Ki67 in breast cancer: prognostic and predictive potential

Identification of potential targetable genes in papillary, follicular, and anaplastic thyroid carcinoma using bioinformatics analysis

PURPOSE

To perform an extensive exploratory analysis to build a deeper insight into clinically relevant molecular biomarkers in Papillary, Follicular, and Anaplastic thyroid carcinomas (PTC, FTC, ATC).

METHODS

Thirteen Thyroid Cancer (THCA) datasets incorporating PTC, FTC, and ATC were derived from the Gene Expression Omnibus. Genes differentially expressed (DEGs) between THCA and normal were identified and subjected to GO and KEGG analyses. Multiple topological properties were harnessed and protein-protein interaction (PPI) networks were constructed to identify the hub genes followed by survival analysis and validation.

RESULTS

There were 70, 87, and 377 DEGs, and 23, 27, and 53 hub genes for PTC, FTC, and ATC samples, respectively. Survival analysis detected 39 overall and 49 relapse-free survival-relevant hub genes. Six hub genes, BCL2, FN1, ITPR1, LYVE1, NTRK2, TBC1D4, were found common to more than one THCA type. The most significant hub genes found in the study were: BCL2, CD44, DCN, FN1, IRS1, ITPR1, MFAP4, MKI67, NTRK2, PCLO, TGFA. The most enriched and significant GO terms were Melanocyte differentiation for PTC, Extracellular region for FTC, and Extracellular exosome for ATC. Prostate cancer for PTC was the most significantly enriched KEGG pathway. The results were validated using TCGA data.

CONCLUSIONS

The findings unravel potential biomarkers and therapeutic targets of thyroid carcinomas.

Inferring replication timing and proliferation dynamics from single-cell DNA sequencing data

Dysregulated DNA replication is a cause and a consequence of aneuploidy in cancer, yet the interplay between copy number alterations (CNAs), replication timing (RT) and cell cycle dynamics remain understudied in aneuploid tumors. We developed a probabilistic method, PERT, for simultaneous inference of cell-specific replication and copy number states from single-cell whole genome sequencing (scWGS) data. We used PERT to investigate clone-specific RT and proliferation dynamics in  >50,000 cells obtained from aneuploid and clonally heterogeneous cell lines, xenografts and primary cancers. We observed bidirectional relationships between RT and CNAs, with CNAs affecting X-inactivation producing the largest RT shifts. Additionally, we found that clone-specific S-phase enrichment positively correlated with ground-truth proliferation rates in genomically stable but not unstable cells. Together, these results demonstrate robust computational identification of S-phase cells from scWGS data, and highlight the importance of RT and cell cycle properties in studying the genomic evolution of aneuploid tumors.

EDB-FN-targeted probes for near infrared fluorescent imaging and positron emission tomography imaging of breast cancer in mice

The extra domain B splice variant of fibronectin (EDB-FN), which is overexpressed in several cancers, is an approved diagnostic and therapeutic target of cancers. The aim of this study was to evaluate the EDB-FN-targeting peptide EDBp as a noninvasive imaging modality for molecular imaging of breast cancer in mice. Western blot, flow cytometry and immunofluorescence were used to assess the expression level of EDB-FN and its binding to EDRp in MCF7, SKBR3, 4T1, EMT6, MDA-MB-231 and MDA-MB-453 cells. Establishment MDA-MB-231-luc cells-based subcutaneous tumor model mice or pulmonary metastasis model mice. The EDRp molecular probes to perform fluorescent probes for near-infrared fluorescence (NIRF)·and PET imaging of model mice. Our results demonstrate that EDBp-Cy5 had a strong binding ability to the MDA-MB-231 cells and exhibited specific tumor accumulation in MDA-MB-231 subcutaneous and pulmonary metastasis model mice. Importantly, the EDBp peptide-based radiotracer [18F]-AlF-NOTA-EDBp provided excellent diagnostic value for positron emission tomography (PET) imaging of breast cancer, especially in subcutaneous model mice. The uptake of [18F]-AlF-NOTA-EDBp in subcutaneous tumors (6.53 ± 0.89%, ID/g) was unexpectedly higher than that in the kidney (4.96 ± 0.20, %ID/g). The high tumor uptake of these probes in mice suggests their potential for application in imaging of EDB-FN-positive breast cancer for disease staging of regional and distant metastases.

Visualization of Biomolecular Radiation Damage at the Single-Particle Level Using Lanthanide-Sensitized DNA Origami

Precise monitoring of biomolecular radiation damage is crucial for understanding X-ray-induced cell injury and improving the accuracy of clinical radiotherapy. We present the design and performance of lanthanide-DNA-origami nanodosimeters for directly visualizing radiation damage at the single-particle level. Lanthanide ions (Tb3+ or Eu3+) coordinated with DNA origami nanosensors enhance the sensitivity of X-ray irradiation. Atomic force microscopy (AFM) revealed morphological changes in Eu3+-sensitized DNA origami upon X-ray irradiation, indicating damage caused by ionization-generated electrons and free radicals. We further demonstrated the practical applicability of Eu3+-DNA-origami integrated chips in precisely monitoring radiation-mediated cancer radiotherapy. Quantitative results showed consistent trends with flow cytometry and histological examination under comparable X-ray irradiation doses, providing an affordable and user-friendly visualization tool for preclinical applications. These findings provide new insights into the impact of heavy metals on radiation-induced biomolecular damage and pave the way for future research in developing nanoscale radiation sensors for precise clinical radiography.

POP1 Facilitates Proliferation in Triple-Negative Breast Cancer via m6A-Dependent Degradation of CDKN1A mRNA

Triple-negative breast cancer (TNBC) is currently the worst prognostic subtype of breast cancer, and there is no effective treatment other than chemotherapy. Processing of precursors 1 (POP1) is the most substantially up-regulated RNA-binding protein (RBP) in TNBC. However, the role of POP1 in TNBC remains clarified. A series of molecular biological experiments in vitro and in vivo and clinical correlation analyses were conducted to clarify the biological function and regulatory mechanism of POP1 in TNBC. Here, we identified that POP1 is significantly up-regulated in TNBC and associated with poor prognosis. We further demonstrate that POP1 promotes the cell cycle and proliferation of TNBC in vitro and vivo. Mechanistically, POP1 directly binds to the coding sequence (CDS) region of CDKN1A mRNA and degrades it. The degradation process depends on the N6-methyladenosine (m6A) modification at the 497th site of CDKN1A and the recognition of this modification by YTH N6-methyladenosine RNA binding protein 2 (YTHDF2). Moreover, the m6A inhibitor STM2457 potently impaired the proliferation of POP1-overexpressed TNBC cells and improved the sensitivity to paclitaxel. In summary, our findings reveal the pivotal role of POP1 in promoting TNBC proliferation by degrading the mRNA of CDKN1A and that inhibition of m6A with STM2457 is a promising therapeutic strategy for TNBC.

Role of the Clinical Features and MRI Parameters on Ki-67 Expression in Hepatocellular Carcinoma Patients: Development of a Predictive Nomogram

PURPOSE

To develop a nomogram using clinical features and the MRI parameters for preoperatively predicting the expression of Ki-67 in patients with hepatocellular carcinoma (HCC).

METHODS

One hundred and forty patients (training cohorts: n = 108; validation cohorts: n = 32) with confirmed HCC were investigated. Mann-Whitney U test, independent sample t-test, and chi-squared test were used to analyze the continuous and categorical variables. Univariate and multivariate logistic regression analyses were performed to examine the clinical variables and parameters from MRI associated with Ki-67 expression. As a result, a nomogram was developed based on these associations in patients with HCC. The performance of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC) and calibration curves.

RESULTS

In the training set, multivariable logistic regression analysis revealed that lens culinaris agglutinin-reactive fraction of alpha-fetoprotein (AFP-L3) levels, protein induced by vitamin K absence or antagonist-II (PIVKA-II) levels, and tumor shape were independent predictors for Ki-67 expression (p < 0.05). These three variables and the apparent diffusion coefficient (ADC) value were used to establish a nomogram, while the ADC value was found to be a marginal significant predictor. The model demonstrated a strong ability to discriminate Ki-67 expression in both the training and validation cohorts (AUC = 0.862, 0.877).

CONCLUSION

A non-invasive preoperative prediction method, which incorporates MRI variables and clinical features was developed, and showed effectiveness in evaluating Ki-67 expression in HCC patients.

Radiomics Nomogram Based on Dual-Sequence MRI for Assessing Ki-67 Expression in Breast Cancer

BACKGROUND

Radiomics has been extensively applied in predicting Ki-67 in breast cancer (BC). However, this is often confined to the exploration of a single sequence, without considering the varying sensitivity and specificity among different sequences.

PURPOSE

To develop a nomogram based on dual-sequence MRI derived radiomic features combined with clinical characteristics for assessing Ki-67 expression in BC.

STUDY TYPE

Retrospective.

POPULATION

227 females (average age, 51 years) with 233 lesions and pathologically confirmed BC, which were divided into the training set (n = 163) and test set (n = 70).

FIELD STRENGTH/SEQUENCE

3.0-T, T1-weighted dynamic contrast-enhanced MRI (DCE-MRI) and apparent diffusion coefficient (ADC) maps from diffusion-weighted MRI (EPI sequence).

ASSESSMENT

The regions of interest were manually delineated on ADC and DCE-MRI sequences. Three radiomics models of ADC, DCE-MRI, and dsMRI (combined ADC and DCE-MRI sequences) were constructed by logistic regression and the radiomics score (Radscore) of the best model was calculated. The correlation between Ki-67 expression and clinical characteristics such as receptor status, axillary lymph node (ALN) metastasis status, ADC value, and time signal intensity curve was analyzed, and the clinical model was established. The Radscore was combined with clinical predictors to construct a nomogram.

