High-Throughput Antigen Microarray Identifies Longitudinal Prognostic Autoantibody for Chemoimmunotherapy in Advanced Non-Small Cell Lung Cancer.

Chemoimmunotherapy has evolved as a standard treatment for advanced non-small cell lung cancer (aNSCLC). However, inevitable drug resistance has limited its efficacy, highlighting the urgent need for biomarkers of chemoimmunotherapy. A three-phase strategy to discover, verify, and validate longitudinal predictive autoantibodies (AAbs) for aNSCLC before and after chemoimmunotherapy was employed. A total of 528 plasma samples from 267 aNSCLC patients before and after anti-PD1 immunotherapy were collected, plus 30 independent formalin-fixed paraffin-embedded samples. Candidate AAbs were firstly selected using a HuProt high-density microarray containing 21,000 proteins in the discovery phase, followed by validation using an aNSCLC-focused microarray. Longitudinal predictive AAbs were chosen for ELISA based on responders versus non-responders comparison and progression-free survival (PFS) survival analysis. Prognostic markers were also validated using immunohistochemistry and publicly available immunotherapy datasets. We identified and validated a panel of two AAbs (MAX and DHX29) as pre-treatment biomarkers and another panel of two AAbs (MAX and TAPBP) as on-treatment predictive markers in aNSCLC patients undergoing chemoimmunotherapy. All three AAbs exhibited a positive correlation with early responses and PFS (p < 0.05). The kinetics of MAX AAb showed an increasing trend in responders (p < 0.05) and a tendency to initially increase and then decrease in non-responders (p < 0.05). Importantly, MAX protein and mRNA levels effectively discriminated PFS (p < 0.05) in aNSCLC patients treated with immunotherapy. Our results present a longitudinal analysis of changes in prognostic AAbs in aNSCLC patients undergoing chemoimmunotherapy.

Identification of novel prognostic autoantibodies in diffuse large B-cell lymphoma treated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone via a high-throughput antigen microarray.

BACKGROUND: R-CHOP (rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone) is a standard first-line treatment for diffuse large B-cell lymphoma (DLBCL). However, 20%-40% of patients survive less than 5 years. Novel prognostic biomarkers remain in demand. METHODS: Baseline plasma autoantibodies (AAbs) were assessed in 336 DLBCLs. In the discovery phase (n = 20), a high-density antigen microarray (∼21,000 proteins) was used to expound AAb profiles. In the verification phase (n = 181), with a DLBCL-focused microarray, comparative results based on event-free survival at 24 months (EFS24) and lasso Cox regression models of progression-free survival (PFS) and overall survival (OS) were integrated to identify potential biomarkers. They were further validated by enzyme-linked immunosorbent assay in validation phase 1 (n = 135) and a dynamic cohort (n = 12). In validation phase 2, a two-AAb-based risk score was established. They were further validated in an immunohistochemistry cohort (n = 55) and four independent Gene Expression Omnibus datasets (n = 1598). RESULTS: Four AAbs (CREB1, N4BP1, UBAP2, and DEAF1) were identified that showed associations with EFS24 status (p < .05) and superior PFS and OS (p < .05). A novel risk score model based on CREB1 and N4BP1 AAbs was developed to predict PFS with areas under the curve of 0.72, 0.71, 0.76, and 0.82 at 1, 3, 5, and 7 years, respectively, in DLBCL treated with R-CHOP independent of the International Prognostic Index (IPI) and provided significant additional recurrence risk discrimination (p < .05) for the IPI. CREB1 and N4BP1 proteins and messenger RNAs were also associated with better PFS and OS (p < .05). CONCLUSIONS: This study identified a novel prognostic panel of CREB1, N4BP1, DEAF1, and UBAP2 AAbs that is independent of the IPI in DLBCL.

Mutational variant allele frequency profile as a biomarker of response to immune checkpoint blockade in non-small cell lung Cancer.

INTRODUCTION: Identifying new biomarkers for predicting immune checkpoint inhibitors (ICIs) response in non-small cell lung cancer (NSCLC) is crucial. We aimed to assess the variant allele frequency (VAF)-related profile as a novel biomarker for NSCLC personalized therapy. METHODS: We utilized genomic data of 915 NSCLC patients via cBioPortal and a local cohort of 23 patients for model construction and mutational analysis. Genomic, transcriptomic data from 952 TCGA NSCLC patients, and immunofluorescence (IF) assessment with the local cohort supported mechanism analysis. RESULTS: Utilizing the random forest algorithm, a 15-gene VAF-related model was established, differentiating patients with durable clinical benefit (DCB) from no durable benefit (NDB). The model demonstrated robust performance, with ROC-AUC values of 0.905, 0.737, and 0.711 across training (n = 313), internal validation (n = 133), and external validation (n = 157) cohorts. Stratification by the model into high- and low-score groups correlated significantly with both progression-free survival (PFS) (training: P < 0.0001, internal validation: P < 0.0001, external validation: P = 0.0066) and overall survival (OS) (n = 341) (P < 0.0001). Notably, the stratification system was independent of PD-L1 (P < 0.0001) and TMB (P < 0.0001). High-score patients exhibited an increased DCB ratio and longer PFS across both PD-L1 and TMB subgroups. Additionally, the high-score group appeared influenced by tobacco exposure, with activated DNA damage response pathways. Whereas, immune/inflammation-related pathways were enriched in the low-score group. Tumor immune microenvironment analyses revealed higher proportions of exhausted/effector memory CD8 + T cells in the high-score group. CONCLUSIONS: The mutational VAF profile is a promising biomarker for ICI therapy in NSCLC, with enhanced therapeutic stratification and management as a supplement to PD-L1 or TMB.

