Radiotherapy Prostate Cancer Research Articles

Prostate cancer risk in African American men evaluated via digital histopathology multi-modal deep learning models developed on NRG Oncology phase III clinical trials.

108 Background: Artificial intelligence (AI) tools can display racial bias as a result of existing systemic health inequities and biased datasets. We have previously developed multi-modal AI (MMAI) prognostic models based on digital pathology images from five phase III randomized radiotherapy prostate cancer trials that outperform NCCN risk groups for prediction of distant metastasis (DM), biochemical failure (BF), prostate cancer-specific mortality (PCSM) and all-cause mortality (OS). In this study, we assessed the algorithmic fairness of the locked MMAI models between African American (AA) and non-AA populations in the five randomized trials. Methods: Patients enrolled in NRG/RTOG 9202, 9408, 9413, 9910, and 0126 with digitized biopsy histopathology slides were included in this study. The locked MMAI models were applied, and subgroup analyses were conducted by comparing distributions of clinical variables and MMAI scores (medians for continuous variables and proportions for categorical variables reported), and evaluating MMAI models’ prognostic ability among AA and non-AA men. The performance of the models were compared using DM as the primary endpoint and secondary endpoints of BF, PCSM, OS (death without an event as a competing risk) with Fine-Gray or Cox Proportional Hazards models. Either Kaplan Meier or cumulative incidence estimates were computed and compared using log-rank or Gray’s test. Results: This study included 5,624 men: 932 (17%) AA, 4503 (80%) white, and 189 (3%) other races. AA had younger median age (69 vs 71 year [yr]), higher median baseline PSA (12 vs 10 ng/mL), more T1-T2a (62% vs 57%), more Gleason < 7 (42% vs 36%) and 8-10 (15% vs 12%), and more NCCN low and high risk (12% vs 10% and 41% vs 33%). AA and non-AA had estimated 5-yr BF rates 27% and 27%, 5-yr DM rates 5% and 5%, 10-yr PCSM 5% and 7%, and 10-yr OS 58% and 60%, respectively. The median (interquartile range) score of the model optimizing for 5-yr DM (5-yr DM MMAI) was 0.044 (0.037–0.059) in AA and 0.043 (0.036–0.057) in non-AA. Similarly, all other MMAI models had differences in the medians between AA and non-AA ranging from 0.001 to 0.02. For all endpoints, the 5-yr DM MMAI model showed strong prognostic signal (hazard ratio [HR] per one standard deviation increase: 1.6 for DM, 1.4 for BF, 1.6 for PCSM and 1.3 for OS, all p-values < 0.001) and had comparable trends within AA vs. non-AA in the entire cohort (e.g., HR for DM 1.4 vs 1.6). Similar results were observed for the MMAI model optimizing for 10-yr PCSM. Conclusions: To our knowledge, this represents the first comparative analyses of a digital pathology AI prognostic model in AA vs. non-AA prostate cancer patients. The prognostic performance of the AI models was found to be comparable between subgroups. Our data supports the use of these models across racial groups, though further validation in AA cohorts is ongoing.

Development and validation of a prognostic AI biomarker using multi-modal deep learning with digital histopathology in localized prostate cancer on NRG Oncology phase III clinical trials.