STATISTICAL TESTS

The independent sample t-test, Mann-Whitney U test, Chi-squared test, Interclass correlation coefficients (ICCs), single factor analysis, least absolute shrinkage and selection operator (LASSO), logistic regression, receiver operating characteristics, Delong test, Hosmer_Lemeshow test, calibration curve, decision curve. A P-value <0.05 was considered statistically significant.

RESULTS

In the test set, the prediction efficiency of the dsMRI model (AUC = 0.862) was higher than ADC model (AUC = 0.797) and DCE-MRI model (AUC = 0.755). With the inclusion of estrogen receptor (ER) and ALN metastasis, the nomogram displayed quality improvement (AUC = 0.876), which was superior to the clinical model (AUC = 0.787) and radiomics model.

DATA CONCLUSION

The nomogram based on dsMRI radiomic features and clinical characteristics may be able to assess Ki-67 expression in BC.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 3.

Deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram for predicting Ki-67 expression in rectal cancer

PURPOSE

To explore the value of deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram in predicting the Ki-67 expression in rectal cancer.

METHODS

The data of 491 patients with rectal cancer from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. They were categorized into high- and low-expression group based on postoperative pathological Ki-67 expression. Each patient's mp-MRI data were analyzed to extract and select the most relevant features of deep learning, and a deep learning model was constructed. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a nomogram for the prediction of Ki-67 expression. The performance characteristics of the DL-model, clinical model, and nomogram were assessed using ROCs, calibration curve, decision curve, and clinical impact curve analysis.

RESULTS

The strongest deep learning features were extracted and screened from mp-MRI data. Two independent predictive factors, namely Magnetic Resonance Imaging T (mrT) staging and differentiation degree, were identified through clinical feature selection. Three models were constructed: a deep learning (DL)-model, a clinical model, and a nomogram. The AUCs of clinical model in the training, internal validation, and external validation set were 0.69, 0.78, and 0.67, respectively. The AUCs of the deep model and nomogram ranged from 0.88 to 0.98. The prediction performance of the deep learning model and nomogram was significantly better than the clinical model (P < 0.001).

CONCLUSION

The nomogram based on deep learning can help clinicians accurately and conveniently predict the expression status of Ki-67 in rectal cancer.

Histogram analysis of intravoxel incoherent motion imaging: Correlation with molecular prognostic factors and combined subtypes of breast cancer

PURPOSE

To look for links between diffusion and IVIM parameters and different molecular subtypes and prognostic factors through histogram analysis.

MATERIALS AND METHODS

A total of 139 patients with breast cancer who had pre-operative MRI examinations were enrolled in this retrospective study. Histograms of the diffusion and IVIM parameters were analyzed for the whole tumor, and an association was investigated between the parameters and the different molecular prognostic factors and subtypes using the nonparametric test, Spearman's rank correlation, and receiver operating characteristic (ROC) curve.

RESULTS

The histogram metrics of the diffusion and IVIM parameters were significantly different for molecular prognostic factors such as human epidermal receptor factor-2 (HER2), progesterone receptor, estrogen receptor, and ki-67. All histogram metrics displayed a poor correlation with all groups (r = -0.28-0.29). There were significant differences in the histogram metrics for the Luminal B-HER2 (-) vs. HER2-positive (non-luminal) subtypes in the mean and 10th percentile D, with the area under the curves (AUCs) of 0.742 and 0.700, respectively, and for the Luminal A and HER2-positive (non-luminal) subtypes in the 90th percentile and entropy of D*, with AUCs of 0.769 and 0.727, respectively.

CONCLUSION

The histogram metrics of IVIM parameters exhibited links with breast cancer prognosis factors and combined subtypes.

A Microfluidic 3D-Tumor-Spheroid Model for the Evaluation of Targeted Therapies from Angiogenesis-Related Cytokines at the Single Spheroid Level

Angiogenesis is a key player in drug resistance to targeted therapies for breast cancer. The average expression of angiogenesis-related cytokines is widely associated with the treatments of target therapies for a population of cells or spheroids, overlooking the distinct responses for individuals. In this work, a highly integrated microfluidic platform is developed for the generation of monodisperse multicellular tumor spheroids (MTSs), drug treatments, and the measurement of cytokines for individual MTSs in a single chip. The platform allows the correlation evaluation between cytokine secretion and drug treatment at the level of individual spheroids. For validation, quantities of six representative proangiogenic cytokines are tested against treatments with four model drugs at varying times and concentrations. By applying a linear regression model, significant correlations are established between cytokine secretion and the treated drug concentration for individual spheroids. The proposed platform provides a high-throughput method for the investigation of the molecular mechanism of the cytokine response to targeted therapies and paves the way for future drug screening using predictive regression models at the single-spheroid level.

Machine-Learning and Radiomics-Based Preoperative Prediction of Ki-67 Expression in Glioma Using MRI Data

BACKGROUND

Gliomas are the most common primary brain tumours and constitute approximately half of all malignant glioblastomas. Unfortunately, patients diagnosed with malignant glioblastomas typically survive for less than a year. In light of this circumstance, genotyping is an effective means of categorising gliomas. The Ki67 proliferation index, a widely used marker of cellular proliferation in clinical contexts, has demonstrated potential for predicting tumour classification and prognosis. In particular, magnetic resonance imaging (MRI) plays a vital role in the diagnosis of brain tumours. Using MRI to extract glioma-related features and construct a machine learning model offers a viable avenue to classify and predict the level of Ki67 expression.

METHODS

This study retrospectively collected MRI data and postoperative immunohistochemical results from 613 glioma patients from the First Affliated Hospital of Nanjing Medical University. Subsequently, we performed registration and skull stripping on the four MRI modalities: T1-weighted (T1), T2-weighted (T2), T1-weighted with contrast enhancement (T1CE), and Fluid Attenuated Inversion Recovery (FLAIR). Each modality's segmentation yielded three distinct tumour regions. Following segmentation, a comprehensive set of features encompassing texture, first-order, and shape attributes were extracted from these delineated regions. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) algorithm with subsequent sorting to identify the most important features. These selected features were further analysed using correlation analysis to finalise the selection for machine learning model development. Eight models: logistic regression (LR), naive bayes, decision tree, gradient boosting tree, and support vector classification (SVM), random forest (RF), XGBoost, and LightGBM were used to objectively classify Ki67 expression.

RESULTS

In total, 613 patients were enroled in the study, and 24,455 radiomic features were extracted from each patient's MRI. These features were eventually reduced to 36 after LASSO screening, RF importance ranking, and correlation analysis. Among all the tested machine learning models, LR and linear SVM exhibited superior performance. LR achieved the highest area under the curve score of 0.912 ± 0.036, while linear SVM obtained the top accuracy with a score of 0.884 ± 0.031.

CONCLUSION

This study introduced a novel approach for classifying Ki67 expression levels using MRI, which has been proven to be highly effective. With the LR model at its core, our method demonstrated its potential in signalling a promising avenue for future research. This innovative approach of predicting Ki67 expression based on MRI features not only enhances our understanding of cell activity but also represents a significant leap forward in brain glioma research. This underscores the potential of integrating machine learning with medical imaging to aid in the diagnosis and prognosis of complex diseases.

The Ki67 dilemma: investigating prognostic cut-offs and reproducibility for automated Ki67 scoring in breast cancer

PURPOSE

Quantification of Ki67 in breast cancer is a well-established prognostic and predictive marker, but inter-laboratory variability has hampered its clinical usefulness. This study compares the prognostic value and reproducibility of Ki67 scoring using four automated, digital image analysis (DIA) methods and two manual methods.

METHODS

The study cohort consisted of 367 patients diagnosed between 1990 and 2004, with hormone receptor positive, HER2 negative, lymph node negative breast cancer. Manual scoring of Ki67 was performed using predefined criteria. DIA Ki67 scoring was performed using QuPath and Visiopharm® platforms. Reproducibility was assessed by the intraclass correlation coefficient (ICC). ROC curve survival analysis identified optimal cutoff values in addition to recommendations by the International Ki67 Working Group and Norwegian Guidelines. Kaplan-Meier curves, log-rank test and Cox regression analysis assessed the association between Ki67 scoring and distant metastasis (DM) free survival.

RESULTS

The manual hotspot and global scoring methods showed good agreement when compared to their counterpart DIA methods (ICC > 0.780), and good to excellent agreement between different DIA hotspot scoring platforms (ICC 0.781-0.906). Different Ki67 cutoffs demonstrate significant DM-free survival (p < 0.05). DIA scoring had greater prognostic value for DM-free survival using a 14% cutoff (HR 3.054-4.077) than manual scoring (HR 2.012-2.056). The use of a single cutoff for all scoring methods affected the distribution of prediction outcomes (e.g. false positives and negatives).

CONCLUSION

This study demonstrates that DIA scoring of Ki67 is superior to manual methods, but further study is required to standardize automated, DIA scoring and definition of a clinical cut-off.

SNX14 inhibits autophagy via the PI3K/AKT/mTOR signaling cascade in breast cancer cells

BACKGROUND

Sorting nexin 14 (SNX14) is a member of the sorting junction protein family. Its specific roles in cancer development remain unclear. Therefore, in this study, we aimed to determine the effects and underlying mechanisms of SNX14 on autophagy of breast cancer cells to aid in the therapeutic treatment of breast cancer.

METHODS

In this study, we performed in vitro experiments to determine the effect of SNX14 on breast cancer cell growth. Moreover, we used an MCF7 breast cancer tumor-bearing mouse model to confirm the effect of SNX14 on tumor cell growth in vivo. We also performed western blotting and quantitative polymerase chain reaction to identify the mechanism by which SNX14 affects breast cancer MCF7 cells.

