Citations

Evidence is provided that the fibroproliferative actions of TGF‐β are dependent on a metabolic adaptation that sustains pathologic growth. Specifically, profibrotic TGF‐β signaling is shown to require fatty acid synthase (FASN), an essential anabolic enzyme responsible for the de novo synthesis of fatty acids. With the use of pharmacologic and genetic approaches, we show that TGF‐β‐stimulated FASN expression is independent of Smad2/ 3 and is mediated via mammalian target of rapamycin complex 1. In the absence of FASN activity or protein, TGF‐β ‐driven fibrogenic processes are reduced with no apparent toxicity. Furthermore, as increased FASN expression was also observed to correlate with the degree of lung fibrosis in bleomycin‐treated mice, inhibition of FASN was examined in a murine‐treatment model of pulmonary fibrosis. Remarkably, inhibition of FASN not only decreased expression of profibrotic targets, but lung function was also stabilized/improved, as assessed by peripheral blood oxygenation.—Jung, M.‐Y., Kang, J.‐H., Hernandez, D. M., Yin, X., Andrianifahanana, M., Wang, Y., Gonzalez‐Guerrico, A., Limper, A. H., Lupu, R., Leof, E. B. Fatty acid synthase is required for profibrotic TGF‐β signaling. FASEB J. 32, 3803–3815 (2018). www.fasebj.org

SummaryThe landscape of oncology drug development has recently changed with the emergence of molecularly targeted agents and immunotherapies. These new therapeutic agents appear more likely to induce multiple low or moderate grade toxicities rather than dose limiting toxicities. Various model-based dose finding designs and toxicity severity scoring systems have been proposed to account for toxicity grades, but they are difficult to implement because of the use of complicated dose–toxicity models and the requirement to refit the model at each decision of dose escalation and de-escalation. We propose a generalized Bayesian optimal interval design, gBOIN, that accommodates various existing toxicity grade scoring systems under a unified framework. As a model-assisted design, gBOIN derives its optimal decision rule on the basis of the exponential family of distributions but is carried out in a simple way as the algorithm-based design: its decision of dose escalation and de-escalation involves only a simple comparison of the sample mean of the end point with two prespecified dose escalation and de-escalation boundaries. No model fitting is needed. We show that gBOIN has the desirable finite property of coherence and a large sample property of consistency. Numerical studies show that gBOIN yields good performance that is comparable with or superior to that of some existing, more complicated model-based designs. A Web application for implementing gBOIN is freely available from http://www.trialdesign.org.

Abstract Purpose: Small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) is a rare, aggressive ovarian cancer in young women that is universally driven by loss of the SWI/SNF ATPase subunits SMARCA4 and SMARCA2. A great need exists for effective targeted therapies for SCCOHT. Experimental Design: To identify underlying therapeutic vulnerabilities in SCCOHT, we conducted high-throughput siRNA and drug screens. Complementary proteomics approaches profiled kinases inhibited by ponatinib. Ponatinib was tested for efficacy in two patient-derived xenograft (PDX) models and one cell-line xenograft model of SCCOHT. Results: The receptor tyrosine kinase (RTK) family was enriched in siRNA screen hits, with FGFRs and PDGFRs being overlapping hits between drug and siRNA screens. Of multiple potent drug classes in SCCOHT cell lines, RTK inhibitors were only one of two classes with selectivity in SCCOHT relative to three SWI/SNF wild-type ovarian cancer cell lines. We further identified ponatinib as the most effective clinically approved RTK inhibitor. Reexpression of SMARCA4 was shown to confer a 1.7-fold increase in resistance to ponatinib. Subsequent proteomic assessment of ponatinib target modulation in SCCOHT cell models confirmed inhibition of nine known ponatinib target kinases alongside 77 noncanonical ponatinib targets in SCCOHT. Finally, ponatinib delayed tumor doubling time 4-fold in SCCOHT-1 xenografts while reducing final tumor volumes in SCCOHT PDX models by 58.6% and 42.5%. Conclusions: Ponatinib is an effective agent for SMARCA4-mutant SCCOHT in both in vitro and in vivo preclinical models through its inhibition of multiple kinases. Clinical investigation of this FDA-approved oncology drug in SCCOHT is warranted. Clin Cancer Res; 24(8); 1932–43. ©2018 AACR.

