The Benefits of Quitting Smoking at Different Ages.

INTRODUCTION: Quantifying the impact of smoking on life expectancy and the potential benefits of smoking cessation is crucial for motivating people who smoke to quit. While previous studies have attempted to estimate these effects, they were conducted more than a decade ago and did not include a significant demographic, people over 65 years old who smoke. METHODS: Mortality rates by age and smoking status were calculated using mortality relative risks derived from Cancer Prevention Study II, 2018 National Health Interview Survey smoking prevalence data, 2018 US population census data, and 2018 US mortality rates. Subsequently, life tables by smoking status - never, current, and former - were constructed. Life expectancies for all three smoking statuses, including those of individuals who had quit smoking at various ages ranging from 35 to 75, were then compared. Additionally, probability distributions of years lost due to smoking and years gained by quitting smoking at different ages were generated. Analyses were conducted in 2023. RESULTS: Compared to people who never smoked, those who smoke currently, aged 35, 45, 55, 65 or 75 years, and who have smoked throughout adulthood until that age, will lose, on average, 9.1, 8.3, 7.3, 5.9, and 4.4 years of life, respectively, if they continue to smoke for the rest of their lives. However, if they quit smoking at each of these ages, they will avoid an average loss of 8.0, 5.6, 3.4, 1.7, and 0.7 years. The chances of gaining at least 1 year of life among those who quit at age 65 and 75 are 23.4% and 14.2%, respectively. CONCLUSIONS: Quitting smoking early will avoid most years otherwise lost due to smoking. Even those who quit at ages 65 and above can still meaningfully increase their life expectancy.

New Estimates of Smoking-Attributable Mortality in the U.S. From 2020 Through 2035.

INTRODUCTION: The often-cited Centers for Disease Control and Prevention (CDC) estimate of 480,000 annual U.S. smoking-attributable deaths (SADs), including 439,000 first-hand smoke deaths, derives from 2005 to 2009 data. Since then, adult smoking prevalence has decreased by 40%, while the population has grown and the smoking population aged. An updated estimate is presented to determine whether the CDC figure remains accurate or has changed substantially. In addition, the likely annual smoking-related mortality toll is projected through 2035. METHODS: A well-established model of smoking prevalence and health effects is employed to estimate annual SADs among individuals exposed to first-hand smoke in the U.S. for two distinct periods: 2005-2009 and 2020-2035. The estimate for 2005-2009 serves as a benchmark to evaluate the reliability of the model's estimate in comparison to CDC's. The projections for 2020-2035 provide up-to-date figures for SADs, predicting how annual SADs are likely to change in the coming years. Data were collected between 2005 and 2020. The analysis was conducted in 2023. RESULTS: This study's estimate of 420,000 first-hand smoke deaths over 2005-2009 is 95.7% of CDC's estimate during the same period. The model projections indicate that SADs among individuals who currently smoke or formerly smoked have increased modestly since 2005-2009. Beginning in 2020, annual SADs will remain relatively stable at approximately 450,000 before starting to decline around 2030. CONCLUSIONS: These findings suggest that the CDC estimate of the annual mortality burden of smoking remains valid. Despite U.S. population growth and the aging of the smoking population, substantial reductions in smoking will finally produce a steady, if gradual, decline in SADs beginning around 2030.

The evolution of age-specific smoking cessation rates in the United States from 2009 to 2017: a Kalman filter based approach.

BACKGROUND: Tracking the US smoking cessation rate over time is of great interest to tobacco control researchers and policymakers since smoking cessation behaviors have a major effect on the public's health. Recent studies have employed dynamic models to estimate the US cessation rate through observed smoking prevalence. However, none of those studies has provided annual estimates of the cessation rate by age group. Hence, the primary objective of this study is to estimate annual smoking cessation rates specific to different age groups in the US from 2009 to 2017. METHODS: We employed a Kalman filter approach to investigate the annual evolution of age-group-specific cessation rates, unknown parameters of a mathematical model of smoking prevalence, during the 2009-2017 period using data from the 2009-2018 National Health Interview Surveys. We focused on cessation rates in the 25-44, 45-64 and 65 + age groups. RESULTS: The findings show that cessation rates followed a consistent u-shaped curve over time with respect to age (i.e., higher among the 25-44 and 65 + age groups, and lower among 45-64-year-olds). Over the course of the study, the cessation rates in the 25-44 and 65 + age groups remained nearly unchanged around 4.5% and 5.6%, respectively. However, the rate in the 45-64 age group exhibited a substantial increase of 70%, from 2.5% to 2009 to 4.2% in 2017. The estimated cessation rates in all three age groups tended to converge to the weighted average cessation rate over time. CONCLUSIONS: The Kalman filter approach offers a real-time estimation of cessation rates that can be helpful for monitoring smoking cessation behavior.