RESULTS

We found that SNX14 regulated the onset and progression of breast cancer by promoting the proliferation and inhibiting the autophagy of MCF7 breast cancer cells. In vivo experiments further confirmed that SNX14 knockdown inhibited the tumorigenicity and inhibited the growth of tumor cells in tumor tissues of nude mice. In addition, western blotting analysis revealed that SNX14 modulate the autophagy of MCF7 breast cancer cells via the phosphoinositide 3-kinase/protein kinase B/mechanistic target of rapamycin kinase signaling pathway.

CONCLUSION

Our findings indicate that SNX14 is an essential tumor-promoting factor in the development of breast cancer.

A pH-triggered N-oxide polyzwitterionic nano-drug loaded system for the anti-tumor immunity activation research

Triple negative breast cancer (TNBC) has the characteristics of low immune cell infiltration, high expression of tumor programmed death ligand 1 (PD-L1), and abundant cancer stem cells. Systemic toxicity of traditional chemotherapy drugs due to poor drug selectivity, and chemotherapy failure due to tumor drug resistance and other problems, so it is particularly important to find new cancer treatment strategies for TNBC with limited treatment options. Both the anti-tumor natural drugs curcumin and ginsenoside Rg3 can exert anti-tumor effects by inducing immunogenic cell death (ICD) of tumor cells, reducing PD-L1 expression, and reducing cancer stem cells. However, they have the disadvantages of poor water solubility, low bioavailability, and weak anti-tumor effect of single agents. We used vinyl ether bonds to link curcumin (Cur) with N-O type zwitterionic polymers and at the same time encapsulated ginsenoside Rg3 to obtain hyperbranched zwitterionic drug-loaded micelles OPDEA-PGED-5HA@Cur@Rg3 (PPH@CR) with pH response. In vitro cell experiments and in vivo animal experiments have proved that PPH@CR could not only promote the maturation of dendritic cells (DCs) and increase the CD4+ T cells and CD8+ T cells by inducing ICD in tumor cells but also reduce the expression of PD-L1 in tumor tissues, and reduce cancer stem cells and showed better anti-tumor effects and good biological safety compared with free double drugs, which is a promising cancer treatment strategy.

The tumor-stroma ratio in giant cell tumor of bone: associations with the immune microenvironment and responsiveness to denosumab treatment

BACKGROUND

Currently, there is limited understanding regarding the clinical significance of the tumor-stroma ratio (TSR) in giant cell tumor of bone (GCTB). Hence, we aimed to investigate the distribution of TSR in GCTB and explore its correlation with various clinicopathologic factors, immune microenvironment, survival prognosis, and denosumab treatment responsiveness.

METHODS

We conducted a multicenter cohort study comprising 426 GCTB patients treated at four centers. TSR was evaluated on hematoxylin and eosin-stained and immunofluorescent sections of tumor specimens. Immunohistochemistry was performed to assess CD3+, CD4+, CD8+, CD20+, PD-1+, PD-L1+, and FoxP3+ TIL subtypes as well as Ki-67 expression levels in 426 tissue specimens. These parameters were then analyzed for their correlations with patient outcomes [local recurrence-free survival (LRFS) and overall survival (OS)], clinicopathological features, and denosumab treatment responsiveness.

RESULTS

Low TSR was significantly associated with poor LRFS and OS in both cohorts. Furthermore, TSR was also correlated with multiple clinicopathological features, TIL subtype expression, and denosumab treatment responsiveness. TSR demonstrated similar predictive capabilities as the conventional Campanacci staging system for predicting patients' LRFS and OS.

CONCLUSION

The results of this study provide evidence supporting the use of TSR as a reliable prognostic tool in GCTB and as a predictor of denosumab treatment responsiveness. These findings may aid in developing individualized treatment strategies for GCTB patients in the future.

Deep learning for automated scoring of immunohistochemically stained tumour tissue sections - Validation across tumour types based on patient outcomes

We aimed to develop deep learning (DL) models to detect protein expression in immunohistochemically (IHC) stained tissue-sections, and to compare their accuracy and performance with manually scored clinically relevant proteins in common cancer types. Five cancer patient cohorts (colon, two prostate, breast, and endometrial) were included. We developed separate DL models for scoring IHC-stained tissue-sections with nuclear, cytoplasmic, and membranous staining patterns. For training, we used images with annotations of cells with positive and negative staining from the colon cohort stained for Ki-67 and PMS2 (nuclear model), the prostate cohort 1 stained for PTEN (cytoplasmic model) and β-catenin (membranous model). The nuclear DL model was validated for MSH6 in the colon, MSH6 and PMS2 in the endometrium, Ki-67 and CyclinB1 in prostate, and oestrogen and progesterone receptors in the breast cancer cohorts. The cytoplasmic DL model was validated for PTEN and Mapre2, and the membranous DL model for CD44 and Flotillin1, all in prostate cohorts. When comparing the results of manual and DL scores in the validation sets, using manual scores as the ground truth, we observed an average correct classification rate of 91.5 % (76.9-98.5 %) for the nuclear model, 85.6 % (73.3-96.6 %) for the cytoplasmic model, and 78.4 % (75.5-84.3 %) for the membranous model. In survival analyses, manual and DL scores showed similar prognostic impact, with similar hazard ratios and p-values for all DL models. Our findings demonstrate that DL models offer a promising alternative to manual IHC scoring, providing efficiency and reproducibility across various data sources and markers.

Predicting Ki-67 expression levels in breast cancer using radiomics-based approaches on digital breast tomosynthesis and ultrasound

Purpose

To construct and validate radiomics models that utilize ultrasound (US) and digital breast tomosynthesis (DBT) images independently and in combination to non-invasively predict the Ki-67 status in breast cancer.

Materials and methods

149 breast cancer women who underwent DBT and US scans were retrospectively enrolled from June 2018 to August 2023 in total. Radiomics features were acquired from both the DBT and US images, then selected and reduced in dimensionality using several screening approaches. Establish radiomics models based on DBT, and US separately and combined. The area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity were utilized to validate the predictive ability of the models. The decision curve analysis (DCA) was used to evaluate the clinical applicability of the models. The output of the classifier with the best AUC performance was converted into Rad-score and was regarded as Rad-Score model. A nomogram was constructed using the logistic regression method, integrating the Rad-Score and clinical factors. The model's stability was assessed through AUC, calibration curves, and DCA.

Results

Support vector machine (SVM), logistic regression (LR), and random forest (RF) were trained to establish radiomics models with the selected features, with SVM showing optimal results. The AUC values for three models (US_SVM, DBT_SVM, and merge_SVM) were 0.668, 0.704, and 0.800 respectively. The DeLong test indicated a notable disparity in the area under the curve (AUC) between merge_SVM and US_SVM (p = 0.048), while there was no substantial variability between merge_SVM and DBT_SVM (p = 0.149). The DCA curve indicates that merge_SVM is superior to unimodal models in predicting high Ki-67 level, showing more clinical values. The nomogram integrating Rad-Score with tumor size obtained the better performance in test set (AUC: 0.818) and had more clinical net.

Conclusion

The fusion radiomics model performed better in predicting the Ki-67 expression level of breast carcinoma, but the gain effect is limited; thus, DBT is preferred as a preoperative diagnosis mode when resources are limited. Nomogram offers predictive advantages over other methods and can be a valuable tool for predicting Ki-67 levels in BC.

Prognostic Value of Ki67 in Epithelial Ovarian Cancer: Post-Neoadjuvant Chemotherapy Ki67 Combined with CA125 Predicting Recurrence

Purpose

To evaluate Ki67 expression and prognostic value during neoadjuvant chemotherapy (NACT) in advanced epithelial ovarian cancer (EOC).

Patients and Methods

95 patients with advanced EOC receiving NACT followed by interval debulking surgery (IDS) were available for tissue samples from matched pre- and post-therapy specimens. The expression of Ki-67 was evaluated by immunohistochemistry and classified by percentage of stained cells. The optimal cutoff values of the Ki67 were assessed by receiver operating characteristic analysis. Kaplan-Meier analysis, the Log rank test, and Cox regression analysis were carried out to analyze survival.

Results

Post-NACT Ki67 was an independent prognostic factor for recurrence by univariate (HR: 1.8, 95% CI: 1.1-3.0, P-value: 0.023) and multivariate (HR: 1.88, 95% CI: 1.08-3.26, P-value: 0.025) analysis. Residual disease >1cm (HR: 2.69, 95% CI: 1.31-5.54, P-value: 0.0070) and pre-treatment CA125 ≥ 1432 U/mL (HR: 2.00, 95% CI: 1.13-3.55, P-value: 0.017) were also independent risk factors for progression-free survival (PFS) in multivariate analysis. Post-NACT Ki67 ≥ 20% was an independent risk factor for PFS, however, baseline Ki67 and Ki67 change did not suggest prognostic significance. In patients with high CA125, the median PFS for patients with high postKi67 (median PFS: 15.0 months, 95% CI: 13.4-16.6 months) was significantly (P-value: 0.013) poorer compared to patients with low postKi67 (median PFS: 30.0 months, 95% CI: 13.5-46.5 months).

Conclusion

Post-NACT Ki67 ≥ 20% was an independent factor associated with poorer PFS in patients with advanced-stage EOC undergoing NACT followed by IDS. The combination of post-NACT Ki67 and pretreatment CA125 could better identify patients with poorer PFS in NACT-administered patients.

