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Objective: Neuromyelitis optica spectrum disorders (NMOSD) represent rare autoimmune diseases of the central nervous system largely targeting optic nerve(s) and spinal cord. The present analysis used real-world data to identify clinical and epidemiological correlates of treatment change in patients with NMOSD. Methods: CIRCLES is a longitudinal, observational study of NMOSD conducted at 15 centers across North America. Patients with ≥ 60 days of follow-up and receiving on-study maintenance treatment were evaluated. The mean annual relapse rate (ARR) was estimated using negative binomial models; the likelihood of treatment change was estimated using Cox proportional hazards models. Relapses were included as time-varying covariates to estimate the relationship to treatment change. Results: Of 542 patients included, 171 (31.5%) experienced ≥ 1 relapse on the study and 133 patients (24.5%) had ≥ 1 change in the treatment regimen. Two categories of variables significantly correlated with the likelihood of treatment change: (1) relapse: any on-study relapse (hazard ratio [HR] = 2.91; p < 0.001), relapse phenotypes (HR range = 2.15–5.49; p < 0.001), and pre-study ARR > 0.75 (HR 2.28; p < 0.001); 2) disease phenotype: brain syndrome only vs transverse myelitis involvement at onset (HR 2.44; p = 0.008), disease duration < 1 vs > 5 years (HR 1.66; p = 0.028), or autoimmune comorbidity (HR 1.55; p = 0.015). A subset of these factors significantly correlated with shorter time to first rituximab discontinuation. Conclusions: In CIRCLES, relapse patterns and disease phenotype significantly correlated with changes in the maintenance treatment regimen. Such findings may facilitate the identification of patients with NMOSD who are likely to benefit from treatment change to reduce relapse risk or disease burden and enhance the quality of life. [ABSTRACT FROM AUTHOR]

Background Although unopposed estrogen exposure is considered a major driver of endometrial carcinogenesis, chronic inflammation and insulin resistance and hyperinsulinemia are also major endometrial cancer risk factors. However, it is unclear whether diets with inflammatory or insulinemic potential are associated with risk of endometrial cancer. Methods We followed 48 330 women from the Nurses' Health Study (1984-2016) and 85 426 women from the Nurses' Health Study II (1989-2017). Using food frequency questionnaires, we calculated repeated measures of empirical dietary inflammatory pattern (EDIP) and empirical dietary index for hyperinsulinemia (EDIH) scores, which characterize the potential of the whole diet to modulate circulating biomarkers of inflammation or C-peptide, respectively. We used multivariable-adjusted Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for type I endometrial cancer risk. Results We documented 1462 type I endometrial cancer cases over 2 823 221 person-years of follow-up. In the pooled multivariable-adjusted analyses, women in the highest compared with lowest quintiles were at higher risk of type I endometrial cancer (EDIP HRQ5vsQ1 = 1.46, 95% CI = 1.24 to 1.73; P trend <.001; EDIH HRQ5vsQ1 = 1.58, 95% CI = 1.34 to 1.87; P trend <.001). Additional adjustment for body mass index attenuated the associations (EDIP HR = 1.03, 95% CI = 0.87 to 1.22; EDIH HR = 1.01, 95% CI = 0.85 to 1.21), and mediation analyses showed that body mass index may explain 60.4% (95% CI = 37.4% to 79.6%; P <.001) and 71.8% (95% CI = 41.0% to 90.4%; P <.001) of the association of endometrial cancer with EDIP and EDIH, respectively. Conclusions In this large cohort study, higher dietary inflammatory and insulinemic potential were each associated with increased endometrial cancer incidence, and this association may be almost entirely mediated by adiposity. [ABSTRACT FROM AUTHOR]

