Cholesterol Metabolism as a Potential Therapeutic Target and a Prognostic Biomarker for Cancer Immunotherapy

Modulation of cholesterol metabolism with Phytoremedies in Alzheimer's disease: A comprehensive review

Alzheimer's disease (AD) is a complex neurological ailment that causes cognitive decline and memory loss. Cholesterol metabolism dysregulation has emerged as a crucial element in AD pathogenesis, contributing to the formation of amyloid-beta (Aβ) plaques and tau tangles, the disease's hallmark neuropathological characteristics. Thus, targeting cholesterol metabolism has gained attention as a potential therapeutic method for Alzheimer's disease. Phytoremedies, which are generated from plants and herbs, have shown promise as an attainable therapeutic option for Alzheimer's disease. These remedies contain bioactive compounds like phytochemicals, flavonoids, and polyphenols, which have demonstrated potential in modulating cholesterol metabolism and related pathways. This comprehensive review explores the modulation of cholesterol metabolism by phytoremedies in AD. It delves into the role of cholesterol in brain function, highlighting disruptions observed in AD. Additionally, it examines the underlying molecular mechanisms of cholesterol-related pathology in AD. The review emphasizes the significance of phytoremedies as a potential therapeutic intervention for AD. It discusses the drawbacks of current treatments and the need for alternative strategies addressing cholesterol dysregulation and its consequences. Through an in-depth analysis of specific phytoremedies, the review presents compelling evidence of their potential benefits. Molecular mechanisms underlying phytoremedy effects on cholesterol metabolism are examined, including regulation of cholesterol-related pathways, interactions with Aβ pathology, influence on tau pathology, and anti-inflammatory effects. The review also highlights challenges and future perspectives, emphasizing standardization, clinical evidence, and personalized medicine approaches to maximize therapeutic potential in AD treatment. Overall, phytoremedies offer promise as a potential avenue for AD management, but further research and collaboration are necessary to fully explore their efficacy, safety, and mechanisms of action.

Plasma proteome profiling reveals dynamic of cholesterol marker after dual blocker therapy

Dual blocker therapy (DBT) has the enhanced antitumor benefits than the monotherapy. Yet, few effective biomarkers are developed to monitor the therapy response. Herein, we investigate the DBT longitudinal plasma proteome profiling including 113 longitudinal samples from 22 patients who received anti-PD1 and anti-CTLA4 DBT therapy. The results show the immune response and cholesterol metabolism are upregulated after the first DBT cycle. Notably, the cholesterol metabolism is activated in the disease non-progressive group (DNP) during the therapy. Correspondingly, the clinical indicator prealbumin (PA), free triiodothyronine (FT3) and triiodothyronine (T3) show significantly positive association with the cholesterol metabolism. Furthermore, by integrating proteome and radiology approach, we observe the high-density lipoprotein partial remodeling are activated in DNP group and identify a candidate biomarker APOC3 that can reflect DBT response. Above, we establish a machine learning model to predict the DBT response and the model performance is validated by an independent cohort with balanced accuracy is 0.96. Thus, the plasma proteome profiling strategy evaluates the alteration of cholesterol metabolism and identifies a panel of biomarkers in DBT.

Inhibition of autophagy-related protein 7 enhances anti-tumor immune response and improves efficacy of immune checkpoint blockade in microsatellite instability colorectal cancer

BACKGROUND

The efficacy of anti-PD-1 therapy is primarily hindered by the limited T-cell immune response rate and immune evasion capacity of tumor cells. Autophagy-related protein 7 (ATG7) plays an important role in autophagy and it has been linked to cancer. However, the role of ATG7 in the effect of immune checkpoint blockade (ICB) treatment on high microsatellite instability (MSI-H)/mismatch repair deficiency (dMMR) CRC is still poorly understood.

METHODS

In this study, patients from the cancer genome altas (TCGA) COAD/READ cohorts were used to investigate the biological mechanism driving ATG7 development. Several assays were conducted including the colony formation, cell viability, qRT-PCR, western blot, immunofluorescence, flow cytometry, ELISA, immunohistochemistry staining and in vivo tumorigenicity tests.

