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PubMed2026

Evolving Role of Immunotherapy in Advanced Esophageal Squamous Cell Carcinoma: Are Programmed Death-Ligand 1 (PD-L1) Cutoffs Still Relevant?

Immune checkpoint inhibitors have transformed the management of advanced esophageal squamous cell carcinoma (ESCC) across first-line, second-line, and perioperative settings. Programmed death-ligand 1 (PD-L1) expression has served as the principal biomarker guiding patient selection for these agents, yet it is measured inconsistently across trials and antibody platforms, and its predictive value has come under renewed scrutiny as follow-up data have matured. This review synthesizes the pivotal randomized trials that established anti-programmed cell death protein-1 therapy in ESCC, critically appraises the pooled and patient-level meta-analyses that have re-examined outcomes across biomarker subgroups, and situates recent regulatory reassessment of PD-L1 thresholds within this broader evidence base. Assay heterogeneity between scoring systems, discordance across antibody clones, and the biological distinction between PD-L1 as a prognostic versus a predictive marker are examined as sources of continued uncertainty. The review concludes by considering emerging genomic and microenvironmental biomarkers that may eventually complement or refine PD-L1-based patient selection, and offers a framework for interpreting a single expression threshold as an approximate, assay-dependent stratifier rather than a precise biological boundary.

combined positive score↗

PubMed2025

A Single-Arm Phase 2 Study of Sotorasib Plus Carboplatin and Pemetrexed in Patients With Advanced Nonsquamous NSCLC With KRAS G12C Mutation (WJOG14821L, SCARLET).

INTRODUCTION: The efficacy and safety of sotorasib plus platinum doublet chemotherapy in KRAS G12C-mutated nonsquamous NSCLC (nonsq NSCLC) have been previously reported with a limited follow-up period. METHODS: SCARLET is a single-arm phase 2 study involving chemotherapy-naive patients with KRAS G12C-mutated nonsq NSCLC. The participants received 960 mg daily plus four cycles of carboplatin (area under the curve = 5)/pemetrexed 500 mg/m2, followed by sotorasib/pemetrexed until disease progression. The primary end point was the overall response rate (ORR) and the secondary end points were progression-free survival (PFS), overall survival, and safety. Using plasma samples, next-generation sequencing was performed at baseline, 3 weeks, and during disease progression (the Japan Registry of Clinical Trials number 2051210086). RESULTS: Thirty patients were enrolled between October 2021 and July 2022 with a median follow-up of 14.8 months. ORR was 88.9% (80% confidence interval [CI]: 78.5%-94.8%, 95% CI: 70.8%-97.6%), median PFS was 6.6 months (95% CI: 5.3-16.7 mo), and median overall survival was 20.6 months (95% CI: 8.1 mo-not estimated). Among patients with programmed death-ligand 1 expression levels of 1% or higher and less than 1%, the ORRs were 82.3 and 100%, respectively, and the median PFS was 7.6 and 9.7 months, respectively. Using plasma samples, patients without KRAS G12C at baseline, without KRAS-related pathway co-alterations, or who cleared KRAS G12C at 3 weeks had better median PFS (16.7, 13.9, 8.7 mo, respectively). Tumor protein 53 mutations and EGFR and MET amplification were detected as acquired resistance. CONCLUSIONS: In patients with KRAS G12C-mutated nonsq NSCLC, sotorasib plus carboplatin/pemetrexed reported favorable efficacy, particularly for patients with less than 1% programmed death-ligand 1, with manageable toxicity.

Humans↗

PubMed2025

Unveiling non-small cell lung cancer treatment effect heterogeneity: a comparative analysis of statistical methods.

