Circumventing drug resistance in gastric cancer: A spatial multi-omics exploration of chemo and immuno-therapeutic response dynamics.

BACKGROUND: Gastric Cancer (GC) characteristically exhibits heterogeneous responses to treatment, particularly in relation to immuno plus chemo therapy, necessitating a precision medicine approach. This study is centered around delineating the cellular and molecular underpinnings of drug resistance in this context. METHODS: We undertook a comprehensive multi-omics exploration of postoperative tissues from GC patients undergoing the chemo and immuno-treatment regimen. Concurrently, an image deep learning model was developed to predict treatment responsiveness. RESULTS: Our initial findings associate apical membrane cells with resistance to fluorouracil and oxaliplatin, critical constituents of the therapy. Further investigation into this cell population shed light on substantial interactions with resident macrophages, underscoring the role of intercellular communication in shaping treatment resistance. Subsequent ligand-receptor analysis unveiled specific molecular dialogues, most notably TGFB1-HSPB1 and LTF-S100A14, offering insights into potential signaling pathways implicated in resistance. Our SVM model, incorporating these multi-omics and spatial data, demonstrated significant predictive power, with AUC values of 0.93 and 0.84 in the exploration and validation cohorts respectively. Hence, our results underscore the utility of multi-omics and spatial data in modeling treatment response. CONCLUSION: Our integrative approach, amalgamating mIHC assays, feature extraction, and machine learning, successfully unraveled the complex cellular interplay underlying drug resistance. This robust predictive model may serve as a valuable tool for personalizing therapeutic strategies and enhancing treatment outcomes in gastric cancer.

In silico analysis reveals PRR11 as a prognostic and oncogenic marker in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is a common lung cancer. Although there are various treatments for LUAD, its prognosis remains poor. Therefore, it is imperative to identify new targets and develop novel therapeutic strategies. In this study, we analyze the expression of proline rich 11 (PRR11) in pan cancer based on The Cancer Genome Atlas (TCGA) database, and explore the prognostic value of PRR11 in LUAD by GEPIA2 (Gene Expression Profiling Interactive Analysis, version 2) database. In addition, the relationship between PRR11 and the clinicopathological features of LUAD was analyzed using UALCAN database. The association between PRR11 expression and immune infiltration was accessed. The PRR11 related genes were screened using LinkOmics and GEPIA2. Gene Ontology Term Enrichment (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was performed by David database. The results suggested that the expression of PRR11 in most tumor tissues was significantly higher than that in normal tissues. In LUAD patients, high expression of PRR11 was associated with shortened first progression survival (FPS), overall survival (OS) and post progression survival (PPS), and correlated with individual cancer stage, race, gender, smoking habit, and tissue subtype. Besides, the high expression of PRR11 was accompanied by a relatively higher infiltration level of cancer-associated fibroblasts (CAFs) and myeloid-derived suppressor cell (MDSC), and decreased infiltration level of CD8+ T cells in the tumor microenvironment. GO analyses showed that PRR11 participated in biological processes such as cell division and cell cycle, and was involved in protein binding and microtubule binding functions. KEGG analyses revealed that PRR11 was implicated in p53 signaling pathway. All the results indicated that PRR11 might be an independent prognostic biomarker and therapeutic target for LUAD.

Precise Delivery of Nanomedicines to M2 Macrophages by Combining "Eat Me/Don't Eat Me" Signals and Its Anticancer Application.

Targeted delivery of nanomedicines to M2 tumor-associated macrophages (TAMs) has been proposed to reduce tumor promotion and enhance the efficacy of anticancer therapy. However, upregulated receptors on M2 TAMs are also expressed on M1 TAMs and other macrophages in normal tissues. Therefore, improving targeting specificity remains a key challenge. Here, we developed a precise M2 TAM-targeted delivery system using "eat-me" and "don't-eat-me" signals. A CD47-derived self-peptide ligand (don't-eat-me signal) and galactose ligand (eat-me signal) were introduced on liposomes. Cleavable phospholipid-polyethylene glycol was covered on the surface and could combine with the self-peptide to inhibit macrophage recognition even after immunoglobulin M adsorption and protect galactose from hepatic clearance to prolong the circulation time and promote the accumulation of liposomes in tumors. This detachable polymer can be removed by the redox microenvironment upon transcytosis through the tumor endothelium and re-expose the self-peptide and galactose. The self-peptide highly reduced M1 macrophage phagocytosis, and the galactose ligand enhanced the interaction between the liposomes and M2 macrophages. Thus, the modified liposomes enabled specific recognition of M1/M2 TAMs. In vitro evidence revealed reduced endocytosis of the liposomes by M1 macrophages. Moreover, in vivo studies demonstrated that doxorubicin-loaded liposomes efficiently eliminated M2 TAMs but did not affect M1 TAMs, enhancing the potency of the antitumor therapy. Collectively, our results demonstrate the potential of combining active escape and active targeting for precisely delivering a drug of interest to M2 macrophages and suggest its application in anticancer therapy.