Direct Comparison of Chol-siRNA Polyplexes and Chol-DsiRNA Polyplexes Targeting STAT3 in a Syngeneic Murine Model of TNBC

RNA interference (RNAi) molecules have tremendous potential for cancer therapy but are limited by insufficient potency after intravenous (IV) administration. We previously found that polymer complexes (polyplexes) formed between 3′-cholesterol-modified siRNA (Chol-siRNA) or DsiRNA (Chol-DsiRNA) and the cationic diblock copolymer PLL[30]-PEG[5K] greatly increase RNAi potency against stably expressed LUC mRNA in primary syngeneic murine breast tumors after daily IV dosing. Chol-DsiRNA polyplexes, however, maintain LUC mRNA suppression for ~48 h longer after the final dose than Chol-siRNA polyplexes, which suggests that they are the better candidate formulation. Here, we directly compared the activities of Chol-siRNA polyplexes and Chol-DsiRNA polyplexes in primary murine 4T1 breast tumors against STAT3, a therapeutically relevant target gene that is overexpressed in many solid tumors, including breast cancer. We found that Chol-siSTAT3 polyplexes suppressed STAT3 mRNA in 4T1 tumors with similar potency (half-maximal ED50 0.3 mg/kg) and kinetics (over 96 h) as Chol-DsiSTAT3 polyplexes, but with slightly lower activity against total Stat3 protein (29% vs. 42% suppression) and tumor growth (11.5% vs. 8.6% rate-based T/C ratio) after repeated IV administration of equimolar, tumor-saturating doses every other day. Thus, both Chol-siRNA polyplexes and Chol-DsiRNA polyplexes may be suitable clinical candidates for the RNAi therapy of breast cancer and other solid tumors.

Dense Tissue Pattern Characterization Using Deep Neural Network

Breast tumors are from the common infections among women around the world. Classifying the various types of breast tumors contribute to treating breast tumors more efficiently. However, this classification task is often hindered by dense tissue patterns captured in mammograms. The present study has been proposed a dense tissue pattern characterization framework using deep neural network. A total of 322 mammograms belonging to the mini-MIAS dataset and 4880 mammograms from DDSM dataset have been taken, and an ROI of fixed size 224 × 224 pixels from each mammogram has been extracted. In this work, tedious experimentation has been executed using different combinations of training and testing sets using different activation function with AlexNet, ResNet-18 model. Data augmentation has been used to create a similar type of virtual image for proper training of the DL model. After that, the testing set is applied on the trained model to validate the proposed model. During experiments, four different activation functions ‘sigmoid’, ‘tanh’, ‘ReLu’, and ‘leakyReLu’ are used, and the outcome for each function has been reported. It has been found that activation function ‘ReLu’ perform always outstanding with respect to others. For each experiment, classification accuracy and kappa coefficient have been computed. The obtained accuracy and kappa value for MIAS dataset using ResNet-18 model is 91.3% and 0.803, respectively. For DDSM dataset, the accuracy of 92.3% and kappa coefficient value of 0.846 are achieved. After the combination of both dataset images, the achieved accuracy is 91.9%, and kappa coefficient value is 0.839 using ResNet-18 model. Finally, it has been concluded that the ResNet-18 model and ReLu activation function yield outstanding performance for the task.

A comprehensive single-cell map of T cell exhaustion-associated immune environments in human breast cancer

Immune checkpoint therapy in breast cancer remains restricted to triple negative patients, and long-term clinical benefit is rare. The primary aim of immune checkpoint blockade is to prevent or reverse exhausted T cell states, but the causes and implications of T cell exhaustion in breast tumors are not well understood. Here, we used single-cell transcriptomics combined with imaging mass cytometry to comprehensively study exhausted and non-exhausted immune environments in human breast tumors, with a focus on Luminal subtypes. We found that the presence of a PD-1high exhaustion-like T cell phenotype was indicative of an inflammatory immune environment with a characteristic cytotoxic profile and spatial features. Accumulation of natural killer T cells and increased myeloid cell activation in exhausted immune environments provide further support for tissue inflammation in these environments. Consistent with this, our comprehensive map of cellular interactions within the breast tumor microenvironment revealed elevated immunomodulatory, chemotactic, and cytokine signaling in exhausted environments. These data reveal fundamental differences between exhausted and non-exhausted immune environments within Luminal breast cancer, and show that expression of PD-1 and CXCL13 on T cells, and MHC-I – but not PD-L1 – on tumor cells are strong distinguishing features between these environments; these factors are potential new biomarkers for patient stratification.

Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

Breast cancer incidence has been rising steadily during the past few decades. It is the second leading cause of death in women. If it is diagnosed early, there is a good possibility of recovery. Mammography is proven to be an excellent screening technique for breast tumor diagnosis, but its detection and classification in mammograms remain a significant challenge. Previous studies’ major limitation is an increase in false positive ratio (FPR) and false negative ratio (FNR), as well as a drop in Matthews correlation coefficient (MCC) value. A model that can lower FPR and FNR while increasing MCC value is required. To overcome prior research limitations, a modified network of YOLOv5 is used in this study to detect and classify breast tumors. Our research is conducted using publicly available datasets Curated Breast Imaging Subset of DDSM (CBIS-DDSM). The first step is to perform preprocessing, which includes image enhancing techniques and the removal of pectoral muscles and labels. The dataset is then annotated, augmented, and divided into 60% for training, 30% for validation, and 10% for testing. The experiment is then performed using a batch size of 8, a learning rate of 0.01, a momentum of 0.843, and an epoch value of 300. To evaluate the performance of our proposed model, our proposed model is compared with YOLOv3 and faster RCNN. The results show that our proposed model performs better than YOLOv3 and faster RCNN with 96% mAP, 93.50% MCC value, 96.50% accuracy, 0.04 FPR, and 0.03 FNR value. The results show that our suggested model successfully identifies and classifies breast tumors while also overcoming previous research limitations by lowering the FPR and FNR and boosting the MCC value.