Key Risk Factors Associated With Electronic Nicotine Delivery Systems Use Among Adolescents.

Importance: The prevalence of electronic nicotine delivery systems (ENDS) use among US youths has increased significantly during the past decade. Identifying key factors highly associated with ENDS use is essential in monitoring and preventing this harmful behavior among youths. Objective: To identify the most important risk factors in wave 4.5 (ie, December 2017 to December 2018) of the Population Assessment of Tobacco and Health Study (PATH) data that are associated with ENDS use in wave 5 (ie, December 2018 to November 2019) among adolescents who were tobacco-naive at baseline. Design, Setting, and Participants: This prognostic study examined data from waves 4.5 and 5 of the PATH youth data set using machine learning techniques. The PATH study is a nationally representative longitudinal cohort study of tobacco use and health in the United States among individuals aged 12 years and older. The data analysis was carried out between January and April 2023. Main Outcomes and Measures: Wave 5 current ENDS use status of wave 4.5 adolescents who were tobacco-naive. Results: The analyzed data set comprised 7943 individuals who were tobacco-naive in wave 4.5. Among this group, 332 participants (4.2%) indicated their present use of ENDS in wave 5, 5047 (63.5%) were aged 12 to 14 years, 4066 (51.2%) were male, and 2455 (30.9%) were Hispanic. The most important risk factors of ENDS use in wave 5 among adolescents who were tobacco-naive in wave 4.5 were the likelihood of using ENDS if offered by a best friend (mean SHAP value, 0.184), the number of best friends using e-cigarettes (mean SHAP value, 0.167), household tobacco usage (mean SHAP value, 0.161), curiosity about ENDS use (mean SHAP value, 0.088), future intention to use ENDS (mean SHAP value, 0.068), youth's total average weekly earnings (mean SHAP value, 0.060), and perceptions of tobacco product safety (mean SHAP value, 0.026). Conclusions and Relevance: The findings of this study suggest that family and friends play an important role in ENDS use among adolescents. The top-ranking factors associated with ENDS use in this study are areas for further exploration, given the increasing prevalence of ENDS use among youths in recent years. Additionally, these findings highlight the important role of families and schools in shaping adolescents' tobacco-related knowledge, which can protect them from using ENDS.

The Evolution of Age-Specific Smoking Cessation Rates in the United States From 2009 to 2018.

Objective: Tracking the US smoking cessation rate over time is of great interest to tobacco control researchers and policymakers since smoking cessation behaviors have a major effect on the public's health. A couple of recent studies have employed dynamic models to estimate the US cessation rate through observed smoking prevalence. However, none of those studies has provided recent annual estimates of the cessation rate by age group. Methods: We employed a Kalman filter approach to investigate the annual evolution of age-group-specific cessation rates, unknown parameters of a mathematical model of smoking prevalence, during the 2009-2018 period using data from the National Health Interview Survey. We focused on cessation rates in the 24-44, 45-64 and 65 + age groups. Results: The findings show that cessation rates follow a consistent u-shaped curve over time with respect to age (i.e., higher among the 25-44 and 65 + age groups, and lower among 45-64-year-olds). Over the course of the study, the cessation rates in the 25-44 and 65 + age groups remained nearly unchanged around 4.5% and 5.6%, respectively. However, the rate in the 45-64 age group exhibited a substantial increase of 70%, from 2.5% in 2009 to 4.2% in 2017. The estimated cessation rates in all three age groups tended to converge to the weighted average cessation rate over time. Conclusions: The Kalman filter approach offers a real-time estimation of cessation rates that would be helpful for monitoring smoking cessation behavior, of interest in general but also for tobacco control policymakers.

