Surrogate endpoints in clinical trials of p16-positive squamous cell carcinoma of the oropharynx: an individual patient data meta-analysis.

BACKGROUND: The increased incidence of human papillomavirus (HPV)-related cancers has motivated efforts to optimise treatment for these patients with excellent prognosis. Validation of surrogates for overall survival could expedite the investigation of new therapies. We sought to evaluate candidate intermediate clinical endpoints in trials assessing definitive treatment of p16-positive oropharyngeal cancer with chemotherapy or radiotherapy. METHODS: We did a retrospective review of five multicentre, randomised trials (NRG/RTOG 9003, 0129, 0234, 0522, and 1016) that tested radiotherapy with or without chemotherapy in patients (aged ≥18 years) with p16-positive localised head or neck squamous-cell carcinomas. Eight intermediate clinical endpoints were considered as potential surrogates for overall survival: freedom from local progression, freedom from regional progression, freedom from distant metastasis, freedom from locoregional progression, freedom from any progression, locoregional progression-free survival, progression-free survival, and distant metastasis-free survival. We used a two-stage meta-analytical framework, which requires high correlation between the intermediate clinical endpoint and overall survival at the patient level (condition 1), and high correlation between the treatment effect on the intermediate clinical endpoint and the treatment effect on overall survival (condition 2). For both, an r2 greater than 0·7 was used as criteria for clinically relevant surrogacy. FINDINGS: We analysed 1373 patients with oropharyngeal cancer from May 9, 2020, to Nov 22, 2023. 1231 (90%) of patients were men, 142 (10%) were women, and 1207 (88%) were White, with a median age of 57 years (IQR 51-62). Median follow-up was 4·2 years (3·1-5·1). For the first condition, correlating the intermediate clinical endpoints with overall survival at the individual and trial level, the three composite endpoints of locoregional progression-free survival (Kendall's τ 0·91 and r2 0·72), distant metastasis-free survival (Kendall's τ 0·93 and r2 0·83), and progression-free survival (Kendall's τ 0·88 and r2 0·70) were highly correlated with overall survival at the patient level and at the trial-group level. For the second condition, correlating treatment effects of the intermediate clinical endpoints and overall survival, the composite endpoints of locoregional progression-free survival (r2 0·88), distant metastasis-free survival (r2 0·96), and progression-free survival (r2 0·92) remained strong surrogates. Treatment effects on the remaining intermediate clinical endpoints were less strongly correlated with overall survival. INTERPRETATION: We identified locoregional progression-free survival, distant metastasis-free survival, and progression-free survival as surrogates for overall survival in p16-positive oropharyngeal cancers treated with chemotherapy or radiotherapy, which could serve as clinical trial endpoints. FUNDING: NRG Oncology Operations, NRG Oncology SDMC, the National Cancer Institute, Eli Lilly, Aventis, and the University of Michigan.

Impact of lung cancer screening on stage migration and mortality among the national Veterans Health Administration population with lung cancer.

BACKGROUND: Despite randomized trials demonstrating a mortality benefit to low-dose computed tomography screening to detect lung cancer, uptake of lung cancer screening (LCS) has been slow, and the benefits of screening remain unclear in clinical practice. METHODS: This study aimed to assess the impact of screening among patients in the Veterans Health Administration (VA) health care system diagnosed with lung cancer between 2011 and 2018. Lung cancer stage at diagnosis, lung cancer-specific survival, and overall survival between patients with cancer who did and did not receive screening before diagnosis were evaluated. We used Cox regression modeling and inverse propensity weighting analyses with lead time bias adjustment to correlate LCS exposure with patient outcomes. RESULTS: Of 57,919 individuals diagnosed with lung cancer in the VA system between 2011 and 2018, 2167 (3.9%) underwent screening before diagnosis. Patients with screening had higher rates of stage I diagnoses (52% vs. 27%; p ≤ .0001) compared to those who had no screening. Screened patients had improved 5-year overall survival rates (50.2% vs. 27.9%) and 5-year lung cancer-specific survival (59.0% vs. 29.7%) compared to unscreened patients. Among screening-eligible patients who underwent National Comprehensive Cancer Network guideline-concordant treatment, screening resulted in substantial reductions in all-cause mortality (adjusted hazard ratio [aHR], 0.79; 95% confidence interval [CI], 0.67-0.92; p = .003) and lung-specific mortality (aHR, 0.61; 95% CI, 0.50-0.74; p < .001). CONCLUSIONS: While LCS uptake remains limited, screening was associated with earlier stage diagnoses and improved survival. This large national study corroborates the value of LCS in clinical practice; efforts to widely adopt this vital intervention are needed.

