Citations

AbstractAlthough cancer is highly heterogeneous, all metastatic cancer is considered American Joint Committee on Cancer (AJCC) Stage IV disease. The purpose of this project was to redefine staging of metastatic cancer. Internal validation of nationally representative patient data from the National Cancer Database (n = 461 357; 2010‐2013), and external validation using the Surveillance, Epidemiology and End Results database (n = 106 595; 2014‐2015) were assessed using the concordance index for evaluation of survival prediction. A Cox proportional hazards model was used for overall survival by considering identified phenotypes (latent classes) and other confounding variables. Latent class analysis was performed for phenotype identification, where Bayesian information criterion (BIC) and sample‐size‐adjusted BIC were used to select the optimal number of distinct clusters. Kappa coefficients assessed external cluster validation. Latent class analysis identified five metastatic phenotypes with differences in overall survival (P < .0001): (Stage IVA) nearly exclusive bone‐only metastases (n = 59 049, 12.8%; median survival 12.7 months; common in lung, breast and prostate cancers); (IVB) predominant lung metastases (n = 62 491, 13.5%; 11.4 months; common in breast, stomach, kidney, ovary, uterus, thyroid, cervix and soft tissue cancers); (IVC) predominant liver/lung metastases (n = 130 014, 28.2%; 7.0 months; common in colorectum, pancreatic, lung, esophagus and stomach cancers); (IVD) bone/liver/lung metastases predominant over brain (n = 61 004, 13.2%; 5.9 months; common in lung and breast cancers); and (IVE) brain/lung metastases predominant over bone/liver (n = 148 799, 32.3%; 5.7 months; lung cancer and melanoma). Long‐term survivors were identified, particularly in Stages IVA‐B. A pan‐cancer nomogram model to predict survival (STARS: site, tumor, age, race, sex) was created, validated and provides 13% better prognostication than AJCC: 1‐month concordance index of 0.67 (95% confidence interval [CI]: 0.66‐0.67) vs 0.61 (95% CI: 0.60‐0.61). STARS is simple, uses easily accessible variables, better prognosticates survival outcomes and provides a platform to develop novel metastasis‐directed clinical trials.

Abstract There are few data on the quality of cancer treatment information available on social media. Here, we quantify the accuracy of cancer treatment information on social media and its potential for harm. Two cancer experts reviewed 50 of the most popular social media articles on each of the 4 most common cancers. The proportion of misinformation and potential for harm were reported for all 200 articles and their association with the number of social media engagements using a 2-sample Wilcoxon rank-sum test. All statistical tests were 2-sided. Of 200 total articles, 32.5% (n = 65) contained misinformation and 30.5% (n = 61) contained harmful information. Among articles containing misinformation, 76.9% (50 of 65) contained harmful information. The median number of engagements for articles with misinformation was greater than factual articles (median [interquartile range] = 2300 [1200-4700] vs 1600 [819-4700], P = .05). The median number of engagements for articles with harmful information was statistically significantly greater than safe articles (median [interquartile range] = 2300 [1400-4700] vs 1500 [810-4700], P = .007).

In medical research, the Brier score (BS) and the area under the receiver operating characteristic (ROC) curves (AUC) are two common metrics used to evaluate prediction models of a binary outcome, such as using biomarkers to predict the risk of developing a disease in the future. The assessment of an existing prediction models using data with missing covariate values is challenging. In this article, we propose inverse probability weighted (IPW) and augmented inverse probability weighted (AIPW) estimates of AUC and BS to handle the missing data. An alternative approach uses multiple imputation (MI), which requires a model for the distribution of the missing variable. We evaluated the performance of IPW and AIPW in comparison with MI in simulation studies under missing completely at random, missing at random, and missing not at random scenarios. When there are missing observations in the data, MI and IPW can be used to obtain unbiased estimates of BS and AUC if the imputation model for the missing variable or the model for the missingness is correctly specified. MI is more efficient than IPW. Our simulation results suggest that AIPW can be more efficient than IPW, and also achieves double robustness from miss‐specification of either the missingness model or the imputation model. The outcome variable should be included in the model for the missing variable under all scenarios, while it only needs to be included in missingness model if the missingness depends on the outcome. We illustrate these methods using an example from prostate cancer.

