement OncotypeDX as an easy-to-implement prescreening methodology.

Clinicopathological correlation of tumor-stroma ratio and inflammatory cell infiltrate with tumor grade and lymph node metastasis in squamous cell carcinoma of buccal mucosa and tongue in 41 cases with review of literature

Introduction

Several studies regarding tumor-stroma ratio (TSR) in colorectal, esophageal, breast, endometrial, and cervical carcinomas have been done in the past with significant results.

Objectives

The objectives of this study were to (1) study and grade TSR in buccal mucosa and tongue squamous cell carcinoma (SCC), (2) grade inflammatory cell infiltrate surrounding the tumor, and (3) correlate the above two parameters with tumor grade, lymph node metastasis, lymphovascular invasion (LVI), and perineural invasion (PNI).

Materials and Methods

Totally, 25 patients of buccal SCC and 16 cases of tongue SCC were included in the study. TSR was assessed visually on the hematoxylin and eosin-stained tissue sections by two independent observers. Cases were categorized into two groups: One with high TSR >50% (stroma poor) and the other with low TSR <50% as the stroma-rich group. TSR was correlated with tumor size, lymph node metastasis, inflammatory cell infiltrate, LVI, and PNI. Data were analyzed by the Statistical Package for the Social Sciences version 16.0 (Chicago, IL, USA) for Windows. The Chi-square and Fischer's exact tests were applied in the analysis of categorical variable.

Results and Conclusion

SCC of buccal mucosa showed a significant correlation between TSR and size of the tumor (P = 0.001). We found that smaller the tumor size ≤2 cm (Stage T1), lesser the TSR, and size >2 cm was found to be associated with higher TSR. Hence, higher TSR (stroma poor) was associated with an adverse pathological characteristic, i.e., advanced T significantly. There was no significant correlation between TSR and inflammatory infiltrate with grade of the tumor, lymph node metastasis, LVI, and PNI. In 16 cases of SCC of the tongue; no correlation was observed between TSR and inflammatory infiltrate with tumor size, grade of the tumor, lymph node metastasis, LVI, and PNI. TSR has been studied in various malignancies (mostly adenocarcinomas) including laryngeal SCCs; however, it has never been studied on oral SCCs.

Increased RAB31 Expression in Cancer-Associated Fibroblasts Promotes Colon Cancer Progression Through HGF-MET Signaling

RAB family proteins participate in the dynamic regulation of cellular membrane compartments and are dysregulated in a variety of tumor types, which may alter the biological properties of cancer cells such as proliferation, migration, and invasion. In our previous study, we found that Ras-related protein Rab-31 (RAB31) expression was increased in late-stage colorectal cancer (CRC). The role of RAB31 has never been investigated in CRC. In this study, we found that expression of RAB31 in the tumor stroma but not cancer cells of colon cancer predicted poor survival. RAB31 can be detected in primary cancer-associated fibroblasts (CAFs) and paired normal fibroblasts. Conditioned medium (CM) from RAB31 overexpressing CAFs significantly promoted migration of colon cancer cell lines in vitro and in vivo. This process may be mediated by paracrine action of hepatocyte growth factor (HGF), which was increased in the CM of RAB31-overexpressing CAFs. Blockade of HGF/MET signaling by drug inhibition, knockdown of mesenchymal to epithelial transition factor (MET) in RKO, or antibody neutralization of HGF abolished migration of RKO cells mediated by RAB31 expression in CAFs. We propose that in colon cancer, increased RAB31 expression in CAFs may contribute to tumor progression by regulating the secretion of HGF in the tumor stroma.

Novel quantitative signature of tumor stromal architecture: polarized light imaging differentiates between myxoid and sclerotic human breast cancer stroma

As a leading cause of death in women, breast cancer is a global health concern for which personalized therapy remains largely unrealized, resulting in over- or under-treatment. Recently, tumor stroma has been shown to carry important prognostic information, both in its relative abundance and morphology, but its current assessment methods are few and suboptimal. Herein, we present a novel stromal architecture signature (SAS) methodology based on polarized light imaging that quantifies patterns of tumor connective tissue. We demonstrate its ability to differentiate between myxoid and sclerotic stroma, two pathology-derived categories associated with significantly different patient outcomes. The results demonstrate a 97% sensitivity and 88% specificity for myxoid stroma identification in a pilot study of 102 regions of interest from human invasive ductal carcinoma breast cancer surgical specimens (20 patients). Additionally, the SAS numerical score is indicative of the wide range of stromal characteristics within these binary classes and highlights ambiguous mixed-morphology regions prone to misclassification. The enabling polarized light microscopy technique is inexpensive, fast, fully automatable, applicable to fresh or embedded tissue without the need for staining and thus potentially translatable into research and/or clinical settings. The SAS metric yields quantifiable and objective stromal characterization with promise for prognosis in many types of cancers beyond breast carcinoma, enabling researchers and clinicians to further investigate the emerging and important role of stromal architectural patterns in solid tumors.

