Construction of a predictive model for immunotherapy efficacy in lung squamous cell carcinoma based on the degree of tumor-infiltrating immune cells and molecular typing.

BACKGROUND: To construct a predictive model of immunotherapy efficacy for patients with lung squamous cell carcinoma (LUSC) based on the degree of tumor-infiltrating immune cells (TIIC) in the tumor microenvironment (TME). METHODS: The data of 501 patients with LUSC in the TCGA database were used as a training set, and grouped using non-negative matrix factorization (NMF) based on the degree of TIIC assessed by single-sample gene set enrichment analysis (GSEA). Two data sets (GSE126044 and GSE135222) were used as validation sets. Genes screened for modeling by least absolute shrinkage and selection operator (LASSO) regression and used to construct a model based on immunophenotyping score (IPTS). RNA extraction and qPCR were performed to validate the prognostic value of IPTS in our independent LUSC cohort. The receiver operating characteristic (ROC) curve was constructed to determine the predictive value of the immune efficacy. Kaplan-Meier survival curve analysis was performed to evaluate the prognostic predictive ability. Correlation analysis and enrichment analysis were used to explore the potential mechanism of IPTS molecular typing involved in predicting the immunotherapy efficacy for patients with LUSC. RESULTS: The training set was divided into a low immune cell infiltration type (C1) and a high immune cell infiltration type (C2) by NMF typing, and the IPTS molecular typing based on the 17-gene model could replace the results of the NMF typing. The area under the ROC curve (AUC) was 0.82. In both validation sets, the IPTS of patients who responded to immunotherapy were significantly higher than those who did not respond to immunotherapy (P = 0.0032 and P = 0.0451), whereas the AUC was 0.95 (95% CI = 1.00-0.84) and 0.77 (95% CI = 0.58-0.96), respectively. In our independent cohort, we validated its ability to predict the response to cancer immunotherapy, for the AUC was 0.88 (95% CI = 1.00-0.66). GSEA suggested that the high IPTS group was mainly involved in immune-related signaling pathways. CONCLUSIONS: IPTS molecular typing based on the degree of TIIC in the TME could well predict the efficacy of immunotherapy in patients with LUSC with a certain prognostic value.

Outcome of patients with primary retroperitoneal solitary fibrous sarcoma.

BACKGROUND: To describe the clinicopathological features of primary retroperitoneal solitary fibrous tumor (RSFT) and define the prognostic factors. METHODS: The comprehensive data of 35 primary RSFT patients who got curative surgery at a tertiary cancer center from April 2004 to October 2018 were retrospectively analyzed. RESULTS: Male patients outnumbered female patients (19 vs. 16), with the age ranging from 19 to 73 years (median, 51 years). 7 (20%) patients had tumors located in special parts, including three in kidney, one in renal pelvis, one in bladder, one in prostate, and one in mesentery. Tumor sizes ranged from 2.5 to 25 cm (median, 9 cm). Microscopic negative margin was reached in 33 (94.3%) cases. 13 (37.1%) were classified as atypical/malignant, while 22 (62.9%) were benign. Concomitant organ excision was performed on 11 (31.4%) patients, with kidney (n = 5) being the most frequent organ. Multifocality was only found in 4 (11.4%) cases. The majority of the patients (31, 88.6%) did not get adjuvant treatment. The median follow-up time was 46 months (range 4-153 months). The 5-year DSS rate and DFS rate were 100% and 63.6%, respectively. In univariate analysis, tumor size ≥ 10 cm (P = 0.002) and atypical/malignant pathology (P = 0.024) were associated with decreased DFS. Multivariate analysis revealed that tumor size was the only independent prognostic factor for DFS (HR 6.03, 95% CI 1.18-30.77, P = 0.031). CONCLUSION: RSFT is uncommon, slow-growing, and recrudescent tumors. Large tumor size and malignant pathology are associated with decreased DFS. Tumor size ≥ 10 cm independently predicts shortened DFS.

Prognostic analysis of surgically treated clear cell sarcoma: an analysis of a rare tumor from a single center.

