Classification of Tumor Immune Microenvironment According to Programmed Death-Ligand 1 Expression and Immune Infiltration Predicts Response to Immunotherapy Plus Chemotherapy in Advanced Patients With NSCLC

Spatial distribution of tertiary lymphoid structures in the molecular and clinical context of non-small cell lung cancer

INTRODUCTION

Tertiary lymphoid structures (TLS) are lymphocyte aggregates resembling secondary lymphoid organs and are pivotal in cancer immunity. The ambiguous morphological definition of TLS makes it challenging to ascertain their clinical impact on patient survival and response to immunotherapy.

OBJECTIVES

This study aimed to characterize TLS in hematoxylin-eosin tissue sections from lung cancer patients, assessing their occurrence in relation to the local immune environment, mutational background, and patient outcome.

METHODS

Two pathologists evaluated one whole tissue section from resection specimens of 680 NSCLC patients. TLS were spatially quantified within the tumor area or periphery and further categorized based on the presence of germinal centers (mature TLS). Metrics were integrated with immune cell counts, genomic and transcriptomic data, and correlated with clinical parameters.

RESULTS

TLS were present in 86% of 536 evaluable cases, predominantly in the tumor periphery, with a median of eight TLS per case. Mature TLS were found in 24% of cases. TLS presence correlated positively with increased plasma cell (CD138+) and lymphocytic cell (CD3+, CD8+, FOXP3+) infiltration. Tumors with higher tumor mutational burden exhibited higher numbers of peripheral TLS. The overall TLS quantity was independently associated with improved patient survival, irrespective of TLS maturation status. This prognostic association held true for peripheral TLS but not for tumor TLS.

CONCLUSION

TLS in NSCLC is common and their correlation with a specific immune phenotype suggests biological relevance in the local immune reaction. The prognostic significance of this scoring system on routine hematoxylin-eosin sections has the potential to augment diagnostic algorithms for NSCLC patients.

Clinical, immune cell, and genetic features predicting survival and long-term response to first-line chemo-immunotherapy treatment for non-small cell lung cancer

INTRODUCTION

Chemo-immunotherapy has become a standard of care for the first-line treatment of non-small cell lung cancer (NSCLC), but currently still lacks reliable markers to predict therapeutic efficacy and long-term response (LTR).

METHODS

In this study, we retrospectively summarized the survival outcome of 319 patients with locally advanced or metastatic NSCLC who received anti-programmed cell death protein-1 (PD-1)/programmed cell death-ligand 1 (PD-L1) based therapy from January 1st, 2018 to February 28th, 2022 at the Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College. Then a comprehensive analysis of the association of LTR or survival outcomes with various characteristics including clinical parameters, peripheral blood lymphocyte subsets and common gene mutations in 167 NSCLC patients who received first-line anti-PD-1 plus chemotherapy treatment was conducted. LTR was defined as progression-free survival (PFS) exceeding 24 months, while non-responders had a PFS of less than 6 months.

RESULTS

With a median follow-up time of 32.1 months (95% confidence interval [CI] 29.2-38.0), the median overall survival (OS) was 29.9 months (95% CI 23.6-37.5) in locally advanced or metastatic NSCLC receiving anti-PD-1/PD-L1 based treatment. Among 167 patients who received the first-line chemo-immunotherapy, 25.1% (n = 42) achieved LTR. Independent baseline predictors of LTR included age < 65 years (odds ratio [OR] = 3.22, p = 0.024), overweight or obesity (body mass index [BMI] ≥ 24 kg/m2, OR = 3.26, p = 0.020), and a C-reactive protein/albumin ratio (CAR) score < 0.07 (OR = 9.94, p = 0.039). In multivariate cox analysis, both patients with higher CAR scores of ≥ 0.07 (hazard ratio [HR] = 2.83, p = 0.016) and those who were underweight (BMI < 18.5 kg/m2) (HR = 4.52, p = 0.005) were observed with significantly shorter OS. A peripheral B cell percentage ≥ 14.5% was more prevalent among LTR patients (OR = 9.23, p = 0.045) after adjusting for age, BMI and TNM stage. Additionally, the presence of TP53 mutation (16/66) was associated with non-response to first-line chemo-immunotherapy (p = 0.048) and shorter PFS (p = 0.028) and OS (p = 0.023) outcomes in univariate analysis.

CONCLUSIONS

This study provides some new insights into the features and predictors significantly associated with LTR and survival in NSCLC patient receiving first-line treatment of anti-PD-1 plus chemotherapy. Those whose age < 65 years, overweight or obesity, or has a baseline CAR score < 0.07 are more likely to achieve optimal benefit from the first-line treatment of chemo-immunotherapy.

Computed tomography-based deep learning radiomics model for preoperative prediction of tumor immune microenvironment in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is a leading cause of cancer-related death globally, with the tumor immune microenvironment (TIME) influencing prognosis and immunotherapy response. Current TIME evaluation relies on invasive biopsies, limiting its clinical application. This study hypothesized that computed tomography (CT)-based deep learning (DL) radiomics models can non-invasively predict key TIME biomarkers: Tumor-stroma ratio (TSR), tumor-infiltrating lymphocytes (TILs), and immune score (IS).

AIM

To develop a non-invasive DL approach using preoperative CT radiomics to evaluate TIME components in CRC patients.

METHODS

In this retrospective study, preoperative CT images of 315 pathologically confirmed CRC patients (220 in training cohort and 95 in validation cohort) were analyzed. Manually delineated regions of interest were used to extract DL features. Predictive models (DenseNet-121/169) for TSR, TILs, IS, and TIME classification were constructed. Performance was evaluated via receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA).

RESULTS

The DL-DenseNet-169 model achieved area under the curve (AUC) values of 0.892 [95% confidence interval (CI): 0.828-0.957] for TSR and 0.772 (95%CI: 0.674-0.870) for TIME score. The DenseNet-121 model yielded AUC values of 0.851 (95%CI: 0.768-0.933) for TILs and 0.852 (95%CI: 0.775-0.928) for IS. Calibration curves demonstrated strong prediction-observation agreement, and DCA confirmed clinical utility across threshold probabilities (P < 0.05 for all models).

CONCLUSION

CT-based DL radiomics provides a reliable non-invasive method for preoperative TIME evaluation, enabling personalized immunotherapy strategies in CRC management.

Durvalumab with or without tremelimumab in combination with chemotherapy in first-line metastatic non-small-cell lung cancer: outcomes by tumor mutational burden in POSEIDON

BACKGROUND

In updated analyses from the phase III POSEIDON study, after a median follow-up of >5 years, tremelimumab plus durvalumab and chemotherapy (T + D + CT) showed durable long-term overall survival (OS) benefit versus CT alone in first-line metastatic non-small-cell lung cancer (mNSCLC). In this article, we report the associations of tumor mutational burden (TMB) with outcomes of D with or without T in combination with CT versus CT alone.

