M. Schipper

High-throughput transcriptomic analysis nominates proteasomal genes as age-specific biomarkers and therapeutic targets in prostate cancer

Background:Although prostate cancer (PCa) is hypothesized to differ in nature between younger versus older patients, the underlying molecular distinctions are poorly understood. We hypothesized that high-throughput transcriptomic analysis would elucidate biological differences in PCas arising in younger versus older men, and would nominate potential age-specific biomarkers and therapeutic targets.Methods:The high-density Affymetrix GeneChip platform, encompassing >1 million genomic loci, was utilized to assess gene expression in 1090 radical prostatectomy samples from patients with long-term follow-up. We identified genes associated with metastatic progression by 10 years post-treatment in younger (age<65) versus older (age⩾65) patients, and ranked these genes by their prognostic value. We performed Gene Set Enrichment Analysis (GSEA) to nominate biological concepts that demonstrated age-specific effects, and validated a target by treating with a clinically available drug in three PCa cell lines derived from younger men.Results:Over 80% of the top 1000 prognostic genes in younger and older men were specific to that age group. GSEA nominated the proteasome pathway as the most differentially prognostic in younger versus older patients. High expression of proteasomal genes conferred worse prognosis in younger but not older men on univariate and multivariate analysis. Bortezomib, a Food and Drug Administration approved proteasome inhibitor, decreased proliferation in three PCa cell lines derived from younger patients.Conclusions:Our data show significant global differences in prognostic genes between older versus younger men. We nominate proteasomeal gene expression as an age-specific biomarker and potential therapeutic target specifically in younger men. Limitations of our study include clinical differences between cohorts, and increased comorbidities and lower survival in older patients. These intriguing findings suggest that current models of PCa biology do not adequately represent genetic heterogeneity of PCa related to age, and future clinical trials would benefit from stratification based on age.

Individualizing Radiation Dose in Locally Advanced Non–Small Cell Lung Cancer Patients Using Pretreatment Serum MicroRNA Signatures

RTOG 0915 was open, and closed on April 15; 2015 after accruing a total of 98 patients. All patients received planned SBRT treatment. Median follow-up was 27 months. In follow-up; 10 patients were lost to follow-up; 1 was in arm 1 and 9 in arm 2. Baseline patient and tumor characteristics were balanced between both arms. Thirteen (27%) patients on arm 1 and 16 (33%) patients on arm 2 experienced RTOG grade 3 AEs; there were no grade 4 AEs. Thoracic grade 3 AEs were experienced by 8 (16%) patients on arm 1 and 6 (12%) patients on arm 2. There were no differences in OS or PFS survival, log-rank PZ.44 and .99, respectively. OS at 2 years was 71% (95% CI; 55-82%) for arm 1 and 61% (95% CI; 44-78%) for arm 2. PFS at 2 years was 63% (95% CI; 46-75%) for arm 1 and 51% (95% CI; 34-65%) for arm 2. Conclusion: This randomized phase 2 study demonstrated that 30 Gy in one fraction was equivalent to 60 Gy in three fractions in terms of toxicity, progression-free survival, and overall survival. Author Disclosure: A.K. Singh: None. J. Gomez Suescun: None. K.L. Stephans: None. J.A. Bogart: None. T. Lili: None. H. Malhotra: None. G.M. Videtic: None. A. Groman: None.

TU-AB-303-05: Clinical Guidelines for Determining When An Adaptive Replan May Be Warranted for Head and Neck Patients

Purpose: Tools are now available to perform daily dose assessment in radiotherapy, however, guidance is lacking as to when to replan to limit increase in normal tissue dose. This work performs statistical analysis to provide guidance for when adaptive replanning may be necessary for head/neck (HN) patients. Methods: Planning CT and daily kVCBCT images for 50 HN patients treated with VMAT were retrospectively evaluated. Twelve of 50 patients were replanned due to anatomical changes noted over their RT course. Daily dose assessment was performed to calculate the variation between the planned and delivered dose for the 38 patients not replanned and the patients replanned using their delivered plan. In addition, for the replanned patients, the dose that would have been delivered if the plan was not modified was also quantified. Deviations in dose were analyzed before and after replanning, the daily variations in patients who were not replanned assessed, and the predictive power of the deviation after 1, 5, and 15 fractions determined. Results: Dose deviations were significantly reduced following replanning, compared to if the original plan would have been delivered for the entire course. Early deviations were significantly correlated with total deviations (p<0.01). Using the criteria that a 10% increase in the final delivered dose indicates a replan may be needed earlier in the treatment course, the following guidelines can be made with a 90% specificity after the first 5 fractions: deviations of 7% in the mean dose to the inferior constrictors and 5% in the mean dose to the parotid glands and submandibular glands. No significant dose deviations were observed in any patients for the CTV _70Gy (max deviation 4%). Conclusions: A 5–7% increase in mean dose to normal tissues within the first 5 fractions strongly correlate to an overall deviatios in the delivered dose for HN patients. This work is funded in part by NIH 2P01CA059827-16

