Circulating tumor cells shielded with extracellular vesicle-derived CD45 evade T cell attack to enable metastasis.

Circulating tumor cells (CTCs) are precursors of distant metastasis in a subset of cancer patients. A better understanding of CTCs heterogeneity and how these CTCs survive during hematogenous dissemination could lay the foundation for therapeutic prevention of cancer metastasis. It remains elusive how CTCs evade immune surveillance and elimination by immune cells. In this study, we unequivocally identified a subpopulation of CTCs shielded with extracellular vesicle (EVs)-derived CD45 (termed as CD45+ CTCs) that resisted T cell attack. A higher percentage of CD45+ CTCs was found to be closely correlated with higher incidence of metastasis and worse prognosis in cancer patients. Moreover, CD45+ tumor cells orchestrated an immunosuppressive milieu and CD45+ CTCs exhibited remarkably stronger metastatic potential than CD45- CTCs in vivo. Mechanistically, CD45 expressing on tumor surfaces was shown to form intercellular CD45-CD45 homophilic interactions with CD45 on T cells, thereby preventing CD45 exclusion from TCR-pMHC synapse and leading to diminished TCR signaling transduction and suppressed immune response. Together, these results pointed to an underappreciated capability of EVs-derived CD45-dressed CTCs in immune evasion and metastasis, providing a rationale for targeting EVs-derived CD45 internalization by CTCs to prevent cancer metastasis.

A20 promotes colorectal cancer immune evasion by upregulating STC1 expression to block "eat-me" signal.

Immune checkpoint inhibitors (ICIs) have induced durable clinical responses in a subset of patients with colorectal cancer (CRC). However, the dis-satisfactory response rate and the lack of appropriate biomarkers for selecting suitable patients to be treated with ICIs pose a major challenge to current immunotherapies. Inflammation-related molecule A20 is closely related to cancer immune response, but the effect of A20 on "eat-me" signal and immunotherapy efficacy remains elusive. We found that A20 downregulation prominently improved the antitumor immune response and the efficacy of PD-1 inhibitor in CRC in vitro and in vivo. Higher A20 expression was associated with less infiltration of immune cells including CD3 (+), CD8 (+) T cells and macrophages in CRC tissues and also poorer prognosis. Gain- and loss-A20 functional studies proved that A20 could decrease the "eat-me" signal calreticulin (CRT) protein on cell membrane translocation via upregulating stanniocalcin 1 (STC1), binding to CRT and detaining in mitochondria. Mechanistically, A20 inhibited GSK3ß phosphorylating STC1 at Thr86 to slow down the degradation of STC1 protein. Our findings reveal a new crosstalk between inflammatory molecule A20 and "eat-me" signal in CRC, which may represent a novel predictive biomarker for selecting CRC patients most likely to benefit from ICI therapy.

Predictive Nomogram for Hyperprogressive Disease During Anti-PD-1/PD-L1 Treatment in Patients with Advanced Non-Small Cell Lung Cancer.

Introduction: Various studies have reported that anti-PD-1/PD-L1 treatment may lead to the rapid development of tumors called hyperprogressive disease (HPD). A nomogram for HPD prediction in NSCLC patients is urgently needed. Methods: This retrospective cohort study included 176 cases for establishing a model of HPD prediction and 85 cases for validation in advanced NSCLC patients treated with PD-1/PD-L1 inhibitors. HPD was defined as tumor growth rate (TGR, ≥ 2), tumor growth kinetics (TGK, ≥ 2) or time to treatment failure (TTF, ≤ 2 months). Univariate and multivariate logistic regression were used to estimate the specified factors associated with HPD. Then, the nomogram was developed and validated. Results: Anti-PD-1/PD-L1 therapy resulted in a 9.66% (17/176) incidence of HPD in advanced NSCLC. The overall survival (OS) and progression-free survival (PFS) in patients with HPD were significantly shorter than those in patients without HPD (OS: 7.00 vs 12.00 months, P<0.01; PFS: 2.00 vs 5.00 months, P<0.001, respectively). The HPD prediction nomogram included APTT (P<0.01), CD4+ CD25+ CD127-low cells (Treg cells) (P<0.01), the presence of liver metastasis (P<0.05), and more than two metastatic sites (P<0.05). Then, patients were divided into two groups by the "HPD score" calculated by the nomogram. The C-index was 0.845, while the area under the curve (AUC) was 0.830 (sensitivity 75.00%, specificity 91.70%). The calibration plot of HPD probability showed an optimal agreement between the actual observation and prediction by the nomogram. In the validation cohort, the AUC was up to 0.960 (sensitivity 88.70%, specificity 89.80%). Conclusions: The nomogram was constructed with the presence of liver metastasis, more than two metastatic sites, lengthened APTT and a high level of Treg cells, which could be used to predict HPD risk.