Time-varying associations of patient and tumor characteristics with cancer survival: an analysis of SEER data across 14 cancer sites, 2004-2017.

PURPOSE: Surveillance, Epidemiology, and End Results (SEER) cancer registries provides information about survival duration and cause of death for cancer patients. Baseline demographic and tumor characteristics such as age, sex, race, year of diagnosis, and tumor stage can inform the expected survival time of patients, but their associations with survival may not be constant over the post-diagnosis period. METHODS: Using SEER data, we examined if there were time-varying associations of patient and tumor characteristics on survival, and we assessed how these relationships differed across 14 cancer sites. Standard Cox proportional hazards models were extended to allow for time-varying associations and incorporated into a competing-risks framework, separately modeling cancer-specific and other-cause deaths. For each cancer site and for each of the five factors, we estimated the relative hazard ratio and absolute hazard over time in the presence of competing risks. RESULTS: Our comprehensive consideration of patient and tumor characteristics when estimating time-varying hazards showed that the associations of age, tumor stage at diagnosis, and race/ethnicity with risk of death (cancer-specific and other-cause) change over time for many cancers; characteristics of sex and year of diagnosis exhibit some time-varying patterns as well. Stage at diagnosis had the largest associations with survival. CONCLUSION: These findings suggest that proportional hazards assumptions are often violated when examining patient characteristics on cancer survival post-diagnosis. We discuss several interesting results where the relative hazards are time-varying and suggest possible interpretations. Based on the time-varying associations of several important covariates on survival after cancer diagnosis using a pan-cancer approach, the likelihood of the proportional hazards assumption being met or corresponding interpretation should be considered in survival analyses, as flawed inference may have implications for cancer care and policy.

Magnetic prestressing for a d32-mode single crystal ultrasonic transducer.

This work describes a 35.9 kHz ultrasonic transducer that incorporates a magnetic arrangement to apply a static-compressive prestress to a d32-mode relaxor ferroelectric single crystal drive-element. The magnetic arrangement produces a 22.5 N static-compressive force, inducing a static compression of ∼630 nm on the drive-element. Operating in air with a continuous-wave 10 V peak drive at ∼35.9 kHz, the measured resonant peak displacement of the transducers head-mass was 127 nm. This is well within the predicted static compression, thus, the drive-element is protected from damaging tensile stress. Under the same drive conditions and at an axial distance of 10 mm from the face of the head-mass, the measured acoustic pressure was ∼12 Pa. Analytical and finite element model predictions and the measured behaviour of a prototype device are presented and show good correlation, demonstrating that magnetic prestressing of the drive-element can be a viable alternative to the traditional bolt-clamp.

Association between sustained virological response and clinical outcomes in patients with hepatitis C infection and hepatocellular carcinoma.

BACKGROUND: Sustained viral response (SVR) improves survival for patients with hepatitis C (HCV) and hepatocellular carcinoma (HCC) after curative treatment; however, the benefit of SVR in those with active HCC with a significant competing risk of mortality is unknown. This study aimed to evaluate the association between SVR and outcomes in patients with active HCC. METHODS: The authors performed a multicenter, retrospective cohort study including consecutive adults with HCV cirrhosis and treatment-naive HCC diagnosed between 2014 and 2018. Patients were stratified into two groups: active viremia (n = 431) and SVR before HCC diagnosis (n = 135). All patients underwent nonsurgical therapy as their initial treatment and were followed until liver transplantation, last follow-up, or death. The primary outcome was incident or worsening hepatic decompensation within 6 months and the secondary outcome was overall survival. All analyses used inverse probability of treatment weights (IPTW) to account for differences between the nonrandomized cohorts. RESULTS: Post-SVR patients had significantly lower odds of hepatic decompensation compared to viremic patients (odds ratio [OR], 0.18; 95% confidence interval [CI], 0.06-0.59). Results were consistent among subgroups of patients with Child Pugh A cirrhosis (OR, 0.22; 95% CI, 0.04-0.77), Barcelona Clinic Liver Cancer stage B/C HCC (OR, 0.20; 95% CI, 0.04-0.65), and those receiving nonablative HCC therapies (OR, 0.21; 95% CI, 0.07-0.67). However, in IPTW multivariable Cox regression, SVR was not associated with improved survival (hazard ratio, 0.79; 95% CI, 0.56-1.12). CONCLUSIONS: Patients with HCV-related HCC and SVR are less likely to experience hepatic decompensation than viremic patients, suggesting patients with HCC who are undergoing nonsurgical therapies may benefit from DAA treatment.

