There are controversies surrounding indications for prostate-specific membrane antigen (PSMA) positron-emission tomography (PET) and the subsequent management of localized disease. Conventional imaging is not a necessary prerequisite to PSMA PET, which serves as an equally effective, if not more effective frontline imaging tool. However, research conducted in different countries has shown conflicting results regarding its cost-effectiveness. Following accurate staging using PSMA PET, subsequent management is discussed by our expert team in this review, which incorporates the latest updates: (1) Brief global overview: the sustainability and cost-effectiveness of routine PET, as well as the treatment sequences of neoadjuvant vs. adjuvant androgen deprivation therapy (ADT) with radiotherapy, require further research. (2) Gonadotropin-releasing hormone antagonists demonstrate better response rates, lower recurrence rates, and fewer complications compared to agonists. (3) The unfavorable intermediate-risk group may undergo prostatectomy or radiotherapy combined with 4–6 months of ADT. Radiotherapy alone may be considered for patients with co-morbidities, Gleason score 7 (3 + 4), and positive biopsy cores < 50%, provided an escalated radiation dose is applied. (4) Three Prostate Advances in Comparative Evidence (PACE) studies demonstrated that stereotactic radiotherapy, greatly relying on PSMA PET, is as effective as surgery or conventional radiotherapy. (5) Findings from clinical trials indicate that pelvic nodal radiotherapy coverage provides a survival benefit. (6) A brachytherapy boost provides better outcomes compared to external beam boost, eliminating the need for ADT in intermediate-risk cancers and reducing ADT duration to 6 months in high-risk cancers. Even short-term use (4–6 months) of gonadotropin releasing hormone agonists can lead to cardiac morbidity. In summary, localized prostate cancer, as identified through the relatively new PSMA PET, can be managed in various ways. This review highlights significant updates on controversial issues relevant to both cancer patients and researchers.

PURPOSETo quantify changes in prostate size and seed movement over time after transperineal implantation of stranded 125I seeds, and to determine their impact on prostate dosimetry.METHODSCT and MR (T2, balanced steady-state free precession) image triplets were acquired on days 0, 3, 10, and 30 for a cohort of 20 patients and registered automatically. Prostate contours were drawn on MR-T2 images; seeds were found and matched in successive CT images. Prostate volume and dimensions, seed movements, and prostate dose metrics V200, V150, V100 and D90 were calculated, and their dynamic behaviors quantified in an operationally defined prostate coordinate system.RESULTSCohort-averaged reductions in prostate A-P dimension (∼8%) and L-R dimension (∼5%) inferred from seed movements agreed with those obtained from contour measurements, whereas prostate volume and S-I dimension (implant direction) reductions inferred from seed movements were overestimated by about 30%. Average overall seed movement was 4.8 ± 3.0 mm, of which the only identifiable systematic component was resolution of prostate edema. Cohort-averaged ratios of prostate V200, V150, V100, and D90 on day 30 relative to day 0 were 1.67, 1.33, 1.02, and 1.08, respectively.CONCLUSIONSPostimplant prostate size reduction in the SI (implant) direction cannot reliably be inferred from stranded seed movements. Apart from large-scale migration, residual seed movements relative to the prostate after accounting for edema resolution appear to be random. Prostate V100 and D90 changes 30 days post implant are modest, whereas those for V150 and V200 are substantial.

