Prognostic significance of the tumor-stroma ratio: validation study in node-negative premenopausal breast cancer patients from the EORTC perioperative chemotherapy (POP) trial (10854)

Tumour-stroma ratio (TSR) in breast cancer: comparison of scoring core biopsies versus resection specimens

PURPOSE

Tumour-stroma ratio (TSR) is an important prognostic and predictive factor in several tumour types. The aim of this study is to determine whether TSR evaluated in breast cancer core biopsies is representative of the whole tumour.

METHOD

Different TSR scoring methods, their reproducibility, and the association of TSR with clinicopathological characteristics were investigated in 178 breast carcinoma core biopsies and corresponding resection specimens. TSR was assessed by two trained scientists on the most representative H&E-stained digitised slides. Patients were treated primarily with surgery between 2010 and 2021 at Semmelweis University, Budapest.

RESULTS

Ninety-one percent of the tumours were hormone receptor (HR)-positive (luminal-like). Interobserver agreement was highest using 100 × magnification (κcore = 0.906, κresection specimen = 0.882). The agreement between TSR of core biopsies and resection specimens of the same patients was moderate (κ = 0.514). Differences between the two types of samples were most frequent in cases with TSR scores close to the 50% cut-off point. TSR was strongly correlated with age at diagnosis, pT category, histological type, histological grade, and surrogate molecular subtype. A tendency was identified for more recurrences among stroma-high (SH) tumours (p = 0.07). Significant correlation was detected between the TSR and tumour recurrence in grade 1 HR-positive breast cancer cases (p = 0.03).

CONCLUSIONS

TSR is easy to determine and reproducible on both core biopsies and in resection specimens and is associated with several clinicopathological characteristics of breast cancer. TSR scored on core biopsies is moderately representative for the whole tumour.

The Conflicting Prognostic Role of the Stroma-Tumor 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 2 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 2 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 data set (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 3 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 outcomes in triple-negative BC (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.

Vague-Segment Technique: Automatic Computation of Tumor Stroma Ratio for Breast Cancer on Whole Slides

The calculation of Tumor Stroma Ratio (TSR) is a challenging medical issue that could improve predictions of neoadjuvant chemotherapy benefits and patient prognoses. Although several studies on breast cancer and deep learning methods have achieved promising results, the drawbacks that pixel-level semantic segmentation processes could not extract core tumor regions containing both tumor pixels and stroma pixels make it difficult to accurately calculate TSR. In this paper, we propose a Vague-Segment Technique (VST) consisting of a designed SwinV2UNet module and a modified Suzuki algorithm. Specifically, the SwinV2UNet identifies tumor pixels and generate pixel-level classification results, based on which the modified Suzuki algorithm extracts the contour of core tumor regions in terms of cosine angle. Through this way, VST obtains vaguely segmentation results of core tumor regions containing both tumor pixels and stroma pixels, where the TSR could be calculated by the formula of Intersection over Union (IOU). For the training and evaluation, we utilize the well-known The Cancer Genome Atlas (TCGA) database to create an annotated dataset, while 150 images with TSR annotations from real cases are also collected. The experimental results illustrate that the proposed VST could generate better tumor identification results compared with state-of-the-art methods, where the extracted core tumor regions lead to more consistencies of calculated TSR with senior experts compared to junior pathologists. The experimental results demonstrate the superiority of our proposed pipeline, which has promise for future clinical application.

Combining the tumor-stroma ratio with tumor-infiltrating lymphocytes improves the prediction of pathological complete response in breast cancer patients

PURPOSE

The tumor-stroma ratio (TSR) is a common histological parameter that measures stromal abundance and is prognostic in breast cancer (BC). However, more evidence is needed on the predictive value of the TSR for the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). The purpose of this study was to determine the importance of the TSR in predicting pCR in NAC settings.

METHOD

We evaluated the TSR on pretreatment biopsies of 912 BC patients from four independent Chinese hospitals and investigated the potential value of the TSR for predicting pCR. Meanwhile, stromal tumor-infiltrating lymphocytes (sTILs) were assessed, and we evaluated the predictive value of the combination of sTILs and TSR (TSRILs).

RESULTS

Patients with low stroma showed a higher pCR rate than those with high stroma among the four independent hospitals, and in multivariate analysis, the TSR was proven to be an independent predictor for pCR to NAC with an odds ratio of 1.945 (95% CI 1.230-3.075, P = 0.004). Moreover, we found that TSRILs could improve the area under the curve (AUC) for predicting pCR from 0.750 to 0.785 (P = 0.039); especially in HER2-negative BCs, the inclusion of TSRILs increased the AUC from 0.801 to 0.835 in the discovery dataset (P = 0.048) and 0.734 to 0.801 in the validation dataset (P = 0.003).

CONCLUSION

TSR and sTILs can be easily measured in pathological routines and provide predictive information without additional cost; with more evidence from clinical trials, TSRILs could be a candidate to better stratify patients in NAC settings.

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.

Magnetic resonance imaging features of invasive breast cancer association with the tumour stromal ratio

OBJECTIVE

To assess the association between breast cancer tumour stroma and magnetic resonance imaging (MRI) features.

MATERIALS AND METHODS

A total of 84 patients with treatment-naïve invasive breast cancer were enrolled into this retrospective study. The tumour stroma ratio (TSR) was estimated from the amount of tumour stroma in the pathology specimen of the breast tumour. The MRI images of the patients were analysed based on Breast Imaging Reporting and Data Systems (ACR-BIRADS) for qualitative features which include T2- weighted, diffusion-weighted images (DWI) and dynamic contrast-enhanced (DCE) for kinetic features. The mean signal intensity (SI) of Short Tau Inversion Recovery (STIR), with the ratio of STIR of the lesion and pectoralis muscle (L/M ratio) and apparent diffusion coefficient (ADC) value, were measured for the quantitative features. Correlation tests were performed to assess the relationship between TSR and MRI features.

RESULTS

There was a significant correlation between the margin of mass, enhancement pattern, and STIR signal intensity of breast cancer and TSR. There were 54.76% (n = 46) in the low stromal group and 45.24% (n = 38) in the high stromal group. A significant association were seen between the margin of the mass and TSR (p = 0.034) between the L/M ratio (p <0.001), and between STIR SI of the lesion and TSR (p<0.001). The median L/M ratio was significantly higher in the high TSR group as compared to the lower TSR group (p < 0.001).

CONCLUSION

Breast cancer with high stroma had spiculated margins, lower STIR signal intensity, and a heterogeneous pattern of enhancement. Hence, in this preliminary study, certain MRI features showed a potential to predict TSR.

Significance of Tumor-Stroma Ratio (TSR) in Predicting Outcomes of Malignant Tumors

Background and Objectives: The present study aimed to elucidate the distribution and the prognostic implications of tumor-stroma ratio (TSR) in various malignant tumors through a meta-analysis. Materials and Methods: This meta-analysis included 51 eligible studies with information for overall survival (OS) or disease-free survival (DFS), according to TSR. In addition, subgroup analysis was performed based on criteria for high TSR. Results: The estimated rate of high TSR was 0.605 (95% confidence interval (CI) 0.565-0.644) in overall malignant tumors. The rates of high TSR ranged from 0.276 to 0.865. The highest rate of high TSR was found in endometrial cancer (0.865, 95% CI 0.827-0.895). The estimated high TSR rates of colorectal, esophageal, and stomach cancers were 0.622, 0.529, and 0.448, respectively. In overall cases, patients with high TSR had better OS and DFS than those with low TSR (hazard ratio (HR) 0.631, 95% CI 0.542-0.734, and HR 0.564, 95% CI 0.0.476-0.669, respectively). Significant correlations with OS were found in the breast, cervical, colorectal, esophagus, head and neck, ovary, stomach, and urinary tract cancers. In addition, there were significant correlations of DFS in breast, cervical, colorectal, esophageal, larynx, lung, and stomach cancers. In endometrial cancers, high TSR was significantly correlated with worse OS and DFS. Conclusions: The rate of high TSR was different in various malignant tumors. TSR can be useful for predicting prognosis through a routine microscopic examination of malignant tumors.