BACKGROUND: For patients with advanced non-small cell lung cancer lacking targetable genomic alterations, the impact of clinicogenomic characteristics on the effectiveness of combining chemotherapy with immunotherapy is unclear. METHODS: We evaluated 4 statistical methods for detecting heterogeneous treatment effects related to clinical factors, including programmed death-ligand 1 expression, tumor mutation burden, and stage at diagnosis, using the American Association for Cancer Research Project Genomics Evidence Neoplasia Exchange BioPharma Collaborative dataset supplemented with institutional data collected under the same data curation model. A 2-sided P value of no more than .05 was used to denote statistical significance for all analyses. RESULTS: The mixture model revealed 2 latent subgroups: in one subgroup, there was no meaningful treatment effect, with average progression-free survival (PFS) only 5% longer with immunotherapy alone (95% confidence interval [CI] = -19% to 35%); in the second subgroup, immunotherapy alone was associated with a 35% decrease in average PFS (95% CI = -59% to 2%), corresponding to a ratio in treatment effects of 1.62 (95% CI = 1.02 to 2.57). There was a marginal association between lower tumor mutation burden levels and membership in the subgroup with improved PFS following receipt of chemoimmunotherapy. The causal survival forest highlighted the importance of tumor mutation burden (variable importance ranking: 1) and programmed death-ligand 1 (variable importance ranking: 3) when assessing heterogeneity. In contrast, the accelerated failure time and Cox proportional hazards models did not detect any statistically significant heterogeneous treatment effects. In simulations, the mixture model identified heterogeneous treatment effects more frequently than other methods, especially with weak covariate relationships, demonstrating its utility for informing personalized treatment approaches. CONCLUSIONS: The application of novel statistical methods to large scale clinico-genomic databases offers an opportunity to more accurately identify heterogeneous treatment effects in some settings as compared to traditional statistical methods. Applying such methods to the AACR Project GENIE BPC non-small cell lung cancer data indicated a potential association between decreasing tumor mutation burden and improved outcomes with chemoimmunotherapy as compared to immunotherapy alone.

Humans↗

PubMed2026

Genetically proxied circulating PD-1/PD-L1 levels and broadly defined myocarditis: A bidirectional Mendelian randomization study with exploratory lipidomic analyses.

Myocarditis is an inflammatory myocardial disease with potentially severe outcomes. Programmed cell death protein 1 (PD-1) and programmed death-ligand 1 (PD-L1) regulate immune tolerance, but the association between lifelong genetically proxied circulating PD-1/PD-L1 levels and broadly defined myocarditis remains uncertain. We investigated these associations and explored related plasma lipid species. We conducted bidirectional 2-sample Mendelian randomization using proteomic genome-wide association data from the UK Biobank Pharma Proteomics Project and INTERVAL. FinnGen Release 10 was the primary broadly defined myocarditis outcome, and an independent myocarditis genome-wide association study (GCST90018882) provided outcome-level validation. Complementary estimators, heterogeneity and pleiotropy diagnostics, influence analyses, MR-RAPS, and supportive meta-analyses were performed. Associations with 179 plasma lipid species were examined in exploratory analyses. Higher genetically proxied circulating PD-L1 was inversely associated with broadly defined myocarditis in UKB-PPP (odds ratio [OR] 0.834, 95% confidence interval [CI] 0.698-0.995; P = .0441), and the independent INTERVAL analysis yielded a concordant inverse estimate (OR 0.619, 95% CI: 0.434-0.883; P = .0083); no clear association was observed for PD-1. The MR-RAPS estimate retained the inverse direction; estimates against the independent broadly defined myocarditis dataset were also inverse, and supportive meta-analyses across protein and outcome sources yielded inverse pooled estimates. Reverse MR did not support effects of broadly defined myocarditis liability on circulating PD-1 or PD-L1. Exploratory lipid analyses identified nominal associations requiring confirmation. Higher genetically proxied circulating PD-L1 may be associated with a lower risk of broadly defined myocarditis, supporting further investigation of PD-L1-related immune regulation. These findings do not directly estimate the effects of pharmacologic PD-1/PD-L1 blockade. The lipid findings are hypothesis-generating.

Myocarditis↗

PubMed2026

Development of m6A-related prognostic models for survival in lung squamous cell carcinoma with different PD-L1 expression levels.