Development and validation of a life expectancy calculator for US patients with prostate cancer.

OBJECTIVE: To develop and validate an accurate, usable prediction model for other-cause mortality (OCM) in patients with prostate cancer diagnosed in the United States. MATERIALS AND METHODS: Model training was performed using the National Health and Nutrition Examination Survey 1999-2010 including men aged >40 years with follow-up to the year 2014. The model was validated in the Prostate, Lung, Colon, and Ovarian Cancer Screening Trial prostate cancer cohort, which enrolled patients between 1993 and 2001 with follow-up to the year 2015. Time-dependent area under the curve (AUC) and calibration were assessed in the validation cohort. Analyses were performed to assess algorithmic bias. RESULTS: The 2420 patient training cohort had 459 deaths over a median follow-up of 8.8 years among survivors. The final model included eight predictors: age; education; marital status; diabetes; hypertension; stroke; body mass index; and smoking. It had an AUC of 0.75 at 10 years for predicting OCM in the validation cohort of 8220 patients. The final model significantly outperformed the Social Security Administration life tables and showed adequate predictive performance across race, educational attainment, and marital status subgroups. There is evidence of major variability in life expectancy that is not captured by age, with life expectancy predictions differing by 10 or more years among patients of the same age. CONCLUSION: Using two national cohorts, we have developed and validated a simple and useful prediction model for OCM for patients with prostate cancer treated in the United States, which will allow for more personalized treatment in accordance with guidelines.

Utility based approach in individualized optimal dose selection using machine learning methods.

The goal in personalized medicine is to individualize treatment using patient characteristics and improve health outcomes. Selection of optimal dose must balance the effect of dose on both treatment efficacy and toxicity outcomes. We consider a setting with one binary efficacy and one binary toxicity outcome. The goal is to find the optimal dose for each patient using clinical features and biomarkers from available dataset. We propose to use flexible machine learning methods such as random forest and Gaussian process models to build models for efficacy and toxicity depending on dose and biomarkers. A copula is used to model the joint distribution of the two outcomes and the estimates are constrained to have non-decreasing dose-efficacy and dose-toxicity relationships. Numerical utilities are elicited from clinicians for each potential bivariate outcome. For each patient, the optimal dose is chosen to maximize the posterior mean of the utility function. We also propose alternative approaches to optimal dose selection by adding additional toxicity based constraints and an approach taking into account the uncertainty in the estimation of the utility function. The proposed methods are evaluated in a simulation study to compare expected utility outcomes under various estimated optimal dose rules. Gaussian process models tended to have better performance than random forest. Enforcing monotonicity during modeling provided small benefits. Whether and how, correlation between efficacy and toxicity, was modeled, had little effect on performance. The proposed methods are illustrated with a study of patients with liver cancer treated with stereotactic body radiation therapy.

Intermediate clinical endpoints for surrogacy in localised prostate cancer: an aggregate meta-analysis.