Fibrocytes enhance mammary gland fibrosis in obesity

Obesity rates continue to rise, and obese individuals are at higher risk for multiple types of cancer, including breast cancer. Obese mammary fat is a site of chronic, macrophage-driven inflammation, which enhances fibrosis within adipose tissue. Elevated fibrosis within the mammary gland may contribute to risk for obesity-associated breast cancer. To understand how inflammation due to obesity enhanced fibrosis within mammary tissue, we utilized a high-fat diet model of obesity and elimination of CCR2 signaling in mice to identify changes in immune cell populations and their impact on fibrosis. We observed that obesity increased a population of CD11b+ cells with the ability to form myofibroblast-like colonies in vitro. This population of CD11b+ cells is consistent with fibrocytes, which have been identified in wound healing and chronic inflammatory diseases but have not been examined in obesity. In CCR2-null mice, which have limited ability to recruit myeloid lineage cells into obese adipose tissue, we observed reduced mammary fibrosis and diminished fibrocyte colony formation in vitro. Transplantation of myeloid progenitor cells, which are the cells of origin for fibrocytes, into the mammary glands of obese CCR2-null mice resulted in significantly increased myofibroblast formation. Gene expression analyses of the myeloid progenitor cell population from obese mice demonstrated enrichment for genes associated with collagen biosynthesis and extracellular matrix remodeling. Together these results show that obesity enhances recruitment of fibrocytes to promote obesity-induced fibrosis in the mammary gland.

Obesity and Fibrosis: Setting the Stage for Breast Cancer

Obesity is a rising health concern and is linked to a worsened breast cancer prognosis. Tumor desmoplasia, which is characterized by elevated numbers of cancer-associated fibroblasts and the deposition of fibrillar collagens within the stroma, may contribute to the aggressive clinical behavior of breast cancer in obesity. A major component of the breast is adipose tissue, and fibrotic changes in adipose tissue due to obesity may contribute to breast cancer development and the biology of the resulting tumors. Adipose tissue fibrosis is a consequence of obesity that has multiple sources. Adipocytes and adipose-derived stromal cells secrete extracellular matrix composed of collagen family members and matricellular proteins that are altered by obesity. Adipose tissue also becomes a site of chronic, macrophage-driven inflammation. Macrophages exist as a diverse population within obese adipose tissue and mediate the development of fibrosis through the secretion of growth factors and matricellular proteins and interactions with other stromal cells. While weight loss is recommended to resolve obesity, the long-term effects of weight loss on adipose tissue fibrosis and inflammation within breast tissue are less clear. Increased fibrosis within breast tissue may increase the risk for tumor development as well as promote characteristics associated with tumor aggressiveness.

Metastasis of breast cancer to bones alters the tumor immune microenvironment

BACKGROUND

Bone is one of the most frequent sites for breast cancer metastasis. Breast cancer bone metastasis (BCBM) leads to skeletal morbidities including pain, fractures, and spinal compression, all of which severely impact quality of life. Immunotherapy is a promising therapy for patients with advanced cancer, but whether it may provide benefit to metastatic bone cancer is currently unknown. Thus, a better understanding of the immune landscape of bone-disseminated breast cancers may reveal new therapeutic strategies. In this study, we use histopathological analysis to investigate changes within the immune microenvironment of primary breast cancer and paired BCBM.

METHODS

Sixty-three patients with BCBM, including 31 with paired primary and bone metastatic lesions, were included in our study. The percentage of stroma and stromal tumor-infiltrating lymphocytes (TILs) was evaluated by histopathological analysis. The quantification of stromal TILs (CD4 + and CD8 +), macrophages (CD68 + and HLA-DR +), programmed cell death protein 1 (PD-1), and programmed cell death protein ligand 1 (PD-L1) was evaluated through immunohistochemical (IHC) staining. Statistical analysis was performed with paired t test, Wilcoxon test, spearman correlation test, and univariate and multivariate cox regression.

RESULTS

Median survival after BCBM pathological diagnosis was 20.5 months (range: 3-95 months). Of the immune parameters measured, none correlated with survival after bone metastasis was diagnosed. Compared to the primary site, bone metastases exhibited more tumor stroma (mean: 58.5% vs 28.87%, p < 0.001) and less TILs (mean: 8.45% vs 14.03%, p = 0.042), as determined by H&E analysis. The quantification of primary vs metastatic tissue area with CD4 + (23.95/mm² vs 51.69/mm², p = 0.027 and with CD8 + (18.15/mm² vs 58.95/mm², p = 0.004) TILs similarly followed this trend and was reduced in number for bone metastases. The number of CD68 + and HLA-DR + macrophages showed no significant difference between primary sites and bone metastases. PD-1 expression was present in 68.25% of the bone metastasis, while PD-L1 expression was only present in 7.94% of the bone metastasis.

CONCLUSIONS

Our findings suggest that compared to the primary breast cancer site, bone metastases harbor a less active immune microenvironment. Despite this relatively dampened immune landscape, expression of PD-1 and PD-L1 in the bone metastasis indicates a potential benefit from immune checkpoint inhibitors for some BCBM cases.

Deep learning in digital pathology for personalized treatment plans of cancer patients

Over the past decade, many new cancer treatments have been developed and made available to patients. However, in most cases, these treatments only benefit a specific subgroup of patients, making the selection of treatment for a specific patient an essential but challenging task for oncologists. Although some biomarkers were found to associate with treatment response, manual assessment is time-consuming and subjective. With the rapid developments and expanded implementation of artificial intelligence (AI) in digital pathology, many biomarkers can be quantified automatically from histopathology images. This approach allows for a more efficient and objective assessment of biomarkers, aiding oncologists in formulating personalized treatment plans for cancer patients. This review presents an overview and summary of the recent studies on biomarker quantification and treatment response prediction using hematoxylin-eosin (H&E) stained pathology images. These studies have shown that an AI-based digital pathology approach can be practical and will become increasingly important in improving the selection of cancer treatments for patients.

Investigating the role of core needle biopsy in evaluating tumor-stroma ratio (TSR) of invasive breast cancer: a retrospective study

PURPOSE

Tumor-stroma ratio (TSR) of invasive breast carcinoma has gained attention in recent years due to its prognostic significance. Previous studies showed TSR is a potential biomarker for indicating the tumor response to neoadjuvant chemotherapy. However, it is not clear how well TSR evaluation in biopsy specimens might reflect the TSR in resection specimens. We conducted a study to investigate whether biopsy evaluation of TSR can be an alternative method.

METHOD

We collected cases with invasive breast carcinoma of no special type (IBC-NST) from University of Yamanashi hospital between 2011 and 2017 whose biopsy and resection specimens both had a pathologically diagnosis of IBC-NST (n = 146). We conceptualized a method for evaluating TSR in biopsy specimens within a preliminary cohort (n = 50). Within the studied cohort (n = 96), biopsy-based TSR (b-TSR) and resection-based TSR (r-TSR) were scored by two pathologists. We then evaluated our method's validity and performance by measuring interobserver variability between the two pathologists, Spearman's correlation between b-TSR and r-TSR, and the receiver operating characteristics (ROC) analysis for defining stroma-rich and stroma-poor tumors.