Purpose Prostatic edema following transperineal interstitial permanent prostate brachytherapy implantation is commonly evaluated based on either prostate or implant volume. The current study compares the edema time course between the MR-delineated prostate contour and the CT-localized stranded seeds, enabling pairwise comparison in the presence of individual patient variation. In addition, unique identification of seeds enables the characterization of stranded implant dynamics. Materials and Methods Twenty patients were implanted with stranded Iodine-125 seeds (0.5 U strength) to the prostate at a prescribed dose of 145 Gy, following standard procedure. Pelvic scans were performed using computer tomography (CT) and magnetic resonance imaging (MRI) (T2-weighted fast spin-echo and balanced steady-state free precession (bSSFP)) on the day of implantation (D0), D3, D10, and D30 (30 days post-implant). A Prostate Coordinate System, based on the MR-delineated prostate contour, served as a common coordinate system across all time points. MR(bSSFP)-CT rigid registration was performed based on the mutual information metric. A strand reconstruction software uniquely matched individual seeds to the strand configuration in the preplan. The relative edema, normalized to D30, was calculated for MR-based contours and CT-based seed positions. Correlated movement of seeds within a strand were quantified: strand movement was calculated from the shift in the strand center-of-mass; strand length was determined as the total length of the line segments connecting sequential seeds in a strand. Simulation of the stranded seed model was performed. Initial D0 seed positions were moved based on the observed strand characteristic movement and compared against actual D30 seed positions. Results Prostatic edema resulted in swelling of the prostate, which peaks at D0 and mostly resolves by D30. The contour- and seed- based relative edema were similar and correlated (p < 0.01) in the lateral and ant-pos directions. The edema magnitudes differed noticeably in the sup-inf direction with no statistically significant correlation (p = 0.11). The average strand movement was 0.09, 0.12, and 0.26 cm in the Lateral, Ant-Pos, and Sup-Inf directions respective, resulting in a more compact seed distribution. The movement was largest between D0 and D3 and smallest between D10 and D30. Conversely, the strand length was relatively constant during the initial time points, followed by a length contraction of 5% between D10 and D30. Thus, the stranding material initially limits independent seed movement (i.e. strands moved as a whole) and subsequently loses integrity over time, allowing for strand contraction. Simulation of the stranded seed model reproduced the observed relative edema, particularly in the Sup-Inf strand direction. The average residual distance between simulated and actual D30 seed positions was 0.27 cm. For comparison, the actual seed movement was 0.38 cm and the residual from a loose seed model was 0.29 cm. Conclusions The study characterized edema resolution based on stranded seeds in permanent prostate brachytherapy. Comparison of contour-based and seed-based relative edema for the same patient cohort revealed statistically significant differences in the strand direction. Dynamic strand-specific behaviours pointed to the potential impact of stranding material integrity. Simulation of stranded seed behaviour reproduced the observed relative edema, presenting a plausible explanation of the dynamics of stranded seeds during the course of edema resolution. Prostatic edema following transperineal interstitial permanent prostate brachytherapy implantation is commonly evaluated based on either prostate or implant volume. The current study compares the edema time course between the MR-delineated prostate contour and the CT-localized stranded seeds, enabling pairwise comparison in the presence of individual patient variation. In addition, unique identification of seeds enables the characterization of stranded implant dynamics. Twenty patients were implanted with stranded Iodine-125 seeds (0.5 U strength) to the prostate at a prescribed dose of 145 Gy, following standard procedure. Pelvic scans were performed using computer tomography (CT) and magnetic resonance imaging (MRI) (T2-weighted fast spin-echo and balanced steady-state free precession (bSSFP)) on the day of implantation (D0), D3, D10, and D30 (30 days post-implant). A Prostate Coordinate System, based on the MR-delineated prostate contour, served as a common coordinate system across all time points. MR(bSSFP)-CT rigid registration was performed based on the mutual information metric. A strand reconstruction software uniquely matched individual seeds to the strand configuration in the preplan. The relative edema, normalized to D30, was calculated for MR-based contours and CT-based seed positions. Correlated movement of seeds within a strand were quantified: strand movement was calculated from the shift in the strand center-of-mass; strand length was determined as the total length of the line segments connecting sequential seeds in a strand. Simulation of the stranded seed model was performed. Initial D0 seed positions were moved based on the observed strand characteristic movement and compared against actual D30 seed positions. Prostatic edema resulted in swelling of the prostate, which peaks at D0 and mostly resolves by D30. The contour- and seed- based relative edema were similar and correlated (p < 0.01) in the lateral and ant-pos directions. The edema magnitudes differed noticeably in the sup-inf direction with no statistically significant correlation (p = 0.11). The average strand movement was 0.09, 0.12, and 0.26 cm in the Lateral, Ant-Pos, and Sup-Inf directions respective, resulting in a more compact seed distribution. The movement was largest between D0 and D3 and smallest between D10 and D30. Conversely, the strand length was relatively constant during the initial time points, followed by a length contraction of 5% between D10 and D30. Thus, the stranding material initially limits independent seed movement (i.e. strands moved as a whole) and subsequently loses integrity over time, allowing for strand contraction. Simulation of the stranded seed model reproduced the observed relative edema, particularly in the Sup-Inf strand direction. The average residual distance between simulated and actual D30 seed positions was 0.27 cm. For comparison, the actual seed movement was 0.38 cm and the residual from a loose seed model was 0.29 cm. The study characterized edema resolution based on stranded seeds in permanent prostate brachytherapy. Comparison of contour-based and seed-based relative edema for the same patient cohort revealed statistically significant differences in the strand direction. Dynamic strand-specific behaviours pointed to the potential impact of stranding material integrity. Simulation of stranded seed behaviour reproduced the observed relative edema, presenting a plausible explanation of the dynamics of stranded seeds during the course of edema resolution.