M2-like macrophage-derived exosomes facilitate metastasis in non-small-cell lung cancer by delivering integrin αVß3.

Metastasis is the most prevalent cause of cancer deaths, and immunological components of the tumor microenvironment, especially tumor-associated macrophages (TAMs), play a vital role in cancer metastasis. However, the underlying mechanisms of TAMs on non-small-cell lung cancer (NSCLC) metastasis remain largely unexplored. Herein, we demonstrated that M2-like TAMs facilitate the migration and invasion of cancer cells in vitro and in vivo through intercellular delivery of M2-like macrophage-derived exosomes (M2-exos). Importantly, we found that M2-exos had considerably higher levels of integrin (ITG) αV and ß3. The impact of M2-like macrophage-mediated invasion and migration of NSCLC cells was clearly decreased when ITG αVß3 was blocked. Mechanistically, exosomal ITG αVß3 produced from M2-like macrophages successfully triggered the focal adhesion kinase signaling pathway in recipient cells, boosting the migratory and invasive abilities of NSCLC cells. Clinically, we found that metastatic NSCLC patients had greater ITG αV and ß3 expression, which was associated with a worse prognosis. This study reveals a novel mechanism by which M2-exos significantly increased NSCLC cell migration and invasion by delivering integrin αVß3. Exosomal ITG αVß3 can be used as a potential prognostic marker, and blocking ITG αVß3 could be a viable treatment option for preventing tumor metastasis.

Molecular Simulation of Electron Traps in Epoxy Resin/Graphene Oxide Nanocomposites.

Trapped space charges in epoxy composite distort the electric field, which will induce the failure of the insulation system, and nano graphene oxide may inhibit the curing behavior of epoxy resin matrix. This paper analyzes how the two interfaces affect the electron traps of epoxy resin/graphene oxide systems with different nanofiller contents. The electron affinity energy of epoxy resin matrix and nano filler molecules in the epoxy resin/graphene oxide system is calculated based on quantum chemistry. It is found that nano graphene oxide has a strong electron affinity energy and is easier to capture electrons. Then the influence of the interface formed by the epoxy resin matrix and the nano graphene oxide on the electron transfer ability is calculated. The epoxy resin matrix contains the electron transfer ability of interfaces formed by nano graphene oxide and the molecular chain is different from that of unreacted molecules. The results can provide a reference for the modification of epoxy resin/graphene oxide nanocomposites.

Lazertinib improves the efficacy of chemotherapeutic drugs in ABCB1 or ABCG2 overexpression cancer cells in vitro, in vivo, and ex vivo.