Machine learning application for predicting smoking cessation among US adults: An analysis of waves 1-3 of the PATH study.

Identifying determinants of smoking cessation is critical for developing optimal cessation treatments and interventions. Machine learning (ML) is becoming more prevalent for smoking cessation success prediction in treatment programs. However, only individuals with an intention to quit smoking cigarettes participate in such programs, which limits the generalizability of the results. This study applies data from the Population Assessment of Tobacco and Health (PATH), a United States longitudinal nationally representative survey, to select primary determinants of smoking cessation and to train ML classification models for predicting smoking cessation among the general population. An analytical sample of 9,281 adult current established smokers from the PATH survey wave 1 was used to develop classification models to predict smoking cessation by wave 2. Random forest and gradient boosting machines were applied for variable selection, and the SHapley Additive explanation method was used to show the effect direction of the top-ranked variables. The final model predicted wave 2 smoking cessation for current established smokers in wave 1 with an accuracy of 72% in the test dataset. The validation results showed that a similar model could predict wave 3 smoking cessation of wave 2 smokers with an accuracy of 70%. Our analysis indicated that more past 30 days e-cigarette use at the time of quitting, fewer past 30 days cigarette use before quitting, ages older than 18 at smoking initiation, fewer years of smoking, poly tobacco past 30-days use before quitting, and higher BMI resulted in higher chances of cigarette cessation for adult smokers in the US.

Are the Relevant Risk Factors Being Adequately Captured in Empirical Studies of Smoking Initiation? A Machine Learning Analysis Based on the Population Assessment of Tobacco and Health Study.

INTRODUCTION: Cigarette smoking continues to pose a threat to public health. Identifying individual risk factors for smoking initiation is essential to further mitigate this epidemic. To the best of our knowledge, no study today has used machine learning (ML) techniques to automatically uncover informative predictors of smoking onset among adults using the Population Assessment of Tobacco and Health (PATH) study. AIMS AND METHODS: In this work, we employed random forest paired with Recursive Feature Elimination to identify relevant PATH variables that predict smoking initiation among adults who have never smoked at baseline between two consecutive PATH waves. We included all potentially informative baseline variables in wave 1 (wave 4) to predict past 30-day smoking status in wave 2 (wave 5). Using the first and most recent pairs of PATH waves was found sufficient to identify the key risk factors of smoking initiation and test their robustness over time. The eXtreme Gradient Boosting method was employed to test the quality of these selected variables. RESULTS: As a result, classification models suggested about 60 informative PATH variables among many candidate variables in each baseline wave. With these selected predictors, the resulting models have a high discriminatory power with the area under the specificity-sensitivity curves of around 80%. We examined the chosen variables and discovered important features. Across the considered waves, two factors, (1) BMI, and (2) dental and oral health status, robustly appeared as important predictors of smoking initiation, besides other well-established predictors. CONCLUSIONS: Our work demonstrates that ML methods are useful to predict smoking initiation with high accuracy, identifying novel smoking initiation predictors, and to enhance our understanding of tobacco use behaviors. IMPLICATIONS: Understanding individual risk factors for smoking initiation is essential to prevent smoking initiation. With this methodology, a set of the most informative predictors of smoking onset in the PATH data were identified. Besides reconfirming well-known risk factors, the findings suggested additional predictors of smoking initiation that have been overlooked in previous work. More studies that focus on the newly discovered factors (BMI and dental and oral health status,) are needed to confirm their predictive power against the onset of smoking as well as determine the underlying mechanisms.

Comparative Efficacy of Mayaro Virus-Like Particle Vaccines Produced in Insect or Mammalian Cells.