Integrated molecular characterization of esophageal basaloid squamous cell carcinoma: a subtype with distinct RNA expression pattern and immune characteristics, but no specific genetic mutations.

Esophageal basaloid squamous cell carcinoma (bSCC) is a subtype of squamous cell carcinoma (SCC) with a different behavior and poor prognosis. Exploring bSCC's molecular characteristics and treatment strategies are of great clinical significance. We performed multi-omics analysis of paired bSCC and common SCC (cSCC) using whole exome sequencing and a NanoString nCounter gene expression panel. Immunohistochemistry was used for verification of candidate biomarkers. Different treatment response was analyzed on both patients receiving neoadjuvant treatment and late-stage patients. The common genetically-clonal origin of bSCC and cSCC was confirmed. No significant differences between their genetic alterations or mutation spectra were observed. Mutation signature 15 (associated with defective DNA damage repair) was less prominent, and tumor mutational burden (TMB) was lower in bSCC. bSCC with an RNA expression pattern resembling cSCC had a better survival than other bSCCs. Moreover, bSCC showed significant upregulation of expression of genes associated with angiogenesis response, basement membranes, and epithelial-mesenchymal transition, and downregulation of KRT14 (squamous differentiation) and CCL21 (associated with immune response). Immunohistochemistry for SFRP1 was shown to be highly sensitive and specific for bSCC diagnosis (p < 0.001). In addition, bSCC receiving neoadjuvant immuno-chemotherapy had a worse pathological response than bSCC receiving neoadjuvant chemotherapy (but without statistical significance), even in bSCC positive for PD-L1. Our results demonstrated the molecular characteristics of esophageal bSCC as a subtype with a distinct RNA expression pattern and immune characteristics, but no specific genetic mutations. We provided a useful biomarker, SFRP1, for diagnosis. After outcome analysis for six bSCCs with neoadjuvant immunotherapy treatment and four late-stage bSCCs with immunotherapy, we found that immunotherapy may not be an effective treatment option for most bSCCs. This may also provide a clue for the same subtypes of lung and head and neck cancer. Our study highlighted the heterogeneity among bSCC patients, and might explain the conflicting results of bSCC outcomes in existing studies. © 2022 The Pathological Society of Great Britain and Ireland.

Low TP53 variant allele frequency as a biomarker for anti-programmed death (ligand) 1 monotherapy in lung adenocarcinoma.

BACKGROUND: TP53 mutation heterogeneity should be considered when using TP53 as a predictive biomarker for anti-programmed death (ligand) 1 (PD-(L)1) monotherapy in lung adenocarcinoma (LUAD). However, whether TP53 variant allele frequency (VAF) should also be considered remains unknown. METHODS: Patients with LUAD from both published research and the local cohort were included to discover and validate the relationship between TP53 VAF and the efficacy of PD-(L)1 inhibitors. The Cancer Genome Atlas (TCGA) LUAD data were included for genomic, transcriptomic, and tumor microenvironment analysis. RESULTS: Among 159 patients in the discovery cohort, low TP53 VAF patients (VAF ≤ 25%) experienced significantly longer progression-free survival (PFS) than both high TP53 VAF (5.4 vs. 3.3 months; p = .021) and TP53-wild-type patients (5.4 vs. 2.5 months; p = .011). Multivariate Cox regression revealed low TP53 VAF as an independent biomarker of better efficacy. Among 50 patients in the combined validation cohort, median PFS of low TP53 VAF patients was also significantly longer than that of high TP53 VAF patients (12.0 vs. 2.1 months; p = .037). Analyzed with 469 TCGA LUAD samples, low TP53 VAF is associated with significantly higher PD-L1 expression, enrichment of gene sets related to T-cell activation, T cell-mediated immunity, and interferon-γ signaling pathways, and independently associated with more tumor-infiltrating CD8+ T cells compared with both high TP53 VAF and TP53-wild type. CONCLUSIONS: TP53 VAF should also be considered when using TP53 as a predictive biomarker. Only low TP53 VAF is independently associated with better efficacy of anti-PD-(L)1 monotherapy, which may result from higher PD-L1 expression and more tumor-infiltrating CD8+ T cells.

Identification of an antigen-presenting cells/T/NK cells-related gene signature to predict prognosis and CTSL to predict immunotherapeutic response for lung adenocarcinoma: an integrated analysis of bulk and single-cell RNA sequencing.