RESULTS

We found that ATG7 plays a crucial role in MSI-H CRC. Its knockdown decreased tumor growth and caused an infiltration of CD8+ T effector cells in vivo. ATG7 inhibition restored surface major histocompatibility complex I (MHC-I) levels, causing improved antigen presentation and anti-tumor T cell response by activating reactive oxygen species (ROS)/NF-κB pathway. Meanwhile, ATG7 inhibition also suppressed cholesterol accumulation and augmentation of anti-tumor immune responses. Combining ATG7 inhibition and statins improved the therapeutic benefit of anti-PD-1 in MSI-H CRC. Importantly, CRC patients with high expression of both ATG7 and recombinant 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR) experienced worse prognosis compared to those with low ATG7 and HMGCR expression.

CONCLUSIONS

Inhibition of ATG7 leads to upregulation of MHC-I expression, augments immune response and suppresses cholesterol accumulation. These findings demonstrate that ATG7 inhibition has therapeutic potential and application of statins can increase the sensitivity to immune checkpoint inhibitors.

Dysregulation of cellular membrane homeostasis as a crucial modulator of cancer risk

Cellular membranes serve as an epicentre combining extracellular and cytosolic components with membranous effectors, which together support numerous fundamental cellular signalling pathways that mediate biological responses. To execute their functions, membrane proteins, lipids and carbohydrates arrange, in a highly coordinated manner, into well-defined assemblies displaying diverse biological and biophysical characteristics that modulate several signalling events. The loss of membrane homeostasis can trigger oncogenic signalling. More recently, it has been documented that select membrane active dietaries (MADs) can reshape biological membranes and subsequently decrease cancer risk. In this review, we emphasize the significance of membrane domain structure, organization and their signalling functionalities as well as how loss of membrane homeostasis can steer aberrant signalling. Moreover, we describe in detail the complexities associated with the examination of these membrane domains and their association with cancer. Finally, we summarize the current literature on MADs and their effects on cellular membranes, including various mechanisms of dietary chemoprevention/interception and the functional links between nutritional bioactives, membrane homeostasis and cancer biology.

Biomarkers and computational models for predicting efficacy to tumor ICI immunotherapy

Numerous studies have shown that immune checkpoint inhibitor (ICI) immunotherapy has great potential as a cancer treatment, leading to significant clinical improvements in numerous cases. However, it benefits a minority of patients, underscoring the importance of discovering reliable biomarkers that can be used to screen for potential beneficiaries and ultimately reduce the risk of overtreatment. Our comprehensive review focuses on the latest advancements in predictive biomarkers for ICI therapy, particularly emphasizing those that enhance the efficacy of programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors and cytotoxic T-lymphocyte antigen-4 (CTLA-4) inhibitors immunotherapies. We explore biomarkers derived from various sources, including tumor cells, the tumor immune microenvironment (TIME), body fluids, gut microbes, and metabolites. Among them, tumor cells-derived biomarkers include tumor mutational burden (TMB) biomarker, tumor neoantigen burden (TNB) biomarker, microsatellite instability (MSI) biomarker, PD-L1 expression biomarker, mutated gene biomarkers in pathways, and epigenetic biomarkers. TIME-derived biomarkers include immune landscape of TIME biomarkers, inhibitory checkpoints biomarkers, and immune repertoire biomarkers. We also discuss various techniques used to detect and assess these biomarkers, detailing their respective datasets, strengths, weaknesses, and evaluative metrics. Furthermore, we present a comprehensive review of computer models for predicting the response to ICI therapy. The computer models include knowledge-based mechanistic models and data-based machine learning (ML) models. Among the knowledge-based mechanistic models are pharmacokinetic/pharmacodynamic (PK/PD) models, partial differential equation (PDE) models, signal networks-based models, quantitative systems pharmacology (QSP) models, and agent-based models (ABMs). ML models include linear regression models, logistic regression models, support vector machine (SVM)/random forest/extra trees/k-nearest neighbors (KNN) models, artificial neural network (ANN) and deep learning models. Additionally, there are hybrid models of systems biology and ML. We summarized the details of these models, outlining the datasets they utilize, their evaluation methods/metrics, and their respective strengths and limitations. By summarizing the major advances in the research on predictive biomarkers and computer models for the therapeutic effect and clinical utility of tumor ICI, we aim to assist researchers in choosing appropriate biomarkers or computer models for research exploration and help clinicians conduct precision medicine by selecting the best biomarkers.

