Effects of antineoplastic and immunomodulating agents on postvaccination SARS-CoV-2 breakthrough infections, antibody response, and serological cytokine profile.

BACKGROUND: Despite immunization, patients on antineoplastic and immunomodulating agents have a heightened risk of COVID-19 infection. However, accurately attributing this risk to specific medications remains challenging. METHODS: An observational cohort study from December 11, 2020 to September 22, 2022, within a large healthcare system in San Diego, California, USA was designed to identify medications associated with greatest risk of postimmunization SARS-CoV-2 infection. Adults prescribed WHO Anatomical Therapeutic Chemical (ATC) classified antineoplastic and immunomodulating medications were matched (by age, sex, race, and number of immunizations) with control patients not prescribed these medications yielding a population of 26 724 patients for analysis. From this population, 218 blood samples were collected from an enrolled subset to assess serological response and cytokine profile in relation to immunization. RESULTS: Prescription of WHO ATC classified antineoplastic and immunomodulatory agents was associated with elevated postimmunization SARS-CoV-2 infection risk (HR 1.50, 95% CI 1.38 to 1.63). While multiple immunization doses demonstrated a decreased association with postimmunization SARS-CoV-2 infection risk, antineoplastic and immunomodulatory treated patients with four doses remained at heightened risk (HR 1.23, 95% CI 1.06 to 1.43). Risk variation was identified among medication subclasses, with PD-1/PD-L1 inhibiting monoclonal antibodies, calcineurin inhibitors, and CD20 monoclonal antibody inhibitors identified to associate with increased risk of postimmunization SARS-CoV-2 infection. Antineoplastic and immunomodulatory treated patients also displayed a reduced IgG antibody response to SARS-CoV-2 epitopes alongside a unique serum cytokine profile. CONCLUSIONS: Antineoplastic and immunomodulating medications associate with an elevated risk of postimmunization SARS-CoV-2 infection in a drug-specific manner. This comprehensive, unbiased analysis of all WHO ATC classified antineoplastic and immunomodulating medications identifies medications associated with greatest risk. These findings are crucial in guiding and refining vaccination strategies for patients prescribed these treatments, ensuring optimized protection for this susceptible population in future COVID-19 variant surges and potentially for other RNA immunization targets.

Doublecortin-like kinase 1 is a therapeutic target in squamous cell carcinoma.

Doublecortin like kinase 1 (DCLK1) plays a crucial role in several cancers including colon and pancreatic adenocarcinomas. However, its role in squamous cell carcinoma (SCC) remains unknown. To this end, we examined DCLK1 expression in head and neck SCC (HNSCC) and anal SCC (ASCC). We found that DCLK1 is elevated in patient SCC tissue, which correlated with cancer progression and poorer overall survival. Furthermore, DCLK1 expression is significantly elevated in human papilloma virus negative HNSCC, which are typically aggressive with poor responses to therapy. To understand the role of DCLK1 in tumorigenesis, we used specific shRNA to suppress DCLK1 expression. This significantly reduced tumor growth, spheroid formation, and migration of HNSCC cancer cells. To further the translational relevance of our studies, we sought to identify a selective DCLK1 inhibitor. Current attempts to target DCLK1 using pharmacologic approaches have relied on nonspecific suppression of DCLK1 kinase activity. Here, we demonstrate that DiFiD (3,5-bis [2,4-difluorobenzylidene]-4-piperidone) binds to DCLK1 with high selectivity. Moreover, DiFiD mediated suppression of DCLK1 led to G2/M arrest and apoptosis and significantly suppressed tumor growth of HNSCC xenografts and ASCC patient derived xenografts, supporting that DCLK1 is critical for SCC growth.

6 month serologic response to the Pfizer-BioNTech COVID-19 vaccine among healthcare workers.