Multidrug resistance (MDR) is the major cause of chemotherapy failure, which is usually caused by the overexpression of ATP-binding cassette (ABC) transporters such as ABCB1 and ABCG2. To date, no MDR modulator has been clinically approved. Here, we found that lazertinib (YH25448; a novel third-generation tyrosine kinase inhibitor [TKI]) could enhance the anticancer efficacy of MDR transporter substrate anticancer drugs in vitro，in vivo, and ex vivo. Mechanistically, lazertinib was shown to inhibit the drug efflux activities of ABCB1 and ABCG2 and thus increase the intracellular accumulation of the transporter substrate anticancer drug. Moreover, lazertinib was found to stimulate the ATPase activity of ABCB1/ABCG2 and inhibit the photolabeling of the transporters by 125I-iodoarylazidoprazosin (IAAP). However, lazertinib neither changed the expression or locolization of ABCB1 and ABCG2 nor blocked the signal pathway of Akt or Erk1/2 at a drug concentration effective for MDR reversal. Overall, our results demonstrate that lazertinib effectively reverses ABCB1- or ABCG2-mediated MDR by competitively binding to the ATP-binding site and inhibiting drug efflux function. This is the first report demonstrating the novel combined use of lazertinib and conventional chemotherapeutical drugs to overcome MDR in ABCB1/ABCG2-overexpressing cancer cells.

Statistical analysis of the community lockdown for COVID-19 pandemic.

As the global pandemic of the COVID-19 continues, the statistical modeling and analysis of the spreading process of COVID-19 have attracted widespread attention. Various propagation simulation models have been proposed to predict the spread of the epidemic and the effectiveness of related control measures. These models play an indispensable role in understanding the complex dynamic situation of the epidemic. Most existing work studies the spread of epidemic at two levels including population and agent. However, there is no comprehensive statistical analysis of community lockdown measures and corresponding control effects. This paper performs a statistical analysis of the effectiveness of community lockdown based on the Agent-Level Pandemic Simulation (ALPS) model. We propose a statistical model to analyze multiple variables affecting the COVID-19 pandemic, which include the timings of implementing and lifting lockdown, the crowd mobility, and other factors. Specifically, a motion model followed by ALPS and related basic assumptions is discussed first. Then the model has been evaluated using the real data of COVID-19. The simulation study and comparison with real data have validated the effectiveness of our model.

Application of Logistic Regression and Artificial Intelligence in the Risk Prediction of Acute Aortic Dissection Rupture.

Logistic regression (LR) and artificial intelligence algorithms were used to analyze the risk factors for the early rupture of acute type A aortic dissection (ATAAD). Data from electronic medical records of 200 patients diagnosed with ATAAD from the Department of Emergency of Guangdong Provincial People's Hospital from April 2012 to March 2017 were collected. Logistic regression and artificial intelligence algorithms were used to establish prediction models, and the prediction effects of four models were analyzed. According to the LR models, we elucidated independent risk factors for ATAAD rupture, which included age > 63 years (odds ratio (OR) = 1.69), female sex (OR = 1.77), ventilator assisted ventilation (OR = 3.05), AST > 80 U/L (OR = 1.59), no distortion of the inner membrane (OR = 1.57), the diameter of the aortic sinus > 41 mm (OR = 0.92), maximum aortic diameter > 48 mm (OR = 1.32), the ratio of false lumen area to true lumen area > 2.12 (OR = 1.94), lactates > 1.9 mmol/L (OR = 2.28), and white blood cell > 14.2 × 109 /L (OR = 1.23). The highest sensitivity and accuracy were found with the convolutional neural network (CNN) model. Its sensitivity was 0.93, specificity was 0.90, and accuracy was 0.90. In this present study, we found that age, sex, select biomarkers, and select morphological parameters of the aorta are independent predictors for the rupture of ATAAD. In terms of predicting the risk of ATAAD, the performance of random forests and CNN is significantly better than LR, but the performance of the support vector machine (SVM) is worse than LR.

PD0325901, an ERK inhibitor, enhances the efficacy of PD-1 inhibitor in non-small cell lung carcinoma.