PATIENTS AND METHODS

A total of 1013 patients with EGFR/ALK wild-type mNSCLC were randomized (1 : 1 : 1) to T + D + CT, D + CT, or CT, stratified by programmed cell death-ligand 1 (PD-L1) tumor cell (TC) expression ≥50% versus <50%, disease stage (IVA versus IVB) and histology (squamous versus nonsquamous). Patient subgroups were defined by a range of blood TMB (bTMB) values, including at a prespecified cut-off of 20 mutations (mut)/megabase (Mb) and across further subdivisions by PD-L1 TC expression ≥1% or <1% and by tissue TMB (tTMB) values.

RESULTS

At the primary OS data cut-off (12 March 2021), at each bTMB or tTMB cut-off, the magnitude of OS benefit appeared greater among patients in the bTMB- or tTMB-high subgroups for the T + D + CT arm versus the CT arm but was similar between subgroups for the D + CT arm versus the CT arm. Updated OS analyses in the bTMB ≥20 and <20 mut/Mb subgroups, after median follow-up of >5 years (data cut-off 24 August 2023), were similar to those obtained at the primary OS data cut-off.

CONCLUSIONS

First-line treatment with T (limited course) plus D (until progression) and four cycles of CT consistently improved clinical outcomes versus CT alone in both bTMB-high and -low subgroups, and also in both high and low tTMB subgroups, in patients with mNSCLC. Benefit appeared greater in the TMB-high versus TMB-low subgroups; the addition of anti-cytotoxic T lymphocyte-associated antigen-4 to anti-PD-L1 and CT seemed to increase the magnitude of this difference.

Metabolic characterization and radiomics-based composite model for breast cancer immune microenvironment types using 18F-FDG PET/CT

PURPOSE

The intricateness of tumor immune microenvironment types (TIMTs) complicates identifying responders to immune checkpoint inhibitors (ICIs). Our purpose was to explore the metabolic characteristics of TIMTs in breast cancer using 18F-fluorodeoxyglucose (FDG) PET/CT and to establish radiomics-based predictive models for TIMTs.

METHODS

Consecutive 207 breast cancer patients (211 primary lesions), who underwent 18F-FDG PET/CT examination from Sep 2022 to Aug 2024 in our hospital, were retrospectively reviewed. The programmed death-ligand 1 (PD-L1) and tumor-infiltrating lymphocytes (TILs) were evaluated for TIMTs: TMIT-I (PD-L1-, TILs-), TMIT-II (PD-L1+, TILs+), TMIT-III (PD-L1-, TILs+), and TMIT-IV (PD-L1+, TILs-). The relationship between metabolic parameters (such as maximum standardized uptake value (SUVmax) and tumor-to-liver SUV ratio (TLR)) and TIMTs was analyzed. Then composite predictive models based on radiomics were further developed.

RESULTS

TIMT-II represented the highest proportion in HER2+ (14/22, 64%) and triple-negative (17/27, 63%) breast cancer. Most metabolic parameters (such as SUVmax and TLR) exhibited significant differences in TIMT-II vs. -I or TIMT-II vs. -III (P < 0.05). TLR (P = 0.03; OR: 1.1) and Nottingham grade (P = 0.006; OR: 3.1) were independent impact factors of TIMT-II. We further developed a composite model that integrated radiomics, metabolic parameter, and clinicopathological data, which demonstrated promising predictive efficacy for TIMT-II (AUC testing set = 0.86).

CONCLUSION

Metabolic differences existed among different TIMTs, with TIMT-II exhibiting markedly elevated metabolic characteristics. The composite model based on radiomics demonstrated high predictive efficacy for TIMT-II and has the potential to screen ICIs responders.

Kolmogorov-Arnold Network Model Integrated with Hypoxia Risk for Predicting PD-L1 Inhibitor Responses in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths, with immunotherapy being a first-line treatment at the advanced stage and beyond. Hypoxia plays a critical role in tumor progression and resistance to therapy. This study develops and validates an artificial intelligence (AI) model based on publicly available genomic datasets to predict hypoxia-related immunotherapy responses. Based on the HCC-Hypoxia Overlap (HHO) and immunotherapy response to hypoxia (IRH) genes selected by differential expression and enrichment analyses, a hypoxia model was built and validated on the TCGA-LIHC and GSE233802 datasets, respectively. The training and test sets were assembled from the EGAD00001008128 dataset of 290 HCC patients, and the response and non-response classes were balanced using the Synthetic Minority Over-sampling Technique. With the genes selected via the minimum Redundancy Maximum Relevance and stepwise forward methods, a Kolmogorov-Arnold Network (KAN) model was trained. Support Vector Machine (SVM) combined the Hypoxia and KAN models to predict immunotherapy response. The hypoxia model was constructed using 10 genes (IRH and HHO). The KAN model with 11 genes achieved a test accuracy of 0.7. The SVM integrating the hypoxia and KAN models achieved a test accuracy of 0.725. The established AI model can predict immunotherapy response based on hypoxia risk and genomic factors potentially intervenable in HCC patients.

Developing and validating a prognostic disulfidptosis-related signature for glioblastoma: predicting radioresistance and synergestic effect with immunotherapy

BACKGROUND

Programmed cell death (PCD) modulated radioresistance is one of the predominant causes of treatment failure in glioblastoma (GBM). Disulfidptosis, a newly discovered form of PCD, plays a crucial role in GBM progression. However, the association among disulfidptosis, radiosensitivity and radiotherapy (RT) in GBM remain unclear.

METHODS

We systematically analyzed disulfidptosis-related genes in 1075 GBM patients and constructed a disulfidptosis-related gene signature (DRS). Correlations among the DRS, patient prognosis and immune microenvironment were fully explored. The effects of DRS and EFEMP2 on radiotherapy efficacy were investigated via single cell sequencing analysis and validated via in vitro and in vivo experiments.

RESULTS

The DRS was identified as a robust and independent prognostic biomarker for GBM by multivariate Cox regression analysis, receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) in multiple cohorts. High DRS is characterized by radioresistance, and EFEMP2 was proven to be the key gene involved in this process by single cell sequencing analysis, CCK-8 assay and a clonogenic survival assay. In high-DRS patients, the cancer-immunity cycle is attenuated because the antitumor cytotoxicity of CD8+ T cells is inhibited by immune checkpoints. Preclinically, the overexpression of EFEMP2 induced radioresistance and enhancing the efficacy of programmed cell death ligand-1 (PD-L1) blockade in GL261-bearing mice. The combination of irradiation and anti-PD-L1 therapy had a synergistic effect on GBM murine models in which EFEMP2 was overexpressed.

