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Objective: A growing number of cancer antineoplastic agents can cause life-threatening acute infusion reactions. Because previous studies have not studied these reactions from the perspective of patients, this study was undertaken with that objective in mind., Methods: Patients who had an acute infusion reaction were interviewed based on the Leventhal model. Once saturation of content was achieved, interviews were transcribed and analyzed with qualitative methodology., Results: Twenty-one patients were enrolled. Most were women (n = 15); the median age was 58 years, and paclitaxel was the most common inciting agent. Three themes emerged. First, these reactions are frightening; patients made remarks such as "I was just thinking oh my God, I am dying." Second, prior education about these reactions seemed to mitigate this fear, "Basically everything the nurses told me potentially could happen, like happened. So, I was prepared." Third, when health-care providers were prompt and attentive during the reaction, patients described less fear with future chemotherapy, "So no, I'm really not fearful about going in tomorrow because I know they'll be there and they'll be watching me.", Conclusion: These reactions evoke fear which can be mitigated with education prior to and with prompt responsiveness during the acute infusion reaction.

Background: Attenuated measles virus (MV) strains are promising agents currently being tested against solid tumors or hematologic malignancies in ongoing phase I and II clinical trials; factors determining oncolytic virotherapy success remain poorly understood, however., Methods: We performed RNA sequencing and gene set enrichment analysis to identify pathways differentially activated in MV-resistant (n = 3) and -permissive (n = 2) tumors derived from resected human glioblastoma (GBM) specimens and propagated as xenografts (PDX). Using a unique gene signature we identified, we generated a diagonal linear discriminant analysis (DLDA) classification algorithm to predict MV responders and nonresponders, which was validated in additional randomly selected GBM and ovarian cancer PDX and 10 GBM patients treated with MV in a phase I trial. GBM PDX lines were also treated with the US Food and Drug Administration-approved JAK inhibitor, ruxolitinib, for 48 hours prior to MV infection and virus production, STAT1/3 signaling and interferon stimulated gene expression was assessed. All statistical tests were two-sided., Results: Constitutive interferon pathway activation, as reflected in the DLDA algorithm, was identified as the key determinant for MV replication, independent of virus receptor expression, in MV-permissive and -resistant GBM PDXs. Using these lines as the training data for the DLDA algorithm, we confirmed the accuracy of our algorithm in predicting MV response in randomly selected GBM PDX ovarian cancer PDXs. Using the DLDA prediction algorithm, we demonstrate that virus replication in patient tumors is inversely correlated with expression of this resistance gene signature (ρ = -0.717, P = .03). In vitro inhibition of the interferon response pathway with the JAK inhibitor ruxolitinib was able to overcome resistance and increase virus production (1000-fold, P = .03) in GBM PDX lines., Conclusions: These findings document a key mechanism of tumor resistance to oncolytic MV therapy and describe for the first time the development of a prediction algorithm to preselect for oncolytic treatment or combinatorial strategies.

Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models minimal information standard (PDX-MI) for reporting on the generation, quality assurance, and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient's tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use of PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models. Cancer Res; 77(21); e62-66. ©2017 AACR ., (©2017 American Association for Cancer Research.)

Insulin-like growth factor (IGF) plays an important role in tissue growth and development. Several studies have demonstrated the association between circulating levels of IGF-1 and -2 and cancer risk, and the IGF system has been implicated in the oncogenesis of essentially all solid and hematologic malignancies. The optimal strategy for targeting IGF signaling in patients with cancer is not clear. The modest benefits reported thus far underscore the need for a better understanding of IGF signaling, which would enable clinicians to identify the subset of patients with the greatest likelihood of attaining benefit from this targeted approach., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Background: Attenuated measles virus (MV) strains are promising agents currently being tested against solid tumors or hematologic malignancies in ongoing phase I and II clinical trials; factors determining oncolytic virotherapy success remain poorly understood, however.Methods: We performed RNA sequencing and gene set enrichment analysis to identify pathways differentially activated in MV-resistant (n = 3) and -permissive (n = 2) tumors derived from resected human glioblastoma (GBM) specimens and propagated as xenografts (PDX). Using a unique gene signature we identified, we generated a diagonal linear discriminant analysis (DLDA) classification algorithm to predict MV responders and nonresponders, which was validated in additional randomly selected GBM and ovarian cancer PDX and 10 GBM patients treated with MV in a phase I trial. GBM PDX lines were also treated with the US Food and Drug Administration-approved JAK inhibitor, ruxolitinib, for 48 hours prior to MV infection and virus production, STAT1/3 signaling and interferon stimulated gene expression was assessed. All statistical tests were two-sided.Results: Constitutive interferon pathway activation, as reflected in the DLDA algorithm, was identified as the key determinant for MV replication, independent of virus receptor expression, in MV-permissive and -resistant GBM PDXs. Using these lines as the training data for the DLDA algorithm, we confirmed the accuracy of our algorithm in predicting MV response in randomly selected GBM PDX ovarian cancer PDXs. Using the DLDA prediction algorithm, we demonstrate that virus replication in patient tumors is inversely correlated with expression of this resistance gene signature (ρ = -0.717, P = .03). In vitro inhibition of the interferon response pathway with the JAK inhibitor ruxolitinib was able to overcome resistance and increase virus production (1000-fold, P = .03) in GBM PDX lines.Conclusions: These findings document a key mechanism of tumor resistance to oncolytic MV therapy and describe for the first time the development of a prediction algorithm to preselect for oncolytic treatment or combinatorial strategies. [ABSTRACT FROM AUTHOR]