Computer-Aided Discovery of Potent Broad-Spectrum Vaccine Adjuvants.

Adjuvants stimulate the immune system to vigorously respond to a vaccine. While current adjuvants such as aluminum salts and oil-in-water emulsions have been used for decades, they do not generate broad and long-lasting responses in many vaccines. Consequently, more potent adjuvants are needed. Here, using computer-aided molecule design and machine learning, we discovered 2 new, broad-spectrum adjuvants that can boost vaccine responses. Our library containing 46 toll-like receptor (TLR)-targeting agonist ligands were assembled on Au nanoparticles. Comprehensive in vitro, ex vivo and in vivo studies showed both leads promoted dendritic cell activation via multiple TLRs and enhanced antigen presentation to T cells. When used together with tumor-specific antigens to immunize mice against B16-OVA melanoma and 4T1-PD1 breast cancer, both adjuvants unleashed strong immune responses that suppressed tumor growth and lung metastases. Our results show computer-aided design and screening can rapidly uncover potent adjuvants for tackling waning immunity in current vaccines.

A new nomogram and risk classification system for predicting survival in small cell lung cancer patients diagnosed with brain metastasis: a large population-based study.

BACKGROUND: The prognosis of patients with small cell lung cancer (SCLC) is poor, most of them are in the extensive stage at the time of diagnosis, and are prone to brain metastasis. In this study, we established a nomogram combined with some clinical parameters to predict the survival of SCLC patients with brain metastasis. METHODS: The 3522 eligible patients selected from the SEER database between 2010 and 2015 were randomly divided into training cohort and validation cohort. Univariate and multivariate Cox regression analysis were used to evaluate the ability of each parameter to predict OS. The regression coefficients obtained in multivariate analysis were visualized in the form of nomogram, thus a new nomogram and risk classification system were established. The calibration curves were used to verify the model. And ROC curves were used to evaluate the discrimination ability of the newly constructed nomogram. Survival curves were made by Kaplan-Meier method and compared by Log rank test. RESULTS: Univariate and multivariate analysis showed that age, race, sex, T stage, N stage and marital status were independent prognostic factors and were included in the predictive model. The calibration curves showed that the predicted value of the 1- and 3-year survival rate by the nomogram was in good agreement with the actual observed value of the 1- and 3-year survival rate. And, the ROC curves implied the good discrimination ability of the predictive model. In addition, the results showed that in the total cohort, training cohort, and validation cohort, the prognosis of the low-risk group was better than that of the high-risk group. CONCLUSIONS: We established a nomogram and a corresponding risk classification system to predict OS in SCLC patients with brain metastasis. This model could help clinicians make clinical decisions and stratify treatment for patients.

Airborne fine particles drive H1N1 viruses deep into the lower respiratory tract and distant organs.

Mounting data suggest that environmental pollution due to airborne fine particles (AFPs) increases the occurrence and severity of respiratory virus infection in humans. However, it is unclear whether and how interactions with AFPs alter viral infection and distribution. We report synergetic effects between various AFPs and the H1N1 virus, regulated by physicochemical properties of the AFPs. Unlike infection caused by virus alone, AFPs facilitated the internalization of virus through a receptor-independent pathway. Moreover, AFPs promoted the budding and dispersal of progeny virions, likely mediated by lipid rafts in the host plasma membrane. Infected animal models demonstrated that AFPs favored penetration of the H1N1 virus into the distal lung, and its translocation into extrapulmonary organs including the liver, spleen, and kidney, thus causing severe local and systemic disorders. Our findings revealed a key role of AFPs in driving viral infection throughout the respiratory tract and beyond. These insights entail stronger air quality management and air pollution reduction policies.

PDL1 high expression without TP53, KEAP1 and EPHA5 mutations could better predict survival for patients with NSCLC receiving atezolizumab.