AIM: Healthcare workers (HCWs) were among the first group of people vaccinated with the Pfizer-BioNTech Covid-19 vaccine (BNT162b2). Characterization of the kinetics of antibody response to vaccination is important to devise future vaccination strategies. To better characterize the antibody response to BNT162b2, we analyzed the kinetics of IgG and IgM antibody response to 5 different SARS-CoV-2 epitopes over a period of 6 months. METHODS AND RESULTS: An observational single-centered study was conducted to evaluate the temporal dynamics of anti-SARS-CoV-2 antibodies following immunization with two doses of BNT162b2. Anti-SARS-CoV-2 antibodies were assessed using the Maverick SARS-CoV-2 multi-antigen panel (Genalyte Inc.). Healthcare workers aged ≥18 receiving BNT162b2 vaccination who self-reported no prior symptoms of COVID-19 nor prior COVID-19 PCR test positivity, were included in this study. HCWs developed an IgG antibody response to SARS-CoV-2 Spike S1, Spike S1 receptor binding domain (RBD), Spike S1S2 and Spike S2 after vaccination. IgG response was observed at two weeks following immunization in most participant samples and continued to increase at week 4, but subsequently decreased significantly starting at 3 months and up to 6 months. In contrast, IgM response to respective epitopes was minimal. CONCLUSION: Multiplex results demonstrate that, contrary to natural infection, immunization with BNT162b2 produces minimal anti-Spike IgM response. Polyclonal IgG response to Spike declined at 3 months and continued to do so up to 6 months.

Machine Learning to Predict Treatment in Oropharyngeal Squamous Cell Carcinoma.

OBJECTIVES: This study seeks to (1) demonstrate how machine learning (ML) can be used for prediction modeling by predicting the treatment patients with T1-2, N0-N1 oropharyngeal squamous cell carcinoma (OPSCC) receive and (2) assess the impact patient, socioeconomic, regional, and institutional factors have in the treatment of this population. METHODS: A retrospective cohort of adults diagnosed with T1-2, N0-N1 OPSCC from 2004 to 2013 was obtained using the National Cancer Database. The data was split into 80/20 distribution for training and testing, respectively. Various ML algorithms were explored for development. Area under the curve (AUC), accuracy, precision, and recall were calculated for the final model. RESULTS: Among the 19,111 patients in the study, the mean (standard deviation) age was 61.3 (10.8) years, 14,034 (73%) were male, and 17,292 (91%) were white. Surgery was the primary treatment in 9,533 (50%) cases and radiation in 9,578 (50%) cases. The model heavily utilized T-stage, primary site, N-stage, grade, and type of treatment facility to predict the primary treatment modality. The final model yielded an AUC of 78% (95% CI, 77-79%), accuracy of 71%, precision of 72%, and recall of 71%. CONCLUSION: This study created a ML model utilizing clinical variables to predict primary treatment modality for T1-2, N0-N1 OPSCC. This study demonstrates how ML can be used for prediction modeling while also highlighting that tumor and facility realted variables impact the decision making process on a national level.

EMR quantity autopopulation removal on hospital discharge prescribing patterns: Implications for opioid stewardship.

WHAT IS KNOWN AND OBJECTIVE: Prescription drug stewardship is critical. Autopopulation(AP) of medication quantities may influence prescriber behaviour. We investigate the impact of AP removal(APR) on opioid prescribing. METHODS: Inpatient and emergency department(ED) discharges with opioid pain medications 2 years before and after APR were identified. Milligrams of morphine equivalents(MMEs) prescribed were recorded. Group comparisons were performed using Mann-Whitney U tests. Spearman's rho was used to analyse correlations between pain level and quantity of prescribed opioids. Mann-Kendall tests assessed trends in prescription patterns. Generalized estimating equations assessed trends in total quantity of prescribed MME. RESULTS AND DISCUSSION: A total of 53 608 patient encounters were included for analysis. In surgical patients, there were no trends in the frequency of prescriptions below, at or above the AP quantity pre-APR. Post-APR, there was a decrease in the percentage of prescriptions written for the AP quantity(τ = -.493, P = .001) and an increase in prescriptions for <30 tablets(τ = .468,P = .001). In non-operative patients, the pre-APR period was associated with a lower percentage of prescriptions >30 tablets and a greater percentage of prescriptions for <30 tablets. Interestingly, APR reversed this trend in prescriptions for >30 tablets and resulted in an increase in larger prescriptions. Multivariate analysis of the total prescribed quantity of MME found no significant trend across months for inpatients prior to and after APR (0.997, P = .065 and 1.003, P = .142; respectively). The ED model found a monthly downward trend in amount of prescribed MME prior to and after APR (0.986, P < .001 and 0.990, P < .001; respectively). In the inpatient setting, pain was positively correlated to discharge MME (ρ = .028, P < .001); with those reporting the highest pain receiving the greatest amount of opioids both pre- and post-APR. Interestingly, in the ED, this finding was negatively correlated (ρ = -.086, P < .001); with those reporting the lowest pain receiving the greatest amount of opioids both pre- and post-APR. WHAT IS NEW AND CONCLUSIONS: AP removal may have unintended consequences, such as increased prescriptions for greater quantities. To drive down prescription amounts, lower anchor values may be of more utility than APR. The poor correlation of pain values with prescribed medications warrants further investigation.