BACKGROUND: Antigen-presenting cells (APC)/T/NK cells are key immune cells that play crucial roles in fighting against malignancies including lung adenocarcinoma (LUAD). In this study, we aimed to identify an APC/T/NK cells-related gene signature (ATNKGS) and potential immune cell-related genes (IRGs) to realize risk stratification, prognosis, and immunotherapeutic response prediction for LUAD patients. METHODS: Based on the univariate Cox regression and the LASSO Cox regression results of 196 APC/T/NK cells-related genes collected from three pathways in the KEGG database, we determined the final genes and established the ATNKGS-related risk model. The single-cell RNA sequencing data were applied for key IRGs identification and investigate their value in immunotherapeutic response prediction. Several GEO datasets and an external immunotherapy cohort from Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, were applied for validation. RESULTS: In this study, nine independent public datasets including 1108 patients were enrolled. An ATNKGS containing 16 genes for predicting overall survival of LUAD patients was constructed with robust prognostic capability. The ATNKGS high risk group was related to significantly worse OS outcomes than those in the low-risk group, which were verified in TCGA and four GEO datatsets. A nomogram combining the ATNKGS risk score with clinical TNM stage achieved the optimal prediction performance. The single-cell RNA sequencing analysis revealed CTSL as an IRG of macrophage and monocyte. Moreover, though CTSL was an indicator for poor prognosis of LUAD patients, CTSL high expression group was associated with higher ESTIMATEScore, immune checkpoints expression, and lower TIDE score. Several immunotherapeutic cohorts have confirmed the response-predicting significance of CTSL in patients receiving immune checkpoint inhibitor (ICI) treatment. CONCLUSIONS: Our study provided an insight into the significant role of APC/T/NK cells-related genes in survival risk stratification and CTSL in response prediction of immunotherapy in patients with LUAD.

Identification of novel serological autoantibodies in Chinese prostate cancer patients using high-throughput protein arrays.

Autoantibody (AAb) has a prominent role in prostate cancer (PCa), with few studies profiling the AAb landscape in Chinese patients. Therefore, the AAb landscape in Chinese patients was characterized using protein arrays. First, in the discovery phase, Huprot arrays outlined autoimmune profiles against ~ 21,888 proteins from 57 samples. In the verification phase, the PCa-focused arrays detected 25 AAbs selected from the discovery phase within 178 samples. Then, PCa was detected using a backpropagation artificial neural network (BPANN) model. In the validation phase, an enzyme-linked immunosorbent assay (ELISA) was used to validate four AAb biomarkers from 196 samples. Huprot arrays profiled distinct PCa, benign prostate diseases (BPD), and health AAb landscapes. PCa-focused array depicted that IFIT5 and CPOX AAbs could distinguish PCa from health with an area under curve (AUC) of 0.71 and 0.70, respectively. PAH and FCER2 AAbs had AUCs of 0.86 and 0.88 in discriminating PCa from BPD. Particularly, PAH AAb detected patients in the prostate-specific antigen (PSA) gray zone with an AUC of 0.86. Meanwhile, the BPANN model of 4-AAb (IFIT5, PAH, FCER2, CPOX) panel attained AUC of 0.83 among the two cohorts for detecting patients with gray-zone PSA. In the validation cohort, the IFIT5 AAb was upregulated in PCa compared to health (p < 0.001). Compared with BPD, PAH and FCER2 AAbs were significantly elevated in PCa (p = 0.012 and 0.039). We have demonstrated the first extensive profiling of autoantibodies in Chinese PCa patients, identifying novel diagnostic AAb biomarkers, especially for identification of gray-zone-PSA patients.

Integrative analyses of bulk and single-cell RNA-seq identified cancer-associated fibroblasts-related signature as a prognostic factor for immunotherapy in NSCLC.

An emerging view regarding cancer-associated fibroblast (CAF) is that it plays a critical role in tumorigenesis and immunosuppression in the tumor microenvironment (TME), but the clinical significance and biological functions of CAFs in non-small cell lung cancer (NSCLC) are still poorly explored. Here, we aimed to identify the CAF-related signature for NSCLC through integrative analyses of bulk and single-cell genomics, transcriptomics, and proteomics profiling. Using CAF marker genes identified in weighted gene co-expression network analysis (WGCNA), we constructed and validated a CAF-based risk model that stratifies patients into two prognostic groups from four independent NSCLC cohorts. The high-score group exhibits a higher abundance of CAFs, decreased immune cell infiltration, increased epithelial-mesenchymal transition (EMT), activated transforming growth factor beta (TGFß) signaling, and a limited survival rate compared with the low-score group. Considering the immunosuppressive feature in the high-score group, we speculated an inferior clinical response for immunotherapy in these patients, and this association was successfully verified in two NSCLC cohorts treated with immune checkpoint blockades (ICBs). Furthermore, single-cell RNA sequence datasets were used to clarify the molecular mechanisms underlying the aggressive and immunosuppressive phenotype in the high-score group. We found that one of the genes in the risk model, filamin binding LIM protein 1 (FBLIM1), is mainly expressed in fibroblasts and upregulated in CAFs compared to fibroblasts from normal tissue. FBLIM1-positive CAF subtype was correlated with increased TGFß expression, higher mesenchymal marker level, and immunosuppressive tumor microenvironment. Finally, we demonstrated that FBLIM1 might serve as a poor prognostic marker for immunotherapy in clinical samples. In conclusion, we identified a novel CAF-based classifier with prognostic value in NSCLC patients and those treated with ICBs. Single-cell transcriptome profiling uncovered FBLIM1-positive CAFs as an aggressive subtype with a high abundance of TGFß, EMT, and an immunosuppressive phenotype in NSCLC.