CONCLUSION

Our study bioinformatically and experimentally reveals the molecular landscape of disulfidptosis in GBM, develops a predictive signature for predicting prognosis as well as radioresistance, and provides a synergistic treatment that combines radiotherapy with immunotherapy for radioresistant GBM patients with high DRS or EFEMP2 expression.

CT-Based Deep Learning Predicts Prognosis in Esophageal Squamous Cell Cancer Patients Receiving Immunotherapy Combined with Chemotherapy

RATIONALE AND OBJECTIVES

Immunotherapy combined with chemotherapy has improved outcomes for some esophageal squamous cell carcinoma (ESCC) patients, but accurate pre-treatment risk stratification remains a critical gap. This study constructed a deep learning (DL) model to predict survival outcomes in ESCC patients receiving immunotherapy combined with chemotherapy.

MATERIALS AND METHODS

A DL model was developed to predict survival outcomes in ESCC patients receiving immunotherapy and chemotherapy. Retrospective data from 482 patients across three institutions were split into training (N=322), internal test (N=79), and external test (N=81) sets. Unenhanced computed tomography (CT) scans were processed to analyze tumor and peritumoral regions. The model evaluated multiple input configurations: original tumor regions of interest (ROIs), ROI subregions, and ROIs expanded by 1 and 3 pixels. Performance was assessed using Harrell's C-index and receiver operating characteristic (ROC) curves. A multimodal model combined DL-derived risk scores with five key clinical and laboratory features. The Shapley Additive Explanations (SHAP) method elucidated the contribution of individual features to model predictions.

RESULTS

The DL model with 1-pixel peritumoral expansion achieved the best accuracy, yielding a C-index of 0.75 for the internal test set and 0.60 for the external test set. Hazard ratios for high-risk patients were 1.82 (95% CI: 1.19-2.46; P=0.02) in internal test set. The multimodal model achieved C-indices of 0.74 and 0.61 for internal and external test sets, respectively. Kaplan-Meier analysis revealed significant survival differences between high- and low-risk groups (P<0.05). SHAP analysis identified tumor response, risk score, and age as critical contributors to predictions.

CONCLUSION

This DL model demonstrates efficacy in stratifying ESCC patients by survival risk, particularly when integrating peritumoral imaging and clinical features. The model could serve as a valuable pre-treatment tool to facilitate the implementation of personalized treatment strategies for ESCC patients undergoing immunotherapy and chemotherapy.

Diagnostic and prognostic values of tsRNAs in lung cancer: a meta-analysis

BACKGROUND

Lung cancer (LC) is the leading cause of cancer-related death in humans. tRNA-derived small RNA (tsRNA) is a novel biomarker that plays a crucial role in the genesis and development of LC. In this study, we aimed to investigate the value of differentially expressed tsRNAs in LC through meta-analysis.

METHODS

PubMed and Web of Science were searched for articles published up to January 10, 2024. Diagnostic odds ratios (DORs) and areas under the receiver operating characteristic curve (AUCs) were used to evaluate the potential of tsRNAs as diagnostic markers for LC. Furthermore, hazard ratios (HRs) and 95% confidence intervals (95% CIs) were used to analyze association of tsRNAs with LC prognosis.

RESULTS

In total, 12 studies were included in the analysis. Our results indicated that the combined DOR of total tsRNAs for LC diagnosis was 7.32; the AUC was 0.81. Subgroup analysis revealed that high levels of tsRNAs in serum had good diagnostic efficacy (DOR = 16.56, AUC = 0.88). Moreover, a high tsRNAs level was associated with a worse prognosis in LC patients (HR = 1.59, 95% CI: 1.33-1.90).

CONCLUSION

Our findings suggest that a high tsRNAs level has potential value for diagnosis and prognosis of LC patients. However, further high-quality studies are needed to validate our results.

Integration of clinical, pathological, radiological, and transcriptomic data improves prediction for first-line immunotherapy outcome in metastatic non-small cell lung cancer

Immunotherapy is improving the survival of patients with metastatic non-small cell lung cancer (NSCLC), yet reliable biomarkers are needed to identify responders prospectively and optimize patient care. In this study, we explore the benefits of multimodal approaches to predict immunotherapy outcome using multiple machine learning algorithms and integration strategies. We analyze baseline multimodal data from a cohort of 317 metastatic NSCLC patients treated with first-line immunotherapy, including positron emission tomography images, digitized pathological slides, bulk transcriptomic profiles, and clinical information. Testing multiple integration strategies, most of them yield multimodal models surpassing both the best unimodal models and established univariate biomarkers, such as PD-L1 expression. Additionally, several multimodal combinations demonstrate improved patient risk stratification compared to models built with routine clinical features only. Our study thus provides evidence of the superiority of multimodal over unimodal approaches, advocating for the collection of large multimodal NSCLC datasets to develop and validate robust and powerful immunotherapy biomarkers.

AXL: shapers of tumor progression and immunosuppressive microenvironments

As research progresses, our understanding of the tumor microenvironment (TME) has undergone profound changes. The TME evolves with the developmental stages of cancer and the implementation of therapeutic interventions, transitioning from an immune-promoting to an immunosuppressive microenvironment. Consequently, we focus intently on the significant role of the TME in tumor proliferation, metastasis, and the development of drug resistance. AXL is highly associated with tumor progression; however, previous studies on AXL have been limited to its impact on the biological behavior of cancer cells. An increasing body of research now demonstrates that AXL can influence the function and differentiation of immune cells, mediating immune suppression and thereby fostering tumor growth. A comprehensive analysis to identify and overcome the causes of immunosuppressive microenvironments represents a novel approach to conquering cancer. In this review, we focus on elucidating the role of AXL within the immunosuppressive microenvironments, discussing and analyzing the effects of AXL on tumor cells, T cells, macrophages, natural killer (NK) cells, fibroblasts, and other immune-stromal cells. We aim to clarify the contributions of AXL to the progression and drug resistance of cancer from its functional role in the immune microenvironment.

