ß-Endorphin mediates radiation therapy fatigue.

Fatigue is a common adverse effect of external beam radiation therapy in cancer patients. Mechanisms causing radiation fatigue remain unclear, although linkage to skin irradiation has been suggested. ß-Endorphin, an endogenous opioid, is synthesized in skin following genotoxic ultraviolet irradiation and acts systemically, producing addiction. Exogenous opiates with the same receptor activity as ß-endorphin can cause fatigue. Using rodent models of radiation therapy, exposing tails and sparing vital organs, we tested whether skin-derived ß-endorphin contributes to radiation-induced fatigue. Over a 6-week radiation regimen, plasma ß-endorphin increased in rats, paralleled by opiate phenotypes (elevated pain thresholds, Straub tail) and fatigue-like behavior, which was reversed in animals treated by the opiate antagonist naloxone. Mechanistically, all these phenotypes were blocked by opiate antagonist treatment and were undetected in either ß-endorphin knockout mice or mice lacking keratinocyte p53 expression. These findings implicate skin-derived ß-endorphin in systemic effects of radiation therapy. Opioid antagonism may warrant testing in humans as treatment or prevention of radiation-induced fatigue.

Comparison of 3D Conformal Proton Therapy, Intensity-Modulated Proton Therapy, and Intensity-Modulated Photon Therapy for Retroperitoneal Sarcoma.

Background: External beam radiation therapy (RT) for retroperitoneal sarcoma often requires treatment of large target volumes close to critical normal tissues. Radiation may be limited by adjacent organs at risk (OAR). Intensity-modulated radiation therapy has been shown to improve target coverage and reduce doses to OAR. Objectives: To compare target coverage and dose to OAR with 3D conformal proton therapy (3D CPT), intensity-modulated proton therapy (IMPT), and intensity-modulated photon therapy (IMXT). Methods: We performed a comparative study of treatment plans with 3D CPT, IMPT, and IMXT for ten patients with retroperitoneal sarcomas. RT was delivered to 50.4 Gy to the clinical target volume (CTV), the structures considered at risk for microscopic disease. Results: CTVs ranged from 74 to 357 cc (mean 188 cc). Dose conformity was improved with IMPT, while 3D CPT provided better dose homogeneity. Mean dose to the liver, small bowel, and stomach was reduced with IMPT compared with 3D CPT or IMXT. Conclusions: IMPT, 3D CPT, and IMXT provide excellent target coverage for retroperitoneal sarcomas. OAR dose is lower with IMPT and 3D CPT, and IMPT achieves the closest conformity. These techniques offer the opportunity for further dose escalation to areas with positive margins.

Screening and Validation of Molecular Targeted Radiosensitizers.

The development of molecular targeted drugs with radiation and chemotherapy is critically important for improving the outcomes of patients with hard-to-treat, potentially curable cancers. However, too many preclinical studies have not translated into successful radiation oncology trials. Major contributing factors to this insufficiency include poor reproducibility of preclinical data, inadequate preclinical modeling of intertumoral genomic heterogeneity that influences treatment sensitivity in the clinic, and a reliance on tumor growth delay instead of local control (TCD50) endpoints. There exists an urgent need to overcome these barriers to facilitate successful clinical translation of targeted radiosensitizers. To this end, we have used 3-dimensional (3D) cell culture assays to better model tumor behavior in vivo. Examples of successful prediction of in vivo effects with these 3D assays include radiosensitization of head and neck cancers by inhibiting epidermal growth factor receptor or focal adhesion kinase signaling, and radioresistance associated with oncogenic mutation of KRAS. To address the issue of tumor heterogeneity, we leveraged institutional resources that allow high-throughput 3D screening of radiation combinations with small-molecule inhibitors across genomically characterized cell lines from lung, head and neck, and pancreatic cancers. This high-throughput screen is expected to uncover genomic biomarkers that will inform the successful clinical translation of targeted agents from the National Cancer Institute Cancer Therapy Evaluation Program portfolio and other sources. Screening "hits" need to be subjected to refinement studies that include clonogenic assays, addition of disease-specific chemotherapeutics, target/biomarker validation, and integration of patient-derived tumor models. The chemoradiosensitizing activities of the most promising drugs should be confirmed in TCD50 assays in xenograft models with or without relevant biomarker and using clinically relevant radiation fractionation. We predict that appropriately validated and biomarker-directed targeted therapies will have a higher likelihood than past efforts of being successfully incorporated into the standard management of hard-to-treat tumors.

Targeting the DNA replication stress phenotype of KRAS mutant cancer cells.

Mutant KRAS is a common tumor driver and frequently confers resistance to anti-cancer treatments such as radiation. DNA replication stress in these tumors may constitute a therapeutic liability but is poorly understood. Here, using single-molecule DNA fiber analysis, we first characterized baseline replication stress in a panel of unperturbed isogenic and non-isogenic cancer cell lines. Correlating with the observed enhanced replication stress we found increased levels of cytosolic double-stranded DNA in KRAS mutant compared to wild-type cells. Yet, despite this phenotype replication stress-inducing agents failed to selectively impact KRAS mutant cells, which were protected by CHK1. Similarly, most exogenous stressors studied did not differentially augment cytosolic DNA accumulation in KRAS mutant compared to wild-type cells. However, we found that proton radiation was able to slow fork progression and preferentially induce fork stalling in KRAS mutant cells. Proton treatment also partly reversed the radioresistance associated with mutant KRAS. The cellular effects of protons in the presence of KRAS mutation clearly contrasted that of other drugs affecting replication, highlighting the unique nature of the underlying DNA damage caused by protons. Taken together, our findings provide insight into the replication stress response associated with mutated KRAS, which may ultimately yield novel therapeutic opportunities.

Urinary metabolites of furan in waterpipe tobacco smokers compared to non-smokers in home settings in the US.

OBJECTIVES: Determine uptake of furan, a potential human carcinogen, in waterpipe tobacco (WPT) smokers in home settings. METHODS: We analysed data from a US convenience sample of 50 exclusive WPT smokers, mean age 25.3 years, and 25 non-smokers, mean age 25.5 years. For WPT smokers, data were collected at a home visit by research assistants during which participants smoked one WPT head of one brand for a mean of 33.1 min in their homes. Research assistants provided and prepared a WP for participants by weighing and loading 10 g of WPT in the WP head. At the completion of the smoking session, research assistants measured the remaining WPT. Cotinine and six furan metabolites were quantified in first morning urine samples provided on 2 consecutive days for non-smokers, and on the morning of a WPT smoking session and on the following morning for smokers. RESULTS: WPT smokers consumed a mean of 2.99 g WPT. In WPT smokers, urinary cotinine levels increased significantly 26.1 times the following morning; however, urinary metabolites of furan did not increase significantly. Compared to non-smokers, 2 furan metabolites, N-acetyl-S-[1-(5-acetylamino-5-carboxylpentyl)-1H-pyrrol-3-yl]-L-cysteine and N-acetyl-S-[1-(5-amino-5-carboxypentyl)-1H-pyrrol-3-yl]-L-cysteine sulfoxide, were significantly higher in WPT smokers in pre and in post WPT smoking levels. CONCLUSIONS: To enable a more rigorous assessment of furan exposure from WPT smoking, future research should determine furan concentrations in WPT smoke, quantify furan metabolites from users of various WPT brands; and extend the investigation to social settings where WPT smoking is habitually practiced.

Disruption of SLX4-MUS81 Function Increases the Relative Biological Effectiveness of Proton Radiation.

PURPOSE: Clinical proton beam therapy has been based on the use of a generic relative biological effectiveness (RBE) of ∼1.1. However, emerging data have suggested that Fanconi anemia (FA) and homologous recombination pathway defects can lead to a variable RBE, at least in vitro. We investigated the role of SLX4 (FANCP), which acts as a docking platform for the assembly of multiple structure-specific endonucleases, in the response to proton irradiation. METHODS AND MATERIALS: Isogenic cell pairs for the study of SLX4, XPF/ERCC1, MUS81, and SLX1 were irradiated at the mid-spread-out Bragg peak of a clinical proton beam (linear energy transfer 2.5 keV/µm) or with 250 kVp x-rays, and the clonogenic survival fractions were determined. To estimate the RBE of the protons relative to cobalt-60 photons (Co60Eq), we assigned a RBE(Co60Eq) of 1.1 to x-rays to correct the physical dose measured. Standard DNA repair foci assays were used to monitor the damage responses, and the cell cycle distributions were assessed by flow cytometry. The poly(ADP-ribose) polymerase inhibitor olaparib was used for comparison. RESULTS: Loss of SLX4 function resulted in an enhanced proton RBE(Co60Eq) of 1.42 compared with 1.11 for wild-type cells (at a survival fraction of 0.1; P<.05), which correlated with increased persistent DNA double-strand breaks in cells in the S/G2 phase. Genetic analysis identified the SLX4-binding partner MUS81 as a mediator of resistance to proton radiation. Both proton irradiation and olaparib treatment resulted in a similar prolonged accumulation of RAD51 foci in SLX4/MUS81-deficient cells, suggesting a common defect in the repair of DNA replication fork-associated damage. CONCLUSIONS: A defect in the FA pathway at the level of SLX4 results in hypersensitivity to proton radiation, which is, at least in part, due to impaired MUS81-mediated processing of replication forks that stall at clustered DNA damage. In vivo and clinical studies are needed to confirm these findings in human cancers.

Test equality between two binary screening tests with a confirmatory procedure restricted on screen positives.

In studies of screening accuracy, we may commonly encounter the data in which a confirmatory procedure is administered to only those subjects with screen positives for ethical concerns. We focus our discussion on simultaneously testing equality of sensitivity and specificity between two binary screening tests when only subjects with screen positives receive the confirmatory procedure. We develop four asymptotic test procedures and one exact test procedure. We derive sample size calculation formula for a desired power of detecting a difference at a given nominal [Formula: see text]-level. We employ Monte Carlo simulation to evaluate the performance of these test procedures and the accuracy of the sample size calculation formula developed here in a variety of situations. Finally, we use the data obtained from a study of the prostate-specific-antigen test and digital rectal examination test on 949 Black men to illustrate the practical use of these test procedures and the sample size calculation formula.

Five interval estimators of the risk difference under stratified randomized clinical trials with noncompliance and repeated measurements.

We often employ stratified analysis to control the confounding effect due to centers in a multicenter trial or the confounding effect due to trials in a meta-analysis. On the basis of a general risk additive model, we focus discussion on interval estimation of the risk difference (RD) in repeated binary measurements under a stratified randomized clinical trial (RCT) in the presence of noncompliance. We develop five asymptotic interval estimators for the RD in closed form. These include the interval estimator using the weighted least-squares (WLS) estimator, the WLS interval estimator with tanh (-1)(x) transformation, the Mantel-Haenszel (MH) type interval estimator, the MH interval estimator with tanh (-1)(x) transformation, and the interval estimator using the idea of Fieller's theorem and a randomization-based variance. We employ Monte Carlo simulation to study and compare the finite-sample performance of these interval estimators in a variety of situations. We include an example studying the use of macrophage colony-stimulating factor to reduce the risk of febrile neutropenia events in acute myeloid leukaemia patients published elsewhere to illustrate the use of these estimators.

Notes on interval estimation of the generalized odds ratio under stratified random sampling.

It is not rare to encounter the patient response on the ordinal scale in a randomized clinical trial (RCT). Under the assumption that the generalized odds ratio (GOR) is homogeneous across strata, we consider four asymptotic interval estimators for the GOR under stratified random sampling. These include the interval estimator using the weighted-least-squares (WLS) approach with the logarithmic transformation (WLSL), the interval estimator using the Mantel-Haenszel (MH) type of estimator with the logarithmic transformation (MHL), the interval estimator using Fieller's theorem with the MH weights (FTMH) and the interval estimator using Fieller's theorem with the WLS weights (FTWLS). We employ Monte Carlo simulation to evaluate the performance of these interval estimators by calculating the coverage probability and the average length. To study the bias of these interval estimators, we also calculate and compare the noncoverage probabilities in the two tails of the resulting confidence intervals. We find that WLSL and MHL can generally perform well, while FTMH and FTWLS can lose either precision or accuracy. We further find that MHL is likely the least biased. Finally, we use the data taken from a study of smoking status and breathing test among workers in certain industrial plants in Houston, Texas, during 1974 to 1975 to illustrate the use of these interval estimators.

Notes on testing non-inferiority under the partial verification design with a confirmatory procedure limited to screen positives.

When a new test with fewer invasions or less expenses to administer than the traditional test is developed, we may be interested in testing whether the former is non-inferior to the latter with respect to test accuracy. We define non-inferiority via both the odds ratio (OR) of correctly identifying a case and the OR of correctly identifying a non-case between two tests under comparison. We focus our discussion on testing the non-inferiority of a new screening test to a traditional screening test when a confirmatory procedure is performed only on patients with screen positives. On the basis of well-established methods for paired-sample data, we derive an asymptotic test procedure and an exact test procedure with respect to the two ORs defined here. Using Monte Carlo simulation, we evaluate the performance of these test procedures in a variety of situations. We note that the test procedures proposed here can also be applicable if we are interested in testing non-inferiority with respect to the ratio of sensitivities and the ratio of specificities. We discuss interval estimation of these ORs and sample size calculation based on the asymptotic test procedure considered here. We use the data taken from a study of the prostate-specific-antigen (PSA) test and the digital rectal examination (DRE) test to illustrate the practical use of these test procedures, interval estimators and sample size calculation formula.

An effective preoperative three-dimensional radiotherapy target volume for extremity soft tissue sarcoma and the effect of margin width on local control.

PURPOSE: There is little information on the appropriate three-dimensional (3D) preoperative radiotherapy (XRT) volume for extremity soft-tissue sarcomas (STS). We retrospectively analyzed the pattern of local failure (LF) to help elucidate optimal field design. METHODS AND MATERIALS: We analyzed the 56 patients who underwent computed tomography-planned XRT for Stage I to III extremity STS between June 2000 and December 2006. Clinical target volume (CTV) included the T1 post-gadolinium-defined gross tumor volume with 1- to 1.5-cm radial and 3.5-cm longitudinal margins. Planning target volume expansion was 5 to 7 mm, and >or=95% of dose was delivered to the planning target volume. Preoperative XRT was 44 to 50.4 Gy (median, 50). Postoperative boost of 10 to 20 Gy was given to 12 patients (6 with positive and 6 with close margins). RESULTS: Follow-up ranged from 15 to 76 months (median, 41 months). The 5-year local control, freedom from distant metastasis, disease-free survival, and overall survival were 88.5%, 80.0%, 77.5% and 82.8%, respectively. Three patients (all with positive margin) experienced local failure (LF) as first relapse (2 isolated, 1 with distant failure), and 2 additional patients (all with margin<1 mm) had late LF after distant metastasis. The LFs were within the CTV in 3 patients and within and also extending beyond the CTV in 2 patients. CONCLUSIONS: These target volume definitions appear to be appropriate for most patients. No local recurrences were observed with surgical margins >or=1 mm, and it appears that these may be adequate for patients with extremity STS treated with preoperative radiotherapy.

Test homogeneity of odds ratio in a randomized clinical trial with noncompliance.

The odds ratio (OR) has been recommended to measure the relative treatment effect in therapeutic equivalence or meta-analysis. When controlling the confounding effect due to strata formed by centers (or trials) on patients' response in a multicenter study (or a meta-analysis), we commonly employ stratified analysis and obtain a summary estimate of the treatment effect. In practice, it is not uncommon to come across data in which there are patients not complying with their assigned treatment. To avoid obtaining a misleading summary estimate due to overlooking the interaction between the stratum and treatment effects as well as the selection bias from noncompliance, it is important to develop test statistics accounting for noncompliance for testing the homogeneity of the OR across strata. In this article, we develop five asymptotic test statistics and employ Monte Carlo simulation to evaluate the performance of these statistics in a variety of situations. We note that the weighted-least-squares (WLS) test statistic can be liberal when the number of strata is moderate or large (>/=5). We find that the logarithmic transformation of the WLS (LWLS) test statistic, the squared-root transformation of the LWLS (SLWLS) test statistic, and Fisher's logarithmic transformation of LWLS (LLWLS) test statistic can perform well with respect to Type I error in all the situations considered here. We further find that the Z-transformation of LWLS (ZLWLS) test statistic can be liberal when the number of strata is small or moderate. We note that the LWLS test statistic is likely preferable to the others for a small number of strata, while the ZLWLS test statistic can be the best for a moderate or large number of strata. Finally, we use the data taken from a multiple-risk-factor intervention trial to illustrate the use of these test statistics.

Spatial dependence of MLC transmission in IMRT delivery.

In complex intensity-modulated radiation therapy cases, a considerable amount of the total dose may be delivered through closed leaves. In such cases an accurate knowledge of spatial characteristics of multileaf collimator (MLC) transmission is crucial, especially for the treatment of large targets with split fields. Measurements with an ionization chamber, radiographic films (EDR2, EBT) and EPID are taken to characterize all relevant effects related to MLC transmission for various field sizes and depths. Here we present a phenomenological model to describe MLC transmission, whereby the main focus is the off-axis decrease of transmission for symmetric and asymmetric fields as well as on effects due to the tongue and groove design of the leaves, such as interleaf transmission and the tongue and groove effect. Data obtained with the four different methods are presented, and the utility of each measurement method to determine the necessary model parameters is discussed. With the developed model, it is possible to predict the relevant MLC effects at any point in the phantom for arbitrary jaw settings and depths.

Four-dimensional imaging and treatment planning of moving targets.

Four-dimensional CT acquisition is commercially available, and provides important information on the shape and trajectory of the tumor and normal tissues. The primary advantage of four-dimensional imaging over light breathing helical scans is the reduction of motion artifacts during scanning that can significantly alter tumor appearance. Segmentation, image registration, visualization are new challenges associated with four-dimensional data sets because of the overwhelming increase in the number of images. Four-dimensional dose calculations, while currently laborious, provide insights into dose perturbations due to organ motion. Imaging before treatment (image guidance) improves accuracy of radiation delivery, and recording transmission images can provide a means of verifying gated delivery.

Interval estimation of the risk difference in non-compliance randomized trials with repeated binary measurements.

In a randomized clinical trial (RCT), we often encounter non-compliance with the treatment protocol for a subset of patients. The intention-to-treat (ITT) analysis is probably the most commonly used method in a RCT with non-compliance. However, the ITT analysis estimates 'the programmatic effectiveness' rather than 'the biological efficacy'. In this paper, we focus attention on the latter index and consider use of the risk difference (RD) to measure the effect of a treatment. Based on a simple additive risk model proposed elsewhere, we develop four asymptotic interval estimators of the RD for repeated binary measurements in a RCT with non-compliance. We apply Monte Carlo simulation to evaluate and compare the finite-sample performance of these interval estimators in a variety of situations. We find that all interval estimators considered here can perform well with respect to the coverage probability. We further find that the interval estimator using a tanh(-1)(x) transformation is probably more precise than the others, while the interval estimator derived from a randomization-based approach may cause a slight loss of precision. When the number of patients per treatment is large and the probability of compliance to an assigned treatment is high, we find that all interval estimators discussed here are essentially equivalent. Finally, we illustrate use of these interval estimators with data simulated from a trial of using macrophage colony-stimulating factor to reduce febrile neutropenia incidence in acute myeloid leukaemia patients.

A Monte Carlo evaluation of five interval estimators for the relative risk in sparse data.

The relative risk (RR) is one of the most frequently used indices to measure the strength of association between a disease and a risk factor in etiological studies or the efficacy of an experimental treatment in clinical trials. In this paper, we concentrate attention on interval estimation of RR for sparse data, in which we have only a few patients per stratum, but a moderate or large number of strata. We consider five asymptotic interval estimators for RR, including a weighted least-squares (WLS) interval estimator with an ad hoc adjustment procedure for sparse data, an interval estimator proposed elsewhere for rare events, an interval estimator based on the Mantel-Haenszel (MH) estimator with a logarithmic transformation, an interval estimator calculated from a quadratic equation, and an interval estimator derived from the ratio estimator with a logarithmic transformation. On the basis of Monte Carlo simulations, we evaluate and compare the performance of these five interval estimators in a variety of situations. We note that, except for the cases in which the underlying common RR across strata is around 1, using the WLS interval estimator with the adjustment procedure for sparse data can be misleading. We note further that using the interval estimator suggested elsewhere for rare events tends to be conservative and hence leads to loss of efficiency. We find that the other three interval estimators can consistently perform well even when the mean number of patients for a given treatment is approximately 3 patients per stratum and the number of strata is as small as 20. Finally, we use a mortality data set comparing two chemotherapy treatments in patients with multiple myeloma to illustrate the use of the estimators discussed in this paper.

Procedures for testing the homogeneity of relative difference in sparse data.

To quantify the excess effect of an experimental treatment over a placebo group in clinical trials, we often consider use of the relative difference, defined as the proportion of patients who would respond to the experimental treatment among those who would not otherwise if they were assigned to the placebo group. To control the effects due to confounders on the response of interest, we frequently employ stratified analysis in practice. Before obtaining a summary estimate of the relative difference, it is desirable to assess whether this measure is constant across strata. Based on the beta-binomial model, we develop simple procedures for testing the homogeneity of relative difference for sparse data in which we have many strata but few patients per stratum. Using Monte Carlo simulations, we demonstrate that the proposed test procedures can generally perform well with respect to type I error in a variety of situations. We further evaluate and study the power of these test procedures. Finally, we note some robustness in using the test procedures proposed here.

Estimation of attributable risk for case-control studies with multiple matching.

Kuritz and Landis considered case-control studies with multiple matching and proposed an asymptotic interval estimator of the attributable risk based on Wald's statistic. Using Monte Carlo simulation, Kuritz and Landis demonstrated that their interval estimator could perform well when the number of matched sets was large (>or=100). However, the number of matched sets may often be moderate or small in practice. In this paper, we evaluate the performance of Kuritz and Landis' interval estimator in small or moderate number of matched sets and compare it with four other interval estimators. We note that the coverage probability of Kuritz and Landis' interval estimator tends to be less than the desired confidence level when the probability of exposure among cases is large. In these cases, the interval estimator using the logarithmic transformation and the two interval estimators derived from the quadratic equations developed here can generally improve the coverage probability of Kuritz and Landis' interval estimator, especially for the case of a small number of matched sets. Furthermore, we find that an interval estimator derived from a quadratic equation is consistently more efficient than Kuritz and Landis' interval estimator. The interval estimator using the logit transformation, although which performs poorly when the underlying odds ratio (OR) is close to 1, can be useful when both the probability of exposure among cases and the underlying OR are moderate or large.

Interval estimation of the proportion ratio under multiple matching.

The discussions on interval estimation of the proportion ratio (PR) of responses or the relative risk (RR) of a disease for multiple matching have been generally focused on the odds ratio (OR) based on the assumption that the latter can approximate the former well. When the underlying proportion of outcomes is not rare, however, the results for the OR would be inadequate for use if the PR or RR was the parameter of our interest. In this paper, we develop five asymptotic interval estimators of the common PR (or RR) for multiple matching. To evaluate and compare the finite sample performance of these estimators, we apply Monte Carlo simulation to calculate the coverage probability and the average length of the resulting confidence intervals in a variety of situations. We note that when we have a constant number of matching, the interval estimator using the logarithmic transformation of the Mantel-Haenszel estimator, the interval estimator derived from the quadratic inequality given in this paper, and the interval estimator using the logarithmic transformation of the ratio estimator can consistently perform well. When the number of matching varies between matched sets, we find that the interval estimator using the logarithmic transformation of the ratio estimator is probably the best among the five interval estimators considered here in the case of a small number (=20) of matched sets. To illustrate the use of these interval estimators, we employ the data studying the supplemental ascorbate in the supportive treatment of terminal cancer patients.

A simple test of the homogeneity of risk difference in sparse data: an application to a multicenter study.

In this paper, we focus discussion on testing the homogeneity of risk difference for sparse data, in which we have few patients in each stratum, but a moderate or large number of strata. When the number of patients per treatment within strata is small (2 to 5 patients), none of test procedures proposed previously for testing the homogeneity of risk difference for sparse data can really perform well. On the basis of bootstrap methods, we develop a simple test procedure that can improve the power of the previous test procedures. Using Monte Carlo simulations, we demonstrate that the test procedure developed here can perform reasonable well with respect to Type I error even when the number of patients per stratum for each treatment is as small as two patients. We evaluate and study the power of the proposed test procedure in a variety of situations. We also include a comparison of the performance between the test statistics proposed elsewhere and the test procedure developed here. Finally, we briefly discuss the limitation of using the proposed test procedure. We use the data comparing two chemotherapy treatments in patients with multiple myeloma to illustrate the use of the proposed test procedure.