Morgan, Todd M.

Introduction. Chromophobe renal cell carcinoma (chromophobe RCC) is the third major subcategory of renal tumors after clear cell RCC and papillary RCC, accounting for approximately 5% of all RCC subtypes. Other oncocytic neoplasms seen commonly in surgical pathology practice include the eosinophilic variant of chromophobe RCC, renal oncocytoma, and low-grade oncocytic unclassified RCC. Methods. In our recent next-generation sequencing based study, we nominated a lineage-specific novel biomarker LINC01187 (long intergenic non-protein coding RNA 1187) which was found to be enriched in chromophobe RCC. Like KIT (cluster of differentiation 117; CD117), a clinically utilized chromophobe RCC related biomarker, LINC01187 is expressed in intercalated cells of the nephron. In this follow-up study, we performed KIT immunohistochemistry and LINC01187 RNA in situ hybridization (RNA-ISH) on a cohort of chromophobe RCC and other renal neoplasms, characterized the expression patterns, and quantified the expression signals of the two biomarkers in both primary and metastatic settings. Results. LINC01187, in comparison to KIT, exhibits stronger and more uniform expression within tumors while maintaining temporal and spatial consistency. LINC01187 also is devoid of intra-tumoral heterogeneous expression pattern, a phenomenon commonly noted with KIT. Conclusions. LINC01187 expression can augment the currently utilized KIT assay and help facilitate easy microscopic analyses in routine surgical pathology practice.

The substantial biological heterogeneity of metastatic prostate cancer has hindered the development of personalized therapeutic approaches. Therefore, it is difficult to predict the course of metastatic hormone-sensitive prostate cancer (mHSPC), with some men remaining on first-line androgen deprivation therapy (ADT) for several years while others progress more rapidly. Improving our ability to risk-stratify patients would allow for the optimization of systemic therapies and support the development of stratified prospective clinical trials focused on patients likely to have the greatest potential benefit. Here, we applied a liquid biopsy approach to identify clinically relevant, blood-based prognostic biomarkers in patients with mHSPC. Gene expression indicating the presence of CTCs was greater in CHAARTED high-volume (HV) patients (52% CTChigh) than in low-volume (LV) patients (23% CTChigh; * p = 0.03). HV disease (p = 0.005, q = 0.033) and CTC presence at baseline prior to treatment initiation (p = 0.008, q = 0.033) were found to be independently associated with the risk of nonresponse at 7 months. The pooled gene expression from CTCs of pre-ADT samples found AR, DSG2, KLK3, MDK, and PCA3 as genes predictive of nonresponse. These observations support the utility of liquid biomarker approaches to identify patients with poor initial response. This approach could facilitate more precise treatment intensification in the highest risk patients.

PURPOSEThere remains a lack of clarity regarding the influence of sequencing of androgen deprivation therapy (ADT) and radiotherapy (RT) on outcomes in prostate cancer (PCa). Herein, we evaluate the optimal sequencing of ADT with prostate-directed RT in localized PCa.METHODSMEDLINE (1966-2018), Embase (1982-2018), ClinicalTrials.gov, and conference proceedings (1990-2018) were searched to identify randomized trials evaluating the sequencing, but not duration, of ADT with RT. Two randomized phase III trials were identified, and individual patient data were obtained: Ottawa 0101 and NRG Oncology's Radiation Therapy Oncology Group 9413. Ottawa 0101 randomly assigned patients to neoadjuvant or concurrent versus concurrent or adjuvant short-term ADT. Radiation Therapy Oncology Group 9413, a 2 × 2 factorial trial, included a random assignment of neoadjuvant or concurrent versus adjuvant short-term ADT. The neoadjuvant or concurrent ADT arms of both trials were combined into the neoadjuvant group, and the arms receiving adjuvant ADT were combined into the adjuvant group. The primary end point of this meta-analysis was progression-free survival (PFS).RESULTSThe median follow-up was 14.9 years. Overall, 1,065 patients were included (531 neoadjuvant and 534 adjuvant). PFS was significantly improved in the adjuvant group (15-year PFS, 29% v 36%, hazard ratio [HR], 1.25 [95% CI, 1.07 to 1.47], P = .01). Biochemical failure (subdistribution HR [sHR], 1.37 [95% CI, 1.12 to 1.68], P = .002), distant metastasis (sHR, 1.40 [95% CI, 1.00 to 1.95], P = .04), and metastasis-free survival (HR, 1.17 [95% CI, 1.00 to 1.37], P = .050) were all significantly improved in the adjuvant group. There were no differences in late grade ≥ 3 gastrointestinal (2% v 3%, P = .33) or genitourinary toxicity (5% v 5%, P = .76) between groups.CONCLUSIONThe sequencing of ADT with prostate-directed RT has significant association with long-term PFS and MFS in localized PCa. Our findings favor use of an adjuvant over a neoadjuvant approach, without any increase in long-term toxicity.

