The BAG3+ CAF-T Cell Neighborhood may serve as a biomarker for predicting non-response to NCIT in NSCLC, with significant potential to inform clinical decision-making.
Abstract
Background Despite significantly improving outcomes in non-small cell lung cancer (NSCLC), neoadjuvant chemoimmunotherapy (NCIT) fails to achieve a major pathological response (MPR) in over 40% of patients. Consequently, the early identification of non-responders prior to treatment initiation remains a critical unmet clinical need. Methods In this study, we performed single-cell RNA sequencing (scRNA-seq) on pre-treatment NSCLC tissue samples and integrated data from two public databases to identify signaling pathways associated with poor treatment response. Key findings were subsequently validated using multiplex immunofluorescence (mIF), and the predictive value of identified molecules was finally assessed in our cohort and GEO datasets. Results Among the 83 patients, 23/57 (40.35%) of these radiological responders failed to achieve MPR. Data analysis revealed activation of stress-related signaling pathways in cancer-associated fibroblasts (CAFs) and T cells from nMPR patients, with elevated expression of stress-related markers, including BAG3 and IFITM2. MIF confirmed that BAG3+IFITM2+ CAFs and BAG3+CD8+ T cells were spatially adjacent and significantly more abundant in nMPR patients. In our cohort and the two public databases, the BAG3+ CAF-T Cell Neighborhood was significantly more abundant in the nMPR group compared to the MPR group (p<0.05). In the MPR group, there was no significant difference in BAG3+ CAF-T Cell Neighborhood between the radiological PR and non-PR subgroups. The AUC values of BAG3+IFITM2+ CAFs, BAG3+CD8+ T cells, and the BAG3+ CAF-T Cell Neighborhood were 0.84 [95%CI: 0.746-0.931], 0.72 [95%CI: 0.603-0.835], and 0.87 [95%CI: 0.787-0.948], respectively. The OS and DFS of the BAG3+ CAF-T Cell Neighborhood high group are significantly decreased than that of the low group (p<0.05). The level of BAG3+ CAF-T Cell Neighborhood outperformed other two indicators in predicting non-response to NCIT. Consistent results were observed in GSE126044 and GSE135222. Conclusion The BAG3+ CAF-T Cell Neighborhood may serve as a biomarker for predicting non-response to NCIT in NSCLC, with significant potential to inform clinical decision-making.
Background: Poor pathological response to neoadjuvant immunotherapy combined with chemotherapy (nICT) limits long-term survival in resectable lung cancer. How to improve response rates and the regulatory mechanism of tumor inflammatory signaling in nICT efficacy remain unclear. Methods: In this study, patients were stratified into molecular subgroups using unsupervised consensus clustering based on inflammatory response genes (IRGs) expression profiles. Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics were applied to characterize tumor inflammatory states, immune microenvironment heterogeneity, and spatial organization between major pathological response (MPR) and non-MPR (NMPR) patients. Differentially expressed gene (DEG) analysis was performed to identify potential therapeutic targets, followed by preliminary functional validation to explore their biological roles. Results: Consensus clustering identified four molecular subgroups with distinct survival outcomes and clinical characteristics. scRNA-seq profiling uncovered that intratumoral inflammatory activation was markedly elevated in NMPR patients relative to MPR patients, with neutrophil subsets exhibiting the most robust inflammatory response among all immune cell populations. Further subset analysis identified two pro-inflammatory neutrophil populations (N3_CXCL8 and N4_VCAN), which were enriched in hypoxic tumor regions and contributed to immune evasion and angiogenesis. OLR1 was identified as a key regulatory hub associated with inflammatory activation, immunosuppression, and pro-angiogenic signaling. In addition, OLR1 + monocytes and macrophages exhibited consistent pro-tumor inflammatory phenotypes. Conclusion: This study identifies an OLR1-associated inflammatory regulatory axis that contributes to impaired response to nICT in lung cancer. Targeting OLR1 may represent a promising strategy to enhance immunotherapy efficacy and improve clinical outcomes in patients with resectable lung cancer. However, the study is limited by a relatively small validation cohort and a strong reliance on bioinformatics analyses, underscoring the need for further translational research and clinical validation to facilitate potential therapeutic application.