Background The clinical utility of a new biomarker should ideally be established in a prospective randomized clinical trial. However, such trials are not always practical. As such, it is common for investigators to identify promising biomarkers using archived specimens and clinical data collected from previously completed therapeutic trials. Simon et al. defined such biomarker studies as prospective–retrospective studies and proposed specific conditions to satisfy for such evaluations to be more than hypothesis generating. One condition they proposed is that archived tissues must be available on a sufficiently large number of patients from the pivotal trials to ensure adequately powered analyses. Purpose The purpose of this article is to provide a new perspective on how to estimate power for assessing the prognostic and predictive values of a single binary biomarker in prospective–retrospective biomarker studies. Computer programs are provided to facilitate the use of these methods in practice. Methods The proposed methods utilize additional information that becomes available during the course of the treatment trial including sample size, accrual time, additional follow-up time, and the observed number of events at time of biomarker analysis. These methods involve solving for the exponential hazard rates that give rise to the event numbers that are consistent with those observed while satisfying other design parameter constraints. Conclusion Simon et al. proposed a new paradigm for biomarker design, conduct, analysis, and evaluation in prospective–retrospective studies that offers a more efficient alternative than fully prospective biomarker studies. As a general rule, they suggest that samples from at least two-thirds of the patients be available for analysis. In this article, I expand on this issue and provide a methodological tool useful for estimating study power in prospective–retrospective biomarker studies. It is my hope that these incremental efforts to improve the quality and statistical rigor in biomarker studies will hasten the introduction of useful tumor biomarkers into clinical practice.

Background: Phase I trials are designed to determine the safety, tolerability, and recommended phase 2 dose of therapeutic agents for subsequent testing. The dose-finding paradigm has thus traditionally focused on identifying the maximum tolerable dose of an agent or combination therapy under the assumption that there is a non-decreasing relationship between dose–toxicity and dose–efficacy. The dose is typically determined based on the probability of severe toxicity observed during the first treatment cycle. A novel endpoint, the total toxicity profile, was previously developed to account for the multiple toxicity types and grades experienced in the first cycle. More recently, this was extended to a repeated measures design based on the total toxicity profile to account for longitudinal toxicities over multiple treatment cycles in the absence of within-patient correlation. Methods: In this work, we propose to extend the design in the presence of within-patient correlation. Furthermore, we provide a framework to detect a toxicity time trend (toxicity increasing, decreasing, or stable) over multiple treatment cycles. We utilize a linear mixed model in the Bayesian framework, with the addition of Bayesian risk functions for decision-making in dose assignment. Results: The performance of this design was evaluated using simulation studies and real data from a phase I trial. We demonstrated that using available toxicity data from all cycles of treatment improves the accuracy of maximum tolerated dose identification and allows for the detection of a time trend. The performance is consistent regardless of the strength of the within-patient correlation. In addition, the use of a quasi-continuous total toxicity profile score significantly increased the power to detect time trends compared to when binary data only were used. Conclusion: The increased interest in molecularly targeted agents and immunotherapies in oncology necessitates innovative phase I study designs. Our proposed framework provides a tool to tackle some of the challenges presented by these novel agents, specifically through the ability to understand patterns of toxicity over time, which is important in the cases of cumulative or late toxicities.