RESULTS

Intra-class coefficient between the two pathologists was 0.59. The correlation coefficients between b-TSR and r-TSR in the two pathologists were 0.45 and 0.37. The ROC areas under the curve were 0.7 and 0.67. By considering an r-TSR of < 50% as stroma-rich, the sensitivity and specificity of detecting stroma-rich tumors were 64.1% and 66.7%, respectively, when b-TSR was < 40%.

CONCLUSION

Our current b-TSR evaluation method can provide information about r-TSR and facilitate pre-treatment therapy follow-up.

Predicting neoadjuvant chemotherapy benefit using deep learning from stromal histology in breast cancer

Neoadjuvant chemotherapy (NAC) is a standard treatment option for locally advanced breast cancer. However, not all patients benefit from NAC; some even obtain worse outcomes after therapy. Hence, predictors of treatment benefit are crucial for guiding clinical decision-making. Here, we investigated the predictive potential of breast cancer stromal histology via a deep learning (DL)-based approach and proposed the tumor-associated stroma score (TS-score) for predicting pathological complete response (pCR) to NAC with a multicenter dataset. The TS-score was demonstrated to be an independent predictor of pCR, and it not only outperformed the baseline variables and stromal tumor-infiltrating lymphocytes (sTILs) but also significantly improved the prediction performance of the baseline variable-based model. Furthermore, we discovered that unlike lymphocytes, collagen and fibroblasts in the stroma were likely associated with a poor response to NAC. The TS-score has the potential to better stratify breast cancer patients in NAC settings.

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.

Artificial intelligence for quantifying immune infiltrates interacting with stroma in colorectal cancer

Background

We proposed an artificial intelligence-based immune index, Deep-immune score, quantifying the infiltration of immune cells interacting with the tumor stroma in hematoxylin and eosin-stained whole-slide images of colorectal cancer.

Methods

A total of 1010 colorectal cancer patients from three centers were enrolled in this retrospective study, divided into a primary (N = 544) and a validation cohort (N = 466). We proposed the Deep-immune score, which reflected both tumor stroma proportion and the infiltration of immune cells in the stroma region. We further analyzed the correlation between the score and CD3⁺ T cells density in the stroma region using immunohistochemistry-stained whole-slide images. Survival analysis was performed using the Cox proportional hazard model, and the endpoint of the event was the overall survival.

Result

Patients were classified into 4-level score groups (score 1–4). A high Deep-immune score was associated with a high level of CD3⁺ T cells infiltration in the stroma region. In the primary cohort, survival analysis showed a significant difference in 5-year survival rates between score 4 and score 1 groups: 87.4% vs. 58.2% (Hazard ratio for score 4 vs. score 1 0.27, 95% confidence interval 0.15–0.48, P < 0.001). Similar trends were observed in the validation cohort (89.8% vs. 67.0%; 0.31, 0.15–0.62, < 0.001). Stratified analysis showed that the Deep-immune score could distinguish high-risk and low-risk patients in stage II colorectal cancer (P = 0.018).

Conclusion

The proposed Deep-immune score quantified by artificial intelligence can reflect the immune status of patients with colorectal cancer and is associate with favorable survival. This digital pathology-based finding might advocate change in risk stratification and consequent precision medicine.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03666-3.

Breast cancer microenvironment and obesity: challenges for therapy

Women with obesity who develop breast cancer have a worsened prognosis with diminished survival rates and increased rates of metastasis. Obesity is also associated with decreased breast cancer response to endocrine and chemotherapeutic treatments. Studies utilizing multiple in vivo models of obesity as well as human breast tumors have enhanced our understanding of how obesity alters the breast tumor microenvironment. Changes in the complement and function of adipocytes, adipose-derived stromal cells, immune cells, and endothelial cells and remodeling of the extracellular matrix all contribute to the rapid growth of breast tumors in the context of obesity. Interactions of these cells enhance secretion of cytokines and adipokines as well as local levels of estrogen within the breast tumor microenvironment that promote resistance to multiple therapies. In this review, we will discuss our current understanding of the impact of obesity on the breast tumor microenvironment, how obesity-induced changes in cellular interactions promote resistance to breast cancer treatments, and areas for development of treatment interventions for breast cancer patients with obesity.

Standardization of the tumor-stroma ratio scoring method for breast cancer research

Purpose

The tumor-stroma ratio (TSR) has repeatedly proven to be correlated with patient outcomes in breast cancer using large retrospective cohorts. However, studies validating the TSR often show variability in methodology, thereby hampering comparisons and uniform outcomes.

Method

This paper provides a detailed description of a simple and uniform TSR scoring method using Hematoxylin and Eosin (H&E)-stained core biopsies and resection tissue, specifically focused on breast cancer. Possible histological challenges that can be encountered during scoring including suggestions to overcome them are reported. Moreover, the procedure for TSR estimation in lymph nodes, scoring on digital images and the automatic assessment of the TSR using artificial intelligence are described.

Conclusion

Digitized scoring of tumor biopsies and resection material offers interesting future perspectives to determine patient prognosis and response to therapy. The fact that the TSR method is relatively easy, quick, and cheap, offers great potential for its implementation in routine diagnostics, but this requires high quality validation studies.

Magnetic Resonance Imaging (MRI) and MR Spectroscopic Methods in Understanding Breast Cancer Biology and Metabolism

A common malignancy that affects women is breast cancer. It is the second leading cause of cancer-related death among women. Metabolic reprogramming occurs during cancer growth, invasion, and metastases. Functional magnetic resonance (MR) methods comprising an array of techniques have shown potential for illustrating physiological and molecular processes changes before anatomical manifestations on conventional MR imaging. Among these, in vivo proton (1H) MR spectroscopy (MRS) is widely used for differentiating breast malignancy from benign diseases by measuring elevated choline-containing compounds. Further, the use of hyperpolarized 13C and 31P MRS enhanced the understanding of glucose and phospholipid metabolism. The metabolic profiling of an array of biological specimens (intact tissues, tissue extracts, and various biofluids such as blood, urine, nipple aspirates, and fine needle aspirates) can also be investigated through in vitro high-resolution NMR spectroscopy and high-resolution magic angle spectroscopy (HRMAS). Such studies can provide information on more metabolites than what is seen by in vivo MRS, thus providing a deeper insight into cancer biology and metabolism. The analysis of a large number of NMR spectral data sets through multivariate statistical methods classified the tumor sub-types. It showed enormous potential in the development of new therapeutic approaches. Recently, multiparametric MRI approaches were found to be helpful in elucidating the pathophysiology of cancer by quantifying structural, vasculature, diffusion, perfusion, and metabolic abnormalities in vivo. This review focuses on the applications of NMR, MRS, and MRI methods in understanding breast cancer biology and in the diagnosis and therapeutic monitoring of breast cancer.

An easy and practical prognostic parameter: tumor-stroma ratio in Luminal, Her2, and triple-negative breast cancers

OBJECTIVE

The stroma surrounding the tumor cells is important in tumor progression and treatment resistance, besides the properties of tumor cells. Studies on the tumor stroma characteristics will contribute to the knowledge for new treatment approaches.

METHODS

A total of 363 breast cancer patients were evaluated for the tumor-stroma ratio. The percentage of stroma was visually assessed on hematoxylin-eosin stained slides. The cases of tumor-stroma ratio more than 50% were categorized as tumor-stroma ratio high, and those less than 50% and below were categorized as tumor-stroma ratio low.