Xinxian Chen, Siang Zhang, Xue-Feng Ai et al.· Journal of Inflammation Rese...· 0 citations
Background Neoadjuvant chemoimmunotherapy (NACI) improves outcomes in resectable lung squamous cell carcinoma (LUSC), yet response varies widely and current biomarkers lack precision. Novel correlates of immunotherapy sensitivity tailored to the LUSC tumor microenvironment (TME) are urgently needed. Methods Using TCGA-LUSC transcriptomic data, we constructed a 25-gene prognostic model and applied three machine learning algorithms in combination with the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm to identify core genes linked to prognosis and immunotherapy response. Immune infiltration and enrichment analyses were performed to characterize the TME. An independent pre-NACI biopsy cohort (n=36) was used for histopathological validation to explore correlations with pathological response, while single-cell RNA-seq (GSE207422) and CellChat were used to infer tumor-stromal crosstalk and explore underlying mechanisms. Results The risk score independently stratified prognosis. Among three core genes, high MBNL2 expression was associated with higher TIDE scores, lower TIDE-predicted response rates, and elevated cancer-associated fibroblast (CAF) scores. scRNA-seq revealed systematically enhanced communication between MBNL2-high tumor cells and FAP+ CAFs, with unique ligand-receptor pairs enriched in WNT and EGF pathways; FAP+ CAFs interacted with regulatory T cells via ECM remodeling and the MDK-NCL axis. Histopathological validation confirmed that low tumor-cell MBNL2 expression correlated with higher pathological response and pCR rates, reduced FAP, and decreased FOXP3 expression. Conclusion This study establishes a 25-gene prognostic model for LUSC and identifies MBNL2 as a novel correlate of poor pathological response to NACI. Elevated MBNL2 expression in tumor cells is associated with enhanced tumor-CAF crosstalk, CAF activation, and an immunosuppressive TME, laying a foundation for future mechanistic investigation.
Hao Wu, Yang Cheng, Hong-Lin Yan et al.· Frontiers in Oncology· 0 citations
After combined treatment, uHCC patients were characterized by macrophages_FOLR2, CD8Teff, and LSEC, indicating a remodeling of the immune microenvironment.
Haifeng Zhou, Bi-Fei Wu, W. Ding et al.· International Immunopharmaco...· 0 citations
Background Platinum-based chemotherapy remains the cornerstone of ovarian cancer treatment, yet acquired resistance severely limits efficacy. Because platinum agents can also influence immunogenic cell death and tumor microenvironment (TME) remodeling, clarifying cellular pharmacological mechanisms of sensitivity and resistance within the ovarian cancer tumor ecosystem is important for understanding therapeutic failure. Methods We integrated single-cell RNA-seq from treatment-naïve and post-neoadjuvant chemotherapy ovarian tumors with bulk multi-omics cohorts to map epithelial tumor heterogeneity, transcriptional reprogramming, pathway activation, immune infiltration, and inferred cell–cell communication networks. DUSP5 was identified as a candidate regulator linked to stress-adaptive programs. Functional validation included qPCR, proliferation, migration, colony-formation, carboplatin dose-response, and xenograft assays following stable DUSP5 knockdown. Results Single-cell analysis revealed chemotherapy-associated epithelial states with enhanced stress, EMT, hypoxia, and inflammatory signatures. Elevated DUSP5 expression correlated with MAPK/JAK-STAT/TGF-β signaling, myeloid/stromal infiltration, and clinical outcome differences in independent cohorts. DUSP5 depletion suppressed proliferation and migration, amplified carboplatin-induced MAPK transcriptional responses and pro-apoptotic signaling (BAX/PUMA upregulation, BCL2 downregulation), reduced IC50 values in both OVCAR8 and SKOV3 cells, and significantly inhibited xenograft tumor growth. Conclusion DUSP5 may contribute to platinum response and resistance by linking tumor-intrinsic adaptive programs with TME-associated features in ovarian cancer. These findings support DUSP5 as a candidate biomarker and therapeutic target that warrants further mechanistic and clinical validation.
Chengfeng Liu, Wehua Li, Tingjun Liao et al.· Frontiers in Pharmacology· 0 citations
Esophageal Cancer: Adjuvant and Neo-Adjuvant Therapies
The predictive utility of the PD-L1 combined positive score (CPS) for neoadjuvant immuno-chemotherapy in locally advanced esophageal squamous cell carcinoma (ESCC) remains suboptimal, highlighting a critical unmet need for better patient stratification.
we performed bulk RNA sequencing (RNA-seq), whole-exome sequencing (WES), and shallow whole-genome sequencing (sWGS) on pretreatment tumor samples from 102 patients enrolled in the HCHTOG1909 trial. Additionally, for a subset of these patients (N=80) for whom matched posttreatment samples available, we conducted a paired longitudinal analysis to investigate therapy-induced molecular changes. Through this approach, we identified baseline predictive biomarkers, including a gene expression signature and genomic alterations (CNVs/SNVs), and investigated therapy-induced dynamic changes associated with treatment response.