Mayaro virus (MAYV) is a mosquito-transmitted alphavirus that causes often debilitating rheumatic disease in tropical Central and South America. There are currently no licensed vaccines or antiviral drugs available for MAYV disease. Here, we generated Mayaro virus-like particles (VLPs) using the scalable baculovirus-insect cell expression system. High-level secretion of MAYV VLPs in the culture fluid of Sf9 insect cells was achieved, and particles with a diameter of 64 to 70 nm were obtained after purification. We characterize a C57BL/6J adult wild-type mouse model of MAYV infection and disease and used this model to compare the immunogenicity of VLPs from insect cells with that of VLPs produced in mammalian cells. Mice received two intramuscular immunizations with 1 µg of nonadjuvanted MAYV VLPs. Potent neutralizing antibody responses were generated against the vaccine strain, BeH407, with comparable activity seen against a contemporary 2018 isolate from Brazil (BR-18), whereas neutralizing activity against chikungunya virus was marginal. Sequencing of BR-18 illustrated that this virus segregates with genotype D isolates, whereas MAYV BeH407 belongs to genotype L. The mammalian cell-derived VLPs induced higher mean neutralizing antibody titers than those produced in insect cells. Both VLP vaccines completely protected adult wild-type mice against viremia, myositis, tendonitis, and joint inflammation after MAYV challenge. IMPORTANCE Mayaro virus (MAYV) is associated with acute rheumatic disease that can be debilitating and can evolve into months of chronic arthralgia. MAYV is believed to have the potential to emerge as a tropical public health threat, especially if it develops the ability to be efficiently transmitted by urban mosquito vectors, such as Aedes aegypti and/or Aedes albopictus. Here, we describe a scalable virus-like particle vaccine against MAYV that induced neutralizing antibodies against a historical and a contemporary isolate of MAYV and protected mice against infection and disease, providing a potential new intervention for MAYV epidemic preparedness.

Monitoring the Increase in the U.S. Smoking Cessation Rate and Its Implication for Future Smoking Prevalence.

INTRODUCTION: We calculate the U.S. adult smoking cessation rate for 2014-2019, compare it to the historical trend, and estimate the implication for future smoking prevalence. METHODS: We repeated an earlier analysis, which examined the cessation rate from 1990 to 2014, extending the period to 2019. Employing National Health Interview Survey (NHIS) and National Survey on Drug Use and Health (NSDUH) data, we estimated the adult cessation rate in 6-year intervals, using weighted nonlinear least squares. We then employed a meta-regression model to test whether the cessation rate has increased beyond expectation. We used cessation rate estimates and smoking initiation rate estimates to project smoking prevalence in 2030 and eventual steady-state prevalence. RESULTS: The annual cessation rate increased 29% using NHIS data (from 4.2% in 2008-2013 to 5.4% in 2014-2019) and 33% with NSDUH data (4.2%-5.6%). The cessation rate increase accounts for 60% of a smoking prevalence decline in the most recent period exceeding the 1990-2013 predicted trend. The remaining 40% owes to declining smoking initiation. With current initiation and cessation rates, smoking prevalence should fall to 8.3% in 2030 and eventually reach a steady state of 3.53%. CONCLUSIONS: The smoking cessation rate continued to increase during 2014-2019. NHIS and NSDUH results are practically identical. The larger share (60%) of the smoking prevalence decrease, beyond expectation, attributable to the increased cessation rate is encouraging since the positive health effects of cessation occur much sooner than those derived from declining initiation. IMPLICATIONS: The smoking cessation rate in the United States continues to increase, accelerating the decline in smoking prevalence. This increase suggests that the Healthy People 2030 goal of 5% adult smoking prevalence, while ambitious, is attainable. Our findings can be used in simulation and statistical models that aim to predict future prevalence and population health effects due to smoking under various scenarios.

The association between smoking behaviors and prices and taxes per cigarette pack in the United States from 2000 through 2019.

OBJECTIVE: The conclusions on how tax and price increases affect smoking behaviors are mixed. This work is devoted to re-evaluating the relationship between cigarette prices and taxes and smoking behaviors. METHODS: Using 2000-2019 Behavioral Risk Factor Surveillance System data, we employed linear mixed-effect models to re-examine the impact of cigarette prices and taxes on smoking prevalence and the proportion of current smokers having tried to quit smoking in the past 12 months. All the analyses were conducted for the general population, then by age group, gender, race/ethnicity, and income level. RESULTS: The results indicate that higher cigarette prices and taxes were associated with a decrease in smoking prevalence and an increased likelihood of quitting smoking. Cigarette tax and price increases produced the most powerful impact on the smoking prevalence of 18- to 24-year-olds. The estimates also show that males tended to be more price-sensitive than females. Raising cigarette prices and taxes was estimated to be more effective in reducing the smoking prevalence among non-Hispanic Blacks and Hispanics when compared to non-Hispanic whites. Cigarette price and tax changes were likely to have a smaller effect on individuals with annual income under $25,000 relative to individuals with higher income levels. CONCLUSIONS: Increases in cigarette prices and taxes are significantly associated with a reduction in smoking prevalence and an increased likelihood of quitting smoking among adults across different demographic and socioeconomic groups. However, as cigarette price and tax changes disproportionately affect low-income individuals, raising cigarette prices and taxes may deepen income disparities.

