Marital Status, Living Arrangement, and Survival among Individuals with Advanced Prostate Cancer in the International Registry for Men with Advanced Prostate Cancer.

BACKGROUND: Studies have shown improved survival among individuals with cancer with higher levels of social support. Few studies have investigated social support and overall survival (OS) in individuals with advanced prostate cancer in an international cohort. We investigated the associations of marital status and living arrangements with OS among individuals with advanced prostate cancer in the International Registry for Men with Advanced Prostate Cancer (IRONMAN). METHODS: IRONMAN is enrolling participants diagnosed with advanced prostate cancer (metastatic hormone-sensitive prostate cancer, mHSPC; castration-resistant prostate cancer, CRPC) from 16 countries. Participants in this analysis were recruited between July 2017 and January 2023. Adjusting for demographics and tumor characteristics, the associations were estimated using Cox regression and stratified by disease state (mHSPC, CRPC), age (<70, ≥70 years), and continent of enrollment (North America, Europe, Other). RESULTS: We included 2,119 participants with advanced prostate cancer, of whom 427 died during up to 5 years of follow-up (median 6 months). Two-thirds had mHSPC. Most were married/in a civil partnership (79%) and 6% were widowed. Very few married participants were living alone (1%), while most unmarried participants were living alone (70%). Married participants had better OS than unmarried participants [adjusted HR: 1.44; 95% confidence interval (CI): 1.02-2.02]. Widowed participants had the worst survival compared with married individuals (adjusted HR: 1.89; 95% CI: 1.22-2.94). CONCLUSIONS: Among those with advanced prostate cancer, unmarried and widowed participants had worse OS compared with married participants. IMPACT: This research highlighted the importance of social support in OS within this vulnerable population.

Pain and Its Association with Survival for Black and White Individuals with Advanced Prostate Cancer in the United States.

Bone pain is a well-known quality-of-life detriment for individuals with prostate cancer and is associated with survival. This study expands previous work into racial differences in multiple patient-reported dimensions of pain and the association between baseline and longitudinal pain and mortality. This is a prospective cohort study of individuals with newly diagnosed advanced prostate cancer enrolled in the International Registry for Men with Advanced Prostate Cancer (IRONMAN) from 2017 to 2023 at U.S. sites. Differences in four pain scores at study enrollment by race were investigated. Cox proportional hazards models and joint longitudinal survival models were fit for each of the scale scores to estimate HRs and 95% confidence intervals (CI) for the association with all-cause mortality. The cohort included 879 individuals (20% self-identifying as Black) enrolled at 38 U.S. sites. Black participants had worse pain at baseline compared with White participants, most notably a higher average pain rating (mean 3.1 vs. 2.2 on a 10-point scale). For each pain scale, higher pain was associated with higher mortality after adjusting for measures of disease burden, particularly for severe bone pain compared with no pain (HR, 2.47; 95% CI: 1.44-4.22). The association between pain and all-cause mortality was stronger for participants with castration-resistant prostate cancer compared with those with metastatic hormone-sensitive prostate cancer and was similar among Black and White participants. Overall, Black participants reported worse pain than White participants, and more severe pain was associated with higher mortality independent of clinical covariates for all pain scales. SIGNIFICANCE: Black participants with advanced prostate cancer reported worse pain than White participants, and more pain was associated with worse survival. More holistic clinical assessments of pain in this population are needed to determine the factors upon which to intervene to improve quality of life and survivorship, particularly for Black individuals.

A harmonized resource of integrated prostate cancer clinical, -omic, and signature features.

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or  not standardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility.

curatedPCaData: Integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource.

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or unstandardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility.

Effect of prior treatments on post-CDK 4/6 inhibitor survival in hormone receptor-positive breast cancer.