Optical coherence tomography (OCT) has become an important tool for measuring the vibratory response of the living cochlea. It stands alone in its capacity to measure the intricate motion of the hearing organ through the surrounding otic capsule bone. Nevertheless, as an extension of phase-sensitive OCT, it is only capable of measuring motion along the optical axis. Hence, measurements are 1-D. To overcome this limitation and provide a measure of the 3-D vector of motion in the cochlea, we developed an OCT system with three sample arms in a single interferometer. Taking advantage of the long coherence length of our swept laser, we depth (frequency) encode the three channels. An algorithm to depth decode and coregister the three channels is followed by a coordinate transformation that takes the vibrational data from the experimental coordinate system to Cartesian or spherical polar coordinates. The system was validated using a piezo as a known vibrating element that could be positioned at various angles. The angular measurement on the piezo was shown to have an RMSE of ≤ 0.30° (5.2 mrad) with a standard deviation of the amplitude of ≤ 120 pm. Finally, we demonstrate the system for in vivo imaging by measuring the vector of motion over a volume image in the apex of the mouse cochlea.

Background Automated catheter localization for ultrasound guided high-dose-rate prostate brachytherapy faces challenges relating to imaging noise and artifacts. To date, catheter reconstruction during the clinical procedure is performed manually. Deep learning has been successfully applied to a wide variety of complex tasks and has the potential to tackle the unique challenges associated with multiple catheter localization on ultrasound. Such a task is well suited for automation, with the potential to improve productivity and reliability. Purpose We developed a deep learning model for automated catheter reconstruction and investigated potential factors influencing model performance. The model was designed to integrate into a clinical workflow, with a proposed reconstruction confidence metric to aid in planner verification. Methods Datasets from 242 patients treated from 2016 to 2020 were collected retrospectively. The anonymized dataset comprises 31,000 transverse images reconstructed from 3D sagittal ultrasound acquisitions and 3500 implanted catheters manually localized by the planner. Each catheter was retrospectively ranked based on the severity of imaging artifacts affecting reconstruction difficulty. The U-NET deep learning architecture was trained to localize implanted catheters on transverse images. A fivefold cross-validation method was used, allowing for evaluation over the entire dataset. The postprocessing software combined the predictions with patient-specific implant information to reconstructed catheters in 3D space, uniquely matched to the implanted grid positions. A reconstruction confidence metric was calculated based on the number and probability of localized predictions per catheter. For each patient, deep learning prediction and postprocessing reconstruction were completed in under 2 min on a nonperformance PC. Results Overall, 80% of catheter reconstructions were accurate, within 2 mm along 90% of the length. The catheter tip was often not detected and required extrapolation during reconstruction. The reconstruction accuracy was 89% for the easiest catheter ranking and decreased to 13% for the highest difficulty ranking, when the aid of live ultrasound would have been recommended. Even when limited to the easiest ranked catheters, the reconstruction accuracy decreased at distal grid positions, down to 50%. Individual implantation style was found to influence the frequency of severe artifacts, slightly impacting the model accuracy. A reconstruction confidence metric identified the difficult catheters, removed the observed individual variation, and increased the overall accuracy to 91% while excluding 27% of the reconstructions. Conclusions The deep learning model localized implanted catheters over a large clinical dataset, with overall promising results. The model faced challenges due to ultrasound artifacts and image degradation distal to the probe, underlining the continued importance of maintaining image quality and minimizing artifacts. A potential workflow for integration into the clinical procedure was demonstrated, including the use of a confidence metric to predict low accuracy reconstructions. Comparison between models evaluated on different datasets should also consider underlying differences, such as the frequency and severity of imaging artifacts.