BACKGROUND: The objective of this retrospective study was to evaluate the prognostic value of various factors in clear cell sarcoma patients after radical surgery. METHODS: Forty-two clear cell sarcoma patients from August 2006 to March 2018 were included in the study. Curves of disease-free survival and overall survival were calculated using the Kaplan-Meier method, and univariate and multivariate analyses of various prognostic factors were performed using a Cox proportional hazard regression model. Laboratory test of peripheral blood was recorded before surgery. The optimal cutoff value of systemic inflammatory markers was defined by receiver-operating curve analysis. RESULTS: The 5-year DFS and 5-year OS rate were 22% and 46%, respectively. The median DFS and OS times were 12 and 41.5 months, respectively. In univariate analysis, there was a significant association between shorter DFS and tumor size larger than 5 cm (p = 0.0043), positive surgical margin (p = 0.0233), and the neutrophil-to-lymphocyte ratio (NLR) higher than 2.73 (p = 0.0009). Furthermore, we observed a significant association between shorter OS and tumor size larger than 5 cm (p = 0.0075), positive surgical margin (p = 0.0101), NLR higher than 2.73 (p = 0.0126), the platelet-to-lymphocyte ratio (PLR) higher than 103.89 (p = 0.0147) and the lymphocyte-to-monocyte ratio (LMR) lower than 4.2 (p = 0.0445). A multivariate analysis demonstrated that the surgical margin (p = 0.013) and NLR (p = 0.001) were significantly associated with DFS. Tumor size (p = 0.010) and NLR (p = 0.013) were independent prognostic factors for OS. CONCLUSIONS: This study had the second largest sample around the world and preoperative NLR may be a useful prognostic factor in CCS patients after radical surgery.

Identification and validation of immune-related gene signature models for predicting prognosis and immunotherapy response in hepatocellular carcinoma.

Background: This study seeks to enhance the accuracy and efficiency of clinical diagnosis and therapeutic decision-making in hepatocellular carcinoma (HCC), as well as to optimize the assessment of immunotherapy response. Methods: A training set comprising 305 HCC cases was obtained from The Cancer Genome Atlas (TCGA) database. Initially, a screening process was undertaken to identify prognostically significant immune-related genes (IRGs), followed by the application of logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods for gene modeling. Subsequently, the final model was constructed using support vector machines-recursive feature elimination (SVM-RFE). Following model evaluation, quantitative polymerase chain reaction (qPCR) was employed to examine the gene expression profiles in tissue samples obtained from our cohort of 54 patients with HCC and an independent cohort of 231 patients, and the prognostic relevance of the model was substantiated. Thereafter, the association of the model with the immune responses was examined, and its predictive value regarding the efficacy of immunotherapy was corroborated through studies involving three cohorts undergoing immunotherapy. Finally, the study uncovered the potential mechanism by which the model contributed to prognosticating HCC outcomes and assessing immunotherapy effectiveness. Results: SVM-RFE modeling was applied to develop an OS prognostic model based on six IRGs (CMTM7, HDAC1, HRAS, PSMD1, RAET1E, and TXLNA). The performance of the model was assessed by AUC values on the ROC curves, resulting in values of 0.83, 0.73, and 0.75 for the predictions at 1, 3, and 5 years, respectively. A marked difference in OS outcomes was noted when comparing the high-risk group (HRG) with the low-risk group (LRG), as demonstrated in both the initial training set (P <0.0001) and the subsequent validation cohort (P <0.0001). Additionally, the SVMRS in the HRG demonstrated a notable positive correlation with key immune checkpoint genes (CTLA-4, PD-1, and PD-L1). The results obtained from the examination of three cohorts undergoing immunotherapy affirmed the potential capability of this model in predicting immunotherapy effectiveness. Conclusions: The HCC predictive model developed in this study, comprising six genes, demonstrates a robust capability to predict the OS of patients with HCC and immunotherapy effectiveness in tumor management.

Pulmonary function test-related prognostic models in non-small cell lung cancer patients receiving neoadjuvant chemoimmunotherapy.