The false vital sign: When pain levels are not predictive of discharge opioid prescriptions.

BACKGROUND: Pain gained recognition as a vital sign in the early 2000s, underscoring the importance of accurate documentation, characterization, and treatment of pain. No prior studies have demonstrated the utility of the 0-10 pain scale with respect to discharge opioid prescriptions, nor characterized the most influential factors in discharge prescriptions. METHODS: Inpatient and emergency department(ED) encounters from July 1, 2012 to April 1, 2018 resulting in a discharge prescription for tablet opioid medications were identified. The primary outcome was to determine if pain levels in 24 h prior to discharge correlated with opioids (in milligrams of morphine equivalents (MME)) prescribed. Secondary outcomes included the impact of patient and prescriber demographics, demographics. A generalized linear model was created to investigate factors affecting the quantity of prescribed opioids. RESULTS: n = 78,691 patient encounters. Overall mean adjusted MME for non-ED visits was 378 versus 197 for ED visits. Whites received the highest quantities; those identifying as non-white and non-black received the lowest. Women received significantly fewer discharge MMEs in both the ED and inpatient cohorts. Provider prescribing patterns exhibited the most profound effect on discharge MMEs. The most prolific (≥300 prescriptions over the study period) writing the largest amount. In the ED, there was a significant negative correlation between documented pain levels and discharge MMEs(ρ = 0.074,p < 0.001). CONCLUSIONS: Pain scale was significantly negatively correlated with discharge MMEs in the ED and positively correlated in the inpatient population. Individual prescriber characteristics were the more influential variable, with prolific high prescribers writing for the largest MME amounts. The inverse association of pain and MMEs at discharge in the ED, and the large effect pre-existing prescriber patterns exhibited, both improved methodology for assessing and appropriately treating pain, and effective prescriber-targeted interventions, must be a priority.

Development and Assessment of a Machine Learning Model to Help Predict Survival Among Patients With Oral Squamous Cell Carcinoma.

Importance: Predicting survival of oral squamous cell carcinoma through the use of prediction modeling has been underused, and the development of prediction models would augment clinicians' ability to provide absolute risk estimates for individual patients. Objectives: To develop a prediction model using machine learning for 5-year overall survival among patients with oral squamous cell carcinoma and compare this model with a prediction model created from the TNM (Tumor, Node, Metastasis) clinical and pathologic stage. Design, Setting, and Participants: A retrospective cohort study was conducted of 33 065 patients with oral squamous cell carcinoma from the National Cancer Data Base between January 1, 2004, and December 31, 2011. Patients were excluded if the treatment was considered palliative, staging demonstrated T0 or Tis, or survival or staging data were missing. Patient, tumor, treatment, and outcome information were obtained from the National Cancer Data Base. The data were split into a distribution of 80% for training and 20% for testing. The model was created using 2-class decision forest architecture. Permutation feature importance scores were used to determine the variables that were used in the model's prediction and their order of significance. Statistical analysis was conducted from August 1, 2018, to January 10, 2019. Main Outcomes and Measures: Ability to predict 5-year overall survival assessed through area under the curve, accuracy, precision, and recall. Results: Among the 33 065 patients in the study, the mean (SD) age was 64.6 (14.0) years, 19 791 were men (59.9%), 13 274 were women (40.1%), and 29 783 (90.1%) were white. At 60 months, there were 16 745 deaths (50.6%). The median time of follow-up was 56.8 months (range, 0-155.6 months). Age, pathologic T stage, positive margins at the time of surgery, lymph node size, and institutional identification were identified among the most significant variables. The calculated area under the curve for this machine learning model was 0.80 (95% CI, 0.79-0.81), accuracy was 71%, precision was 71%, and recall was 68%. In comparison, the calculated area under the curve of the TNM staging system was 0.68 (95% CI, 0.67-0.70), accuracy was 65%, precision was 69%, and recall was 52%. Conclusions and Relevance: Using machine learning algorithms, a prediction model was created based on patient social, demographic, clinical, and pathologic features. The developed prediction model proved to be better than a prediction model that exclusively used TNM pathologic and clinical stage according to all performance metrics. This study highlights the role that machine learning may play in individual patient risk estimation in the era of big data.

