Targeting TNF-α for cancer therapy

As the tumor vasculature is a key element of the tumor stroma, angiogenesis is the target of many cancer therapies. Recent work published in BMC Cell Biology describes a fusion protein that combines a peptide previously shown to home in on the gastric cancer vasculature with the anti-tumor cytokine TNF-α, and assesses its potential for gastric cancer therapy

Antiangiogenic gene therapy of cancer: recent developments

With the role of angiogenesis in tumor growth and progression firmly established, considerable effort has been directed to antiangiogenic therapy as a new modality to treat human cancers. Antiangiogenic agents have recently received much widespread attention but strategies for their optimal use are still being developed. Gene therapy represents an attractive alternative to recombinant protein administration for several reasons. This review evaluates the potential advantages of gene transfer for antiangiogenic cancer therapy and describes preclinical gene transfer work with endogenous angiogenesis inhibitors demonstrating the feasibility of effectively suppressing and even eradicating tumors in animal models. Additionally, we describe the advantages and disadvantages of currently available gene transfer vectors and update novel developments in this field. In conclusion, gene therapy holds great promise in advancing antiangiogenesis as an effective cancer therapy and will undoubtedly be evaluated in human clinical trials in the near future

A novel technique for quantifying changes in vascular density, endothelial cell proliferation and protein expression in response to modulators of angiogenesis using the chick chorioallantoic membrane (CAM) assay

Reliable quantitative evaluation of molecular pathways is critical for both drug discovery and treatment monitoring. We have modified the CAM assay to quantitatively measure vascular density, endothelial proliferation, and changes in protein expression in response to anti-angiogenic and pro-angiogenic agents. This improved CAM assay can correlate changes in vascular density with changes seen on a molecular level. We expect that these described modifications will result in a single in vivo assay system, which will improve the ability to investigate molecular mechanisms underlying the angiogenic response

Tumor-associated endothelial cells display GSTP1 and RARβ2 promoter methylation in human prostate cancer

BACKGROUND: A functional blood supply is essential for tumor growth and proliferation. However, the mechanism of blood vessel recruitment to the tumor is still poorly understood. Ideally, a thorough molecular assessment of blood vessel cells would be critical in our comprehension of this process. Yet, to date, there is little known about the molecular makeup of the endothelial cells of tumor-associated blood vessels, due in part to the difficulty of isolating a pure population of endothelial cells from the heterogeneous tissue environment. METHODS: Here we describe the use of a recently developed technique, Expression Microdissection, to isolate endothelial cells from the tumor microenvironment. The methylation status of the dissected samples was evaluated for GSTP1 and RARβ2 promoters via the QMS-PCR method. RESULTS: Comparing GSTP1 and RARβ2 promoter methylation data, we show that 100% and 88% methylation is detected, respectively, in the tumor areas, both in epithelium and endothelium. Little to no methylation is observed in non-tumor tissue areas. CONCLUSION: We applied an accurate microdissection technique to isolate endothelial cells from tissues, enabling DNA analysis such as promoter methylation status. The observations suggest that epigenetic alterations may play a role in determining the phenotype of tumor-associated vasculature

Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling

Background:: Checkpoint inhibitor therapy of cancer has led to markedly improved survival of a subset of patients in multiple solid malignant tumor types, yet the factors driving these clinical responses or lack thereof are not known. We have developed a mechanistic mathematical model for better understanding these factors and their relations in order to predict treatment outcome and optimize personal treatment strategies. Methods:: Here, we present a translational mathematical model dependent on three key parameters for describing efficacy of checkpoint inhibitors in human cancer: tumor growth rate (α), tumor-immune infiltration (Λ), and immunotherapy-mediated amplification of anti-tumor response (µ). The model was calibrated by fitting it to a compiled clinical tumor response dataset (n = 189 patients) obtained from published anti-PD-1 and anti-PD-L1 clinical trials, and then validated on an additional validation cohort (n = 64 patients) obtained from our in-house clinical trials. Results:: The derived parameters Λ and µ were both significantly different between responding versus nonresponding patients. Of note, our model appropriately classified response in 81.4% of patients by using only tumor volume measurements and within 2 months of treatment initiation in a retrospective analysis. The model reliably predicted clinical response to the PD-1/PD-L1 class of checkpoint inhibitors across multiple solid malignant tumor types. Comparison of model parameters to immunohistochemical measurement of PD-L1 and CD8+ T cells confirmed robust relationships between model parameters and their underlying biology. Conclusions:: These results have demonstrated reliable methods to inform model parameters directly from biopsy samples, which are conveniently obtainable as early as the start of treatment. Together, these suggest that the model parameters may serve as early and robust biomarkers of the efficacy of checkpoint inhibitor therapy on an individualized per-patient basis. Funding:: We gratefully acknowledge support from the Andrew Sabin Family Fellowship, Center for Radiation Oncology Research, Sheikh Ahmed Center for Pancreatic Cancer Research, GE Healthcare, Philips Healthcare, and institutional funds from the University of Texas M.D. Anderson Cancer Center. We have also received Cancer Center Support Grants from the National Cancer Institute (P30CA016672 to the University of Texas M.D. Anderson Cancer Center and P30CA072720 the Rutgers Cancer Institute of New Jersey). This research has also been supported in part by grants from the National Science Foundation Grant DMS-1930583 (ZW, VC), the National Institutes of Health (NIH) 1R01CA253865 (ZW, VC), 1U01CA196403 (ZW, VC), 1U01CA213759 (ZW, VC), 1R01CA226537 (ZW, RP, WA, VC), 1R01CA222007 (ZW, VC), U54CA210181 (ZW, VC), and the University of Texas System STARS Award (VC). BC acknowledges support through the SER Cymru II Programme, funded by the European Commission through the Horizon 2020 Marie Skłodowska-Curie Actions (MSCA) COFUND scheme and the Welsh European Funding Office (WEFO) under the European Regional Development Fund (ERDF). EK has also received support from the Project Purple, NIH (U54CA210181, U01CA200468, and U01CA196403), and the Pancreatic Cancer Action Network (16-65-SING). MF was supported through NIH/NCI center grant U54CA210181, R01CA222959, DoD Breast Cancer Research Breakthrough Level IV Award W81XWH-17-1-0389, and the Ernest Cockrell Jr. Presidential Distinguished Chair at Houston Methodist Research Institute. RP and WA received serial research awards from AngelWorks, the Gillson-Longenbaugh Foundation, and the Marcus Foundation. This work was also supported in part by grants from the National Cancer Institute to SHC (R01CA109322, R01CA127483, R01CA208703, and U54CA210181 CITO pilot grant) and to PYP (R01CA140243, R01CA188610, and U54CA210181 CITO pilot grant). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Gene expression profiles derived from fine needle aspiration correlate with response to systemic chemotherapy in breast cancer

BACKGROUND: Drug resistance in breast cancer is a major obstacle to successful chemotherapy. In this study we used cDNA microarray technology to examine gene expression profiles obtained from fine needle aspiration (FNA) of primary breast tumors before and after systemic chemotherapy. Our goal was to determine the feasibility of obtaining representative expression array profiles from limited amounts of tissue and to identify those expression profiles that correlate with treatment response. METHODS: Repeat presurgical FNA samples were taken from six patients who were to undergo primary surgical treatment. Additionally, a group of 10 patients who were to receive neoadjuvant chemotherapy underwent two FNAs before chemotherapy (adriamycin 60 mg/m(2) and cyclophosphamide 600 mg/m(2)) followed by another FNA on day 21 after the first cycle. Total RNA was amplified with T7 Eberwine's procedure and labeled cDNA was hybridized onto a 7600-feature glass cDNA microarray. RESULTS: We identified candidate gene expression profiles that might distinguish tumors with complete response to chemotherapy from tumors that do not respond, and found that the number of genes that change after one cycle of chemotherapy was 10 times greater in the responding group than in the non-responding group. CONCLUSION: This study supports the suitability of FNA-derived cDNA microarray expression profiling of breast cancers as a comprehensive genomic approach for studying the mechanisms of drug resistance. Our findings also demonstrate the potential of monitoring post-chemotherapy changes in expression profiles as a measure of pharmacodynamic effect and suggests that these approaches might yield useful results when validated by larger studies

Deciphering von Hippel-Lindau (VHL/Vhl)-Associated Pancreatic Manifestations by Inactivating Vhl in Specific Pancreatic Cell Populations