Background: This study aimed to establish a comprehensive clinical prognostic risk model based on pulmonary function tests. This model was intended to guide the evaluation and predictive management of patients with resectable stage I-III non-small cell lung cancer (NSCLC) receiving neoadjuvant chemoimmunotherapy. Methods: Clinical pathological characteristics and prognostic survival data for 175 patients were collected. Univariate and multivariate Cox regression analyses, and least absolute shrinkage and selection operator (LASSO) regression analysis were employed to identify variables and construct corresponding models. These variables were integrated to develop a ridge regression model. The models' discrimination and calibration were evaluated, and the optimal model was chosen following internal validation. Comparative analyses between the risk scores or groups of the optimal model and clinical factors were conducted to explore the potential clinical application value. Results: Univariate regression analysis identified smoking, complete pathologic response (CPR), and major pathologic response (MPR) as protective factors. Conversely, T staging, D-dimer/white blood cell ratio (DWBCR), D-dimer/fibrinogen ratio (DFR), and D-dimer/minute ventilation volume actual ratio (DMVAR) emerged as risk factors. Evaluation of the models confirmed their capability to accurately predict patient prognosis, exhibiting ideal discrimination and calibration, with the ridge regression model being optimal. Survival analysis demonstrated that the disease-free survival (DFS) in the high-risk group (HRG) was significantly shorter than in the low-risk group (LRG) (P=2.57×10-13). The time-dependent receiver operating characteristic (ROC) curve indicated that the area under the curve (AUC) values at 1 year, 2 years, and 3 years were 0.74, 0.81, and 0.79, respectively. Clinical correlation analysis revealed that men with lung squamous cell carcinoma or comorbid chronic obstructive pulmonary disease (COPD) were predominantly in the LRG, suggesting a better prognosis and potentially identifying a beneficiary population for this treatment combination. Conclusion: The prognostic model developed in this study effectively predicts the prognosis of patients with NSCLC receiving neoadjuvant chemoimmunotherapy. It offers valuable predictive insights for clinicians, aiding in developing treatment plans and monitoring disease progression.

Targeting immune checkpoints on tumor-associated macrophages in tumor immunotherapy.

Unprecedented breakthroughs have been made in cancer immunotherapy in recent years. Particularly immune checkpoint inhibitors have fostered hope for patients with cancer. However, immunotherapy still exhibits certain limitations, such as a low response rate, limited efficacy in certain populations, and adverse events in certain tumors. Therefore, exploring strategies that can improve clinical response rates in patients is crucial. Tumor-associated macrophages (TAMs) are the predominant immune cells that infiltrate the tumor microenvironment and express a variety of immune checkpoints that impact immune functions. Mounting evidence indicates that immune checkpoints in TAMs are closely associated with the prognosis of patients with tumors receiving immunotherapy. This review centers on the regulatory mechanisms governing immune checkpoint expression in macrophages and strategies aimed at improving immune checkpoint therapies. Our review provides insights into potential therapeutic targets to improve the efficacy of immune checkpoint blockade and key clues to developing novel tumor immunotherapies.

Identification of a Four-Gene Signature Associated with the Prognosis Prediction of Lung Adenocarcinoma Based on Integrated Bioinformatics Analysis.

Lung adenocarcinoma (LUAD) is often diagnosed at an advanced stage, so it is necessary to identify potential biomarkers for the early diagnosis and prognosis of LUAD. In our study, a gene co-expression network was constructed using weighted gene co-expression network analysis (WGCNA) in order to obtain the key modules and genes correlated with LUAD prognosis. Four hub genes (HLF, CHRDL1, SELENBP1, and TMEM163) were screened out using least absolute shrinkage and selection operator (LASSO)-Cox regression analysis; then, a prognostic model was established for predicting overall survival (OS) based on these four hub genes..Furthermore, the prognostic values of this four-gene signature were verified in four validation sets (GSE26939, GSE31210, GSE72094, and TCGA-LUAD) as well as in the GEPIA database. To assess the prognostic values of hub genes, receiver operating characteristic (ROC) curves were constructed and a nomogram was created. We found that a higher expression of four hub genes was associated with a lower risk of patient death. In a training set, it was demonstrated that this four-gene signature was a better prognostic factor than clinical factors such as age and stage of disease. Moreover, our results revealed that these four genes were suppressor factors of LUAD and that their high expression was associated with a lower risk of death. In summary, we demonstrated that this four-gene signature could be a potential prognostic factor for LUAD patients. These findings provide a theoretical basis for exploring potential biomarkers for LUAD prognosis prediction in the future.

CCNDBP1, a Prognostic Marker Regulated by DNA Methylation, Inhibits Aggressive Behavior in Dedifferentiated Liposarcoma via Repressing Epithelial Mesenchymal Transition.