Prognostic Significance of the Tumor-Stromal Ratio in Invasive Breast Cancer and a Proposal of a New Ts-TNM Staging System

Background

Previous studies have demonstrated that the tumor-stromal ratio (TSR) was an independent prognostic factor in several types of carcinomas. This study aimed at exploring the prognostic significance of the TSR in invasive breast cancer using immunohistochemistry (IHC)-stained tissue microarrays (TMAs) and integrating the TSR into the traditional tumor-node-metastasis (TNM) staging system.

Methods

The prepared 7 TMAs containing 240 patients with 480 invasive BC specimens were stained with cytokeratin (CK) by the IHC staining method. The ratio of tumor cells and stromal cells was visually assessed. TSR > 1 and TSR ≤ 1 were categorized as the high TSR (low stroma) and low TSR (high stroma) groups, respectively, and the prognostic value of the TSR at 5-year disease-free survival (5-DFS) was analyzed. A new Ts-TNM (tumor stroma-tumor-node-metastasis) staging system was established and assessed.

Results

IHC staining of CK could specifically label tumor cells with clear contrast, making it easy to manually assess TSR. High TSR (low stroma) and low TSR (high stroma) were observed in 52.5% (n = 126) and 47.5 (n = 114) of the cases, according to the division of value 1. A Kaplan-Meier analysis showed that patients in the low TSR group had a worse 5-DFS compared with patients in the high TSR group (P=0.022). Multivariable analysis indicated that the T stage (P=0.014), N status (P < 0.001), histological grade (P < 0.001), estrogen receptor status (P=0.015), and TSR (P=0.011) were independent prognostic factors of invasive BC patients. The new Ts-TNM staging system combining TSR, tumor staging, lymph node status, and metastasis staging was established. The receiver operating characteristic (ROC) curve analysis demonstrated that the ability of the Ts-TNM staging system to predict recurrence was not lower than that of the TNM staging system.

Conclusions

This study confirms that the TSR is a prognostic indicator for invasive breast cancer. The Ts-TNM staging system containing stromal and tumor information may optimize risk stratification for invasive breast cancer.

The intra-tumoural stroma in patients with breast cancer increases with age

PURPOSE

The tumour microenvironment in older patients is subject to changes. The tumour-stroma ratio (TSR) was evaluated in order to estimate the amount of intra-tumoural stroma and to evaluate the prognostic value of the TSR in older patients with breast cancer (≥ 70 years).

METHODS

Two retrospective cohorts, the FOCUS study (N = 619) and the Nottingham Breast Cancer series (N = 1793), were used for assessment of the TSR on haematoxylin and eosin stained tissue slides.

RESULTS

The intra-tumoural stroma increases with age in the FOCUS study and the Nottingham Breast Cancer series (B 0.031, 95% CI 0.006-0.057, p = 0.016 and B 0.034, 95% CI 0.015-0.054, p < 0.001, respectively). Fifty-one per cent of the patients from the Nottingham Breast Cancer series < 40 years had a stroma-high tumour compared to 73% of the patients of ≥ 90 years from the FOCUS study. The TSR did not validate as an independent prognostic parameter in patients ≥ 70 years.

CONCLUSIONS

The intra-tumoural stroma increases with age. This might be the result of an activated tumour microenvironment. The TSR did not validate as an independent prognostic parameter in patients ≥ 70 years in contrast to young women with breast cancer as published previously.

Novel methodology to image stromal tissue and assess its morphological features with polarized light: towards a tumour microenvironment prognostic signature

The amount and organization details of peri-tumoural stroma have been linked to patient outcomes in various cancers. In this study, we propose a novel and relatively simple methodology using polarized light microscopy (PLM) to image fibrillar structures within a tumour microenvironment, using only linear crossed polarizers. We demonstrate the technique's ability to image and extract measurement-geometry-independent quantitative morphological metrics related to stromal density and alignment in human invasive breast cancer samples. The findings are promising towards quantitative characterization of peri-tumoural stroma, with potential to develop a PLM signature of tumour microenvironment for providing clinically important information such as breast cancer behaviour or treatment outcome prognosis.