Unravelling the Reasons Behind Limited Response to Anti-PD Therapy in ATC: A Comprehensive Evaluation of Tumor-Infiltrating Immune Cells and Checkpoints

Inhibiting the immune checkpoint (ICP) PD-1 based on PD-L1 expression status has revolutionized the treatment of various cancers, yet its efficacy in anaplastic thyroid carcinoma (ATC) remains limited. The therapeutic response depends upon multiple factors, particularly the conduciveness of the tumor's immune milieu. This study comprehensively evaluated and classified ATC's immune microenvironment (IME) to elucidate the factors behind suboptimal response to anti-PD therapy. Utilizing multiplex-immunofluorescence and immunohistochemistry, we retrospectively analyzed 26 cases of ATC for expression of ICPs PD-L1, PD-1, CTLA4, TIM3, and Galectin-9 and tumor-infiltrating cytotoxic T lymphocytes (CTL)-the effector cells, the anti-tumor NK cells, the immune-inhibitory myeloid-derived suppressor (MDSC) and regulatory T (Treg) cells, and B lymphocytes. Most ATCs (65%) exhibited PD-L1 positivity, but only 31%, in addition, had abundant CTL (type I IME), a combination associated with a better response to ICP inhibition. Additionally, PD-1 expression levels on CTL were low/absent in most cases-a "target-missing" situation-unfavorable for an adequate therapeutic response. All but one ATC showed nuclear Galectin-9 expression. The documentation of nuclear expression of Galectin-9 akin to benign thyroid is a first, and its role in ATC pathobiology needs further elucidation. In addition to less abundant PD-1 expression on CTL, the presence of MDSC, Treg, and exhausted cytotoxic T lymphocytes in the immune milieu of ATC can contribute to anti-PD resistance. TIM3, the most frequently expressed ICP on CTL, followed by CTLA4, provides alternate therapeutic targets in ATC. The co-expression of multiple immune checkpoints is of great interest for ATC since these data also open the avenue for combination therapies.

Has_circ_0002360 facilitates immune evasion by enhancing heterogeneous nuclear ribonucleoprotein A1 stability, thereby promoting malignant progression in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is marked by complex molecular aberrations including differential expression of circular RNAs (circRNAs). hsa_circ_0002360, a circRNA, has been identified as overexpressed in NSCLC. This study aimed to evaluate the expression patterns of hsa_circ_0002360 and its potential role as an oncogenic factor in NSCLC. We analyzed two GEO datasets (GSE112214 and GSE158695) using R software to identify differentially expressed circRNAs. Quantitative reverse transcription PCR (qRT-PCR) assessed the expression of hsa_circ_0002360 in NSCLC tissues and cell lines compared to controls. We used siRNA and overexpression vectors to modulate hsa_circ_0002360 levels in A549 cells, followed by assays to assess proliferation, migration, invasion, apoptosis, and epithelial-mesenchymal transition (EMT). Interactions with RNA-binding proteins, specifically HNRNPA1, were investigated using RNA-pull down and RIP assays. In GEO datasets GSE112214 and GSE158695, hsa_circ_0002360 was identified as significantly overexpressed in NSCLC, a finding supported by qRT-PCR analyses showing higher levels in NSCLC tissues and cell lines compared to controls. Functional assays demonstrated that knockdown of hsa_circ_0002360 in A549 cells decreased proliferation, migration, invasion, and altered epithelial-mesenchymal transition marker expression, while inducing apoptosis, suggesting its oncogenic role. Conversely, overexpression promoted tumor characteristics, corroborated by in vivo xenograft models showing increased tumor growth. Hsa_circ_0002360's interaction with HNRNPA1, evidenced through RNA-pull down and RIP assays, implicates it in regulatory pathways that enhance NSCLC progression. This expression was also correlated with advanced TNM stages and metastasis, highlighting its potential as a therapeutic target. hsa_circ_0002360 acts as an oncogene in NSCLC, promoting tumor progression and metastasis through regulation of cell growth, apoptosis, and EMT processes. The interaction between hsa_circ_0002360 and HNRNPA1 suggests a novel mechanism of circRNA-mediated modulation of NSCLC pathology, providing potential targets for therapeutic intervention.

Association of artificial intelligence-based immunoscore with the efficacy of chemoimmunotherapy in patients with advanced non-squamous non-small cell lung cancer: a multicentre retrospective study

Purpose

Currently, chemoimmunotherapy is effective only in a subset of patients with advanced non-squamous non-small cell lung cancer. Robust biomarkers for predicting the efficacy of chemoimmunotherapy would be useful to identify patients who would benefit from chemoimmunotherapy. The primary objective of our study was to develop an artificial intelligence-based immunoscore and to evaluate the value of patho-immunoscore in predicting clinical outcomes in patients with advanced non-squamous non-small cell lung cancer (NSCLC).

Methods

We have developed an artificial intelligence-powered immunoscore analyzer based on 1,333 whole-slide images from TCGA-LUAD. The predictive efficacy of the model was further validated in the CPTAC-LUAD cohort and the biomarker cohort of the ORIENT-11 study, a randomized, double-blind, phase 3 study. Finally, the clinical significance of the patho-immunoscore was evaluated using the ORIENT-11 study cohort.

Results

Our immunoscore analyzer achieved good accuracy in all the three cohort mentioned above (TCGA-LUAD, mean AUC: 0.783; ORIENT-11 cohort, AUC: 0.741; CPTAC-LUAD cohort, AUC: 0.769). In the 259 patients treated with chemoimmunotherapy, those with high patho-immunoscore (n = 146) showed significantly longer median progression-free survival than those with low patho-immunoscore (n = 113) (13.8 months vs 7.13 months, hazard ratio [HR]: 0.53, 95% confidence interval [CI]: 0.38 - 0.73; p < 0.001). In contrast, no significant difference was observed in patients who were treated with chemotherapy only (5.07 months vs 5.07 months, HR: 1.04, 95% CI: 0.71 - 1.54; p = 0.83). Similar trends were observed in overall survival.

Conclusion

Our study indicates that AI-powered immunoscore applied on LUAD digital slides can serve as a biomarker for survival outcomes in patients with advanced non-squamous NSCLC who received chemoimmunotherapy. This methodology could be applied to other cancers and facilitate cancer immunotherapy.

Identification of novel markers for neuroblastoma immunoclustering using machine learning

Background

Due to the unique heterogeneity of neuroblastoma, its treatment and prognosis are closely related to the biological behavior of the tumor. However, the effect of the tumor immune microenvironment on neuroblastoma needs to be investigated, and there is a lack of biomarkers to reflect the condition of the tumor immune microenvironment.

Methods

The GEO Database was used to download transcriptome data (both training dataset and test dataset) on neuroblastoma. Immunity scores were calculated for each sample using ssGSEA, and hierarchical clustering was used to categorize the samples into high and low immunity groups. Subsequently, the differences in clinicopathological characteristics and treatment between the different groups were examined. Three machine learning algorithms (LASSO, SVM-RFE, and Random Forest) were used to screen biomarkers and synthesize their function in neuroblastoma.

Results

In the training set, there were 362 samples in the immunity_L group and 136 samples in the immunity_H group, with differences in age, MYCN status, etc. Additionally, the tumor microenvironment can also affect the therapeutic response of neuroblastoma. Six characteristic genes (BATF, CXCR3, GIMAP5, GPR18, ISG20, and IGHM) were identified by machine learning, and these genes are associated with multiple immune-related pathways and immune cells in neuroblastoma.

Conclusions

BATF, CXCR3, GIMAP5, GPR18, ISG20, and IGHM may serve as biomarkers that reflect the conditions of the immune microenvironment of neuroblastoma and hold promise in guiding neuroblastoma treatment.