ExoDS: a versatile exosome-based drug delivery platform to target cancer cells and cancer stem cells

Chemotherapy drugs like doxorubicin (Dox) are widely used in middle-income countries around the world to treat various types of cancers, including breast cancer. Although they are toxic, they are still widely used to treat cancer. Delivering chemotherapy drugs directly to cancer cells to reduce side effects remains a challenge. Moreover, modern research gave rise to cancer stem cell theory, which implicated cancer stem cells in tumor initiation, progression, and relapse. This makes it imperative to target cancer stem cells to achieve complete remission. Our work highlights the development of an exosome-based targeted drug delivery vehicle. These exosomes were isolated from mature dendritic cells (mDCs) and encapsulated with doxorubicin (ExoDS). Our results showed that ExoDS specifically targeted breast cancer cells and breast cancer stem cells. Further analysis revealed that ExoDS did not induce any significant apoptosis in healthy mammary cells and peripheral blood mononuclear cells (PBMCs) isolated from healthy individuals and breast cancer patients. ExoDS was also found to target circulating tumor cells (CTCs) isolated from patient blood. ExoDS also showed equal efficiency compared to free doxorubicin in vivo. We also observed that ExoDS reduced the expression of cancer stem cell markers in murine tumor tissues. Altogether, this work provides novel insights into how mDC-derived exosomes can be used to specifically target cancer cells and cancer stem cells.

Photodynamic therapy inhibits cancer progression and induces ferroptosis and apoptosis by targeting P53/GPX4/SLC7A11 signaling pathways in cholangiocarcinoma

BACKGROUND

Cholangiocarcinoma (CCA) is a malignant tumor with a poor prognosis. The specific mechanism of photodynamic therapy (PDT) in treating CCA remains unclear. This study aims to investigate the mechanisms of PDT in the treatment of CCA and try to improve the therapeutic effect of PDT by intervening associated signaling pathways.

METHODS

The Cell Counting Kit-8 (CCK-8) was used to examine the cytotoxicity of CCA cell lines following PDT. Apoptosis and reactive oxygen species (ROS) levels were measured by flow cytometry. A transmission electron microscope was used to study the changes in cell mitochondria after PDT. The levels of glutathione (GSH), malondialdehyde (MDA), ferrous iron (Fe2+), lactate dehydrogenase (LDH), and lipid peroxide (LPO) were determined. Changes in the expression of apoptosis and ferroptosis-related proteins were determined using quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting. Xenograft tumor models were developed to investigate the effects of PDT on tumor proliferation, apoptosis, and ferroptosis in vivo.

RESULTS

PDT inhibited tumor proliferation and induced apoptosis both in vivo and in vitro. This treatment led to swelling and damage of the mitochondria in affected cells. Furthermore, ROS levels rose, accompanied by an increase in the proportion of apoptotic-positive cells. The expressions of Bax and Caspase-3 were upregulated, while the Bcl-2 was downregulated. Meanwhile, PDT triggered ferroptosis, marked by decreased expressions of GPX4 and SLC7A11, and reduced GSH levels. This was accompanied by upregulation of P53 expression and heightened levels of Fe2+, LPO, MDA, and LDH. After inducing the ferroptosis pathway, the therapeutic effect of PDT was enhanced, the tumor tissue was further reduced, and the degree of malignancy was reduced.

CONCLUSION

PDT promotes apoptosis and ferroptosis of cholangiocarcinoma cells by activating the P53/SLC7A11/GPX4 signaling pathway and inhibits the growth of cholangiocarcinoma. Inducing ferroptosis can enhance the effectiveness of photodynamic therapy.

A Molecular Typing Method for Invasive Breast Cancer by Serum Raman Spectroscopy

BACKGROUND

The incidence of breast cancer ranks highest among cancers and is exceedingly heterogeneous. Immunohistochemical staining is commonly used clinically to identify the molecular subtype for subsequent treatment and prognosis.

PURPOSE

Raman spectroscopy and support vector machine (SVM) learning algorithm were utilized to identify blood samples from breast cancer patients in order to investigate a novel molecular typing approach.

METHOD

Tumor tissue coarse needle aspiration biopsy samples, and peripheral venous blood samples were gathered from 459 invasive breast cancer patients admitted to the breast department of Sichuan Cancer Hospital between June 2021 and September 2022. Immunohistochemical staining and in situ hybridization were performed on the coarse needle aspiration biopsy tissues to obtain their molecular typing pathological labels, including: 70 cases of Luminal A, 167 cases of Luminal B (HER2-positive), 57 cases of Luminal B (HER2-negative), 84 cases of HER2-positive, and 81 cases of triple-negative. Blood samples were processed to obtained Raman spectra taken for SVM classification models establishment with machine algorithms (using 80% of the sample data as the training set), and then the performance of the SVM classification models was evaluated by the independent validation set (20% of the sample data).

RESULTS

The AUC values of SVM classification models remained above 0.85, demonstrating outstanding model performance and excellent subtype discrimination of breast cancer molecular subtypes.

CONCLUSION

Raman spectroscopy of serum samples can promptly and precisely detect the molecular subtype of invasive breast cancer, which has the potential for clinical value.

Assessment of novel prognostic biomarkers to predict pathological complete response in patients with non-metastatic triple-negative breast cancer using a window of opportunity design

Background

Triple-negative breast cancer (TNBC) includes approximately 20% of all breast cancer and is characterized by its aggressive nature, high recurrence rates, and visceral metastasis. Pathological complete response (pCR) is an established surrogate endpoint for survival. The window of opportunity studies provide valuable information on the disease biology prior to definitive treatment.

Objectives

To study the association of dynamic change in pathological, imagining, and genomic biomarkers that can prognosticate pCR. The study aims to develop a composite prognostic score.

Design

Clinical, interventional, and prognostic biomarker study using the novel window of opportunity design.

Methods

The study aims to enroll 80 treatment-naïve, pathologically confirmed TNBC patients, administering a single dose of paclitaxel and carboplatin during the window period before neoadjuvant chemotherapy (NACT). Tumor tissue will be obtained through a tru-cut biopsy, and positron emission tomography and computed tomography scans will be performed for each patient at two time points aiming to evaluate biomarker alterations. This will be followed by the administration of standard dose-dense NACT containing anthracyclines and taxanes, with the study culminating in surgery to assess pCR.

Results

The study would develop a composite prognostic risk score derived from the dynamic change in the Ki-67, tumor-infiltrating lymphocytes, Standardized Uptake Value (SUV max), Standardized Uptake Value for lean body mass (SUL max), and gene expression level pre- and post-intervention during the window period prior to the start of definitive treatment. This outcome will aid in categorizing the disease biology into risk categories.

Trial registration

The current study is approved by the Institutional Ethics Committee [Ethics: Protocol. no. JIP/IEC/2020/019]. This study was registered with ClinicalTrials.gov [CTRI Registration: CTRI/2022/06/043109].

Conclusion

The validated biomarker score will help to personalize NACT protocols in patients in TNBC planned for definitive treatment.

Prognostic predictive value of Ki-67 in stage I-II triple-negative breast cancer

Aim: Our research aimed to determine an optimal cutoff value and investigate the prognostic predictive function of Ki-67. Materials & methods: We retrospectively enrolled 1146 patients diagnosed with stage I-II triple-negative breast cancer. Disease-free and overall survival were analyzed using the Kaplan-Meier method and the Cox regression model. Results: We classified Ki-67 >45% as the high group (n = 716). A Ki-67 level of >45% was associated with poorer disease-free survival (p = 0.039) and overall survival (p = 0.029). Lymph node stage, neoadjuvant chemotherapy, and radiotherapy were independent predictive variables of prognosis. Conclusion: Triple-negative breast cancer may be further subcategorized according to the Ki-67 level. Neoadjuvant chemotherapy and postoperative radiotherapy can improve the prognosis of early triple-negative breast cancer.

Axillary lymph node metastasis in pure mucinous carcinoma of breast: clinicopathologic and ultrasonographic features

BACKGROUND

The purpose of this research is to study the sonographic and clinicopathologic characteristics that associate with axillary lymph node metastasis (ALNM) for pure mucinous carcinoma of breast (PMBC).

METHODS

A total of 176 patients diagnosed as PMBC after surgery were included. According to the status of axillary lymph nodes, all patients were classified into ALNM group (n = 15) and non-ALNM group (n = 161). The clinical factors (patient age, tumor size, location), molecular biomarkers (ER, PR, HER2 and Ki-67) and sonographic features (shape, orientation, margin, echo pattern, posterior acoustic pattern and vascularity) between two groups were analyzed to unclose the clinicopathologic and ultrasonographic characteristics in PMBC with ALNM.

RESULTS

The incidence of axillary lymph node metastasis was 8.5% in this study. Tumors located in the outer side of the breast (upper outer quadrant and lower outer quadrant) were more likely to have lymphatic metastasis, and the difference between the two group was significantly (86.7% vs. 60.3%, P = 0.043). ALNM not associated with age (P = 0.437). Although tumor size not associated with ALNM(P = 0.418), the tumor size in ALNM group (32.3 ± 32.7 mm) was bigger than non-ALNM group (25.2 ± 12.8 mm). All the tumors expressed progesterone receptor (PR) positively, and 90% of all expressed estrogen receptor (ER) positively, human epidermal growth factor receptor 2 (HER2) were positive in two cases of non-ALNM group. Ki-67 high expression was observed in 36 tumors in our study (20.5%), and it was higher in ALNM group than non-ALNM group (33.3% vs. 19.3%), but the difference wasn't significantly (P = 0.338).

CONCLUSIONS

Tumor location is a significant factor for ALNM in PMBC. Outer side location is more easily for ALNM. With the bigger size and/or Ki-67 higher expression status, the lymphatic metastasis seems more likely to present.