BACKGROUND: Programmed death-ligand 1 (PD-L1) is widely used in the clinical context of immune checkpoint inhibitor therapy, but its relationship with N6-methyladenosine (m6A) RNA methylation in lung squamous cell carcinoma (LUSC) has not been well defined. This study aimed to investigate the association between PD-L1 messenger RNA (mRNA) expression and m6A regulator expression patterns and to develop exploratory m6A-based prognostic models in LUSC. METHODS: Transcriptome data from 502 patients with LUSC were obtained from The Cancer Genome Atlas (TCGA). Patients were divided into PD-L1 high-expression (PHE) and PD-L1 low-expression (PLE) groups according to the median PD-L1 mRNA level. Differential expression and correlation analyses were performed for 30 m6A regulators. Transcriptome sequencing data from surgical specimens from 28 Asian patients with LUSC were used for expression-pattern comparison. Principal component analysis (PCA), univariate Cox regression, and least absolute shrinkage and selection operator (LASSO)-Cox regression were used to construct prognostic models in the TCGA cohort. RESULTS: In the TCGA cohort, the main differentially expressed m6A regulators between the two PD-L1 groups were YTHDF2 (P<0.001), IGF2BP3 (P<0.001), and YTHDC2 (P<0.001). In the Asian cohort, ALKBH5 (P=0.008) and ZC3H13 (P=0.03) showed significant differences. LASSO-Cox models were constructed for the overall LUSC cohort and for the PHE and PLE subgroups. The overall model included METTL3, HNRNPC, and CBLL1, with a 5-year time-dependent area under the receiver operating characteristic curve (AUC) of 0.579. The 5-year AUCs were 0.742 in the PHE subgroup and 0.652 in the PLE subgroup. The risk score remained independently associated with prognosis in multivariate Cox analysis. CONCLUSIONS: In LUSC, PD-L1 mRNA status was associated with distinct m6A regulator expression profiles. In the TCGA cohort, the PHE subgroup showed higher expression of CBLL1, G3BP1, IGF2BP3, FMR1, and YTHDC2, but lower expression of VIRMA, YTHDF2, and PRRC2A compared with the PLE subgroup. In the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences (CICAMS) cohort, ALKBH5 and ZC3H13 were more highly expressed in the PHE subgroup. Moreover, m6A-based risk models were associated with survival outcomes, with significant prognostic separation in the overall TCGA cohort and the PHE subgroup, whereas the PLE subgroup showed a weaker survival separation.

Lung squamous cell carcinoma (LUSC)↗

PubMed2026

Gastric carcinoma classification in the WHO 6th edition (2026): Updated framework and emerging entities.

The sixth edition of the WHO Classification of Digestive System Tumours (2026) represents an important step in the continuing evolution of gastric carcinoma classification. While preserving morphology as the foundation of diagnosis, it incorporates advances in molecular pathology, genotype-phenotype correlations, tumour evolution, and predictive biomarker assessment. This review summarizes the development of the WHO classification from the third edition (2000) to the sixth edition (2026) and highlights its relationship with other major classification systems, including those of Laurén, Nakamura, and the Japanese Gastric Carcinoma Association (JGCA). Major histological categories remain largely unchanged; however, several important conceptual and diagnostic refinements have been introduced. These include recognition of crawling-type adenocarcinoma as a distinctive variant of tubular adenocarcinoma, subclassification of poorly cohesive carcinoma into signet-ring cell and non-signet-ring cell subtypes, introduction of the concept of pure signet-ring cell carcinoma, and increased emphasis on tumour evolution. The sixth edition also expands and refines the spectrum of uncommon gastric carcinoma subtypes, including gastric carcinoma with lymphoid stroma, AFP-producing carcinoma, micropapillary adenocarcinoma, gastric adenocarcinoma of fundic-gland type, and gastric sarcomatoid carcinoma. Crucially, molecular subgroups originally proposed by The Cancer Genome Atlas (TCGA) and actionable biomarkers-including HER2 (ERBB2), Claudin 18.2, mismatch repair deficiency/microsatellite instability (dMMR/MSI), and programmed death-ligand 1 (PD-L1)-have transitioned from research-based categories into essential tools for precision oncology. Rather than providing exhaustive diagnostic criteria, this review offers a conceptual framework and encourages consultation of the original WHO text for full details. These advances illustrate the transition of gastric carcinoma classification from a predominantly morphology-based system toward an integrated histomolecular framework that more closely links pathological diagnosis with tumour biology, prognostication, and therapeutic stratification.

Crawling-type adenocarcinoma↗

PubMed2026

Respiratory viral infections prime accelerated lung cancer growth.