Multiplex immunohistochemistry defines two cholesterol metabolism patterns predicting immunotherapeutic outcomes in gastric cancer

BACKGROUND

The role of cholesterol metabolism in gastric cancer (GC) and its implications for tumor characteristics and immunotherapy response remain poorly understood. In this study, our aim was to investigate this role, identify associated metabolic subtypes, and assess their clinical implications in GC.

METHODS

We conducted a comprehensive analysis of cholesterol metabolism genes (CMGs) using transcriptomic data from TCGA and GEO. Based on 23 representative CMGs, we classified GC into metabolic subtypes. We evaluated clinical features and immune cell infiltration between these subtypes. Additionally, we identified a CMG signature and assessed its clinical relevance in GC. We retrospectively enrolled thirty-five GC patients receiving chemotherapy plus a PD-1 inhibitor to assess the CMG signature using multiplex immunohistochemistry.

RESULTS

Our analysis revealed two cholesterol metabolism subtypes in GC: Cholesterol Metabolism Type 1 (CMT1) and Cholesterol Metabolism Type 2 (CMT2). These subtypes exhibited distinct patterns: CMT1 indicated heightened cholesterol biosynthesis, while CMT2 showed abnormal cholesterol transport. CMT2 was associated with unfavorable clinical features, enriched malignant pathways, and a pro-tumor immune microenvironment. Furthermore, we developed a five-CMG prognostic signature (ABCA1, NR1H3, TSPO, NCEH1, and HMGCR) that effectively predicted the prognosis of patients with GC and their response to chemotherapy plus a PD-1 inhibitor. This signature was validated in a clinical cohort using multiplex immunohistochemistry.

CONCLUSION

Our results highlight the effectiveness of cholesterol metabolism patterns as biomarkers for predicting the prognosis and immunotherapy response in GC. The expression of cholesterol metabolism genes and the assessment of cholesterol metabolism patterns have the potential to predict the outcome of immunotherapy and guide treatment strategies.

Raman imaging and chemometric methods in human normal bronchial and cancer lung cells: Raman biomarkers of lipid reprogramming

This paper presents an approach to study biochemical changes in human normal bronchial cells (BEpiC) and human cancer lung cells (A549) by Raman spectroscopy and Raman imaging combined with chemometric methods. Based on Raman spectra and Raman imaging combined with chemometric methods we have proved that peaks at 845 cm-1, 2845 cm-1, 2936 cm-1, 1444 cm-1, 750 cm-1, 1126 cm-1, 1584 cm-1, can be treated as Raman biomarkers probing phosphorylation, lipid reprogramming, oxidative phosphorylation and changes in cholesterol and cytochrome in normal and cancer cells. Raman analysis of the bands at 845 cm-1, 2845 cm-1, 1444 cm-1, and 1126 cm-1 in human cancer lung cells and human normal bronchial cells demonstrate enhanced phosphorylation and triglycerides de novo synthesis, reduced levels of cholesterol and cytochrome c in cancer cells. The sensitivity is equal to 100% (nucleus), 87.5% (mitochondria), 100% (endoplasmic reticulum), 87.5% (lipid droplets), 87.5% (cytoplasm), 87.5% (cell membrane) for A549 cell line and 83.3% (nucleus), 100% (mitochondria), 83.3% (endoplasmic reticulum), 100% (lipid droplets), 100% (cytoplasm), 83.3% (cell membrane) for BEpiC. The values of specificity for cross-validation equal 93.4% (nucleus), 85.5% (mitochondria), 89.5% (endoplasmic reticulum), 90.8% (lipid droplets), 61.8% (cytoplasm), 94.7% (cell membrane) for A549 cell line and 88.5% (nucleus), 85.9% (mitochondria), 85.9% (endoplasmic reticulum), 97.4% (lipid droplets), 75.6% (cytoplasm), 92.3% (cell membrane) for BEpiC. We have confirmed that Raman spectroscopy methods combined with PLS-DA are useful tools to monitor changes in human cancer lung cells and human normal bronchial cells.