222 Background: Prognostication in localized prostate cancer is reliant on non-specific tools, an issue that leads to the over- and under-treatment of patients. Various tissue-based molecular biomarkers have attempted to fill this unmet need, but most lack prospective randomized trial validation. Herein, we train and validate prognostic biomarkers in localized prostate cancer using five phase III randomized trials, by leveraging multi-modal deep learning on digital histopathology. Methods: Histopathology image data was generated from pre-treatment biopsy slides in five NRG Oncology phase III randomized radiotherapy prostate cancer trials (RTOG 9202, 9408, 9413, 9910, and 0126). The trials were randomly split into training (80%) and validation (20%) cohorts. A multi-modal artificial intelligence (MMAI) architecture was developed to take clinicopathologic and image-based (histopathology) data as input and predict binary outcomes. Using this architecture, various models were trained to predict relevant clinical endpoints: biochemical recurrence (BCR), distant metastasis (DM), prostate cancer-specific survival (PCaSS), and overall survival (OS). These models were then validated for measures of prognostic discrimination using the time-based area under the curve (AUC) method. Results: Clinicopathologic and histopathology image data was available for 5,654 of 7,957 eligible patients (71.1%), yielding 16.1 TB of data from 16,204 histopathology slides of pretreatment biopsy samples. After training the models, locking them, and evaluating them on the validation cohort, we found that the MMAI prognostic model had superior discrimination compared to the NCCN model (PSA, T-stage, and Gleason score) for 5-year DM (AUC of 0.84 vs 0.73), 5-year BCR (AUC of 0.69 vs 0.58), 10-year PCaSS (AUC of 0.79 vs 0.66), and 10-year OS (AUC of 0.65 vs 0.58). Within each of the individual trials in the validation cohort, the MMAI-model had superior performance compared to NCCN risk groups for all clinical endpoints. Conclusions: This represents the first ever development and validation of prognostic biomarkers in localized prostate cancer using multiple large phase III clinical trials. We have successfully validated that our MMAI-prognostic biomarkers are superior to standard clinical and pathologic variables in identifying future BCR, DM, PCaSS, and OS. This massively scalable technology is feasible and can help personalize the management of prostate cancer patients. Funding: This project was supported by grants U10CA180868 (NRG Oncology Operations), U10CA180822 (NRG Oncology SDMC), UG1CA189867 (NCORP), U24CA196067 (NRG Specimen Bank) from the National Cancer Institute (NCI).

Prediction of recurrence from metabolites and expression of TOP2A and EZH2 in prostate cancer patients treated with radiotherapy.

The dual upregulation of TOP2A and EZH2 gene expression has been proposed as a biomarker for recurrence in prostate cancer patients to be treated with radical prostatectomy. A low tissue level of the metabolite citrate has additionally been connected to aggressive disease and recurrence in this patient group. However, for radiotherapy prostate cancer patients, few prognostic biomarkers have been suggested. The main aim of this study was to use an integrated tissue analysis to evaluate metabolites and expression of TOP2A and EZH2 as predictors for recurrence among radiotherapy patients. From 90 prostate cancer patients (56 received neoadjuvant hormonal treatment), 172 transrectal ultrasound-guided (TRUS) biopsies were collected prior to radiotherapy. Metabolic profiles were acquired from fresh frozen TRUS biopsies using high resolution-magic angle spinning MRS. Histopathology and immunohistochemistry staining for TOP2A and EZH2 were performed on TRUS biopsies containing cancer cells (n = 65) from 46 patients, where 24 of these patients (n = 31 samples) received hormonal treatment. Eleven radical prostatectomy cohorts of a total of 2059 patients were used for validation in a meta-analysis. Among radiotherapy patients with up to 11 years of follow-up, a low level of citrate was found to predict recurrence, p = 0.001 (C-index = 0.74). Citrate had a higher predictive ability compared with individual clinical variables, highlighting its strength as a potential biomarker for recurrence. The dual upregulation of TOP2A and EZH2 was suggested as a biomarker for recurrence, particularly for patients not receiving neoadjuvant hormonal treatment, p = 0.001 (C-index = 0.84). While citrate was a statistically significant biomarker independent of hormonal treatment status, the current study indicated a potential of glutamine, glutamate and choline as biomarkers for recurrence among patients receiving neoadjuvant hormonal treatment, and glucose among patients not receiving neoadjuvant hormonal treatment. Using an integrated approach, our study shows the potential of citrate and the dual upregulation of TOP2A and EZH2 as biomarkers for recurrence among radiotherapy patients.

Transcriptome profiling of NRG Oncology/RTOG 9601: Validation of a prognostic genomic classifier in salvage radiotherapy prostate cancer patients from a prospective randomized trial.