RESULTS

Tumor-stroma ratio-high tumors had shorter overall survival (p=0.002). Disease-free survival tended to be shorter in tumor-stroma ratio-high tumors (p=0.082) compared with tumor-stroma ratio-low tumors. Tumor-stroma ratio was an independent prognostic parameter for the total group of patients (p=0.003) and also axillary lymph node metastasis and tumor-stroma ratio was statistically associated (p=0.004). Also, tumor-stroma ratio was an independent prognostic parameter in node-positive Luminal A and B subgroups for overall survival (p<0.001).

CONCLUSION

Tumor-stroma ratio is an independent prognostic parameter that can be evaluated quite easily in all molecular subtypes of all breast cancers and does not require extra cost and time to evaluate.

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.

Second-Harmonic Generation Imaging Reveals Changes in Breast Tumor Collagen Induced by Neoadjuvant Chemotherapy

Breast cancer is the most common invasive cancer in women, with most deaths attributed to metastases. Neoadjuvant chemotherapy (NACT) may be prescribed prior to surgical removal of the tumor for subsets of breast cancer patients but can have diverse undesired and off-target effects, including the increased appearance of the 'tumor microenvironment of metastasis', image-based multicellular signatures that are prognostic of breast tumor metastasis. To assess whether NACT can induce changes in two other image-based prognostic/predictive signatures derived from tumor collagen, we quantified second-harmonic generation (SHG) directionality and fiber alignment in formalin-fixed, paraffin-embedded sections of core needle biopsies and primary tumor excisions from 22 human epidermal growth factor receptor 2-overexpressing (HER2+) and 22 triple-negative breast cancers. In both subtypes, we found that SHG directionality (i.e., the forward-to-backward scattering ratio, or F/B) is increased by NACT in the bulk of the tumor, but not the adjacent tumor-stroma interface. Overall collagen fiber alignment is increased by NACT in triple-negative but not HER2+ breast tumors. These results suggest that NACT impacts the collagenous extracellular matrix in a complex and subtype-specific manner, with some prognostic features being unchanged while others are altered in a manner suggestive of a more metastatic phenotype.

The effect of the low stromal ratio induced by neoadjuvant chemotherapy on recurrence patterns in borderline resectable pancreatic ductal adenocarcinoma

The optimal regimens of neoadjuvant chemotherapy (NAC) and its biological and physiological modification of the tumor microenvironment (TME) in patients with borderline resectable pancreatic ductal adenocarcinoma (BR PDAC) remain unknown. A deeper understanding of the complex stromal biology of the TME will identify new avenues to establish treatment strategies for PDAC patients. Herein, we sought to clarify whether stromal remodeling by NAC affects recurrence patterns and prognosis in BR PDAC patients. We retrospectively analyzed data from 104 BR PDAC patients who underwent pancreatectomy with or without NAC (upfront surgery [UpS], n = 44; gemcitabine + nab-paclitaxel [GnP], n = 28; and gemcitabine + S-1 [GS], n = 32) to assess the correlations of treatment with early recurrence, the stromal ratio, and Ki-67 levels. Eighty-six patients experienced recurrence, and those with liver metastasis had significantly shorter recurrence-free survival than those with other recurrence patterns. The frequency of liver metastasis was significantly higher in patients with a low stromal ratio than in those with a high stromal ratio in the NAC group but not in the UpS group. Patients in the GnP group had significantly higher Ki-67 than those in the GS and UpS groups. A low stromal ratio was positively correlated with high Ki-67 in the NAC group but not in the UpS group. The low stromal ratio induced by NAC promoted early liver metastasis in patients with BR PDAC. Our findings provide new insights into the complexity of stromal biology, leading to consideration of the optimal NAC regimen.

Stromal scoring in advanced colon and rectal cancer: Stroma-rich tumors and their association with aggressive phenotypes

Background: Our aim was to explore relevance of the proportion between neoplastic cell component and tumor-associated stroma in order to assess its association with confirmed aggressive phenotypes of right/left colon and rectum cancers in a large series of patients. Methods: The quantification of stroma component was performed in patients diagnosed with colorectal adenocarcinoma who underwent surgical resection. The analyzed variables were age, gender, anatomical/pathological features, and tumor-stroma proportion. Tumor-stroma proportion was estimated based on slides used in routine pathology for determination of T status and was described as low, with a stromal percentage ?50% or high, with a stromal percentage >50%. The tumor-stroma proportion was estimated by two observers, and the inter-observer agreement was assessed. Results: The sample included 390 colorectal adenocarcinoma patients. Stroma-rich tumors were observed in 53.3% of cases. Well-differentiated tumors had the lowest stromal proportions (p = 0.028). Stroma-poor tumors showed less depth of invasion (p<0.001). High stromal content was observed in association with tumor budding, perineural, angiolymphatic, and lymph node involvement, and distant metastasis (p?0.001). Colorectal adenocarcinoma without lymph node or distant metastasis involvement had lower stromal proportion, while metastatic ones exhibited high stromal content (p <0.001). The inter-rater reliability (concordance) between the estimations of pathologists for tumor-stroma proportions was high (?=0.746). Conclusion: The tumorstroma proportion in colorectal adenocarcinoma was associated with adverse prognostic factors, reflecting the stage of the disease. Stroma-rich tumors showed a significant correlation with advancement of the disease and its aggressiveness. Due to its availability tumor-stroma proportion evaluation has high application potential and can complement current staging system for colorectal adenocarcinoma.

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.

Prognostic significance of tumour budding, tumour-stroma ratio and desmoplastic stromal reaction in gall bladder carcinoma

AIMS AND METHODS

The prognostic role of tumour budding (TBd) and its interaction with the stromal microenvironment has gained a lot of attention recently, but remains unexplored in gall bladder cancer (GBC). We aimed to study the interrelationship of TBd by International Tumour Budding Consensus Conference scoring system, tumour-stroma ratio (TSR) and desmoplastic stromal reaction (DSR) with the conventional clinicopathological prognostic factors, mortality and overall survival (OS) in 96 patients of operated GBC.

RESULTS

Higher age, high TNM stage, lymphovascular and perineural invasion, positive resection margins, higher TBd score, low TSR and immature DSR were significantly associated with worse OS. However, on multivariate analysis, only metastases, positive resection margins and TSR <50% proved to be independent prognostic factors. The TBd score of stroma-rich tumour group (6.40±4.69) was significantly higher than that of stroma-poor group (2.77±3.79, p≤0.001). The TBd score of immature and intermediate DSR groups was significantly higher than that of mature group (p≤0.001 and p=0.002, respectively). There was a strong interobserver agreement for TBd score, TSR and type of DSR (Cohen's Kappa=0.726 to 0.864, p≤0.001). Stroma-rich tumours were significantly associated with immature DSR and fibrotic DSR with high TSR (p≤0.001).

CONCLUSION

A high TBd, low TSR and immature DSR were significantly associated with several high-risk clinicopathological parameters and poor OS in GBC. These novel, simple, reproducible and cost-effective parameters may be included in the routine reporting checklist for GBC as additional prognostic parameters that can substratify the high-risk patients.

On the Additional Information Provided by 3T-MRI ADC in Predicting Tumor Cellularity and Microscopic Behavior

BACKGROUND

to evaluate whether Apparent Diffusion Coefficient (ADC) values of invasive breast cancer, provided by 3T Diffusion Weighted-Images (DWI), may represent a non-invasive predictor of pathophysiologic tumor aggressiveness.

