Quantification of stromal reaction in breast carcinoma and its correlation with tumor grade and free progression survival

Exploration of crucial stromal risk genes associated with prognostic significance and chemotherapeutic opportunities in invasive ductal breast carcinoma

BACKGROUND

Few studies revealed that stromal genes regulate the tumor microenvironment (TME). However, identification of key-risk genes in the invasive ductal breast carcinoma-associated stroma (IDBCS) and their associations with the prediction of risk group remains lacking.

METHODS

This study used the GSE9014, GSE10797, GSE8977, GSE33692, and TGGA BRCA datasets. We explored the differentially expressed transcriptional markers, hub genes, gene modules, and enriched KEGG pathways. We employed a variety of algorithms, such as the log-rank test, the LASSO-cox model, the univariate regression model, and the multivariate regression model, to predict prognostic-risk genes and the prognostic-risk model. Finally, we employed a molecular docking-based study to explore the interaction of sensitive drugs with prognostic-risk genes.

RESULTS

In comparing IDBCS and normal stroma, we discovered 1472 upregulated genes and 1400 downregulated genes (combined ES > 0585 and adjusted p-value < 0.05). The hub genes enrich cancer, immunity, and cellular signaling pathways. We explored the 12 key risk genes (ADAM8, CD86, CSRP1, DCTN2, EPHA1, GALNT10, IGFBP6, MIA, MMP11, RBM22, SLC39A4, and SYT2) in the IDBCS to identify the high-risk group and low-risk group patients. The high-risk group had a lower survival rate, and the constructed ROC curves evaluated the validity of the risk model. Expression validation and diagnostic efficacy revealed that the key stromal risk genes are consistently deregulated in the high-risk group and high stromal samples of the TCGA BRCA cohort. The expression of crucial risk genes, including CD86, CSRP1, EPHA1, GALNT10, IGFBP6, MIA, and RBM22 are associated with drug resistance and drug sensitivity. Finally, a molecular docking study explored several sensitive drugs (such as QL-XII-61, THZ-2-49, AZ628, NG-25, lapatinib, dasatinib, SB590885, and dabrafenib) interacted with these essential risk genes through hydrogen bonds and other chemical interactions.

CONCLUSIONS

Exploring essential prognostic-risk genes and their association with the prognosis, diagnostic efficacy, and risk-group prediction may provide substantial clues for targeting the breast cancer stromal key-risk genes.

Inverse correlation between the amounts of lymphocytic infiltrate and stroma in breast carcinoma

Background

Previous breast carcinoma studies focused on the evaluation of tumour-infiltrating lymphocytes (TILs) or of tumoural stroma via the tumour stroma ratio (TSR). Few studies assessed peritumoural lymphocytes and almost no studies investigated a possible relationship between lymphocytes and stroma. This prompted us to evaluate the amount of tumour cells, intra- and peritumoural lymphocytes, and stroma in breast cancer to support the hypothesis that the stroma may block the infiltration of lymphocytes inside the tumour.

Methods

We collected a retrospective series of 158 breast cancers (<25 mm). In addition to standard TILs and TSR evaluations, we assessed the percentages of tumour cells, stromal myofibroblasts, intra- and peritumoural lymphocytes on full-section tumours with haematoxylin and eosin and immunohistochemical staining.

Results

We showed significant negative correlations between the amounts of stroma and both intra- and peritumoural lymphocyte percentages. Considering the estrogen receptor positive invasive breast cancer of no special type cases, we showed that TSR had a positive prognostic value with an optimal threshold of 10 %.

Conclusions

This study is one of the first to show inverse correlations between tumoural stroma amount and intra- and peritumoural lymphocyte percentages, which supports the hypothesis that tumoural stroma can prevent the recruitment of lymphocytes around and within the tumour.