Incidence, survival comparison, and novel prognostic evaluation approaches for stage iii-iv pulmonary large cell neuroendocrine carcinoma and small cell lung cancer.

BACKGROUND: Pulmonary large cell neuroendocrine carcinoma (LCNEC) and small cell lung cancer (SCLC) are two types of high-grade neuroendocrine carcinomas of the lung with poor prognosis. LCNEC has not been thoroughly studied due to its rarity, data are also lacking regarding the survival comparison and prognosis analysis of patients with locally advanced or metastatic LCNEC and SCLC. METHODS: Data of patients with LCNEC, SCLC, and other NSCLC who were diagnosed from 1975 to 2019 were extracted from the Surveillance, Epidemiology and End Results (SEER) database to estimate incidence. Those in stage III-IV and being diagnosed from 2010 to 2015 were utilized further to investigate their clinical characteristics and prognosis. Propensity score matching (PSM) analyses at a ratio of 1:2 was used to compare their survival outcomes. Nomograms of LCNEC and SCLC were established with internal validation, and the nomogram of SCLC was externally validated by 349 patients diagnosed in Cancer hospital, Chinese Academy of Medical Sciences & Peking Union Medical College from January 1, 2012 to December 31, 2018. RESULTS: The incidence of LCNEC has been increasing in recent decades, meanwhile that of SCLC and other types of NSCLC were decreasing. A total of 91,635 lung cancer patients, including 785 with LCNEC, 15,776 with SCLC, and 75,074 with other NSCLC were enrolled for further analysis. The survival of stage III-IV LCNEC resembles that of SCLC, and significantly worse than other types of NSCLC before and after PSM analysis. In pretreatment prognostic analysis, age, T stage, N stage, M stage, bone metastasis, liver metastasis, and brain metastasis were found to be associated with the survival of both LCNEC and SCLC, besides sex, bilaterality, and lung metastasis were additional prognostic factors for SCLC. Two nomograms and convenient online tools respectively for LCNEC and SCLC were established accordingly with favorable predicting accuracy of < 1-year, < 2-year, < 3-year survival probabilities. In external validation of the SCLC nomogram with a Chinese cohort, the AUCs of 1-year, 2-year and 3-year ROC were 0.652, 0.669, and 0.750, respectively. All the results of 1-, 2-, 3- year variable-dependent ROC curves verified the superior prognostic value of our nomograms for LCNEC and SCLC over the traditional T/N/M staging system. CONCLUSIONS: Based on large sample-based cohort, we compared the epidemiological trends and survival outcomes between locally advanced or metastatic LCNEC, SCLC, and other NSCLC. Furthermore, two prognostic evaluation approaches respectively for LCNEC and SCLC might present as practical tools for clinicians to predict the survival outcome of these patients and facilitate risk stratification.

Multiomics analysis revealed the mechanisms related to the enhancement of proliferation, metastasis and EGFR-TKI resistance in EGFR-mutant LUAD with ARID1A deficiency.

INTRODUCTION: Dysregulated ARID1A expression is frequently detected in lung adenocarcinoma (LUAD) and mediates significant changes in cancer behaviors and a poor prognosis. ARID1A deficiency in LUAD enhances proliferation and metastasis, which could be induced by activation of the Akt signaling pathway. However, no further exploration of the mechanisms has been performed. METHODS: Lentivirus was used for the establishment of the ARID1A knockdown (ARID1A-KD) cell line. MTS and migration/invasion assays were used to examine changes in cell behaviors. RNA-seq and proteomics methods were applied. ARID1A expression in tissue samples was determined by IHC. R software was used to construct a nomogram. RESULTS: ARID1A KD significantly promoted the cell cycle and accelerated cell division. In addition, ARID1A KD increased the phosphorylation level of a series of oncogenic proteins, such as EGFR, ErbB2 and RAF1, activated the corresponding pathways and resulted in disease progression. In addition, the bypass activation of the ErbB pathway, the activation of the VEGF pathway and the expression level changes in epithelial-mesenchymal transformation biomarkers induced by ARID1A KD contributed to the insensitivity to EGFR-TKIs. The relationship between ARID1A and the sensitivity to EGFR-TKIs was also determined using tissue samples from LUAD patients. CONCLUSION: Loss of ARID1A expression influences the cell cycle, accelerates cell division, and promotes metastasis. EGFR-mutant LUAD patients with low ARID1A expression had poor overall survival. In addition, low ARID1A expression was associated with a poor prognosis in EGFR-mutant LUAD patients who received first-generation EGFR-TKI treatment. Video abstract.