You have accessJournal of UrologyCME1 May 2022MP33-09 RADIOGENOMIC CORRELATES OF CLINICALLY RELEVANT CLEAR CELL RENAL CELL CANCER BIOMARKERS Derek Liu, Bino Varghese, Darryl Hwang, Xiaomeng Lei, Afshin Azadikhah, Komal Dani, Alex Raman, Steven Cen, Manju Aron, Harris Zahoor, Imran Siddiqi, Inderbir Gill, and Vinay Duddalwar Derek LiuDerek Liu More articles by this author , Bino VargheseBino Varghese More articles by this author , Darryl HwangDarryl Hwang More articles by this author , Xiaomeng LeiXiaomeng Lei More articles by this author , Afshin AzadikhahAfshin Azadikhah More articles by this author , Komal DaniKomal Dani More articles by this author , Alex RamanAlex Raman More articles by this author , Steven CenSteven Cen More articles by this author , Manju AronManju Aron More articles by this author , Harris ZahoorHarris Zahoor More articles by this author , Imran SiddiqiImran Siddiqi More articles by this author , Inderbir GillInderbir Gill More articles by this author , and Vinay DuddalwarVinay Duddalwar More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002587.09AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The study was conducted to investigate how quantitative texture analysis can be used to non-invasively identify novel radiogenomic correlations with Clear Cell Renal Cell Carcinoma (ccRCC) biomarkers which are relevant in IO/VEGFi therapy as well as prognostic biomarker panels viz Clear code 34. METHODS: The Cancer Genome Atlas–Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) open-source database was used to identify 190 sets of patient genomic data that had corresponding multiphase contrast-enhanced CT images in The Cancer Imaging Archive (TCIA-KIRC). Only CT images in which the tumor was more than 8 pixels were included for analysis. Twelve clinically relevant biomarkers were identified from the literature. 2824 radiomic features spanning fifteen texture families were extracted from CT images using a custom-built software package in MATLAB. Robust radiomic features with strong inter-scanner reproducibility were selected. Random Forest (RF), AdaBoost and Elastic Net machine learning (ML) algorithms were used to evaluate the ability of the selected radiomic features to predict the presence of the previously identified biomarkers. ML analysis was repeated with cases stratified by stage (I/II vs. III/IV) and grade (1/2 vs. 3/4). 10-fold cross validation was used to evaluate model performance. RESULTS: Before stratification, radiomics predicted the presence of several biomarkers with weak discrimination (AUC 0.60-0.66). Among patients with high stage (III/IV), radiomics predicted the presence of TeffhighMyeloidlow gene expression subtype and high indel burden with acceptable discrimination (AUC 0.71 and 0.73, respectively). Among high-grade patients, radiomics predicted the presence of Teffhigh/Myeloidlow gene expression subtype with acceptable discrimination (AUC 0.71 and 0.72, respectively), and high indel burden with excellent accuracy (AUC 0.83). Additionally, radiomics predicted ClearCode34 risk class with acceptable accuracy in low-stage patients (AUC 0.73). CONCLUSIONS: Radiomic texture analysis has the potential to identify a variety of clinically relevant biomarkers in patients with ccRCC and may be used in addition to biopsy results to predict prognosis and select treatment. Source of Funding: Clinical and Translational Science Institute; American Cancer Society © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e573 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Derek Liu More articles by this author Bino Varghese More articles by this author Darryl Hwang More articles by this author Xiaomeng Lei More articles by this author Afshin Azadikhah More articles by this author Komal Dani More articles by this author Alex Raman More articles by this author Steven Cen More articles by this author Manju Aron More articles by this author Harris Zahoor More articles by this author Imran Siddiqi More articles by this author Inderbir Gill More articles by this author Vinay Duddalwar More articles by this author Expand All Advertisement PDF downloadLoading ...

Objectives:To identify computed tomography (CT)-based radiomic signatures of cluster of differentiation 8 (CD8)-T cell infiltration and programmed cell death ligand 1 (PD-L1) expression levels in patients with clear-cell renal cell carcinoma (ccRCC). Methods:Seventy-eight patients with pathologically confirmed localized ccRCC, preoperative multiphase CT and tumor resection specimens were enrolled in this retrospective study. Regions of interest (ROI) of the ccRCC volume were manually segmented from the CT images and processed using a radiomics panel comprising of 1708 metrics. The extracted metrics were used as inputs to three machine learning classifiers: Random Forest, AdaBoost, and ElasticNet to create radiomic signatures for CD8-T cell infiltration and PD-L1 expression, respectively. Results:Using a cut-off of 80 lymphocytes per high power field, 59 % were classified to CD8 highly infiltrated tumors and 41 % were CD8 non highly infiltrated tumors, respectively. An ElasticNet classifier discriminated between these two groups of CD8-T cells with an AUC of 0.68 (95 % CI, 0.55-0.80). In addition, based on tumor proportion score with a cut-off of > 1 % tumor cells expressing PD-L1, 76 % were PD-L1 positive and 24 % were PD-L1 negative. An Adaboost classifier discriminated between PD-L1 positive and PD-L1 negative tumors with an AUC of 0.8 95 % CI: (0.66, 0.95). 3D radiomics metrics of graylevel co-occurrence matrix (GLCM) and graylevel run-length matrix (GLRLM) metrics drove the performance for CD8-Tcell and PD-L1 classification, respectively. Conclusions:CT-radiomic signatures can differentiate tumors with high CD8-T cell infiltration with moderate accuracy and positive PD-L1 expression with good accuracy in ccRCC.