Objective We aimed to evaluate the contribution of acinar-to-ductal metaplasia (ADM) to the accumulation of cells with a ductal phenotype in cultured human exocrine pancreatic tissues and reveal the underlying mechanism. Methods We sorted and cultured viable cell populations in human exocrine pancreatic tissues with a flow cytometry–based lineage tracing method to evaluate possible mechanisms of ADM. Cell surface markers, gene expression pattern, and sphere formation assay were used to examine ADM. Results A large proportion of acinar cells gained CD133 expression during the 2-dimensional culture and showed down-regulation of acinar markers and up-regulation of ductal markers, assuming an ADM phenotype. In a serum-free culture condition, ADM induction was mainly dependent on transforming growth factor β (TGF-β) secreted from cultured ductal cells. Human acinar cells when cultured alone for a week in a serum-free condition do not undergo ADM. However, serum may contain other factors besides TGF-β to induce ADM in human acinar cells. In addition, we found that TGF-β cannot induce ADM of murine acinar cells. Conclusions Ductal cells are the major source of TGF-β that induces ADM in cultured human exocrine pancreatic tissues. This culture system might be a useful model to investigate the mechanism of ADM in human cells.

PurposeTo assess the success rate, image quality, and the ability to stage liver fibrosis of a standard 2D gradient‐recalled echo (GRE) and four different spin‐echo (SE) magnetic resonance elastography (MRE) sequences in patients with different liver iron concentrations.Materials and MethodsA total of 332 patients who underwent 3T MRE examinations that included liver fat and iron quantification were enrolled, including 136 patients with all five MRE techniques. Thirty‐four patients had biopsy results for fibrosis staging. The liver stiffness, region of interest area, image quality, and success rate of the five sequences were compared in 115/136 patients. The area under the receiver operating characteristic curves (AUCs) and the accuracies for diagnosing early‐stage fibrosis and advanced fibrosis were compared. The effect of BMI (body mass index), the R2* relaxation time, and fat fraction on the image quality and liver stiffness measurements were analyzed.ResultsThe success rates were significantly higher in the four SE sequences (99.1–100%) compared with GRE MRE (85.3%) (all P < 0.001). There were significant differences of the mean ROI area between every pair of sequences (all P < 0.0001). There were no significant differences in the AUC of the five MRE sequences for discriminating advanced fibrosis (10 P‐values ranging from 0.2410–0.9171). R2* had a significant effect on the success rate and image quality for the noniron 2D echo‐planar imaging (EPI), 3D EPI and 2D GRE (all P < 0.001) sequences. BMI had a significant effect on the iron 2D EPI (P = 0.0230) and iron 2D SE (P = 0.0040) sequences.ConclusionAll five techniques showed good diagnostic performance in staging liver fibrosis. The SE MRE sequences had higher success rates and better image quality than GRE MRE in 3T clinical hepatic imaging.Level of Evidence: 3Technical Efficacy: Stage 5J. Magn. Reson. Imaging 2018;47:976–987.

Delivery of biomolecules to the correct subcellular locales is critical for proper physiological function. To that end, we have previously determined that type I and II transforming growth factor beta (TGF-β) receptors (TβRI and TβRII, respectively) localize to the basolateral domain in polarized epithelia. While TβRII targeting was shown to be regulated by sequences between amino acids 529 and 538, the analogous region(s) within TβRI is unknown. To address that question, sequential cytoplasmic TβRI truncations and point mutations identified a targeting motif between residues 158 and 163 (VxxEED) required for basolateral TβRI expression. Further studies documented that receptor internalization, down-regulation, direct recycling, or Smad signaling were unaffected by motif mutations that caused TβRI mislocalization. However, inclusion of amino acids 148–217 containing the targeting motif was able to direct basolateral expression of the apically sorted nerve growth factor receptor (NGFR, p75; extracellular and transmembrane regions) in a dominant manner. Finally, coexpression of apically targeted type I and type II TGF-β receptors mediated Smad3 signaling from the apical membrane of polarized epithelial cells. These findings demonstrate that the absence of apical TGF-β signaling in normal epithelia is primarily a reflection of domain-specific receptor expression and not an inability to couple with the signaling machinery.