Increasing the efficiency of cone-beam CT based delta-radiomics using automated contours to predict radiotherapy-related toxicities in prostate cancer.

Extracting longitudinal image quantitative data, known as delta-radiomics, has the potential to capture changes in a patient's anatomy throughout the course of radiation treatment for prostate cancer. Some of the major challenges of delta-radiomics studies are contouring the structures for individual fractions and accruing patients' data in an efficient manner. The manual contouring process is often time consuming and would limit the efficiency of accruing larger sample sizes for future studies. The problem is amplified because the contours are often made by highly trained radiation oncologists with limited time to dedicate to research studies of this nature. This work compares the use of automated prostate contours generated using a deformable image-based algorithm to make predictive models of genitourinary and changes in total international prostate symptom score in comparison to manually contours for a cohort of fifty patients. Area under the curve of manual and automated models were compared using the Delong test. This study demonstrated that the delta-radiomics models were similar for both automated and manual delta-radiomics models.

Uncovering prostate cancer aggressiveness signal in T2-weighted MRI through a three-reference tissues normalization technique.

Quantitative T2-weighted MRI (T2W) interpretation is impeded by the variability of acquisition-related features, such as field strength, coil type, signal amplification, and pulse sequence parameters. The main purpose of this work is to develop an automated method for prostate T2W intensity normalization. The procedure includes the following: (i) a deep learning-based network utilizing MASK R-CNN for automatic segmentation of three reference tissues: gluteus maximus muscle, femur, and bladder; (ii) fitting a spline function between average intensities in these structures and reference values; and (iii) using the function to transform all T2W intensities. The T2W distributions in the prostate cancer regions of interest (ROIs) and normal appearing prostate tissue (NAT) were compared before and after normalization using Student's t-test. The ROIs' T2W associations with the Gleason Score (GS), Decipher genomic score, and a three-tier prostate cancer risk were evaluated with Spearman's correlation coefficient (rS ). T2W differences in indolent and aggressive prostate cancer lesions were also assessed. The MASK R-CNN was trained with manual contours from 32 patients. The normalization procedure was applied to an independent MRI dataset from 83 patients. T2W differences between ROIs and NAT significantly increased after normalization. T2W intensities in 231 biopsy ROIs were significantly negatively correlated with GS (rS = -0.21, p = 0.001), Decipher (rS = -0.193, p = 0.003), and three-tier risk (rS = -0.235, p < 0.001). The average T2W intensities in the aggressive ROIs were significantly lower than in the indolent ROIs after normalization. In conclusion, the automated triple-reference tissue normalization method significantly improved the discrimination between prostate cancer and normal prostate tissue. In addition, the normalized T2W intensities of cancer exhibited a significant association with tumor aggressiveness. By improving the quantitative utilization of the T2W in the assessment of prostate cancer on MRI, the new normalization method represents an important advance over clinical protocols that do not include sequences for the measurement of T2 relaxation times.

Predicting response to immunotherapy in non-small cell lung cancer- from bench to bedside.

Background: Immune checkpoint inhibitor (ICI) therapy is first-line treatment for many advanced non-small cell lung cancer (aNSCLC) patients. Predicting response could help guide selection of intensified or alternative anti-cancer regimens. We hypothesized that radiomics and laboratory variables predictive of ICI response in a murine model would also predict response in aNSCLC patients. Methods: Fifteen mice with lung carcinoma tumors implanted in bilateral flanks received ICI. Pre-ICI laboratory and computed tomography (CT) data were evaluated for association with systemic ICI response. Baseline clinical and CT data for 117 aNSCLC patients treated with nivolumab were correlated with overall survival (OS). Models for predicting treatment response were created and subjected to internal cross-validation, with the human model further tested on 42 aNSCLC patients who received pembrolizumab. Results: Models incorporating baseline NLR and identical radiomics (surface-to-mass ratio, average Gray, and 2D kurtosis) predicted ICI response in mice and OS in humans with AUCs of 0.91 and 0.75, respectively. The human model successfully sorted pembrolizumab patients by longer vs. shorter predicted OS (median 35 months vs. 6 months, p=0.026 by log-rank). Discussion: This study advances precision oncology by non-invasively classifying aNSCLC patients according to ICI response using pre-treatment data only. Interestingly, identical radiomics features and NLR correlated with outcomes in the preclinical study and with ICI response in 2 independent patient cohorts, suggesting translatability of the findings. Future directions include using a radiogenomic approach to optimize modeling of ICI response.

Genomic Classifiers in Personalized Prostate Cancer Radiation Therapy Approaches: A Systematic Review and Future Perspectives Based on International Consensus.