TH-AB-304-07: A Two-Stage Signature-Based Data Fusion Mechanism to Predict Radiation Pneumonitis in Patients with Non-Small-Cell Lung Cancer (NSCLC)

Purpose: For NSCLC radiotherapy, toxicity outcomes such as radiation pneumonitis ≥G2 (RP2) may depend on patients’ physical, clinical, biological and genomic characteristics, and on biomarkers measured during the course of radiotherapy. This can include 100s of predictors. To reduce complexity, a two-step, signature-based data fusion mechanism was developed to estimate a relationship between patient specific characteristics and the probability of RP2 in terms of a modifying effect on mean lung dose (MLD). Methods: Data came from 82 NSCLC patients, 15 with RP2. Besides MLD, each had 179 predictors including 10 clinical factors (eg, age, gender, KPS), cytokines before (30) and during (30) treatment, microRNAs (49), and single-nucleotide polymorphisms (SNPs) (60). In stage1, cytokines, microRNAs, and SNPs were used to build separate “signatures” via ridge regression. In stage2, a logistic regression predictive model for RP2 was determined in terms of MLD, the other clinical factors, and the signatures using the least absolute shrinkage and selection operator (LASSO). Leave-one-out cross-validation was conducted. This was all implemented via ‘glmnet’ in the R programming environment. Results: For stage1, signatures modifying the effect of MLD for cytokine_pre, cytokine_during, microRNA and SNP included 2, 19, 3, 12 important predictors, respectively. For stage2, only the cytokine_during and SNP signatures remained as important modifying effects to MLD. The cross-validated area under curve (AUC) reaches 0.81 (95% CI 0.70–0.89 based on 2000 stratified bootstrap replicates); significantly better than a null value of 0.50 (p<0.01). Conclusions: As implemented here, the two-stage, signature-based data fusion mechanism approach includes many patient specific measurements in generation of the signatures (a characteristic of ridge regression), then only includes important signatures and other clinical factors for RP2 prediction (a characteristic of LASSO). This potentially more intuitive approach to handling high dimensional predictors could be an important component of decision support for personalized adaptive radiation treatment.

Bayesian Decision Support for Adaptive Lung Treatments

Purpose: A Bayesian Decision Network will be demonstrated to provide clinical decision support for adaptive lung response-driven treatment management based on evidence that physiologic metrics may correlate better with individual patient response than traditional (population-based) dose and volume-based metrics. Further, there is evidence that information obtained during the course of radiation therapy may further improve response predictions. Methods: Clinical factors were gathered for 58 patients including planned mean lung dose, and the bio-markers IL-8 and TGF-β1 obtained prior to treatment and two weeks into treatment along with complication outcomes for these patients. A Bayesian Decision Network was constructed using Netica 5.0.2 from Norsys linking these clinical factors to obtain a prediction of radiation induced lung disese (RILD) complication. A decision node was added to the network to provide a plan adaption recommendation based on the trade-off between the RILD prediction and complexity of replanning. A utility node provides the weighting cost between the competing factors. Results: The decision node predictions were optimized against the data for the 58 cases. With this decision network solution, one can consider the decision result for a new patient with specific findings to obtain a recommendation to adaptively modify the originally planned treatment course. Conclusions: A Bayesian approach allows handling and propagating probabilistic data in a logical and principled manner. Decision networks provide the further ability to provide utility-based trade-offs, reflecting non-medical but practical cost/benefit analysis. The network demonstrated illustrates the basic concept, but many other factors may affect these decisions and work on building better models are being designed and tested. Acknowledgement: Supported by NIH-P01-CA59827

Impact of Intensity Modulated Radiation Therapy on Acute Toxicity in Locally Advanced Lung Cancer: Results of a Large Statewide Multi-center Cohort

Secondary analysis of RTOG 0617 has shown lower rates of pneumonitis and less decline in patient quality of life with the use of intensity modulated radiation therapy (IMRT) vs. three-dimensional conformal external beam radiation therapy (3D-CRT) in locally advanced non-small cell lung cancer patients undergoing definitive radiation therapy. In a large statewide radiation oncology quality consortium, we sought to evaluate impact of IMRT vs. 3D-CRT treatment technique on acute esophagitis & pneumonitis.