BACKGROUND: The international Intermediate Clinical Endpoints in Cancer of the Prostate working group has established metastasis-free survival as a surrogate for overall survival in localised prostate cancer based on the findings of 19 predominantly radiotherapy-based trials. We sought to comprehensively assess aggregate trial-level performance of commonly reported intermediate clinical endpoints across all randomised trials in localised prostate cancer. METHODS: For this meta-analysis, we searched PubMed for all trials in localised or biochemically recurrent prostate cancer published between Jan 1, 1970, and Jan 15, 2020. Eligible trials had to be randomised, therapeutic, reporting overall survival and at least one intermediate clinical endpoint, and with a sample size of at least 70 participants. Trials of metastatic disease were excluded. Intermediate clinical endpoints included biochemical failure, local failure, distant metastases, biochemical failure-free survival, progression-free survival, and metastasis-free survival. Candidacy for surrogacy was assessed using the second condition of the meta-analytical approach (ie, correlation of the treatment effect of the intermediate clinical endpoint and overall survival), using R2 weighted by the inverse variance of the log intermediate clinical endpoint hazard ratio. The intermediate clinical endpoint was deemed to be a surrogate for overall survival if R2 was 0·7 or greater. FINDINGS: 75 trials (53 631 patients) were included in our analysis. Median follow-up was 9·1 years (IQR 5·7-10·6). Biochemical failure (R2 0·38 [95% CI 0·11-0·64]), biochemical failure-free survival (R2 0·12 [0·0030-0·33]), biochemical failure and clinical failure (R2 0·28 [0·0045-0·65]), and local failure (R2 0·085 [0·00-0·37]) correlated poorly with overall survival. Progression-free survival (R2 0·46 [95% CI 0·22-0·67]) showed moderate correlation with overall survival, and metastasis-free survival (R2 0·78 [0·59-0·89]) correlated strongly. INTERPRETATION: Intermediate clinical endpoints based on biochemical and local failure did not meet the second condition of the meta-analytical approach and are not surrogate endpoints for overall survival in localised prostate cancer. Our findings validate metastasis-free survival as the only identified surrogate endpoint for overall survival to date. FUNDING: Prostate Cancer Foundation and National Institutes of Health.

Patient Burden with Current Surveillance Paradigm and Factors Associated with Interest in Altered Surveillance for Early Stage HPV-Related Oropharyngeal Cancer.

INTRODUCTION: Optimal surveillance paradigms for survivors of early stage human papillomavirus (HPV)-related oropharyngeal cancer are not well defined. This study aimed to characterize patient interest in and factors associated with an altered surveillance paradigm. MATERIALS AND METHODS: We surveyed patients with Stage I or II HPV-related oropharyngeal cancer treated at a tertiary care institution from 2016 to 2019. Primary outcomes were descriptive assessment of patient knowledge, interest in altered surveillance, burdens of in-person appointments, and priorities for surveillance visits. Ordinal regression was used to identify correlates of interest in altered surveillance. RESULTS: Sixty-seven patients completed surveys from February to April 2020 at a median of 21 months since completing definitive treatment. A majority (61%) of patients were interested in a surveillance approach that decreased in-person clinic visits. Patients who self-identified as medical maximizers, had higher worry of cancer recurrence, or were in long-term relationships were less likely to be interested. Patients reported significant burdens associated with surveillance visits, including driving distance, time off work, and nonmedical costs. Patients were most concerned with discussing cancer recurrence (76%), physical quality of life (70%), mortality (61%), and mental quality of life (52%) with their providers at follow-up visits. CONCLUSION: Patients with early stage HPV-related oropharyngeal cancers are interested in altered surveillance approaches, experience significant burdens related to surveillance visits, and have concerns that are not well addressed with current surveillance approaches, including physical and mental quality of life. Optimized surveillance approaches should incorporate patient priorities and minimize associated burdens. IMPLICATIONS FOR PRACTICE: The number of patients with HPV-related oropharyngeal cancers is increasing, and numerous clinical trials are investigating novel approaches to treating these good-prognosis patients. There has been limited work assessing optimal surveillance paradigms in these patients. Patients experience significant appointment-related burdens and have concerns such as physical and mental quality of life. Additionally, patients with early stage HPV-related oropharyngeal cancers express interest in altered surveillance approaches that decrease in-person clinic visits. Optimization of surveillance paradigms to promote broader survivorship care in clinical practice is needed.

Design and analysis considerations for utilizing a mapping function in a small sample, sequential, multiple assignment, randomized trials with continuous outcomes.

Small sample, sequential, multiple assignment, randomized trials (snSMARTs) are multistage trials with the overall goal of determining the best treatment after a fixed amount of time. In snSMART trials, patients are first randomized to one of three treatments and a binary (e.g. response/nonresponse) outcome is measured at the end of the first stage. Responders to first stage treatment continue their treatment. Nonresponders to first stage treatment are rerandomized to one of the remaining treatments. The same binary outcome is measured at the end of the first and second stages, and data from both stages are pooled together to find the best first stage treatment. However, in many settings the primary endpoint may be continuous, and dichotomizing this continuous variable may reduce statistical efficiency. In this article, we extend the snSMART design and methods to allow for continuous outcomes. Instead of requiring a binary outcome at the first stage for rerandomization, the probability of staying on the same treatment or switching treatment is a function of the first stage outcome. Rerandomization based on a mapping function of a continuous outcome allows for snSMART designs without requiring a binary outcome. We perform simulation studies to compare the proposed design with continuous outcomes to standard snSMART designs with binary outcomes. The proposed design results in more efficient treatment effect estimates and similar outcomes for trial patients.