Efficacy of first-line treatments in the elderly and non-elderly patients with advanced epidermal growth factor receptor mutated, non-small cell lung cancer: a network meta-analysis.

OBJECTIVE: Epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) are the current standard of care for advanced or metastatic non-small cell lung cancer (NSCLC) patients harboring EGFR activating mutations. However, the optimal strategy for elderly NSCLC patients is still under debate. This study was designed to explore the optimal first-line regimens by comparing diverse strategies for elderly and non-elderly EGFR-mutated NSCLC patients. METHODS: A systematic review was conducted to summarize all available randomized controlled trials (RCTs) from PubMed, EMBASE, Cochrane Central Register of Controlled Trials databases, and international conferences before September 30, 2020. The primary outcome was progression free survival (PFS), and the secondary outcome was overall survival (OS). A network meta-analysis (NMA) was constructed using the Bayesian statistical model to synthesize the survival outcomes of all the treatments. RESULTS: In total, 12 RCTs were deemed eligible for inclusion with 3779 patients who have received 10 diverse treatments including EGFR-TKIs. Results from the Bayesian ranking suggested that osimertinib was most likely to rank the first in overall population and in elderly patients in PFS, with the cumulative probabilities of 42.20% and 31.46%, respectively. In non-elderly group (younger than 65 years old), standard of care (SoC, representing first-generation EGFR-TKIs in this NMA) + chemotherapy ranked the first (31.66%). As for OS, SoC + chemotherapy ranked first in all patients (64.33%), patients younger than 65 years old (61.98%), or older than 65 years old (34.45%). CONCLUSION: The regimen of osimertinib is associated with the most favorable PFS in elderly advanced EGFR-mutated NSCLC patients, while SoC + chemotherapy is the optimal strategy in PFS for non-elderly NSCLC patients harboring EGFR activating mutations, and in OS for both elderly and non-elderly EGFR-mutated advanced NSCLC patients. TRIAL REGISTRATION: INPLASY protocol 2020100061 https://doi.org/10.37766/inplasy2020.20.0061 .

ARID1A deficiency reverses the response to anti-PD(L)1 therapy in EGFR-mutant lung adenocarcinoma by enhancing autophagy-inhibited type I interferon production.

INTRODUCTION: EGFR mutations in non-small cell lung cancer (NSCLC) are associated with a poor response to immune checkpoint inhibitors (ICIs), and only 20% of NSCLC patients harboring EGFR mutations benefit from immunotherapy. Novel biomarkers or therapeutics are needed to predict NSCLC prognosis and enhance the efficacy of ICIs in NSCLC patients harboring EGFR mutations, especially lung adenocarcinoma (LUAD) patients, who account for approximately 40-50% of all NSCLC cases. METHODS: An ARID1A-knockdown (ARID1A-KD) EGFR-mutant LUAD cell line was constructed using lentivirus. RNA-seq and mass spectrometry were performed. Western blotting and IHC were used for protein expression evaluation. Effects of 3-MA and rapamycin on cells were explored. Immunofluorescence assays were used for immune cell infiltration examination. RESULTS: ARID1A expression was negatively associated with immune cell infiltration and immune scores for ICIs in LUAD with EGFR mutations. In vitro experiments suggested that ARID1A-KD activates the EGFR/PI3K/Akt/mTOR pathway and inhibits autophagy, which attenuates the inhibition of Rig-I-like receptor pathway activity and type I interferon production in EGFR-mutant LUAD cells. In addition, 3-MA upregulated production of type I interferon in EGFR-mutant LUAD cells, with an similar effect to ARID1A-KD. On the other hand, rapamycin attenuated the enhanced production of type I interferon in ARID1A-KD EGFR-mutant LUAD cells. ARID1A function appears to influence the tumor immune microenvironment and response to ICIs. CONCLUSION: ARID1A deficiency reverses response to ICIs in EGFR-mutant LUAD by enhancing autophagy-inhibited type I interferon production. Video Abstract.

Single-cell and spatial analyses revealed the co-location of cancer stem cells and SPP1+ macrophage in hypoxic region that determines the poor prognosis in hepatocellular carcinoma.