PURPOSE: Multiple treatment options exist for patients with metastatic breast cancer (MBC). However, limited information is available on the impact of prior treatment duration and class on survival outcome for novel therapies, such as cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) for patients with hormone receptor-positive, human epidermal growth factor receptor 2-negative (HR+ HER2-) MBC. METHODS: This study used a nationwide, de-identified electronic health record-derived database to identify women with HR+ HER2- MBC who received at least one CDK 4/6i between 2011 and 2020. Hazard ratios (HR) and 95% confidence intervals (CI) were estimated for the association between prior duration and class of cancer treatment (both early-stage and metastatic) and prior CDK 4/6i survival as well as for those with multiple CDK 4/6i. RESULTS: Of 5363 patients, the median survival from first CDK 4/6 inhibitor administration was 3.3 years. When compared to patients with no prior treatments, patients with < 1 year of prior treatment duration had a 30% increased hazard of death (HR, 1.30; 95% CI 1.15-1.46), those with 1 to < 3 years a 68% increased hazard of death (HR 1.68; 95% CI 1.49-1.88), and those with 3 or more years a 55% increased hazard of death (HR 1.55; 95% CI 1.36, 1.76). Patients who received prior therapy (endocrine or chemotherapy) before their CDK 4/6i had worse outcomes than those who received no prior therapy. Similar results were seen when comparing patients in the metastatic setting alone. Finally, patients who received a different CDK 4/6i after their first saw a lower hazard of death compared to patients who received subsequent endocrine or chemotherapy after their first CDK 4/6i. CONCLUSION: Prior treatment duration and class are associated with a decreased overall survival after CDK 4/6 inhibitor administration. This highlights the importance for clinicians to consider prior treatment and duration in treatment decision-making and for trialists to stratify by these factors when randomizing patients or reporting results of future studies.

IRONMAN: A Novel International Registry of Men With Advanced Prostate Cancer.

PURPOSE: To describe a newly established international registry recruiting diverse patients with advanced prostate cancer across academic and community practices to address unmet needs in this population. PATIENTS AND METHODS: Initiated in 2017, IRONMAN (International Registry for Men with Advanced Prostate Cancer) is a prospective cohort of patients with advanced prostate cancer. The study will enroll 5,000 patients with metastatic hormone-sensitive prostate cancer (mHSPC) or castration-resistant prostate cancer (CRPC), recruited from Australia, the Bahamas, Barbados, Brazil, Canada, Ireland, Jamaica, Kenya, Nigeria, Norway, South Africa, Spain, Sweden, Switzerland, the United Kingdom, and the United States. The study is collecting datatypes to study variation in care and treatment of advanced prostate cancer across countries and across academic, community-based, and government practices with a focus on clinical outcomes, patient-reported outcomes, epidemiologic data, biologic subtypes, and clinician questionnaires. RESULTS: Through July 2022, 2,682 eligible patients were enrolled in 11 of 12 active countries. Sixty-six percent of patients have mHSPC, and 34% have CRPC. On the basis of self-report, 11% of patients are Black and 9% are Hispanic. Five Veterans Affairs Medical Centers are enrolling patients. Globally, 23% of patients report being veterans of military service. CONCLUSION: To our knowledge, this is the first international cohort of people newly diagnosed with advanced prostate cancer designed to describe variations in patient management, experiences, and outcomes. IRONMAN aims to identify optimal treatment sequences to improve survival, understand patient-reported outcomes, and explore novel biomarkers to understand treatment resistance mechanisms. Insights from IRONMAN will inform and guide future clinical management of people with mHSPC and CRPC. This cohort study will provide real-world evidence to facilitate a better understanding of the survivorship of people with advanced prostate cancer.

Tumor immune cell clustering and its association with survival in African American women with ovarian cancer.