BACKGROUND: Non-small cell lung cancer (NSCLC) patients with high expression of PDL1 are more likely to benefit from atezolizumab. There are no relevant research focusing on the relationship between the PDL1 expression and clinical variables and gene mutation types among NSCLC patients. METHODS: NSCLC patients with confirmed PDL1 expression and gene mutation information from OAK study were included in our study. Logistic regression proportional model was applied to analyze the risk factors on PDL1 high expression. The biomarker evaluable population (BEP) was screened to analyze the gene mutation informaion among these patients. High frequency gene mutations were screened based on different PDL1 expressions. Moreover, the log rank test was applied to analyze the overall survival (OS) difference based on different gene mutation types. RESULTS: A total of 838 patients with NSCLC were included in our study. White patients are more likely to have PDL1 ≥ 1% (P = 0.004). ERBB4, EP300, PREX2, SLIT2, EPHB1 and IGF2R mutations were high frequency mutations in patients with high PDL1 expression, and the patients with EGFR, SMARCA4, EPHA5, FAT1, STK11, TET2 mutations were more likely to be seen in negative PDL1 expression group. Worse survival could be found in patients with KEAP1 (P < 0.001), TP53 (P = 0.004) and EPHA5 (P = 0.013) mutations who received atezolizumab compared with those who had none of these gene mutations. Importantly, for PDL1 high patients without KEAP1, EPHA5, TP53 mutations receiving atezolizumab, they all showed relatively longer median survival with 22.47, 22.18 and 23.33 months, respectively (all, P < 0.01). CONCLUSIONS: Different high frequency gene mutations could be found between the patients with high and negative PDL1. PDL1 expression combined with specific gene mutation may better predict the survival for patients receiving atezolizumab.

Duration of immunotherapy in patients with advanced lung adenocarcinoma with negative driver genes: case report and literature review.

Here, we report two cases of advanced non-small cell lung cancer (NSCLC) in patients with negative driver genes who received ICI treatment for less than two years but continued to benefit from their administration after drug withdrawal. The first patient was diagnosed with left lung adenocarcinoma, cT1cN3M1c, stage IVb, and after four cycles achieved a completed response (CR). After 10 cycles of camrelizumab treatment, immunotherapy was discontinued because of hepatotoxicity. When the drug was discontinued, the curative effect was evaluated as CR. At the last follow-up, the drug withdrawal time had been more than 20 months, and the response was maintained at CR, with PFS of over 30 months. In the second case, the patient was diagnosed with left lung adenocarcinoma, cT1N3M1c, stage IVb. The patient was treated with sintilimab, and due to cardiac and skin toxicity, the patient withdrew from the trial after five cycles of immunotherapy. After drug withdrawal, the curative effect of the patients was maintained at PR. At the last follow-up, the drug withdrawal time was more than three months, and the curative effect was evaluated as PR. The PFS was more than nine months. In conclusion, whether the drug can be discontinued in advance after immune checkpoint inhibitor (ICI) therapy has been effective remains a concern, and at present there is no final conclusion in the medical profession. However, the results of this study indicate that early withdrawal of immunotherapy due to adverse reactions might also benefit patients with advanced lung adenocarcinoma with negative driver genes who achieve an early response to immunotherapy.

Tumor Primary Location May Affect Metastasis Pattern for Patients with Stage IV NSCLC: A Population-Based Study.

BACKGROUND: Most patients with nonsmall cell lung cancer (NSCLC) were initially diagnosed with distant metastasis. At present, there is no study to clarify the correlation between the primary location of the tumor and the metastasis pattern in advanced NSCLC. So we conducted this study to explored the relationship between the tumor primary location and metastasis pattern in stage IV NSCLC. METHODS: A total of 19,295 eligible patients were identified from 2010 to 2012 in the SEER database. The main endpoint of our study was overall survival (OS). The survival curves were created by using the Kaplan-Meier method and compared by the usage of the Log Rank test. The clinical variable characteristics were compared by the chi-square test, and multivariate logistic regression analyses were used to evaluate the risk factors on metastasis patterns. All statistical P values were two-sided, and it was considered statistically significant when P ≤ 0.05. RESULTS: We found that different proportions of metastatic sites could be found in different tumor primary locations. In addition, the prognosis of lung metastasis was relatively good in patients with tumor location in main bronchus (P < 0.001), upper lobe (P < 0.001), lower lobe (P < 0.001) , and middle lobe (P = 0.005). Besides, there was no significant OS difference for patients whose primary location was overlapping lesion (P = 0.226). The results also demonstrated that compared with patients with primary tumor located in the main bronchus, those in the upper lobe were more likely to have brain metastasis (P = 0.01) and lung metastasis (P = 0.024), those in the middle lobe were more prone to develop lung metastasis (P = 0.035) and those in the lower lobe were more apt to cause bone metastasis (P = 0.005) and lung metastasis (P = 0.001). In addition, there was no statistical difference in metastasis patterns among patients with overlapping lesions (P > 0.05). CONCLUSIONS: Different primary tumor locations might affect the metastasis pattern in patients with stage IV NSCLC.