METHODS

100 Patients with histologically proven invasive breast cancers who underwent a 3T-MRI examination were included in the study. All MRI examinations included dynamic contrast-enhanced and DWI/ADC sequences. ADC value were calculated for each lesion. Tumor grade was determined according to the Nottingham Grading System, and immuno-histochemical analysis was performed to assess molecular receptors, cellularity rate, on both biopsy and surgical specimens, and proliferation rate (Ki-67 index). Spearman's Rho test was used to correlate ADC values with histological (grading, Ki-67 index and cellularity) and MRI features. ADC values were compared among the different grading (G1, G2, G3), Ki-67 (<20% and >20%) and cellularity groups (<50%, 50-70% and >70%), using Mann-Whitney and Kruskal-Wallis tests. ROC curves were performed to demonstrate the accuracy of the ADC values in predicting the grading, Ki-67 index and cellularity groups.

RESULTS

ADC values correlated significantly with grading, ER receptor status, Ki-67 index and cellularity rates. ADC values were significantly higher for G1 compared with G2 and for G1 compared with G3 and for Ki-67 < 20% than Ki-67 > 20%. The Kruskal-Wallis test showed that ADC values were significantly different among the three grading groups, the three biopsy cellularity groups and the three surgical cellularity groups. The best ROC curves were obtained for the G3 group (AUC of 0.720), for G2 + G3 (AUC of 0.835), for Ki-67 > 20% (AUC of 0.679) and for surgical cellularity rate > 70% (AUC of 0.805).

CONCLUSIONS

3T-DWI ADC is a direct predictor of cellular aggressiveness and proliferation in invasive breast carcinoma, and can be used as a supporting non-invasive factor to characterize macroscopic lesion behavior especially before surgery.

Deep learning-based virtual cytokeratin staining of gastric carcinomas to measure tumor-stroma ratio

The tumor-stroma ratio (TSR) determined by pathologists is subject to intra- and inter-observer variability. We aimed to develop a computational quantification method of TSR using deep learning-based virtual cytokeratin staining algorithms. Patients with 373 advanced (stage III [n = 171] and IV [n = 202]) gastric cancers were analyzed for TSR. Moderate agreement was observed, with a kappa value of 0.623, between deep learning metrics (dTSR) and visual measurement by pathologists (vTSR) and the area under the curve of receiver operating characteristic of 0.907. Moreover, dTSR was significantly associated with the overall survival of the patients (P = 0.0024). In conclusion, we developed a virtual cytokeratin staining and deep learning-based TSR measurement, which may aid in the diagnosis of TSR in gastric cancer.

Tumor-stroma ratio is associated with Miller-Payne score and pathological response to neoadjuvant chemotherapy in HER2-negative early breast cancer

The tumor-stroma ratio (TSR) has proven to be a strong prognostic factor in breast cancer, demonstrating better survival for patients with stroma-low tumors. Since the role of the TSR as a predictive marker for neoadjuvant chemotherapy outcome is yet unknown, this association was evaluated for HER2-negative breast cancer in the prospective DIRECT and NEOZOTAC trials. The TSR was assessed on 375 hematoxylin and eosin-stained sections of pre-treatment biopsies. Associations between the TSR and chemotherapy response according to the Miller-Payne (MP) grading system, and between the TSR and pathological response were examined using Pearson's chi-square, Cochran-Armitage test for trend and regression analyses. A stroma-low tumor prior to neoadjuvant chemotherapy was significantly associated with a higher MP score (P = .005). This relationship remained significant in the estrogen receptor (ER)-negative subgroup (P = .047). The univariable odds ratio (OR) of a stroma-low tumor on pathological complete response (pCR) was 2.46 (95% CI 1.34-4.51, P = .004), which attenuated to 1.90 (95% CI 0.85-4.25, P = .119) after adjustment for relevant prognostic factors. Subgroup analyses revealed an OR of 5.91 in univariable analyses for ER-negativity (95% CI 1.19-29.48, P = .030) and 1.48 for ER-positivity (95% CI 0.73-3.01, P = .281). In conclusion, a low amount of stroma on pre-treatment biopsies is associated with a higher MP score and pCR rate. Therefore, the TSR is a promising biomarker in predicting neoadjuvant treatment outcome. Incorporating this parameter in routine pathological diagnostics could be worthwhile to prevent overtreatment and undertreatment.

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.

e-Learning for Instruction and to Improve Reproducibility of Scoring Tumor-Stroma Ratio in Colon Carcinoma: Performance and Reproducibility Assessment in the UNITED Study

BACKGROUND

The amount of stroma in the primary tumor is an important prognostic parameter. The tumor-stroma ratio (TSR) was previously validated by international research groups as a robust parameter with good interobserver agreement.

OBJECTIVE

The Uniform Noting for International Application of the Tumor-Stroma Ratio as an Easy Diagnostic Tool (UNITED) study was developed to bring the TSR to clinical implementation. As part of the study, an e-Learning module was constructed to confirm the reproducibility of scoring the TSR after proper instruction.

METHODS

The e-Learning module consists of an autoinstruction for TSR determination (instruction video or written protocol) and three sets of 40 cases (training, test, and repetition sets). Scoring the TSR is performed on hematoxylin and eosin-stained sections and takes only 1-2 minutes. Cases are considered stroma-low if the amount of stroma is ≤50%, whereas a stroma-high case is defined as >50% stroma. Inter- and intraobserver agreements were determined based on the Cohen κ score after each set to evaluate the reproducibility.

RESULTS

Pathologists and pathology residents (N=63) with special interest in colorectal cancer participated in the e-Learning. Forty-nine participants started the e-Learning and 31 (63%) finished the whole cycle (3 sets). A significant improvement was observed from the training set to the test set; the median κ score improved from 0.72 to 0.77 (P=.002).

CONCLUSIONS

e-Learning is an effective method to instruct pathologists and pathology residents for scoring the TSR. The reliability of scoring improved from the training to the test set and did not fall back with the repetition set, confirming the reproducibility of the TSR scoring method.

TRIAL REGISTRATION

The Netherlands Trial Registry NTR7270; https://www.trialregister.nl/trial/7072.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/13464.

The prognostic impact of the tumour stroma fraction: A machine learning-based analysis in 16 human solid tumour types

Background

The development of a reactive tumour stroma is a hallmark of tumour progression and pronounced tumour stroma is generally considered to be associated with clinical aggressiveness. The variability between tumour types regarding stroma fraction, and its prognosis associations, have not been systematically analysed.

Methods

Using an objective machine-learning method we quantified the tumour stroma in 16 solid cancer types from 2732 patients, representing retrospective tissue collections of surgically resected primary tumours. Image analysis performed tissue segmentation into stromal and epithelial compartment based on pan-cytokeratin staining and autofluorescence patterns.

Findings

The stroma fraction was highly variable within and across the tumour types, with kidney cancer showing the lowest and pancreato-biliary type periampullary cancer showing the highest stroma proportion (median 19% and 73% respectively). Adjusted Cox regression models revealed both positive (pancreato-biliary type periampullary cancer and oestrogen negative breast cancer, HR(95%CI)=0.56(0.34-0.92) and HR(95%CI)=0.41(0.17-0.98) respectively) and negative (intestinal type periampullary cancer, HR(95%CI)=3.59(1.49-8.62)) associations of the tumour stroma fraction with survival.

Interpretation

Our study provides an objective quantification of the tumour stroma fraction across major types of solid cancer. Findings strongly argue against the commonly promoted view of a general associations between high stroma abundance and poor prognosis. The results also suggest that full exploitation of the prognostic potential of tumour stroma requires analyses that go beyond determination of stroma abundance.

Funding

The Swedish Cancer Society, The Lions Cancer Foundation Uppsala, The Swedish Government Grant for Clinical Research, The Mrs Berta Kamprad Foundation, Sweden, Sellanders foundation, P.O.Zetterling Foundation, and The Sjöberg Foundation, Sweden.