New technologies, such as multiplex immunofluorescence microscopy (mIF), are being developed and used for the assessment and visualization of the tumor immune microenvironment (TIME). These assays produce not only an estimate of the abundance of immune cells in the TIME, but also their spatial locations. However, there are currently few approaches to analyze the spatial context of the TIME. Therefore, we have developed a framework for the spatial analysis of the TIME using Ripley's K, coupled with a permutation-based framework to estimate and measure the departure from complete spatial randomness (CSR) as a measure of the interactions between immune cells. This approach was then applied to epithelial ovarian cancer (EOC) using mIF collected on intra-tumoral regions of interest (ROIs) and tissue microarrays (TMAs) from 160 high-grade serous ovarian carcinoma patients in the African American Cancer Epidemiology Study (AACES) (94 subjects on TMAs resulting in 263 tissue cores; 93 subjects with 260 ROIs; 27 subjects with both TMA and ROI data). Cox proportional hazard models were constructed to determine the association of abundance and spatial clustering of tumor-infiltrating lymphocytes (CD3+), cytotoxic T-cells (CD8+CD3+), and regulatory T-cells (CD3+FoxP3+) with overall survival. Analysis was done on TMA and ROIs, treating the TMA data as validation of the findings from the ROIs. We found that EOC patients with high abundance and low spatial clustering of tumor-infiltrating lymphocytes and T-cell subsets in their tumors had the best overall survival. Additionally, patients with EOC tumors displaying high co-occurrence of cytotoxic T-cells and regulatory T-cells had the best overall survival. Grouping women with ovarian cancer based on both cell abundance and spatial contexture showed better discrimination for survival than grouping ovarian cancer cases only by cell abundance. These findings underscore the prognostic importance of evaluating not only immune cell abundance but also the spatial contexture of the immune cells in the TIME. In conclusion, the application of this spatial analysis framework to the study of the TIME could lead to the identification of immune content and spatial architecture that could aid in the determination of patients that are likely to respond to immunotherapies.

Prognostic and therapeutic significance of COP9 signalosome subunit CSN5 in prostate cancer.

Chromosome 8q gain is associated with poor clinical outcomes in prostate cancer, but the underlying biological mechanisms remain to be clarified. CSN5, a putative androgen receptor (AR) partner that is located on chromosome 8q, is the key subunit of the COP9 signalosome, which deactivates ubiquitin ligases. Deregulation of CSN5 could affect diverse cellular functions that contribute to tumor development, but there has been no comprehensive study of its function in prostate cancer. The clinical significance of CSN5 amplification/overexpression was evaluated in 16 prostate cancer clinical cohorts. Its oncogenic activity was assessed by genetic and pharmacologic perturbations of CSN5 activity in prostate cancer cell lines. The molecular mechanisms of CSN5 function were assessed, as was the efficacy of the CSN5 inhibitor CSN5i-3 in vitro and in vivo. Finally, the transcription cofactor activity of CSN5 in prostate cancer cells was determined. The prognostic significance of CSN5 amplification and overexpression in prostate cancer was independent of MYC amplification. Inhibition of CSN5 inhibited its oncogenic function by targeting AR signaling, DNA repair, multiple oncogenic pathways, and spliceosome regulation. Furthermore, inhibition of CSN5 repressed metabolic pathways, including oxidative phosphorylation and glycolysis in AR-negative prostate cancer cells. Targeting CSN5 with CSN5i-3 showed potent antitumor activity in vitro and in vivo. Importantly, CSN5i-3 synergizes with PARP inhibitors to inhibit prostate cancer cell growth. CSN5 functions as a transcription cofactor to cooperate with multiple transcription factors in prostate cancer. Inhibiting CSN5 strongly attenuates prostate cancer progression and could enhance PARP inhibition efficacy in the treatment of prostate cancer.

Dynamic expression of SNAI2 in prostate cancer predicts tumor progression and drug sensitivity.

Prostate cancer is a highly heterogeneous disease, understanding the crosstalk between complex genomic and epigenomic alterations will aid in developing targeted therapeutics. We demonstrate that, even though snail family transcriptional repressor 2 (SNAI2) is frequently amplified in prostate cancer, it is epigenetically silenced in this disease, with dynamic changes in SNAI2 levels showing distinct clinical relevance. Integrative clinical data from 18 prostate cancer cohorts and experimental evidence showed that gene fusion between transmembrane serine protease 2 (TMPRSS2) and ETS transcription factor ERG (ERG) (TMPRSS2-ERG fusion) is involved in the silencing of SNAI2. We created a silencer score to evaluate epigenetic repression of SNAI2, which can be reversed by treatment with DNA methyltransferase inhibitors and histone deacetylase inhibitors. Silencing of SNAI2 facilitated tumor cell proliferation and luminal differentiation. Furthermore, SNAI2 has a major influence on the tumor microenvironment by reactivating tumor stroma and creating an immunosuppressive microenvironment in prostate cancer. Importantly, SNAI2 expression levels in part determine sensitivity to the cancer drugs dasatinib and panobinostat. For the first time, we defined the distinct clinical relevance of SNAI2 expression at different disease stages. We elucidated how epigenetic silencing of SNAI2 controls the dynamic changes of SNAI2 expression that are essential for tumor initiation and progression and discovered that restoring SNAI2 expression by treatment with panobinostat enhances dasatinib sensitivity, indicating a new therapeutic strategy for prostate cancer.