The present study aimed to explore the prognostic value, function, and mechanism of CCNDBP1 in dedifferentiated liposarcoma (DDL). Immunohistochemistry staining was used to analyze the protein expression of CCNDBP1 in tissue specimens. After silencing CCNDBP1 in LPS853 and overexpressing CCNDBP1 in LPS510, CCK-8, clone formation, transwell migration, and invasion assays were used to detect cell proliferation, migration, and invasion ability. CCNDBP1-induced cell apoptosis was analyzed by flow cytometry. The altered expression of epithelial-mesenchymal transition (EMT)-related proteins were detected by Western blot. The methylation, gene expression, and clinical data of 58 samples with DDL were analyzed using the cancer genome atlas (TCGA) database. Low expression of CCNDBP1 was associated with a poor prognosis of patients with DDL and was considered an independent prognostic factor of the progression-free survival (PFS). CCNDBP1 significantly inhibited the clone formation, proliferation, migration, and invasion of cancer cells in vitro and promoted cancer cell apoptosis. CCNDBP1 could repress the pathological EMT, thereby inhibiting the malignant behaviors of DDL cells. The high degree of DNA methylation sites cg05194114 and cg22184989 could decrease the expression of CCNDBP1 and worsen the prognosis of DDL patients. This is the first study reporting that CCNDBP1 is a tumor suppressor gene of DDL and putative prognostic marker in DDL patients. CCNDBP1 might inhibit the ability of cell proliferation and invasion by repressing pathological EMT, and the expression of CCNDBP1 could be regulated by DNA methylation in DDL.

Identification and Validation of a Novel Six-lncRNA-Based Prognostic Model for Lung Adenocarcinoma.

OBJECTIVE: This study was conducted in order to establish a long non-coding RNA (lncRNA)-based model for predicting overall survival (OS) in patients with lung adenocarcinoma (LUAD). METHODS: Original RNA-seq data of LUAD samples were extracted from The Cancer Genome Atlas (TCGA) database. Univariate Cox survival analysis was performed to select lncRNAs associated with OS. The least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox analysis were performed for building an OS-associated lncRNA prognostic model. Moreover, receiver operating characteristic (ROC) curves were generated to assess predictive values of the hub lncRNAs. Consequently, qRT-PCR was conducted to validate its prognostic value. The potential roles of these lncRNAs in immunotherapy and anti-angiogenic therapy were also investigated. RESULTS: The lncRNA-associated risk score of OS (LARSO) was established based on the LASSO coefficient of six individual lncRNAs, including CTD-2124B20.2, CTD-2168K21.1, DEPDC1-AS1, RP1-290I10.3, RP11-454K7.3, and RP11-95M5.1. Kaplan-Meier analysis revealed that LUAD patients with higher LARSO values had a shorter OS. Furthermore, a new risk score (NRS), including LARSO, stage, and N stage, could better predict the prognosis of LUAD patients compared with LARSO alone. Evaluation of the prognostic model in our cohort demonstrated that patients with higher scores had a worse prognosis. In addition, correlation analysis between these six lncRNAs and immune checkpoints or anti-angiogenic targets suggested that LUAD patients with high LARSO might not be sensitive to immunotherapy or anti-angiogenic therapy. CONCLUSIONS: This robust six-lncRNA prognostic signature may be used as a novel and powerful prognostic biomarker for lung adenocarcinoma.

Mefatinib as first-line treatment of patients with advanced EGFR-mutant non-small-cell lung cancer: a phase Ib/II efficacy and biomarker study.

EGFR inhibitors have revolutionized the treatment of advanced non-small-cell lung cancer (NSCLC). Mefatinib is a novel, bioavailable, second-generation, irreversible pan-EGFR inhibitor. This phase Ib/II open-label, single-arm, multi-center study investigated the efficacy, safety, biomarker, and resistance mechanisms of mefatinib in the first-line treatment of patients with advanced EGFR-mutant NSCLC. This study included 106 patients with EGFR-mutant stage IIIB-IV NSCLC who received first-line mefatinib at a daily dose of either 60 mg (n = 51) or 80 mg (n = 55). The primary endpoint was progression-free survival (PFS). Secondary endpoints were overall response rate (ORR), disease control rate (DCR), overall survival (OS), and safety. The cohort achieved an ORR of 84.9% and DCR of 97.2%. The median PFS was 15.4 months and the median OS was 31.6 months. Brain metastasis was detected in 29% of patients (n = 31) at diagnosis and demonstrated an ORR of 87.1%, PFS of 12.8 months, and OS of 25.2 months. Adverse events primarily involved skin and gastrointestinal toxicities, which were well-tolerated and manageable. Analyses of mutation profiles were performed using targeted sequencing of plasma samples at baseline, first follow-up 6 weeks from starting mefatinib therapy (F1), and at progression. Patients with concurrent TP53 mutations had comparable PFS as wild-type TP53 (14.0 vs 15.4 months; p = 0.315). Furthermore, circulating tumor DNA clearance was associated with longer PFS (p = 0.040) and OS (p = 0.002). EGFR T790M was the predominant molecular mechanism of mefatinib resistance (42.1%, 16/38). First-line mefatinib provides durable PFS and an acceptable toxicity profile in patients with advanced EGFR-mutant NSCLC.