Evaluation of predictive model performance of an existing model in the presence of missing data.

In medical research, the Brier score (BS) and the area under the receiver operating characteristic (ROC) curves (AUC) are two common metrics used to evaluate prediction models of a binary outcome, such as using biomarkers to predict the risk of developing a disease in the future. The assessment of an existing prediction models using data with missing covariate values is challenging. In this article, we propose inverse probability weighted (IPW) and augmented inverse probability weighted (AIPW) estimates of AUC and BS to handle the missing data. An alternative approach uses multiple imputation (MI), which requires a model for the distribution of the missing variable. We evaluated the performance of IPW and AIPW in comparison with MI in simulation studies under missing completely at random, missing at random, and missing not at random scenarios. When there are missing observations in the data, MI and IPW can be used to obtain unbiased estimates of BS and AUC if the imputation model for the missing variable or the model for the missingness is correctly specified. MI is more efficient than IPW. Our simulation results suggest that AIPW can be more efficient than IPW, and also achieves double robustness from miss-specification of either the missingness model or the imputation model. The outcome variable should be included in the model for the missing variable under all scenarios, while it only needs to be included in missingness model if the missingness depends on the outcome. We illustrate these methods using an example from prostate cancer.

A Bayesian dose-finding design for outcomes evaluated with uncertainty.

INTRODUCTION: In some phase I trial settings, there is uncertainty in assessing whether a given patient meets the criteria for dose-limiting toxicity. METHODS: We present a design which accommodates dose-limiting toxicity outcomes that are assessed with uncertainty for some patients. Our approach could be utilized in many available phase I trial designs, but we focus on the continual reassessment method due to its popularity. We assume that for some patients, instead of the usual binary dose-limiting toxicity outcome, we observe a physician-assessed probability of dose-limiting toxicity specific to a given patient. Data augmentation is used to estimate the posterior probabilities of dose-limiting toxicity at each dose level based on both the fully observed and partially observed patient outcomes. A simulation study is used to assess the performance of the design relative to using the continual reassessment method on the true dose-limiting toxicity outcomes (available in simulation setting only) and relative to simple thresholding approaches. RESULTS: Among the designs utilizing the partially observed outcomes, our proposed design has the best overall performance in terms of probability of selecting correct maximum tolerated dose and number of patients treated at the maximum tolerated dose. CONCLUSION: Incorporating uncertainty in dose-limiting toxicity assessment can improve the performance of the continual reassessment method design.

Feasibility of function-guided lung treatment planning with parametric response mapping.

PURPOSE: Recent advancements in functional lung imaging have been developed to improve clinicians' knowledge of patient pulmonary condition prior to treatment. Ultimately, it may be possible to employ these functional imaging modalities to tailor radiation treatment plans to optimize patient outcome and mitigate pulmonary complications. Parametric response mapping (PRM) is a computed tomography (CT)-based functional lung imaging method that utilizes a voxel-wise image analysis technique to classify lung abnormality phenotypes, and has previously been shown to be effective at assessing lung complication risk in diagnostic applications. The purpose of this work was to demonstrate the implementation of PRM guidance in radiotherapy treatment planning. METHODS AND MATERIALS: A retrospective study was performed with 18 lung cancer patients to test the incorporation of PRM into a radiotherapy planning workflow. Paired inspiration/expiration pretreatment CT scans were acquired and PRM analysis was utilized to classify each voxel as normal, parenchymal disease, small airway disease, and emphysema. Density maps were generated for each PRM classification to contour high density regions of pulmonary abnormalities. Conventional volumetric-modulated arc therapy and PRM-guided treatment plans were designed for each patient. RESULTS: PRM guidance was successfully implemented into the treatment planning process. The inclusion of PRM priorities resulted in statistically significant (p < 0.05) improvements to the V20Gy within the PRM avoidance contours. On average, reductions of 5.4% in the V20Gy(%) were found. The PRM-guided treatment plans did not significantly increase the dose to the organs at risk or result in insufficient planning target volume coverage, but did increase plan complexity. CONCLUSIONS: PRM guidance was successfully implemented into a treatment planning workflow and shown to be effective for dose redistribution within the lung. This work has provided a framework for the potential clinical implementation of PRM-guided treatment planning.