The prognostic value of tumour-stroma ratio in primary breast cancer with special attention to triple-negative tumours: a review

Purpose

There is a strong need to improve the prognostication of breast cancer patients in order to prevent over- and undertreatment, especially when considering adjuvant chemotherapy. Tumour stroma characteristics might be valuable in predicting disease progression.

Methods

Studies regarding the prognostic value of tumour–stroma ratio (TSR) in breast cancer are evaluated.

Results

A high stromal content is related to a relatively poor prognosis. The most pronounced prognostic effect of this parameter seems to be observed in the triple-negative breast cancer (TNBC) subtype.

Conclusions

TSR assessment might represent a simple, fast and reproducible prognostic factor at no extra costs, and could possibly be incorporated into routine pathological diagnostics. Despite these advantages, a robust clinical validation of this parameter has yet to be established in prospective studies.

Electronic supplementary material

The online version of this article (10.1007/s10549-018-4987-4) contains supplementary material, which is available to authorized users.

Tumor stroma-containing 3D spheroid arrays: A tool to study nanoparticle penetration

Nanoparticle penetration through tumor tissue after extravasation is considered as a key issue for tumor distribution and therapeutic effects. Most tumors possess abundant stroma, a fibrotic tissue composed of cancer-associated fibroblasts (CAFs) and extracellular matrix (ECM), which acts as a barrier for nanoparticle penetration. There is however a lack of suitable in vitro systems to study the tumor stroma penetration of nanoparticles. In the present study, we developed and thoroughly characterized a 3D co-culture spheroidal array to mimic tumor stroma and investigated the penetration of silica and PLGA nanoparticles in these spheroids. First, we examined human breast tumor patient biopsies to characterize the content and organization of stroma and found a high expression of alpha-smooth muscle actin (α-SMA; 40% positive area) and collagen-1 (50% positive area). Next, we prepared homospheroids of 4T1 mouse breast cancer cells or 3T3 mouse fibroblasts alone as well as heterospheroids combining 3T3 and 4T1 cells in different ratios (1:1 and 5:1) using a microwell array platform. Confocal live imaging revealed that fibroblasts distributed and reorganized within 48h in heterospheroids. Furthermore, immunohistochemical staining and gene expression analysis showed a proportional increase of α-SMA and collagen in heterospheroids with higher fibroblast ratios attaining 35% and 45% positive area at 5:1 (3T3:4T1) ratio, in a good match with the clinical breast tumor stroma. Subsequently, we studied the penetration of high and low negatively charged fluorescent silica nanoparticles (30nm; red and 100 or 70nm; green; zeta potential: -40mV and -20mV) and as well as Cy5-conjugated pegylated PLGA nanoparticles (200nm, -7mV) in both homo- and heterospheroid models. Fluorescent microscopy on spheroid cryosections after incubation with silica nanoparticles showed that 4T1 homospheroids allowed a high penetration of about 75-80% within 24h, with higher penetration in case of the 30nm nanoparticles. In contrast, spheroids with increasing fibroblast amounts significantly inhibited NP penetration. Silica nanoparticles with a less negative zeta potential exhibited lesser penetration compared to highly negative charged nanoparticles. Subsequently, similar experiments were conducted using Cy5-conjugated pegylated PLGA nanoparticles and confocal laser scanning microscopy; an increased nanoparticle penetration was found in 4T1 homospheroids until 48h, but significantly lower penetration in heterospheroids. Furthermore, we also developed human homospheroids (MDA-MB-231 or Panc-1 tumor cells) and heterospheroids (MDA-MB-231/BJ-hTert and Panc-1/pancreatic stellate cells) and performed silica nanoparticle (30 and 100nm) penetration studies. As a result, heterospheroids had significantly a lesser penetration of the nanoparticles compared to homospheroids. In conclusion, our data demonstrate that tumor stroma acts as a strong barrier for nanoparticle penetration. The 30-nm nanoparticles with low zeta potential favor deeper penetration. Furthermore, the herein proposed 3D co-culture platform that mimics the tumor stroma, is ideally suited to systematically investigate the factors influencing the penetration characteristics of newly developed nanomedicines to allow the design of nanoparticles with optimal penetration characteristics.