SIAH1 ubiquitination-modified HMGCR inhibits lung cancer progression and promotes drug sensitivity through cholesterol synthesis

BACKGROUNDS

Lung cancer is one of the most frequently diagnosed cancers and the leading cause of cancer-related deaths worldwide. Deep understanding of chemoresistance will lead to remarkable progress in lung cancer treatment strategy. Cholesterol accumulation was associated with cisplatin resistance in lung cancer treatment. And we found the degree of cisplatin resistance was correlated with the expression of the cholesterol synthesis HMGCR.

METHODS

We analyzed a group of 42 lung cancer patients who received cisplatin treatment after lung resection surgery. The expression of HMGCR and its correlation with cholesterol in lung cancer cell lines were determined by qRT-PCR and ELISA analyses. We focus on the function and mechanism of HMGCR in lung cancer and reveal that knockdown of HMGCR expression inhibits the proliferation, colony formation, and migration of lung cancer cell lines in vitro or in vivo and dramatically enhances the efficacy of cisplatin.

RESULTS

Through mechanism studies, we illustrate that SIAH1, an E3 ubiquitin-protein ligase, ubiquitination modifies HMGCR and inhibits efflux protein activity via regulating cholesterol synthesis. In vivo experiments showed that SIAH1 overexpression or using HMGCR knockdown retard tumor growth and enhanced the efficacy of cisplatin. In summary, HMGCR affects cholesterol metabolism by regulating key enzymes in cholesterol synthesis, thereby reducing drug sensitivity.

CONCLUSION

This study indicates that lung cancer patients with lower HMGCR levels may lead to a better prognosis and provide a potential treatment by SIAH1 overexpression for lung cancer patients with cisplatin resistance.

Targeting inhibition of prognosis-related lipid metabolism genes including CYP19A1 enhances immunotherapeutic response in colon cancer

BACKGROUND

Lipid metabolic reprogramming in colon cancer shows a potential impact on tumor immune microenvironment and is associated with response to immunotherapy. Therefore, this study aimed to develop a lipid metabolism-related prognostic risk score (LMrisk) to provide new biomarkers and combination therapy strategies for colon cancer immunotherapy.

METHODS

Differentially expressed lipid metabolism-related genes (LMGs) including cytochrome P450 (CYP) 19A1 were screened to construct LMrisk in TCGA colon cancer cohort. The LMrisk was then validated in three GEO datasets. The differences of immune cell infiltration and immunotherapy response between LMrisk subgroups were investigated via bioinformatic analysis. These results were comfirmed by in vitro coculture of colon cancer cells with peripheral blood mononuclear cells, human colon cancer tissue microarray analysis, multiplex immunofluorescence staining and mouse xenograft models of colon cancer.

RESULTS

Six LMGs including CYP19A1, ALOXE3, FABP4, LRP2, SLCO1A2 and PPARGC1A were selected to establish the LMrisk. The LMrisk was positively correlated with the abundance of macrophages, carcinoma-associated fibroblasts (CAFs), endothelial cells and the levels of biomarkers for immunotherapeutic response including programmed cell death ligand 1 (PD-L1) expression, tumor mutation burden and microsatellite instability, but negatively correlated with CD8⁺ T cell infiltration levels. CYP19A1 protein expression was an independent prognostic factor, and positively correlated with PD-L1 expression in human colon cancer tissues. Multiplex immunofluorescence analyses revealed that CYP19A1 protein expression was negatively correlated with CD8⁺ T cell infiltration, but positively correlated with the levels of tumor-associated macrophages, CAFs and endothelial cells. Importantly, CYP19A1 inhibition downregulated PD-L1, IL-6 and TGF-β levels through GPR30-AKT signaling, thereby enhancing CD8⁺ T cell-mediated antitumor immune response in vitro co-culture studies. CYP19A1 inhibition by letrozole or siRNA strengthened the anti-tumor immune response of CD8⁺ T cells, induced normalization of tumor blood vessels, and enhanced the efficacy of anti-PD-1 therapy in orthotopic and subcutaneous mouse colon cancer models.

