Influence of Macrophages on Vascular Invasion of Inflammatory Breast Cancer Emboli Measured Using an In Vitro Microfluidic Multi-Cellular Platform

Mechanical forces in the tumor microenvironment: roles, pathways, and therapeutic approaches

Tumors often exhibit greater stiffness compared to normal tissues, primarily due to increased deposition within the tumor stroma. Collagen, proteoglycans, laminin, and fibronectin are key components of the extracellular matrix (ECM), interacting to facilitate ECM assembly. Enhanced fiber density and cross-linking within the ECM result in elevated matrix stiffness and interstitial fluid pressure, subjecting tumors to significant physical stress during growth. This mechanical stress is transduced intracellularly via integrins, the Rho signaling pathway, and the Hippo signaling pathway, thereby promoting tumor invasion. Additionally, mechanical pressure fosters glycolysis in tumor cells, boosting energy production to support metastasis. Mechanical cues also regulate macrophage polarization, maintaining an inflammatory microenvironment conducive to tumor survival. In summary, mechanical signals within tumors play a crucial role in tumor growth and invasion. Understanding these signals and their involvement in tumor progression is essential for advancing our knowledge of tumor biology and enhancing therapeutic approaches.

Radiomics Analysis of Intratumoral and Various Peritumoral Regions From Automated Breast Volume Scanning for Accurate Ki-67 Prediction in Breast Cancer Using Machine Learning

RATIONALE AND OBJECTIVES

Current radiomics research primarily focuses on intratumoral regions and fixed peritumoral areas, lacking optimization for accurate Ki-67 prediction. This study aimed to develop machine learning (ML) models to analyze radiomic features from Automated Breast Volume Scanning (ABVS) images of different peritumoral region sizes to identify the optimal size for accurate preoperative Ki-67 prediction.

MATERIALS AND METHODS

A total of 668 breast cancer patients were enrolled and divided into training (486) and testing (182) cohorts. In the training cohort, ML models were developed for intratumoral and peritumoral regions (2, 4, 6, 8, and 10 mm). Relevant Ki-67 features for each ROI were identified, and different models were compared to determine the optimal one. These models were validated using a testing cohort to find the most accurate peritumoral region for Ki-67 prediction. SHAP (Shapley Additive Explanations) analysis was performed to identify key radiomic features from the optimal model.

RESULTS

The Extreme Gradient Boosting (XGBoost) model for the intratumoral region combined with the 6 mm peritumoral region achieved the highest predictive accuracy, with an AUC of 0.957 in the training cohort and 0.920 in the testing cohort. Calibration curves confirmed reliability, and decision curve analysis demonstrated the highest net benefit. SHAP indicated that 6 mm peritumoral radiomic features are more significant than intratumoral features.

CONCLUSION

The XGBoost model using ABVS-derived radiomic features from both the intratumoral and 6 mm peritumoral regions provides the most accurate preoperative Ki-67 prediction.

Reprogramming Tumor-Associated Macrophage Using Nanocarriers: New Perspectives to Halt Cancer Progression

Cancer remains a significant challenge for public healthcare systems worldwide. Within the realm of cancer treatment, considerable attention is focused on understanding the tumor microenvironment (TME)-the complex network of non-cancerous elements surrounding the tumor. Among the cells in TME, tumor-associated macrophages (TAMs) play a central role, traditionally categorized as pro-inflammatory M1 macrophages or anti-inflammatory M2 macrophages. Within the TME, M2-like TAMs can create a protective environment conducive to tumor growth and progression. These TAMs secrete a range of factors and molecules that facilitate tumor angiogenesis, increased vascular permeability, chemoresistance, and metastasis. In response to this challenge, efforts are underway to develop adjuvant therapy options aimed at reprogramming TAMs from the M2 to the anti-tumor M1 phenotype. Such reprogramming holds promise for suppressing tumor growth, alleviating chemoresistance, and impeding metastasis. Nanotechnology has enabled the development of nanoformulations that may soon offer healthcare providers the tools to achieve targeted drug delivery, controlled drug release within the TME for TAM reprogramming and reduce drug-related adverse events. In this review, we have synthesized the latest data on TAM polarization in response to TME factors, highlighted the pathological effects of TAMs, and provided insights into existing nanotechnologies aimed at TAM reprogramming and depletion.

The use of microphysiological systems to model metastatic cancer

Cancer is one of the leading causes of death in the 21st century, with metastasis of cancer attributing to 90% of cancer-related deaths. Therefore, to improve patient outcomes there is a need for better preclinical models to increase the success of translating oncological therapies into the clinic. Current traditional staticin vitromodels lack a perfusable network which is critical to overcome the diffusional mass transfer limit to provide a mechanism for the exchange of essential nutrients and waste removal, and increase their physiological relevance. Furthermore, these models typically lack cellular heterogeneity and key components of the immune system and tumour microenvironment. This review explores rapidly developing strategies utilising perfusable microphysiological systems (MPS) for investigating cancer cell metastasis. In this review we initially outline the mechanisms of cancer metastasis, highlighting key steps and identifying the current gaps in our understanding of the metastatic cascade, exploring MPS focused on investigating the individual steps of the metastatic cascade before detailing the latest MPS which can investigate multiple components of the cascade. This review then focuses on the factors which can affect the performance of an MPS designed for cancer applications with a final discussion summarising the challenges and future directions for the use of MPS for cancer models.