276 Background: Decipher is a genomic classifier (GC) that estimates risk of prostate cancer (PCa) distant metastases (DM) post-radical prostatectomy (RP). Herein, we validate the GC within the context of a randomized phase 3 trial. Methods: RP specimens from patients on the NRG/RTOG 9601 phase 3 placebo-controlled randomized trial of salvage radiotherapy (sRT) +/- 2 years of bicalutamide (NCT00002874) were centrally reviewed and the highest-grade tumor underwent RNA extraction. Samples passing quality control (QC) were run on a clinical-grade whole-transcriptome assay to assign the GC score (scale 0-1). Our NCTN-CCSC approved pre-specified statistical plan (NRG-GU-TS002 CSC0075) included the primary objective of validating the ability of the GC to independently prognosticate the cumulative incidence of DM, with secondary endpoints of prostate cancer-specific mortality (PCSM) and overall survival (OS). Results: Of patients with tissue available, 352 passed QC and were included for analysis. The final GC cohort was a representative sample of the overall cohort, with a median follow-up of 13 years. On multivariable analysis, the GC (continuous variable) was independently associated with DM (HR 1.19 [95%CI 1.06-1.35], p=0.003), PCSM (HR 1.37 [95%CI 1.18-1.61], p<0.001), and OS (HR 1.16 [95%CI 1.06-1.28], p=0.002) after adjusting for age, race, Gleason score, T-stage, margin status, entry PSA, and treatment arm. The estimated absolute impact of bicalutamide on 12-year OS was less in patients with lower vs higher GC scores (2.4% vs 8.9%), further demonstrated in men receiving early sRT at PSA <0.7 ng/mL (-2.0% vs 5.0%). Conclusions: This is the first validation of this GC in a prospective randomized trial cohort and demonstrates association of the GC with DM and PCSM independent of standard clinicopathologic variables. The GC may help personalize shared decision-making to weigh the absolute benefit from the addition of bicalutamide to sRT.

VMAT treatment plan acceptability and quality assurance study for prostate cancer in radiotherapy

AbstractAim:The aim of this work was to study the acceptability of plans prepared for prostate patients treated by volumetric modulated arc therapy (VMAT) with the vision to evaluate the quality of plans and test pre-treatment quality assurance (QA).Material methods:VMAT plans of 35 patients, planned on the Eclipse Treatment Planning System (Aria 15), were included in the study. Plan acceptability was checked using statistical analysis, which includes homogeneity index, radical and median homogeneity index, coverage and uniformity index. Dose–volume histograms (DVH) of the plans were also studied to check prescribed dose (PD), Dmax, Dmin, D5 and D95. Portal dosimetry was also done by gamma analysis using 3%/3 mm criterion. SD and mean SD error were also calculated and analysed.Results:Statistical analysis showed a mean HI of 1·054, coverage 0·959, UI 1·055, mDHI 0·962 and rDHI 0·866. SD of HI, coverage, UI, mDHI and rDHI was 0·019, 0·019, 0·014, 0·013 and 0·030, respectively. From the DVHs, mean of D5, D95, Dmin and Dmax was calculated at 6,252·9, 5,757·4, 6,413·3 and 5,657·7 cGy, respectively, with a prescribed dose of 6,000 cGy. According to gamma analysis, area gamma < 1 was 99·12% with a tolerance limit of 95%, maximum gamma was 1·466 with a tolerance limit of 3·5, average gamma was 0·388 with a tolerance limit of 0·5, area gamma > 1·2 was 0·242% with a tolerance limit of 0·5%, maximum dose difference was 0·6 with a tolerance limit of 1·0 and average dose difference was 0·029 with a tolerance limit of 0·2.Conclusion:All three computations showed the results to be within acceptable limits. VMAT possesses a unique feature of delivering the whole treatment with only two rotations of the gantry. VMAT has an improved efficiency of delivery for equivalent dosimetric quality.

Photosensitizers in prostate cancer therapy.

The search for new therapeutics for the treatment of prostate cancer is ongoing with a focus on the balance between the harms and benefits of treatment. New therapies are being constantly developed to offer treatments similar to radical therapies, with limited side effects. Photodynamic therapy (PDT) is a promising strategy in delivering focal treatment in primary as well as post radiotherapy prostate cancer. PDT involves activation of a photosensitizer (PS) by appropriate wavelength of light, generating transient levels of reactive oxygen species (ROS). Several photosensitizers have been developed with a focus on treating prostate cancer like mTHPC, motexafin lutetium, padoporfin and so on. This article will review newly developed photosensitizers under clinical trials for the treatment of prostate cancer, along with the potential advantages and disadvantages in delivering focal therapy.