The von Hippel-Lindau (VHL) syndrome is a pleomorphic familial disease characterized by the development of highly vascularized tumors, such as hemangioblastomas of the central nervous system, pheochromocytomas, renal cell carcinomas, cysts and neuroendocrine tumors of the pancreas. Up to 75% of VHL patients are affected by VHL-associated pancreatic lesions; however, very few reports in the published literature have described the cellular origins and biological roles of VHL in the pancreas. Since homozygous loss of Vhl in mice resulted in embryonic lethality, this study aimed to characterize the functional significance of VHL in the pancreas by conditionally inactivating Vhl utilizing the Cre/LoxP system. Specifically, Vhl was inactivated in different pancreatic cell populations distinguished by their roles during embryonic organ development and their endocrine lineage commitment. With Cre recombinase expression directed by a glucagon promoter in α-cells or an insulin promoter in β-cells, we showed that deletion of Vhl is dispensable for normal functions of the endocrine pancreas. In addition, deficiency of VHL protein (pVHL) in terminally differentiated α-cells or β-cells is insufficient to induce pancreatic neuroendocrine tumorigenesis. Most significantly, we presented the first mouse model of VHL-associated pancreatic disease in mice lacking pVHL utilizing Pdx1-Cre transgenic mice to inactivate Vhl in pancreatic progenitor cells. The highly vascularized microcystic adenomas and hyperplastic islets that developed in Pdx1-Cre;Vhl f/f homozygous mice exhibited clinical features similar to VHL patients. Establishment of three different, cell-specific Vhl knockouts in the pancreas have allowed us to provide evidence suggesting that VHL is functionally important for postnatal ductal and exocrine pancreas, and that VHL-associated pancreatic lesions are likely to originate from progenitor cells, not mature endocrine cells. The novel model systems reported here will provide the basis for further functional and genetic studies to define molecular mechanisms involved in VHL-associated pancreatic diseases

Development of a clinical decision model for thyroid nodules

<p>Abstract</p> <p>Background</p> <p>Thyroid nodules represent a common problem brought to medical attention. Four to seven percent of the United States adult population (10–18 million people) has a palpable thyroid nodule, however the majority (>95%) of thyroid nodules are benign. While, fine needle aspiration remains the most cost effective and accurate diagnostic tool for thyroid nodules in current practice, over 20% of patients undergoing FNA of a thyroid nodule have indeterminate cytology (follicular neoplasm) with associated malignancy risk prevalence of 20–30%. These patients require thyroid lobectomy/isthmusectomy purely for the purpose of attaining a definitive diagnosis. Given that the majority (70–80%) of these patients have benign surgical pathology, thyroidectomy in these patients is conducted principally with diagnostic intent. Clinical models predictive of malignancy risk are needed to support treatment decisions in patients with thyroid nodules in order to reduce morbidity associated with unnecessary diagnostic surgery.</p> <p>Methods</p> <p>Data were analyzed from a completed prospective cohort trial conducted over a 4-year period involving 216 patients with thyroid nodules undergoing ultrasound (US), electrical impedance scanning (EIS) and fine needle aspiration cytology (FNA) prior to thyroidectomy. A Bayesian model was designed to predict malignancy in thyroid nodules based on multivariate dependence relationships between independent covariates. Ten-fold cross-validation was performed to estimate classifier error wherein the data set was randomized into ten separate and unique train and test sets consisting of a training set (90% of records) and a test set (10% of records). A receiver-operating-characteristics (ROC) curve of these predictions and area under the curve (AUC) were calculated to determine model robustness for predicting malignancy in thyroid nodules.</p> <p>Results</p> <p>Thyroid nodule size, FNA cytology, US and EIS characteristics were highly predictive of malignancy. Cross validation of the model created with Bayesian Network Analysis effectively predicted malignancy [AUC = 0.88 (95%CI: 0.82–0.94)] in thyroid nodules. The positive and negative predictive values of the model are 83% (95%CI: 76%–91%) and 79% (95%CI: 72%–86%), respectively.</p> <p>Conclusion</p> <p>An integrated predictive decision model using Bayesian inference incorporating readily obtainable thyroid nodule measures is clinically relevant, as it effectively predicts malignancy in thyroid nodules. This model warrants further validation testing in prospective clinical trials.</p