Expression and Purification of Recombinant Bowman-Birk Trypsin Inhibitor from Foxtail Millet Bran and Its Anticolorectal Cancer Effect In Vitro and In Vivo

Trypsin inhibitors derived from plants have various pharmacological activities and promising clinical applications. In our previous study, a Bowman-Birk-type major trypsin inhibitor from foxtail millet bran (FMB-BBTI) was extracted with antiatherosclerotic activity. Currently, we found that FMB-BBTI possesses a prominent anticolorectal cancer (anti-CRC) activity. Further, a recombinant FMB-BBTI (rFMB-BBTI) was successfully expressed in a soluble manner in host strain Escherichia coli. BL21 (DE3) was induced by isopropyl-β-d-thiogalactoside (0.1 mM) at 37 °C for 3.5 h by the pET28a vector system. Fortunately, a purity greater than 93% of rFMB-BBTI with anti-CRC activity was purified by nickel-nitrilotriacetic acid affinity chromatography. Subsequently, we found that rFMB-BBTI displays a strikingly anti-CRC effect, characterized by the inhibition of cell proliferation and clone formation ability, cell cycle arrest at the G2/M phase, and induction of cell apoptosis. It is interesting that the rFMB-BBTI treatment had no obvious effect on normal colorectal cells in the same concentration range. Importantly, the anti-CRC activity of rFMB-BBTI was further confirmed in the xenografted nude mice model. Taken together, our study highlights the anti-CRC activity of rFMB-BBTI in vitro and in vivo, uncovering the clinical potential of rFMB-BBTI as a targeted agent for CRC in the future.

Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment

Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors before surgery while simultaneously allowing for controlled evaluation of biomarkers, toxicity, and suppression of distant (occult) metastatic disease. Yet neoadjuvant clinical trials are rarely preceded by preclinical testing involving neoadjuvant treatment, surgery, and post-surgery monitoring of the disease. Here we used a mouse model of spontaneous metastasis occurring after surgical removal of orthotopically implanted primary tumors to develop a predictive mathematical model of neoadjuvant treatment response to sunitinib, a receptor tyrosine kinase inhibitor (RTKI). Treatment outcomes were used to validate a novel mathematical kinetics-pharmacodynamics model predictive of perioperative disease progression. Longitudinal measurements of presurgical primary tumor size and postsurgical metastatic burden were compiled using 128 mice receiving variable neoadjuvant treatment doses and schedules (released publicly at https://zenodo.org/records/10607753). A non-linear mixed-effects modeling approach quantified inter-animal variabilities in metastatic dynamics and survival, and machine-learning algorithms were applied to investigate the significance of several biomarkers at resection as predictors of individual kinetics. Biomarkers included circulating tumor- and immune-based cells (circulating tumor cells and myeloid-derived suppressor cells) as well as immunohistochemical tumor proteins (CD31 and Ki67). Our computational simulations show that neoadjuvant RTKI treatment inhibits primary tumor growth but has little efficacy in preventing (micro)-metastatic disease progression after surgery and treatment cessation. Machine learning algorithms that included support vector machines, random forests, and artificial neural networks, confirmed a lack of definitive biomarkers, which shows the value of preclinical modeling studies to identify potential failures that should be avoided clinically.

Toxoplasma gondii suppresses proliferation and migration of breast cancer cells by regulating their transcriptome

BACKGROUND

Breast cancer is the most common cancer in women worldwide. Toxoplasma gondii (T. gondii) has shown anticancer activity in breast cancer mouse models, and exerted beneficial effect on the survival of breast cancer patients, but the mechanism was unclear.

METHODS

The effect of tachyzoites of T. gondii (RH and ME49 strains) on human breast cancer cells (MCF-7 and MDA-MB-231 cells) proliferation and migration was assessed using cell growth curve and wound healing assays. Dual RNA-seq was performed for T. gondii-infected and non-infected cells to determine the differentially expressed genes (DEGs). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Protein-Protein Interaction Networks analysis (PPI) were performed to explore the related signaling pathway and hub genes. Hub genes were validated using the Kaplan-Meier plotter database, and Pathogen Host Interaction (PHI-base) database. The results were verified by qRT-PCR.

RESULTS

The tachyzoites of T. gondii decreased the expression of Ki67 and increased the expression of E-cadherin, resulting in suppressing the proliferation and migration of infected human breast cancer cells. The inhibitory effect of T. gondii on breast cancer cells showed a significant dose-response relationship. Compared with the control group, 2321 genes were transcriptionally regulated in MCF-7 cells infected with T. gondii, while 169 genes were transcriptionally regulated in infected MDA-MB-231 cells. Among these genes, 698 genes in infected MCF-7 cells and 67 genes in infected MDA-MB-231 cells were validated by the publicly available database. GO and KEGG analyses suggested that several pathways were involved in anticancer function of T. gondii, such as ribosome, interleukin-17 signaling, coronavirus disease pathway, and breast cancer pathway. BRCA1, MYC and IL-6 were identified as the top three hub genes in infected-breast cancer cells based on the connectivity of PPI analysis. In addition, after interacting with breast cancer cells, the expression of ROP16 and ROP18 in T. gondii increased, while the expression of crt, TgIST, GRA15, GRA24 and MIC13 decreased.

CONCLUSIONS

T. gondii transcriptionally regulates several signaling pathways by altering the hub genes such as BRCA1, MYC and IL-6, which can inhibit the breast tumor growth and migration, hinting at a potential therapeutic strategy.

Circ_0003945: an emerging biomarker and therapeutic target for human diseases

Due to the rapid development of RNA sequencing techniques, a circular non-coding RNA (ncRNA) known as circular RNAs (circRNAs) has gradually come into focus. As a distinguished member of the circRNA family, circ_0003945 has garnered attention for its aberrant expression and biochemical functions in human diseases. Subsequent studies have revealed that circ_0003945 could regulate tumor cells proliferation, migration, invasion, apoptosis, autophagy, angiogenesis, drug resistance, and radio resistance through the molecular mechanism of competitive endogenous RNA (ceRNA) during tumorigenesis. The expression of circ_0003945 is frequently associated with some clinical parameters and implies a poorer prognosis in the majority of cancers. In non-malignant conditions, circ_0003945 also holds considerable importance in diseases pathogenesis. This review aims to recapitulate molecular mechanism of circ_0003945 and elucidates its potential as a diagnostic and therapeutic target in neoplasms and other diseases.

Integrating molecular and clinical variables to predict myocardial recovery

Mechanical unloading and circulatory support with left ventricular assist devices (LVADs) mediate significant myocardial improvement in a subset of advanced heart failure (HF) patients. The clinical and biological phenomena associated with cardiac recovery are under intensive investigation. Left ventricular (LV) apical tissue, alongside clinical data, were collected from HF patients at the time of LVAD implantation (n=208). RNA was isolated and mRNA transcripts were identified through RNA sequencing and confirmed with RT-qPCR. To our knowledge this is the first study to combine transcriptomic and clinical data to derive predictors of myocardial recovery. We used a bioinformatic approach to integrate 59 clinical variables and 22,373 mRNA transcripts at the time of LVAD implantation for the prediction of post-LVAD myocardial recovery defined as LV ejection fraction (LVEF) ≥40% and LV end-diastolic diameter (LVEDD) ≤5.9cm, as well as functional and structural LV improvement independently by using LVEF and LVEDD as continuous variables, respectively. To substantiate the predicted variables, we used a multi-model approach with logistic and linear regressions. Combining RNA and clinical data resulted in a gradient boosted model with 80 features achieving an AUC of 0.731±0.15 for predicting myocardial recovery. Variables associated with myocardial recovery from a clinical standpoint included HF duration, pre-LVAD LVEF, LVEDD, and HF pharmacologic therapy, and LRRN4CL (ligand binding and programmed cell death) from a biological standpoint. Our findings could have diagnostic, prognostic, and therapeutic implications for advanced HF patients, and inform the care of the broader HF population.

Clinical and Analytical Validation of Two Methods for Ki-67 Scoring in Formalin Fixed and Paraffin Embedded Tissue Sections of Early Breast Cancer

Proliferation determined by Ki-67 immunohistochemistry has been proposed as a useful prognostic and predictive marker in breast cancer. However, the clinical validity of Ki-67 is questionable. In this study, Ki-67 was retrospectively evaluated by three pathologists using two methods: a visual assessment of the entire slide and a quantitative assessment of the tumour margin in 411 early-stage breast cancer patients with a median follow-up of 26.8 years. We found excellent agreement between the three pathologists for both methods. The risk of recurrence for Ki-67 was time-dependent, as the high proliferation group (Ki-67 ≥ 30%) had a higher risk of recurrence initially, but after 4.5 years the risk was higher in the low proliferation group. In estrogen receptor (ER)-positive patients, the intermediate Ki-67 group initially followed the high Ki-67 group, but eventually followed the low Ki-67 group. ER-positive pN0-1 patients with intermediate Ki-67 treated with endocrine therapy alone had a similar outcome to patients treated with chemotherapy. A cut-off value of 20% appeared to be most appropriate for distinguishing between the high and low Ki-67 groups. To summarize, a simple visual whole slide Ki-67 assessment turned out to be a reliable method for clinical decision-making in early breast cancer patients. We confirmed Ki-67 as an important prognostic and predictive biomarker.

Knockdown of KIF23 alleviates the progression of asthma by inhibiting pyroptosis

BACKGROUND

Asthma is a chronic disease affecting the lower respiratory tract, which can lead to death in severe cases. The cause of asthma is not fully known, so exploring its potential mechanism is necessary for the targeted therapy of asthma.

METHOD

Asthma mouse model was established with ovalbumin (OVA). H&E staining, immunohistochemistry and ELISA were used to detect the inflammatory response in asthma. Transcriptome sequencing was performed to screen differentially expressed genes (DEGs). The role of KIF23 silencing in cell viability, proliferation and apoptosis was explored by cell counting kit-8, EdU assay and flow cytometry. Effects of KIF23 knockdown on inflammation, oxidative stress and pyroptosis were detected by ELISA and western blot. After screening KIF23-related signalling pathways, the effect of KIF23 on p53 signalling pathway was explored by western blot.