A prognostic scoring model to determine which patients with metastatic extensive-stage small cell lung cancer could benefit from local radiotherapy: a large population-based study.

OBJECTIVE: Our study was performed to clarify which patients with metastatic extensive-stage small cell lung cancer (ES-SCLC) could gain survival benefit from local radiotherapy. PATIENTS AND METHODS: A chi-square test was used to compare baseline characteristics of different groups. Kaplan-Meier method was applied to analyzing the survival difference. The prognostic factors for cancer-specific survival (CSS) in patients receiving radiotherapy were analyzed by the Cox proportional hazard model. According to the above information, we established a prognostic score model. Based on the prognostic score model, the Kaplan-Meier method was used to make survival curves to find the difference in prognosis. CSS based on different subgroup variables were analyzed by the Cox proportional hazard model and concrete results were shown in forest plots. RESULTS: Our study revealed that CSS improved in the radiotherapy group compared with the control/none group (P<0.001). The multivariate analysis identified that both female and metastasis number=1 as significant prognostic factors for favorable CSS. We established a prognostic score (PS) model with a score of 0 to 2. Based on the prognostic score model, survival curves were made and showed that CSS of the radiotherapy group improved significantly with the decreasing patient propensity score (P<0.001). In subgroup analyses, patients with PS=0 (P<0.001) and PS=1 (P<0.001) could benefit from receiving local radiotherapy; patients with PS=2 (P=0.160) demonstrated no significant difference in CSS after receiving local radiotherapy. CONCLUSIONS: Local radiotherapy could improve the CSS of patients with metastatic ES-SCLC, only for patients with a 0-1 score.

Relationship between tumor size and metastatic site in patients with stage IV non-small cell lung cancer: A large SEER-based study.

OBJECTIVE: To analyze the relationship between tumor size and metastatic site in stage IV NSCLC patients. METHODS: A total of 40,196 stage IV NSCLC patients from 2010 to 2015 were screened by SEER database. Chi-square test was used to compare the characteristics of clinical variables. At the same time, multivariate Logistic regression analysis was used to evaluate the relationship between tumor size and organ metastasis. RESULTS: Regardless of tumor size, the proportion of bone metastasis and lung metastasis was higher and similar in patients with squamous cell carcinoma, while in patients with adenocarcinoma, bone metastasis accounted for the highest proportion. We found that whether the metastatic site was bone, brain, liver or lung, the proportion of patients with a tumor size of 3-7 cm was the highest. Multivariate regression analysis demonstrated that patients with a tumor size of 3-7 cm and a tumor size ≥7 cm were more likely to develop brain metastasis and lung metastasis compared with patients with a tumor size ≤3 cm (all P < 0.001), which meant the larger the tumor, the greater the risk of brain or lung metastasis. At the same time, the results indicated that patients with a tumor size of 3-7 cm had a tendency to develop liver metastasis (P = 0.004), while the statistical significance was not found for patients with a tumor size ≥7 cm (P = 0.524). The results also revealed that patients with a tumor size of 3-7cm had no significant difference to develop bone metastasis (P = 0.116), while the statistical significance was found for patients with a tumor size ≥7 cm (P < 0.001). CONCLUSIONS: There was statistical significance between tumor size and metastatic site in patients with stage IV NSCLC. For brain or lung metastasis, the larger the tumor, the higher the risk of brain or lung metastasis. For liver metastasis, patients with a tumor size of 3-7 cm were more prone to develop liver metastasis. For bone metastasis, patients with a tumor size ≥7 cm were more likely to have bone metastasis.