Pleotropic role of RNA binding protein CELF2 in autophagy induction.

We previously reported that ionizing radiation (IR) mediates cell death through the induction of CUGBP elav-like family member 2 (CELF2), a tumor suppressor. CELF2 is an RNA binding protein that modulates mRNA stability and translation. Since IR induces autophagy, we hypothesized that CELF2 regulates autophagy-mediated colorectal cancer (CRC) cell death. For clinical relevance, we determined CELF2 levels in The Cancer Genome Atlas (TCGA). Role of CELF2 in radiation response was carried out in CRC cell lines by immunoblotting, immunofluorescence, autophagic vacuole analyses, RNA stability assay, quantitative polymerase chain reaction and electron microscopy. In vivo studies were performed in a xenograft tumor model. TCGA analyses demonstrated that compared to normal tissue, CELF2 is expressed at significantly lower levels in CRC, and is associated with better overall 5-year survival in patients receiving radiation. Mechanistically, CELF2 increased levels of critical components of the autophagy cascade including Beclin-1, ATG5, and ATG12 by modulating mRNA stability. CELF2 also increased autophagic flux in CRC. IR significantly induced autophagy in CRC which correlates with increased levels of CELF2 and autophagy associated proteins. Silencing CELF2 with siRNA, mitigated IR induced autophagy. Moreover, knockdown of CELF2 in vivo conferred tumor resistance to IR. These studies elucidate an unrecognized role for CELF2 in inducing autophagy and potentiating the effects of radiotherapy in CRC.

Autophagy-dependent secretion: mechanism, factors secreted, and disease implications.

Although best understood as a degradative pathway, recent evidence demonstrates pronounced involvement of the macroautophagic/autophagic molecular machinery in cellular secretion. With either overexpression or inhibition of autophagy mediators, dramatic alterations in the cellular secretory profile occur. This affects secretion of a plethora of factors ranging from cytokines, to granule contents, and even viral particles. Encompassing a wide range of secreted factors, autophagy-dependent secretion is implicated in diseases ranging from cancer to neurodegeneration. With a growing body of evidence shedding light onto the molecular mediators, this review delineates the molecular machinery involved in selective targeting of the autophagosome for either degradation or secretion. In addition, we summarize the current understanding of factors and cargo secreted through this unconventional route, and describe the implications of this pathway in both health and disease. Abbreviations: BECN1, beclin 1; CAF, cancer associated fibroblast; CUPS, compartment for unconventional protein secretion; CXCL, C-X-C motif chemokine ligand; ER, endoplasmic reticulum; FGF2, fibroblast growth factor 2; HMGB1, high mobility group box 1; IDE, insulin degrading enzyme; IL, Interleukin; MAP1LC3/LC3, microtubule associated protein 1 light chain 3; MAPS, misfolding associated protein secretion; MEF, mouse embryonic fibroblast; MTORC1, MTOR complex I; PtdIns, phosphatidyl inositol; SEC22B, SEC22 homolog B, vesicle trafficking protein (gene/pseudogene); SFV, Semliki forest virus; SNCA, synuclein alpha; SQSTM1, sequestosome 1; STX, Syntaxin; TASCC, TOR-associated spatial coupling compartment; TGFB, transforming growth factor beta; TRIM16, tripartite motif containing 16; UPS, unconventional protein secretion; VWF, von Willebrand factor.

Using Machine Learning to Predict Sensorineural Hearing Loss Based on Perilymph Micro RNA Expression Profile.