Consequences of a match made in hell: the harm caused by menthol smoking to the African American population over 1980-2018.

BACKGROUND: For many years, national surveys have shown a consistently disproportionately high prevalence of menthol smokers among African Americans compared with the general population. However, to our knowledge, no prior study has quantified the harm that menthol smoking has caused on that population. In this work, we estimate the public health harm that menthol cigarettes have caused to the African American community over the last four decades. METHODS: Using National Health Interview Survey data, we employed a well-established simulation model to reproduce the observed smoking trajectory over 1980-2018 in the African American population. Then, we repeat the experiment, removing the effects of menthol on the smoking initiation and cessation rates over that period, obtaining a new hypothetical smoking trajectory. Finally, we compared both scenarios to calculate the public health harm attributable to menthol cigarettes over 1980-2018. RESULTS: Our results show that menthol cigarettes were responsible for 1.5 million new smokers, 157 000 smoking-related premature deaths and 1.5 million life-years lost among African Americans over 1980-2018. While African Americans constitute 12% of the total US population, these figures represent, respectively, a staggering 15%, 41% and 50% of the total menthol-related harm. DISCUSSION: Our results show that menthol cigarettes disproportionally harmed African Americans significantly over the last 38 years and are responsible for exacerbating health disparities among that population. Removing menthol cigarettes from the market would benefit the overall US population but, particularly, the African American community.

Is mammography screening beneficial: An individual-based stochastic model for breast cancer incidence and mortality.

The benefits of mammography screening have been controversial, with conflicting findings from various studies. We hypothesize that unmeasured heterogeneity in tumor aggressiveness underlies these conflicting results. Based on published data from the Canadian National Breast Screening Study (CNBSS), we develop and parameterize an individual-based mechanistic model for breast cancer incidence and mortality that tracks five stages of breast cancer progression and incorporates the effects of age on breast cancer incidence and all-cause mortality. The model accurately reproduces the reported outcomes of the CNBSS. By varying parameters, we predict that the benefits of mammography depend on the effectiveness of cancer treatment and tumor aggressiveness. In particular, patients with the most rapidly growing or potentially largest tumors have the highest benefit and least harm from the screening, with only a relatively small effect of age. However, the model predicts that confining mammography to populations with a high risk of acquiring breast cancer increases the screening benefit only slightly compared with the full population.

Model-Based Simulation of Maintenance Therapy of Childhood Acute Lymphoblastic Leukemia.

Acute lymphoblastic leukemia is the most common malignancy in childhood. Successful treatment requires initial high-intensity chemotherapy, followed by low-intensity oral maintenance therapy with oral 6-mercaptopurine (6MP) and methotrexate (MTX) until 2-3 years after disease onset. However, intra- and inter-individual variability in the pharmacokinetics (PK) and pharmacodynamics (PD) of 6MP and MTX make it challenging to balance the desired antileukemic effects with undesired excessive myelosuppression during maintenance therapy. A model to simulate the dynamics of different cell types, especially neutrophils, would be a valuable contribution to improving treatment protocols (6MP and MTX dosing regimens) and a further step to understanding the heterogeneity in treatment efficacy and toxicity. We applied and modified a recently developed semi-mechanistic PK/PD model to neutrophils and analyzed their behavior using a non-linear mixed-effects modeling approach and clinical data obtained from 116 patients. The PK model of 6MP influenced the accuracy of absolute neutrophil count (ANC) predictions, whereas the PD effect of MTX did not. Predictions based on ANC were more accurate than those based on white blood cell counts. Using the new cross-validated mathematical model, simulations of different treatment protocols showed a linear dose-effect relationship and reduced ANC variability for constant dosages. Advanced modeling allows the identification of optimized control criteria and the weighting of specific influencing factors for protocol design and individually adapted therapy to exploit the optimal effect of maintenance therapy on survival.