CONCLUSION

A risk model based on lipid metabolism-related genes may predict prognosis and immunotherapeutic response in colon cancer. CYP19A1-catalyzed estrogen biosynthesis promotes vascular abnormality and inhibits CD8⁺ T cell function through the upregulation of PD-L1, IL-6 and TGF-β via GPR30-AKT signaling. CYP19A1 inhibition combined with PD-1 blockade represents a promising therapeutic strategy for colon cancer immunotherapy.

The role of blood cholesterol quality in patients with advanced cancer receiving immune checkpoint inhibitors

INTRODUCTION

Immune checkpoint inhibitors (ICIs) became the standard of care for several solid tumors. A limited fraction of patients (pts) achieves a long-term benefit. Plasmatic and intracellular cholesterol levels have emerged as promising biomarkers. The aim of the present study was to determine whether cholesterol efflux capacity (CEC), mediated by serum transporters (ABCA1 and ABCG1) and passive diffusion (PD), impacts on clinical outcome of advanced non-small cell lung cancer (NSCLC) and metastatic renal cell carcinoma (mRCC) pts treated with ICIs.

MATERIAL AND METHODS

We retrospectively enrolled advanced NSCLC and mRCC pts consecutively treated with ICIs between October 2013 and October 2018. CEC and cholesterol loading capacity (CLC) were assessed by well-established specific cell models. As primary endpoint, CEC, PD and CLC were correlated with overall survival (OS) while the effects of these parameters on progression-free survival (PFS) and clinical benefit (CB), defined as complete/partial response or stable disease, represented secondary endpoints.

RESULTS

NSCLC accounted for 94.2% of 70 enrolled cases, and serum sample suitable for CEC and PD determination was available in 68. Blood cholesterol and serum ABCA1, ABCG1, PD and CLC were associated with outcomes (OS, PFS and CB) at univariate analysis. At the multivariate analysis, only PD confirmed its positive prognostic value in terms of OS, PFS and CB.

CONCLUSION

The favorable impact of cholesterol PD on clinical outcome might reflect its main conformation in mature HDL particles which potentially shape an inflamed context, ultimately promoting ICI efficacy. Further prospective studies are needed to support our findings and uncover targetable pathways.

Tumor regression rate, PD-L1 expression, pembrolizumab/nab-paclitaxel-based regimens, squamous cell carcinoma, and comorbidities were independently associated with efficacy of neoadjuvant chemoimmunotherapy in non-small cell lung cancer

Background

Neoadjuvant chemoimmunotherapy (NCIO) is more effective than neoadjuvant immunotherapy alone for pathological response in non-small cell lung cancer (NSCLC) patients, but the processes for determining patient suitability for its implementation are not clear. We aimed to identify the most relevant factors and build a convenient model to select NSCLC patients who would benefit most from NCIO.

Methods

 We retrospectively collected the clinical data of patients with locally advanced NSCLC who received NCIO followed by surgery at our institution between January 2019 and July 2022.

Results

A total of 101 eligible stage IIB-IIIC NSCLC patients were included. After NCIO, all patients successfully underwent surgical resection. A total of 46.53% (47/101) of patients achieved pathological complete response (pCR), and 70.30% (71/101) achieved major pathologic response (MPR). Tumor regression rate (adjusted odds ratio OR = 12.33), PD-L1 expression (adjusted odds ratio (OR) = 9.66), pembrolizumab/nab-paclitaxel-based regimens (adjusted OR = 4.92), and comorbidities (adjusted OR = 0.16) were independently associated with pCR rate (all P < 0.05). Tumor regression rate (adjusted OR = 8.45), PD-L1 expression (adjusted OR = 5.35), and presence of squamous cell carcinoma (adjusted OR = 7.02) were independently associated with MPR rate (all P < 0.05). We established and validated an easy-to-use clinical model to predict pCR (with an area under the curve [AUC] of 0.848) and MPR (with an AUC of 0.847). Of note, the present study showed that CD4⁺ T-cell count/rate and total cholesterol (TC) and high-density lipoprotein cholesterol (HDL-C) levels in the peripheral blood of pre-NCIO patients were also significantly correlated with pathological response in univariate analyses.