The Tumor Microenvironment in Liver Metastases from Colorectal Carcinoma in the Context of the Histologic Growth Patterns

Colorectal carcinoma (CRC) is the third most common cancer. Likewise, it is a disease that has a long survival if it is prematurely detected. However, more than 50% of patients will develop metastases, mainly in the liver (LM-CRC), throughout the evolution of their disease, which accounts for most CRC-related deaths. Treatment it is certainly a controversial issue, since it has not been shown to increase overall survival in the adjuvant setting, although it does improve disease free survival (DFS). Moreover, current chemotherapy combinations are administered based on data extrapolated from primary tumors (PT), not considering that LM-CRC present a very particular tumor microenvironment that can radically condition the effectiveness of treatments designed for a PT. The liver has a particular histology and microenvironment that can determine tumor growth and response to treatments: double blood supply, vascularization through fenestrated sinusoids and the presence of different mesenchymal cell types, among other particularities. Likewise, the liver presents a peculiar immune response against tumor cells, a fact that correlates with the poor response to immunotherapy. All these aspects will be addressed in this review, putting them in the context of the histological growth patterns of LM-CRC, a particular pathologic feature with both prognostic and predictive repercussions.

3D Tumor Spheroid Models for In Vitro Therapeutic Screening of Nanoparticles

The anticancer activity of compounds and nanoparticles is most often determined in the cell monolayer. However, three-dimensional (3D) systems, such as tumor spheroids, are more representing the natural tumor microenvironment. They have been shown to have higher invasiveness and resistance to cytotoxic agents and radiotherapy compared to cells growing in 2D monolayer. Furthermore, to improve the prediction of clinical efficacy of drugs, in the past decades, even more sophisticated systems, such as multicellular 3D cultures, closely representing natural tumor microenvironment have been developed. Those cultures are formed from either cell lines or patient-derived tumor cells. Such models are very attractive and could improve the selection of tested materials for clinical trials avoiding unnecessary expensive tests in vivo. The microenvironment in tumor spheroids is different, and those differences or the interaction between several cell populations may contribute to different tumor response to the treatment. Also, different types of nanoparticles may have different behavior in 3D models, depending on their nature, physicochemical properties, the presence of targeting ligands on the surface, etc. Therefore, it is very important to understand in which cases which type of tumor spheroid is more suitable for testing specific types of nanoparticles, which conditions should be used, and which analytical method should be applied.

The prognostic value of tumor-stromal ratio combined with TNM staging system in esophagus squamous cell carcinoma

Background: Tumor stroma is a crucial component of the tumor environment that interacted with tumor cells and modulated tumor cell proliferation, immune evasion, and metastasis. Tumor-stromal ratio (TSR) has been confirmed as an influential independent prognostic factor for diverse types of cancer, but it was seldom discussed in esophagus squamous cell carcinoma (ESCC). Methods: In present study, pathological sections from the most invasive part of the ESCC of 270 patients were analyzed for their TSR by visual inspection and software. The TSR was combined with the TNM staging system to further explain its predictive value of prognosis. The 57 cases ESCC from TCGA database also were included as an independently validated cohort. Results: Our results indicated that TSR was a robust prognostic factor for ESCC patients. TSR by visual inspection was dependable to reflect the stroma percent of the tumor compared to software calculation. Compared with stroma-low groups, the risk of death increased by 153.1% for patients in the stroma-high group [HR=2.531 (95%CI 1.657-3.867), P<0.001]. The results of ROC analysis in two cohorts indicated that TSNM staging system had better resolving ability with the largest area under the curve [0.698 95%CI (0.635-0.760), 0.691 95%CI (0.555-0.807)], compare to TNM. The novel TSNM staging system revealed strong predictive performance (P<0.001). Conclusion: TSR was a reliable dependent indicator for ESCC prognosis. The TSNM staging system has a better discriminative ability than the conventional TNM staging system, especially for III stage patients.

Concurrent Comparison of the Prognostic Values of Tumor Budding, Tumor Stroma Ratio, Tumor Infiltrating Pattern and Lymphocyte-to-Monocyte Ratio in Colorectal Cancer Patients

Objectives: Tumor budding (TB), tumor stroma ratio (TSR), tumor infiltrating pattern (TIP), and preoperative lymphocyte-to-monocyte ratio (LMR) were previously reported to be useful prognostic factors in colorectal cancer (CRC); however, the correlation among these markers and their individual prognostic potency have not been extensively studied. Methods: A cohort of 147 stage I-IV CRC patients was obtained retrospectively, and the patients were divided into subgroups based on low or high TB/TSR/LMR, TIPa (expansile + intermediate) and TIPb (infiltrative) values. The differences in relapse-free survival (RFS) and overall survival (OS) intervals among these subgroups were determined by Kaplan-Meier analysis followed by log-rank tests. The Cox proportional hazard model was applied for the univariate and multivariate analysis of RFS and OS rates. Results:TB, TIP, and LMR, but not TSR, are useful markers for predicting patient survival. Patients with a poor histological grade and large tumor diameter were more likely to present with high TB, TIPb, and low LMR values; in addition, those with advanced T, N, and TNM stages and elevated preoperative CA199 levels had high TB and TIPb levels. TB, TIP, and LMR were significant prognostic factors for the RFS (TB: HR [hazard ratio] = 2.28, 95% CI = 1.30-4.00, P < .01; TIP: HR = 2.60, 95% CI = 1.46-4.60, P < .01; LMR: HR = 0.79, 95% CI = 0.65-0.96, P = .02) and OS (TB: HR = 2.43, 95% CI = 1.32-4.48, P < .01; TIP: HR = 2.49, 95% CI = 1.34-4.63, P < .01; LMR: HR = 0.79, 95% CI = 0.64-0.98, P = .03) intervals. In addition, TB and LMR were independent prognostic factors for the RFS interval (TB: HR = 1.80, 95% CI = 1.01-3.19, P = .05; LMR: HR = 0.80, 95% CI = 0.67-0.96, P = .01), but only LMR was an independent factor for OS rates (HR = 0.80, 95% CI = 0.65-0.98, P = .03). Conclusion: Although TB, TIP, and LMR are useful prognostic markers for CRC, the LMR is likely to be the only independent prognostic factor for both RFS and OS outcomes in practice.

Standardized Assessment of the Tumor-Stroma Ratio in Colorectal Cancer: Interobserver Validation and Reproducibility of a Potential Prognostic Factor

The tumor stroma plays a relevant role in the initiation and evolution of solid tumors. Tumor-stroma ratio (TSR) is a histological feature that expresses the proportion of the stromal component that surrounds cancer cells. In different studies, the TSR represents a potential prognostic factor: a rich stroma in tumor tissue can promote invasion and aggressiveness. The aim of this study was to evaluate the reproducibility and determine the interobserver agreement in the TSR score. The stromal estimate was evaluated in patients diagnosed with colorectal adenocarcinoma (CRA), who underwent surgical resection. We also evaluated age, gender, and other anatomopathological features. Tumor-stroma ratio was calculated based on the slide used in routine diagnostic pathology to determine the T-status. Stromal percentages were separated into 2 categories: ⩽50%-low stroma and >50%-high stroma. The interobserver agreement in the TSR scoring was evaluated among 4 pathologists at different stages of professional experience, using 2 different ways to learn the scoring system. In total, 98 patients were included in this study; 54.1% were male, with a mean age of 61.9 years. Localized disease was diagnosed in 60.2% of patients. Stromal-poor CRA was predominant. The concordance between the TSR percentages of the 4 pathologists was substantial (Kappa > 0.6). There was greater agreement among pathologists for stromal-poor tumors. Substantial agreement and high reproducibility were observed in the determination of TSR score. The TSR score is feasible, suggesting that the presented methodology can be used to facilitate the determination of the stromal proportion of potential prognostic factor.