Heterogeneity between subgroups of first-line chemoimmunotherapy for extensive-stage small cell lung cancer patients: a meta-analysis and systematic review

Objectives

Differences in clinicopathological characteristics of extensive-stage small cell lung cancer (ES-SCLC) patients may influence the immune response. This study aims to evaluate the heterogeneity of response to first-line chemoimmunotherapy between subgroups in ES-SCLC to screen out suitable populations.

Materials and methods

We searched the PubMed, EMBASE, and Cochrane Library databases from inception to December 3, 2022 for randomized controlled trials (RCTs) of ES-SCLC chemoimmunotherapy. We also reviewed main conferences from January 1, 2021 to October 1, 2023. A trial-specific hazard ratio (HR) ratio for each subgroup was calculated, and these ratios were then pooled using the deft approach.

Results

A total of 9 RCTs with 4099 patients were finally included. The pooled ratios were 0.92 (95% CI = 0.77 to 1.09) for OS-HRs and 0.79 (95% CI = 0.55 to 1.13) for PFS-HRs in women versus men. The pooled ratios of OS-HRs and PFS-HRs in patients with positive versus negative PD-L1 expression were 1.26 (95% CI = 0.91 to 1.73) and 1.08 (95% CI = 0.77 to 1.52), respectively. The pooled ratios of OS-HRs and PFS-HRs in patients without versus with brain metastasis were 0.77 (95% CI = 0.59 to 1.01) and 0.71 (95% CI = 0.44 to 1.12). No statistically significant differences were also found in terms of subgroups for age, liver metastasis, smoking status, ECOG PS, LDH level, type of platinum salt and race.

Conclusion

Women or patients with negative PD-L1 expression or with LDH ≤ ULN or without brain metastasis tend to benefit more from first-line chemoimmunotherapy in ES-SCLC. More trials are needed to prospectively validate the therapeutic heterogeneity among clinicopathological characteristics.

Systematic review registration

https://inplasy.com/inplasy-2023-3-0064/ identifier, INPLASY202330064.

Assessment of PD-L1 expression and tumour infiltrating lymphocytes in early-stage non-small cell lung carcinoma with artificial intelligence algorithms

AIMS

To study programmed death ligand 1 (PD-L1) expression and tumour infiltrating lymphocytes (TILs) in patients with early-stage non-small cell lung carcinoma (NSCLC) with artificial intelligence (AI) algorithms.

METHODS

The study included samples from 50 early-stage NSCLCs. PD-L1 immunohistochemistry (IHC) stained slides (clone SP263) were scored manually and with two different AI tools (PathAI and Navify Digital Pathology) by three pathologists. TILs were digitally assessed on H&E and CD8 IHC stained sections with two different algorithms (PathAI and Navify Digital Pathology, respectively). The agreement between observers and methods for each biomarker was analysed. For PD-L1, the turn-around time (TAT) for manual versus AI-assisted scoring was recorded.

RESULTS

Agreement was higher in tumours with low PD-L1 expression regardless of the approach. Both AI-powered tools identified a significantly higher number of cases equal or above 1% PD-L1 tumour proportion score as compared with manual scoring (p=0.00015), a finding with potential therapeutic implications. Regarding TAT, there were significant differences between manual scoring and AI use (p value <0.0001 for all comparisons). The total TILs density with the PathAI algorithm and the total density of CD8+ cells with the Navify Digital Pathology software were significantly correlated (τ=0.49 (95% CI 0.37, 0.61), p value<0.0001).

CONCLUSIONS

This preliminary study supports the use of AI algorithms for the scoring of PD-L1 and TILs in patients with NSCLC.

Efficacy and toxicity of anlotinib plus camrelizumab versus anlotinib plus S-1 as second-line therapy for advanced esophageal squamous cell carcinoma: A real-world retrospective study

Background

No data exist on the efficacy and safety of anlotinib plus camrelizumab doublet as second-line therapy for advanced esophageal squamous cell carcinoma (ESCC). Although anlotinib and the programmed death-1 (PD-1) inhibitor camrelizumab are used as treatments for ESCC, the combined use of anlotinib and camrelizumab as a second-line therapy has not been reported. Therefore, this study explored the efficacy and toxicity of anlotinib plus camrelizumab as second-line therapy for advanced ESCC.

Methods

Fifty-eight patients with advanced ESCC undergoing second-line therapy, either with anlotinib plus camrelizumab or anlotinib plus S-1, were enrolled and retrospectively analyzed at Jiangsu Province Hospital of Chinese Medicine from January 2020 to December 2021. The primary endpoint was progression-free survival (PFS), with secondary endpoints including the objective response rate (ORR), disease control rate (DCR), and assessment of toxicity.

Results

In patients with advanced ESCC, the anlotinib plus camrelizumab group (N = 32) exhibited longer PFS (8.00 vs. 4.53 months, P < 0.001), higher ORR (28.1 vs. 19.2%, P = 0.431), and higher DCR (87.5 vs. 65.4%, P = 0.045) than those in the anlotinib plus S-1 group (N = 26). Treatment-related adverse events (TRAEs) were predominantly grade 1/2 in both groups, with a higher incidence of grade 1/2 skin toxicity in patients treated with anlotinib plus camrelizumab (P = 0.033). Two patients (6.3%) developed grade 1/2 immune-related pneumonia. The incidence of grade 3/4 TRAEs did not differ significantly between the two groups. Multivariable Cox regression analysis identified that the drug regimen (P < 0.001), Eastern Cooperative Oncology Group performance status (P = 0.008), and differentiation grade (P = 0.008) were independent prognostic factors for PFS.

Conclusions

Anlotinib plus camrelizumab exhibited promising antitumor efficacy and manageable toxicity when used as a second-line treatment for advanced ESCC.

Bojungikki-Tang Augments Pembrolizumab Efficacy in Human PBMC-Injected H460 Tumor-Bearing Mice

Bojungikki-Tang (BJIKT) is traditionally used to enhance digestive function and immunity. It has gained attention as a supplement to chemotherapy or targeted therapy owing to its immune-boosting properties. This study aimed to evaluate the synergistic anti-tumor effects of BJIKT in combination with pembrolizumab in a preclinical model. MHC I/II double knockout NSG mice were humanized with peripheral blood mononuclear cells (PBMCs) and injected subcutaneously with H460 lung tumor cells to establish a humanized tumor model. Both agents were administered to evaluate their impact on tumor growth and immune cell behavior. Immunohistochemistry showed decreased exhaustion markers in CD8(+) and CD4(+) T cells within the tumor, indicating enhanced T cell activity. Additionally, RNA sequencing, transcriptome analysis, and quantitative PCR analysis were performed on tumor tissues to investigate the molecular mechanisms underlying the observed effects. The results confirmed that BJIKT improved T cell function and tumor necrosis factor signaling while suppressing transforming growth factor-β signaling. This modulation led to cell cycle arrest and apoptosis. These findings demonstrate that BJIKT, when combined with pembrolizumab, produces significant anti-tumor effects by altering immune pathways and enhancing the anti-tumor immune response. This study provides valuable insights into the role of BJIKT in the tumor microenvironment and its potential to improve therapeutic outcomes.