ERK pathway regulated the programmed death ligand-1 (PD-L1) expression which was linked to the response of programmed death-1 (PD-1)/PD-L1 blockade therapy. So it is deducible that ERK inhibitor could enhance the efficacy of PD-1 inhibitor in cancer immunotherapy. In this study, PD0325901, an oral potent ERK inhibitor, strongly enhanced the efficacy of PD-1 antibody in vitro and in vivo models in non-small cell lung carcinoma (NSCLC) cells. Mechanistically, PD0325901 or shRNA-ERK1/2 significantly downregulated the PD-L1 expression in NSCLC cells and increased the CD3+ T cells infiltration and functions in tumor tissue. There was a positive correlation between the p-ERK1/2 expression and PD-L1 expression in patients with NSCLC. And the patients with low p-ERK1/2 expression were observed a high response rate of PD-1/PD-L1 blockage therapy. Our results demonstrate that PD0325901, an ERK inhibitor, can enhance the efficacy of PD-1 blockage against NSCLC in vitro and in vivo models. And the combination of ERK inhibitor such as PD0325901 and PD-1/PD-L1 blockage is a promising regimen and encouraged to be further confirmed in the treatment of patients with NSCLC.

[Design and Realization of Remote Infusion Monitoring System].

In order to reduce the working intensity of medical staff in inspecting patients during traditional infusion, a remote monitoring system for intravenous infusion is designed for solving the problem of delay in handling treatment during infusion process and to reduce the incidence of medical accidents. The system uses Visual Basic.NET language to develop the upper computer platform for infusion monitoring. It uses the Arduino control board and infrared photoelectric sensor to form a monitoring device to detect relevant information. At the same time, it uses Zigbee wireless sensing technology to transmit data and upload it to the software platform. The results show that the system can receive data from multiple monitoring terminal devices in the upper computer platform application interface at the same time. It can display the data in the nurse station in a graphical way, and perform alarm warning and information storage during the infusion process. The infusion monitoring system can observe the monitoring situation in real time, reduce the workload of medical staff, and further improve the operating efficiency and safety of the hospital.

PFC: A Novel Perceptual Features-Based Framework for Time Series Classification.

Time series classification (TSC) is a significant problem in data mining with several applications in different domains. Mining different distinguishing features is the primary method. One promising method is algorithms based on the morphological structure of time series, which are interpretable and accurate. However, existing structural feature-based algorithms, such as time series forest (TSF) and shapelet traverse, all features through many random combinations, which means that a lot of training time and computing resources are required to filter meaningless features, important distinguishing information will be ignored. To overcome this problem, in this paper, we propose a perceptual features-based framework for TSC. We are inspired by how humans observe time series and realize that there are usually only a few essential points that need to be remembered for a time series. Although the complex time series has a lot of details, a small number of data points is enough to describe the shape of the entire sample. First, we use the improved perceptually important points (PIPs) to extract key points and use them as the basis for time series segmentation to obtain a combination of interval-level and point-level features. Secondly, we propose a framework to explore the effects of perceptual structural features combined with decision trees (DT), random forests (RF), and gradient boosting decision trees (GBDT) on TSC. The experimental results on the UCR datasets show that our work has achieved leading accuracy, which is instructive for follow-up research.

A Novel Time-Sensitive Composite Similarity Model for Multivariate Time-Series Correlation Analysis.

Finding the correlation between stocks is an effective method for screening and adjusting investment portfolios for investors. One single temporal feature or static nontemporal features are generally used in most studies to measure the similarity between stocks. However, these features are not sufficient to explore phenomena such as price fluctuations similar in shape but unequal in length which may be caused by multiple temporal features. To research stock price volatilities entirely, mining the correlation between stocks should be considered from the point view of multiple features described as time series, including closing price, etc. In this paper, a time-sensitive composite similarity model designed for multivariate time-series correlation analysis based on dynamic time warping is proposed. First, a stock is chosen as the benchmark, and the multivariate time series are segmented by the peaks and troughs time-series segmentation (PTS) algorithm. Second, similar stocks are screened out by similarity. Finally, the rate of rising or falling together between stock pairs is used to verify the proposed model's effectiveness. Compared with other models, the composite similarity model brings in multiple temporal features and is generalizable for numerical multivariate time series in different fields. The results show that the proposed model is very promising.

GM-CSF mediates immune evasion via upregulation of PD-L1 expression in extranodal natural killer/T cell lymphoma.