Conclusions

The tumor regression rate, PD-L1 expression, pembrolizumab/nab-paclitaxel-based regimens, presence of squamous cell carcinoma, and comorbidities were the main influential factors for incidence of pCR/MPR in patients with stage IIB-IIIC NSCLC in the present study. Through predictive models, we can predict who will benefit most from NCIO prior to the emergence of clinical outcomes in locally advanced NSCLC.

The tangled web of dyslipidemia and cancer: Is there any association?

Cancer is a primary cause of mortality around the world and imposes a significant physiological, psychological, and financial burden on patients. Lipids regulate cell cycle progression and affect cell proliferation, migration, and apoptosis. Therefore, alterations in serum lipid levels might contribute to carcinogenesis. In this article, we review the relationships between triglyceride (TG), total cholesterol, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) levels and different types of cancer. Then, we examine the association between cancer and familial hypercholesterolemia. Finally, we evaluate the impact of statins on different types of cancer. Increased total cholesterol has been reported to increase cellular proliferation and angiogenesis in tumors and inhibit apoptosis. Increased LDL-C has been reported to induce inflammation and increase susceptibility to oxidative damage. HDL-C has anti-oxidation, anti-inflammatory, and antiproliferative properties. Increased levels of serum TG can induce oxidative stress and a chronic inflammatory state and therefore contribute to the proliferation and progression of cancer cells. Statins decrease downstream products of cholesterol synthesis that are crucial in cell proliferation and growth. Thus, lipid components can have prognostic value in cancer and management of serum lipid levels through lifestyle changes and medical therapy can be beneficial in cancer prevention and treatment.

Cholesterol: An important actor on the cancer immune scene

Based on the structural and signaling roles of cholesterol, which are necessary for immune cell activity, high concentrations of cholesterol and its metabolites not only trigger malignant cell activities but also impede immune responses against cancer cells. To proliferate and evade immune responses, tumor cells overcome environmental restrictions by changing their metabolic and signaling pathways. Overexpression of mevalonate pathway enzymes and low-density lipoprotein receptor cause elevated cholesterol synthesis and uptake, respectively. Accordingly, cholesterol can be considered as both a cause and an effect of cancer. Variations in the effects of blood cholesterol levels on the outcome of different types of cancer may depend on the stage of cancer. However, positive effects of cholesterol-lowering drugs have been reported in the treatment of patients with some malignancies.

High-affinity SOAT1 ligands remodeled cholesterol metabolism program to inhibit tumor growth

BACKGROUND

Although cholesterol metabolism is a common pathway for the development of antitumor drugs, there are no specific targets and drugs for clinical use. Here, based on our previous study of sterol O-acyltransferase 1 (SOAT1) in hepatocelluar carcinoma, we sought to screen an effective targeted drug for precise treatment of hepatocelluar carcinoma and, from the perspective of cholesterol metabolism, clarify the relationship between cholesterol regulation and tumorigenesis and development.

METHODS

In this study, we developed a virtual screening integrated affinity screening technology for target protein drug screening. A series of in vitro and in vivo experiments were used for drug activity verification. Multi-omics analysis and flow cytometry analysis were used to explore antitumor mechanisms. Comparative analysis of proteome and transcriptome combined with survival follow-up information of patients reveals the clinical therapeutic potential of screened drugs.

RESULTS

We screened three compounds, nilotinib, ABT-737, and evacetrapib, that exhibited optimal binding with SOAT1. In particular, nilotinib displayed a high affinity for SOAT1 protein and significantly inhibited tumor activity both in vitro and in vivo. Multi-omics analysis and flow cytometry analysis indicated that SOAT1-targeting compounds reprogrammed the cholesterol metabolism in tumors and enhanced CD8⁺ T cells and neutrophils to suppress tumor growth.

CONCLUSIONS

Taken together, we reported several high-affinity SOAT1 ligands and demonstrated their clinical potential in the precision therapy of liver cancer, and also reveal the potential antitumor mechanism of SOAT1-targeting compounds.