The purpose of this study is to analyze outcomes of combined antegrade-retrograde dilations (CARD). This retrospective study was conducted on 14 patients with a history of head and neck cancer, treated with radiation therapy that was complicated by either complete or near-complete esophageal stenosis. All patients had minimal oral intake and depended on a gastrostomy tube for nutrition. Swallow function before and after CARD was assessed using the Functional Oral Intake Scale, originally developed for stroke patients and applied to head and neck cancer patients. Patients undergoing CARD demonstrated a quantifiable improvement in swallow function (p = 0.007) that persisted at last known follow-up (p = 0.015) but only a minority (23.1%) achieved oral intake sufficient to obviate the need for tube feeds. Complication rates were 24% per procedure or 36% per patient, almost all complications required procedural intervention, and all complications occurred in patients with complete stenosis. Our study suggests further caution when considering CARD, careful patient selection, and close post-operative monitoring.

Resistance to oncogene-targeted therapies involves discrete drug-tolerant persister cells, originally discovered through in vitro assays. Whether a similar phenomenon limits efficacy of programmed cell death 1 (PD-1) blockade is poorly understood. Here, we performed dynamic single-cell RNA-Seq of murine organotypic tumor spheroids undergoing PD-1 blockade, identifying a discrete subpopulation of immunotherapy persister cells (IPCs) that resisted CD8+ T cell-mediated killing. These cells expressed Snai1 and stem cell antigen 1 (Sca-1) and exhibited hybrid epithelial-mesenchymal features characteristic of a stem cell-like state. IPCs were expanded by IL-6 but were vulnerable to TNF-α-induced cytotoxicity, relying on baculoviral IAP repeat-containing protein 2 (Birc2) and Birc3 as survival factors. Combining PD-1 blockade with Birc2/3 antagonism in mice reduced IPCs and enhanced tumor cell killing in vivo, resulting in durable responsiveness that matched TNF cytotoxicity thresholds in vitro. Together, these data demonstrate the power of high-resolution functional ex vivo profiling to uncover fundamental mechanisms of immune escape from durable anti-PD-1 responses, while identifying IPCs as a cancer cell subpopulation targetable by specific therapeutic combinations.

You have accessJournal of UrologyKidney Cancer: Advanced (including Drug Therapy) II (PD39)1 Apr 2020PD39-04 PREDICTING CD8+ T CELL INFILTRATION AND PD-L1 EXPRESSION IN RENAL CELL CARCINOMA USING CT RADIOMIC SIGNATURES Vinay Duddalwar*, Haris Zahoor, Imran Siddiqui, Manju Aron, Bino Varghese, Austin Fullenkamp, Steven Cen, Akash Sali, Anishka D'Souza, Suhn Rhie, Xiaomeng Lei, Marielena Rivas, Derek Liu, Darryl Hwang, David Quinn, Mihir Desai, and Inderbir Gill Vinay Duddalwar*Vinay Duddalwar* More articles by this author , Haris ZahoorHaris Zahoor More articles by this author , Imran SiddiquiImran Siddiqui More articles by this author , Manju AronManju Aron More articles by this author , Bino VargheseBino Varghese More articles by this author , Austin FullenkampAustin Fullenkamp More articles by this author , Steven CenSteven Cen More articles by this author , Akash SaliAkash Sali More articles by this author , Anishka D'SouzaAnishka D'Souza More articles by this author , Suhn RhieSuhn Rhie More articles by this author , Xiaomeng LeiXiaomeng Lei More articles by this author , Marielena RivasMarielena Rivas More articles by this author , Derek LiuDerek Liu More articles by this author , Darryl HwangDarryl Hwang More articles by this author , David QuinnDavid Quinn More articles by this author , Mihir DesaiMihir Desai More articles by this author , and Inderbir GillInderbir Gill More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000918.04AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: CD8+T cell infiltration and programmed death-1 ligand (PD-L1) expression have been associated with enhanced treatment response to immune checkpoint inhibitors (ICI). However, they are not part of clinical practice due to limitations such as pathologic specimen requirement (biopsy), tumor heterogeneity and sampling variability. Radiomic analysis can non-invasively quantify tumor phenotype using metrics extracted from routine clinical images. We investigated the feasibility of computed tomography (CT)-based radiomic signatures of CD8+T cell infiltration and PD-L1 expression in patients with clear-cell renal cell carcinoma (ccRCC). METHODS: We evaluated 50 patients with pathologically confirmed localized ccRCC, and who had preoperative multiphase CT and had available tumor resection specimens from 2009 -2018. Immunohistochemistry for CD8+ T cells and PD-L1 were performed. The tumor volume was manually segmented from the CT images and evaluated using a custom Matlab-based radiomics panel comprising of 1708 metrics to create radiomic signatures. Four statistical learning methods were attempted: Random Forest, Adaboost, MARS, LASSO. The area under the curve (AUC) based on predicted probability from 48x10 iterations of Repeated Leave-One-Out Bootstrap (RLOOB) cross-validation testing data was used to assess robust discrimination accuracy. RESULTS: 48/50 patients were evaluable for CD8 staining. Using a cut-off of 80 lymphocytes per high power field, 25 (52%) were classified to CD8 high tumors and 23 (48%) were CD8 low tumors respectively. A predictive radiomic discriminator between these two groups of CD8+ T cells was observed using LASSO with an AUC of 0.9 (95% CI, 0.8 to 1). Distribution of predicted probability using histogram showed a complete separation between CD8 high and CD8 low tumors was around 0.5. Of the 50 patients, based on tumor proportion score with a cut-off of ≥1% tumor cells expressing, 16 (32%) were PD-L1 positive and 34 (68%) were PD-L1 negative. A radiomic signature of PD-L1 expression which discriminated PD-L1 positive and PD-L1 negative tumors was observed using Adaboost with an AUC of 0.67 (95% CI, 0.5 to 0.84). CONCLUSIONS: CT-based radiomic signatures can predict CD8+ T cells and PD-L1 expression in ccRCC . This may help in treatment decisions Source of Funding: ACS; SC CTSI © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e809-e810 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Vinay Duddalwar* More articles by this author Haris Zahoor More articles by this author Imran Siddiqui More articles by this author Manju Aron More articles by this author Bino Varghese More articles by this author Austin Fullenkamp More articles by this author Steven Cen More articles by this author Akash Sali More articles by this author Anishka D'Souza More articles by this author Suhn Rhie More articles by this author Xiaomeng Lei More articles by this author Marielena Rivas More articles by this author Derek Liu More articles by this author Darryl Hwang More articles by this author David Quinn More articles by this author Mihir Desai More articles by this author Inderbir Gill More articles by this author Expand All Advertisement PDF downloadLoading ...

Understanding tumor resistance to T cell immunotherapies is critical to improve patient outcomes. Our study revealed a role for transcriptional suppression of the tumor-intrinsic HLA class I (HLA-I) antigen processing and presentation machinery (APM) in therapy resistance. Low HLA-I APM mRNA levels in melanoma metastases prior to immune checkpoint blockade (ICB) correlated with non-responsiveness to therapy and poor clinical outcome. Patient-derived melanoma cells with silenced HLA-I APM escaped recognition by autologous CD8+ T cells. However, targeted activation of the innate immunoreceptor RIG-I initiated de novo HLA-I APM transcription thereby overcoming T cell resistance. Antigen presentation was restored in interferon (IFN)-sensitive but also immunoedited IFN-resistant melanoma models through RIG-I-dependent stimulation of an IFN-independent salvage pathway involving IRF1 and IRF3. Likewise, enhanced HLA-I APM expression was detected in RIG-I (DDX58)-high melanoma biopsies, correlating with improved patient survival. Induction of HLA-I APM by RIG-I synergized with antibodies blocking PD-1 and TIGIT inhibitory checkpoints in boosting the anti-tumor T cell activity of ICB non-responders. Overall, the herein identified IFN-independent effect of RIG-I on tumor antigen presentation and T cell recognition proposes innate immunoreceptor targeting as a strategy to overcome intrinsic T cell resistance of IFN-sensitive and IFN-resistant melanomas and improve clinical outcomes in immunotherapy.

You have accessJournal of UrologyKidney Cancer: Basic Research & Pathophysiology II (MP18)1 Apr 2020MP18-20 DEVELOPING A RADIOMIC SIGNATURE TO EVALUATE THE RAS-MAPK PATHWAY IN CLEAR CELL RENAL CELL CARCINOMA Derek Liu, Steven Cen, Gangning Liang, Kimberly Siegmund, Bino Varghese, Suhn Rhie, Xiaomeng Lei, Simin Hajian, Manju Aron, Haris Zahoor, Darryl hwang, Mihir Desai, Inderbir Gill, and Vinay Duddalwar* Derek LiuDerek Liu More articles by this author , Steven CenSteven Cen More articles by this author , Gangning LiangGangning Liang More articles by this author , Kimberly SiegmundKimberly Siegmund More articles by this author , Bino VargheseBino Varghese More articles by this author , Suhn RhieSuhn Rhie More articles by this author , Xiaomeng LeiXiaomeng Lei More articles by this author , Simin HajianSimin Hajian More articles by this author , Manju AronManju Aron More articles by this author , Haris ZahoorHaris Zahoor More articles by this author , Darryl hwangDarryl hwang More articles by this author , Mihir DesaiMihir Desai More articles by this author , Inderbir GillInderbir Gill More articles by this author , and Vinay Duddalwar*Vinay Duddalwar* More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000843.020AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The RAS-MAPK pathways play an important role in orchestrating