In hepatocellular carcinoma (HCC), classical cancer stem cells (CSC) markers were shared by normal stem cells, targeting which may hinder hepatic regeneration and cause liver failure. Additionally, the spatial structure of CSC still remained elusive. To address these limitations, we undertook a comprehensive study combining single-cell data (56,022 cells from 20 samples) and spatial data (38,191 spots from eight samples) to obtain CSC signature and uncover its spatial structure. Utilizing the CytoTRACE algorithm, we discretely identified CSC, which displayed upregulated proliferation pathways regulated by HIF1A. A CSC signature of 107 genes was then developed using Weighted Gene Co-expression Network Analysis (WGCNA). Notably, HCC patients with high CSC levels exhibited an accumulation of SPP1+ macrophages (Macro_SPP1) expressing metalloproteinases (MMP9, MMP12, and MMP7) regulated by HIF1A, suggesting a hypoxic tumor region connecting Macro_SPP1 and CSC. Both CSC and Macro_SPP1 correlated with worse prognosis and undesirable immunotherapy response. Spatial analysis revealed the co-location of CSC and Macro_SPP1, with CD8 T cells excluded from the tumor region. The co-location area and non-tumor area of boundary exhibited a high level of hypoxia, with the HAVRC2 checkpoint highly expressed. Within the co-location area, the SPP1 signaling pathway was most active in cell-cell communication, with SPP1-CD44 and SPP1-ITGA/ITGB identified as the main ligand-receptor pairs. This study successfully constructed a CSC signature and demonstrated the co-location of CSC and Macro_SPP1 in a hypoxic region that exacerbates the tumor microenvironment in HCC.

Single-cell and spatial transcriptomics reveal a high glycolysis B cell and tumor-associated macrophages cluster correlated with poor prognosis and exhausted immune microenvironment in diffuse large B-cell lymphoma.

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous malignancy characterized by varied responses to treatment and prognoses. Understanding the metabolic characteristics driving DLBCL progression is crucial for developing personalized therapies. METHODS: This study utilized multiple omics technologies including single-cell transcriptomics (n = 5), bulk transcriptomics (n = 966), spatial transcriptomics (n = 10), immunohistochemistry (n = 34), multiple immunofluorescence (n = 20) and to elucidate the metabolic features of highly malignant DLBCL cells and tumor-associated macrophages (TAMs), along with their associated tumor microenvironment. Metabolic pathway analysis facilitated by scMetabolism, and integrated analysis via hdWGCNA, identified glycolysis genes correlating with malignancy, and the prognostic value of glycolysis genes (STMN1, ENO1, PKM, and CDK1) and TAMs were verified. RESULTS: High-glycolysis malignant DLBCL tissues exhibited an immunosuppressive microenvironment characterized by abundant IFN_TAMs (CD68+CXCL10+PD-L1+) and diminished CD8+ T cell infiltration. Glycolysis genes were positively correlated with malignancy degree. IFN_TAMs exhibited high glycolysis activity and closely communicating with high-malignancy DLBCL cells identified within datasets. The glycolysis score, evaluated by seven genes, emerged as an independent prognostic factor (HR = 1.796, 95% CI: 1.077-2.995, p = 0.025 and HR = 2.631, 95% CI: 1.207-5.735, p = 0.015) along with IFN_TAMs were positively correlated with poor survival (p < 0.05) in DLBCL. Immunohistochemical validation of glycolysis markers (STMN1, ENO1, PKM, and CDK1) and multiple immunofluorescence validation of IFN_TAMs underscored their prognostic value (p < 0.05) in DLBCL. CONCLUSIONS: This study underscores the significance of glycolysis in tumor progression and modulation of the immune microenvironment. The identified glycolysis genes and IFN_TAMs represent potential prognostic markers and therapeutic targets in DLBCL.

Front-line therapy for brain metastases and non-brain metastases in advanced epidermal growth factor receptor-mutated non-small cell lung cancer: a network meta-analysis.