Simple Summary: Colorectal cancer risk and survival have previously been associated with telomere length in peripheral blood leukocytes and tumor tissues. We quantitatively assessed these associations through a systematic review and meta-analysis. Following PRISMA guidelines, we identified relevant studies through database searches, and performed meta-analyses using random effects models. We found no association between telomere length in circulating leukocytes and the risk of developing colorectal cancer, however, shorter leukocyte telomeres were associated with a worse survival in patients with colorectal cancer. Therefore, telomere length may serve as a potential biomarker especially for colorectal cancer prognosis. Larger prospective cohort studies are needed to further confirm this potential association. (1) Background: Colorectal cancer risk and survival have previously been associated with telomere length in peripheral blood leukocytes and tumor tissue. A systematic review and meta-analysis of the literature was conducted. The PubMed, Embase, and Web of Science databases were searched through March 2022. (2) Methods: Relevant studies were identified through database searching following PRISMA guidelines. Risk estimates were extracted from identified studies; meta-analyses were conducted using random effects models. (3) Results: Fourteen studies were identified (eight on risk; six on survival) through systematic review. While no association was observed between circulating leukocyte telomere length and the risk of colorectal cancer [overall OR (95% CI) = 1.01 (0.82–1.24)], a worse survival for those with shorter telomeres in leukocytes and longer telomeres in tumor tissues was observed [Quartile1/Quartile2–4 overall HR (95% CI) = 1.41 (0.26–7.59) and 0.82 (0.69–0.98), respectively]. (4) Conclusions: Although there was no association with colorectal cancer risk, a poorer survival was observed among those with shorter leukocyte telomere length. Future larger studies evaluating a potentially non-linear relationship between telomeres and colorectal cancer are needed. [ABSTRACT FROM AUTHOR]

Background: Oncology provider discussions of treatment options, outcomes of treatment, and end of life planning are essential to care for patients with advanced malignancies. Studies have shown that despite this, many patients do not have adequate care planning, including end of life planning. It is thought that the accessibility of information outside of clinical encounters and individual factors and/or beliefs may influence the patient's perception of disease. Aims: The objective of this study was to evaluate if patient understanding of treatment goals matched the provider and if there were areas of discrepancy. If a discrepancy was found, the survey inquired further into more specific aspects. Methods: A questionnaire-based survey was performed at a cancer hospital outpatient clinic. 100 consecutive and consenting patients who had stage IV non-curable lung, gastrointestinal (GI), or other cancer were included in the study. Patients must have had at least 2 visits with their oncologist. Results: 40 patients reported their disease might be curable and 60 reported their disease was not curable. Patients who reported their disease was not curable were more likely to be 65 years or older (P-value: 0.055). They were more likely to report that their doctor discussed the possibility of their cancer getting worse (78.3% VS 55%; P-value 0.024), that their doctor discussed end of life plans (58.3% VS 30%; P- value: 0.01), and that they had appointed a health care decision-maker (86.7% VS 62.5%; P-value: 0.01). 65% of patients who thought their disease might be curable reported that their doctor said it might be curable, compared with only 6.7% of patients who thought their disease was not curable (p < 0.001). Or, equivalently, 35% of patients who thought their disease might be curable reported that their doctor's opinion was that it was not curable, compared with 93% of patients who thought their disease was not curable (p < 0.001). Patients who had lung cancer were more likely to believe their cancer was not curable than patients with gastrointestinal or other cancer, though the difference was not statistically significant (p = 0.165). Patients who said their disease might be curable selected as possible reasons that a miracle (50%) or alternative medicine (66.7%) would get rid of the cancer, or said their family wanted them to believe the cancer would go away (16.7%) or that another doctor said it would (4.2%). Patients who said their disease might be curable said they did so due to alternative medications, another doctor, or their family. Restricting to the 70 patients who reported their doctors telling them their disease was not curable, 20% of them still said that they personally felt their disease might be curable. Patients below 65 years of age were more likely to disagree with the doctor in this case (P-value: 0.047). Conclusion: This survey of patients diagnosed with stage IV cancer shows that a significant number of patients had misunderstandings of the treatment and curability of their disease. Findings suggest that a notable proportion kept these beliefs even after being told by treating physicians that their disease is not curable. [ABSTRACT FROM AUTHOR]

Circulating insulin-like growth factor-1 (IGF-1) is consistently associated with prostate cancer risk. IGF-1 binds to IGF-1 receptor (IGF1R) and insulin receptor (IR), activating cancer hallmark pathways. Experimental evidence suggests that TMPRSS2:ERG may interact with IGF/insulin signaling to influence progression. We investigated IGF1R and IR expression and its association with lethal prostate cancer among 769 men. Protein expression of IGF1R, IR and ERG (i.e. a surrogate of ERG fusion genes) were assayed by immunohistochemistry. Cox models estimated hazard ratios (HR) and 95% confidence intervals (CI) adjusted for clinical characteristics. Among patients, 29% had strong tumor IGF1R expression and 10% had strong IR expression. During a mean follow-up of 13.2 years through 2012, 80 men (11%) developed lethal disease. Tumors with strong IGF1R or IR expression showed increased cell proliferation, decreased apoptosis and a higher prevalence of ERG. In multivariable models, strong IGF1R was associated with a borderline increased risk of lethal prostate cancer (HR 1.7; 95% CI 0.9–3.1). The association appeared greater in ERG-positive tumors (HR 2.8; 95% CI 0.9–8.4) than in ERG-negative tumors (HR 1.3; 95% CI 0.6–3.0, p-heterogeneity 0.08). There was no association between IR and lethal prostate cancer (HR 0.8; 95% CI 0.4–1.9). These results suggest that tumor IGF1R expression may play a role in prostate cancer progression to a lethal phenotype and that ERG-positive tumors may be more sensitive to IGF signaling. These data may improve our understanding of IGF signaling in prostate cancer and suggest therapeutic options for disease subtypes. [ABSTRACT FROM AUTHOR]