signaling events that contribute to tumorigenesis and angiogenesis. Several small molecule inhibitors such as tyrosine and MEK inhibitors act by regulating RAS-MAPK activity. Our study aims to identify associations between radiomic metrics and the activation/deactivation of the RAS-MAPK pathway in clear cell RCC (ccRCC). Our goal is to identify new predictors for selecting treatment strategies using clinical imaging alone. METHODS: We retrospectively identified 78 patients with localized ccRCC with available imaging from a prospectively maintained database at our institution, University of Southern California. From a doublet biopsy of tumor tissue, one sample was used for histopathological confirmation and the other for epigenetics analysis. Methylation was calculated as beta-values that represent the ratio of methylated to unmethylated DNA at a unique locus. Gene methylation was defined as the mean of probes within 1500bp of the transcription start site. Imaging data was analyzed using a previously reported radiomics pipeline that includes nine different texture methods for a total of 1708 radiomic features. Each radiomics method was assessed for internal concordance by calculating Spearman’s Rank Correlation for pairs of radiomic features within each method. Each gene and radiomic feature were then correlated, also using Spearman’s Rank Correlation. This data was entered into a modified Gene Set Enrichment Analysis (GSEA) to identify pathways implicated in ccRCC including the RAS-MAPK pathway. RESULTS: Gray-Level Difference Matrix (GLDM) showed significant internal concordance compared to the other radiomic methods, with mean R-squared values of 0.47 (2D) and 0.51 (3D), compared to a range of 0.1 to 0.18 for other methods. Six pathways with significant roles in ccRCC including RAS and MAPK were identified for GSEA analysis. A stringent filter of p < 0.01 resulted in two features within the GLDM2D and GLDM3D methods with significantly enriched pathways. GLDM 2D Correlation metric in corticomedullary phase was significantly enriched for correlations to genes in the RAS pathway (p < .007) and GLDM 3D MCC metric in nephrographic phase for genes in the MAPK pathway (p < .005). CONCLUSIONS: CT-based radiomic metrics showed significant correlations with underlying methylation patterns in the RAS and MAPK pathways, which are signaling pathways targeted by small molecule inhibitors. Our discovery study warrants a future prospective validation study. Source of Funding: Whittier Foundation, RSNA Medical Student Grant © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e243-e243 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Derek Liu More articles by this author Steven Cen More articles by this author Gangning Liang More articles by this author Kimberly Siegmund More articles by this author Bino Varghese More articles by this author Suhn Rhie More articles by this author Xiaomeng Lei More articles by this author Simin Hajian More articles by this author Manju Aron More articles by this author Haris Zahoor More articles by this author Darryl hwang More articles by this author Mihir Desai More articles by this author Inderbir Gill More articles by this author Vinay Duddalwar* More articles by this author Expand All Advertisement PDF downloadLoading ...

Background To understand fundamental mechanisms of immune escape, we leveraged our functional ex vivo platform of murine derived organotypic tumor spheroids (DOTS)1 to determine if drug-tolerant persister cells analogous to oncogene targeted therapies limit efficacy of programmed death (PD)-1 blockade, and to identify therapeutic vulnerabilities to overcome anti-PD-1 (αPD-1) resistance. Methods Murine syngeneic cancer models with well-characterized response to αPD-1 therapy were chosen: MC38 (sensitive) and CT26 (partially resistant). Bulk and single-cell (sc) RNA-sequencing (RNA-seq) were performed on αPD-1 treated DOTS. In vitro culture studies were conducted with or without cytokines (100 ng/ml) or drugs (500 nM). In vivo studies in mice bearing MC38 or CT26 tumors evaluated the combinatorial strategy with PD-1 blockade. We further evaluated our findings in scRNA-seq of an αPD-1 refractory colorectal cancer (CRC) patient tumor.2 Results Bulk RNA-seq of αPD-1 treated DOTS revealed a mesenchymal resistant phenotype with upregulated TNF-α/NFκB signaling (figure 1). scRNA-seq further identified a discrete sub-population of immunotherapy persister cells (IPCs). These cells expressed a stem-like phenotype including downregulation of E2F targets indicative of quiescence, suppression of interferon-γ response genes, induction of hybrid epithelial-to-mesenchymal state, and active IL-6 signaling (figure 1). Ly6a/stem cell antigen-1 (Sca-1) and Snai1 were found to be differentially upregulated in IPCs resistant to PD-1 blockade (not shown). Sca-1 positivity was confirmed in pre-existing tumor populations in vitro (figure 2). When enriched via sorting, these cells remained more