Metabolite signaling promotes the recruitment of immunosuppressive cells to tumors
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High baseline levels of PD-L1 reduce the heterogeneity of immune checkpoint signature and sensitize anti-PD1 therapy in lung and colorectal cancers
Immune checkpoint blockade (ICB) therapy only induces durable responses in a subset of cancer patients. The underlying mechanisms of such selective efficacy remain largely unknown. By analyzing the expression profiles of immune checkpoint molecules in different statuses of murine tumors, we found that tumor progression generally randomly upregulated multiple immune checkpoints, thus increased the Heterogeneity of Immune checkpoint Signature (HIS) and resulted in immunotherapeutic resistance. Interestingly, overexpressing one pivotal immune checkpoint in a tumor hindered the upregulation of a majority of other immune checkpoint genes during tumor progression via suppressing interferon γ, resulting in HIS-low. Indeed, PD-L1 high-expression sensitized baseline large tumors to anti-PD1 therapy without altering the sensitivity of baseline small tumors. In line with these preclinical results, a retrospective analysis of a phase III study involving patients with non-small cell lung cancer (NSCLC) revealed that PD-L1 tumor proportion score (TPS) ≥ 50% more reliably predicted therapeutic response in NSCLC patients with baseline tumor volume (BTV)-large compared to patients with BTV-small. Notably, TPS combined with BTV significantly improved the predictive accuracy. Collectively, the data suggest that HIS reflects the dynamic features of tumor immune evasion and dictates the selective efficacy of ICB in a tumor size-dependent manner, providing a potential novel strategy to improve precision ICB. These findings highlight the application of ICB to earlier stages of cancer patients. The integration of PD-L1 with BTV may immediately improve patient stratification and prediction performance in the clinic.
Targeting of TAMs: can we be more clever than cancer cells?
With increasing incidence and geography, cancer is one of the leading causes of death, reduced quality of life and disability worldwide. Principal progress in the development of new anticancer therapies, in improving the efficiency of immunotherapeutic tools, and in the personification of conventional therapies needs to consider cancer-specific and patient-specific programming of innate immunity. Intratumoral TAMs and their precursors, resident macrophages and monocytes, are principal regulators of tumor progression and therapy resistance. Our review summarizes the accumulated evidence for the subpopulations of TAMs and their increasing number of biomarkers, indicating their predictive value for the clinical parameters of carcinogenesis and therapy resistance, with a focus on solid cancers of non-infectious etiology. We present the state-of-the-art knowledge about the tumor-supporting functions of TAMs at all stages of tumor progression and highlight biomarkers, recently identified by single-cell and spatial analytical methods, that discriminate between tumor-promoting and tumor-inhibiting TAMs, where both subtypes express a combination of prototype M1 and M2 genes. Our review focuses on novel mechanisms involved in the crosstalk among epigenetic, signaling, transcriptional and metabolic pathways in TAMs. Particular attention has been given to the recently identified link between cancer cell metabolism and the epigenetic programming of TAMs by histone lactylation, which can be responsible for the unlimited protumoral programming of TAMs. Finally, we explain how TAMs interfere with currently used anticancer therapeutics and summarize the most advanced data from clinical trials, which we divide into four categories: inhibition of TAM survival and differentiation, inhibition of monocyte/TAM recruitment into tumors, functional reprogramming of TAMs, and genetic enhancement of macrophages.
The radiogenomic and spatiogenomic landscapes of glioblastoma and their relationship to oncogenic drivers
Glioblastoma is a highly heterogeneous brain tumor, posing challenges for precision therapies and patient stratification in clinical trials. Understanding how genetic mutations influence tumor imaging may improve patient management and treatment outcomes. This study investigates the relationship between imaging features, spatial patterns of tumor location, and genetic alterations in IDH-wildtype glioblastoma, as well as the likely sequence of mutational events.
Implantation of engineered adipocytes suppresses tumor progression in cancer models
Tumors exhibit an increased ability to obtain and metabolize nutrients. Here, we implant engineered adipocytes that outcompete tumors for nutrients and show that they can substantially reduce cancer progression, a technology termed adipose manipulation transplantation (AMT). Adipocytes engineered to use increased amounts of glucose and fatty acids by upregulating UCP1 were placed alongside cancer cells or xenografts, leading to significant cancer suppression. Transplanting modulated adipose organoids in pancreatic or breast cancer genetic mouse models suppressed their growth and decreased angiogenesis and hypoxia. Co-culturing patient-derived engineered adipocytes with tumor organoids from dissected human breast cancers significantly suppressed cancer progression and proliferation. In addition, cancer growth was impaired by inducing engineered adipose organoids to outcompete tumors using tetracycline or placing them in an integrated cell-scaffold delivery platform and implanting them next to the tumor. Finally, we show that upregulating UPP1 in adipose organoids can outcompete a uridine-dependent pancreatic ductal adenocarcinoma for uridine and suppress its growth, demonstrating the potential customization of AMT.
Professional demand analysis for teaching Chinese to speakers of other languages: a text mining approach on internet recruitment platforms
The rapid development of international education in China highlights the growing importance of employment analysis in Teaching Chinese to Speakers of Other Languages (TCSOL). This study explores the enterprise demands for TCSOL professionals using text mining techniques to analyze recruitment data collected from four major platforms: Boss Zhipin, Zhaopin.com, 51job.com, and Liepin.com. Combining descriptive statistics, LDA topic modeling, BERT-BiLSTM-CRF-based named entity recognition, and co-occurrence network analysis were used. Results show that there is a high demand for TCSOL professionals, especially for small-scale enterprises located in first-tier cities such as Beijing, Shanghai, Guangzhou, and Shenzhen. Employers tend to favor candidates with at least a bachelor’s degree and 1–3 years of work experience. The topic model highlighted three central themes in job descriptions, emphasizing a shift toward a more diverse skill set. Named entity recognition identified essential attributes such as “communication ability”, “teaching experience”, “bachelor’s degree or above” and “responsibility” as core recruitment requirements. The co-occurrence network analysis revealed the importance of “teaching” and “priority” as core skill nodes. Time series analysis showed seasonal fluctuations in recruitment demand, peaking during spring recruitment and graduation periods. A hierarchical model of talent demand and development in TCSOL is proposed, integrating the perspectives of employers, job seekers, educators, and policymakers. This study provides valuable insights for aspiring TCSOL professionals, offering guidance to better align talent training with market needs and improve employment prospects.
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