Objective: Standard approaches for large scale phenotypic screens using electronic health record (EHR) data apply thresholds, such as ≥2 diagnosis codes, to define subjects as having a phenotype. However, the variation in the accuracy of diagnosis codes can impair the power of such screens. Our objective was to develop and evaluate an approach which converts diagnosis codes into a probability of a phenotype (PheProb). We hypothesized that this alternate approach for defining phenotypes would improve power for genetic association studies.Methods: The PheProb approach employs unsupervised clustering to separate patients into 2 groups based on diagnosis codes. Subjects are assigned a probability of having the phenotype based on the number of diagnosis codes. This approach was developed using simulated EHR data and tested in a real world EHR cohort. In the latter, we tested the association between low density lipoprotein cholesterol (LDL-C) genetic risk alleles known for association with hyperlipidemia and hyperlipidemia codes (ICD-9 272.x). PheProb and thresholding approaches were compared.Results: Among n = 1462 subjects in the real world EHR cohort, the threshold-based p-values for association between the genetic risk score (GRS) and hyperlipidemia were 0.126 (≥1 code), 0.123 (≥2 codes), and 0.142 (≥3 codes). The PheProb approach produced the expected significant association between the GRS and hyperlipidemia: p = .001.Conclusions: PheProb improves statistical power for association studies relative to standard thresholding approaches by leveraging information about the phenotype in the billing code counts. The PheProb approach has direct applications where efficient approaches are required, such as in Phenome-Wide Association Studies. [ABSTRACT FROM AUTHOR]

Attempts to predict prognosis in cancer patients using high-dimensional genomic data such as gene expression in tumor tissue can be made difficult by the large number of features and the potential complexity of the relationship between features and the outcome. Integrating prior biological knowledge into risk prediction with such data by grouping genomic features into pathways and networks reduces the dimensionality of the problem and could improve prediction accuracy. Additionally, such knowledge-based models may be more biologically grounded and interpretable. Prediction could potentially be further improved by allowing for complex nonlinear pathway effects. The kernel machine framework has been proposed as an effective approach for modeling the nonlinear and interactive effects of genes in pathways for both censored and noncensored outcomes. When multiple pathways are under consideration, one may efficiently select informative pathways and aggregate their signals via multiple kernel learning (MKL), which has been proposed for prediction of noncensored outcomes. In this paper, we propose MKL methods for censored survival outcomes. We derive our approach for a general survival modeling framework with a convex objective function and illustrate its application under the Cox proportional hazards and semiparametric accelerated failure time models. Numerical studies demonstrate that the proposed MKL-based prediction methods work well in finite sample and can potentially outperform models constructed assuming linear effects or ignoring the group knowledge. The methods are illustrated with an application to 2 cancer data sets. [ABSTRACT FROM AUTHOR]

In cancer studies, patients often experience two different types of events: a non-terminal event such as recurrence or metastasis, and a terminal event such as cancer-specific death. Identifying pathways and networks of genes associated with one or both of these events is an important step in understanding disease development and targeting new biological processes for potential intervention. These correlated outcomes are commonly dealt with by modeling progression-free survival, where the event time is the minimum between the times of recurrence and death. However, identifying pathways only associated with progression-free survival may miss out on pathways that affect time to recurrence but not death, or vice versa. We propose a combined testing procedure for a pathway's association with both the cause-specific hazard of recurrence and the marginal hazard of death. The dependency between the two outcomes is accounted for through perturbation resampling to approximate the test's null distribution, without any further assumption on the nature of the dependency. Even complex non-linear relationships between pathways and disease progression or death can be uncovered thanks to a flexible kernel machine framework. The superior statistical power of our approach is demonstrated in numerical studies and in a gene expression study of breast cancer. [ABSTRACT FROM AUTHOR]