A utility approach to individualized optimal dose selection using biomarkers.

In many settings, including oncology, increasing the dose of treatment results in both increased efficacy and toxicity. With the increasing availability of validated biomarkers and prediction models, there is the potential for individualized dosing based on patient specific factors. We consider the setting where there is an existing dataset of patients treated with heterogenous doses and including binary efficacy and toxicity outcomes and patient factors such as clinical features and biomarkers. The goal is to analyze the data to estimate an optimal dose for each (future) patient based on their clinical features and biomarkers. We propose an optimal individualized dose finding rule by maximizing utility functions for individual patients while limiting the rate of toxicity. The utility is defined as a weighted combination of efficacy and toxicity probabilities. This approach maximizes overall efficacy at a prespecified constraint on overall toxicity. We model the binary efficacy and toxicity outcomes using logistic regression with dose, biomarkers and dose-biomarker interactions. To incorporate the large number of potential parameters, we use the LASSO method. We additionally constrain the dose effect to be non-negative for both efficacy and toxicity for all patients. Simulation studies show that the utility approach combined with any of the modeling methods can improve efficacy without increasing toxicity relative to fixed dosing. The proposed methods are illustrated using a dataset of patients with lung cancer treated with radiation therapy.

Dose-intensified chemoradiation is associated with altered patterns of failure and favorable survival in patients with newly diagnosed glioblastoma.

BACKGROUND AND PURPOSE: We evaluated whether dose-intensified chemoradiation alters patterns of failure and is associated with favorable survival in the temozolomide era. MATERIALS AND METHODS: Between 2003 and 2015, 82 patients with newly diagnosed glioblastoma were treated with 66-81 Gy in 30 fractions using conventional magnetic resonance imaging. Progression-free (PFS) and overall survival (OS) were calculated using Kaplan-Meier methods. Factors associated with improved PFS, OS, and time to progression were assessed using multivariate Cox model and linear regression. RESULTS: Median follow-up was 23 months (95% CI 4-124 months). Sixty-one percent of patients underwent subtotal resection or biopsy, and 38% (10/26) of patients with available data had MGMT promoter methylation. Median PFS was 8.4 months (95% CI 7.3-11.0) and OS was 18.7 months (95% CI 13.1-25.3). Only 30 patients (44%) experienced central recurrence, 6 (9%) in-field, 16 (23.5%) marginal and 16 (23.5%) distant. On multivariate analysis, younger age (HR 0.95, 95% CI 0.93-0.97, p = 0.0001), higher performance status (HR 0.39, 95% CI 0.16-0.95, p = 0.04), gross total resection (GTR) versus biopsy (HR 0.37, 95% CI 0.16-0.85, p = 0.02) and MGMT methylation (HR 0.25, 95% CI 0.09-0.71, p = 0.009) were associated with improved OS. Only distant versus central recurrence (p = 0.03) and GTR (p = 0.02) were associated with longer time to progression. Late grade 3 neurologic toxicity was rare (6%) in patients experiencing long-term survival. CONCLUSION: Dose-escalated chemoRT resulted in lower rates of central recurrence and prolonged time to progression compared to historical controls, although a significant number of central recurrences were still observed. Advanced imaging and correlative molecular studies may enable targeted treatment advances that reduce rates of in- and out-of-field progression.

Erectile function after stereotactic body radiotherapy for localized prostate cancer.