BACKGROUND: The brain is a common metastatic site in patients with non-small cell lung cancer (NSCLC), resulting in a relatively poor prognosis. Systemic therapy with epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) is recommended as the first-line treatment for EGFR -mutated, advanced NSCLC patients. However, intracranial activity varies in different drugs. Thus, brain metastasis (BM) should be considered when choosing the treatment regimens. We conducted this network meta-analysis to explore the optimal first-line therapeutic schedule for advanced EGFR -mutated NSCLC patients with different BM statuses. METHODS: Randomized controlled trials focusing on EGFR-TKIs (alone or in combination) in advanced and EGFR -mutant NSCLC patients, who have not received systematic treatment, were systematically searched up to December 2021. We extracted and analyzed progression-free survival (PFS) and overall survival (OS). A network meta-analysis was performed with the Bayesian statistical model to determine the survival outcomes of all included therapy regimens using the R software. Hazard ratios (HRs) and 95% confidence intervals (CIs) were used to compare intervention measures, and overall rankings of therapies were estimated under the Bayesian framework. RESULTS: This analysis included 17 RCTs with 5077 patients and 12 therapies, including osimertinib + bevacizumab, aumolertinib, osimertinib, afatinib, dacomitinib, standards of care (SoC, including gefitinib, erlotinib, or icotinib), SoC + apatinib, SoC + bevacizumab, SoC + ramucirumab, SoC + pemetrexed based chemotherapy (PbCT), PbCT, and pemetrexed free chemotherapy (PfCT). For patients with BM, SoC + PbCT improved PFS compared with SoC (HR = 0.40, 95% CI: 0.17-0.95), and osimertinib + bevacizumab was most likely to rank first in PFS, with a cumulative probability of 34.5%, followed by aumolertinib, with a cumulative probability of 28.3%. For patients without BM, osimertinib + bevacizumab, osimertinib, aumolertinib, SoC + PbCT, dacomitinib, SoC + ramucirumab, SoC + bevacizumab, and afatinib showed superior efficacy compared with SoC (HR = 0.43, 95% CI: 0.20-0.90; HR = 0.46, 95% CI: 0.31-0.68; HR = 0.51, 95% CI: 0.34-0.77; HR = 0.50, 95% CI: 0.38-0.66; HR = 0.62, 95% CI: 0.43-0.89; HR = 0.64, 95% CI: 0.44-0.94; HR = 0.61, 95% CI: 0.48-0.76; HR = 0.71, 95% CI: 0.50-1.00), PbCT (HR = 0.29, 95% CI: 0.11-0.74; HR = 0.31, 95% CI: 0.15-0.62; HR = 0.34, 95% CI: 0.17-0.69; HR = 0.34, 95% CI: 0.18-0.64; HR = 0.42, 95% CI: 0.21-0.82; HR = 0.43, 95% CI: 0.22-0.87; HR = 0.41, 95% CI: 0.22-0.74; HR = 0.48, 95% CI: 0.31-0.75), and PfCT (HR = 0.14, 95% CI: 0.06-0.32; HR = 0.15, 95% CI: 0.09-0.26; HR = 0.17, 95% CI: 0.09-0.29; HR = 0.16, 95% CI: 0.10-0.26; HR = 0.20, 95% CI: 0.12-0.35; HR = 0.21, 95% CI: 0.12-0.39; HR = 0.20, 95% CI: 0.12-0.31; HR = 0.23, 95% CI: 0.16-0.34) in terms of PFS. And, SoC + apatinib showed relatively superior PFS when compared with PbCT (HR = 0.44, 95% CI: 0.22-0.92) and PfCT (HR = 0.21, 95% CI: 0.12-0.39), but similar PFS to SoC (HR = 0.65, 95% CI: 0.42-1.03). No statistical differences were observed for PFS in patients without BM between PbCT and SoC (HR = 1.49, 95% CI: 0.84-2.64), but both showed favorable PFS when compared with PfCT (PfCT vs. SoC, HR = 3.09, 95% CI: 2.06-4.55; PbCT vs. PfCT, HR = 0.14, 95% CI: 0.06-0.32). For patients without BM, osimertinib + bevacizumab was most likely to rank the first, with cumulative probabilities of 47.1%. For OS, SoC + PbCT was most likely to rank first in patients with and without BM, with cumulative probabilities of 46.8%, and 37.3%, respectively. CONCLUSION: Osimertinib + bevacizumab is most likely to rank first in PFS in advanced EGFR -mutated NSCLC patients with or without BM, and SoC + PbCT is most likely to rank first in OS.

An immunosuppressive subtype of senescent tumor cells predicted worse immunotherapy response in lung adenocarcinoma.

Senescent tumor cells (STCs) can induce immunosuppression, promoting tumor progression and therapy resistance. However, the specific characteristics of immunosuppressive STC have not been thoroughly investigated. This study aimed to characterize and elucidate the immunosuppressive phenotype of STC in lung adenocarcinoma by employing single-cell and bulk transcriptomics, as well as serum proteomics profiling. We identified senescence-related genes specific to tumors and identified Cluster10 of STC as the immunomodulatory subtype. Cluster10 exhibited a distinct secretome dominated by cytokines such as CXCL1, CXCL2, and CXCL8 and showed activation of transcription factors associated with cytokine secretion, including NFKB1, RELA, and STAT3. Notably, Cluster10 demonstrated the highest degree of intercellular communication among all cell types, with interactions as LGALS9-TIM3 and MIF-CD74. Furthermore, Cluster10 showed significant associations with poor prognosis and diminished response to immunotherapy. Analysis of serum proteomics data from our in-house cohort identified CXCL8 as a potential marker for predicting immunotherapeutic outcomes.

Evolutionary Characteristics and Immunologic Divergence of Lung and Brain Metastasis Lesions in NSCLC.

Brain metastases (BM) is one of the main reasons for lung cancer-related deaths but lack prediction methods. Many patients with BMs do not benefit from immunotherapy. A comprehensive genomic analysis of matched primary tumors (PT) and their BM lesions may provide new insight into the evolutionary and immune characteristics. To describe evolutionary features and immune characteristic differences, we analyzed whole-exome sequencing data for 28 paired PT and BM samples from 14 patients with non-small cell lung cancer. In addition, we used another 26 matched PT and BM samples as a validation cohort. We found that total mutational signatures were relatively consistent between paired primary and brain metastatic tumors. Nevertheless, the shared mutations of the two lesions were fewer than the mutations present in each of the lesions alone. In the process of BM, driver genes undergo evolutionary branches. Typical driver genes, including EGFR and TP53, appear relatively conserved throughout evolution; however, specific signals are enriched in BM lesions. We found several main characteristics of lung cancer BMs that were different from primary lung cancer, such as genomic instability, novel driver genes, tumor mutation burden, and BM lesion private neoantigens. In addition, the estimated timing of dissemination showed that BMs might occur early in lung cancer. IMPLICATIONS: Mechanistic insight from this study provides new insight into the biology of the metastatic brain process and a new beneficial approach for preventing and treating lung cancer BMs.

