Development and validation of a deep learning-based model to predict response and survival of T790M mutant non-small cell lung cancer patients in early clinical phase trials using electronic medical record and pharmacokinetic data.

Background: Epidermal growth factor receptor (EGFR) T790M mutation is the standard predictive biomarker for third-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) treatment. While not all T790M-positive patients respond to third-generation EGFR-TKIs and have a good prognosis, it necessitates novel tools to supplement EGFR genotype detection for predicting efficacy and stratifying EGFR-mutant patients with various prognoses. Mixture-of-experts (MoE) is designed to disassemble a large model into many small models. Meanwhile, it is also a model ensembling method that can better capture multiple patterns of intrinsic subgroups of enrolled patients. Therefore, the combination of MoE and Cox algorithm has the potential to predict efficacy and stratify survival in non-small cell lung cancer (NSCLC) patients with EGFR mutations. Methods: We utilized the electronic medical record (EMR) and pharmacokinetic parameters of 326 T790M-mutated NSCLC patients, including 283 patients treated with Abivertinib in phase I (n=177, for training) and II (n=106, for validation) clinical trials and an additional validation cohort 2 comprising 43 patients treated with BPI-7711. Furthermore, 18 patients underwent whole-exome sequencing for biological interpretation of CoxMoE. We evaluated the predictive performance for therapeutic response using the area under the curve (AUC) and the Concordance index (C-index) for progression-free survival (PFS). Results: CoxMoE exhibited AUCs of 0.73-0.83 for predicting efficacy defined by best overall response (BoR) and achieved C-index values of 0.64-0.65 for PFS prediction in training and validating cohorts. The PFS of 198 patients with a low risk [median, 6.0 (range, 1.0-23.3) months in the abivertinib treated cohort; median 16.5 (range, 1.4-27.4) months in BPI-7711 treated cohort] of being non-responder increased by 43% [hazard ratio (HR), 0.56; 95% confidence interval (CI), 0.40-0.78; P=0.0013] and 50% (HR, 0; 95% CI, 0-0; P=0.01) compared to those at high-risk [median, 4.2 (range, 1.0-35) months in the abivertinib treated cohort; median, 11.0 (range, 1.4-25.1) months in BPI-7711 treated cohort]. Additionally, activated partial thromboplastin time (APTT), creatinine clearance (Ccr), monocyte, and steady-state plasma trough concentration utilited to construct model were found significantly associated with drug resistance and aggressive tumor pathways. A robust correlation was observed between APTT and Ccr with PFS (log-rank test; P<0.01) and treatment response (Wilcoxon test; P<0.05), respectively. Conclusions: CoxMoE offers a valuable approach for patient selection by forecasting therapeutic response and PFS utilizing laboratory tests and pharmacokinetic parameters in the setting of early-phase clinical trials. Simultaneously, CoxMoE could predict the efficacy of third-generation EGFR-TKI non-invasively for T790M-positive NSCLC patients, thereby complementing existing EGFR genotype detection.

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.

Longitudinal plasma proteomic profiling of EML4-ALK positive lung cancer receiving ALK-TKIs therapy.

BACKGROUND: Anaplastic lymphoma kinase-tyrosine kinase inhibitors (ALK-TKIs) has demonstrated remarkable therapeutic effects in ALK-positive non-small cell lung cancer (NSCLC) patients. Identifying prognostic biomarkers can enhance the clinical efficacy of relapsed or refractory patients. METHODS: We profiled 737 plasma proteins from 159 pre-treatment and on-treatment plasma samples of 63 ALK-positive NSCLC patients using data-independent acquisition-mass spectrometry (DIA-MS). The consensus clustering algorithm was used to identify subtypes with distinct biological features. A plasma-based prognostic model was constructed using the LASSO-Cox method. We performed the Mfuzz analysis to classify the patterns of longitudinal changes in plasma proteins during treatment. 52 baseline plasma samples from another independent ALK-TKI treatment cohort were collected to validate the potential prognostic markers using ELISA. RESULTS: We identified three subtypes of ALK-positive NSCLC with distinct biological features and clinical efficacy. Patients in subgroup 1 exhibited activated humoral immunity and inflammatory responses, increased expression of positive acute-phase response proteins, and the worst prognosis. Then we constructed and verified a prognostic model that predicts the efficacy of ALK-TKI therapy using the expression levels of five plasma proteins (SERPINA4, ATRN, APOA4, TF, and MYOC) at baseline. Next, we explored the longitudinal changes in plasma protein expression during treatment and identified four distinct change patterns (Clusters 1-4). The longitudinal changes of acute-phase proteins during treatment can reflect the treatment status and tumor progression of patients. Finally, we validated the prognostic efficacy of baseline plasma CRP, SAA1, AHSG, SERPINA4, and TF in another independent NSCLC cohort undergoing ALK-TKI treatment. CONCLUSIONS: This study contributes to the search for prognostic and drug-resistance biomarkers in plasma samples for ALK-TKI therapy and provides new insights into the mechanism of drug resistance and the selection of follow-up treatment.