Immunometabolic Markers in a Small Patient Cohort Undergoing Immunotherapy

Although the discovery of immune checkpoints was hailed as a major breakthrough in cancer therapy, generating a sufficient response to immunotherapy is still limited. Thus, the objective of this exploratory, hypothesis-generating study was to identify potentially novel peripheral biomarkers and discuss the possible predictive relevance of combining scarcely investigated metabolic and hormonal markers with immune subsets. Sixteen markers that differed significantly between responders and non-responders were identified. In a further step, the correlation with progression-free survival (PFS) and false discovery correction (Benjamini and Hochberg) revealed potential predictive roles for the immune subset absolute lymphocyte count (rs = 0.51; p = 0.0224 *), absolute basophil count (rs = 0.43; p = 0.04 *), PD-1+ monocytes (rs = −0.49; p = 0.04 *), hemoglobin (rs = 0.44; p = 0.04 *), metabolic markers LDL (rs = 0.53; p = 0.0224 *), free androgen index (rs = 0.57; p = 0.0224 *) and CRP (rs = −0.46; p = 0.0352 *). The absolute lymphocyte count, LDL and free androgen index were the most significant individual markers, and combining the immune subsets with the metabolic markers into a biomarker ratio enhanced correlation with PFS (rs = −0.74; p ≤ 0.0001 ****). In summary, in addition to well-established markers, we identified PD-1+ monocytes and the free androgen index as potentially novel peripheral markers in the context of immunotherapy. Furthermore, the combination of immune subsets with metabolic and hormonal markers may have the potential to enhance the power of future predictive scores and should, therefore, be investigated further in larger trials.

An unexpected role for PD-L1 in front-rear polarization and directional migration

Programmed cell death-ligand 1 (PD-L1)-mediated T cell inhibition through PD-1 is a key checkpoint frequently exploited by tumors to evade immunity. In this issue, Wang et al. (2022. J. Cell Biol.https://doi.org/10.1083/jcb.202108083) reveal an unexpected role for PD-L1 in promoting tumor cell front-rear polarity and directionally persistent cell migration, independently of PD-1.

Artificial Intelligence-Powered Hematoxylin and Eosin Analyzer Reveals Distinct Immunologic and Mutational Profiles among Immune Phenotypes in Non-Small-Cell Lung Cancer

The tumor microenvironment can be classified into three immune phenotypes: inflamed, immune excluded, and immune-desert. Immunotherapy efficacy has been shown to vary by phenotype; yet, the mechanisms are poorly understood and demand further investigation. This study unveils the mechanisms using an artificial intelligence-powered software called Lunit SCOPE. Artificial intelligence was used to classify 965 samples of non-small-cell lung carcinoma from The Cancer Genome Atlas into the three immune phenotypes. The immune and mutational profiles that shape each phenotype using xCell, gene set enrichment analysis with RNA-sequencing data, and cBioportal were described. In the inflamed subtype, which showed higher cytolytic score, the enriched pathways were generally associated with immune response and immune-related cell types were highly expressed. In the immune excluded subtype, enriched glycolysis, fatty acid, and cholesterol metabolism pathways were observed. The KRAS mutation, BRAF mutation, and MET splicing variant were mostly observed in the inflamed subtype. The two prominent mutations found in the immune excluded subtype were EGFR and PIK3CA mutations. This study is the first to report the distinct immunologic and mutational landscapes of immune phenotypes, and demonstrates the biological relevance of the classification. In light of these findings, the study offers insights into potential treatment options tailored to each immune phenotype.

Prognostic significance of Naples prognostic score in non-small-cell lung cancer patients with brain metastases

Aims: The authors aimed to evaluate the prognostic value of Naples prognostic score (NPS) in advanced non-small-cell lung cancer patients with brain metastases. Materials & methods: A total of 186 consecutive advanced non-small-cell lung cancer patients were retrospectively analyzed. Kaplan-Meier survival analysis and Cox proportional regression models were used to assess the significance of NPS in overall survival and disease-free survival. Results: Multivariate Cox proportional regression analysis revealed that NPS was a significant independent predictive indicator for overall survival (hazard ratio: 1.897; 95% CI: 1.184-3.041; p = 0.008) and disease-free survival (hazard ratio: 2.169; 95% CI: 1.367-3.44; p = 0.001). Conclusion: NPS was a powerful prognostic indicator for outcome in advanced non-small-cell lung cancer patients with brain metastases.