Unselective Measurement of Tumor-to-Stroma Proportion in Colon Cancer at the Invasion Front-An Elusive Prognostic Factor: Original Patient Data and Review of the Literature

The tumor-to-stroma ratio is a highly debated prognostic factor in the management of several solid tumors and there is no universal agreement on its practicality. In our study, we proposed confirming or dismissing the hypothesis that a simple measurement of stroma quantity is an easy-to-use and strong prognostic tool. We have included 74 consecutive patients with colorectal cancer who underwent primary curative abdominal surgery. The tumors have been grouped into stroma-poor (stroma < 10%), medium-stroma (between 10 and 50%) and stroma-rich (over 50%). The proportion of tumor stroma ranged from 5% to 70% with a median of 25%. Very few, only 6.8% of patients, had stroma-rich tumors, 4% had stroma-poor tumors and 89.2% had tumors with a medium quantity of stroma. The proportion of stroma, at any cut-off, had no statistically significant influence on the disease-specific survival. This can be explained by the low proportion of stroma-rich tumors in our patient group and the inverse correlation between stroma proportion and tumor grade. The real-life proportion of stroma-rich tumors and the complex nature of the stroma-tumor interaction has to be further elucidated.

Alterations in the mammary gland and tumor microenvironment of formerly obese mice

BACKGROUND

Obesity is a risk factor for breast cancer, and women with obesity that develop breast cancer have a worsened prognosis. Within the mammary gland, obesity causes chronic, macrophage-driven inflammation and adipose tissue fibrosis. Weight loss is a recommended intervention to resolve obesity, but the impact of weight loss on the mammary gland microenvironment and in tumors has not been well identified.

METHODS

To examine the effects of weight loss following obesity, mice were fed a high-fat diet for 16 weeks to induce obesity, then switched to a low-fat diet for 6 weeks. We examined changes in immune cells, including fibrocytes, which are myeloid lineage cells that have attributes of both macrophages and myofibroblasts, and collagen deposition within the mammary glands of non-tumor-bearing mice and within the tumors of mice that were transplanted with estrogen receptor alpha positive TC2 tumor cells.

RESULTS

In formerly obese mice, we observed reduced numbers of crown-like structures and fibrocytes in mammary glands, while collagen deposition was not resolved with weight loss. Following transplant of TC2 tumor cells into the mammary glands of lean, obese, and formerly obese mice, diminished collagen deposition and cancer-associated fibroblasts were observed in tumors from formerly obese mice compared to obese mice. Within tumors of obese mice, increased myeloid-derived suppressor cells and diminished CD8+ T cells were identified, while the microenvironment of tumors of formerly obese mice were more similar to tumors from lean mice. When TC2 tumor cells were mixed with CD11b+CD34+ myeloid progenitor cells, which are the cells of origin for fibrocytes, and transplanted into mammary glands of lean and obese mice, collagen deposition within the tumors of both lean and obese was significantly greater than when tumor cells were mixed with CD11b+CD34- monocytes or total CD45+ immune cells.

CONCLUSIONS

Overall, these studies demonstrate that weight loss resolved some of the microenvironmental conditions within the mammary gland that may contribute to tumor progression. Additionally, fibrocytes may contribute to early collagen deposition in mammary tumors of obese mice leading to the growth of desmoplastic tumors.

Reassessing Breast Cancer-Associated Fibroblasts (CAFs) Interactions with Other Stromal Components and Clinico-Pathologic Parameters by Using Immunohistochemistry and Digital Image Analysis (DIA)

BACKGROUND

Breast cancer (BC) stroma has CD34- and αSMA-positive cancer-associated fibroblasts (CAFs) differently distributed. During malignant transformation, CD34-positive fibroblasts decrease while αSMA-positive CAFs increase. The prevalence of αSMA-positive CAFs in BC stroma makes microscopic examination difficult without digital image analysis processing (DIA). DIA was used to compare CD34- and αSMA-positive CAFs among breast cancer molecular subgroups. DIA-derived data were linked to age, survival, tumor stroma vessels, tertiary lymphoid structures (TLS), invasion, and recurrence.