OBJECTIVE: To elucidate the functional erection rate after prostate stereotactic body radiotherapy (SBRT) and to develop a comprehensive prognostic model of outcomes after treatment. PATIENTS AND METHODS: Between 2008 and 2013, 373 consecutive men with localized prostate cancer were treated with SBRT at a single academic institution as part of a prospective clinical trial or prospective registry. Prospective longitudinal patient-reported health-related quality of life (HRQoL) data was collected using the Expanded Prostate Cancer Index Composite (EPIC)-26 instrument. Functional erections were strictly defined as 'firm enough for intercourse' according to EPIC-26. Detailed comorbidity data were also collected. Logistic regression models were used to predict 24- and 60-month functional erection rates. Observed erection rates after SBRT were compared with those after other radiation therapies (external beam radiation therapy [EBRT] and brachytherapy) using prospectively validated models. RESULTS: The median (interquartile range) follow-up was 56 (37-73) months and the response rate at 2 years was 84%. For those with functional erections at baseline, 57% and 45% retained function at 24 and 60 months, respectively. On multivariable analysis for 24-month erectile function, significant variables included higher baseline sexual HRQoL (adjusted odds ratio [aOR] 1.55 per 10 points, 95% confidence interval [CI] 1.37-1.74; P < 0.001) and older age (aOR 0.66 per 10 years, 95% CI 0.43-1.00; P = 0.05). At 60 months, baseline HRQoL and age remained associated with erectile function, along with body mass index (aOR 0.45, 95% CI 0.26-0.78; P < 0.001). The 24- and 60-month models had excellent discrimination (c-index 0.81 and 0.84, respectively). Erection rates after SBRT were not statistically different from model-predicted rates after EBRT or brachytherapy for the whole cohort and the cohort with baseline erectile function. CONCLUSIONS: Intermediate- to long-term post-SBRT erectile function results are promising and not significantly different from other radiotherapy techniques. Clinicians can use our prognostic model to counsel patients regarding expected erectile function after SBRT.

Immunohistochemical analysis of H3K27me3 demonstrates global reduction in group-A childhood posterior fossa ependymoma and is a powerful predictor of outcome.

Posterior fossa ependymomas (EPN_PF) in children comprise two morphologically identical, but biologically distinct tumor entities. Group-A (EPN_PFA) tumors have a poor prognosis and require intensive therapy. In contrast, group-B tumors (EPN_PFB) exhibit excellent prognosis and the current consensus opinion recommends future clinical trials to test the possibility of treatment de-escalation in these patients. Therefore, distinguishing these two tumor subtypes is critical. EPN_PFA and EPN_PFB can be distinguished based on DNA methylation signatures, but these assays are not routinely available. We have previously shown that a subset of poorly prognostic childhood EPN_PF exhibits global reduction in H3K27me3. Therefore, we set out to determine whether a simple immunohistochemical assay for H3K27me3 could be used to segregate EPN_PFA from EPN_PFB tumors. We assembled a cohort of 230 childhood ependymomas and H3K27me3 immunohistochemistry was assessed as positive or negative in a blinded manner. H3K27me3 staining results were compared with DNA methylation-based subgroup information available in 112 samples [EPN_PFA (n = 72) and EPN_PFB tumors (n = 40)]. H3K27me3 staining was globally reduced in EPN_PFA tumors and immunohistochemistry showed 99% sensitivity and 100% specificity in segregating EPN_PFA from EPN_PFB tumors. Moreover, H3K27me3 immunostaining was sufficient to delineate patients with worse prognosis in two independent, non-overlapping cohorts (n = 133 and n = 97). In conclusion, immunohistochemical evaluation of H3K27me3 global reduction is an economic, easily available and readily adaptable method for defining high-risk EPN_PFA from low-risk posterior fossa EPN_PFB tumors to inform prognosis and to enable the design of future clinical trials.

RNA biomarkers associated with metastatic progression in prostate cancer: a multi-institutional high-throughput analysis of SChLAP1.