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.

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.

Proteomics Identifies Circulating TIMP-1 as a Prognostic Biomarker for Diffuse Large B-Cell Lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, although disease stratification using in-depth plasma proteomics has not been performed to date. By measuring more than 1000 proteins in the plasma of 147 DLBCL patients using data-independent acquisition mass spectrometry and antibody array, DLBCL patients were classified into four proteomic subtypes (PS-I-IV). Patients with the PS-IV subtype and worst prognosis had increased levels of proteins involved in inflammation, including a high expression of metalloproteinase inhibitor-1 (TIMP-1) that was associated with poor survival across two validation cohorts (n = 180). Notably, the combination of TIMP-1 with the international prognostic index (IPI) identified 64.00% to 88.24% of relapsed and 65.00% to 80.49% of deceased patients in the discovery and two validation cohorts, which represents a 24.00% to 41.67% and 20.00% to 31.70% improvement compared to the IPI score alone, respectively. Taken together, we demonstrate that DLBCL heterogeneity is reflected in the plasma proteome and that TIMP-1, together with the IPI, could improve the prognostic stratification of patients.

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.

Overexpression of RACGAP1 by E2F1 Promotes Neuroendocrine Differentiation of Prostate Cancer by Stabilizing EZH2 Expression.

Neuroendocrine prostate cancer (NEPC) is a lethal subtype of prostate cancer. It is characterized by the loss of androgen receptor (AR) signaling in neuroendocrine transdifferentiation, and finally, resistance to AR-targeted therapy. With the application of a new generation of potent AR inhibitors, the incidence of NEPC is gradually increasing. The molecular mechanism of neuroendocrine differentiation (NED) after androgen deprivation therapy (ADT) remains largely unclear. In this study, using NEPC-related genome sequencing database analyses, we screened RACGAP1, a common differentially expressed gene. We investigated RACGAP1 expression in clinical prostate cancer specimens by IHC. Regulated pathways were examined by Western blotting, qRT-PCR, luciferase reporter, chromatin immunoprecipitation, and immunoprecipitation assays. The corresponding function of RACGAP1 in prostate cancer was analyzed by CCK-8 and Transwell assays. The changes of neuroendocrine markers and AR expression in C4-2-R and C4-2B-R cells were detected in vitro. We confirmed that RACGAP1 contributed to NE transdifferentiation of prostate cancer. Patients with high tumor RACGAP1 expression had shorter relapse-free survival time. The expression of RACGAP1 was induced by E2F1. RACGAP1 promoted neuroendocrine transdifferentiation of prostate cancer by stabilizing EZH2 expression in the ubiquitin-proteasome pathway. Moreover, overexpression of RACGAP1 promoted enzalutamide resistance of castration-resistant prostate cancer (CRPC) cells. Our results showed that the upregulation of RACGAP1 by E2F1 increased EZH2 expression, which drove NEPC progression. This study explored the molecular mechanism of NED and may provide novel methods and ideas for targeted therapy of NEPC.

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.

TRPM2 facilitates tumor progression of clear cell renal cell carcinoma by relieving Endoplasmic Reticulum Stress.

Clear cell renal cell carcinoma (ccRCC) has the highest incidence rate among all pathological types of kidney cancers. Although the role of transient receptor potential (TRP) ion channel TRPM2 has been studied in many cancers, its function in ccRCC is still unexplored. In this study, using the KIRC module of TCGA, we found that TRPM2 was upregulated in ccRCC tissues and was related to poor prognosis. Gene set enrichment analysis (GSEA) showed that TRPM2 was related to epithelial-to-mesenchymal transition (EMT), TCA cycle, fatty acid metabolism, and immune system-related functions. Functional experimental results indicated that TRPM2 could promote ccRCC progression. Furthermore, mechanism analysis showed that knocking out TRPM2 can reverse these phenotypes by increasing endoplasmic reticulum stress and decreasing EMT. We also investigated the potential role of TRPM2 in immune cell infiltration in the tumor microenvironment. Our study indicated that TRPM2 promotes ccRCC progression and may be a novel target for ccRCC therapy.

High Expression of DNTTIP1 Predicts Poor Prognosis in Clear Cell Renal Cell Carcinoma.