METHODS

Double immunostaining for CD34 and αSMA showed different CAF distribution patterns in normal and BC tissues. Single CD34 immunohistochemistry on supplemental slides quantified tumor stroma CD34_CAFs. Digital image analysis (DIA) data on CAF density, intensity, stromal score, and H-score were correlated with clinico-pathologic factors.

RESULTS

CD34/αSMA CAF proportion was significantly related to age in Luminal A (LA), Luminal B (LB), and HER2 subtypes. CD34_CAF influence on survival, invasion, and recurrence of LA, LB-HER2, and TNBC subtypes was found to be significant. The CD34/αSMA-expressing CAFs exhibited a heterogeneous impact on stromal vasculature and TLS.

CONCLUSION

BC stromal CD34_CAFs/αSMA_CAFs have an impact on survival, invasion, and recurrence differently between BC molecular subtypes. The tumor stroma DIA assessment may have predictive potential to prognosis and long-term follow-up of patients with breast cancer.

Alterations in the Mammary Gland and Tumor Microenvironment of Formerly Obese Mice

Obesity is a risk factor for breast cancer, and women with obesity that develop breast cancer have a worsened prognosis. Within the mammary gland, obesity causes chronic, macrophage-driven inflammation and adipose tissue fibrosis. To examine the impact of weight loss on the mammary microenvironment, mice were fed high-fat diet to induce obesity, then switched to a low-fat diet. In formerly obese mice, we observed reduced numbers of crown-like structures and fibrocytes in mammary glands, while collagen deposition was not resolved with weight loss. Following transplant of TC2 tumor cells into the mammary glands of lean, obese, and formerly obese mice, diminished collagen deposition and cancer-associated fibroblasts were observed in tumors from formerly obese mice compared to obese mice. When TC2 tumor cells were mixed with CD11b+CD34+ myeloid progenitor cells, collagen deposition within the tumors was significantly greater compared to when tumor cells were mixed with CD11b+CD34- monocytes, suggesting that fibrocytes contribute to early collagen deposition in mammary tumors of obese mice. Overall, these studies show that weight loss resolved some of the microenvironmental conditions within the mammary gland that may contribute to tumor progression.

Prostate cancer tissue classification by multiphoton imaging, automated image analysis and machine learning

Prostate carcinoma, a slow-growing and often indolent tumour, is the second most commonly diagnosed cancer among men worldwide. The prognosis is mainly based on the Gleason system through prostate biopsy analysis. However, new treatment and monitoring strategies depend on a more precise diagnosis. Here, we present results by multiphoton imaging for prostate tumour samples from 120 patients that allow to obtain quantitative parameters leading to specific tumour aggressiveness signatures. An automated image analysis was developed to recognise and quantify stromal fibre and neoplastic cell regions in each image. The set of metrics was able to distinguish between non-neoplastic tissue and carcinoma areas by linear discriminant analysis and random forest with accuracy of 89% ± 3%, but between Gleason groups of only 46% ± 6%. The reactive stroma analysis improved the accuracy to 65% ± 5%, clearly demonstrating that stromal parameters should be considered as additional criteria for a more accurate diagnosis.

Phosphodiesterase type 5 inhibitors enhance chemotherapy in preclinical models of esophageal adenocarcinoma by targeting cancer-associated fibroblasts

The chemotherapy resistance of esophageal adenocarcinomas (EACs) is underpinned by cancer cell extrinsic mechanisms of the tumor microenvironment (TME). We demonstrate that, by targeting the tumor-promoting functions of the predominant TME cell type, cancer-associated fibroblasts (CAFs) with phosphodiesterase type 5 inhibitors (PDE5i), we can enhance the efficacy of standard-of-care chemotherapy. In ex vivo conditions, PDE5i prevent the transdifferentiation of normal fibroblasts to CAF and abolish the tumor-promoting function of established EAC CAFs. Using shotgun proteomics and single-cell RNA-seq, we reveal PDE5i-specific regulation of pathways related to fibroblast activation and tumor promotion. Finally, we confirm the efficacy of PDE5i in combination with chemotherapy in close-to-patient and in vivo PDX-based model systems. These findings demonstrate that CAFs drive chemotherapy resistance in EACs and can be targeted by repurposing PDE5i, a safe and well-tolerated class of drug administered to millions of patients world-wide to treat erectile dysfunction.