BACKGROUND: Granulocyte-macrophage colony stimulating factor (GM-CSF) is a cytokine that is used as an immunopotentiator for anti-tumor therapies in recent years. We found that some of the extranodal natural killer/T cell lymphoma (ENKTL) patients with the treatment of hGM-CSF rapidly experienced disease progression, but the underlying mechanisms remain to be elucidated. Here, we aimed to explore the mechanisms of disease progression triggered by GM-CSF in ENKTL. METHODS: The mouse models bearing EL4 cell tumors were established to investigate the effects of GM-CSF on tumor growth and T cell infiltration and function. Human ENKTL cell lines including NK-YS, SNK-6, and SNT-8 were used to explore the expression of programmed death-ligand 1 (PD-L1) induced by GM-CSF. To further study the mechanisms of disease progression of ENKTL in detail, the mutations and gene expression profile were examined by next-generation sequence (NGS) in the ENKTL patient's tumor tissue samples. RESULTS: The mouse-bearing EL4 cell tumor exhibited a faster tumor growth rate and poorer survival in the treatment with GM-CSF alone than in treatment with IgG or the combination of GM-CSF and PD-1 antibody. The PD-L1 expression at mRNA and protein levels was significantly increased in ENKTL cells treated with GM-CSF. STAT5A high-frequency mutation including p.R131G, p.D475N, p.F706fs, p.V707E, and p.S710F was found in 12 ENKTL cases with baseline tissue samples. Importantly, STAT5A-V706fs mutation tumor cells exhibited increased activation of STAT5A pathway and PD-L1 overexpression in the presence of GM-CSF. CONCLUSIONS: These findings demonstrate that GM-CSF potentially triggers the loss of tumor immune surveillance in ENKTL patients and promotes disease progression, which is associated with STAT5 mutations and JAK2 hyperphosphorylation and then upregulates the expression of PD-L1. These may provide new concepts for GM-CSF application and new strategies for the treatment of ENKTL.

Aldehyde Dehydrogenase 2 Mediates Alcohol-Induced Colorectal Cancer Immune Escape through Stabilizing PD-L1 Expression.

Despite the great success of immunotherapy in a small subset of cancer patients, most colorectal cancer (CRC) patients do not respond to programmed cell death receptor 1 (PD-1) blockade immunotherapy. There is an urgent medical need to elucidate how cancer cells evade immune response and to develop novel means to boost the efficacy of immune checkpoint inhibitors. In this study, alcohol induces ligand programmed cell death receptor 1 (PD-L1) expression of CRC cells in vitro and in vivo. Alcohol exposure is shown to induce aldehyde dehydrogenase 2 (ALDH2) expression that is a crucial enzyme involved in alcohol metabolism, and low level of lymphocytes infiltration in the murine CRC model and patients. Intriguingly, ALDH2 and PD-L1 protein expression are positively correlated in tumor tissues from the CRC patients. Mechanistically, ALDH2 stabilizes PD-L1 protein expression by physically interacting with the intracellular segment of PD-L1 and inhibiting its proteasome-dependent degradation mediated by an E3 ubiquitin ligase Speckle Type POZ Protein (SPOP). Importantly, inhibition of ALDH2 reduces PD-L1 protein in CRC cells and promotes tumor-infiltrating T cells (TILs) infiltration, presumably leading to the significant potentiation of anti-PD-1 antibody efficacy in a mouse CT26 CRC model. The findings highlight a crucial role played by ALDH2 to facilitate alcohol-mediated tumor escape from immunity surveillance and promote tumor progression.

A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction.

The trend prediction of the stock is a main challenge. Accidental factors often lead to short-term sharp fluctuations in stock markets, deviating from the original normal trend. The short-term fluctuation of stock price has high noise, which is not conducive to the prediction of stock trends. Therefore, we used discrete wavelet transform (DWT)-based denoising to denoise stock data. Denoising the stock data assisted us to eliminate the influences of short-term random events on the continuous trend of the stock. The denoised data showed more stable trend characteristics and smoothness. Extreme learning machine (ELM) is one of the effective training algorithms for fully connected single-hidden-layer feedforward neural networks (SLFNs), which possesses the advantages of fast convergence, unique results, and it does not converge to a local minimum. Therefore, this paper proposed a combination of ELM- and DWT-based denoising to predict the trend of stocks. The proposed method was used to predict the trend of 400 stocks in China. The prediction results of the proposed method are a good proof of the efficacy of DWT-based denoising for stock trends, and showed an excellent performance compared to 12 machine learning algorithms (e.g., recurrent neural network (RNN) and long short-term memory (LSTM)).