Background: Invasion and metastasis led to poor prognosis and death of clear cell renal cell carcinoma (ccRCC) patients. The deoxynucleotidyl transferase terminal interacting protein 1 (DNTTIP1) was reported to promote multiple tumor progression. However, there is no research about DNTTIP1 in ccRCC. Methods: Kaplan-Meier survival analysis, multivariate analysis demonstrated the prognostic indicator in overall survival (OS) and disease-free survival (DFS) of ccRCC with DNTTIP1 expression in the Cancer Genome Atlas Kidney Clear Cell Carcinoma (TCGA-KIRC). Receiver operator characteristic (ROC) curve analyzed diagnostic ability of DNTTIP1 in TCGA-KIRC and validation dataset. The quantitative real-time polymerase chain reaction (qRT-PCR) detected the DNTTIP1 expression in renal cancer tissues, and the Office of Cancer Clinical Proteomics Research (CPTAC) verified the protein expression of DNTTIP1. Moreover, nomogram predicted the role of DNTTIP1 in ccRCC patient. Single-sample Gene Set Enrichment Analysis (SsGSEA) and GSEA evaluated the pathogenesis role of DNTTIP1 in TCGA-KIRC. Results: DNTTIP1 expression was higher in ccRCC tumor tissues. High expression of DNTTIP1 was associated with poor OS (HR = 1.618, P < 0.0001), and poor DFS (HR = 1.789, P < 0.0001). SsGSEA and GSEA showed DNTTIP1 was associated with hypoxia, epithelial-mesenchymal transition (EMT), angiogenesis, G2M checkpoint. DNTTIP1 had a positive correlation with EMT biomarkers in ccRCC, and might be an effective target for ccRCC. Conclusion: This study provided that higher expression of DNTTIP1 predicted poor prognosis in ccRCC, and DNTTIP1 might be a novel detection biomarker and therapeutic target of tumor malignant in the future.

Association of immune-related adverse events and efficacy in advanced non-small-cell lung cancer: a systematic review and meta-analysis.

Aim: This study aimed to explore the association of immune-related adverse events (irAEs) with efficacy in advanced non-small-cell lung cancer (NSCLC). Materials & methods: A literature search was conducted under preselected criteria. Primary outcomes were hazard ratio (HR) and 95% CI of irAEs on objective response rate, overall survival (OS) and progression-free survival (PFS). Results: 35 studies covering 8435 patients with advanced NSCLC were included. Patients with irAEs exhibited significantly longer PFS and OS (for PFS, HR: 0.481; 95% CI: 0.370-0.568; p < 0.001 and for OS, HR: 0.470; 95% CI: 0.410-0.539; p < 0.001), and also showed significantly higher objective response rate compared with those without irAEs (pooled OR: 0.023 [95% CI: 0.009-0.590]). Conclusion: This meta-analysis showed that irAEs were associated with efficacy for advanced NSCLC.

Immune-related adverse events (irAEs) are a series of adverse events that occur during the application of immune checkpoint inhibitors. The correlation between irAEs and ICI efficacy is controversial. In this meta-analysis, we analyzed the association of irAEs with the efficacy of immune checkpoint inhibitors in patients with advanced non-small-cell lung cancer (NSCLC). A total of 35 studies covering 8435 patients with NSCLC at advanced stage were included. Pooled analysis demonstrated that patients with irAEs got a significantly higher objective response rate than those without irAEs. Besides this, patients with irAEs had a more favorable survival outcome than those without. This study indicated that the occurrence of irAEs was associated with better survival outcome and higher tumor efficacy for patients with advanced NSCLC.

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.

Early administration of dobutamine in the treatment of septic shock patients with tumor-a retrospective comparative cohort study.

Background: Studies have found that dobutamine may be beneficial to protect organs function in patients with septic shock, but there is still a lack of relevant research in septic shock patients with tumor. The study sought to explore the role of the early administration of dobutamine in the treatment of septic shock patients with tumors. Methods: We retrospectively collected the data of tumor patients who developed septic shock at Sun Yat-sen University Cancer Center between June 2008 and November 2021. All the patients were divided into the following 3 groups: (I) the early administration group (<3 days, n=15); (II) the late administration group (≥3 days, n=22); and (III) the non-administration group (n=85). The primary observation indicator was 28-day mortality, and the secondary observation indicators included the shock reversal rate, the length of stay in the intensive care unit (ICU) and the duration of mechanical ventilation. There was no statistical difference in the basic data of the three groups. Results: The early administration group had a significant decrease in 28-day mortality compared to the late and non-administration groups (log-rank P=0.018). The comparison between the groups showed that the 28-day mortality of the early administration group was significantly lower than that of the non-administration group [20.0% vs. 58.8%, P=0.013, hazard ratios (HRs) =0.248, 95% confidence intervals (CIs): 0.077-0.796]. There was no statistically significant difference in 28-day mortality between the late administration group and the non-administration group (63.6% vs. 58.8%, P=0.682, HR =0.983, 95% CI: 0.543-1.778). Additionally, the early administration group had a significantly increased shock reversal rate (P=0.014), shortened length of stay in the ICU (P<0.001), and reduced duration of mechanical ventilation (P=0.049). Conclusions: Early use of dobutamine may be beneficial to reduce the in-hospital mortality of septic shock patients with tumor, but the sample size of this study was small, which still needs to be confirmed by a multi-center randomized controlled clinical study.

