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Metabolic engineering reprograms cells to synthesize value-added products. In doing so, endogenous genes are altered and heterologous genes can be introduced to achieve the necessary enzymatic reactions. Dynamic regulation of metabolic flux is a powerful control scheme to alleviate and overcome the competing cellular objectives that arise from the introduction of these production pathways. This review explores dynamic regulation strategies that have demonstrated significant production benefits by targeting the metabolic node corresponding to a specific challenge. We summarize the stimulus-responsive control circuits employed in these strategies that determine the criterion for actuating a dynamic response and then examine the points of control that couple the stimulus-responsive circuit to a shift in metabolic flux.

Metabolic engineering seeks to reprogram cells to efficiently produce value-added chemicals. Traditionally, this is achieved by overexpressing the production pathway and/or knocking out competing endogenous pathways. However, limitations in some pathways are more effectively addressed through dynamic metabolic flux control to favor different cellular objectives over the course of the fermentation. Dynamic control circuits can autonomously actuate changes in metabolic fluxes in response to changing fermentation conditions, cell density, or metabolite concentrations. In this review, we discuss recent studies focused on multiplexed autonomous strategies which (1) combine regulatory circuits to control metabolic flux at multiple nodes or (2) respond to more than one input signal. These strategies have the potential to address challenging pathway scenarios, actuate more complex response profiles, and improve the specificity of the criteria that actuate the dynamic response., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

As synthetic biology and metabolic engineering tools improve, it is feasible to construct more complex microbial synthesis systems that may be limited by the machinery and resources available in an individual cell. Coculture fermentation is a promising strategy for overcoming these constraints by distributing objectives between subpopulations, but the primary method for controlling the composition of the coculture of production systems has been limited to control of the inoculum composition. We have developed a quorum sensing (QS)-based growth-regulation circuit that provides an additional parameter for regulating the composition of a coculture over the course of the fermentation. Implementation of this tool in a naringenin-producing coculture resulted in a 60% titer increase over a system that was optimized by varying inoculation ratios only. We additionally demonstrated that the growth control circuit can be implemented in combination with a communication module that couples transcription in one subpopulation to the cell-density of the other population for coordination of behavior, resulting in an additional 60% improvement in naringenin titer.

Metabolic engineering seeks to reprogram microbial cells to efficiently and sustainably produce value-added compounds. Since chemical production can be at odds with the cell's natural objectives, strategies have been developed to balance conflicting goals. For example, dynamic regulation modulates gene expression to favor biomass and metabolite accumulation at low cell densities before diverting key metabolic fluxes toward product formation. To trigger changes in gene expression in a pathway-independent manner without the need for exogenous inducers, researchers have coupled gene expression to quorum-sensing (QS) circuits, which regulate transcription based on cell density. While effective, studies thus far have been limited to one control point. More challenging pathways may require layered dynamic regulation strategies, motivating the development of a generalizable tool for regulating multiple sets of genes. We have developed a QS-based regulation tool that combines components of the lux and esa QS systems to simultaneously and dynamically up- and down-regulate expression of 2 sets of genes. Characterization of the circuit revealed that varying the expression level of 2 QS components leads to predictable changes in switching dynamics and that using components from 2 QS systems allows for independent tuning capability. We applied the regulation tool to successfully address challenges in both the naringenin and salicylic acid synthesis pathways. Through these case studies, we confirmed the benefit of having multiple control points, predictable tuning capabilities, and independently tunable regulation modules., Competing Interests: The authors declare no competing interest.

Background and Purpose: High-risk prostate cancer patients are frequently treated with external-beam radiotherapy (EBRT). Of all patients receiving EBRT, 15-35% will experience biochemical recurrence (BCR) within five years. Magnetic resonance imaging (MRI) is commonly acquired as part of the diagnostic procedure and imaging-derived features have shown promise in tumour characterisation and biochemical recurrence prediction. We investigated the value of imaging features extracted from pre-treatment T2w anatomical MRI to predict five year biochemical recurrence in high-risk patients treated with EBRT., Materials and Methods: In a cohort of 120 high-risk patients, imaging features were extracted from the whole-prostate and a margin surrounding it. Intensity, shape and textural features were extracted from the original and filtered T2w-MRI scans. The minimum-redundancy maximum-relevance algorithm was used for feature selection. Random forest and logistic regression classifiers were used in our experiments. The performance of a logistic regression model using the patient's clinical features was also investigated. To assess the prediction accuracy we used stratified 10-fold cross validation and receiver operating characteristic analysis, quantified by the area under the curve (AUC)., Results: A logistic regression model built using whole-prostate imaging features obtained an AUC of 0.63 in the prediction of BCR, outperforming a model solely based on clinical variables (AUC = 0.51). Combining imaging and clinical features did not outperform the accuracy of imaging alone., Conclusions: These results illustrate the potential of imaging features alone to distinguish patients with an increased risk of recurrence, even in a clinically homogeneous cohort., (© 2018 The Authors.)