RESULTS

In the asthma model, the levels of caspase-3, IgG in serum and inflammatory factors (interleukin (IL)-1β, KC and tumour necrosis factor (TNF)-α) in serum and bronchoalveolar lavage fluid were increased. Transcriptome sequencing showed that there were 352 DEGs in the asthma model, and 7 hub genes including KIF23 were identified. Knockdown of KIF23 increased cell proliferation and inhibited apoptosis, inflammation and pyroptosis of BEAS-2B cells induced by IL-13 in vitro. In vivo experiments verified that knockdown of KIF23 inhibited oxidative stress, inflammation and pyroptosis to alleviate OVA-induced asthma mice. In addition, p53 signalling pathway was suppressed by KIF23 knockdown.

CONCLUSION

Knockdown of KIF23 alleviated the progression of asthma by suppressing pyroptosis and inhibited p53 signalling pathway.

Contrast-Enhanced Ultrasound and Conventional Ultrasound Characteristics of Breast Cancer With Different Molecular Subtypes

BACKGROUND

Identifying molecular subtypes of breast cancer (BC) is of great significance in selecting optimal treatment strategy. Different molecular subtypes of BC have various vascular distribution characteristics. Contrast-enhanced ultrasound (CEUS) can dynamically display the microcirculation of tumor. This study intends to explore the conventional ultrasound and CEUS characteristics of different molecular subtypes of BC.

METHODS

During this prospective study, 86 patients with BC who were divided into Luminal A (LA), Luminal B (LB), HER2 over-expression (H2), and triple-negative (TN). The CEUS qualitative and quantitative characteristics of BC with different molecular subtypes was explored, as well as the conventional ultrasound features. In addition, the diagnostic efficiency of CEUS quantitative parameters in differentiating molecular subtypes of BC was analyzed.

RESULTS

Our study found that the Adler grade differed significantly among 4 molecular subtypes (P < .05). The enhancement speed, enhancement degree and size after enhancement of 4 molecular subtypes were statistically different (P < .05). The wash in slope (WIS), peak intensity (PI), and wash-in area under the curve (WiAUC) differed significantly among 4 subtypes (P < .05). The diagnostic efficiency of PI was better for detecting LA and H2 subtype with the areas under the receiver operating characteristic curve was 0.778 and 0.734, respectively.

CONCLUSION

Different molecular subtypes of BC have different CEUS and conventional ultrasound characteristics. CEUS can provide valuable imaging basis for precise clinical diagnosis and individualized therapy of BC with different molecular subtypes.

Potential biomarkers in breast cancer drug development: application of the biomarker qualification evidentiary framework

Breast cancer treatments have evolved rapidly, and clinically meaningful biomarkers have been used to guide therapy. These biomarkers hold utility within the drug development process to increase the efficiency and effectiveness. To this purpose, the US FDA developed an evidentiary framework. Literature searches conducted of literature published between 2016 and 2022 identified biomarkers in breast cancer. These biomarkers were reviewed for drug development utility through the biomarker qualification evidentiary framework. In the breast cancer setting, several promising biomarkers (ctDNA, Ki-67 and PIK3CA) were identified. There is a need for increased transparency regarding the requirements for qualification of specific biomarkers and increased awareness of the processes involved in biomarker qualification.

Ki-67 Testing in Breast Cancer: Assessing Variability With Scoring Methods and Specimen Types and the Potential Subsequent Impact on Therapy Eligibility

Abemaciclib was originally FDA approved for patients with ER-positive/HER2-negative breast cancer with Ki-67 expression ≥20%. However, there were no guidelines provided on which specimen to test or which scoring method to use. We performed a comprehensive study evaluating the variation in Ki-67 expression in breast specimens from 50 consecutive patients who could have been eligible for abemaciclib therapy. Three pathologists with breast expertise each performed a blinded review with 3 different manual scoring methods [estimated (EST), unweighted (UNW), and weighted (WT) (WT recommended by the International Ki-67 in Breast Cancer Working Group)]. Quantitative image analysis (QIA) using the HALO platform was also performed. Three different specimen types [core needle biopsy (CNB) (n=63), resection (RES) (n=52), and axillary lymph node metastasis (ALN) (n=50)] were evaluated for each patient. The average Ki-67 for all specimens was 14.68% for EST, 14.46% for UNW, 14.15% for WT, and 11.15% for QIA. For the manual methods, the range between the lowest and highest Ki-67 for each specimen between the 3 pathologists was 8.44 for EST, 5.94 for WT, and 5.93 for UNW. The WT method limited interobserver variability with ICC1=0.959 (EST ICC1=0.922 and UNW=0.949). Using the aforementioned cutoff of Ki-67 ≥20% versus <20% to determine treatment eligibility, the averaged EST method yields 20 of 50 patients (40%) who would have been treatment-eligible, versus 15 (30%) for the UNW, 17 (34%) for the WT, and 12 (24%) for the QIA. There was no statistically significant difference in Ki-67 among the 3 specimen types. The average Ki-67 difference was 4.36 for CNB vs RES, 6.95 for CNB versus ALN, and RES versus ALN (P=0.93, 0.99, and 0.94, respectively). Our study concludes that further refinement in Ki-67 scoring is advisable to reduce clinically significant variation.

The effects of prenatal azithromycin exposure on offspring ovarian development at different stages, doses, and courses

Azithromycin, a commonly used macrolide antibiotic for treating chlamydial infections during pregnancy, has sparked investigations into its potential effects on offspring development. Despite these inquiries, there remains uncertainty about the specific impact of prenatal azithromycin exposure (PAzE) on offspring ovarian development and the precise "effect window". Pregnant mice, following clinical guidelines for azithromycin dosing, were orally administered azithromycin at different gestational stages [(gestational day, GD) 10-12 or GD 15-17], doses (50, 100, or 200 mg/kg·d), and courses (single or multiple). On GD 18, we collected offspring blood and ovaries to examine changes in fetal serum estradiol (E2) levels, fetal ovarian morphology, pre-granulosa cell function, and oocyte development. Multiple courses of PAzE resulted in abnormal fetal ovarian morphological development, disorganized germ cell nests, enhanced ovarian cell proliferation, and reduced apoptosis. Simultaneously, multiple courses of PAzE significantly increased fetal serum E2 levels, elevated ovarian steroidogenic function (indicated by Star, 3β-hsd, and Cyp19 expression), disrupted oocyte development (indicated by Figlα and Nobox expression), and led to alterations in the MAPK signal pathway in fetal ovaries, particularly in the high-dose treatment group. In contrast, a single course of PAzE reduced fetal ovarian cell proliferation, decreased steroidogenic function, and inhibited oocyte development, particularly through the downregulation of Mek2 expression in the MAPK signal pathway. These findings suggest that PAzE can influence various aspects of fetal mouse ovarian cell development. Multiple courses enhance pre-granulosa cell estrogen synthesis function and advance germ cell development, while a single terminal gestation dose inhibits germ cell development. These differential effects may be associated with changes in the MAPK signal pathway.

Pediatric Pterygopalatine Fossa Schwannoma Presenting as Vision Loss: A Case Report and Literature Review

Neurosynovial tumors, originating from Schwann cells within nerve sheaths, are benign entities, with 25% to 45% manifesting in the head and neck region. However, occurrences in the pterygopalatine fossa (PPF) are exceptionally rare, and only a handful of cases have been documented. In this report, we present the unique case of a 6-year-old child exhibiting a sizable soft tissue mass in the left PPF, extending into the inferior orbital fissure. The patient underwent successful intranasal endoscopic removal of PPF schwannoma utilizing the prelacrimal recess approach, with postoperative pathology confirming the diagnosis of schwannoma. Schwannomas within the PPF are particularly uncommon, and instances of such tumors in pediatric patients are even more exceptional. This case highlights the diagnostic and therapeutic challenges associated with PPF schwannomas in children, emphasizing the significance of a multidisciplinary approach for optimal management. In addition, a comprehensive literature review is presented to provide insights into the existing knowledge on this rare entity, further contributing to the understanding of pediatric PPF schwannomas.

Long noncoding RNA TMPO-AS1 accelerates glycolysis by regulating the miR-1270/PKM2 axis in colorectal cancer

BACKGROUND

Long noncoding RNA thymopoietin-antisense RNA 1 (TMPO-AS1) is recognized as a participant in cancer progression. Nevertheless, its biological function in colorectal cancer remains obscure and needs further elucidation.

METHODS AND RESULTS

First, we discovered enriched TMPO-AS1 in the tumor tissues that were related to poor prognosis. TMPO-AS1 knockdown enhanced SW480 cell apoptosis but inhibited invasion, proliferation, migration, and glucose metabolism. Further, MiR-1270 is directly bound with TMPO-AS1. MiR-1270 mimics were confirmed to inhibit cell proliferation, invasion, and glucose metabolism in our study. Mechanistically, miR-1270 directly is bound with the 3' untranslated regions (3'UTR) of PKM2 to downregulate PKM2. MiR-1270 inhibitors reversed the TMPO-AS1 knockdown's effect on suppressing the tumor cell proliferation, invasion, and glycolysis, while the knockdown of PKM2 further inverted the function of miR-1270 inhibitors on the TMPO-AS1 knockdown.

CONCLUSIONS

This study illustrated that TMPO-AS1 advanced the development and the glycolysis of colorectal cancer by modulating the miR-1270/PKM2 axis, which provided a new insight into the colorectal cancer therapeutic strategy.