Hearing loss (HL) is the most common neurodegenerative disease worldwide. Despite its prevalence, clinical testing does not yield a cell or molecular based identification of the underlying etiology of hearing loss making development of pharmacological or molecular treatments challenging. A key to improving the diagnosis of inner ear disorders is the development of reliable biomarkers for different inner ear diseases. Analysis of microRNAs (miRNA) in tissue and body fluid samples has gained significant momentum as a diagnostic tool for a wide variety of diseases. In previous work, we have shown that miRNA profiling in inner ear perilymph is feasible and may demonstrate distinctive miRNA expression profiles unique to different diseases. A first step in developing miRNAs as biomarkers for inner ear disease is linking patterns of miRNA expression in perilymph to clinically available metrics. Using machine learning (ML), we demonstrate we can build disease specific algorithms that predict the presence of sensorineural hearing loss using only miRNA expression profiles. This methodology not only affords the opportunity to understand what is occurring on a molecular level, but may offer an approach to diagnosing patients with active inner ear disease.

Artificial Intelligence for the Otolaryngologist: A State of the Art Review.

OBJECTIVE: To provide a state of the art review of artificial intelligence (AI), including its subfields of machine learning and natural language processing, as it applies to otolaryngology and to discuss current applications, future impact, and limitations of these technologies. DATA SOURCES: PubMed and Medline search engines. REVIEW METHODS: A structured search of the current literature was performed (up to and including September 2018). Search terms related to topics of AI in otolaryngology were identified and queried to identify relevant articles. CONCLUSIONS: AI is at the forefront of conversation in academic research and popular culture. In recent years, it has been touted for its potential to revolutionize health care delivery. Yet, to date, it has made few contributions to actual medical practice or patient care. Future adoption of AI technologies in otolaryngology practice may be hindered by misconceptions of what AI is and a fear that machine errors may compromise patient care. However, with potential clinical and economic benefits, it is vital for otolaryngologists to understand the principles and scope of AI. IMPLICATIONS FOR PRACTICE: In the coming years, AI is likely to have a major impact on biomedical research and the practice of medicine. Otolaryngologists are key stakeholders in the development and clinical integration of meaningful AI technologies that will improve patient care. High-quality data collection is essential for the development of AI technologies, and otolaryngologists should seek opportunities to collaborate with data scientists to guide them toward the most impactful clinical questions.

Machine Learning to Predict Delays in Adjuvant Radiation following Surgery for Head and Neck Cancer.

OBJECTIVE: To apply a novel methodology with machine learning (ML) to a large national cancer registry to help identify patients who are high risk for delayed adjuvant radiation. STUDY DESIGN: Observational cohort study. SETTING: National Cancer Database (NCDB). SUBJECTS AND METHODS: A total of 76,573 patients were identified from the NCDB who had invasive head and neck cancer and underwent surgery, followed by radiation. The model was constructed from 80% of the patient data and subsequently evaluated and scored with the remaining 20%. Permutation feature importance analysis was used to understand the weighted model construction. RESULTS: A total of 76,573 patients met inclusion and exclusion criteria. Our ML model was able to predict whether patients would start adjuvant therapy beyond 50 days after surgery with an overall accuracy of 64.41% and a precision of 58.5%. The 2 most important variables used to build the model were treating facility and urban versus rural demographics. CONCLUSION: Statistics can provide inferences within an overall system, while ML is a novel methodology that can make predictions. We can identify patients who are "high risk" for delayed radiation using information from >75,000 patient experiences, which has the potential for a direct impact on clinical care. Our inability to achieve greater accuracy is due to limitations of the data captured by the NCDB, and we need to continue to identify new variables that are correlated with delayed radiation therapy. ML will prove to be a valuable clinical tool in years to come, but its utility is limited by available data.

Potent Antitumor Effects of a Combination of Three Nutraceutical Compounds.