persistently Sca-1+ at 96 hours in culture of CT26 compared to MC38 cells, related to increased autocrine IL-6 production by CT26 Sca-1+ cells. Indeed, IL-6 supplementation was capable of expanding Sca-1+ cells in culture (figure 2). Sca-1+ cells expressing ovalbumin peptide were refractory to OT-1 T cell mediated killing and failed to upregulate MHC class-1 antigen presentation (H-2Kb) in response to IL-6, in contrast to interferon-γ (not shown). Analysis of RNA-seq data further identified Birc2/3 as potential targets limiting TNF-mediated apoptosis of these cells (not shown). Notably, Birc2/3 antagonism depleted Sca-1+ IPCs in vitro and significantly potentiated the impact of PD-1 blockade in vivo in MC38, and less robustly in CT26 (figure 3). Evaluation in a microsatellite-instability high CRC patient identified a pre-existent IPC subpopulation within the αPD-1 refractory pre-treatment tumor, with high SNAI1 expression compared to CRC samples in TCGA (figure 4). Conclusions High-resolution functional ex vivo profiling identified Sca-1+/Snai1high stem-like 'immunotherapy persister cells' and uncovered their anti-apoptotic dependencies targetable with Birc2/3 antagonism to augment αPD-1 efficacy. Ethics Approval This study was approved by the Dana-Farber Animal Care and Use Committee and Novartis Institutional Animal Care and Use Committee. Informed written consent to participate in Dana-Farber/Harvard Cancer Center institutional review board (IRB)-approved research protocols was obtained from the human subject. A copy of the written consent is available for review by the Editor of this journal. The study was conducted per the WMA Declaration of Helsinki and IRB-approved protocols. References Jenkins RW, Aref AR, Lizotte PH, Ivanova E, Stinson S, Zhou CW, et al. Ex Vivo Profiling of PD-1 Blockade using organotypic tumor spheroids. Cancer Discov. 2018;8(2):196–668 215. Gurjao C, Liu D, Hofree M, AlDubayan SH, Wakiro I, Su MJ, et al. intrinsic resistance to immune checkpoint blockade in a mismatch repair-deficient colorectal cancer. Cancer Immunol Res 2019;7(8):1230–6.

3627 Background: Differential gene expression (DGE) methods, initially developed for analyzing bulk RNA changes in pure tumor cell lines under experimental settings, are commonly used to identify biomarkers in and infer biological differences between patient tumor samples, which are admixtures of tumor and non-tumor components. Methods to sensitively and accurately detect cell type-specific expression differences in admixed patient samples are not well characterized but may greatly affect emerging targeted and immunotherapy biomarker strategies. To address this issue, we developed a simulation framework to benchmark our ability to detect changes in tumor-intrinsic gene expression. Methods: Pseudobulk RNAseq melanoma cohorts were simulated by sampling from melanoma single cell RNAseq data. Simulation parameters were optimized to maximize concordance of gene expression means and variances (Spearman r = 0.81, 0.68, respectively) between the TCGA SKCM cohort (n = 462) and matched simulated cohort, and then validated in two independent melanoma cohorts (n = 42, 129; means Spearman r = 0.80, 0.78; variances Spearman r = 0.68, 0.63). Using this simulation framework, we benchmarked the effect of sample size, magnitude of differential expression, and differences in cell type proportions on the sensitivity and positive predictive value (PPV) of detecting true differentially expressed genes in the tumor-intrinsic compartment. Results: Reference cohorts of 50 total tumors (n = 10) were simulated to contain a 2 standard deviation tumor-intrinsic expression change in 50 randomly selected genes and a 11% difference in mean purity between two equally sized 25-tumor subgroups. DGE analysis using DESeq2 with an FDR q-value threshold of 0.1 yielded a sensitivity of 0.37 and PPV of 0.29. DGE analysis of the same simulated cohorts using a non-parametric Mann-Whitney U test with an FDR q-value threshold of 0.1 yielded a sensitivity of 0.13 and PPV of 0.76. Conclusions: Commonly used DGE methods for existing expression-based biomarker strategies have poor sensitivity and PPV in admixed tumor samples, limiting our ability to find meaningful transcriptional biomarkers in clinical cohorts. We are currently developing methods to more accurately detect true differentially expressed genes in admixed bulk RNAseq samples and applying these approaches for biomarker discovery in immunotherapy-treated patient cohorts and other clinical tumor cohorts.

You have accessJournal of UrologyImaging/Radiology: Uroradiology III (MP80)1 Apr 2019Imaging/Radiology: Uroradiology III (MP80) View All Author Informationhttps://doi.org/10.1097/01.JU.0000558674.79094.e1AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail © 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue Supplement 4April 2019 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Expand All Advertisement PDF downloadLoading ...