Deep learning-based tumor microenvironment analysis in colon adenocarcinoma histopathological whole-slide images

BACKGROUND AND OBJECTIVE

Colon cancer is a fatal disease, and a comprehensive understanding of the tumor microenvironment (TME) could lead to better risk stratification, prognosis prediction, and therapy management. In this paper, we focused on the automatic evaluation of TME in giga-pixel digital histopathology whole-slide images.

METHODS

A convolutional neural network is used to recognize nine different content presented in colon cancer whole-slide images. Several implementation details, including the foreground filtering and stain normalization are discussed. Based on the whole-slide segmentation, several TME descriptors are quantified and correlated with the clinical outcome by Kaplan-Meier analysis and Cox regression. Specifically, the stroma, tumor, necrosis, and lymphocyte components are discussed.

RESULTS

We validated the method on colon adenocarcinoma cases from The Cancer Genome Atlas project. The result shows that the stroma is an independent predictor of progression-free interval (PFI) after corrected by age and pathological stage, with a hazard ratio of 1.665 (95%CI: 1.110~2.495, p = 0.014). High-level necrosis component and lymphocytes component tend to be correlated with poor PFI, with a hazard ratio of 1.552 (95%CI: 0.943~2.554, p = 0.084) and 1.512 (95%CI: 0.979~2.336, p = 0.062), respectively.

CONCLUSIONS

The result reveals the complex role of the tumor microenvironment in colon adenocarcinoma, and the quantified descriptors are potential predictors of disease progression. The method could be considered for risk stratification and targeted therapy and extend to other types of cancer, leading to a better understanding of the tumor microenvironment.

Advanced co-culture 3D breast cancer model for investigation of fibrosis induced by external stimuli: optimization study

Radiation-induced fibrosis (RIF) is the main late radiation toxicity in breast cancer patients. Most of the current 3D in vitro breast cancer models are composed by cancer cells only and are unable to reproduce the complex cellular homeostasis within the tumor microenvironment to study RIF mechanisms. In order to account complex cellular interactions within the tumor microenvironment, an advanced 3D spheroid model, consisting of the luminal breast cancer MCF-7 cells and MRC-5 fibroblasts, was developed. The spheroids were generated using the liquid overlay technique in culture media into 96-well plates previously coated with 1% agarose (m/v, in water). In total, 21 experimental setups were tested during the optimization of the model. The generated spheroids were characterized using fluorescence imaging, immunohistology and immunohistochemistry. The expression of ECM components was confirmed in co-culture spheroids. Using α-SMA staining, we confirmed the differentiation of healthy fibroblasts into myofibroblasts upon the co-culturing with cancer cells. The induction of fibrosis was studied in spheroids treated 24 h with 10 ng/mL TGF-β and/or 2 Gy irradiation. Overall, the developed advanced 3D stroma-rich in vitro model of breast cancer provides a possibility to study fibrosis mechanisms taking into account 3D arrangement of the complex tumor microenvironment.

Canine mammary cancer diagnosis from quantitative properties of nonlinear optical images

We present nonlinear microscopy imaging results and analysis from canine mammary cancer biopsies. Second harmonic generation imaging allows information of the collagen structure in the extracellular matrix that together with the fluorescence of the cell regions of the biopsies form a base for comprehensive image analysis. We demonstrate an automated image analysis method to classify the histological type of canine mammary cancer using a range of parameters extracted from the images. The software developed for image processing and analysis allows for the extraction of the collagen fibre network and the cell regions of the images. Thus, the tissue properties are obtained after the segmentation of the image and the metrics are measured specifically for the collagen and the cell regions. A linear discriminant analysis including all the extracted metrics allowed to clearly separate between the healthy and cancerous tissue with a 91%-accuracy. Also, a 61%-accuracy was achieved for a comparison of healthy and three histological cancer subtypes studied.

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.