BACKGROUND: Improved clinical predictors for disease progression are needed for localised prostate cancer, since only a subset of patients develop recurrent or refractory disease after first-line treatment. Therefore, we undertook an unbiased analysis to identify RNA biomarkers associated with metastatic progression after prostatectomy. METHODS: Prostate cancer samples from patients treated with radical prostatectomy at three academic institutions were analysed for gene expression by a high-density Affymetrix GeneChip platform, encompassing more than 1 million genomic loci. In a discovery cohort, all protein-coding genes and known long non-coding RNAs were ranked by fold change in expression between tumours that subsequently metastasised versus those that did not. The top ranked gene was then validated for its prognostic value for metastatic progression in three additional independent cohorts. 95% of the gene expression assays were done in a Clinical Laboratory Improvements Amendments certified laboratory facility. All genes were assessed for their ability to predict metastatic progression by receiver-operating-curve area-under-the-curve analyses. Multivariate analyses were done for the primary endpoint of metastatic progression, with variables including Gleason score, preoperative prostate-specific antigen concentration, seminal vesicle invasion, surgical margin status, extracapsular extension, lymph node invasion, and expression of the highest ranked gene. FINDINGS: 1008 patients were included in the study: 545 in the discovery cohort and 463 in the validation cohorts. The long non-coding RNA SChLAP1 was identified as the highest-ranked overexpressed gene in cancers with metastatic progression. Validation in three independent cohorts confirmed the prognostic value of SChLAP1 for metastatic progression. On multivariate modelling, SChLAP1 expression (high vs low) independently predicted metastasis within 10 years (odds ratio [OR] 2·45, 95% CI 1·70-3·53; p<0·0001). The only other variable that independently predicted metastasis within 10 years was Gleason score (8-10 vs 5-7; OR 2·14, 95% CI 1·77-2·58; p<0·0001). INTERPRETATION: We identified and validated high SChLAP1 expression as significantly prognostic for metastatic disease progression of prostate cancer. Our findings suggest that further development of SChLAP1 as a potential biomarker, for treatment intensification in aggressive prostate cancer, warrants future study. FUNDING: Prostate Cancer Foundation, National Institutes of Health, Department of Defense, Early Detection Research Network, Doris Duke Charitable Foundation, and Howard Hughes Medical Institute.

Diet and proinflammatory cytokine levels in head and neck squamous cell carcinoma.

BACKGROUND: Proinflammatory cytokine levels may be associated with cancer stage, recurrence, and survival. The objective of this study was to determine whether cytokine levels were associated with dietary patterns and fat-soluble micronutrients in patients with previously untreated head and neck squamous cell carcinoma (HNSCC). METHODS: This was a cross-sectional study of 160 patients with newly diagnosed HNSCC who completed pretreatment food frequency questionnaires (FFQs) and health surveys. Dietary patterns were derived from FFQs using principal component analysis. Pretreatment serum levels of the proinflammatory cytokines interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-α), and interferon gamma (IFN-γ) were measured using an enzyme-linked immunosorbent assay, and serum carotenoid and tocopherol levels were measured by high-performance liquid chromatography. Multivariable ordinal logistic regression models examined associations between cytokines and quartiles of reported and serum dietary variables. RESULTS: Three dietary patterns emerged: whole foods, Western, and convenience foods. In multivariable analyses, higher whole foods pattern scores were significantly associated with lower levels of IL-6, TNF-α, and IFN-γ (P ≤ .001, P = .008, and P = .03, respectively). Significant inverse associations were reported between IL-6, TNF-α, and IFN-γ levels and quartiles of total reported carotenoid intake (P = .006, P = .04, and P = .04, respectively). There was an inverse association between IFN-γ levels and serum α-tocopherol levels (P = .03). CONCLUSIONS: Consuming a pretreatment diet rich in vegetables, fruit, fish, poultry, and whole grains may be associated with lower proinflammatory cytokine levels in patients with HNSCC.

Personalized dose selection in radiation therapy using statistical models for toxicity and efficacy with dose and biomarkers as covariates.

Selection of dose for cancer patients treated with radiation therapy (RT) must balance the increased efficacy with the increased toxicity associated with higher dose. Historically, a single dose has been selected for a population of patients (e.g., all stage III non-small cell lung cancer). However, the availability of new biologic markers for toxicity and efficacy allows the possibility of selecting a more personalized dose. We consider the use of statistical models for toxicity and efficacy as a function of RT dose and biomarkers to select an optimal dose for an individual patient, defined as the dose that maximizes the probability of efficacy minus the sum of weighted toxicity probabilities. This function can be shown to be equal to the expected value of the utility derived from a particular family of bivariate outcome utility matrices. We show that if dose is linearly related to the probability of toxicity and efficacy, then any marker that only acts additively with dose cannot improve efficacy, without also increasing toxicity. Using a dataset of lung cancer patients treated with RT, we illustrate this approach and compare it to non-marker-based dose selection. Because typical metrics used in evaluating new markers (e.g., area under the ROC curve) do not directly address the ability of a marker to improve efficacy at a fixed probability of toxicity, we utilize a simulation study to assess the effects of marker-based dose selection on toxicity and efficacy outcomes.