Profiling of a panel of radioresistant prostate cancer cells identifies deregulation of key miRNAs

BackgroundmiRNAs are increasingly associated with the aggressive phenotype of prostate tumours. Their ability to control radiobiologically-relevant cellular processes strengthens their potential as novel markers of response to radiation therapy. PurposeTo identify miRNAs associated with increased clonogenic survival following radiation exposure. Material and methodsThe miRNA expression profiles of a panel of 22RV1 cells with varying levels of radiosensitivities (hypoxic H-22Rv1 cells, RR-22Rv1 cells derived from WT-22Rv1 cells through 2-Gy fractionated repeated exposure, the associated aged matched cells (AMC-22Rv1) and the WT-22Rv1 cell lines) were generated and cross-analysed to identify common miRNAs associated with a radioresistant phenotype. ResultsIncreased clonogenic survival following irradiation was associated with significant modifications in miRNA expression pattern. miR-221 (up) and miR-4284 (down) in RR-22Rv1 and MiR-31 and miR-200c in AMC-22Rv1 were the most uniquely significantly deregulated miRNAs when compared to WT-22Rv1 cells. miR-200c ranked as the most downregulated miRNAs in hypoxic, when compared to RR-22Rv1 cells. miR-200a was the only differentially expressed miRNA between RR-22Rv1 and AMC-22Rv1 cells. miR-210 yielded the highest fold change in expression in H-22Rv1, when compared to WT-22RV1 cells. ConclusionThis study identifies candidate miRNAs for the development of novel prognostic biomarkers for radiotherapy prostate cancer patients.

Volumetric modulated arc therapy treatment planning assessment for low-risk prostate cancer in radiotherapy

Aims: This research study was carried out for dose verification of volumetric modulated arc therapy (VMAT) plan in the patients of low-risk prostate cancer using different indices in radiotherapy. Materials and Methods: Twenty patients with low-risk prostate cancer after histopathological confirmation were included which were divided into two groups with prescribed dose of 7400 and 6000 cGy according to their disease and 3 mm sliced for each patient by computed tomography was obtained for planning. To evaluate the dose conformity and its distribution using VMAT plans such as homogeneity index (HI), radial dose HI, moderate dose HI, coverage, and uniformity index for each patient. These plans were constructed for each patient separately using Eclipse Treatment planning software. Statistical Analysis Used: Statistical Package for the Social Sciences statistical software is used to calculate mean standard deviation and standard error of all these indices. Results: Obtained results and findings enabled this research work to conclude that VMAT is a better treatment technique for the patients of low-risk prostate cancer with uniform dose distribution. Conclusion: It has been suggested that VMAT with Eclipse Treatment planning system is a good treatment modality to treat the patients of low-risk prostate cancer with better results.

Abstract 3444: Identification of a miRNAs signature of radioresistance in a prostate cancer model

Abstract Introduction Radiotherapy (RT) has a prominent role when used as a single modality or in combination with androgen deprivation therapy (ADT) in the management of prostate cancer patients. However, even with currently available high-dose RT protocols, many patients develop recurrence or “biochemical failure” associated with a rise in PSA, and ultimately reduced overall survival. Key regulators of cancer cell behavior, dysregulation of miRNAs expression is associated with both prostate cancer pathogenesis and treatment resistance. This study aimed to identify a miRNAs signature of radioresistance in a prostate cancer model, as a prerequisite for the future development of a novel pre-treatment assay for the identification of radiotherapy prostate cancer patients at risk of biochemical failure. Methods Two models of radioresistance were used : (1) an isogenic radiation resistant model generated through weekly exposure to 2-Gy fractionated ionising radiation of 22Rv1 (primary) prostate cancer (CaP) cells to a cumulative dose of 60Gy. (2) chronically hypoxic 22Rv1 cells (48hrs). miRNA profiling of radioresistant and wild type cells was performed using the Exiqon miRCURY array and overlapping differentially expressed miRs were identified. Results The radiation survival curve of the resulting subline RR22Rv1 was associated with a significant increase in clonogenic survival (1.3 fold increase in survival after 2Gy and 2.2 fold increase after 10Gy), when compared to both parent 22Rv1 and aged-matched controls. The RR22Rv1 cell line is associated with decreased sensitivity to DNA damage (comet assay), enlargement of the S-phase cell population (PI staining) but no evidence for expansion of the senescent cell fraction (β-Gal assay). miRNA profiling identified 105 significantly differentially expressed miRNAs in RR22Rv1, when compared to the parent and age-matched control 22RV1 cells. A total of 12 miRNA were differentially expressed in chronically hypoxic compared to normoxic 22Rv1 cells. Three candidate miRNAs were associated with a radioresistant phenotype across the models: miR200a, miR210 and miR4284. Functional validation of these candidates is currently under way. Conclusion Available in tissues, serum and urine, miRNAs are ideal candidates for the development of novel biological tests. This study identifies a miRNAs signature of radioresistance in prostate cancer cells with potential for the development of a miRNA-based prognostic assay for radiotherapy prostate cancer patients. Citation Format: Niamh McDermott, Armelle Meunier, Christopher Hernandez, Karen J. Bowman, George D. Jones, Laure Marignol. Identification of a miRNAs signature of radioresistance in a prostate cancer model. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3444. doi:10.1158/1538-7445.AM2015-3444