A mathematical model of white blood cell dynamics during maintenance therapy of childhood acute lymphoblastic leukemia.

Acute lymphoblastic leukemia is the most common malignancy in childhood and requires prolonged oral maintenance chemotherapy to prevent disease relapse after remission induction with intensive intravenous chemotherapy. In maintenance therapy, drug doses of 6-mercaptopurine (6-MP) and methotrexate (MTX) are adjusted to achieve sustained antileukemic activity without excessive myelosuppression. However, uncertainty exists regarding timing and extent of drug dose responses and optimal dose adaptation strategies. We propose a novel comprehensive mathematical model for 6-MP and MTX pharmacokinetics, pharmacodynamics and myelosuppression in acute lymphoblastic maintenance therapy. We personalize and cross-validate the mathematical model using clinical data and propose a real-time algorithm to predict chemotherapy responses with a clinical decision support system as a potential future application.

Effective cutaneous vaccination using an inactivated chikungunya virus vaccine delivered by Foroderm.

Foroderm is a new cutaneous delivery technology that uses high-aspect ratio, cylindrical silica microparticles, that are massaged into the skin using a 3D-printed microtextured applicator, in order to deliver payloads across the epidermis. Herein we show that this technology is effective for delivery of a non-adjuvanted, inactivated, whole-virus chikungunya virus vaccine in mice, with minimal post-vaccination skin reactions. A single topical Foroderm-based vaccination induced T cell, Th1 cytokine and antibody responses, which provided complete protection against viraemia and disease after challenge with chikungunya virus. Foroderm vaccination was shown to deliver fluorescent, virus-sized beads across the epidermis, with beads subsequently detected in draining lymph nodes. Foroderm vaccination also stimulated the egress of MHC II(+) antigen presenting cells from the skin. Foroderm thus has potential as a simple, cheap, effective, generic, needle-free technology for topical delivery of vaccines.

A physiological function of inflammation-associated SerpinB2 is regulation of adaptive immunity.

SerpinB2 (plasminogen activator inhibitor-2) is widely described as an inhibitor of urokinase plasminogen activator; however, SerpinB2(-/-) mice show no detectable increase in urokinase plasminogen activator activity. In this study, we describe an unexpected immune phenotype in SerpinB2(-/-) mice. After immunization with OVA in CFA, SerpinB2(-/-) mice made approximately 6-fold more IgG2c and generated approximately 2.5-fold more OVA-specific IFN-gamma-secreting T cells than SerpinB2(+/+) littermate controls. In SerpinB2(+/+) mice, high inducible SerpinB2 expression was seen at the injection site and in macrophages low levels in draining lymph nodes and conventional dendritic cells, and no expression was seen in plasmacytoid dendritic, B, T, or NK cells. SerpinB2(-/-) macrophages promoted greater IFN-gamma secretion from wild-type T cells in vivo and in vitro and, when stimulated with anti-CD40/IFN-gamma or cultured with wild-type T cells in vitro, secreted more Th1-promoting cytokines than macrophages from littermate controls. Draining lymph node SerpinB2(-/-) myeloid APCs similarly secreted more Th1-promoting cytokines when cocultured with wild-type T cells. Regulation of Th1 responses thus appears to be a physiological function of inflammation-associated SerpinB2; an observation that may shed light on human inflammatory diseases like pre-eclampsia, lupus, asthma, scleroderma, and periodontitis, which are associated with SerpinB2 polymorphisms or dysregulated SerpinB2 expression.

Immunostimulatory cancer chemotherapy using local ingenol-3-angelate and synergy with immunotherapies.

Ingenol-3-angelate is a new local chemotherapeutic agent in clinical trails that induces primary necrosis of tumour cells and transient local inflammation. Here we show that cure of subcutaneous tumours with ingenol-3-angelate (PEP005) resulted in the generation of anti-cancer CD8 T cells that could regress metastases. Furthermore, PEP005-mediated cure synergized with several CD8 T cell-based immunotherapies to regress further distant metastases. PEP005 was shown to have adjuvant properties, being able to upregulate CD80 and CD86 expression on dendritic cells in vivo, and to promote CD8 T cell induction when co-delivered with a protein antigen. PEP005 thus emerges as a unique local chemotherapeutic immunostimulatory debulking agent that could be used in conjunction with immunotherapies to promote regression of metastases.