The NCCN Guidelines for Prostate Cancer address staging and risk assessment after a prostate cancer diagnosis and include management options for localized, regional, and metastatic disease. Recommendations for disease monitoring and treatment of recurrent disease are also included. The NCCN Prostate Cancer Panel meets annually to reevaluate and update their recommendations based on new clinical data and input from within NCCN Member Institutions and from external entities. This article summarizes the panel’s discussions for the 2021 update of the guidelines with regard to systemic therapy for metastatic castration-resistant prostate cancer.

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.

Abstract New approaches are needed to overcome intrinsic therapy resistance in glioblastoma (GBM). Because GBMs exhibit sexual dimorphism and are reported to express steroid hormone receptors, we reasoned that signaling through the androgen receptor (AR) could mediate therapy resistance in GBM, as it does in AR-positive prostate and breast cancers. Using RNAseq, immunoblot and immunohistochemistry, we found that nearly half of GBM cell lines, patient-derived xenografts and human tumors express AR transcript and protein with levels that overlap those of primary prostate cancer. AR expression in GBM did not vary by sex, age or common molecular alterations. We identified two cell line models of GBM that expressed AR protein (LN18 and T98G: termed “AR positive”) and two that did not (8MGBA and AM38: termed “AR negative”). Seviteronel, a blood-brain barrier permeable CYP17 lyase inhibitor and antiandrogen slowed growth in AR positive GBM cell lines (GI50 3-4 µM) but not AR negative lines (GI50 &gt; 500 µM) as measured by the colony formation assay. The antiandrogen enzalutamide, which also penetrates the blood brain barrier, similarly preferentially slowed growth in AR positive GBM cell lines. Seviteronel and enzalutamide sensitized AR positive GBM cell lines to radiation with enhancement ratios of 1.3-1.6 as measured by the clonogenic survival assay. Antiandrogens had no effect on the radiosensitivity of AR negative GBM cell lines. Seviteronel treatment did not affect the growth of AR positive T98G xenografts grown in vivo, but did sensitize these tumors to radiation (median time to tripling: 15 d with radiation alone and not reached with radiation combined with seviteronel). Enzalutamide similarly had modest single agent effects on an AR positive GBM patient-derived xenograft (GBM26 from the Mayo Clinic GBM PDX national resource) grown in vivo but sensitized these tumors to radiation (median time to tripling: 25.5 d with radiation alone and 39 d with radiation combined with enzalutamide). RNAseq performed on GBM26 tumors grown in vivo revealed that enzalutamide treatment caused minimal transcriptional changes when given as monotherapy but, when given in combination with radiation, blocked the ability of AR-positive GBMs to engage adaptive transcriptional programs related to multiple DNA repair pathways. We confirmed these mechanistic findings in vitro, as antiandrogens selectively impaired the repair of radiation-induced double strand DNA breaks in AR positive GBM cell lines. These results suggest that AR signaling may mediate therapy resistance in AR positive GBMs, and patients with these tumors could derive clinical benefit from combination therapies involving radiation and blood-brain-barrier permeable antiandrogens. Citation Format: Christian K. Werner, Uchechi Nna, Hanshi Sun, Kari Wilder-Romans, Joseph Dresser, Ayesha Kothari, Weihua Zhou, Yangyang Yao, Arvind Rao, Stefanie Stallard, Carl Koschmann, Tarik Bor, Waldemar Debinski, Alexander Hegedus, Meredith Morgan, Sriram Venneti, Edwina Baskin-Bey, Daniel Spratt, Howard Colman, Jann Sarkaria, Arul Chinnayain, Joel Eisner, Corey Speers, Theodore S. Lawrence, Roy Strowd, Daniel R. Wahl. Repurposing antiandrogens to overcome therapy resistance in androgen receptor-positive glioblastoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6267.