RNA sequencing identifies lung cancer lineage and facilitates drug repositioning

Recent breakthrough therapies have improved survival rates in non-small cell lung cancer (NSCLC), but a paradigm for prospective confirmation is still lacking. Patientdatasets were mainly downloaded from TCGA, CPTAC and GEO. We conducted downstream analysis by collecting metagenes and generated 42-gene subtype classifiers to elucidate biological pathways. Subsequently, scRNA, eRNA, methylation, mutation, and copy number variation were depicted from a phenotype perspective. Enhancing the clinical translatability of molecular subtypes, preclinical models including CMAP, CCLE, and GDSC were utilized for drug repositioning. Importantly, we verified the presence of previously described three phenotypes including bronchioid, neuroendocrine, and squamoid. Poor prognosis was seen in squamoid and neuroendocrine clusters for treatment-naive and immunotherapy populations. The neuroendocrine cluster was dominated by STK11 mutations and 14q13.3 amplifications, whose related methylated loci are predictive of immunotherapy. And the greatest therapeutic potential lies in the bronchioid cluster. We further estimated the relative cell abundance of the tumor microenvironment (TME), specific cell types could be reflected among three clusters. Meanwhile, the higher portion of immune cell infiltration belonged to bronchioid and squamoid, not the neuroendocrine cluster. In drug repositioning, MEK inhibitors resisted bronchioid but were squamoid-sensitive. To conceptually validate compounds/targets, we employed RNA-seq and CCK-8/western blot assays. Our results indicated that dinaciclib and alvocidib exhibited similar activity and sensitivity in the neuroendocrine cluster. Also, a lineage factor named KLF5 recognized by inferred transcriptional factors activity could be suppressed by verteporfin.

From bench to bedside: an interdisciplinary journey through the gut-lung axis with insights into lung cancer and immunotherapy

This comprehensive review undertakes a multidisciplinary exploration of the gut-lung axis, from the foundational aspects of anatomy, embryology, and histology, through the functional dynamics of pathophysiology, to implications for clinical science. The gut-lung axis, a bidirectional communication pathway, is central to understanding the interconnectedness of the gastrointestinal- and respiratory systems, both of which share embryological origins and engage in a continuous immunological crosstalk to maintain homeostasis and defend against external noxa. An essential component of this axis is the mucosa-associated lymphoid tissue system (MALT), which orchestrates immune responses across these distant sites. The review delves into the role of the gut microbiome in modulating these interactions, highlighting how microbial dysbiosis and increased gut permeability ("leaky gut") can precipitate systemic inflammation and exacerbate respiratory conditions. Moreover, we thoroughly present the implication of the axis in oncological practice, particularly in lung cancer development and response to cancer immunotherapies. Our work seeks not only to synthesize current knowledge across the spectrum of science related to the gut-lung axis but also to inspire future interdisciplinary research that bridges gaps between basic science and clinical application. Our ultimate goal was to underscore the importance of a holistic understanding of the gut-lung axis, advocating for an integrated approach to unravel its complexities in human health and disease.

The co-location of CD14+APOE+ cells and MMP7+ tumour cells contributed to worse immunotherapy response in non-small cell lung cancer

Intra-tumour immune infiltration is a crucial determinant affecting immunotherapy response in non-small cell lung cancer (NSCLC). However, its phenotype and related spatial structure have remained elusive. To overcome these restrictions, we undertook a comprehensive study comprising spatial transcriptomic (ST) data (28 712 spots from six samples). We identified two distinct intra-tumour infiltration patterns: immune exclusion (characterised by myeloid cells) and immune activation (characterised by plasma cells). The immune exclusion and immune activation signatures showed adverse and favourable roles in NSCLC patients' survival, respectively. Notably, CD14+APOE+ cells were recognised as the main cell type in immune exclusion samples, with increased epithelial‒mesenchymal transition and decreased immune activities. The co-location of CD14+APOE+ cells and MMP7+ tumour cells was observed in both ST and bulk transcriptomics data, validated by multiplex immunofluorescence performed on 20 NSCLC samples. The co-location area exhibited the upregulation of proliferation-related pathways and hypoxia activities. This co-localisation inhibited T-cell infiltration and the formation of tertiary lymphoid structures. Both CD14+APOE+ cells and MMP7+ tumour cells were associated with worse survival. In an immunotherapy cohort from the ORIENT-3 clinical trial, NSCLC patients who responded unfavourably exhibited higher infiltration of CD14+APOE+ cells and MMP7+ tumour cells. Within the co-location area, the MK, SEMA3 and Macrophage migration inhibitory factor (MIF) signalling pathway was most active in cell‒cell communication. This study identified immune exclusion and activation patterns in NSCLC and the co-location of CD14+APOE+ cells and MMP7+ tumour cells as contributors to immune resistance.

Impacts of tumor microenvironment during neoadjuvant chemotherapy in patients with esophageal squamous cell carcinoma

With the advent of immune checkpoint inhibitors (ICIs), a better understanding of tumor microenvironment (TME) is becoming crucial in managing esophageal squamous cell carcinoma (ESCC) patients. We investigated the survival impact of TME status and changes in patients with ESCC who underwent neoadjuvant chemotherapy (NAC) followed by surgery (n = 264). We examined immunohistochemical status (CD4+, CD8+, CD20+, Foxp3+, HLA class-1+, CD204+, and programmed death ligand-1 [PD-L1+]) on 264 pre-NAC and 204 paired post-NAC specimens. Patients were classified by their pre- and post-NAC immune cell status and their changes following NAC. Our findings showed that pre-NAC TME status was not significantly associated with survival outcomes. In contrast, post-NAC TME status, such as low level of T cells, CD4+ T cells, and high PD-L1 combined positive score (CPS), were significantly associated with poor overall survival (OS). Notably, TME changes through NAC exerted significant survival impacts; patients with consistently low levels of T cells, low levels of CD4+ T cells, or high levels of PD-L1 (CPS) had very poor OS (3-year OS: 35.5%, 40.2%, and 33.3%, respectively). Tumor microenvironment changes of consistently low T cells, low CD4+ T cells, and high PD-L1 were independent predictors of poor OS in multivariate Cox hazards analyses, while factors indicating post-NAC status (T cells, CD4+, and PD-L1 [CPS]) alone were not. Therefore, we suggest that the consistently low T/high PD-L1 group could benefit from additional therapies, such as ICIs, and the importance of stratification by the TME, which has recently been recognized.