Background: Obese men are at higher risk of advanced prostate cancer and cancer-specific mortality; however, the biology underlying this association remains unclear. This study examined gene expression profiles of prostate tissue to identify biological processes differentially expressed by obesity status and lethal prostate cancer.Methods: Gene expression profiling was performed on tumor (n = 402) and adjacent normal (n = 200) prostate tissue from participants in 2 prospective cohorts who had been diagnosed with prostate cancer from 1982 to 2005. Body mass index (BMI) was calculated from the questionnaire immediately preceding cancer diagnosis. Men were followed for metastases or prostate cancer-specific death (lethal disease) through 2011. Gene Ontology biological processes differentially expressed by BMI were identified using gene set enrichment analysis. Pathway scores were computed by averaging the signal intensities of member genes. Odds ratios (ORs) for lethal prostate cancer were estimated with logistic regression.Results: Among 402 men, 48% were healthy weight, 31% were overweight, and 21% were very overweight/obese. Fifteen gene sets were enriched in tumor tissue, but not normal tissue, of very overweight/obese men versus healthy-weight men; 5 of these were related to chromatin modification and remodeling (false-discovery rate < 0.25). Patients with high tumor expression of chromatin-related genes had worse clinical characteristics (Gleason grade > 7, 41% vs 17%; P = 2 × 10-4 ) and an increased risk of lethal disease that was independent of grade and stage (OR, 5.26; 95% confidence interval, 2.37-12.25).Conclusions: This study improves our understanding of the biology of aggressive prostate cancer and identifies a potential mechanistic link between obesity and prostate cancer death that warrants further study. Cancer 2017;123:4130-4138. © 2017 American Cancer Society. [ABSTRACT FROM AUTHOR]

When a moderate number of potential predictors are available and a survival model is fit with regularization to achieve variable selection, providing accurate inference on the predicted survival can be challenging. We investigate inference on the predicted survival estimated after fitting a Cox model under regularization guaranteeing the oracle property. We demonstrate that existing asymptotic formulas for the standard errors of the coefficients tend to underestimate the variability for some coefficients, while typical resampling such as the bootstrap tends to overestimate it; these approaches can both lead to inaccurate variance estimation for predicted survival functions. We propose a two-stage adaptation of a resampling approach that brings the estimated error in line with the truth. In stage 1, we estimate the coefficients in the observed data set and in [Formula: see text] resampled data sets, and allow the resampled coefficient estimates to vote on whether each coefficient should be 0. For those coefficients voted as zero, we set both the point and interval estimates to [Formula: see text] In stage 2, to make inference about coefficients not voted as zero in stage 1, we refit the penalized model in the observed data and in the [Formula: see text] resampled data sets with only variables corresponding to those coefficients. We demonstrate that ensemble voting-based point and interval estimators of the coefficients perform well in finite samples, and prove that the point estimator maintains the oracle property. We extend this approach to derive inference procedures for survival functions and demonstrate that our proposed interval estimation procedures substantially outperform estimators based on asymptotic inference or standard bootstrap. We further illustrate our proposed procedures to predict breast cancer survival in a gene expression study. [ABSTRACT FROM AUTHOR]

Prostate tumors arise primarily in the peripheral zone (PZ) of the prostate, but 20–30% arise in the transition zone (TZ). Zone of origin may have prognostic value or reflect distinct molecular subtypes; however, it can be difficult to determine in practice. Using whole-genome gene expression, we built a signature of zone using normal tissue from five individuals and found that it successfully classified nine tumors of known zone. Hypothesizing that this signature captures tumor zone of origin, we assessed its relationship with clinical factors among 369 tumors of unknown zone from radical prostatectomies (RPs) and found that tumors that molecularly resembled TZ tumors showed lower mortality (P = 0.09) that was explained by lower Gleason scores (P = 0.009). We further applied the signature to an earlier study of 88 RP and 333 transurethral resection of the prostate (TURP) tumor samples, also of unknown zone, with gene expression on ~6000 genes. We had observed previously substantial expression differences between RP and TURP specimens, and hypothesized that this might be because RPs capture primarily PZ tumors, whereas TURPs capture more TZ tumors. Our signature distinguished these two groups, with an area under the receiver operating characteristic curve of 87% (P < 0.0001). Our findings that zonal differences in normal tissue persist in tumor tissue and that these differences are associated with Gleason score and sample type suggest that subtypes potentially resulting from different etiologic pathways might arise in these zones. Zone of origin may be important to consider in prostate tumor biomarker research. [ABSTRACT FROM AUTHOR]