Head and neck squamous cell carcinoma (HNSCC) is associated with low survival, and the current aggressive therapies result in high morbidity. Nutraceuticals are dietary compounds with few side effects. However, limited antitumor efficacy has restricted their application for cancer therapy. Here, we examine combining nutraceuticals, establishing a combination therapy that is more potent than any singular component, and delineate the mechanism of action. Three formulations were tested: GZ17-S (combined plant extracts from Arum palaestinum, Peganum harmala and Curcuma longa); GZ17-05.00 (16 synthetic components of GZ17-S); and GZ17-6.02 (3 synthetic components of GZ17S; curcumin, harmine and isovanillin). We tested the formulations on HNSCC proliferation, migration, invasion, angiogenesis, macrophage viability and infiltration into the tumor and tumor apoptosis. GZ17-6.02, the most effective formulation, significantly reduced in vitro assessments of HNSCC progression. When combined with cisplatin, GZ17-6.02 enhanced anti-proliferative effects. Molecular signaling cascades inhibited by GZ17-6.02 include EGFR, ERK1/2, and AKT, and molecular docking analyses demonstrate GZ17-6.02 components bind at distinct binding sites. GZ17-6.02 significantly inhibited growth of HNSCC cell line, patient-derived xenografts, and murine syngeneic tumors in vivo (P < 0.001). We demonstrate GZ17-6.02 as a highly effective plant extract combination and pave the way for future clinical application in HNSCC.

Cancer-Associated Fibroblasts Drive Glycolysis in a Targetable Signaling Loop Implicated in Head and Neck Squamous Cell Carcinoma Progression.

Despite aggressive therapies, head and neck squamous cell carcinoma (HNSCC) is associated with a less than 50% 5-year survival rate. Late-stage HNSCC frequently consists of up to 80% cancer-associated fibroblasts (CAF). We previously reported that CAF-secreted HGF facilitates HNSCC progression; however, very little is known about the role of CAFs in HNSCC metabolism. Here, we demonstrate that CAF-secreted HGF increases extracellular lactate levels in HNSCC via upregulation of glycolysis. CAF-secreted HGF induced basic FGF (bFGF) secretion from HNSCC. CAFs were more efficient than HNSCC in using lactate as a carbon source. HNSCC-secreted bFGF increased mitochondrial oxidative phosphorylation and HGF secretion from CAFs. Combined inhibition of c-Met and FGFR significantly inhibited CAF-induced HNSCC growth in vitro and in vivo (P < 0.001). Our cumulative findings underscore reciprocal signaling between CAF and HNSCC involving bFGF and HGF. This contributes to metabolic symbiosis and a targetable therapeutic axis involving c-Met and FGFR.Significance: HNSCC cancer cells and CAFs have a metabolic relationship where CAFs secrete HGF to induce a glycolytic switch in HNSCC cells and HNSCC cells secrete bFGF to promote lactate consumption by CAFs. Cancer Res; 78(14); 3769-82. ©2018 AACR.

Differential Gene Expression and Pathway Analysis in Juvenile Nasopharyngeal Angiofibroma Using RNA Sequencing.

Juvenile nasopharyngeal angiofibroma (JNA) is a highly vascularized and locally aggressive tumor that typically presents in adolescent males. The molecular biology of this tumor remains understudied. We sought to identify differentially expressed genes in the JNA transcriptome through messenger RNA sequencing of primary fibroblasts from 2 tumor explants and tonsil tissue from tumor-free subjects. In total, 1088 significant, differentially expressed genes were identified with 749 upregulated and 339 downregulated. Pathway analysis identified a number of activated signaling pathways, most notably, the vascular endothelial growth factor (VEGF) pathway (adjusted overlap P = .03). VEGF-A showed a 4.4-fold upregulation in JNA samples. In addition, the angiogenic receptor, fibroblast growth factor receptor 2 (FGFR2), was not present in tumor-free samples but increased in JNA. We validate these findings with immunohistochemistry, demonstrating upregulation of VEGF and FGFR2 in patient sections. Inhibition of the VEGF or FGFR signaling axes may have therapeutic potential in the treatment of JNA.

Development and Characterization of an In Vitro Model for Radiation-Induced Fibrosis.