Intercellular transfer of exosomal wild type EGFR triggers osimertinib resistance in non-small cell lung cancer.

BACKGROUND: Epidermal growth factor receptor (EGFR)-mutated lung cancer constitutes a major subgroup of non-small cell lung cancer (NSCLC) and osimertinib is administrated as first-line treatment. However, most patients with osimertinib treatment eventually relapse within one year. The underlying mechanisms of osimertinib resistance remain largely unexplored. METHODS: Exosomes isolation was performed by differential centrifugation. Co-culture assays were conducted to explore the alteration of drug sensitivity by cell viability and apoptosis assays. Immunofluorescence and flow cytometry were performed to visualize the formation or absorption of exosomes. Exosomes secretion was measured by Nanoparticle Tracking Analysis or ELISA. The xenograft tumor model in mice was established to evaluate the effect of exosomes on osimertinib sensitivity in vivo. RESULTS: Intercellular transfer of exosomal wild type EGFR protein confers osimertinib resistance to EGFR-mutated sensitive cancer cells in vitro and in vivo. Co-culture of EGFR-mutated sensitive cells and EGFR-nonmutated resistant cells promoted osimertinib resistance phenotype in EGFR-mutated cancer cells, while depletion of exosomes from conditioned medium or blockade of exosomal EGFR by neutralizing antibody alleviated this phenotype. Mechanistically, osimertinib promoted the release of exosomes by upregulated a Rab GTPase (RAB17). Knockdown of RAB17 resulted in the decrease of exosomes secretion. Moreover, exosomes could be internalized by EGFR-mutated cancer cells via Clathrin-dependent endocytosis and then the encapsulated exosomal wild type EGFR protein activated downstream PI3K/AKT and MAPK signaling pathways and triggered osimertinib resistance. CONCLUSIONS: Intercellular transfer of exosomal wild type EGFR promotes osimertinib resistance in NSCLC, which may represent a novel resistant mechanism of osimertinib and provide a proof of concept for targeting exosomes to prevent and reverse the osimertinib resistance.

A Labeling Method for Financial Time Series Prediction Based on Trends.

Time series prediction has been widely applied to the finance industry in applications such as stock market price and commodity price forecasting. Machine learning methods have been widely used in financial time series prediction in recent years. How to label financial time series data to determine the prediction accuracy of machine learning models and subsequently determine final investment returns is a hot topic. Existing labeling methods of financial time series mainly label data by comparing the current data with those of a short time period in the future. However, financial time series data are typically non-linear with obvious short-term randomness. Therefore, these labeling methods have not captured the continuous trend features of financial time series data, leading to a difference between their labeling results and real market trends. In this paper, a new labeling method called "continuous trend labeling" is proposed to address the above problem. In the feature preprocessing stage, this paper proposed a new method that can avoid the problem of look-ahead bias in traditional data standardization or normalization processes. Then, a detailed logical explanation was given, the definition of continuous trend labeling was proposed and also an automatic labeling algorithm was given to extract the continuous trend features of financial time series data. Experiments on the Shanghai Composite Index and Shenzhen Component Index and some stocks of China showed that our labeling method is a much better state-of-the-art labeling method in terms of classification accuracy and some other classification evaluation metrics. The results of the paper also proved that deep learning models such as LSTM and GRU are more suitable for dealing with the prediction of financial time series data.

PBA2, a novel compound, enhances radiosensitivity in various carcinoma cells by activating the p53 pathway in vitro and in vivo.