Current risk-stratification systems for prostate cancer (PCa) do not sufficiently reflect the disease heterogeneity. Genomic classifiers (GC) enable improved risk stratification after surgery, but less data exist for patients treated with definitive radiation therapy (RT) or RT in oligo-/metastatic disease stages. To guide future perspectives of GCs for RT, we conducted (1) a systematic review on the evidence of GCs for patients treated with RT and (2) a survey of experts using the Delphi method, addressing the role of GCs in personalized treatments to identify relevant fields of future clinical and translational research. We performed a systematic review and screened ongoing clinical trials on ClinicalTrials.gov. Based on these results, a multidisciplinary international team of experts received an adapted Delphi method survey. Thirty-one and 30 experts answered round 1 and round 2, respectively. Questions with ≥75% agreement were considered relevant and included in the qualitative synthesis. Evidence for GCs as predictive biomarkers is mainly available to the postoperative RT setting. Validation of GCs as prognostic markers in the definitive RT setting is emerging. Experts used GCs in patients with PCa with extensive metastases (30%), in postoperative settings (27%), and in newly diagnosed PCa (23%). Forty-seven percent of experts do not currently use GCs in clinical practice. Expert consensus demonstrates that GCs are promising tools to improve risk-stratification in primary and oligo-/metastatic patients in addition to existing classifications. Experts were convinced that GCs might guide treatment decisions in terms of RT-field definition and intensification/deintensification in various disease stages. This work confirms the value of GCs and the promising evidence of GC utility in the setting of RT. Additional studies of GCs as prognostic biomarkers are anticipated and form the basis for future studies addressing predictive capabilities of GCs to optimize RT and systemic therapy. The expert consensus points out future directions for GC research in the management of PCa.

Distinct Profiles of DNA Repair Activity Define Favorable-risk Prostate Cancer Subtypes With Divergent Outcome.

INTRODUCTION: Understanding if divergent molecular profiles of DNA damage and repair (DDR) pathway activity, a biomarker of disease progression, exist in prostate tumors with favorable-risk features is an unmet need, which this study aim to unearth. MATERIALS AND METHODS: This was a multicenter registry genome-wide expression profiling study of prospectively collected radical prostatectomy (RP) tumor samples from 2014 to 2016. DDR activity was calculated from average expression of 372 DDR genes. Consensus hierarchical clustering was used to arrive at a robust clustering solution based on DDR gene expression patterns. Genome-wide differential expression between clusters was performed, and outcomes were evaluated across expression patterns. RESULTS: Of 5239 patients from the prospective registry, 376 had favorable-risk disease (Grade group [GG] 1 to 2, PSA prior to RP <10ng/ml, pT2 or less). DDR activity score was correlated with prognostic genomic signatures that predict for metastatic risk (r = 0.37, P < 2e-16) and high grade groups (P < .001). High DDR activity (top-quartile) was observed in 28% of patients with favorable-risk disease. In favorable-risk disease, 3 distinct clusters with varied DDR activity emerged with consensus clustering. Cluster I (compared with cluster II-III and GG3-GG5 disease) had the highest expression of all DDR sub-pathways, MYC, PAPR1, AR, and AR activity (P < .001 for all). Furthermore, cluster I was associated with poorer metastasis-free survival (MFS) and Overall survival (OS) compared with other clusters (MFS; HR: 2.43, 95%CI, [1.22-4.83], P = .01; OS; HR: 2.77, 95%CI, [1.18-6.5], P = .01). CONCLUSIONS: Cluster I is a novel subgroup of favorable-risk disease with high DDR activity, AR activity, PARP1 and chr8q/MYC expression, and poorer MFS and OS.

Adherence to Contouring and Treatment Planning Requirements Within a Multicentric Trial: Results of the Quality Assurance of the SAKK 09/10 trial.