Radiation-induced fibrosis (RIF) is a major side effect of radiotherapy in cancer patients with no effective therapeutic options. RIF involves excess deposition and aberrant remodeling of the extracellular matrix (ECM) leading to stiffness in tissues and organ failure. Development of preclinical models of RIF is crucial to elucidate the molecular mechanisms regulating fibrosis and to develop therapeutic approaches. In addition to radiation, the main molecular perpetrators of fibrotic reactions are cytokines, including transforming growth factor-ß (TGF-ß). We hypothesized that human oral fibroblasts would develop an in vitro fibrotic reaction in response to radiation and TGF-ß. We demonstrate here that fibroblasts exposed to radiation followed by TGF-ß exhibit a fibrotic phenotype with increased collagen deposition, cell proliferation, migration and invasion. In this in vitro model of RIF (RIFiv), the early biological processes involved in fibrosis are demonstrated, along with increased levels of several molecules including collagen 1α1, collagen XIα1, integrin-α2 and cyclin D1 mRNA in irradiated cells. A clinically relevant antifibrotic agent, pentoxifylline, and a curcumin analogue both mitigated collagen deposition in irradiated fibroblast cultures. In summary, we have established an in vitro model for RIF that facilitates the elucidation of molecular mechanisms in radiation-induced fibrosis and the development of effective therapeutic approaches.

Secretory Autophagy in Cancer-Associated Fibroblasts Promotes Head and Neck Cancer Progression and Offers a Novel Therapeutic Target.

Despite therapeutic advancements, there has been little change in the survival of patients with head and neck squamous cell carcinoma (HNSCC). Recent results suggest that cancer-associated fibroblasts (CAF) drive progression of this disease. Here, we report that autophagy is upregulated in HNSCC-associated CAFs, where it is responsible for key pathogenic contributions in this disease. Autophagy is fundamentally involved in cell degradation, but there is emerging evidence that suggests it is also important for cellular secretion. Thus, we hypothesized that autophagy-dependent secretion of tumor-promoting factors by HNSCC-associated CAFs may explain their role in malignant development. In support of this hypothesis, we observed a reduction in CAF-facilitated HNSCC progression after blocking CAF autophagy. Studies of cell growth media conditioned after autophagy blockade revealed levels of secreted IL6, IL8, and other cytokines were modulated by autophagy. Notably, when HNSCC cells were cocultured with normal fibroblasts, they upregulated autophagy through IL6, IL8, and basic fibroblast growth factor. In a mouse xenograft model of HNSCC, pharmacologic inhibition of Vps34, a key mediator of autophagy, enhanced the antitumor efficacy of cisplatin. Our results establish an oncogenic function for secretory autophagy in HNSCC stromal cells that promotes malignant progression. Cancer Res; 77(23); 6679-91. ©2017 AACR.

Inhibition of fibroblast growth factor receptor with AZD4547 mitigates juvenile nasopharyngeal angiofibroma.

BACKGROUND: Juvenile nasopharyngeal angiofibroma (JNA) is a benign tumor that presents in adolescent males. Although surgical excision is the mainstay of treatment, recurrences complicate treatment. There is a need to develop less invasive approaches for management. JNA tumors are composed of fibroblasts and vascular endothelial cells. We identified fibroblast growth factor receptor (FGFR) and vascular endothelial growth factor (VEGF) expression in JNA-derived fibroblasts. FGFR influences fibroblast proliferation and VEGF is necessary for angiogenesis. We hypothesized that targeting FGFR would mitigate JNA fibroblast proliferation, invasion, and migration, and that targeting the VEGF receptor would attenuate endothelial tubule formation. METHODS: After informed consent, fibroblasts from JNA explants of 3 patients were isolated. Fibroblasts were treated with FGFR inhibitor AZD4547, 0 to 25 µg/mL for 72 hours and proliferation was quantified using CyQuant assay. Migration and invasion of JNA were assessed using 24-hour transwell assays with subsequent fixation and quantification. Mitigation of FGFR and downstream signaling was evaluated by immunoblotting. Tubule formation was assessed in human umbilical vein endothelial cells (HUVECs) treated with vehicle control (dimethylsulfoxide [DMSO]) or semaxanib (SU5416) as well as in serum-free media (SFM) or JNA conditioned media (CM). Tubule length was compared between treatment groups. RESULTS: Compared to control, AZD4547 inhibited JNA fibroblast proliferation, migration, and invasion through inhibition of FGFR and downstream signaling, specifically phosphorylation of - p44/42 mitogen activated protein kinase (p44/42 MAPK). JNA fibroblast CM significantly increased HUVEC tubule formation (p = 0.0039). CONCLUSION: AZD4547 effectively mitigates FGFR signaling and decreases JNA fibroblast proliferation, migration, and invasion. SU5416 attenuated JNA fibroblast-induced tubule formation. AZD4547 may have therapeutic potential in the treatment of JNA.