Radiotherapy is the main method used to treat human carcinoma; however, certain types of carcinomas are radiation-insensitive. The present study aimed to explore whether a novel compound, PBA2, could enhance the radiosensitivity of various carcinoma cells in vitro and in vivo, and investigate its underlying mechanism. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay was used to assess the cytotoxicity of PBA2. Colony formation assays were used to observe the radiosensitivity effect of PBA2 in vitro. Cell cycle distributions and cell apoptosis were estimated using flow cytometry. Comet assays and Immunofluorescence assays were used to analyze DNA damage. The intracellular RNA was extracted and analyzed by sequencing. Western blotting was used to determine protein levels. A stable cell line with TP53 (encoding p53) knockdown was constructed by cell transfection. A mouse xenograft model was used to assess the radiosensitivity effect of PBA2 in vivo. We found that PBA2 at a low concentration (0.1 µM) enhanced radiosensitivity in various carcinoma cells, including CNE1, MG63, KB, HEP2, GLC82, and SMMC7221, in vitro. Combined with PBA2, radiation induced significant cell apoptosis in CNE1 and MG63 cells, accompanied by increased DNA damage, but did not affect cell cycle arrest. Mechanistically, PBA2 promoted p53 expression significantly; however, when p53 was mutated, functionally impaired, or knocked down, PBA2 could not enhance the radiosensitivity of these cells. Additionally, the combination of PBA2 and radiation reduced the tumor volume and tumor weight in CNE1 xenograft models significantly, without obvious toxicities. Our results demonstrated that PBA2 enhanced the radiosensitivity of various carcinoma cells in vitro and in vivo. The underlying mechanism might involve increasing DNA damage and cell apoptosis via activating the p53 pathway.

Mitomycin C enhanced the efficacy of PD-L1 blockade in non-small cell lung cancer.

Programmed death ligand 1 (PD-L1) immune checkpoint inhibitors are promising therapeutic agents for treating cancers but the response rate is <20%. Some chemotherapeutic drugs could also activate an anticancer immune response to kill cancer cells, apart from their direct cytotoxicity. Our study investigated the combination of chemotherapeutic drugs with PD-L1 antibody to enhance the response rate of PD-L1 blockade. Non-small cell lung cancer (NSCLC) cells were pre-treated with mitomycin C (MMC) and then co-cultured with peripheral blood mononuclear cells (PBMCs) to investigate the effect of the combination of MMC with PD-L1 antibody. The drug combination was also evaluated in vivo in Lewis lung cancer (LLC) cells-bearing C57BL/6 mice. MMC increased the expressions of PD-L1 and MHC-I in NSCLC cells in vitro and in vivo and enhanced the cytotoxic effect of lymphocytes on NSCLC in vitro. In LLC-bearing mouse model, the combination of MMC and PD-L1 antibody was found to be more effective in retarding tumor growth and prolonging overall survival than either single treatment alone, which was associated with increased lymphocyte infiltration and granzyme B release. Mechanistically, MMC activated the ERK pathway, which subsequently enhanced the binding of c-JUN to the PD-L1 promoter and recruited its co-factor STAT3 to increase PD-L1 expression. The upregulated ERK pathway was shown to activate p65 to increase the MHC-I expression. MMC was shown to enhance the efficacy of PD-L1 blockade in NSCLC cells. Further study is warranted to translate the findings to clinical application.

Tyrosine kinase inhibitors enhanced the efficacy of conventional chemotherapeutic agent in multidrug resistant cancer cells.

Multidrug resistance (MDR) triggered by ATP binding cassette (ABC) transporter such as ABCB1, ABCC1, ABCG2 limited successful cancer chemotherapy. Unfortunately, no commercial available MDR modulator approved by FDA was used in clinic. Tyrosine kinase inhibitors (TKIs) have been administrated to fight against cancer for decades. Almost TKI was used alone in clinic. However, drug combinations acting synergistically to kill cancer cells have become increasingly important in cancer chemotherapy as an approach for the recurrent resistant disease. Here, we summarize the effect of TKIs on enhancing the efficacy of conventional chemotherapeutic drug in ABC transporter-mediated MDR cancer cells, which encourage to further discuss and study in clinic.