PIK3CA Mutational Status Is Associated with High Glycolytic Activity in ER+/HER2- Early Invasive Breast Cancer: a Molecular Imaging Study Using [18F]FDG PET/CT

CRISPR/Cas9-mediated knockout strategies for enhancing immunotherapy in breast cancer

Breast cancer, a prevalent disease with significant mortality rates, often presents treatment challenges due to its complex genetic makeup. This review explores the potential of combining Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) gene knockout strategies with immunotherapeutic approaches to enhance breast cancer treatment. The CRISPR/Cas9 system, renowned for its precision in inducing genetic alterations, can target and eliminate specific cancer cells, thereby minimizing off-target effects. Concurrently, immunotherapy, which leverages the immune system's power to combat cancer, has shown promise in treating breast cancer. By integrating these two strategies, we can potentially augment the effectiveness of immunotherapies by knocking out genes that enable cancer cells to evade the immune system. However, safety considerations, such as off-target effects and immune responses, necessitate careful evaluation. Current research endeavors aim to optimize these strategies and ascertain the most effective methods to stimulate the immune response. This review provides novel insights into the integration of CRISPR/Cas9-mediated knockout strategies and immunotherapy, a promising avenue that could revolutionize breast cancer treatment as our understanding of the immune system's interplay with cancer deepens.

A prismatic view of the epigenetic-metabolic regulatory axis in breast cancer therapy resistance

Epigenetic regulation established during development to maintain patterns of transcriptional expression and silencing for metabolism and other fundamental cell processes can be reprogrammed in cancer, providing a molecular mechanism for persistent alterations in phenotype. Metabolic deregulation and reprogramming are thus an emerging hallmark of cancer with opportunities for molecular classification as a critical preliminary step for precision therapeutic intervention. Yet, acquisition of therapy resistance against most conventional treatment regimens coupled with tumor relapse, continue to pose unsolved problems for precision healthcare, as exemplified in breast cancer where existing data informs both cancer genotype and phenotype. Furthermore, epigenetic reprograming of the metabolic milieu of cancer cells is among the most crucial determinants of therapeutic resistance and cancer relapse. Importantly, subtype-specific epigenetic-metabolic interplay profoundly affects malignant transformation, resistance to chemotherapy, and response to targeted therapies. In this review, we therefore prismatically dissect interconnected epigenetic and metabolic regulatory pathways and then integrate them into an observable cancer metabolism-therapy-resistance axis that may inform clinical intervention. Optimally coupling genome-wide analysis with an understanding of metabolic elements, epigenetic reprogramming, and their integration by metabolic profiling may decode missing molecular mechanisms at the level of individual tumors. The proposed approach of linking metabolic biochemistry back to genotype, epigenetics, and phenotype for specific tumors and their microenvironment may thus enable successful mechanistic targeting of epigenetic modifiers and oncometabolites despite tumor metabolic heterogeneity.

Translational Potential of Fluorescence Polarization for Breast Cancer Cytopathology

Breast cancer is the most common malignancy in women. The standard of care for diagnosis involves invasive core needle biopsy followed by time-consuming histopathological evaluation. A rapid, accurate, and minimally invasive method to diagnose breast cancer would be invaluable. Therefore, this clinical study investigated the fluorescence polarization (Fpol) of the cytological stain methylene blue (MB) for the quantitative detection of breast cancer in fine needle aspiration (FNA) specimens. Cancerous, benign, and normal cells were aspirated from excess breast tissues immediately following surgery. The cells were stained in aqueous MB solution (0.05 mg/mL) and imaged using multimodal confocal microscopy. The system provided MB Fpol and fluorescence emission images of the cells. Results from optical imaging were compared to clinical histopathology. In total, we imaged and analyzed 3808 cells from 44 breast FNAs. Fpol images displayed quantitative contrast between cancerous and noncancerous cells, whereas fluorescence emission images showed the morphological features comparable to cytology. Statistical analysis demonstrated that MB Fpol is significantly higher (p < 0.0001) in malignant vs. benign/normal cells. It also revealed a correlation between MB Fpol values and tumor grade. The results indicate that MB Fpol could provide a reliable, quantitative diagnostic marker for breast cancer at the cellular level.