Novel Transcriptomic Interactions Between Immune Content and Genomic Classifier Predict Lethal Outcomes in High-grade Prostate Cancer.

Grade group 4 and 5 (GG-45) prostate cancer (PCa) patients are at the highest risk of lethal outcomes, yet lack genomic risk stratification for prognosis and treatment selection. Here, we assess whether transcriptomic interactions between tumor immune content score (ICS) and the Decipher genomic classifier can identify most lethal subsets of GG-45 PCa. We utilized whole transcriptome data from 8071 tumor tissue (6071 prostatectomy and 2000 treatment-naïve biopsy samples) to derive four immunogenomic subtypes using ICS and Decipher. When compared across all grade groups, GG-45 samples had the highest proportion of most aggressive subtype-ICSHigh/DecipherHigh. Subsequent analyses within the GG-45 patient samples (n = 1420) revealed that the ICSHigh/DecipherHigh subtype was associated with increased genomic radiosensitivity. Additionally, in a multivariable model (n = 335), ICSHigh/DecipherHigh subtype had a significantly higher risk of distant metastasis (hazard ratio [HR] = 5.41; 95% confidence interval [CI], 2.76-10.6; p ≤ 0.0001) and PCa-specific mortality (HR = 10.6; 95% CI, 4.18-26.94; p ≤ 0.0001) as compared with ICSLow/DecipherLow. The novel immunogenomic subtypes establish a very strong synergistic interaction between ICS and Decipher in identifying GG-45 patients who experience the most lethal outcomes. PATIENT SUMMARY: In this analysis, we identified a novel interaction between the total immune content of prostate tumors and genomic classifier to identify the most lethal subset of patients with grade groups 4 and 5. Our results will aid in the subtyping of aggressive prostate cancer patients who may benefit from combined immune-radiotherapy modalities.

Prostate Cancer Racial Disparities: A Systematic Review by the Prostate Cancer Foundation Panel.

CONTEXT: Prostate cancer (PCa) is a complex disease that disproportionately impacts Black men in the USA. The structural factors that drive heterogeneous outcomes for patients of differing backgrounds are probably the same ones that result in population-level disparities. The relative contribution of drivers along the PCa disease continuum is an active area of investigation and debate. OBJECTIVE: To critically synthesize the available evidence on PCa disparities from a population-level perspective in comparison to data from "equal access and equal care settings" and to provide a consensus summary of the state of PCa disparities. EVIDENCE ACQUISITION: A plenary panel on PCa disparities presented at the Prostate Cancer Foundation meeting on October 24, 2019 and ensuing discussions are reported here. We used a systematic literature review approach and the Preferred Reporting Items for Systematic Reviews and Meta-analyses to select the most relevant publications. A total of 3333 publications between 2011 and 2021 were retrieved, of which 52 were included in the review; an additional 13 articles on screening guidelines, seminal clinical trials, and statistical methodology were used in the evidence synthesis. EVIDENCE SYNTHESIS: Race disparities in PCa are a result of a complex interaction between socioeconomic factors impacting access to care and ancestral/genetic factors that may influence tumor biology. Black men in the USA continue to have a nearly 1.8 times higher population-level incidence rate than White men. Failure to account for the race-specific incidence burden would continue to lead to residual disparity even after achieving relatively similar outcomes after primary treatment, resulting in a higher long-term mortality burden. Selection bias remains possible in PCa studies, which often rely on highly specific cohorts of Black men with higher use of health care resources that may not represent the average Black patient in the USA. Novel methods including mediation analysis and genetic ancestry rather than self-identified race can optimize analytical models investigating racial disparities and may lead to a better understanding of PCa genomic diversity and behavior. CONCLUSIONS: Our findings emphasize the importance of racially diverse studies, including precision -omics, prevention, and targeted therapy initiatives, to elucidate mechanisms underlying racial differences in outcomes and response to therapy. We propose novel approaches for studying and addressing PCa disparities. Contemporary methods, particularly in the domain of mediation analysis, can promote scientific rigor in understanding these disparities. PATIENT SUMMARY: Inaccurate data interpretation or lack of data altogether for Black men can impact policy and ultimately affect millions of individuals of African origin worldwide. Our review identifies a need to develop and prioritize a strategy for including Black and other men with prostate cancer in intervention studies and randomized clinical trials to halt the widening prostate cancer disparities.