PO-0733: Postoperative radiotherapy prostate cancer: survival, toxicity and use of an in house analysis system (PRODVH)

PO-0733: Postoperative radiotherapy prostate cancer: survival, toxicity and use of an in house analysis system (PRODVH)

Sexual Dimorphism In The Effect of Maternal Adiposity on Placental miR-210 Expression

miRNAs are increasingly associated with the aggressive phenotype of prostate tumours. Their ability to control radiobiologically-relevant cellular processes strengthens their potential as novel markers of response to radiation therapy.To identify miRNAs associated with increased clonogenic survival following radiation exposure.The miRNA expression profiles of a panel of 22RV1 cells with varying levels of radiosensitivities (hypoxic H-22Rv1 cells, RR-22Rv1 cells derived from WT-22Rv1 cells through 2-Gy fractionated repeated exposure, the associated aged matched cells (AMC-22Rv1) and the WT-22Rv1 cell lines) were generated and cross-analysed to identify common miRNAs associated with a radioresistant phenotype.Increased clonogenic survival following irradiation was associated with significant modifications in miRNA expression pattern. miR-221 (up) and miR-4284 (down) in RR-22Rv1 and MiR-31 and miR-200c in AMC-22Rv1 were the most uniquely significantly deregulated miRNAs when compared to WT-22Rv1 cells. miR-200c ranked as the most downregulated miRNAs in hypoxic, when compared to RR-22Rv1 cells. miR-200a was the only differentially expressed miRNA between RR-22Rv1 and AMC-22Rv1 cells. miR-210 yielded the highest fold change in expression in H-22Rv1, when compared to WT-22RV1 cells.This study identifies candidate miRNAs for the development of novel prognostic biomarkers for radiotherapy prostate cancer patients.

Image Guided Intensive Modulated Radiosurgery Boost Combined with Conformal Radiotherapy Prostate Cancer Patients

Image Guided Intensive Modulated Radiosurgery Boost Combined with Conformal Radiotherapy Prostate Cancer Patients

Comparison of prostate cancer treatment in two institutions: a quality control study

Purpose: To minimize differences in the treatment planning procedure between two institutions within the context of a radiotherapy prostate cancer trial. Patients and Methods: Twenty-two patients with N0 M0 prostate cancer underwent a computed tomography (CT) scan for radiotherapy treatment planning. For all patients, the tumor and organs at risk were delineated, and a treatment plan was generated for a three-field technique giving a dose of 78 Gy to the target volume. Ten of the 22 cases were delineated and planned in the other institution as well. The delineated volumes and dose distributions were compared. Results: All treatments fulfilled the trial criteria. The mean volume ratio of the gross tumor volumes (GTVs) in both institutions was 1.01, while the mean volume ratio of the planning target volumes (PTVs) was 0.88. The three-dimensional (3D) PTV difference was 3 mm at the prostate apex and 6–8 mm at the seminal vesicles. This PTV difference was mainly caused by a difference in the method of 3D expansion, and disappeared when applying an improved algorithm in one institution. The treated volume (dose ≥ 95% of isocenter dose) reflects the size of the PTV and the conformity of the treatment technique. This volume was on average 66 cm 3 smaller in institution A than in institution B; the effect of the PTV difference was 31 cm 3 and the difference in technique accounted for 36 cm 3. The mean delineated rectal volume including filling was 112 cm 3 and 125 cm 3 for institution A and B, respectively. This difference had a significant impact on the relative dose volume histogram (DVH) of the rectum. Conclusion: Differences in GTV delineation were small and comparable to earlier quantified differences between observers in one institution. Different expansion methods for generation of the PTV significantly influenced the amount of irradiated tissue. Strict definitions of target and normal structures are mandatory for reliable trial results.