Cell cycle plasticity underlies fractional resistance to palbociclib in ER+/HER2- breast tumor cells

The CDK4/6 inhibitor palbociclib blocks cell cycle progression in Estrogen receptor-positive, human epidermal growth factor 2 receptor-negative (ER+/HER2-) breast tumor cells. Despite the drug's success in improving patient outcomes, a small percentage of tumor cells continues to divide in the presence of palbociclib-a phenomenon we refer to as fractional resistance. It is critical to understand the cellular mechanisms underlying fractional resistance because the precise percentage of resistant cells in patient tissue is a strong predictor of clinical outcomes. Here, we hypothesize that fractional resistance arises from cell-to-cell differences in core cell cycle regulators that allow a subset of cells to escape CDK4/6 inhibitor therapy. We used multiplex, single-cell imaging to identify fractionally resistant cells in both cultured and primary breast tumor samples resected from patients. Resistant cells showed premature accumulation of multiple G1 regulators including E2F1, retinoblastoma protein, and CDK2, as well as enhanced sensitivity to pharmacological inhibition of CDK2 activity. Using trajectory inference approaches, we show how plasticity among cell cycle regulators gives rise to alternate cell cycle "paths" that allow individual tumor cells to escape palbociclib treatment. Understanding drivers of cell cycle plasticity, and how to eliminate resistant cell cycle paths, could lead to improved cancer therapies targeting fractionally resistant cells to improve patient outcomes.

Deconvolution-Based Pharmacokinetic Analysis to Improve the Prediction of Pathological Information of Breast Cancer

Pharmacokinetic (PK) parameters, revealing changes in the tumor microenvironment, are related to the pathological information of breast cancer. Tracer kinetic models (e.g., Tofts-Kety model) with a nonlinear least square solver are commonly used to estimate PK parameters. However, the method is sensitive to noise in images. To relieve the effects of noise, a deconvolution (DEC) method, which was validated on synthetic concentration-time series, was proposed to accurately calculate PK parameters from breast dynamic contrast-enhanced magnetic resonance imaging. A time-to-peak-based tumor partitioning method was used to divide the whole tumor into three tumor subregions with different kinetic patterns. Radiomic features were calculated from the tumor subregion and whole tumor-based PK parameter maps. The optimal features determined by the fivefold cross-validation method were used to build random forest classifiers to predict molecular subtypes, Ki-67, and tumor grade. The diagnostic performance evaluated by the area under the receiver operating characteristic curve (AUC) was compared between the subregion and whole tumor-based PK parameters. The results showed that the DEC method obtained more accurate PK parameters than the Tofts method. Moreover, the results showed that the subregion-based Ktrans (best AUCs = 0.8319, 0.7032, 0.7132, 0.7490, 0.8074, and 0.6950) achieved a better diagnostic performance than the whole tumor-based Ktrans (AUCs = 0.8222, 0.6970, 0.6511, 0.7109, 0.7620, and 0.5894) for molecular subtypes, Ki-67, and tumor grade. These findings indicate that DEC-based Ktrans in the subregion has the potential to accurately predict molecular subtypes, Ki-67, and tumor grade.

Pretreatment synthetic MRI features for triple-negative breast cancer

AIM

To evaluate the quantitative parameters derived from synthetic magnetic resonance imaging (SyMRI) for predicting triple-negative breast cancer (TNBC).

MATERIALS AND METHODS

This prospective study enrolled participants with invasive ductal breast carcinoma (IDBC) and separated them into a TNBC group and a Non-TNBC group. Preoperative breast MRI included both the SyMRI and conventional MRI sequences. The quantitative parameters derived from the SyMRI included T1 and T2 relaxation times, proton density (PD), and their standard deviations (SD). Clinicopathological characteristics, conventional MRI findings, and quantitative synthetic parameters were assessed for all participants. Multivariable logistic regression analysis was performed to determine the potential independent imaging predictors for TNBC preoperatively. Receiver operating characteristic (ROC) curve analysis was used to evaluate the performance of these parameters.

RESULTS

A total of 231 participants with histopathological proven IDBC were included in this study (n=46 in the TNBC group and n=185 in the Non-TNBC group). The TNBC group had significantly larger tumour size (p=0.011) and more frequent intratumoural cystic or necrotic lesions (p<0.001) as compared to the Non-TNBC group. The univariate analysis showed that the TNBC tumours had significantly higher T1 (p=0.006) and T2 (p<0.001) values than Non-TNBC tumours. Subsequent multivariable analysis indicated that T2 values and the presence of cystic or necrotic lesions were the independent predictors for TNBC.

CONCLUSION

The T2 from synthetic imaging and the presence of cystic degeneration or necrosis within the breast cancer may serve as potential imaging biomarkers for preoperative differentiation of TNBC from Non-TNBC.

Ki-67 Change in Anthracyline-containing Neoadjuvant Chemotherapy Response in Breast Cancer

OBJECTIVE

Anthracycline-containing regimens are irreplaceable in neoadjuvant chemotherapy (NAC) for breast cancer (BC) at present. However, 30% of early breast cancer (EBC) patients are resistant to anthracycline-containing chemotherapy, leading to poor prognosis and higher mortality. Ki-67 is associated with the prognosis and response to therapy, and it changes after NAC.

METHODS

A total of 105 BC patients who received anthracycline-containing NAC were enrolled. Then, the optimal model of Ki-67 was selected, and its predictive efficacy was analyzed. Immunohistochemistry (IHC) was used to determine the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2) status and Ki-67 level. Fluorescent in situ hybridization (FISH) was used to verify the HER-2 when the IHC score was 2+.

RESULTS

The post-NAC Ki67 level after treatment with anthracycline drugs was lower than pre-NAC Ki-67 (19.6%±23.3% vs. 45.6%±23.1%, P<0.001). Furthermore, patients with the Ki-67 decrease had a border line higher pathological complete response (pCR) rate (17.2% vs. 0.0%, P=0.068), and a higher overall response rate (ORR) (73.6% vs. 27.8%, P<0.001), when compared to patients without the Ki-67 decrease. The ΔKi-67 and ΔKi-67% were valuable markers for the prediction of both the pCR rate and ORR. The area under the curve (AUC) for ΔKi-67 on pCR and ORR was 0.809 (0.698-0.921) and 0.755 (0.655-0.855), respectively, while the AUC for ΔKi-67% on pCR and ORR was 0.857 (0.742-0.972) and 0.720 (0.618-0.822), respectively. Multivariate logistic regression model 1 revealed that ΔKi-67 was an independent predictor for both pCR [odds ratio (OR)=61.030, 95% confidence interval (CI)=4.709-790.965; P=0.002] and ORR (OR=10.001, 95% CI: 3.044-32.858; P<0.001). Multivariate logistic regression model 2 revealed that ΔKi-67% was also an independent predictor for both pCR (OR=408.922, 95% CI=8.908-18771.224; P=0.002) and ORR (OR=5.419, 95% CI=1.842-15.943; P=0.002).

CONCLUSIONS

The present study results suggest that ΔKi67 and ΔKi67% are candidate predictors for anthracycline-containing NAC response, and that they may provide various information for further systematic therapy after surgery in clinical practice.

Extensive heterogeneity in the expression of steroid receptors in superficial peritoneal endometriotic lesions

RESEARCH QUESTION

Is the expression of steroid hormone receptors (oestrogen receptor-α and progesterone receptor A/B) and proliferative markers (Bcl-2 and Ki67) uniform among superficial peritoneal endometriotic lesions?

DESIGN

A retrospective cohort study of 24 patients with surgically and histologically confirmed endometriosis. Immunofluorescence was used to determine the proportion of oestrogen receptor-α (ERα), progesterone receptor A/B, Bcl-2 and Ki67 positive cells in 271 endometriotic lesions (defined as endometriotic gland profile/s within an individual region of CD10 stromal immunostaining from a single biopsy) from 67 endometriotic biopsies from 24 patients. Data were analysed to examine associations related to menstrual cycle stage, lesion location and gland morphology.

RESULTS

Oestrogen receptor-α and progesterone receptor A/B expression in superficial peritoneal endometriotic lesions was extremely heterogeneous. Bcl-2 immunostaining in endometriotic lesions was also variable, whereas Ki67 immunostaining was minimal. Menstrual cycle stage associations were limited in steroid hormone receptor and Bcl-2 expression in lesions. Patterns in progesterone receptor A/B and Bcl-2 immunostaining were associated with lesion location. Bcl-2 was differentially expressed, based on lesion gland morphology.

CONCLUSIONS

These data demonstrate considerable diversity in the expression of steroid hormone receptors and Bcl-2 between lesions, even within an individual patient.

Targeting TAM-secreted S100A9 effectively enhances the tumor-suppressive effect of metformin in treating lung adenocarcinoma

Metformin's effect on tumor treatment was complex, because it significantly reduced cancer cell proliferation in vitro, but made no difference in prognosis in several clinical cohorts. Our transcriptome sequencing results revealed that tumor-associated macrophage (TAM) infiltration significantly increased in active lung adenocarcinoma (LUAD) patients with long-term metformin use. We further identified that the tumor suppressive effect of metformin was more significant in mice after the depletion of macrophages, suggesting that TAMs might play an important role in metformin's effects in LUAD. Combining 10X Genomics single-cell sequencing of tumor samples, transcriptome sequencing of metformin-treated TAMs, and the ChIP-Seq data of the Encode database, we identified and validated that metformin significantly increased the expression and secretion of S100A9 of TAMs through AMPK-CEBP/β pathway. For the downstream, S100A9 binds to RAGE receptors on the surface of LUAD cells, and then activates the NF-κB pathway to promote EMT and progression of LUAD, counteracting the inhibitory effect of metformin on LUAD cells. In cell-derived xenograft models (CDX) and patient-derived xenograft models (PDX) models, our results showed that neutralizing antibodies targeting TAM-secreted S100A9 effectively enhanced the tumor suppressive effect of metformin in treating LUAD. Our results will enable us to better comprehend the complex role of metformin in LUAD, and advance its clinical application in cancer treatment.

Pretreatment Circulating Albumin, Platelet, and RDW-SD Associated with Worse Disease-Free Survival in Patients with Breast Cancer

Objective

Breast cancer is the second most common malignancy globally and a leading cause of cancer death in women. Analysis of factors related to disease-free survival (DFS) has improved understanding of the disease and characteristics related to recurrence. The aim of this study was to investigate the predictors of DFS in patients with breast cancer to enable the identification of patients at high risk who may benefit from prevention interventions.