Impact of Comorbidities on Acute Toxicity in Patients Receiving Radiation Therapy for Locally Advanced Lung Cancer

Radiation Therapy for Locally Advanced Lung Cancer MR McFarlane1, Y Sun2, M Schipper1-2, C Maurino1, AM Laucis1, A Saripalli1, R Ten-Haken1, FM Kong4, M Matuszak1, DE Spratt1, L Pierce1, J Hayman1, P Paximadis5, S Jolly1 Departments of 1Radiation Oncology, 2Biostatistics and 3Pulmonary Medicine, University of Michigan, Ann Arbor; 4Department of Radiation Oncology, Indiana University, Indianapolis; 5Lakeland Radiation Oncology, St. Joseph

Statistical controversies in clinical research: building the bridge to phase II—efficacy estimation in dose-expansion cohorts

Background Regulatory agencies and others have expressed concern about the uncritical use of dose expansion cohorts (DECs) in phase I oncology trials. Nonetheless, by several metrics-prevalence, size, and number-their popularity is increasing. Although early efficacy estimation in defined populations is a common primary endpoint of DECs, the types of designs best equipped to identify efficacy signals have not been established. Methods We conducted a simulation study of six phase I design templates with multiple DECs: three dose-assignment/adjustment mechanisms multiplied by two analytic approaches for estimating efficacy after the trial is complete. We also investigated the effect of sample size and interim futility analysis on trial performance. Identifying populations in which the treatment is efficacious (true positives) and weeding out inefficacious treatment/populations (true negatives) are competing goals in these trials. Thus, we estimated true and false positive rates for each design. Results Adaptively updating the MTD during the DEC improved true positive rates by 8-43% compared with fixing the dose during the DEC phase while maintaining false positive rates. Inclusion of an interim futility analysis decreased the number of patients treated under inefficacious DECs without hurting performance. Conclusion A substantial gain in efficiency is obtainable using a design template that statistically models toxicity and efficacy against dose level during expansion. Design choices for dose expansion should be motivated by and based upon expected performance. Similar to the common practice in single-arm phase II trials, cohort sample sizes should be justified with respect to their primary aim and include interim analyses to allow for early stopping.

Adapting Therapy Based on Tumor Response

Though radiotherapy techniques have improved significantly over the past few decades, high rates of toxicity, recurrence and mortality persist for many cancer types. This chapter describes how traditional radiation therapy, which involves application of common dose prescriptions and normal tissue constraints across heterogeneous populations, limits personalization of treatment. It presents an explanatory model of the potential benefits of individualizing and adapting radiotherapy and places these benefits in the appropriate clinical context. It highlights the importance of utilizing novel imaging sequences and biomarkers, and illuminates the advantages of incorporating intra-treatment data into adaptation algorithms. It outlines a framework for identifying opportunities to investigate and develop individualized radiotherapy protocols. Using various tumor types as examples, it discusses the rationale for radiotherapy, current standards of care, and emerging evidence for the utility of adaptation strategies. Finally, it suggests areas for future adaptive radiotherapy research, with the goal of improving cancer outcomes through precision radiation medicine.

SU-F-J-89: Assessment of Delivered Dose in Understanding HCC Tumor Progression Following SBRT.

PURPOSE It is well documented that the delivered dose to patients undergoing radiotherapy (RT) is often different from the planned dose due to geometric variability and uncertainties in patient positioning. Recent work suggests that accumulated dose to the GTV is a better predictor of progression compared to the minimum planned dose to the PTV. The purpose of this study is to evaluate if deviations from the planned dose can contributed to tumor progression. METHODS From 2010 to 2014 an in-house Phase II clinical trial of adaptive stereotactic body RT was completed. Of the 90 patients enrolled, 7 patients had a local recurrence defined on contrast enhanced CT or MR imaging 3-21 months after completion of RT. Retrospective dose accumulation was performed using a biomechanical model-based deformable image registration algorithm (DIR) to accumulate the dose based on the kV CBCT acquired prior to each fraction for soft tissue alignment of the patient. The DIR algorithm was previously validated for geometric accuracy in the liver (target registration error = 2.0 mm) and dose accumulation in a homogeneous image, similar to a liver CBCT (gamma index = 91%). Following dose accumulation, the minimum dose to 0.5 cc of the GTV was compared between the planned and accumulated dose. Work is ongoing to evaluate the tumor control probability based on the planned and accumulated dose. RESULTS DIR and dose accumulation was performed on all fractions for 6 patients with local recurrence. The difference in minimum dose to 0.5 cc of the GTV ranged from -0.3-2.3 Gy over 3-5 fractions. One patient had a potentially significant difference in minimum dose of 2.3 Gy. CONCLUSION Dose accumulation can reveal tumor underdosage, improving our ability to understand recurrence and tumor progression patterns, and could aid in adaptive re-planning during therapy to correct for this. This work was supported in part by NIH P01CA059827.