Changes in Pulmonary Microenvironment Aids Lung Metastasis of Breast Cancer

4T1 Cell Membrane Biomimetic Nanovehicle for Enhanced Breast Cancer Treatment

In this study, hollow mesoporous silica nanoparticles (HMSN) coated with a 4T1 tumor cell membrane were used to construct biomimetic nanomaterials (DTX@CHMSN) for the treatment of breast cancer. The nanodrug can improve the water solubility of polyenetaxel (DTX) by taking advantage of the special structure, good biocompatibility, and adjustable surface chemical properties of HMSN. Hollow mesoporous silica nanoparticles are coated with 4T1 cell membranes derived from homologous tumors (CHMSN). Adhesion glycoproteins on cancer cell membranes specifically bind to receptors on the cell membranes of the same cancer cell to target specific breast cancer tissues. At the same time, the cell membrane of the 4T1 tumor also contains CD47 protein, which can be specifically recognized by the immune system to produce immune escape. Therefore, the biomimetic nanomedicine DTX@CHMSN, with homologous targeting and immune escape ability, can accumulate in large quantities at the tumor site, reduce systemic toxicity, and thus improve the therapeutic effect.

Metabolic shifts in lipid utilization and reciprocal interactions within the lung metastatic niche of triple-negative breast cancer revealed by spatial multi-omics

The Triple-Negative Breast Cancer (TNBC) subtype constitutes 15-20% of breast cancer cases and is associated with the poorest clinical outcomes. Distant metastasis, particularly to the lungs, is a major contributor to the high mortality rates in breast cancer patients. Despite this, there has been a lack of comprehensive insights into the heterogeneity of metastatic tumors and their surrounding ecosystem in the lungs. In this study, we utilized spatial RNA sequencing technology to investigate the heterogeneity of lung metastatic tumors and their microenvironment in two spontaneous lung metastatic mouse models. Our findings revealed an increase in metabolic-related genes within the cancer cells, with the hub gene Dlat (Dihydrolipoamide S-Acetyltransferase) showing a significant association with the development of lung metastatic tumors. Upregulation of Dlat led to the reprogramming of fatty acid utilization, markedly enhancing the bioenergetic capacity of cancer cells. This finding was corroborated by the increased dependence on fatty acid utilization in lung metastatic cancer cells, and inhibition of Dlat in breast cancer cells exhibited a reduced oxygen consumption rate. Consequently, inhibition of Dlat resulted in decreased survival capacity of breast cancer by reducing cancer stem cell properties and cell adhesion in the lung in vivo. The three cell components within the lung metastatic niche, including CD163+ macrophages, neutrophils, and endothelial cells, expressed elevated levels of ApoE, leading to the secretion of various protumorigenic molecules that promote cancer cell growth in the lung. These molecules include galectin-1, S100A10, S100A4, and S100A6. Collectively, our findings highlight the lipid metabolism reprogramming of cancer and components of the tumor microenvironment that support lung metastasis of TNBC breast cancer.

Early death prediction model for breast cancer with synchronous lung metastases: an analysis of the SEER database

Background

Breast cancer with lung metastases (BCLM) is a serious condition that often leads to early death. This study aims to screen the risk factors of early death in BCLM patients and establish a simple and accurate nomogram prediction model. Identifying prognostic markers and developing accurate prediction models can help guide clinical decision-making.

Methods

The Surveillance, Epidemiology, and End Results (SEER) database was used to analyze a sizable sample of data, encompassing 4,238 BCLM patients diagnosed between 2010 and 2015. Stepwise regression was used to manage the collinearity of variables and to construct a prediction model based on the histogram. The results were subjected to internal validation and contrasted with those of related investigations.

Results

Of the 4,238 BCLM patients in this study, 3,232 did not die early. Of the 1,006 premature deaths, 891 were cancer specific. Lymph node involvement, tumor size, age, and race were all recognized as prognostic markers for premature mortality. A nomogram was constructed based on these factors to reliably predict cancer-specific death and early all-cause death.

Conclusions

This study gives new insights into the prognosis of individuals with BCLM and finds critical prognostic variables for early mortality. The created nomogram might assist physicians in identifying individuals at high risk of early mortality and making treatment options.