The COVID-19 pandemic has highlighted the long-term consequences of viral pneumonia, yet its impact on cancer development remains unclear. Here, we show that patients previously hospitalized with severe COVID-19 have an increased risk of subsequent lung cancer. Across multiple murine models, severe respiratory viral infections accelerated lung cancer growth, whereas vaccination mitigated infection-enhanced tumor progression. Mechanistically, prior viral pneumonia reprogrammed the lung into a pro-tumor microenvironment marked by the sustained accumulation of tumor-associated neutrophils and heightened immunosuppression. We observed persistent chromatin remodeling at key cytokine loci in immune and structural cells, linking inflammatory memory to tumor-promoting signals. Therapeutically, combined blockade of neutrophil recruitment and programmed death-ligand 1 (PD-L1) restored CD8+ T cell function and suppressed tumor growth. Together, these findings establish a causal link between prior viral pneumonia and lung tumorigenesis, underscoring the need for enhanced surveillance and targeted interventions to reduce post-COVID cancer risk.

Animals↗

PubMed2026

DESTINY-Lung06: trastuzumab deruxtecan and pembrolizumab as first-line treatment for HER2-overexpressing, PD-L1 TPS <50% NSCLC.

Non-small cell lung cancer (NSCLC) that overexpresses human epidermal growth factor receptor 2 (HER2) is associated with poor prognosis, and no HER2-targeting therapies for HER2-overexpressing NSCLC are currently approved in the first-line setting. Pembrolizumab plus pemetrexed and platinum-based chemotherapy is a recommended first-line treatment option for nonsquamous NSCLC with no actionable genomic alterations. However, improved outcomes with pembrolizumab-based therapies correlate with higher programmed death-ligand 1 (PD-L1) levels, underscoring the need for more targeted treatment options for patients with lower PD-L1 tumor proportion scores (TPS; <50%). DESTINY-Lung06 is a multicenter, open-label, randomized, phase III trial evaluating the efficacy and safety of trastuzumab deruxtecan (T-DXd) in combination with pembrolizumab in the first-line setting in patients with HER2-overexpressing (≥25% moderate/strong membrane staining), PD-L1 TPS <50% NSCLC. Planned enrollment will be approximately 686 patients randomly assigned to either T-DXd 5.4 mg/kg with pembrolizumab 200 mg intravenously every 3 weeks (Q3W) or pembrolizumab 200 mg plus pemetrexed 500 mg/m2 with platinum-based chemotherapy (cisplatin 75 mg/m2 or carboplatin under the concentration-time curve 5 mg min/mL) intravenously Q3W. Progression-free survival by blinded independent central review and overall survival are the primary and key secondary endpoints of the study, respectively.Clinical trial registration: www.ClinicalTrials.gov identifier is NCT06899126; EudraCT identifier is 2024-515658-26-00.

First-line↗

PubMed2026

Predictive Biomarkers for Immune Checkpoint Inhibitor Efficacy: Challenges, Innovations, and a Pathway to Precision Medicine in the Era of Cancer Immunotherapy.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have transformed oncology practice. However, treatment response remains heterogeneous, rendering predictive biomarkers critical for optimal patient care. The 3 established biomarkers, programmed death-ligand 1, tumor mutational burden (TMB), and microsatellite instability-high/deficient mismatch repair, are approved and clinically validated but are modest predictors of benefit. As a result, multiple novel predictive biomarkers remain under investigation. CONTENT: This review highlights established and investigational predictive ICI efficacy biomarkers. For established biomarkers, we describe biology, assay modalities, approved companion diagnostics, landmark studies, and notable limitations. Due to the multisystem nature of antitumor immune effects, investigational biomarkers span multiple domains, including tumor genomic biomarkers (e.g., mutational signatures, TMB, neoantigen clonality), tumor microenvironment (e.g., tumor-infiltrating lymphocytes [TILs], tertiary lymphoid structures), systemic immune biomarkers (e.g., cytokines, autoantibodies, glycoproteins, peripheral blood mononuclear cells), and the microbiome (e.g., gastrointestinal microbial diversity, responder-enriched taxa). SUMMARY: The established biomarkers PD-L1, TMB, and microsatellite instability-high/deficient mismatch repair inform ICI use in clinical practice but have important limitations. Multiple investigational biomarkers show promise in refining patient selection and optimizing therapy. Moving forward, increased assay harmonization, prospective validation, and standardized parameters may improve performance. Composite models integrating complementary signals across domains may further individualize treatment and lead to an era of personalized cancer immunotherapy.

