The significance of tumour microarchitectural features in breast cancer prognosis: a digital image analysis

Assessing Spectral Analysis of Phytoconstituents and Their In Silico Interactions with Target Proteins in Plant Seed Extracts

The pharmacological and preventive attributes of extracts from vegetable seeds have garnered widespread recognition within the scientific community. This study systematically assessed the in vitro antibacterial, antioxidant, and anti-breast cancer properties of phytochemicals present in various solvent-based vegetable seed extracts. We also conducted molecular docking simulations to ascertain their interactions with specific target proteins. Besides, nine distinct chemical constituents were identified using gas chromatography-mass spectrometry (GCMS). Remarkably, the ethyl acetate extract exhibited robust inhibitory effects against Gram-positive and Gram-negative bacterial strains. Furthermore, its capacity for 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging was found to be noteworthy, with an IC50 value of 550.82 ± 1.7 µg/mL, representing a scavenging efficiency of 64.1 ± 2.8%. Additionally, the ethyl acetate extract demonstrated significant hydrogen peroxide (H2O2) scavenging activity, with a maximal scavenging rate of 44.1 ± 1.70% (IC50) at a concentration of 761.17 ± 1.8 µg/mL. Intriguingly, in vitro cytotoxicity assays against human breast cancer (MCF-7) cells revealed varying levels of cell viability at different extract concentrations, suggesting potential anticancer properties. Importantly, these ethyl acetate extracts did not display toxicity to L929 cells across the concentration range tested. Subsequently, we conducted in-silico molecular docking experiments utilizing Discovery Studio 4.0 against the c-Met kinase protein (hepatocyte growth factor; PDB ID: 1N0W). Among the various compounds assessed, 3,4-Dihydroxy-1,6-bis-(3-methoxy-phenyl)-hexa-2,4-diene-1,6-dione exhibited a notable binding energy of -9.1 kcal/mol, warranting further investigation into its potential anticancer properties, clinical applications, and broader pharmacological characteristics.

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.

Comparative and mechanistic study of pharmacodynamic difference in anti-breast cancer activity between water and liquor extracts of Xiaojin Pills

ETHNOPHARMACOLOGICAL RELEVANCE

Xiaojin Pills was first recorded during the Qing Dynasty and have a history of nearly 300 years. It is the first choice among Chinese patent medicines for the clinical treatment of diseases of the mammary glands in contemporary traditional Chinese medicine. It was also widely used in the treatment of breast cancer, lung cancer, thyroid cancer, and other malignant tumors. Its initial administration method was "taken orally after soaking with Chinese baijiu"; however, the method was changed to "taken orally with water" within the last 40 years. There is no scientific evidence for the difference in efficacy against breast cancer between the two methods of administration.

AIM OF THE STUDY

In vitro and in vivo experiments were carried out to confirm the therapeutic advantages of the liquor extract of Xiaojin Pills to improve the efficacy against breast cancer, and the mechanism was explained in terms of metabolomics and molecular biology.

MATERIALS AND METHODS

In vitro, a cell counting kit-8 cell activity assay and flow cytometry were used to detect the activity and apoptosis of MCF-7 breast cancer cells. In vivo, pharmacodynamic evaluation was performed by constructing a heterotopic transplantation model of breast cancer in BALB/c-nu mice. TUNEL staining was used to observe the apoptosis of cells in tumor tissues. The expression of proteins associated with the phosphoinositide 3-kinase (PI3K)/Akt pathway in BALB/c-nu mice tissue was investigated by metabolomics analysis, immunohistochemistry and western blotting.

RESULTS

CCK-8 assay showed that the IC₅₀ of XJP-L for the inhibition of the activity of MCF-7 cells was less than that of XJP-W at different times. Flow cytometry assay suggested that the apoptosis rate in the XJP-L group was higher than that in the normal control group (p < 0.01). Animal experiment results indicated that both XJP-W group and XJP-L group reduced the volume and quality of the tumor after administration, and the reduction was more significant in the XJP-L group (p < 0.01). Metabolomics analysis results demonstrated that there are about 26 different metabolites have been screened in the serum metabolites between the liquor and water extract, mainly involved in glycerophospholipid, glutamic acid, aspartic acid, nitrogen and pyrimidine metabolism. In addition, immunohistochemistry and WB results showed that compared with the model group, the protein expression of PTEN, AKT, BAX and in tumor tissues of XJP-L and XJP-W groups both exhibited an upward trend, while the expression of BCL-2, p-PI3K and p-AKT exhibited a downward trend, which was much more obvious in XJP-L group.

CONCLUSIONS

This study demonstrates that the liquor extract of Xiaojin Pills had a stronger anti-breast cancer effect than that of the water extract. The PI3K/Akt signaling pathway might play an important role in the mechanism of the liquor extract of Xiaojin Pills and thus improve the efficacy against breast cancer.

A FIJI macro for quantifying pattern in extracellular matrix

Diverse extracellular matrix patterns are observed in both normal and pathological tissue. However, most current tools for quantitative analysis focus on a single aspect of matrix patterning. Thus, an automated pipeline that simultaneously quantifies a broad range of metrics and enables a comprehensive description of varied matrix patterns is needed. To this end, we have developed an ImageJ plugin called TWOMBLI, which stands for The Workflow Of Matrix BioLogy Informatics. This pipeline includes metrics of matrix alignment, length, branching, end points, gaps, fractal dimension, curvature, and the distribution of fibre thickness. TWOMBLI is designed to be quick, versatile and easy-to-use particularly for non-computational scientists. TWOMBLI can be downloaded from https://github.com/wershofe/TWOMBLI together with detailed documentation and tutorial video. Although developed with the extracellular matrix in mind, TWOMBLI is versatile and can be applied to vascular and cytoskeletal networks. Here we present an overview of the pipeline together with examples from a wide range of contexts where matrix patterns are generated.