Inhalable nanoparticles with enhanced cuproptosis and cGAS-STING activation for synergistic lung metastasis immunotherapy

Due to the insufficient Cu+ accumulation, Cu+ efflux mechanism, and highly immunosuppressive tumor microenvironment (TME) in lung metastasis, the cuproptosis efficacy is limited. Herein, an inhalable nanodevice (CLDCu) is constructed to successfully overcome the drawbacks of cuproptosis. CLDCu consists of a Cu2+-chitosan shell and low molecular weight heparin-tocopherol succinate (LMWH-TOS, LT) core with disulfiram (DSF) loading. The prepared CLDCu can be inhaled and accumulate in large amounts in lung lesions (63.6%) with 56.5 times higher than intravenous injection. Within tumor cells, the mild acidity triggers the co-release of DSF and Cu2+, thus generating bis(diethyldithiocarbamate)-copper (CuET) to block Cu+ efflux protein ATP7B and forming toxic Cu+, leading to enhanced cuproptosis. Meanwhile, the released chitosan cooperates with CLDCu-induced cuproptosis to activate stimulator of interferon genes (STING) pathway, which significantly potentiates dendritic cells (DCs) maturation, as wells as evokes innate and adaptive immunity. In lung metastatic mice model, CLDCu is found to induce cuproptosis and reverse the immunosuppressive TME by inhalation administration. Moreover, CLDCu combined with anti-programmed cell death protein ligand-1 antibody (aPD-L1) provokes stronger antitumor immunity. Therefore, nanomedicine that combines cuproptosis with STING activation is a novel strategy for tumor immunotherapy.

Immunomodulatory effects of BAFF and APRIL cytokines in post-pulmonary infection lung cancer: Implications for drug resistance and progression

Lung cancer is a complicated and challenging disease and is one of the most common causes of cancer-related mortality worldwide. Within the lung microenvironment, specific cytokines, including the B cell activation factor (BAFF) and the A proliferation-inducing ligand (APRIL), are produce by various cells, notably airway epithelial cells, in response allergic inflammation or pulmonary infection. These cytokines play a critical role in maintaining local immune responses and fostering the survival of immune cells. The BAFF and APRIL system have been connected in a range of malignancies and have shown their potential in inducing drug resistance and promoting cancer progression. This review highlights recent studies on the involvement of BAFF and APRIL in various cancers, focusing mainly on their role in lung cancer, and discusses the possibility of these molecules in contributing to drug resistance and cancer progression following pulmonary infection. We suggest consideration the targeting BAFF and APRIL or their respective receptors as promising novel therapies for effective treatment of lung cancer, especially post pulmonary infection. However, it remains important to conduct further investigations to fully elucidate the precise mechanisms underlying how the BAFF and APRIL systems enhance cancer survival and drug resistance subsequent pulmonary infections.

Phenotypic Heterogeneity, Bidirectionality, Universal Cues, Plasticity, Mechanics, and the Tumor Microenvironment Drive Cancer Metastasis

Tumor diseases become a huge problem when they embark on a path that advances to malignancy, such as the process of metastasis. Cancer metastasis has been thoroughly investigated from a biological perspective in the past, whereas it has still been less explored from a physical perspective. Until now, the intraluminal pathway of cancer metastasis has received the most attention, while the interaction of cancer cells with macrophages has received little attention. Apart from the biochemical characteristics, tumor treatments also rely on the tumor microenvironment, which is recognized to be immunosuppressive and, as has recently been found, mechanically stimulates cancer cells and thus alters their functions. The review article highlights the interaction of cancer cells with other cells in the vascular metastatic route and discusses the impact of this intercellular interplay on the mechanical characteristics and subsequently on the functionality of cancer cells. For instance, macrophages can guide cancer cells on their intravascular route of cancer metastasis, whereby they can help to circumvent the adverse conditions within blood or lymphatic vessels. Macrophages induce microchannel tunneling that can possibly avoid mechanical forces during extra- and intravasation and reduce the forces within the vascular lumen due to vascular flow. The review article highlights the vascular route of cancer metastasis and discusses the key players in this traditional route. Moreover, the effects of flows during the process of metastasis are presented, and the effects of the microenvironment, such as mechanical influences, are characterized. Finally, the increased knowledge of cancer metastasis opens up new perspectives for cancer treatment.