Purpose: To date no guidelines are available for contouring prostate cancer inside the gland, as visible on multiparametric (mp-) MRI. We assessed inter-institutional differences in interpretation of mp-MRI in the multicenter phase III FLAME trial., Methods: We analyzed clinical delineations on mp-MRI and clinical characteristics from 260 patients across three institutes. We performed a logistic regression analysis to examine each institute's weighting of T2w, ADC and K trans intensity maps in the delineation of the cancer. As reviewing of all delineations by an expert panel is not feasible, we made a selection based on discrepancies between a published tumor probability (TP) model and each institute's clinical delineations using Areas Under the ROC Curve (AUC) analysis., Results: Regression coefficients for the three institutes were -0.07, -0.27 and -0.11 for T2w, -1.96, -0.53 and -0.65 for ADC and 0.15, 0.20 and 0.62 for K trans , with significant differences between institutes for ADC and K trans . AUC analysis showed median AUC values of 0.92, 0.80 and 0.79. Five patients with lowest AUC values were reviewed by a uroradiologist., Conclusion: Regression coefficients revealed considerably different interpretations of mp-MRI in tumor contouring between institutes and demonstrated the need for contouring guidelines. Based on AUC values outlying delineations could efficiently be identified for review., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Dose painting by numbers (DPBN) refers to a voxel-wise prescription of radiation dose modelled from functional image characteristics, in contrast to dose painting by contours which requires delineations to define the target for dose escalation. The direct relation between functional imaging characteristics and DPBN implies that random variations in images may propagate into the dose distribution. The stability of MR-only prostate cancer treatment planning based on DPBN with respect to these variations is as yet unknown. We conducted a test-retest study to investigate the stability of DPBN for prostate cancer in a semi-automated MR-only treatment planning workflow. Twelve patients received a multiparametric MRI on two separate days prior to prostatectomy. The tumor probability (TP) within the prostate was derived from image features with a logistic regression model. Dose mapping functions were applied to acquire a DPBN prescription map that served to generate an intensity modulated radiation therapy (IMRT) treatment plan. Dose calculations were done on a pseudo-CT derived from the MRI. The TP and DPBN map and the IMRT dose distribution were compared between both MRI sessions, using the intraclass correlation coefficient (ICC) to quantify repeatability of the planning pipeline. The quality of each treatment plan was measured with a quality factor (QF). Median ICC values for the TP and DPBN map and the IMRT dose distribution were 0.82, 0.82 and 0.88, respectively, for linear dose mapping and 0.82, 0.84 and 0.94 for square root dose mapping. A median QF of 3.4% was found among all treatment plans. We demonstrated the stability of DPBN radiotherapy treatment planning in prostate cancer, with excellent overall repeatability and acceptable treatment plan quality. Using validated tumor probability modelling and simple dose mapping techniques it was shown that despite day-to-day variations in imaging data still consistent treatment plans were obtained.

Purpose: Tumor localization provides crucial information for radiotherapy dose differentiation treatments, such as focal dose escalation and dose painting by numbers, which aim at achieving tumor control with minimal side effects. Multiparametric (mp-)MRI is increasingly used for tumor detection and localization in prostate because of its ability to visualize tissue structure and to reveal tumor characteristics. However, it can be challenging to distinguish cancer, particularly in the transition zone. In this study, we enhance the performance of a mp-MRI-based tumor localization model by incorporating prior knowledge from two sources: a population-based tumor probability atlas and patient-specific biopsy examination results. This information typically would be considered by a physician when carrying out a manual tumor delineation., Materials and Methods: Our study involves 40 patients from two centers: 23 patients from the University Hospital Leuven (Leuven), Leuven, Belgium and 17 patients from the Netherlands Cancer Institute (NKI), Amsterdam, the Netherlands. All patients received a mp-MRI exam consisting of a T2-weighted, diffusion-weighted, and dynamic contrast-enhanced MRI before prostatectomy. Thirty-one features were extracted for each voxel in the prostate. Among these, 29 were from the multiparametric-MRI, one was from the population-based tumor probability atlas and one from the biopsy map. T2-weighted images of each patient were registered to whole-mount section pathology slices to obtain the ground truth. The study was validated in two settings: single-center (training and test sets were from the same cohort); and cross-center (training and test sets were from different cohorts). In addition, automatic delineations created by our model were compared with manual tumor delineations done by six different teams on a subset of Leuven cohort including 15 patients., Results: In the single-center setting, mp-MRI-based features yielded area under the ROC curves (AUC) of 0.690 on a pooled set of patients from both cohorts. Including prevalence into mp-MRI-based features increased the AUC to 0.751 and including all features achieved the best performance with AUC of 0.775. Using all features always showed better results when varying the size of the training set. In addition, its performance is comparable with the average performance of six teams delineating the tumors manually. The error rate using all features was 0.22. The two prior knowledge features ranked among the top four most important features out of the 31 features. In the cross-center setting, combining all features also yielded the best performance in terms of the mean AUC of 0.777 on the pooled set of patients from both cohorts. In addition, the difference in performance between the single-center setting and cross-center setting was not significant., Conclusions: The results showed significant improvements when including prior knowledge features in addition to mp-MRI-based features in both single- and cross-center settings., (© 2016 The Netherlands Cancer Institute-Antoni van Leeuwenhoek. Medical Physics published by Wiley periodicals, Inc. on behalf of American Association of Physicists in Medicine.)