To investigate the role of adiponectin receptor 2 (AdipoR2) in aggressive prostate cancer we used immunohistochemistry to characterize AdipoR2 protein expression in tumor tissue for 866 men with prostate cancer from the Physicians’ Health Study and the Health Professionals Follow-up Study. AdipoR2 tumor expression was not associated with measures of obesity, pathological tumor stage or prostate-specific antigen (PSA) at diagnosis. However, AdipoR2 expression was positively associated with proliferation as measured by Ki-67 expression quartiles (P-trend < 0.0001), with expression of fatty acid synthase (P-trend = 0.001), and with two measures of angiogenesis (P-trend < 0.1). An inverse association was observed with apoptosis as assessed by the TUNEL assay (P-trend = 0.006). Using Cox proportional hazards regression and controlling for age at diagnosis, Gleason score, year of diagnosis category, cohort and baseline BMI, we identified a statistically significant trend for the association between quartile of AdipoR2 expression and lethal prostate cancer (P-trend = 0.02). The hazard ratio for lethal prostate cancer for the two highest quartiles, as compared to the two lowest quartiles, of AdipoR2 expression was 1.9 (95% confidence interval [CI]: 1.2–3.0). Results were similar when additionally controlling for categories of PSA at diagnosis and Ki-67 expression quartiles. These results strengthen the evidence for the role of AdipoR2 in prostate cancer progression. [ABSTRACT FROM AUTHOR]

To reduce costs and improve clinical relevance of genetic studies, there has been increasing interest in performing such studies in hospital-based cohorts by linking phenotypes extracted from electronic medical records (EMRs) to genotypes assessed in routinely collected medical samples. A fundamental difficulty in implementing such studies is extracting accurate information about disease outcomes and important clinical covariates from large numbers of EMRs. Recently, numerous algorithms have been developed to infer phenotypes by combining information from multiple structured and unstructured variables extracted from EMRs. Although these algorithms are quite accurate, they typically do not provide perfect classification due to the difficulty in inferring meaning from the text. Some algorithms can produce for each patient a probability that the patient is a disease case. This probability can be thresholded to define case-control status, and this estimated case-control status has been used to replicate known genetic associations in EMR-based studies. However, using the estimated disease status in place of true disease status results in outcome misclassification, which can diminish test power and bias odds ratio estimates. We propose to instead directly model the algorithm-derived probability of being a case. We demonstrate how our approach improves test power and effect estimation in simulation studies, and we describe its performance in a study of rheumatoid arthritis. Our work provides an easily implemented solution to a major practical challenge that arises in the use of EMR data, which can facilitate the use of EMR infrastructure for more powerful, cost-effective, and diverse genetic studies. [ABSTRACT FROM AUTHOR]

Integrating genomic information with traditional clinical risk factors to improve the prediction of disease outcomes could profoundly change the practice of medicine. However, the large number of potential markers and possible complexity of the relationship between markers and disease make it difficult to construct accurate risk prediction models. Standard approaches for identifying important markers often rely on marginal associations or linearity assumptions and may not capture non-linear or interactive effects. In recent years, much work has been done to group genes into pathways and networks. Integrating such biological knowledge into statistical learning could potentially improve model interpretability and reliability. One effective approach is to employ a kernel machine (KM) framework, which can capture nonlinear effects if nonlinear kernels are used (Scholkopf and Smola, 2002; Liu et al., 2007, 2008). For survival outcomes, KM regression modeling and testing procedures have been derived under a proportional hazards (PH) assumption (Li and Luan, 2003; Cai, Tonini, and Lin, 2011). In this article, we derive testing and prediction methods for KM regression under the accelerated failure time (AFT) model, a useful alternative to the PH model. We approximate the null distribution of our test statistic using resampling procedures. When multiple kernels are of potential interest, it may be unclear in advance which kernel to use for testing and estimation. We propose a robust Omnibus Test that combines information across kernels, and an approach for selecting the best kernel for estimation. The methods are illustrated with an application in breast cancer. [ABSTRACT FROM AUTHOR]