Nano-Assisted Radiotherapy Strategies: New Opportunities for Treatment of Non-Small Cell Lung Cancer

Lung cancer is the second most commonly diagnosed cancer and a leading cause of cancer-related death, with non-small cell lung cancer (NSCLC) being the most prevalent type. Over 70% of lung cancer patients require radiotherapy (RT), which operates through direct and indirect mechanisms to treat cancer. However, RT can damage healthy tissues and encounter radiological resistance, making it crucial to enhance its precision to optimize treatment outcomes, minimize side effects, and overcome radioresistance. Integrating nanotechnology into RT presents a promising method to increase its efficacy. This review explores various nano-assisted RT strategies aimed at achieving precision treatment. These include using nanomaterials as radiosensitizers, applying nanotechnology to modify the tumor microenvironment, and employing nano-based radioprotectors and radiation-treated cell products for indirect cancer RT. We also explore recent advancements in nano-assisted RT for NSCLC, such as biomimetic targeting that alters mesenchymal stromal cells, magnetic targeting strategies, and nanosensitization with high-atomic number nanomaterials. Finally, we address the existing challenges and future directions of precision RT using nanotechnology, highlighting its potential clinical applications.

Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis

Background

Pathomics has emerged as a promising biomarker that could facilitate personalized immunotherapy in lung cancer. It is essential to elucidate the global research trends and emerging prospects in this domain.

Methods

The annual distribution, journals, authors, countries, institutions, and keywords of articles published between 2018 and 2023 were visualized and analyzed using CiteSpace and other bibliometric tools.

Results

A total of 109 relevant articles or reviews were included, demonstrating an overall upward trend; The terms "deep learning", "tumor microenvironment", "biomarkers", "image analysis", "immunotherapy", and "survival prediction", etc. are hot keywords in this field.

Conclusion

In future research endeavors, advanced methodologies involving artificial intelligence and pathomics will be deployed for the digital analysis of tumor tissues and the tumor microenvironment in lung cancer patients, leveraging histopathological tissue sections. Through the integration of comprehensive multi-omics data, this strategy aims to enhance the depth of assessment, characterization, and understanding of the tumor microenvironment, thereby elucidating a broader spectrum of tumor features. Consequently, the development of a multimodal fusion model will ensue, enabling precise evaluation of personalized immunotherapy efficacy and prognosis for lung cancer patients, potentially establishing a pivotal frontier in this domain of investigation.

New insights into immune cells in cancer immunotherapy: from epigenetic modification, metabolic modulation to cell communication

Cancer is one of the leading causes of death worldwide, and more effective ways of attacking cancer are being sought. Cancer immunotherapy is a new and effective therapeutic method after surgery, radiotherapy, chemotherapy, and targeted therapy. Cancer immunotherapy aims to kill tumor cells by stimulating or rebuilding the body's immune system, with specific efficiency and high safety. However, only few tumor patients respond to immunotherapy and due to the complex and variable characters of cancer immune escape, the behavior and regulatory mechanisms of immune cells need to be deeply explored from more dimensions. Epigenetic modifications, metabolic modulation, and cell-to-cell communication are key factors in immune cell adaptation and response to the complex tumor microenvironment. They collectively determine the state and function of immune cells through modulating gene expression, changing in energy and nutrient demands. In addition, immune cells engage in complex communication networks with other immune components, which are mediated by exosomes, cytokines, and chemokines, and are pivotal in shaping the tumor progression and therapeutic response. Understanding the interactions and combined effects of such multidimensions mechanisms in immune cell modulation is important for revealing the mechanisms of immunotherapy failure and developing new therapeutic targets and strategies.

Predictive value of PD-L1 and TMB for short-term efficacy prognosis in non-small cell lung cancer and construction of prediction models

Objective

To investigate the correlation between programmed death ligand 1(PD-L1), tumor mutation burden (TMB) and the short-term efficacy and clinical characteristics of anti-PD-1 immune checkpoint inhibitor combination chemotherapy in NSCLC patients. The efficacy of the prediction model was evaluated.

Methods

A total of 220 NSCLC patients receiving first-line treatment with anti-PD-1 immune checkpoint inhibitor combined with chemotherapy were retrospectively collected. The primary endpoint was short-term efficacy ORR. The correlation between short-term efficacy, PD-L1, TMB, and clinical characteristics using χ2 test or t-test was evaluated. Screen the independent prognostic factors using univariate and multivariate logistic regression analyses, and construct a nomogram prediction model using the "rms" package in R software. Using receiver operating characteristic (ROC) curve analysis to evaluate the independent Prognostic factors and the prediction model. Using decision curve analysis (DCA) to verify the superiority of the prediction model.

Results

The mean values of PD-L1, TMB, neutrophils, lymphocytes, neutrophil-to-lymphocyte ratio, and albumin were the highest in the ORR group, PD-L1 expression and TMB correlated with epidermal growth factor receptor expression. Multivariate analyses showed that PD-L1, TMB, and neutrophil were independent prognostic factors for ORR. The area under the ROC curve (AUC) values of the ROC constructed based on these three indicators were 0.7104, 0.7139, and 0.7131, respectively. The AUC value under the ROC of the nomogram model was 0.813. The DCA of the model showed that all three indicators used together to build the prediction model of the net return were higher than those of the single indicator prediction model.

Conclusion

PD-L1, TMB, and neutrophils are independent prognostic factors for short-term efficacy. The nomogram prediction model constructed using these three indicators can further improve predictive efficacy of ICIs in patients with NSCLC.

Biomarkers of immunotherapy for non-small cell lung cancer

Immunotherapy is revolutionizing the treatment of non-small cell lung cancer by targeting immune checkpoint proteins, including programmed death-1, programmed death ligand 1 and cytotoxic T-lymphocyte-associated antigen 4. Several immune checkpoint inhibitors, including programmed death ligand 1 inhibitors, programmed death-1 inhibitors and cytotoxic T-lymphocyte-associated antigen 4 inhibitors, were approved for the treatment of patients with advanced non-small cell lung cancer. Programmed death ligand 1 expression is currently the only predictive biomarker for immune checkpoint inhibitors to guide the treatment strategy in these patients. However, programmed death ligand 1 expression is not a perfect biomarker for predicting the efficacy of immunotherapy. Therefore, various biomarkers such as tumour mutation burden, tumour microenvironment, gut microbiome and T-cell receptor repertoire have been proposed to predict the efficacy of immunotherapy more accurately. Additionally, combining different biomarkers may provide a more accurate prediction of response to immunotherapy. This article reports the review of the latest evidence of the predictive marker of immunotherapy in patients with advanced non-small cell lung cancer.