PURPOSE: To evaluate the results of the radiation therapy (RT) quality assurance (QA) program of the phase 3 randomized SAKK 09/10 trial in patients with biochemically recurrent prostate cancer after prostatectomy. METHODS AND MATERIALS: Within the Schweizerische Arbeitsgemeinschaft für Klinische Krebsforschung (SAKK) 09/10 trial testing 64-Gy versus 70-Gy salvage RT, a central collection of treatment plans was performed and thoroughly reviewed by a dedicated medical physicist and radiation oncologist. Adherence to the treatment protocol and specifically to the European Organization for the Research and Treatment of Cancer (EORTC) guidelines for target volume definition (classified as deviation observed yes vs no) and its potential correlation with acute and late toxicity (Common Terminology Criteria for Adverse Events version 4.0) and freedom from biochemical progression (FFBP) were investigated. RESULTS: The treatment plans for 344 patients treated between February 2011 and April 2014 depicted important deviations from the EORTC guidelines and the recommendations per trial protocol. For example, in up to half of the cases, the delineated structures deviated from the protocol (eg, prostate bed in 48.8%, rectal wall [RW] in 41%). In addition, variations in clinical target volume (CTV) and planning target volume (PTV) occurred frequently (eg, CTV and PTV deviations in up to 42.4% and 25.9%, respectively). The detected deviations showed a significant association with a lower risk of grade ≥2 gastrointestinal acute toxicity when the CTV did not overlap the RW versus when the CTV overlapped the RW (odds ratio [OR], 0.43; 95% confidence interval [CI], 0.22-0.85; P = .014), and a higher rate of grade ≥2 late genitourinary (GU) toxicity when the CTV overlapped the RW (OR, 2.58; 95% CI, 1.17-5.72; P = .019). A marginally significant lower risk of grade ≥2 late GU toxicity was observed when the prostate bed did not overlap versus did overlap the RW (OR, 0.51; 95% CI, 0.25-1.03; P = .06). In addition, a marginally significant decrease in FFBP was observed in patients with PTV not including surgical clips as potential markers of the limits of the prostate bed (hazard ratio, 1.44; 95% CI, 0.96-2.17; P = .07). CONCLUSIONS: Despite a thorough QA program, the central review of a phase 3 trial showed limited adherence to treatment protocol recommendations, which was associated with a higher risk of toxicity by means of acute or late gastrointestinal or GU toxicity and showed a trend toward worse FFBP. Data from this QA review might help to refine future QA programs and prostate bed delineation guidelines.

Novel genomic signature predictive of response to immune checkpoint blockade: A pan-cancer analysis from project Genomics Evidence Neo-plasia Information Exchange (GENIE).

BACKGROUND: High tumor mutation burden (TMB) and total mutation count (TMC) can be predictive of better response to immune checkpoint blockade (ICB). Nevertheless, TMB and TMC are limited by variation across cancers and inconsistent definitions due to different profiling methods (targeted vs whole genome sequencing). Our objective was to identify genomic alterations (GAs) associated with ICB response and builds a novel genomic signature predictive of ICB response, independent of TMB/TMC. METHODS: This was a pan-cancer next generation sequencing (NGS)-association study using January 2014-May 2016 data from AACR Project Genomics Evidence Neo-plasia Information Exchange (GENIE). Participants included 6619 patients with metastatic or un-resectable cancer across 9 cancer types (including 1572 ICB-treated patients). GA data was collected using next-generation sequencing (NGS) assays and downloaded from cbioportal.org. Predictive analyses for ICB response were performed to develop the signature (ImmGA). RESULTS: GAs in 16 genes were associated with improved OS in ICB-treated patients (p < 0.005). 13 GAs were associated with an OS benefit in ICB-treated patients (Pinteraction < 0.05); these genes composed the ImmGA signature. High ImmGA score (≥2 alterations out of 13 predictive GAs) was associated with better OS in ICB-treated patients (AHR:0.67, 95%CI [0.6-0.75], p = 1.4e-12), even after accounting for TMC (Pinteraction = 8e-16). High ImmGA was associated with better OS in ICB-treated patients across most cancers and across different ICB treatment modalities. CONCLUSION: A novel signature predictive of ICB response (ImmGA) was developed from 13 GAs. Further investigation of the utility of ImmGA for treatment and trial selection is warranted.

Heterogeneity in Genomic Risk Assessment from Tissue Based Prognostic Signatures Used in the Biopsy Setting and the Impact of Magnetic Resonance Imaging Targeted Biopsy.

PURPOSE: Genomic prognostic signatures are used on prostate biopsy tissue for cancer risk assessment, but tumor heterogeneity and multifocality may be an issue. We evaluated the variability in genomic risk assessment from different biopsy cores within the prostate using 3 prognostic signatures (Decipher, CCP, GPS). MATERIALS AND METHODS: Men in this study came from 2 prospective prostate cancer trials of patients undergoing multiparametric magnetic resonance imaging and magnetic resonance imaging targeted biopsy with genomic profiling of positive biopsy cores. We explored the relationship among tumor grade, magnetic resonance imaging risk and genomic risk for each signature. We evaluated the variability in genomic risk assessment between different biopsy cores and assessed how often magnetic resonance imaging targeted biopsy or the current standard of care (profiling the core with the highest grade) resulted in the highest genomic risk level. RESULTS: In all, 224 positive biopsy cores from 78 men with prostate cancer were profiled. For each signature, higher biopsy grade (p <0.001) and magnetic resonance imaging risk level (p <0.001) were associated with higher genomic scores. Genomic scores from different biopsy cores varied with risk categories changing by 21% to 62% depending on which core or signature was used. Magnetic resonance imaging targeted biopsy and profiling the core with the highest grade resulted in the highest genomic risk level in 72% to 84% and 75% to 87% of cases, respectively, depending on the signature used. CONCLUSIONS: There is variation in genomic risk assessment from different biopsy cores regardless of the signature used. Magnetic resonance imaging directed biopsy or profiling the highest grade core resulted in the highest genomic risk level in most cases.