Neutrophils are a key component of the antitumor efficacy of topical chemotherapy with ingenol-3-angelate.

Harnessing neutrophils for the eradication of cancer cells remains an attractive but still controversial notion. In this study, we provide evidence that neutrophils are required to prevent relapse of skin tumors following topical treatment with a new anticancer agent, ingenol-3-angelate (PEP005). Topical PEP005 treatment induces primary necrosis of tumor cells, potently activates protein kinase C, and was associated with an acute T cell-independent inflammatory response characterized by a pronounced neutrophil infiltrate. In Foxn1(nu) mice depleted of neutrophils and in CD18-deficient mice (in which neutrophil extravasation is severely impaired) PEP005 treatment was associated with a >70% increase in tumor relapse rates. NK cell or monocyte/macrophage deficiency had no effect on relapse rates. Both in vitro and in mice, PEP005 induced MIP-2/IL-8, TNF-alpha, and IL-1beta, all mediators of neutrophil recruitment and activation. In vitro, PEP005 activated human endothelial cells resulting in neutrophil adhesion and also induced human neutrophils to generate tumoricidal-reactive oxygen intermediates. Treatment of tumors with PEP005 significantly elevated the level of anticancer Abs, which were able to promote neutrophil-mediated Ab-dependent cellular cytotoxicity (ADCC) in vitro. PEP005 treatment of tumors grown in SCID mice was also associated with >70% increase in tumor relapse rates. Taken together, these data suggest a central role for neutrophil-mediated ADCC in preventing relapse. PEP005-mediated cure of tumors therefore appears to involve initial chemoablation followed by a neutrophil-dependent ADCC-mediated eradication of residual disease, illustrating that neutrophils can be induced to mediate important anticancer activity with specific chemotherapeutic agents.

Heat shock protein 10 inhibits lipopolysaccharide-induced inflammatory mediator production.

Heat shock protein 10 (Hsp10) and heat shock protein 60 (Hsp60) were originally described as essential mitochondrial proteins involved in protein folding. However, both proteins have also been shown to have a number of extracellular immunomodulatory activities. Here we show that purified recombinant human Hsp10 incubated with cells in vitro reduced lipopolysaccharide (LPS)-induced nuclear factor-kappaB activation and secretion of several inflammatory mediators from RAW264.7 cells, murine macrophages, and human peripheral blood mononuclear cells. Induction of tolerance by contaminating LPS was formally excluded as being responsible for Hsp10 activity. Treatment of mice with Hsp10 before endotoxin challenge resulted in the reduction of serum tumor necrosis factor-alpha and RANTES (regulated upon activation, normal T cell expressed and secreted) levels and an elevation of serum interleukin-10 levels. Hsp10 treatment also delayed mortality in a murine graft-versus-host disease model, where gut-derived LPS contributes to pathology. We were unable to confirm previous reports that Hsp10 has tumor growth factor properties and suggest that Hsp10 exerts anti-inflammatory activity by inhibiting Toll-like receptor signaling possibly by interacting with extracellular Hsp60.

ISCOM based vaccines for cancer immunotherapy.

Immunostimulating complex (ISCOM) vaccines are particulate antigen delivery vehicles composed of saponin, cholesterol, phospholipid and immunogen. Here we illustrate that ISCOM-based vaccines represent an attractive modality for the development of anti-cancer vaccines. Using murine models and a model cancer antigen, ISCOM vaccines were shown to induce potent CD8 T cell responses, to mediate protection in three different tumor models, to promote Th1-biased immunity, and to induce CD8 T cell responses in the absence of CD4+ T cell help. The former three activities were also found to be substantially improved when the vaccine antigen was associated with the ISCOM structure. Furthermore, the presence in vivo of pre-existing antibodies against the vaccine antigen did not inhibit CD8 T cell induction by the ISCOM vaccine. Although vaccination was effective against challenge with vaccine-antigen expressing tumors, no activity against neighboring vaccine-antigen negative tumor cells was observed, indicating that determinant spreading or bystander activity does not lead to significant anti-cancer activity.