Deep learning identifies morphological features in breast cancer predictive of cancer ERBB2 status and trastuzumab treatment efficacy

The treatment of patients with ERBB2 (HER2)-positive breast cancer with anti-ERBB2 therapy is based on the detection of ERBB2 gene amplification or protein overexpression. Machine learning (ML) algorithms can predict the amplification of ERBB2 based on tumor morphological features, but it is not known whether ML-derived features can predict survival and efficacy of anti-ERBB2 treatment. In this study, we trained a deep learning model with digital images of hematoxylin–eosin (H&E)-stained formalin-fixed primary breast tumor tissue sections, weakly supervised by ERBB2 gene amplification status. The gene amplification was determined by chromogenic in situ hybridization (CISH). The training data comprised digitized tissue microarray (TMA) samples from 1,047 patients. The correlation between the deep learning–predicted ERBB2 status, which we call H&E-ERBB2 score, and distant disease-free survival (DDFS) was investigated on a fully independent test set, which included whole-slide tumor images from 712 patients with trastuzumab treatment status available. The area under the receiver operating characteristic curve (AUC) in predicting gene amplification in the test sets was 0.70 (95% CI, 0.63–0.77) on 354 TMA samples and 0.67 (95% CI, 0.62–0.71) on 712 whole-slide images. Among patients with ERBB2-positive cancer treated with trastuzumab, those with a higher than the median morphology–based H&E-ERBB2 score derived from machine learning had more favorable DDFS than those with a lower score (hazard ratio [HR] 0.37; 95% CI, 0.15–0.93; P = 0.034). A high H&E-ERBB2 score was associated with unfavorable survival in patients with ERBB2-negative cancer as determined by CISH. ERBB2-associated morphology correlated with the efficacy of adjuvant anti-ERBB2 treatment and can contribute to treatment-predictive information in breast cancer.

Review of the current state of digital image analysis in breast pathology

Advances in digital image analysis have the potential to transform the practice of breast pathology. In the near future, a move to a digital workflow offers improvements in efficiency. Coupled with artificial intelligence (AI), digital pathology can assist pathologist interpretation, automate time-consuming tasks, and discover novel morphologic patterns. Opportunities for digital enhancements abound in breast pathology, from increasing reproducibility in grading and biomarker interpretation, to discovering features that correlate with patient outcome and treatment. Our objective is to review the most recent developments in digital pathology with clear impact to breast pathology practice. Although breast pathologists currently undertake limited adoption of digital methods, the field is rapidly evolving. Care is needed to validate emerging technologies for effective patient care.

Plasma C-MYC level manifesting as an indicator in progression of breast cancer

Aim: To investigate whether plasma C-MYC level could be an indicator in clinical progression of breast cancer. Materials & methods: Plasma level of C-MYC expression was detected by quantitative real time PCR and the level of c-myc protein in breast cancer tissues was detected by immunohistochemistry. The expression level of C-MYC mRNA in supernatant of cancer cells culture was measured compared with the nonbreast cancer cells. Results: Plasma C-MYC level was significantly higher in patients with breast cancer than that in the controls, which associated with clinical stages, lymph node status, etc. Receiver operating characteristic curve analysis showed the sensitivity and specificity of plasma C-MYC level for diagnosis of breast cancer were 63.6 and 81.8%, respectively. The expression of c-myc protein in breast cancer tissues was associated with plasma C-MYC level, even C-MYC level in supernatant of cancer cells was elevated. Conclusion: Plasma C-MYC level might be a potential indicator in progression of breast cancer.

Breast cancer outcome prediction with tumour tissue images and machine learning

PURPOSE

Recent advances in machine learning have enabled better understanding of large and complex visual data. Here, we aim to investigate patient outcome prediction with a machine learning method using only an image of tumour sample as an input.

METHODS

Utilising tissue microarray (TMA) samples obtained from the primary tumour of patients (N = 1299) within a nationwide breast cancer series with long-term-follow-up, we train and validate a machine learning method for patient outcome prediction. The prediction is performed by classifying samples into low or high digital risk score (DRS) groups. The outcome classifier is trained using sample images of 868 patients and evaluated and compared with human expert classification in a test set of 431 patients.

RESULTS

In univariate survival analysis, the DRS classification resulted in a hazard ratio of 2.10 (95% CI 1.33-3.32, p = 0.001) for breast cancer-specific survival. The DRS classification remained as an independent predictor of breast cancer-specific survival in a multivariate Cox model with a hazard ratio of 2.04 (95% CI 1.20-3.44, p = 0.007). The accuracy (C-index) of the DRS grouping was 0.60 (95% CI 0.55-0.65), as compared to 0.58 (95% CI 0.53-0.63) for human expert predictions based on the same TMA samples.

CONCLUSIONS

Our findings demonstrate the feasibility of learning prognostic signals in tumour tissue images without domain knowledge. Although further validation is needed, our study suggests that machine learning algorithms can extract prognostically relevant information from tumour histology complementing the currently used prognostic factors in breast cancer.