[MUC16: The Novel Target for Tumor Therapy].

Mucin16 (MUC16), also known as carbohydrate antigen 125 (CA125), is a glycoprotein antigen that can be recognized by the monoclonal antibody OC125 detected from epithelial ovarian carcinoma antigen by Bast et al in 1981. CA125 is not present in normal ovarian tissue but is usually elevated in the serum of epithelial ovarian carcinoma patients. CA125 is the most commonly used serologic biomarker for the diagnosis and recurrence monitoring of epithelial ovarian carcinoma. MUC16 is highly expressed in varieties of tumors. MUC16 can interact with galectin-1/3, mesothelin, sialic acid-binding immunoglobulin-type lectins-9 (Siglec-9), and other ligands. MUC16 plays an important role in tumor genesis, proliferation, migration, invasion, and tumor immunity through various signaling pathways. Besides, therapies targeting MUC16 have some significant achievements. Related preclinical studies and clinical trials are in progress. MUC16 may be a potential novel target for tumor therapy. This article will review the mechanism of MUC16 in tumor genesis and progression, and focus on the research actuality of MUC16 in tumor therapy. This article also provides references for subsequent tumor therapy studies targeting MUC16.
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Role of chemokine-mediated angiogenesis in resistance towards crizotinib and its reversal by anlotinib in EML4-ALK positive NSCLC.

BACKGROUND: The identification of early plasma biomarkers for clinical outcomes and drug resistance has key importance for risk stratification in anaplastic lymphoma kinase (ALK)-positive advanced non-small cell lung cancer (NSCLC) patients. Moreover, it remains unclear whether the anti-angiogenic drug anlotinib can reverse the resistance of ALK-tyrosine kinase inhibitor (ALK-TKI) crizotinib, and no research has explored the effect of anlotinib combined with crizotinib on ALK-positive patients. METHODS: In this study, 76 baseline and longitudinal plasma samples from 61 ALK-positive NSCLC patients receiving crizotinib treatment were analyzed by Luminex liquid suspension chip for 40 chemokines. RNA sequence (RNA-seq) was used to identify differentially expressed genes (DEGs) between H3122 and H3122-crizotinib resistant (H3122CR) cells. Tube formation assay was performed to investigate the effect of chemokines on angiogenesis. And H3122CR-derived xenograft model was constructed to validate the efficacy and safety of anlotinib combined with crizotinib in vivo. RESULTS: Baseline and progression plasma samples detection suggested that CCL20 played a crucial role in monitoring and predicting the clinical response of crizotinib (hazard ratio for progression-free survival: 2.27 (1.13-4.58); for overall survival: 2.7 (1.23-5.8)). RNA-seq results for H3122 and H3122CR cells showed that high expression of chemokines and angiogenesis pathways were involved in crizotinib resistance. Subsequently, in vitro experiments indicated that CCL20 may induce crizotinib resistance by activation of angiogenesis via JAK2/STAT3-CCL20-VEGFA/IL6 axis. We further found that anti-angiogenic TKI anlotinib could reverse crizotinib resistance by inhibiting chemokines-induced angiogenesis, and anlotinib combined with crizotinib has a better antitumor effect than monotherapy in vitro & in vivo. CONCLUSIONS: Overall, CCL20-mediated angiogenesis is involved in crizotinib resistance and could be overcome by using anlotinib in EML4-ALK positive NSCLC. The combination of anlotinib and crizotinib is a promising strategy for patients resistant to ALK-TKIs.

High-mobility group box 2 reflects exacerbated disease characteristics and poor prognosis in non-small cell lung cancer patients.

