Blocking TLR2 Activity Attenuates Pulmonary Metastases of Tumor

Background: Metastasis is the most pivotal cause of mortality in cancer patients. Immune tolerance plays a crucial role in tumor progression and metastasis. Methods and Findings: In this study, we investigated the potential roles and mechanisms of TLR2 signaling on tumor metastasis in a mouse model of intravenously injected B16 melanoma cells. Multiple subtypes of TLRs were expressed on B16 cells and several human cancer cell lines; TLR2 mediated the invasive activity of these cells. High metastatic B16 cells released more heat shock protein 60 than poor metastatic B16-F1 cells. Importantly, heat shock protein 60 released by tumor cells caused a persistent activation of TLR2 and was critical in the constitutive activation of transcription factor Stat3, leading to the release of immunosuppressive cytokines and chemokines. Moreover, targeting TLR2 markedly reduced pulmonary metastases and increased the survival of B16-bearing mice by reversing B16 cells induced immunosuppressive microenvironment and restoring tumor-killing cells such as CD8 + T cells and M1 macrophages. Combining an anti-TLR2 antibody and a cytotoxic agent, gemcitabine, provided a further improvement in the survival of tumor-bearing mice. Conclusions and Significance: Our results demonstrate that TLR2 is an attractive target against metastasis and that targeting immunosuppressive microenvironment using anti-TLR2 antibody is a novel therapeutic strategy for combating

A Study of B0 -> J/psi K(*)0 pi+ pi- Decays with the Collider Detector at Fermilab

We report a study of the decays B0 -> J/psi K(*)0 pi+ pi-, which involve the creation of a u u-bar or d d-bar quark pair in addition to a b-bar -> c-bar(c s-bar) decay. The data sample consists of 110 1/pb of p p-bar collisions at sqrt{s} = 1.8 TeV collected by the CDF detector at the Fermilab Tevatron collider during 1992-1995. We measure the branching ratios to be BR(B0 -> J/psi K*0 pi+ pi-) = (8.0 +- 2.2 +- 1.5) * 10^{-4} and BR(B0 -> J/psi K0 pi+ pi-) = (1.1 +- 0.4 +- 0.2) * 10^{-3}. Contributions to these decays are seen from psi(2S) K(*)0, J/psi K0 rho0, J/psi K*+ pi-, and J/psi K1(1270)

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

Therapeutic targeting of CK2 in acute and chronic leukemias

Phosphorylation can regulate almost every property of a protein and is involved in all fundamental cellular processes. Thus, proper regulation of phosphorylation events is critical to the homeostatic functions of cell signaling. Indeed, deregulation of signaling pathways underlies many human diseases, including cancer.[1] The importance of phosphorylation makes protein kinases and phosphatases promising therapeutic targets for a wide variety of disorders.[2] CK2, formerly known as casein kinase II, was discovered in 1954, [3] although only recently, and especially over the last two decades, it has become one of the most studied protein kinases, due to its ubiquity, pleiotropy and constitutive activity. In particular, appreciation of its pleiotropy has completely changed our vision of CK2 biology, from an ordinary cell homeostasis-maintaining enzyme to a master kinase potentially implicated in many human physiological and pathological events. CK2 is responsible for about 25% of the phosphoproteome,[4] as it catalyzes the phosphorylation of >300 substrates.[5] This partly explains the CK2 interconnected roles that underlie its involvement in many signaling pathways. However, CK2 prevalent roles are promotion of cell growth and suppression of apoptosis. Accordingly, several lines of evidence support the notion that CK2 is a key player in the pathogenesis of cancer. High levels of CK2 transcript and protein expression, as well as increased kinase activity are associated with the pathological functions of CK2 in a number of neoplasias.[6] It was only over the last decade, after extensive analyses in solid tumors, that basic and translational studies have provided evidence for a pivotal role of CK2 in driving the growth of different blood cancers as well, although the first report demonstrating increased CK2 expression in acute myelogenous leukemia (AML) dates back to 1985.[7] Since then, CK2 overexpression/activity has been demonstrated in other hematological malignancies, including acute lymphoblastic leukemia (ALL), chronic lymphocytic leukemia (CLL) and chronic myelogenous leukemia (CML). [8] With the notable exceptions of CML and pediatric ALL, many patients with leukemias still have a poor outcome, despite the development of protocols with optimized chemotherapy combinations. Insufficient response to first-line therapy and unsalvageable relapses present major therapeutic challenges. Moreover, chemotherapy, even if successful, could have deleterious long-term biological and psychological effects, especially in children.[9] Furthermore, CML patients can develop resistance to tyrosine kinase inhibitors (TKIs), while both primary chemoresistant and relapsed pediatric ALL cases still remain an unresolved issue.[9

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe