In this paper, we consider estimation of survivor functions from groups of observations with right-censored data when the groups are subject to a stochastic ordering constraint. Many methods and algorithms have been proposed to estimate distribution functions under such restrictions, but none have completely satisfactory properties when the observations are censored. We propose a pointwise constrained nonparametric maximum likelihood estimator, which is defined at each time t by the estimates of the survivor functions subject to constraints applied at time t only. We also propose an efficient method to obtain the estimator. The estimator of each constrained survivor function is shown to be nonincreasing in t, and its consistency and asymptotic distribution are established. A simulation study suggests better small and large sample properties than for alternative estimators. An example using prostate cancer data illustrates the method.
Censored data; Constrained nonparametric maximum likelihood estimator; Kaplan–Meier estimator; Maximum likelihood estimator; Order restriction
Patients who were previously treated for prostate cancer with radiation therapy are monitored at regular intervals using a laboratory test called Prostate Specific Antigen (PSA). If the value of the PSA test starts to rise, this is an indication that the prostate cancer is more likely to recur, and the patient may wish to initiate new treatments. Such patients could be helped in making medical decisions by an accurate estimate of the probability of recurrence of the cancer in the next few years. In this paper, we describe the methodology for giving the probability of recurrence for a new patient, as implemented on a web-based calculator. The methods use a joint longitudinal survival model. The model is developed on a training dataset of 2,386 patients and tested on a dataset of 846 patients. Bayesian estimation methods are used with one Markov chain Monte Carlo (MCMC) algorithm developed for estimation of the parameters from the training dataset and a second quick MCMC developed for prediction of the risk of recurrence that uses the longitudinal PSA measures from a new patient.
Joint longitudinal-survival model; Online calculator; Predicted probability; Prostate cancer; PSA
Randomized trials with dropouts or censored data and discrete time-to-event type outcomes are frequently analyzed using the Kaplan–Meier or product limit (PL) estimation method. However, the PL method assumes that the censoring mechanism is noninformative and when this assumption is violated, the inferences may not be valid. We propose an expanded PL method using a Bayesian framework to incorporate informative censoring mechanism and perform sensitivity analysis on estimates of the cumulative incidence curves. The expanded method uses a model, which can be viewed as a pattern mixture model, where odds for having an event during the follow-up interval (tk−1,tk], conditional on being at risk at tk−1, differ across the patterns of missing data. The sensitivity parameters relate the odds of an event, between subjects from a missing-data pattern with the observed subjects for each interval. The large number of the sensitivity parameters is reduced by considering them as random and assumed to follow a log-normal distribution with prespecified mean and variance. Then we vary the mean and variance to explore sensitivity of inferences. The missing at random (MAR) mechanism is a special case of the expanded model, thus allowing exploration of the sensitivity to inferences as departures from the inferences under the MAR assumption. The proposed approach is applied to data from the TRial Of Preventing HYpertension.
Clinical trials; Hypertension; Ignorability index; Missing data; Pattern-mixture model; TROPHY trial
Motivation: RNA sequencing (RNA-Seq) is a powerful new technology for mapping and quantifying transcriptomes using ultra high-throughput next-generation sequencing technologies. Using deep sequencing, gene expression levels of all transcripts including novel ones can be quantified digitally. Although extremely promising, the massive amounts of data generated by RNA-Seq, substantial biases and uncertainty in short read alignment pose challenges for data analysis. In particular, large base-specific variation and between-base dependence make simple approaches, such as those that use averaging to normalize RNA-Seq data and quantify gene expressions, ineffective.
Results: In this study, we propose a Poisson mixed-effects (POME) model to characterize base-level read coverage within each transcript. The underlying expression level is included as a key parameter in this model. Since the proposed model is capable of incorporating base-specific variation as well as between-base dependence that affect read coverage profile throughout the transcript, it can lead to improved quantification of the true underlying expression level.
Availability and implementation: POME can be freely downloaded at http://www.stat.purdue.edu/~yuzhu/pome.html.
Contact: email@example.com; firstname.lastname@example.org
Supplementary information: Supplementary data are available at Bioinformatics online.
Genetic anticipation, described by earlier age of onset (AOO) and more aggressive symptoms in successive generations, is a phenomenon noted in certain hereditary diseases. Its extent may vary between families and/or between mutation sub-types known to be associated with the disease phenotype. In this paper, we posit a Bayesian approach to infer genetic anticipation under flexible random effects models for censored data that capture the effect of successive generations on AOO. Primary interest lies in the random effects. Misspecifying the distribution of random effects may result in incorrect inferential conclusions. We compare the fit of four candidate random effects distributions via Bayesian model fit diagnostics. A related statistical issue here is isolating the confounding effect of changes in secular trends, screening and medical practices that may affect time to disease detection across birth cohorts. Using historic cancer registry data, we borrow from relative survival analysis methods to adjust for changes in age-specific incidence across birth cohorts. Our motivating case-study comes from a Danish cancer register of 124 families with mutations in mismatch repair genes known to cause hereditary non-polyposis colorectal cancer, also called Lynch syndrome. We find evidence for a decrease in AOO between generations in this study. Our model predicts family level anticipation effects which are potentially useful in genetic counseling clinics for high risk families.
Birth-death process; Brier score; Conditional predictive ordinate; Deviance information criterion; Dirichlet Process; Hereditary non-polyposis colorectal cancer; Prediction of random effects; Relative survival analysis
In longitudinal biomedical studies, there is often interest in the rate functions, which describe the functional rates of change of biomarker profiles. This paper proposes a semiparametric approach to model these functions as the realizations of stochastic processes defined by stochastic differential equations. These processes are dependent on the covariates of interest and vary around a specified parametric function. An efficient Markov chain Monte Carlo algorithm is developed for inference. The proposed method is compared with several existing methods in terms of goodness-of-fit and more importantly the ability to forecast future functional data in a simulation study. The proposed methodology is applied to prostate-specific antigen profiles for illustration. Supplementary materials for this paper are available online.
Euler approximation; Functional data analysis; Gaussian process; Rate function; Stochastic differential equation; Semiparametric stochastic velocity model
In clinical trials, a biomarker (S) that is measured after randomization and is strongly associated with the true endpoint (T) can often provide information about T and hence the effect of a treatment (Z) on T. A useful biomarker can be measured earlier than T and cost less than T. In this paper we consider the use of S as an auxiliary variable and examine the information recovery from using S for estimating the treatment effect on T, when S is completely observed and T is partially observed. In an ideal but often unrealistic setting, when S satisfies Prentice’s definition for perfect surrogacy, there is the potential for substantial gain in precision by using data from S to estimate the treatment effect on T. When S is not close to a perfect surrogate, it can provide substantial information only under particular circumstances. We propose to use a targeted shrinkage regression approach that data-adaptively takes advantage of the potential efficiency gain yet avoids the need to make a strong surrogacy assumption. Simulations show that this approach strikes a balance between bias and efficiency gain. Compared with competing methods, it has better mean squared error properties and can achieve substantial efficiency gain, particularly in a common practical setting when S captures much but not all of the treatment effect and the sample size is relatively small. We apply the proposed method to a glaucoma data example.
Auxiliary Variable; Biomarker; Randomized Trials; Ridge Regression; Missing Data
This paper presents a new modeling strategy in functional data analysis. We consider the problem of estimating an unknown smooth function given functional data with noise. The unknown function is treated as the realization of a stochastic process, which is incorporated into a diffusion model. The method of smoothing spline estimation is connected to a special case of this approach. The resulting models offer great flexibility to capture the dynamic features of functional data, and allow straightforward and meaningful interpretation. The likelihood of the models is derived with Euler approximation and data augmentation. A unified Bayesian inference method is carried out via a Markov Chain Monte Carlo algorithm including a simulation smoother. The proposed models and methods are illustrated on some prostate specific antigen data, where we also show how the models can be used for forecasting.
Diffusion model; Euler approximation; Nonparametric regression; Simulation smoother; Stochastic differential equation; Stochastic velocity model
Intermediate outcome variables can often be used as auxiliary variables for the true outcome of interest in randomized clinical trials. For many cancers, time to recurrence is an informative marker in predicting a patient’s overall survival outcome, and could provide auxiliary information for the analysis of survival times.
To investigate whether models linking recurrence and death combined with a multiple imputation procedure for censored observations can result in efficiency gains in the estimation of treatment effects, and be used to shorten trial lengths.
Recurrence and death times are modeled using data from 12 trials in colorectal cancer. Multiple imputation is used as a strategy for handling missing values arising from censoring. The imputation procedure uses a cure model for time to recurrence and a time-dependent Weibull proportional hazards model for time to death. Recurrence times are imputed, and then death times are imputed conditionally on recurrence times. To illustrate these methods, trials are artificially censored 2-years after the last accrual, the imputation procedure is implemented, and a log-rank test and Cox model are used to analyze and compare these new data with the original data.
The results show modest, but consistent gains in efficiency in the analysis by using the auxiliary information in recurrence times. Comparison of analyses show the treatment effect estimates and log rank test results from the 2-year censored imputed data to be in between the estimates from the original data and the artificially censored data, indicating that the procedure was able to recover some of the lost information due to censoring.
The models used are all fully parametric, requiring distributional assumptions of the data.
The proposed models may be useful to improve the efficiency in estimation of treatment effects in cancer trials and shortening trial length.
Auxiliary Variables; Colon Cancer; Cure Models; Multiple Imputation; Surrogate Endpoints
Diet is associated with cancer prognosis, including head and neck cancer (HNC), and has been hypothesized to influence epigenetic state by determining the availability of functional groups involved in the modification of DNA and histone proteins. The goal of this study was to describe the association between pretreatment diet and HNC tumor DNA methylation. Information on usual pretreatment food and nutrient intake was estimated via food frequency questionnaire (FFQ) on 49 HNC cases. Tumor DNA methylation patterns were assessed using the Illumina Goldengate Methylation Cancer Panel. First, a methylation score, the sum of individual hypermethylated tumor suppressor associated CpG sites, was calculated and associated with dietary intake of micronutrients involved in one-carbon metabolism and antioxidant activity, and food groups abundant in these nutrients. Second, gene specific analyses using linear modeling with empirical Bayesian variance estimation were conducted to identify if methylation at individual CpG sites was associated with diet. All models were controlled for age, sex, smoking, alcohol and HPV status. Individuals reporting in the highest quartile of folate, vitamin B12 and vitamin A intake, compared with those in the lowest quartile, showed significantly less tumor suppressor gene methylation, as did patients reporting the highest cruciferous vegetable intake. Gene specific analyses identified differential associations between DNA methylation and vitamin B12 and vitamin A intake when stratifying by HPV status. These preliminary results suggest that intake of folate, vitamin A and vitamin B12 may be associated with the tumor DNA methylation profile in HNC and enhance tumor suppression.
DNA methylation; diet; tumor suppressor; folate; vitamin B12
When the true end points (T) are difficult or costly to measure, surrogate markers (S) are often collected in clinical trials to help predict the effect of the treatment (Z). There is great interest in understanding the relationship among S, T, and Z. A principal stratification (PS) framework has been proposed by Frangakis and Rubin (2002) to study their causal associations. In this paper, we extend the framework to a multiple trial setting and propose a Bayesian hierarchical PS model to assess surrogacy. We apply the method to data from a large collection of colon cancer trials in which S and T are binary. We obtain the trial-specific causal measures among S, T, and Z, as well as their overall population-level counterparts that are invariant across trials. The method allows for information sharing across trials and reduces the nonidentifiability problem. We examine the frequentist properties of our model estimates and the impact of the monotonicity assumption using simulations. We also illustrate the challenges in evaluating surrogacy in the counterfactual framework that result from nonidentifiability.
Bayesian estimation; Counterfactual model; Identifiability; Multiple trials; Principal stratification; Surrogate marker
A surrogate marker (S) is a variable that can be measured earlier and often easier than the true endpoint (T) in a clinical trial. Most previous research has been devoted to developing surrogacy measures to quantify how well S can replace T or examining the use of S in predicting the effect of a treatment (Z). However, the research often requires one to fit models for the distribution of T given S and Z. It is well known that such models do not have causal interpretations because the models condition on a post-randomization variable S. In this paper, we directly model the relationship among T, S and Z using a potential outcomes framework introduced by Frangakis and Rubin (2002). We propose a Bayesian estimation method to evaluate the causal probabilities associated with the cross-classification of the potential outcomes of S and T when S and T are both binary. We use a log-linear model to directly model the association between the potential outcomes of S and T through the odds ratios. The quantities derived from this approach always have causal interpretations. However, this causal model is not identifiable from the data without additional assumptions. To reduce the non-identifiability problem and increase the precision of statistical inferences, we assume monotonicity and incorporate prior belief that is plausible in the surrogate context by using prior distributions. We also explore the relationship among the surrogacy measures based on traditional models and this counterfactual model. The method is applied to the data from a glaucoma treatment study.
Bayesian Estimation; Counterfactual Model; Randomized Trial; Surrogate Marker
Purpose. Screening for depression, sleep-related disturbances, and anxiety in patients with diagnosed adenocarcinoma of the pancreas. Materials and Methods. Patients were evaluated at initial consultation and subsequent visits at the multidisciplinary pancreatic cancer clinic at our University Cancer Center. Cross-sectional and longitudinal psychosocial distress was assessed utilizing Personal Health Questionnaire 9 (PHQ9) to screen for depression and monitor symptoms, the Penn State Worry Questionnaire (PSWQ) for generalized anxiety, and the University of Michigan Sleep Questionnaire to monitor sleep symptoms. Results. Twenty-two patients diagnosed with pancreatic cancer participated during the 6-month pilot study with longitudinal followup for thirteen patients. In this study, mild-to-moderate depressive symptoms, anxiety, and potential sleep problems were common. The main finding of the study was 23% of the patients who were part of this pilot project screened positive for moderately severe major depressive symptoms, likely anxiety disorder or a potential sleep disorder during the study. One patient screened positive for moderately severe depressive symptoms in longitudinal followup. Conclusions. Depression, anxiety, and sleep problems are evident in patients with pancreatic cancer. Prospective, longitudinal studies, with larger groups of patients, are needed to determine if these comorbid symptoms impact outcome and clinical course.
There has been substantive interest in the assessment of surrogate endpoints in medical research. These are measures which could potentially replace “true” endpoints in clinical trials and lead to studies that require less follow-up. Recent research in the area has focused on assessments using causal inference frameworks. Beginning with a simple model for associating the surrogate and true endpoints in the population, we approach the problem as one of endogenous covariates. An instrumental variables estimator and general two-stage algorithm is proposed. Existing surrogacy frameworks are then evaluated in the context of the model. In addition, we define an extended relative effect estimator as well as a sensitivity analysis for assessing what we term the treatment instrumentality assumption. A numerical example is used to illustrate the methodology.
Clinical Trial; Counterfactual; Nonlinear response; Prentice Criterion; Structural equations model
We consider using observational data to estimate the effect of a treatment on disease recurrence, when the decision to initiate treatment is based on longitudinal factors associated with the risk of recurrence. The effect of salvage androgen deprivation therapy (SADT) on the risk of recurrence of prostate cancer is inadequately described by existing literature. Furthermore, standard Cox regression yields biased estimates of the effect of SADT, since it is necessary to adjust for prostate-specific antigen (PSA), which is a time-dependent confounder and an intermediate variable. In this paper, we describe and compare two methods which appropriately adjust for PSA in estimating the effect of SADT. The first method is a two-stage method which jointly estimates the effect of SADT and the hazard of recurrence in the absence of treatment by SADT. In the first stage, PSA is predicted in the absence of SADT, and in the second stage, a time-dependent Cox model is used to estimate the benefit of SADT, adjusting for PSA. The second method, called sequential stratification, reorganizes the data to resemble a sequence of experiments in which treatment is conditionally randomized given the time-dependent covariates. Strata are formed, each consisting of a patient undergoing SADT and a set of appropriately matched controls, and analysis proceeds via stratified Cox regression. Both methods are applied to data from patients initially treated with radiation therapy for prostate cancer and give similar SADT effect estimates.
treatment by indication; time-dependent confounder; proportional hazards model; causal effect; prostate cancer
Prostate-specific antigen (PSA) is a biomarker routinely and repeatedly measured on prostate cancer patients treated by radiation therapy (RT). It was shown recently that its whole pattern over time rather than just its current level was strongly associated with prostate cancer recurrence. To more accurately guide clinical decision making, monitoring of PSA after RT would be aided by dynamic powerful prognostic tools that incorporate the complete posttreatment PSA evolution. In this work, we propose a dynamic prognostic tool derived from a joint latent class model and provide a measure of variability obtained from the parameters asymptotic distribution. To validate this prognostic tool, we consider predictive accuracy measures and provide an empirical estimate of their variability. We also show how to use them in the longitudinal context to compare the dynamic prognostic tool we developed with a proportional hazard model including either baseline covariates or baseline covariates and the expected level of PSA at the time of prediction in a landmark model. Using data from 3 large cohorts of patients treated after the diagnosis of prostate cancer, we show that the dynamic prognostic tool based on the joint model reduces the error of prediction and offers a powerful tool for individual prediction.
Error of prediction; Joint latent class model; Mixed model; Posterior probability; Predictive accuracy; Prostate cancer prognosis
The treatment effect of a colorectal polyp prevention trial is often evaluated from the colorectal adenoma recurrence status at the end of the trial. However, early colonoscopy from some participants complicates estimation of the final study end recurrence rate. The early colonoscopy could be informative of status of recurrence and induce informative differential follow-up into the data. In this paper we use mid-point imputation to handle interval-censored observations. We then apply a weighted Kaplan-Meier method to the imputed data to adjust for potential informative differential follow-up, while estimating the recurrence rate at the end of the trial. In addition, we modify the weighted Kaplan-Meier method to handle a situation with multiple prognostic covariates by deriving a risk score of recurrence from a working logistic regression model and then use the risk score to define risk groups to perform weighted Kaplan-Meier estimation. We argue that mid-point imputation will produce an unbiased estimate of recurrence rate at the end of the trial under an assumption that censoring only depends on the status of early colonoscopy. The method described here is illustrated with an example from a colon polyp prevention study.
current status data; mid-point imputation; weighted Kaplan-Meier estimator
Coupling chromatin immunoprecipitation (ChIP) with recently developed massively parallel sequencing technologies has enabled genome-wide detection of protein–DNA interactions with unprecedented sensitivity and specificity. This new technology, ChIP-Seq, presents opportunities for in-depth analysis of transcription regulation. In this study, we explore the value of using ChIP-Seq data to better detect and refine transcription factor binding sites (TFBS). We introduce a novel computational algorithm named Hybrid Motif Sampler (HMS), specifically designed for TFBS motif discovery in ChIP-Seq data. We propose a Bayesian model that incorporates sequencing depth information to aid motif identification. Our model also allows intra-motif dependency to describe more accurately the underlying motif pattern. Our algorithm combines stochastic sampling and deterministic ‘greedy’ search steps into a novel hybrid iterative scheme. This combination accelerates the computation process. Simulation studies demonstrate favorable performance of HMS compared to other existing methods. When applying HMS to real ChIP-Seq datasets, we find that (i) the accuracy of existing TFBS motif patterns can be significantly improved; and (ii) there is significant intra-motif dependency inside all the TFBS motifs we tested; modeling these dependencies further improves the accuracy of these TFBS motif patterns. These findings may offer new biological insights into the mechanisms of transcription factor regulation.
To assess the relationship between prognostic factors, post-radiation prostate-specific antigen (PSA) dynamics, and clinical failure following prostate cancer radiation therapy using contemporary statistical models.
Methods and materials
Data from 4,247 patients with 40,324 PSA measurements treated with external beam radiation monotherapy in five cohorts were analyzed. Temporal change of PSA following treatment completion was described by a specially developed linear mixed model (LMM), including standard prognostic factors. These factors, along with predicted PSA evolution, were incorporated into a Cox model to establish their predictive value for the risk of clinical recurrence over time.
Consistent relationships were found across cohorts. The initial PSA decline after radiation therapy was associated with baseline PSA and T-stage (p<0.001). The long-term PSA rise was associated with baseline PSA, T-stage and Gleason score (p<0.001). The risk of clinical recurrence increased with current level (p<0.001) and current slope of PSA (p<0.001). In a pooled analysis, higher doses of radiation were associated with a lower long-term PSA rise (p<0.001) but not with the risk of recurrence after adjusting for PSA trajectory (p=0.63). Conversely, after adjusting for other factors, increased age at diagnosis was not associated with long-term PSA rise (p=0.85) but directly associated with increased risk of recurrence (p<0.001).
LMM can be reliably used to construct typical patient PSA profiles following prostate cancer radiation therapy. Pre-treatment factors along with PSA evolution and the associated risk of recurrence provide an efficient and quantitative way to assess impact of risk factors on disease progression.
Prostate cancer; Prostate-specific Antigen; PSA velocity; Radiation therapy; Prognostic calculator
To prospectively identify markers of response to therapy and outcome in an organ-sparing trial for advanced oropharyngeal cancer.
Patients and Methods
Pretreatment biopsies were examined for expression of epidermal growth factor receptor (EGFR), p16, Bcl-xL, and p53 as well as for p53 mutation. These markers were assessed for association with high-risk human papillomavirus (HPV), response to therapy, and survival. Patient variables included smoking history, sex, age, primary site, tumor stage, and nodal status.
EGFR expression was inversely associated with response to induction chemotherapy (IC) (P = .01), chemotherapy/radiotherapy (CRT; P = .055), overall survival (OS; P = .001), and disease-specific survival (DSS; P = .002) and was directly associated with current smoking (P = .04), female sex (P = .053), and lower HPV titer (P = .03). HPV titer was significantly associated with p16 expression (P < .0001); p16 was significantly associated with response to IC (P = .008), CRT (P = .009), OS (P = .001), and DSS (P = .003). As combined markers, lower HPV titer and high EGFR expression were associated with worse OS (ρEGFR = 0.008; ρHPV = 0.03) and DSS (ρEGFR = 0.01; ρHPV = 0.016). In 36 of 42 biopsies, p53 was wild-type, and only one HPV-positive tumor had mutant p53. The combination of low p53 and high Bcl-xL expression was associated with poor OS (P = .005) and DSS (P = .002).
Low EGFR and high p16 (or higher HPV titer) expression are markers of good response to organ-sparing therapy and outcome, whereas high EGFR expression, combined low p53/high Bcl-xL expression, female sex, and smoking are associated with a poor outcome. Smoking cessation and strategies to target EGFR and Bcl-xL are important adjuncts to the treatment of oropharyngeal cancer.
To test induction chemotherapy (IC) followed by concurrent chemoradiotherapy (CRT) or surgery/ radiotherapy (RT) for advanced oropharyngeal cancer and to assess the effect of human papilloma virus (HPV) on response and outcome.
Patients and Methods
Sixty-six patients (51 male; 15 female) with stage III to IV squamous cell carcinoma of the oropharynx (SCCOP) were treated with one cycle of cisplatin (100 mg/m2) or carboplatin (AUC 6) and with fluorouracil (1,000 mg/m2/d for 5 days) to select candidates for CRT. Those achieving a greater than 50% response at the primary tumor received CRT (70 Gy; 35 fractions with concurrent cisplatin 100 mg/m2 or carboplatin (AUC 6) every 21 days for three cycles). Adjuvant paclitaxel was given to patients who were complete histologic responders. Patients with a response of 50% or less underwent definitive surgery and postoperative radiation. Pretreatment biopsies from 42 patients were tested for high-risk HPV.
Fifty-four of 66 patients (81%) had a greater than 50% response after IC. Of these, 53 (98%) received CRT, and 49 (92%) obtained complete histologic response with a 73.4% (47 of 64) rate of organ preservation. The 4-year overall survival (OS) was 70.4%, and the disease-specific survival (DSS) was 75.8% (median follow-up, 64.1 months). HPV16, found in 27 of 42 (64.3%) biopsies, was associated with younger age (median, 55 v 63 years; P = .016), sex (22 of 30 males [73.3%] and five of 12 females [41.7%]; P = .08), and nonsmoking status (P = .037). HPV titer was significantly associated with IC response (P = .001), CRT response (P = .005), OS (P = .007), and DSS (P = .008).
Although the numbers in this study are small, IC followed by CRT is an effective treatment for SCCOP, especially in patients with HPV-positive tumors; however, for patients who do not respond to treatment, alternative treatments must be developed.
High rates of overall survival (OS) and laryngeal preservation were achieved in two sequential phase II clinical trials in patients with stage III/IV laryngeal squamous cell carcinoma (SCC). Patients were treated with chemoradiation after a >50% primary tumor response to one cycle of neoadjuvant chemotherapy (IC). We analyzed outcomes for T4 patients with cartilage invasion from both studies.
Records from 36 patients with T4 SCC of the larynx with cartilage invasion alone (n = 16) or cartilage invasion and extralaryngeal spread (n = 20) were retrospectively reviewed. All were treated with one cycle of cisplatin (100 mg/m2) [or carboplatin (AUC 6)] and 5-fluorouracil (1,000 mg/m2/d for 5 days) (P+5FU). Those achieving >50% response at the primary tumor received chemoradiation (70 Gy; 35 fractions with concurrent cisplatin-100 mg/m2 [carboplatin (AUC 6)] every 21 days for 3 cycles), followed by adjuvant P+5FU for complete histologic responders (CHR). Patients with <50% response after IC underwent total laryngectomy and postoperative radiation.
Twenty-nine of 36 patients (81%) had >50% response following IC. Of these, 27 received definitive chemoradiation, 23 (85%) obtained CHR, with 58% laryngeal preservation rate. The 3-year OS was 78%, and the disease-specific survival was 80% (median follow-up 69 months). Following chemoradiation, 8/11 (73%) patients with an intact larynx had >75% understandable speech, 6/36 (17%) were g-tube dependent and 6/36 (17%) were tracheostomy dependent.
Our results suggest that chemo-selection is a feasible organ preservation alternative to total laryngectomy for patients with T4 laryngeal SCC with cartilage invasion.
Although prognostic gene expression signatures for survival in early stage lung cancer have been proposed, for clinical application it is critical to establish their performance across different subject populations and in different laboratories. Here we report a large, training-testing, multi-site blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) can be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas.
A common side effect experienced by head and neck cancer patients after radiotherapy (RT) is impairment of the parotid glands’ ability to produce saliva. Our purpose is to investigate the relationship between radiation dose and saliva changes in the two years following treatment.
Methods and Materials
The study population includes 142 patients treated with conformal or intensity modulated radiotherapy. Saliva flow rates from 266 parotid glands are measured before and 1, 3, 6, 12, 18 and 24 months after treatment. Measurements are collected separately from each gland under both stimulated and unstimulated conditions. Bayesian nonlinear hierarchical models were developed and fit to the data.
Parotids receiving higher radiation produce less saliva. The largest reduction is at 1–3 months after RT followed by gradual recovery. When mean doses are lower (e.g. <25Gy), the model-predicted average stimulated saliva recovers to pre-treatment levels at 12 months and exceeds it at 18 and 24 months. For higher doses (e.g. >30Gy), the stimulated saliva does not return to original levels after two years. Without stimulation, at 24 months, the predicted saliva is 86% of pre-treatment levels for 25Gy and <31% for >40Gy. We do not find evidence to support that the over-production of stimulated saliva at 18 and 24 months after low dose in one parotid gland is due to low saliva production from the other parotid gland.
Saliva production is impacted significantly by radiation, but with doses <25–30Gy, recovery is substantial and returns to pre-treatment levels two years after RT.
Head and neck cancer; Intensity modulated radiation therapy; Parotid salivary glands; Radiation dose; Bayesian analysis