PURPOSE Pretreatment estimates of seminal vesicle invasion (SVI) are challenging and significantly influence the management of prostate cancer. We sought to improve current models to predict SVI through the development of an SVI prediction genomic signature. PATIENTS AND METHODS A total of 15,889 patients who underwent radical prostatectomy (RP) with available baseline clinical, pathology, and transcriptome data were retrieved from the GRID registry (ClinicalTrials.gov identifier: NCT02609269 ) and other retrospective cohorts. These data were divided into a training (n = 6,766), test (n = 3,363), and two validation (n = 5,062 and 698) cohorts. Multivariable logistic regression was performed to assess the predictive effect of the genomic SVI (gSVI) classifier in the presence of established nomograms (Partin Tables and Memorial Sloan Kettering Cancer Center [MSKCC]). RESULTS In the training cohort, univariable filtering identified 2,132 genes that were differentially expressed between RP tumors with and without SVI. Model parameters were tuned to maximize the area under the curve (AUC) in the testing cohort, resulting in a logistic generalized linear model with 581 genes. The gSVI model scores range from 0 to 1. In the first validation set, gSVI showed superior discrimination of patients with and without SVI at RP compared with other prognostic signatures trained to predict distant metastasis or clinical recurrence. Of the 698 patients in the second validation set, gSVI combined with the MSKCC nomogram had a superior AUC (0.86) compared with either nomogram individually (0.81). CONCLUSION The gSVI represents a novel and validated expression signature to predict the presence of SVI before treatment with surgery. This genomic tool adds discriminatory power to existing clinical predictive nomograms and may help with pretreatment counseling and decision making.

PURPOSE Germline testing (GT) is a central feature of prostate cancer (PCA) treatment, management, and hereditary cancer assessment. Critical needs include optimized multigene testing strategies that incorporate evolving genetic data, consistency in GT indications and management, and alternate genetic evaluation models that address the rising demand for genetic services. METHODS A multidisciplinary consensus conference that included experts, stakeholders, and national organization leaders was convened in response to current practice challenges and to develop a genetic implementation framework. Evidence review informed questions using the modified Delphi model. The final framework included criteria with strong (> 75%) agreement (Recommend) or moderate (50% to 74%) agreement (Consider). RESULTS Large germline panels and somatic testing were recommended for metastatic PCA. Reflex testing—initial testing of priority genes followed by expanded testing—was suggested for multiple scenarios. Metastatic disease or family history suggestive of hereditary PCA was recommended for GT. Additional family history and pathologic criteria garnered moderate consensus. Priority genes to test for metastatic disease treatment included BRCA2, BRCA1, and mismatch repair genes, with broader testing, such as ATM, for clinical trial eligibility. BRCA2 was recommended for active surveillance discussions. Screening starting at age 40 years or 10 years before the youngest PCA diagnosis in a family was recommended for BRCA2 carriers, with consideration in HOXB13, BRCA1, ATM, and mismatch repair carriers. Collaborative (point-of-care) evaluation models between health care and genetic providers was endorsed to address the genetic counseling shortage. The genetic evaluation framework included optimal pretest informed consent, post-test discussion, cascade testing, and technology-based approaches. CONCLUSION This multidisciplinary, consensus-driven PCA genetic implementation framework provides novel guidance to clinicians and patients tailored to the precision era. Multiple research, education, and policy needs remain of importance.

610 Background: Personalized treatment for clinical T1 renal cortical masses (RCMs) should account for competing risks related to tumor and patient characteristics. Using a contemporary multi-institutional cohort, we developed treatment-specific prediction models for cancer-specific mortality (CSM), other-cause mortality (OCM), and 90-day complication rates for patients managed with surgery, thermal ablation (TA), and active surveillance (AS). Methods: Preoperative clinical and radiological features were collected for eligible patients aged 18-91 years treated at four academic centers from 2000-2016. Prediction models used competing risks regressions for CSM and OCM and logistic regressions for 90-day Clavien >3 complications, adjusting for tumor size as well as patient age, sex, ECOG performance status (PS), and Charlson comorbidity index (CCI). Predictions accounted for missing data using multiple imputation. Results: After excluding 25 patients with no follow-up, the cohort included 4995 patients treated with radical nephrectomy (RN, n=1270), partial nephrectomy (PN, n=2842), thermal ablation (n=479), or active surveillance (n=404). Median follow-up was 5.1 years (IQR 2.5-8.5). Predictions from the fitted model are shown in an online calculator ( https://rgulati.shinyapps.io/rcc-risk-calculator ). To illustrate the use of this calculator for a specific patient, a 70-year-old female with a 5.5 cm RCM, PS of 2, and CCI of 3 has a predicted 5-year CSM of 4-7% across treatments, 5-year OCM of 34-49%, and 90-day risk of Clavien ≥3 complications of 4%, 10%, and 6% for RN, PN, and TA respectively. Conclusions: Personalized treatment selection for cT1 RCM is challenging. We present a competing risk calculator that incorporates pretreatment features to quantify competing causes of mortality and treatment-associated complications. Pending validation, this tool may be used in clinical practice to provide patients with estimated individualized treatment-specific probabilities of competing causes of death and complication risks to facilitate shared decision-making.