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.

A Ticket to Ride: The Implications of Direct Intercellular Communication via Tunneling Nanotubes in Peritoneal and Other Invasive Malignancies

It is well established that the role of the tumor microenvironment (TME) in cancer progression and therapeutic resistance is crucial, but many of the underlying mechanisms are still being elucidated. Even with better understanding of molecular oncology and identification of genomic drivers of these processes, there has been a relative lag in identifying and appreciating the cellular drivers of both invasion and resistance. Intercellular communication is a vital process that unifies and synchronizes the diverse components of the tumoral infrastructure. Elucidation of the role of extracellular vesicles (EVs) over the past decade has cast a brighter light on this field. And yet even with this advance, in addition to diffusible soluble factor-mediated paracrine and endocrine cell communication as well as EVs, additional niches of intratumoral communication are filled by other modes of intercellular transfer. Tunneling nanotubes (TNTs), tumor microtubes (TMs), and other similar intercellular channels are long filamentous actin-based cellular conduits (in most epithelial cancer cell types, ~15-500 µm in length; 50–1000+ nm in width). They extend and form direct connections between distant cells, serving as conduits for direct intercellular transfer of cell cargo, such as mitochondria, exosomes, and microRNAs; however, many of their functional roles in mediating tumor growth remain unknown. These conduits literally create a physical bridge to create a syncytial network of dispersed cells amidst the intercellular stroma-rich matrix. Emerging evidence suggests that they provide a cellular mechanism for induction and emergence of drug resistance and contribute to increased invasive and metastatic potential. They have been imaged in vitro and also in vivo and ex vivo in tumors from human patients as well as animal models, thus not only proving their existence in the TME, but opening further speculation about their exact role in the dynamic niche of tumor ecosystems. TNT cellular networks are upregulated between cancer and stromal cells under hypoxic and other conditions of physiologic and metabolic stress. Furthermore, they can connect malignant cells to benign cells, including vascular endothelial cells. The field of investigation of TNT-mediated tumor-stromal, and tumor-tumor, cell-cell communication is gaining momentum. The mixture of conditions in the microenvironment exemplified by hypoxia-induced ovarian cancer TNTs playing a crucial role in tumor growth, as just one example, is a potential avenue of investigation that will uncover their role in relation to other known factors, including EVs. If the role of cancer heterocellular signaling via TNTs in the TME is proven to be crucial, then disrupting formation and maintenance of TNTs represents a novel therapeutic approach for ovarian and other similarly invasive peritoneal cancers.

Offerhaus G, Siersema PD, Tollenaar RAEM, Hardwick JCH, Hawinkels LJAC, Moons LMG, Lacle MM, Mesker WE, Boonstra JJ. Tumour-stroma ratio has poor prognostic value in non-pedunculated T1 colorectal cancer: A multi-centre case-cohort study

BACKGROUND

Current risk stratification models for early invasive (T1) colorectal cancer are not able to discriminate accurately between prognostic favourable and unfavourable tumours, resulting in over-treatment of a large (>80%) proportion of T1 colorectal cancer patients. The tumour-stroma ratio (TSR), which is a measure for the relative amount of desmoplastic tumour stroma, is reported to be a strong independent prognostic factor in advanced-stage colorectal cancer, with a high stromal content being associated with worse prognosis and survival. We aimed to investigate whether the TSR predicts clinical outcome in patients with non-pedunculated T1 colorectal cancer.

METHODS

Hematoxylin and eosin (H&E)-stained tumour tissue slides from a retrospective multi-centre case cohort of patients with non-pedunculated surgically treated T1 colorectal cancer were assessed for TSR by two independent observers who were blinded for clinical outcomes. The primary end point was adverse outcome, which was defined as the presence of lymph node metastasis in the resection specimen or colorectal cancer recurrence during follow-up.

RESULTS

All 261 patients in the case cohort had H&E slides available for TSR scoring. Of these, 183 were scored as stroma-low, and 78 were scored as stroma-high. There was moderate inter-observer agreement (κ = 0.42). In total, 41 patients had lymph node metastasis, 17 patients had recurrent cancer and five had both. Stroma-high tumours were not associated with an increased risk for an adverse outcome (adjusted hazard ratio = 0.66, 95% confidence interval 0.37-1.18; p = 0.163).

CONCLUSIONS

Our study emphasises that existing prognosticators may not be simply extrapolated to T1 colorectal cancers, even though their prognostic value has been widely validated in more advanced-stage tumours.

Correlation of the tumour-stroma ratio with diffusion weighted MRI in rectal cancer

OBJECTIVE

This study evaluated the correlation between intratumoural stroma proportion, expressed as tumour-stroma ratio (TSR), and apparent diffusion coefficient (ADC) values in patients with rectal cancer.

METHODS

This multicentre retrospective study included all consecutive patients with rectal cancer, diagnostically confirmed by biopsy and MRI. The training cohort (LUMC, Netherlands) included 33 patients and the validation cohort (VHIO, Spain) 69 patients. Two observers measured the mean and minimum ADCs based on single-slice and whole-volume segmentations. The TSR was determined on diagnostic haematoxylin & eosin stained slides of rectal tumour biopsies. The correlation between TSR and ADC was assessed by Spearman correlation (r_s).

RESULTS

The ADC values between stroma-low and stroma-high tumours were not significantly different. Intra-class correlation (ICC) demonstrated a good level of agreement for the ADC measurements, ranging from 0.84-0.86 for single slice and 0.86-0.90 for the whole-volume protocol. No correlation was observed between the TSR and ADC values, with ADC_meanr_s= -0.162 (p= 0.38) and ADC_minr_s= 0.041 (p= 0.82) for the single-slice and r_s= -0.108 (p= 0.55) and r_s= 0.019 (p= 0.92) for the whole-volume measurements in the training cohort, respectively. Results from the validation cohort were consistent; ADC_meanr_s= -0.022 (p= 0.86) and ADC_minr_s = 0.049 (p= 0.69) for the single-slice and r_s= -0.064 (p= 0.59) and r_s= -0.063 (p= 0.61) for the whole-volume measurements.

CONCLUSIONS

Reproducibility of ADC values is good. Despite positive reports on the correlation between TSR and ADC values in other tumours, this could not be confirmed for rectal cancer.

Chemotherapy resistance and stromal targets in breast cancer treatment: a review

Therapy resistance is a known problem in breast cancer and is associated with a variety of mechanisms. The role of the tumor microenvironment in cancer development and resistance mechanisms is becoming increasingly understood. Tumor–stroma is the main component of the tumor microenvironment. Stromal cells like cancer-associated fibroblasts (CAFs) are believed to contribute to chemotherapy resistance via the production of several secreted factors like cytokines and chemokines. CAFs are found to influence disease progression; patients with primary tumors with a high amount of tumor–stroma have a significantly worse outcome. Therefore the role of CAFs resistance mechanisms makes them a promising target in anti-cancer therapy. An overview of recent advances in strategies to target breast cancer stroma is given and the current literature regarding these stromal targets is discussed. CAF-specific proteins as well as secreted molecules involved in tumor–stroma interactions provide possibilities for stroma-specific therapy. The development of stroma-specific therapy is still in its infancy and the available literature is limited. Within the scope of personalized treatment, biomarkers based on the tumor–stroma have future potential for the improvement of treatment via image-guided surgery (IGS) and PET scanning.