Ultrasmall iron-gallic acid coordination polymer nanodots with antioxidative neuroprotection for PET/MR imaging-guided ischemia stroke therapy

Oxidative stress from reactive oxygen species (ROS) is a reperfusion injury factor that can lead to cell damage and death. Here, ultrasmall iron-gallic acid coordination polymer nanodots (Fe-GA CPNs) were developed as antioxidative neuroprotectors for ischemia stroke therapy guided by PET/MR imaging. As proven by the electron spin resonance spectrum, the ultrasmall Fe-GA CPNs with ultrasmall size, scavenged ROS efficiently. In vitro experiments revealed that Fe-GA CPNs could protect cell viability after being treated with hydrogen peroxide (H2O2) and displayed the effective elimination of ROS by Fe-GA CPNs, which subsequently restores oxidation balance. When analyzing the middle cerebral artery occlusion model, the neurologic damage displayed by PET/MR imaging revealed a distinct recovery after treatment with Fe-GA CPNs, which was proved by 2,3,5-triphenyl tetrazolium chloride staining. Furthermore, immunohistochemistry staining indicated that Fe-GA CPNs inhibited apoptosis through protein kinase B (Akt) restoration, whereas western blot and immunofluorescence indicated the activation of the nuclear factor erythroid 2-related factor 2 (Nrf2) and heme oxygenase-1 (HO-1) pathway following Fe-GA CPNs application. Therefore, Fe-GA CPNs exhibit an impressive antioxidative and neuroprotective role via redox homeostasis recovery by Akt and Nrf2/HO-1 pathway activation, revealing its potential for clinical ischemia stroke treatment.

Analysis of Cross-Combinations of Feature Selection and Machine-Learning Classification Methods Based on [18F]F-FDG PET/CT Radiomic Features for Metabolic Response Prediction of Metastatic Breast Cancer Lesions

Simple Summary

Breast cancer is a leading cause of morbidity and mortality worldwide. The metastatic disease is largely responsible for cancer patient deaths, and its treatment implies usually different therapies. In this context, the prediction of response to treatment or depiction of treatment-resistant phenotypes is essential in clinical practice, especially in the new era of precision medicine. In this study, we used several combinations of feature selection methods and machine-learning classifiers to construct predictive models of the metabolic response to the treatment of metastatic breast cancer lesions. These models were based on clinical variables and radiomic features extracted from 2-deoxy-2-[¹⁸F]fluoro-D-glucose positron emission tomography/computed tomography ([¹⁸F]F-FDG PET/CT) images, obtained prior to the treatment. Our main goal was to know if this prediction was feasible and to identify those combinations with better predictive performance. We found that several combinations were successful to predict the metabolic response to treatment, of which the least absolute shrinkage and selection operator (Lasso) + support vector machines (SVM) had the best mean performance in terms of area under the curve, in both training and validation cohorts. Model performances depended largely on the selected combinations.

Abstract

Background: This study aimed to identify optimal combinations between feature selection methods and machine-learning classifiers for predicting the metabolic response of individual metastatic breast cancer lesions, based on clinical variables and radiomic features extracted from pretreatment [¹⁸F]F-FDG PET/CT images. Methods: A total of 48 patients with confirmed metastatic breast cancer, who received different treatments, were included. All patients had an [¹⁸F]F-FDG PET/CT scan before and after the treatment. From 228 metastatic lesions identified, 127 were categorized as responders (complete or partial metabolic response) and 101 as non-responders (stable or progressive metabolic response), by using the percentage changes in SULpeak (peak standardized uptake values normalized for body lean body mass). The lesion pool was divided into training (n = 182) and testing cohorts (n = 46); for each lesion, 101 image features from both PET and CT were extracted (202 features per lesion). These features, along with clinical and pathological information, allowed the prediction model’s construction by using seven popular feature selection methods in cross-combination with another seven machine-learning (ML) classifiers. The performance of the different models was investigated with the receiver-operating characteristic curve (ROC) analysis, using the area under the curve (AUC) and accuracy (ACC) metrics. Results: The combinations, least absolute shrinkage and selection operator (Lasso) + support vector machines (SVM), or random forest (RF) had the highest AUC in the cross-validation, with 0.93 ± 0.06 and 0.92 ± 0.03, respectively, whereas Lasso + neural network (NN) or SVM, and mutual information (MI) + RF, had the higher AUC and ACC in the validation cohort, with 0.90/0.72, 0.86/0.76, and 87/85, respectively. On average, the models with Lasso and models with SVM had the best mean performance for both AUC and ACC in both training and validation cohorts. Conclusions: Image features obtained from a pretreatment [¹⁸F]F-FDG PET/CT along with clinical vaiables could predict the metabolic response of metastatic breast cancer lesions, by their incorporation into predictive models, whose performance depends on the selected combination between feature selection and ML classifier methods.