Everolimus downregulates estrogen receptor and induces autophagy in aromatase inhibitor-resistant breast cancer cells.

BACKGROUND: mTOR inhibition of aromatase inhibitor (AI)-resistant breast cancer is currently under evaluation in the clinic. Everolimus/RAD001 (Afinitor®) has had limited efficacy as a solo agent but is projected to become part of combination therapy for AI-resistant breast cancer. This study was conducted to investigate the anti-proliferative and resistance mechanisms of everolimus in AI-resistant breast cancer cells. METHODS: In this study we utilized two AI-resistant breast cancer cell lines, MCF-7:5C and MCF-7:2A, which were clonally derived from estrogen receptor positive (ER+) MCF-7 breast cancer cells following long-term estrogen deprivation. Cell viability assay, colony formation assay, cell cycle analysis and soft agar anchorage-independent growth assay were used to determine the efficacy of everolimus in inhibiting the proliferation and tumor forming potential of MCF-7, MCF-7:5C, MCF-7:2A and MCF10A cells. Confocal microscopy and transmission electron microscopy were used to evaluate LC3-II production and autophagosome formation, while ERE-luciferase reporter, Western blot, and RT-PCR analyses were used to assess ER expression and transcriptional activity. RESULTS: Everolimus inhibited the proliferation of MCF-7:5C and MCF-7:2A cells with relatively equal efficiency to parental MCF-7 breast cancer cells. The inhibitory effect of everolimus was due to G1 arrest as a result of downregulation of cyclin D1 and p21. Everolimus also dramatically reduced estrogen receptor (ER) expression (mRNA and protein) and transcriptional activity in addition to the ER chaperone, heat shock protein 90 protein (HSP90). Everolimus restored 4-hydroxy-tamoxifen (4OHT) sensitivity in MCF-7:5C cells and enhanced 4OHT sensitivity in MCF-7 and MCF-7:2A cells. Notably, we found that autophagy is one method of everolimus insensitivity in MCF-7 breast cancer cell lines. CONCLUSION: This study provides additional insight into the mechanism(s) of action of everolimus that can be used to enhance the utility of mTOR inhibitors as part of combination therapy for AI-resistant breast cancer.

Radiation-induced fibrosis: mechanisms and implications for therapy.

PURPOSE: Radiation-induced fibrosis (RIF) is a long-term side effect of external beam radiation therapy for the treatment of cancer. It results in a multitude of symptoms that significantly impact quality of life. Understanding the mechanisms of RIF-induced changes is essential to developing effective strategies to prevent long-term disability and discomfort following radiation therapy. In this review, we describe the current understanding of the etiology, clinical presentation, pathogenesis, treatment, and directions of future therapy for this condition. METHODS: A literature review of publications describing mechanisms or treatments of RIF was performed. Specific databases utilized included PubMed and clinicaltrials.gov, using keywords "Radiation-Induced Fibrosis," "Radiotherapy Complications," "Fibrosis Therapy," and other closely related terms. RESULTS: RIF is the result of a misguided wound healing response. In addition to causing direct DNA damage, ionizing radiation generates reactive oxygen and nitrogen species that lead to localized inflammation. This inflammatory process ultimately evolves into a fibrotic one characterized by increased collagen deposition, poor vascularity, and scarring. Tumor growth factor beta serves as the primary mediator in this response along with a host of other cytokines and growth factors. Current therapies have largely been directed toward these molecular targets and their associated signaling pathways. CONCLUSION: Although RIF is widely prevalent among patients undergoing radiation therapy and significantly impacts quality of life, there is still much to learn about its pathogenesis and mechanisms. Current treatments have stemmed from this understanding, and it is anticipated that further elucidation will be essential for the development of more effective therapies.