The greatest obstacle to clinical application of cancer gene therapy is lack of effective delivery tools. Gene delivery vehicles must protect against degradation, avoid immunogenic effects and prevent off target delivery which can cause harmful side effects. PEGylated liposomes have greatly improved tumor localization of small molecule drugs and are a promising tool for nucleic acid delivery as the polyethylene glycol (PEG) coating protects against immune recognition and blood clearance. In this study, small interfering RNA (siRNA) was fully encapsulated within PEGylated liposomes by complexing the siRNA with a cationic polymer, polyethyleneimine (PEI), before encapsulation. Formation methods and material compositions were then investigated for their effects on encapsulation. This technology was translated for protective delivery of siRNA designed for human papillomavirus (HPV) viral gene silencing and cervical cancer treatment. PEGylated liposomes encapsulating siRNA were functionalized with the AG86 targeting peptide-amphiphile which binds to the α 6 β 4 integrin, a cervical cancer biomarker. It was found that both targeting and polymer complexation before encapsulation were critical components to effective transfection.

For radiotherapy of prostate cancer, MRI is used increasingly for delineation of the prostate gland. For focal treatment of low-risk prostate cancer or focal dose escalation for intermediate and high-risk cancer, delineation of the tumor is also required. While multi-parametric MRI is well established for detection of tumors and for staging of the disease, delineation of the tumor inside the prostate is not common practice. Guidelines, such as the PI-RADS classification, exist for tumor detection and staging, but no such guidelines are available for tumor delineation. Indeed, interobserver studies show substantial variation in tumor contours. Computer-aided tumor detection and delineation may help improve the robustness of the interpretation of multi-parametric MRI data. Comparing the performance of an earlier developed model for tumor segmentation with expert delineations, we found a significant correlation between tumor probability in a voxel and the number of experts identifying this voxel as tumor. This suggests that the model agrees with 'the wisdom of the crowd', and thus could serve as a reference for individual physicians in their decision making. With multi-parametric MRI it becomes feasible to revisit the GTV-CTV concept in radiotherapy of prostate cancer. While detection of index lesions is quite reliable, contouring variability and the low sensitivity to small lesions suggest that the remainder of the prostate should be treated as CTV. Clinical trials that investigate the options for dose differentiation, for example with dose escalation to the visible tumor or dose reduction to the CTV, are therefore warranted., (Copyright © 2016 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.)

Background and Purpose: Boosting the dose to the largest (dominant) lesion in radiotherapy of prostate cancer may improve treatment outcome. The success of this approach relies on the detection and delineation of tumors. The agreement among teams of radiation oncologists and radiologists delineating lesions on multiparametric magnetic resonance imaging (mp-MRI) was assessed by measuring the distances between observer contours. The accuracy of detection and delineation was determined using whole-mount histopathology specimens as reference., Material and Methods: Six observer teams delineated tumors on mp-MRI of 20 prostate cancer patients who underwent a prostatectomy. To assess the inter-observer agreement, the inter-observer standard deviation (SD) of the contours was calculated for tumor sites which were identified by all teams., Results: Eighteen of 89 lesions were identified by all teams, all were dominant lesions. The median histological volume of these was 2.4cm(3). The median inter-observer SD of the delineations was 0.23cm. Sixty-six of 69 satellites were missed by all teams., Conclusion: Since all teams identify most dominant lesions, dose escalation to the dominant lesion is feasible. Sufficient dose to the whole prostate may need to be maintained to prevent under treatment of smaller lesions and undetected parts of larger lesions., (Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.)