BACKGROUND: High-mobility group box 2 (HMGB2) is considered as oncogene in non-small cell lung cancer (NSCLC), while its clinical implication is still unknown. This study aimed to explore the correlation of HMGB2 with clinicopathological characteristics and prognosis in NSCLC patients. METHODS: A total of 133 NSCLC patients who received radical excision were enrolled. HMGB2 expression in the tumor specimens and paired adjacent tissue specimens was determined by immunohistochemical assay (for protein expression) and reverse transcription quantitative polymerase chain reaction assay (for gene expression), respectively. RESULTS: HMGB2 protein expression was higher in tumor tissue compared with adjacent tissue, and it could distinguish tumor tissue from adjacent tissue (area under the curve (AUC): 0.775, 95%confidence interval (95%CI): 0.720-0.830). Meanwhile, tumor HMGB2 protein high expression correlated with lymph node (LYN) metastasis and advanced TNM stage. Additionally, tumor HMGB2 protein high expression associated with worse disease-free survival (DFS), while HMGB2 protein expression did not correlate with overall survival (OS). Besides, HMGB2 mRNA expression was raised in tumor tissue compared with adjacent tissue, and it had a good value in differentiating tumor tissue from adjacent tissue (AUC: 0.875, 95% CI: 0.834-0.915). Furthermore, tumor HMGB2 mRNA high expression correlated with higher Eastern Cooperative Oncology Group performance status score, LYN metastasis, and advanced TNM stage. Meanwhile, tumor HMGB2 mRNA high expression associated with shorter DFS and OS. CONCLUSION: HMGB2 could be a biomarker that reflects disease features and prognosis of NSCLC, which is beneficial to improve clinical efficacy in NSCLC patients.

Anti-PD1/PDL1 IgG subclass distribution in ten cancer types and anti-PD1 IgG4 as biomarker for the long time survival in NSCLC with anti-PD1 therapy.

BACKGROUND: Antibodies targeting programmed cell death-1(PD1) and its ligand (PDL1) have revolutionized cancer therapy. However, little is known about the preexisted anti-PD1/PDL1 autoantibodies (AAbs) distribution in multiple cancer types, nor is their potential biomarker role for anti-PD1 therapy. METHOD: Plasma anti-PD1/PDL1 AAb IgG and subclasses (IgG1-4) were detected by enzyme-linked immune sorbent assay (ELISA) in 190 cancer patients, covering 10 cancer types (lung, breast, esophageal, colorectal, liver, prostatic, cervical, ovarian, gastric cancers and lymphoma), the comprehensive correlation of AAbs with multiple clinical parameters was analyzed. We further tested these AAbs in 76 non-small cell lung cancer (NSCLC) samples receiving anti-PD1 therapy, the association of AAbs level with survival was analyzed and validated in an independent cohort (n = 32). RESULTS: Anti-PD1/PDL1 AAb IgG were globally detected in 10 types of cancer patients. IgG1 and IgG2 were the major subtypes for anti-PD1/PDL1 AAbs. Correlation analysis revealed a distinct landscape between various cancer types. The random forest model indicated that IgG4 subtype was mostly associated with cancer. In discovery cohort of 76 NSCLC patients, high anti-PD1 IgG4 was associated with a reduced overall survival (OS, p = 0.019), not progression-free survival (PFS, p = 0.088). The negative association of anti-PD1 IgG4 with OS was validated in 32 NSCLC patients (p = 0.032). CONCLUSION: This study reports for the first time the distribution of preexisted anti-PD1/PDL1 AAb IgG and subclasses across 10 cancer types. Moreover, the anti-PD1 AAb IgG4 subclass was identified to associate with OS, which may serve as a potential biomarker for anti-PD1 therapeutic survival benefit in NSCLC patients.

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.

High Levels of LINC01140 Expression Predict a Good Prognosis and Improve Radiotherapy in Sarcoma Patients.

Long intergenic non-protein coding RNA has an important biological role in tumors. But, LINC01140 in sarcoma has not been studied yet. This study investigates the expression and prognosis role of LINC01140 in sarcoma. LINC01140 was lower in metastatic sarcoma, and low LINC01140 expression predicted poor overall survival, disease-free survival, and disease-specific survival in sarcoma. High LINC01140 expression and radiotherapy could promote survival of sarcoma. Gene set enrichment analysis showed LINC01140 was involved in interferon-gamma response, epithelial-mesenchymal transition, the interaction between cytokine receptors, and cholesterol homeostasis. Gene ontology enrichment analysis showed LINC01140 was involved in immunity, fatty acid metabolism, amino acid metabolism, cell division, serine/threonine-protein kinase. LINC01140 expression was negatively correlated with various epithelial-mesenchymal transition factors and positively correlated with the expression of anti-cancer factor hypermethylated-in-cancer 1. These results confirmed that LINC01140 may be a potential novel prognostic molecule in sarcoma.