Heterogeneity of resistant mechanisms in an EGFR-TKI relapsed patient with EGFR amplification and response to nimotuzumab: A case report.

EGFR mutations are the most important drivers of gene alterations in lung adenocarcinomas and are sensitive to EGFR-TKIs. However, resistance to EGFR-TKIs is inevitable in the majority of EGFR-mutated lung cancer patients. Numerous resistant mechanisms have been revealed to date, and more are still under investigation. Owing to the selective pressure, intratumoral heterogeneity may exist after resistance, especially in patients after multiple lines of treatment. For those patients, it is important to choose therapies focused on the trunk/major clone of the tumor in order to achieve optimal clinical benefit. Here, we will report an EGFR-mutated lung adenocarcinoma patient with heterogeneity of resistant mechanisms including EGFR amplification, large fragment deletion of RB1, and histological transformations after targeted treatments. In our case, EGFR amplification seemed to be the major clone of the resistant mechanism according to the next-generation sequencing (NGS) results of both liquid biopsy monitoring and tissue biopsies. In consideration of the high EGFR amplification level, the patient was administered by combination treatment with EGFR-TKI plus nimotuzumab, an anti-EGFR monoclonal antibody (mAb), and achieved a certain degree of clinical benefit. Our case sheds light on the treatment of EGFR-mutant patients with EGFR amplification and indicates that a combination of EGFR-TKI with anti-EGFR mAb might be one of the possible treatment options based on genetic tests. Moreover, the decision on therapeutic approaches should focus on the major clone of the tumor and should make timely adjustments according to the dynamic changes of genetic characteristics during treatment.

DLX2 Is a Potential Immune-Related Prognostic Indicator Associated with Remodeling of Tumor Microenvironment in Lung Squamous Cell Carcinoma: An Integrated Bioinformatical Analysis.

Background: It is still an unmet clinical need to identify potent biomarkers for immunotherapy on patients with lung squamous cell carcinoma (LUSC). Methods: In this study, we explored the differentially expressed genes (DEGs) that were simultaneously correlated with four pathways (i.e. CD8+ αßT cell proliferation/differentiation/activation pathways and ferroptosis pathway) and possibly related to the remodeling of tumor microenvironment via the TCGA-LUSC dataset. Besides, four GEO datasets (GSE157009, GSE157010, GSE19188, and GSE126045) and IMvigor210 dataset were utilized for confirmation and validation. Results: The co-downregulated DEG DLX2 was selected for further analysis. Function enrichment analysis revealed that low-expression of DLX2 was closely related to various immune-related pathways like T/B/NK cell mediated immunity, interferon gamma/alpha response, and various autoimmune disease. DLX2-downregulated group was enriched in more immune-activating cells and lower tumor immune dysfunction and exclusion (TIDE) score. Via the Cancer Immunome Atlas (TCIA) database, lower expression of DLX2 was also found to be associated with better IPS score of PD-1/PD-L1 blockade (p < 0.001) as well as CTLA-4 combined with PD-1/PD-L1 blockade (p < 0.001). Furthermore, patients in DLX2-low group were found to have significant longer median OS than those in DLX2-high group in IMvigor210 dataset (10.8 vs 7.4 months; hazard ratio [HR]=0.74, 95% confidence interval [95%CI] 0.57-0.96; p = 0.024). Conclusions: Our study on an integrated bioinformatical analysis implied that DLX2 could be served as a promising indicator for remodeling tumor microenvironment status and predicting ICI response of patients with LUSC.

Analysis on methylation and expression of PSMB8 and its correlation with immunity and immunotherapy in lung adenocarcinoma.

Aim: To find biomarkers for immunity and immunotherapy in lung adenocarcinoma (LUAD) through multiomics analysis. Materials & methods: The multiomics data of patients with LUAD were downloaded from the TCGA and GEO databases. CIBERSORT, quanTIseq, ESTIMATEScore, k-means clustering, gene set enrichment analysis, gene set variation analysis, immunophenoscore and logistic regression were used in this study. Results: PSMB8 HypoMet-HighExp group patients have more active immune-related pathways, more antitumor immune cells, less protumor immune cells, higher immunophenoscore and longer progression-free survival of immune checkpoint inhibitor therapy than HyperMet-LowExp group. In multivariate analysis, PSMB8 showed an independent value. Conclusion: The combination of DNA methylation and mRNA expression of PSMB8 could independently distinguish types of tumor immune microenvironment and predict programmed cell death protein 1/programmed cell death-ligand 1 inhibitors' effects in patients with LUAD.

Our research provides a new and robust method to select biomarkers based on the tumor immune microenvironment. Our research finds that a new epigenomic and transcriptomic biomarker could independently distinguish the types of tumor immune microenvironment and predict immunotherapy effects in patients with lung adenocarcinoma.