Roles of tissue-resident immune cells in immunotherapy of non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is the most common and lethal type of lung cancer, with limited treatment options and poor prognosis. Immunotherapy offers hope for improving the survival and quality of life of NSCLC patients, but its efficacy depends on the tumor immune microenvironment (TME). Tissue-resident immune cells are a subset of immune cells that reside in various tissues and organs, and play an important role in fighting tumors. In NSCLC, tissue-resident immune cells are heterogeneous in their distribution, phenotype, and function, and can either promote or inhibit tumor progression and response to immunotherapy. In this review, we summarize the current understanding on the characteristics, interactions, and roles of tissue-resident immune cells in NSCLC. We also discuss the potential applications of tissue-resident immune cells in NSCLC immunotherapy, including immune checkpoint inhibitors (ICIs), other immunomodulatory agents, and personalized cell-based therapies. We highlight the challenges and opportunities for developing targeted therapies for tissue-resident immune cells and optimizing existing immunotherapeutic approaches for NSCLC patients. We propose that tissue-resident immune cells are a key determinant of NSCLC outcome and immunotherapy response, and warrant further investigation in future research.

Immunogenic chemotherapy: great potential for improving response rates

The activation of anti-tumor immunity is critical in treating cancers. Recent studies indicate that several chemotherapy agents can stimulate anti-tumor immunity by inducing immunogenic cell death and durably eradicate tumors. This suggests that immunogenic chemotherapy holds great potential for improving response rates. However, chemotherapy in practice has only had limited success in inducing long-term survival or cure of cancers when used either alone or in combination with immunotherapy. We think that this is because the importance of dose, schedule, and tumor model dependence of chemotherapy-activated anti-tumor immunity is under-appreciated. Here, we review immune modulation function of representative chemotherapy agents and propose a model of immunogenic chemotherapy-induced long-lasting responses that rely on synergetic interaction between killing tumor cells and inducing anti-tumor immunity. We comb through several chemotherapy treatment schedules, and identify the needs for chemotherapy dose and schedule optimization and combination therapy with immunotherapy when chemotherapy dosage or immune responsiveness is too low. We further review tumor cell intrinsic factors that affect the optimal chemotherapy dose and schedule. Lastly, we review the biomarkers indicating responsiveness to chemotherapy and/or immunotherapy treatments. A deep understanding of how chemotherapy activates anti-tumor immunity and how to monitor its responsiveness can lead to the development of more effective chemotherapy or chemo-immunotherapy, thereby improving the efficacy of cancer treatment.

Association between pretreatment C-reactive protein level and survival in non-small cell lung cancer patients treated with immune checkpoint inhibitors: A meta-analysis

BACKGROUND

Current evidence suggests that C-reactive protein (CRP) levels may affect cancer prognosis. However, the effect of CRP has not been validated in immunotherapy recipients with non-small cell lung cancer (NSCLC). Therefore, we performed a meta-analysis to explore the prognostic value of CRP level in patients with NSCLC treated with immune checkpoint inhibitors.

METHODS

PubMed, Web of Science, Embase, and Scopus databases were systematically retrieved for eligible publications, and hazard ratios (HRs) with corresponding 95% confidence intervals (95%CIs) were extracted and merged to evaluate the correlation between pretreatment CRP levels and overall survival (OS) and progression-free survival (PFS). Subgroup and sensitivity analyses were conducted to confirm these findings.

RESULTS

Thirty-five cohorts consisting of 4698 patients were included in the primary analysis. Pooled results demonstrated that a higher pretreatment CRP level is associated with worse OS and PFS (OS: HR = 1.13, 95 %CI:1.09-1.18; PFS: HR = 1.16, 95 %CI:1.10-1.22). These findings remained robust after further statistical analyses.

CONCLUSION

Pretreatment CRP level could be a promising biomarker for NSCLC immunotherapy. However, prospective studies are required to validate these findings.

It's high-time to re-evaluate the value of induced-chemotherapy for reinforcing immunotherapy in colorectal cancer

Immunotherapy has made significant advances in the treatment of colorectal cancer (CRC), revolutionizing the therapeutic landscape and highlighting the indispensable role of the tumor immune microenvironment. However, some CRCs have shown poor response to immunotherapy, prompting investigation into the underlying reasons. It has been discovered that certain chemotherapeutic agents possess immune-stimulatory properties, including the induction of immunogenic cell death (ICD), the generation and processing of non-mutated neoantigens (NM-neoAgs), and the B cell follicle-driven T cell response. Based on these findings, the concept of inducing chemotherapy has been introduced, and the combination of inducing chemotherapy and immunotherapy has become a standard treatment option for certain cancers. Clinical trials have confirmed the feasibility and safety of this approach in CRC, offering a promising method for improving the efficacy of immunotherapy. Nevertheless, there are still many challenges and difficulties ahead, and further research is required to optimize its use.

Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis

The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has given rise to the prominence of the tumor microenvironment (TME) as a critical area of research. The clinical implications of an improved understanding of the TME are significant and far-reaching. Radiomics has been increasingly utilized in the comprehensive assessment of the TME and cancer prognosis. Similarly, the advancement of pathomics, which is based on pathological images, can offer additional insights into the panoramic view and microscopic information of tumors. The combination of pathomics and radiomics has revolutionized the concept of a "digital biopsy". As genomics and transcriptomics continue to evolve, integrating radiomics with genomic and transcriptomic datasets can offer further insights into tumor and microenvironment heterogeneity and establish correlations with biological significance. Therefore, the synergistic analysis of digital image features (radiomics, pathomics) and genetic phenotypes (genomics) can comprehensively decode and characterize the heterogeneity of the TME as well as predict cancer prognosis. This review presents a comprehensive summary of the research on important radiomics biomarkers for predicting the TME, emphasizing the interplay between radiomics, genomics, transcriptomics, and pathomics, as well as the application of multiomics in decoding the TME and predicting cancer prognosis. Finally, we discuss the challenges and opportunities in multiomics research. In conclusion, this review highlights the crucial role of radiomics and multiomics associations in the assessment of the TME and cancer prognosis. The combined analysis of radiomics, pathomics, genomics, and transcriptomics is a promising research direction with substantial research significance and value for comprehensive TME evaluation and cancer prognosis assessment.

Immune checkpoint inhibitors in the treatment of oesophageal squamous cell carcinoma: where are we and where are we going?

Oesophageal squamous cell carcinoma (ESCC) is a kind of malignant tumour with high invasiveness and a poor prognosis. Immunotherapy, especially immune checkpoint inhibitors (ICIs), is a rapidly growing therapeutic method that activates and enhances anti-tumour immunity to treat patients with malignancy. Several clinical trials have confirmed the efficacy of ICIs in the treatment of ESCC. ICIs have been approved for the treatment of patients with ESCC. However, only a subset of patients can obtain excellent benefits from ICI therapy. In recent years, there has been a growing interest in exploring predictive biomarkers of immunotherapy response. In this review, we highlighted the predictive biomarkers for the prognosis of ESCC patients treated with ICIs and pointed out the existing problems and the direction of future research in this field.