Humans↗

PubMed2026

Dual-dimensional profiling of host genomic variations and HPV integration in PD-L1-stratified cervical cancer via Oxford Nanopore Technology.

BACKGROUND: The integration of human papillomavirus (HPV) DNA into the host genome is a key step in the development of HPV-associated cervical cancer (CC). However, the genomic characteristics of host genomic variations and HPV integration within the context of programmed death-ligand 1 (PD-L1) expression stratification have not been systematically investigated. METHODS: Whole-genome sequencing was performed using Oxford Nanopore Technology (ONT) on six samples (three from the high PD-L1 expression group and three from the low PD-L1 expression group). The characteristics of host genomic variations under different PD-L1 expression stratifications were explored, including structural variations (SV), copy number variations (CNV), single nucleotide polymorphisms (SNP), and insertion-deletions (Indel). Subsequently, the distribution features of HPV integration sites were analyzed, different integration types were identified, and pathway analysis was conducted. RESULTS: Whole-genome SV analysis revealed that the total number of SVs and the composition of mutation types were similar between the high and low PD-L1 expression groups, with insertions (INS) and deletions (DEL) predominating in both. These variations were primarily enriched in intergenic regions and introns. In the low PD-L1 expression group, integration events were observed at multiple chromosomal loci, with the most frequent integration occurring in the KLF5 gene region on chromosome 13. No frequently integrated loci were identified in the high PD-L1 expression group. Additionally, four distinct HPV integration breakpoint patterns were preliminarily identified and analyzed. CONCLUSION: PD-L1 expression stratification did not significantly alter the overall genomic instability of the host. However, differences were observed in the distribution patterns of HPV integration sites. These findings provide new insights into the genomic heterogeneity of CC under different PD-L1 expression backgrounds and may lay the groundwork for future research exploring stratified immunotherapy based on HPV integration features.

Humans↗

PubMed2026

A per- and polyfluoroalkyl substances-based gene signature links prognosis to immune landscapes in thyroid cancer.

BACKGROUND: Thyroid cancer (THCA) is the most common endocrine malignancy with a rising global incidence and significant heterogeneity. Although per- and polyfluoroalkyl substances (PFAS) exposure is linked to thyroid dysfunction, the prognostic value of per- and polyfluoroalkyl substances-related genes (PFASRGs) and their role in the tumor immune microenvironment (TME) remain poorly understood. This study aims to systematically screen key PFASRGs and evaluate their prognostic value as biomarkers for THCA. METHODS: Utilizing The Cancer Genome Atlas (TCGA)-THCA transcriptomic data and PFASRGs, we constructed a prognostic model through differential expression analysis, univariate and multivariate Cox regression analyses, and the least absolute shrinkage and selection operator (LASSO). The model's robustness was validated using receiver operating characteristic (ROC) curves, Kaplan-Meier analysis, and clinical nomograms. Furthermore, the TME, immunotherapy response, and drug sensitivities were systematically evaluated. Distinct molecular landscapes were characterized by stratifying the cohort via unsupervised consensus clustering analysis. RESULTS: The eight-gene prognostic model demonstrated robust performance, with area under the curve (AUC) values exceeding 0.85 across all validation cohorts. High-risk patients exhibited significantly shorter overall survival and an "inflamed" TME characterized by high immune scores and checkpoint expression. In contrast, the therapeutic efficacy of anti-programmed death-ligand 1 (PD-L1) agents was more pronounced in the low-risk category, as evidenced by a superior objective response. Furthermore, distinct molecular subtypes and risk-specific sensitivities to targeted agents, such as sorafenib and sunitinib, were identified, highlighting the model's clinical utility for personalized treatment. CONCLUSIONS: We established a novel THCA prognostic framework based on eight PFASRGs. This model exhibits superior performance in risk stratification, effectively distinguishing cohorts with divergent clinical trajectories, unique immune microenvironment features, and varied therapeutic responses. Our findings provide a powerful predictive tool for refining prognostic evaluation and facilitating the implementation of personalized management strategies for THCA patients.

Per- and polyfluoroalkyl substances-related genes ↗

PubMed2026

Enterocutaneous Fistula-Associated Sepsis and Mortality: Development and Validation of a Multimodal Artificial Intelligence Prediction Model.