By integrating these molecular features with macroscopic type and PD-L1 status, we developed the esophageal cancer multimodal immuno-chemotherapy classifier (EMIC), which effectively stratifies patients into two groups with distinct therapeutic responses: EMIC1 (predicted to benefit from combined immuno-chemotherapy) and EMIC2 (predicted to have equivalent outcomes with chemotherapy alone), providing a rationale for personalized treatment de-escalation.
In conclusion, by integrating bulk RNA-seq, whole-exome sequencing, and shallow whole-genome sequencing data from pretreatment tumor samples of 102 patients in the HCHTOG1909 trial, we have identified robust baseline predictive biomarkers. This multi-omics framework enables precise stratification of neoadjuvant chemotherapy patients who do not require addition of PD-1 antibody(EMIC2), allowing them to avoid the burden of ineffective treatment and immune-related adverse events. Future external validation and prospective clinical assessment will be essential to translate these integrative biomarkers into routine practice for personalized treatment decision-making.
Mei Zheng, Mao-ying Xing, M. Wang et al.· Diseases of the esophagus· 0 citations
Abstract Background Therapeutic resistance to immune checkpoint inhibitors (ICIs) and antiangiogenic therapies remains a significant clinical challenge in advanced renal cell carcinoma (RCC). The prognostic value of CD8+ T-cell infiltration—a central player in cancer immunity—is often equivocal across RCC studies, supporting the need for a systematic characterization of response-associated T cell states. Here, we used a machine learning-based approach that integrated bulk RNA-sequencing (RNA-seq) data from multiple ICI-based clinical trials with an in-house single-cell RNA-sequencing (scRNA-seq) dataset to identify CD8+ T-cell states associated with progression-free survival (PFS), providing novel prognostic insights for advanced RCC. Methods We collected bulk RNA-seq data of baseline tumor samples and patient outcomes from four major clinical trials of advanced RCC with at least one treatment arm receiving ICI monotherapy or ICI combined with tyrosine kinase inhibitors (ICI+TKI): HCRN GU16-260, JAVELIN Renal 101, COSMIC-313, and CheckMate 9ER. An in-house scRNA-seq dataset of tumor-infiltrating CD8+ T cells from 70 advanced RCC patients was used as a cellular reference. Our analysis focused on the CD8+ exhausted T cells (Tex), which generally overlap with tumor-specific T-cell population. Using non-negative matrix factorization (NMF), we classified the Tex into four functional states. To link the scRNA-seq-derived T-cell states with the PFS and bulk RNA-seq data, we applied Scissor (Sun et al., 2022), a machine learning model that identifies phenotype-associated cell subsets through cell-sample correlations. For the bulk cohort meta-analysis, shared genes were normalized, scaled, and batch-corrected using ComBat (Johnson et al., 2007). Statistical significance was established via a permutation test of the PFS data, and calculating empirical p-values based on the concordance index (C-index). Results Our final bulk dataset comprised 1,527 RNA-seq tumor samples from four clinical trials of RCC, including 1,004 tumor samples from patients receiving ICI or ICI+TKI regimens. We first applied Scissor to each treatment arm of each trial individually, and found that worse PFS-associated Tex cells exhibited elevated expression of tissue-resident markers, such as ZNF683 and ITGAE, consistently across all ICI and ICI+TKI arms. These markers represent one specific NMF program defined in Tex, which we have termed the resident-memory-like exhausted state (Tex-rm). Notably, the Tex-rm population was enriched among worse PFS-associated cells in all ICI-based treatment arms (ratio of observed to expected [Ro/e] = 1.37-2.33) but not in any TKI monotherapy arms. Subsequently, we combined all ICI-based treatment arms and applied Scissor to the batch-corrected bulk dataset. This again identified Tex-rm as associated with worse PFS. Such a correlation was supported by a permutation test with p-value < 0.001. Conclusions Through machine learning-based integration of bulk and single-cell RNA-seq datasets, we found that baseline tumor infiltration by exhausted T cells with a resident memory phenotype (Tex-rm) is significantly associated with worse PFS in advanced RCC patients receiving ICI monotherapy or ICI+TKI combination therapy. This finding highlights the Tex-rm state as a potential predictive biomarker for ICI efficacy. DOD CDMRP Funding no
Zhao-Chen Ye, Soki Kashima, Rishabh Rout et al.· The Oncologist· 0 citations
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