Onco-Breastomics: An Eco-Evo-Devo Holistic Approach

Known as a diverse collection of neoplastic diseases, breast cancer (BC) can be hyperbolically characterized as a dynamic pseudo-organ, a living organism able to build a complex, open, hierarchically organized, self-sustainable, and self-renewable tumor system, a population, a species, a local community, a biocenosis, or an evolving dynamical ecosystem (i.e., immune or metabolic ecosystem) that emphasizes both developmental continuity and spatio-temporal change. Moreover, a cancer cell community, also known as an oncobiota, has been described as non-sexually reproducing species, as well as a migratory or invasive species that expresses intelligent behavior, or an endangered or parasite species that fights to survive, to optimize its features inside the host's ecosystem, or that is able to exploit or to disrupt its host circadian cycle for improving the own proliferation and spreading. BC tumorigenesis has also been compared with the early embryo and placenta development that may suggest new strategies for research and therapy. Furthermore, BC has also been characterized as an environmental disease or as an ecological disorder. Many mechanisms of cancer progression have been explained by principles of ecology, developmental biology, and evolutionary paradigms. Many authors have discussed ecological, developmental, and evolutionary strategies for more successful anti-cancer therapies, or for understanding the ecological, developmental, and evolutionary bases of BC exploitable vulnerabilities. Herein, we used the integrated framework of three well known ecological theories: the Bronfenbrenner's theory of human development, the Vannote's River Continuum Concept (RCC), and the Ecological Evolutionary Developmental Biology (Eco-Evo-Devo) theory, to explain and understand several eco-evo-devo-based principles that govern BC progression. Multi-omics fields, taken together as onco-breastomics, offer better opportunities to integrate, analyze, and interpret large amounts of complex heterogeneous data, such as various and big-omics data obtained by multiple investigative modalities, for understanding the eco-evo-devo-based principles that drive BC progression and treatment. These integrative eco-evo-devo theories can help clinicians better diagnose and treat BC, for example, by using non-invasive biomarkers in liquid-biopsies that have emerged from integrated omics-based data that accurately reflect the biomolecular landscape of the primary tumor in order to avoid mutilating preventive surgery, like bilateral mastectomy. From the perspective of preventive, personalized, and participatory medicine, these hypotheses may help patients to think about this disease as a process governed by natural rules, to understand the possible causes of the disease, and to gain control on their own health.

Combining radiomics and deep learning features of intra-tumoral and peri-tumoral regions for the classification of breast cancer lung metastasis and primary lung cancer with low-dose CT

PURPOSE

To investigate the performance of deep learning and radiomics features of intra-tumoral region (ITR) and peri-tumoral region (PTR) in the diagnosing of breast cancer lung metastasis (BCLM) and primary lung cancer (PLC) with low-dose CT (LDCT).

METHODS

We retrospectively collected the LDCT images of 100 breast cancer patients with lung lesions, comprising 60 cases of BCLM and 40 cases of PLC. We proposed a fusion model that combined deep learning features extracted from ResNet18-based multi-input residual convolution network with traditional radiomics features. Specifically, the fusion model adopted a multi-region strategy, incorporating the aforementioned features from both the ITR and PTR. Then, we randomly divided the dataset into training and validation sets using fivefold cross-validation approach. Comprehensive comparative experiments were performed between the proposed fusion model and other eight models, including the intra-tumoral deep learning model, the intra-tumoral radiomics model, the intra-tumoral deep-learning radiomics model, the peri-tumoral deep learning model, the peri-tumoral radiomics model, the peri-tumoral deep-learning radiomics model, the multi-region radiomics model, and the multi-region deep-learning model.

RESULTS

The fusion model developed using deep-learning radiomics feature sets extracted from the ITR and PTR had the best classification performance, with the area under the curve of 0.913 (95% CI 0.840-0.960). This was significantly higher than that of the single region's radiomics model or deep learning model.

CONCLUSIONS

The combination of radiomics and deep learning features was effective in discriminating BCLM and PLC. Additionally, the analysis of the PTR can mine more comprehensive tumor information.