Methods

We retrospectively analyzed 559 women with breast cancer who underwent treatment between 2004 and 2022. The study endpoint was DFS. Recurrence was defined as local recurrence, regional recurrence, distant metastases, contralateral breast cancer, other second primary cancer, and death. Baseline tumor-related characteristics, treatment-related characteristics, sociodemographic and biochemical data were analyzed using Cox proportional hazards analysis.

Results

The median DFS was 45 months (range, 2 to 225 months). Breast cancer recurred in 86 patients (15.4%), of whom 10 had local recurrence, 10 had regional recurrence, 17 had contralateral breast cancer, 29 had distant metastases, 10 had second primary cancer, and 10 patients died. Multivariate forward stepwise Cox regression analysis showed that AJCC stage III, Ki67 ≥14%, albumin, platelet, and red cell distribution width-standard deviation (RDW-SD) were predictors of worse DFS. In addition, the effects of albumin, platelet, and RDW-SD on disease recurrence were confirmed by structural equation model (SEM) analysis.

Conclusion

In addition to the traditional predictors of worse DFS such as AJCC stage III and Ki67 ≥14%, lower pretreatment circulating albumin, higher pretreatment circulating platelet count and RDW-SD could significantly predict worse DFS in this study, and SEM delineated possible causal pathways and inter-relationships of albumin, platelet, and RDW-SD contributing to the disease recurrence among Chinese women with breast cancer.

AI improves accuracy, agreement and efficiency of pathologists for Ki67 assessments in breast cancer

The Ki-67 proliferation index (PI) guides treatment decisions in breast cancer but suffers from poor inter-rater reproducibility. Although AI tools have been designed for Ki-67 assessment, their impact on pathologists' work remains understudied. 90 international pathologists were recruited to assess the Ki-67 PI of ten breast cancer tissue microarrays with and without AI. Accuracy, agreement, and turnaround time with and without AI were compared. Pathologists' perspectives on AI were collected. Using AI led to a significant decrease in PI error (2.1% with AI vs. 5.9% without AI, p < 0.001), better inter-rater agreement (ICC: 0.70 vs. 0.92; Krippendorff's α: 0.63 vs. 0.89; Fleiss' Kappa: 0.40 vs. 0.86), and an 11.9% overall median reduction in turnaround time. Most pathologists (84%) found the AI reliable. For Ki-67 assessments, 76% of respondents believed AI enhances accuracy, 82% said it improves consistency, and 83% trust it will improve efficiency. This study highlights AI's potential to standardize Ki-67 scoring, especially between 5 and 30% PI-a range with low PI agreement. This could pave the way for a universally accepted PI score to guide treatment decisions, emphasizing the promising role of AI integration into pathologist workflows.

Radiomics signatures for predicting the Ki-67 level and HER-2 status based on bone metastasis from primary breast cancer

This study explores the potential of radiomics to predict the proliferation marker protein Ki-67 levels and human epidermal growth factor receptor 2 (HER-2) status based on MRI images of patients with spinal metastasis from primary breast cancer. A total of 110 patients with pathologically confirmed spinal metastases from primary breast cancer were enrolled between Dec. 2017 and Dec. 2021. All patients underwent T1-weighted contrast-enhanced MRI scans. The PyRadiomics package was used to extract features from the MRI images based on the intraclass correlation coefficient and least absolute shrinkage and selection operator. The most predictive features were used to develop the radiomics signature. The Chi-Square test, Fisher's exact test, Student's t-test, and Mann-Whitney U test were used to evaluate the clinical and pathological characteristics between the high- and low-level Ki-67 groups and the HER-2 positive/negative groups. The radiomics models were compared using receiver operating characteristic curve analysis. The area under the receiver operating characteristic curve (AUC), sensitivity (SEN), and specificity (SPE) were generated as comparison metrics. From the spinal MRI scans, five and two features were identified as the most predictive for the Ki-67 level and HER-2 status, respectively. The developed radiomics signatures generated good prediction performance for the Ki-67 level in the training (AUC = 0.812, 95% CI: 0.710-0.914, SEN = 0.667, SPE = 0.846) and validation (AUC = 0.799, 95% CI: 0.652-0.947, SEN = 0.722, SPE = 0.833) cohorts. Good prediction performance for the HER-2 status was also achieved in the training (AUC = 0.796, 95% CI: 0.686-0.906, SEN = 0.720, SPE = 0.776) and validation (AUC = 0.705, 95% CI: 0.506-0.904, SEN = 0.733, SPE = 0.762) cohorts. The results of this study provide a better understanding of the potential clinical implications of spinal MRI-based radiomics on the prediction of Ki-67 levels and HER-2 status in breast cancer.

An Automated Breast Volume Scanner-Based Intra- and Peritumoral Radiomics Nomogram for the Preoperative Prediction of Expression of Ki-67 in Breast Malignancy

RATIONALE AND OBJECTIVES

This study aimed to create and verify a nomogram for preoperative prediction of Ki-67 expression in breast malignancy to assist in the development of personalized treatment strategies.

MATERIALS AND METHODS

This retrospective study received approval from the institutional review board and included a cohort of 197 patients with breast malignancy who were admitted to our hospital. Ki-67 expression was divided into two groups based on a 14% threshold: low and high. A radiomics signature was built utilizing 1702 radiomics features based on an intra- and peritumoral (10 mm) regions of interest. Using multivariate logistic regression, radiomics signature, and ultrasound (US) characteristics, the nomogram was developed. To evaluate the model's calibration, clinical application, and predictive ability, decision curve analysis (DCA), the calibration curve, and the receiver operating characteristic curve were used, respectively.

RESULTS

The final nomogram included three independent predictors: tumor size (P = .037), radiomics signature (P < .001), and US-reported lymph node status (P = .018). The nomogram exhibited satisfactory performance in the training cohort, demonstrating a specificity of 0.944, a sensitivity of 0.745, and an area under the curve (AUC) of 0.905. The validation cohort recorded a specificity of 0.909, a sensitivity of 0.727, and an AUC of 0.882. The DCA showed the nomogram's clinical utility, and the calibration curve revealed a high consistency among the expected and detected values.

CONCLUSION

The nomogram used in this investigation can accurately predict Ki-67 expression in people with malignant breast tumors, helping to develop personalized treatment approaches.

Inhibitory actions of oxyresveratrol on the PI3K/AKT signaling cascade in cervical cancer cells

The phosphatidyl inositol 3-kinase (PI3K)/AKT signaling plays a critical role in cancer cell proliferation, migration, and invasion. This signal transduction axis in HPV-positive cervical cancer has been proved to be directly activated by E6/E7 proteins of the virus enhancing cervical cancer progression. Hence, the PI3K/AKT pathway is one of the key therapeutic targets for HPV-positive cervical cancer. Here we discovered that oxyresveratrol (Oxy) at noncytotoxic concentration specifically suppressed the phosphorylation of AKT but not ERK1/2. This potent inhibitory effect of Oxy was still observed even when cells were stimulated with fetal bovine serum. Inhibition of AKT phosphorylation at serine 473 by Oxy resulted in a significant decrease in serine 9 phosphorylation of GSK-3β, a downstream target of AKT. Dephosphorylation of GSK-3β at this serine residue activates its function in promoting the degradation of MCL-1, an anti-apoptotic protein. Results clearly demonstrated that in association with GSK-3β activation, Oxy preferentially downregulated the expression of anti-apoptotic protein MCL-1. Furthermore, results from the functional analyses revealed that Oxy inhibited cervical cancer cell proliferation, at least in part through suppressing nuclear expression of Ki-67. Besides, the compound retarded cervical cancer cell migration even the cells were exposed to a potent enhancer of epithelial-mesenchymal transition, TGF-β1. In consistent with these data, Oxy reduced the expression of β-catenin, N-cadherin, and vimentin. In conclusion, the study disclosed that Oxy specifically inhibits the AKT/GSK-3β/MCL-1 axis resulting in reduction in cervical cancer cell viability, proliferation, and migration.

Study of Drug Targets Associated With Oncogenesis and Cancer Cell Survival and the Therapeutic Activity of Engineered Ashwagandha Extract Having Differential Withanolide Constitutions

Ashwagandha (Withania somnifera) has gained worldwide popularity for a multitude of health benefits inclusive of cancer-preventive and curative effects. Despite numerous research data supporting the benefits of this wonder herb, the actual use of ashwagandha for cancer treatment in clinics is limited. The primary reason for this is the inconsistent therapeutic outcome due to highly variable composition and constitution of active ingredients in the plant extract impacting ashwagandha's pharmacology. We investigate here an engineered yield: an ashwagandha extract (Oncowithanib) that has a unique and fixed portion of active ingredients to achieve consistent and effective therapeutic activity. Using the MCF7 cell line, Oncowithanib was studied for its anti-neoplastic efficacy and drug targets associated with cell cycle regulation, translation machinery, and cell survival and apoptosis. Results demonstrate a dose-dependent decline in Oncowithanib-treated MCF7 cell viability and reduced colony-forming ability. Treated cells showed increased cell death as evidenced by enhancement of Caspase 3 enzyme activity and decreased expressions of cell proliferation markers such as Ki67 and Aurora Kinase A. Oncowithanib treatment was also found to be associated with expressional suppression of key cellular kinases such as RSK1, Akt1, and mTOR in MCF7 cells. Our findings indicate that Oncowithanib decreases MCF7 cell survival and propagation, and sheds light on common drug targets that might be good candidates for the development of cancer therapeutics. Further in-depth investigations are required to fully explore the potency and pharmacology of this novel extract. This study also highlights the importance of the standardization of herbal extracts to get consistent therapeutic activity for the disease indication.