The role of metabolic ecosystem in cancer progression — metabolic plasticity and mTOR hyperactivity in tumor tissues

Despite advancements in cancer management, tumor relapse and metastasis are associated with poor outcomes in many cancers. Over the past decade, oncogene-driven carcinogenesis, dysregulated cellular signaling networks, dynamic changes in the tissue microenvironment, epithelial-mesenchymal transitions, protein expression within regulatory pathways, and their part in tumor progression are described in several studies. However, the complexity of metabolic enzyme expression is considerably under evaluated. Alterations in cellular metabolism determine the individual phenotype and behavior of cells, which is a well-recognized hallmark of cancer progression, especially in the adaptation mechanisms underlying therapy resistance. In metabolic symbiosis, cells compete, communicate, and even feed each other, supervised by tumor cells. Metabolic reprogramming forms a unique fingerprint for each tumor tissue, depending on the cellular content and genetic, epigenetic, and microenvironmental alterations of the developing cancer. Based on its sensing and effector functions, the mechanistic target of rapamycin (mTOR) kinase is considered the master regulator of metabolic adaptation. Moreover, mTOR kinase hyperactivity is associated with poor prognosis in various tumor types. In situ metabolic phenotyping in recent studies highlights the importance of metabolic plasticity, mTOR hyperactivity, and their role in tumor progression. In this review, we update recent developments in metabolic phenotyping of the cancer ecosystem, metabolic symbiosis, and plasticity which could provide new research directions in tumor biology. In addition, we suggest pathomorphological and analytical studies relating to metabolic alterations, mTOR activity, and their associations which are necessary to improve understanding of tumor heterogeneity and expand the therapeutic management of cancer.

Radiogenomics Based on PET Imaging

Radiogenomics or imaging genomics is a novel omics strategy of associating imaging data with genetic information, which has the potential to advance personalized medicine. Imaging features extracted from PET or PET/CT enable assessment of in vivo functional and physiological activity and provide comprehensive tumor information non-invasively. However, PET features are considered secondary to features on conventional imaging, and there has not yet been a review of the radiogenomic approach using PET features. This review article summarizes the current state of PET-based radiogenomic research for cancer, which discusses some of its limitations and directions for future study.

Glycolysis promotes the progression of pancreatic cancer and reduces cancer cell sensitivity to gemcitabine

Previous studies have reported that increased glycolytic activity enhances chemotherapy resistance in some types of malignancies. However, whether glycolysis influences the curative effect of gemcitabine (GEM) on pancreatic cancer (PC) cells remains unclear. The aim of this study was to investigate the status of glycolysis in PC and its association with tolerance to GEM. Data from The Cancer Genome Atlas (TCGA) were used to analyze the correlation between glycolysis-related gene (GRG) expression and PC progression and prognosis. 2-Deoxy-D-glucose (2-DG) was applied to assess the effect of glycolysis inhibition on PC cell death and GEM tolerance. Expression of some GRGs, such as HK1, GAPDH, PKM2, and LDHA, was significantly associated with the prognosis of PC. Furthermore, HK1, PKLR, and LDHA expression correlated positively with PC progression. Further analysis revealed that cancer cell death was markedly enhanced following glycolysis inhibition and that the sensitivity of cancer cells to GEM was notably increased in the presence of 2-DG. Our findings indicate that abnormally increased glycolytic activity promotes the development of PC and enhances drug tolerance to GEM. 2-DG combined with GEM is a potential therapy for PC.

Assessment of the Spatial Heterogeneity of Breast Cancers: Associations Between Computed Tomography and Immunohistochemistry

BACKGROUND

Tumour heterogeneity is considered an important mechanism of treatment failure. Imaging-based assessment of tumour heterogeneity is showing promise but the relationship between these mathematically derived measures and accepted 'gold standards' of tumour biology such as immunohistochemical measures is not established.

METHODS

A total of 20 women with primary breast cancer underwent a research dynamic contrast-enhanced computed tomography prior to treatment with data being available for 15 of these. Texture analysis was performed of the primary tumours to extract 13 locoregional and global parameters. Immunohistochemical analysis associations were assessed by the Spearman rank correlation.

RESULTS

Hypoxia-inducible factor-1α was correlated with first-order kurtosis (r = -0.533, P = .041) and higher order neighbourhood grey-tone difference matrix coarseness (r = 0.54, P = .038). Vascular maturity-related smooth muscle actin was correlated with higher order grey-level run-length long-run emphasis (r = -0.52, P = .047), fractal dimension (r = 0.613, P = .015), and lacunarity (r = -0.634, P = .011). Micro-vessel density, reflecting angiogenesis, was also associated with lacunarity (r = 0.547, P = .035).

CONCLUSIONS

The associations suggest a biological basis for these image-based heterogeneity features and support the use of imaging, already part of standard care, for assessing intratumoural heterogeneity.