BACKGROUND: Predicting enterocutaneous fistula (ECF)-associated sepsis and mortality poses significant challenges in digital health care due to the disease's complexity and heterogeneous clinical manifestations. Current approaches that rely on single-modal data or traditional scoring systems often fail to capture the intricate immune-inflammatory dynamics and multisystem involvement in patients with ECF. OBJECTIVE: This study aims to develop an artificial intelligence (AI)-driven multimodal fusion model integrating clinical, imaging, and transcriptomic data for early prediction of ECF-associated sepsis and 28-day mortality, addressing the limitations of conventional single-dimensional models. METHODS: This study leveraged publicly available datasets (Medical Information Mart for Intensive Care III [MIMIC-III], electronic Intensive Care Unit [eICU], and The Cancer Genome Atlas) to construct a multimodal framework. Clinical parameters were processed using Extreme Gradient Boosting, abdominal imaging features were extracted via convolutional neural networks, and transcriptomic profiles were analyzed with variational autoencoders. A Transformer-based fusion network was employed for joint prediction and validated through cross-validation and external testing. Key features were identified using Shapley Additive Explanations and Local Interpretable Model-Agnostic Explanations interpretability algorithms, while immune regulatory mechanisms were explored via weighted gene co-expression network analysis. RESULTS: The multimodal model achieved an area under the curve (AUC) of 0.89 for predicting sepsis and 28-day mortality, outperforming unimodal models (clinical-only model, AUC 0.72, and imaging-only model, AUC 0.78). Critical predictors included Sequential Organ Failure Assessment score, lactate levels, intra-abdominal free fluid on imaging, and immunoregulatory genes (programmed death-ligand 1 [PD-L1] and indoleamine 2,3-dioxygenase 1 [IDO1]). Mechanistic analysis revealed distinct immune reprogramming in patients with sepsis, characterized by increased regulatory T cells and M2 macrophages, along with downregulated cluster of differentiation 8+ (CD8+) T cells. CONCLUSIONS: This multimodal AI model offers an innovative digital solution in medical informatics, enabling precise early risk stratification for ECF-associated sepsis. By integrating multisource data and providing interpretable insights into immune-inflammatory pathways, the model enhances health care quality for patients with ECF and paves the way for personalized intervention strategies.

Humans↗

PubMed2026

RPN1 at the crossroads of glycosylation, tumor immunity, and disulfidptosis.

Ribophorin I (RPN1), a core component of the oligosaccharyltransferase complex, is traditionally known for its role in endoplasmic reticulum-associated N-glycosylation. Recent studies have identified RPN1 as an emerging regulator of tumor progression and immunity. Aberrant RPN1 overexpression has been reported in multiple malignancies, including glioma, hepatocellular carcinoma, sarcoma, and triple-negative breast cancer, where it is frequently associated with aggressive clinicopathological features and poor prognosis. RPN1 promotes tumor immune evasion by promoting N-glycosylation and stabilization of programmed death-ligand 1 (PD-L1), thereby enhancing immune checkpoint signaling and directly inhibiting anti-tumor T-cell responses. Consequently, elevated RPN1 expression is consistently associated with an immunosuppressive tumor microenvironment rich in M2 macrophages and poor in CD8+ T cells. More importantly, multiple omics signature analyses indicate RPN1 is integrated into several disulfidptosis-related risk models; however, direct experimental evidence confirming the causal linkage between RPN1 and disulfidptosis remains limited. Correlative database data also show potential associations between RPN1 upregulation and genomic instability and treatment resistance. Based on tiered classification of existing evidence (biochemical functional validation vs. multi-omics correlation), this review systematically summarizes the biological roles of RPN1 in cancer, its functions in tumor immunity and disulfidptosis-associated pathways and finally evaluates its potential as a therapeutic target in precision oncology.

PDL1↗

PubMed2024

Case report: response to immunotherapy and association with the fh gene in hereditary leiomyomatosis and renal cell cancer-associated renal cell cancer.