Stromal CCL2 Signaling Promotes Mammary Tumor Fibrosis through Recruitment of Myeloid-Lineage Cells

Obesity is correlated with breast tumor desmoplasia, leading to diminished chemotherapy response and disease-free survival. Obesity causes chronic, macrophage-driven inflammation within breast tissue, initiated by chemokine ligand 2 (CCL2) signaling from adipose stromal cells. To understand how CCL2-induced inflammation alters breast tumor pathology, we transplanted oncogenically transformed human breast epithelial cells with breast stromal cells expressing CCL2 or empty vector into murine mammary glands and examined tumor formation and progression with time. As tumors developed, macrophages were rapidly recruited, followed by the emergence of cancer-associated fibroblasts (CAFs) and collagen deposition. Depletion of CD11b + myeloid lineage cells early in tumor formation reduced tumor growth, CAF numbers, and collagen deposition. CCL2 expression within developing tumors also enhanced recruitment of myeloid progenitor cells from the bone marrow into the tumor site. The myeloid progenitor cell population contained elevated numbers of fibrocytes, which exhibited platelet-derived growth factor receptor-alpha (PDGFRα)-dependent colony formation and growth in vitro. Together, these results suggest that chronic inflammation induced by CCL2 significantly enhances tumor growth and promotes the formation of a desmoplastic stroma through early recruitment of macrophages and fibrocytes into the tumor microenvironment. Fibrocytes may be a novel target in the tumor microenvironment to reduce tumor fibrosis and enhance treatment responses for obese breast cancer patients.

Human Plasma-Derived 3D Cultures Model Breast Cancer Treatment Responses and Predict Clinically Effective Drug Treatment Concentrations

Lack of efficacy and a low overall success rate of phase I-II clinical trials are the most common failures when it comes to advancing cancer treatment. Current drug sensitivity screenings present several challenges including differences in cell growth rates, the inconsistent use of drug metrics, and the lack of translatability. Here, we present a patient-derived 3D culture model to overcome these limitations in breast cancer (BCa). The human plasma-derived 3D culture model (HuP3D) utilizes patient plasma as the matrix, where BCa cell lines and primary BCa biopsies were grown and screened for drug treatments. Several drug metrics were evaluated from relative cell count and growth rate curves. Correlations between HuP3D metrics, established preclinical models, and clinical effective concentrations in patients were determined. HuP3D efficiently supported the growth and expansion of BCa cell lines and primary breast cancer tumors as both organoids and single cells. Significant and strong correlations between clinical effective concentrations in patients were found for eight out of ten metrics for HuP3D, while a very poor positive correlation and a moderate correlation was found for 2D models and other 3D models, respectively. HuP3D is a feasible and efficacious platform for supporting the growth and expansion of BCa, allowing high-throughput drug screening and predicting clinically effective therapies better than current preclinical models.

Expression of receptor activator of NFkB (RANK) drives stemness and resistance to therapy in ER+HER2- breast cancer

The role of RANKL-RANK pathway in progesterone-driven mammary carcinogenesis and triple negative breast cancer tumorigenesis has been well characterized. However, and despite evidences of the existence of RANK-positive hormone receptor (HR)-positive breast tumors, the implication of RANK expression in HR-positive breast cancers has not been addressed before. Here, we report that RANK pathway affects the expression of cell cycle regulators and decreases sensitivity to fulvestrant of estrogen receptor (ER)-positive (ER+)/HER2- breast cancer cells, MCF-7 and T47D. Moreover, RANK overexpressing cells had a staminal and mesenchymal phenotype, with decreased proliferation rate and decreased susceptibility to chemotherapy, but were more invasive in vivo. In silico analysis of the transcriptome of human breast tumors, confirmed the association between RANK expression and stem cell and mesenchymal markers in ER+HER2- tumors. Importantly, exposure of ER+HER2- cells to continuous RANK pathway activation by exogenous RANKL, in vitro and in vivo, induced a negative feedback effect, independent of RANK levels, leading to the downregulation of HR and increased resistance to hormone therapy. These results suggest that ER+HER2- RANK-positive cells may constitute an important reservoir of slow cycling, therapy-resistance cancer cells; and that RANK pathway activation is deleterious in all ER+HER2- breast cancer cells, independently of RANK levels.

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.

Three-dimensional models of breast cancer-fibroblasts interactions

IMPACT STATEMENT

Tumor stroma plays an important role in progression of cancers to a fatal metastatic disease. Modern treatment strategies are considering targeting tumor stroma to improve outcomes for cancer patients. A current challenge to develop stroma-targeting therapeutics is the lack of preclinical physiologic tumor models. Animal models widely used in cancer research lack human stroma and are not amenable to screening of chemical compounds for cancer drug discovery. In this review, we outline in vitro three-dimensional tumor models that we have developed to study the interactions among cancer cells and stromal cells. We describe development of the tumor models in a modular fashion, from a spheroid model to a sophisticated organotypic model, and discuss the importance of using correct physiologic models to recapitulate tumor-stromal signaling. These biomimetic tumor models will facilitate understanding of tumor-stromal signaling biology and provide a scalable approach for testing and discovery of cancer drugs.

Tunneling Nanotubes and Tumor Microtubes in Cancer

Intercellular communication among cancer cells and their microenvironment is crucial to disease progression. The mechanisms by which communication occurs between distant cells in a tumor matrix remain poorly understood. In the last two decades, experimental evidence from different groups proved the existence of thin membranous tubes that interconnect cells, named tunneling nanotubes, tumor microtubes, cytonemes or membrane bridges. These highly dynamic membrane protrusions are conduits for direct cell-to-cell communication, particularly for intercellular signaling and transport of cellular cargo over long distances. Tunneling nanotubes and tumor microtubes may play an important role in the pathogenesis of cancer. They may contribute to the resistance of tumor cells against treatments such as surgery, radio- and chemotherapy. In this review, we present the current knowledge about the structure and function of tunneling nanotubes and tumor microtubes in cancer and discuss the therapeutic potential of membrane tubes in cancer treatment.

Differentiation between Luminal A and B Molecular Subtypes of Breast Cancer Using Pharmacokinetic Quantitative Parameters with Histogram and Texture Features on Preoperative Dynamic Contrast-Enhanced Magnetic Resonance Imaging

OBJECTIVE

The aim of the present study was to use pharmacokinetic quantitative parameters with histogram and texture features on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to differentiate between the luminal A and luminal B molecular subtypes of breast cancer.

METHODS

We retrospectively reviewed the data of 94 patients with histopathologically proven breast cancer. The pharmacokinetic quantitative parameters (K^trans, K_ep, and V_e) with their corresponding histogram and texture features based on preoperative DCE-MRI were obtained. The parameters were compared using the Mann-Whitney U-test between the luminal A and luminal B groups, the human epidermal growth factor receptor-2 (HER2)-positive luminal B and HER2-negative luminal B groups, and the lymph node metastasis (LNM)-positive and LNM-negative groups. Receiver operating characteristic curves were generated for parameters that presented significant between-group differences.

RESULTS

The maximum values of K^trans, K_ep, and V_e, and the mean and 90th percentile values of V_e were significantly higher in the luminal B group than in the luminal A group. Among the texture features, only skewness of K^trans significantly differed between the luminal A and B groups. All histogram features of K^trans were higher in the HER2-positive luminal B group than in the HER2-negative luminal B group. However, no parameter differed between the LNM-positive and LNM-negative groups.

CONCLUSION

Pharmacokinetic quantitative parameters with histogram and texture features obtained from DCE-MRI are associated with the molecular subtypes of breast cancer, and may serve as potential imaging biomarkers to differentiate between the luminal A and luminal B molecular subtypes.