A common treatment of advanced prostate cancer involves the deprivation of androgens. Despite the initial response to hormonal therapy, eventually all the patients relapse. In the present study, we sought to determine whether dietary polyunsaturated fatty acid (PUFA) affects the development of castration-resistant prostate cancer. Cell culture, patient tissue microarray, allograft, xenograft, prostate-specific Pten knockout and omega-3 desaturase transgenic mouse models in conjunction with dietary manipulation, gene knockdown and knockout approaches were used to determine the effect of dietary PUFA on castration-resistant Pten-null prostate cancer. We found that deletion of Pten increased androgen receptor (AR) expression and Pten-null prostate cells were castration resistant. Omega-3 PUFA slowed down the growth of castration-resistant tumors as compared with omega-6 PUFA. Omega-3 PUFA decreased AR protein to a similar extent in tumor cell cytosolic and nuclear fractions but had no effect on AR messenger RNA level. Omega-3 PUFA treatment appeared to accelerate AR protein degradation, which could be blocked by proteasome inhibitor MG132. Knockdown of AR significantly slowed down prostate cancer cell proliferation in the absence of androgens. Our data suggest that omega-3 PUFA inhibits castration-resistant prostate cancer in part by accelerating proteasome-dependent degradation of the AR protein. Dietary omega-3 PUFA supplementation in conjunction with androgen ablation may significantly delay the development of castration-resistant prostate cancer in patients compared with androgen ablation alone. [ABSTRACT FROM PUBLISHER]

Including previously genotyped controls in a genome-wide association study can provide cost-savings, but can also create design biases. When cases and controls are genotyped on different platforms, the imputation needed to provide genome-wide coverage will introduce differential measurement error and may lead to false positives. We compared genotype frequencies of two healthy control groups from the Nurses' Health Study genotyped on different platforms [Affymetrix 6.0 ( n = 1,672) and Illumina HumanHap550 ( n = 1,038)]. Using standard imputation quality filters, we observed 9,841 single-nucleotide polymorphisms (SNPs) out of 2,347,809 (0.4%) significant at the 5 × 10 level. We explored three methods for controlling for this Type I error inflation. One method was to remove platform effects using principal components; another was to restrict to SNPs of highest quality imputation; and a third was to genotype some controls alongside cases to exclude SNPs that are statistical artifact. The first method could not reduce the Type I error rate; the other two could dramatically reduce the error rate, although both required that a portion of SNPs be excluded from analysis. Ideally, the biases we describe would be eliminated at the design stage, by genotyping sufficient numbers of cases and controls on each platform. Researchers using imputation to combine samples genotyped on different platforms with severely unbalanced case-control ratios should be aware of the potential for inflated Type I error rates and apply appropriate quality filters. Every SNP found with genome-wide significance should be validated on another platform to verify that its significance is not an artifact of study design. [ABSTRACT FROM AUTHOR]

Simple Summary: Novel hormonal therapies (such as abiraterone and enzalutamide) and docetaxel are approved treatments for metastatic prostate cancer. Upfront use of these agents has been shown to improve overall survival. However, we do not know the real-world treatment patterns of these agents or the comparative effectiveness of these agents after treatment with a prior novel hormonal therapy in patients with metastatic prostate cancer. In this large study, we found that most patients with metastatic prostate cancer received only androgen deprivation therapy as upfront therapy without novel hormonal therapies or docetaxel. In patients treated with one novel hormonal therapy, alternate novel hormonal therapy was the most common next therapy and was associated with improved overall survival over docetaxel with the caveat of this being a non-randomized comparison. The study's limitations also include its retrospective design. Background: Both novel hormonal therapies and docetaxel are approved for treatment of metastatic prostate cancer (mPC; in castration sensitive or refractory settings). Present knowledge gaps include lack of real-world data on treatment patterns in patients with newly diagnosed mPC, and comparative effectiveness of novel hormonal therapies (NHT) versus docetaxel after treatment with a prior NHT. Methods: Herein we extracted patient-level data from a large real-world database of patients with mPC in United States. Utilization of NHT or docetaxel for mPC and comparative effectiveness of an alternate NHT versus docetaxel after one prior NHT was evaluated. Comparative effectiveness was examined via Cox proportional hazards model with propensity score matching weights. Each patient's propensity for treatment was modeled via random forest based on 22 factors potentially driving treatment selection. Results: The majority of patients (54%) received only androgen deprivation therapy for mPC. In patients treated with an NHT, alternate NHT was the most common next therapy and was associated with improved median overall survival over docetaxel (abiraterone followed by docetaxel vs. enzalutamide (8.7 vs. 15.6 months; adjusted hazards ratio; aHR 1.32; p = 0.009; and enzalutamide followed by docetaxel vs. abiraterone (9.7 vs. 13.2 months aHR 1.40; p = 0.009). Limitations of the study include retrospective design. [ABSTRACT FROM AUTHOR]