Hereditary leiomyomatosis and renal cell cancer (HLRCC) is a rare autosomal dominant syndrome caused by a germline mutation in the fumarate hydratase (FH) gene that manifests with cutaneous leiomyomas, uterine fibroids, and renal cell cancer (RCC). Patients with HLRCC-associated RCC (HLRCC-RCC) have aggressive clinical courses, but there is no standardized therapy for advanced HLRCC-RCC. In this study, we described a case of aggressive HLRCC in a 33-year-old female who exhibited a novel heterozygous germline insertion mutation in exon 8 of the FH gene (c.1126 C > T; p.Q376*). The patient underwent laparoscopic resection of the right kidney, but metastases appeared within 3 months after surgery. Histological staining of the resected tumor revealed high expression levels of programmed cell death-ligand 1 (PD-L1). Therefore, the patient was treated with immunotherapy. The patient achieved a partial response to immunotherapy, and the treatment of metastatic lesions has continued to improve. A thorough literature review pinpointed 76 historical cases of HLRCC-RCC that had undergone immunotherapy. From this pool, 46 patients were selected for this study to scrutinize the association between mutations in the FH gene and the effectiveness of immunotherapy. Our results indicate that immunotherapy could significantly improve the overall survival (OS) of patients with HLRCC-RCC. However, no influence of different mutations in the FH germline gene on the therapeutic efficacy of immunotherapy was observed. Therefore, our study suggested that immunotherapy was an effective therapeutic option for patients with HLRCC regardless of the type of FH germline mutation.

Humans↗

PubMed2026

Lipid metabolic reprogramming of tumor-associated macrophages drives resistance to immune checkpoint blockade in lung cancer: a narrative review of mechanisms and therapeutic strategies.

BACKGROUND AND OBJECTIVE: Immune checkpoint inhibitors (ICIs), represented by programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1), have shown remarkable efficacy in non-small cell lung cancer (NSCLC); however, many patients still develop resistance to immunotherapy. Although small cell lung cancer (SCLC) is also an important histological type of lung cancer, NSCLC accounts for the majority of lung cancer cases. Current research on ICI development, first-line treatment efficacy, and the mechanisms of lipid metabolism in tumor-associated macrophages (TAMs) is predominantly focused on NSCLC. In patients with advanced NSCLC, objective response rates (ORRs) with PD-1/PD-L1 inhibitor monotherapy remain limited. Only in patients with high PD-L1 expression [tumor proportion score (TPS) ≥50%] and without sensitizing epidermal growth factor receptor (EGFR) mutations or anaplastic lymphoma kinase (ALK) rearrangements does the ORR increase to approximately 40-45%. TAMs are a key component of the immunosuppressive tumor microenvironment (TME). Lipid metabolic reprogramming profoundly influences the functional and transcriptional features of TAMs. This review aims to integrate relevant evidence, elucidate how TAM lipid metabolism promotes immunosuppression and resistance to ICIs, and outline potential therapeutic strategies. METHODS: We searched PubMed/MEDLINE, Web of Science, and Scopus for publications up to June 2026 using terms combining lung cancer, TAMs, lipid metabolism, and immune checkpoint blockade/resistance. Mechanistic, translational, and clinically relevant studies were selected by author consensus. KEY CONTENT AND FINDINGS: Lipid uptake, de novo lipogenesis, fatty acid oxidation (FAO), cholesterol remodeling, and eicosanoid metabolism are not independent processes in TAMs. Lipid metabolic reprogramming in TAMs ultimately suppresses type I interferon (IFN-I) signaling, upregulates PD-L1 expression, and impairs the function of CD8+ T cells with stem-like features, thereby establishing an immunosuppressive TME and leading to resistance to ICIs. In lung cancer, hypoxia, high lactate levels, and tobacco exposure further shape the lipid phenotype of TAMs, such as lipid raft enrichment and lipid-laden macrophage subsets like SPP1+ macrophages. Different driver genomic backgrounds differentially impact tumor cell-intrinsic metabolism and the lipid metabolic programs of myeloid cells. In preclinical models, interventions targeting these metabolic axes, including TAM-directed delivery systems, have demonstrated potential therapeutic benefit when combined with anti-PD-1/PD-L1 therapy. CONCLUSIONS: Targeting TAM lipid metabolism to convert immunologically cold tumors into more inflamed, ICI-responsive tumors is a promising strategy to overcome resistance in NSCLC. Identification of predictive biomarkers of therapeutic response and development of cell-selective drug delivery systems come to be major challenges.

Non-small cell lung cancer (NSCLC)↗