The storage spaces of carbonate reservoir are complicated, matrix pores, vugs, fractures and large caves are coexistence. Traditional numerical simulation methods have harsh requirement for geology model and computing method, these methods are not suitable for carbonate reservoir. A comparatively perfect equivalent permeability and porosity model for multi-media reservoir was developed based on the theory of equivalent continuum media and the law of equivalent seepage resistance. The equivalent parameters of a practical reservoir were calculated by this model, and a numerical simulation was carried out by using these parameters, the results showed that the equivalent numerical simulation of fractured-vuggy carbonate reservoir was reasonable.

Lung cancer is the leading cause of cancer-related death. In particular, non-small cell lung cancer (NSCLC) accounts for approximately 85% of all lung cancer cases. Due to tumor resistance and the toxicity of chemotherapeutic agents, it is increasingly critical to discover novel, potent antitumorigenic drugs for treating NSCLC. Lutein, a carotenoid, has been reported to exert toxic effects on cells in several tumor types. However, the detailed functions and underlying mechanisms of lutein in NSCLC remain elusive.The present study showed that lutein significantly and dose-dependently inhibited cell proliferation, arrested the cell cycle at the G0/G1 phase, and induced apoptosis in NSCLC cells. RNA-sequencing analysis revealed that the p53 signaling pathway was the most significantly upregulated in lutein-treated A549 cells. Mechanistically, lutein exerted antitumorigenic effects by inducing DNA damage and subsequently activating the ATR/Chk1/p53 signaling pathway in A549 cells. In vivo, lutein impeded tumor growth in mice and prolonged their survival. In conclusion, our findings demonstrate the antitumorigenic potential of lutein and reveal its molecular mechanism of action, suggesting that lutein is a promising candidate for clinical NSCLC treatment.

Background: Immune checkpoint inhibitors (ICIs) have shown remarkable antitumor effects in non-small cell lung cancer (NSCLC), however, only a subset of patients show a response. Therefore, more accurate biomarkers are highly needed. The aim of this study was to examine the prevalence of INPP4B and its association with response to ICIs among patients with NSCLC. Methods: Formalin-fixed, paraffin-embedded (FFPE) tumor and matched blood samples of 3433 NSCLC patients from OrigiMed were collected for targeted next-generation sequencing (NGS) panel sequencing from December 2017 to January 2019. Genomic alterations (GAs) including single nucleotide variations, short and long insertions/deletions, copy number variations, and gene rearrangements were assessed. PD-L1 expression positive was defined as ≥1% of tumor cells with membranous staining (22C3, DAKO). Genomic data and ICIs treatment outcome of a cohort of 240 NSCLC patients were derived from cBioPortal (MSKCC, J Clin Oncol 2018). Results: INPP4B GAs were found in 1% of patients (33/3433) in the OrigiMed database and 3.75% of patients (9/240) in the MSKCC database. Sites of INPP4B mutations were found scattered throughout the gene, and no significant difference was observed in the frequency of INPP4B GAs between age, stage, and histologic subtype. INPP4B GAs were associated with a significantly higher tumor mutational burden (TMB) compared with wild-type (WT) INPP4B (14.7 vs. 4.6 muts/Mb, respectively, p<0.001). PD-L1 expression was detected in 20.9% of patients (718/3433), including 9 patients with INPP4B GAs. INPP4B GAs were significantly associated with higher PD-L1 expression compared with WT (66.7% vs 29.1%, respectively, p=0.006). Survival analysis from the MSKCC cohort confirmed that patients with INPP4B GAs had a remarkable clinical benefit to ICIs compared to WT patients in both progression free survival (PFS) (13.17 months vs 3.23 months, respectively, p=0.041) and durable clinical benefit (DCB) (50% vs 29.7%, respectively, p=0.25). Furthermore, INPP4B GAs were independent risk factors of PFS (HR: 0.34, 95%CI: 0.134-0.86, p=0.023). Conclusion: This study's findings suggest that the GAs of INPP4B occur in a subgroup of patients with NSCLC and are associated with an increased TMB and response to ICIs, suggesting that GAs of INPP4B may have implications as a biomarker for guiding ICI treatment. Keywords: non-small cell lung cancer, INPP4B, tumor mutational burden, immunotherapy Citation Format: Zhuojian Shen, Hongbiao Wang, Jianjiang Xie, Yuan Su, Shiyue zhang, Jiqiang He, Hui Chen, Shaohua Yuan, Xiaowei Dong. INPP4B mutation as a novel biomarker for immunotherapy in patients with non-small cell lung cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4287.

PurposeTo build a machine learning model to predict histology (type I and type II), stage, and grade preoperatively for endometrial carcinoma to quickly give a diagnosis and assist in improving the accuracy of the diagnosis, which can help patients receive timely, appropriate, and effective treatment.Materials and MethodsThis study used a retrospective database of preoperative examinations (tumor markers, imaging, diagnostic curettage, etc.) in patients with endometrial carcinoma. Three algorithms (random forest, logistic regression, and deep neural network) were used to build models. The AUC and accuracy were calculated. Furthermore, the performance of machine learning models, doctors’ prediction, and doctors with the assistance of models were compared.ResultsA total of 329 patients were included in this study with 16 features (age, BMI, stage, grade, histology, etc.). A random forest algorithm had the highest AUC and Accuracy. For histology prediction, AUC and accuracy was 0.69 (95% CI=0.67-0.70) and 0.81 (95%CI=0.79-0.82). For stage they were 0.66 (95% CI=0.64-0.69) and 0.63 (95% CI=0.61-0.65) and for differentiation grade 0.64 (95% CI=0.63-0.65) and 0.43 (95% CI=0.41-0.44). The average accuracy of doctors for histology, stage, and grade was 0.86 (with AI) and 0.79 (without AI), 0.64 and 0.53, 0.5 and 0.45, respectively. The accuracy of doctors’ prediction with AI was higher than that of Random Forest alone and doctors’ prediction without AI.ConclusionA random forest model can predict histology, stage, and grade of endometrial cancer preoperatively and can help doctors in obtaining a better diagnosis and predictive results.