Extent, impact, and mitigation of batch effects in tumor biomarker studies using tissue microarrays.

Tissue microarrays (TMAs) have been used in thousands of cancer biomarker studies. To what extent batch effects, measurement error in biomarker levels between slides, affects TMA-based studies has not been assessed systematically. We evaluated 20 protein biomarkers on 14 TMAs with prospectively collected tumor tissue from 1448 primary prostate cancers. In half of the biomarkers, more than 10% of biomarker variance was attributable to between-TMA differences (range, 1-48%). We implemented different methods to mitigate batch effects (R package batchtma), tested in plasmode simulation. Biomarker levels were more similar between mitigation approaches compared to uncorrected values. For some biomarkers, associations with clinical features changed substantially after addressing batch effects. Batch effects and resulting bias are not an error of an individual study but an inherent feature of TMA-based protein biomarker studies. They always need to be considered during study design and addressed analytically in studies using more than one TMA.

To understand cancer, researchers need to know which molecules tumor cells use. These so-called 'biomarkers' tag cancer cells as being different from healthy cells, and can be used to predict how aggressive a tumor may be, or how well it might respond to treatment. A popular technique for assessing biomarkers across multiple tumors is to use tissue microarrays. This involves taking samples from different tumors and embedding them in a block of wax, which is then cut into micro-thin slices and stained with reagents that can detect specific biomarkers, such as proteins. Each block contains hundreds of samples, which all experience the same conditions. So, any patterns detected in the staining are likely to represent real variations in the biomarkers present. Many cancer studies, however, often compare samples from multiple tissue microarrays, which may increase the risk of technical artifacts: for example, staining may look stronger in one batch of tissue samples than another, even though the amount of biomarker present in these different arrays is roughly the same. These 'batch effects' could potentially bias the results of the experiment and lead to the identification of misleading patterns. To evaluate how batch effects impact tissue microarray studies, Stopsack et al. examined 14 wax blocks which contained tumor samples from 1,448 men with prostate cancer. This revealed that for some biomarkers, but not others, there were noticeable differences between tissue microarrays that were clearly the result of batch effects. Stopsack et al. then tested six different ways of fixing these discrepancies using statistical methods. All six approaches were successful, even if the arrays included tumors with different characteristics, such as tumors that had been diagnosed more or less recently. This work highlights the importance of considering batch effects when using tissue microarrays to study cancer. Stopsack et al. have used their statistical approaches to develop freely available software which can reduce the biases that sometimes arise from these technical artifacts. This could help researchers avoid misleading patterns in their data and make it easier to detect real variations in the biomarkers present between tumor samples.

A prospective study of inflammatory biomarkers and growth factors and risk of glioma in the UK Biobank.

PURPOSE: The role of growth factors and inflammation in the onset of glioma is poorly understood, and conflicting reports of associations of circulating IGF-1 and inflammatory biomarkers with glioma risk exist in the literature. We examined associations between C-reactive protein (CRP), white blood cell count (WBC), neutrophil-to-lymphocyte ratio (NLR), and insulin-like growth factor-1 (IGF-1) and glioma risk in the UK Biobank cohort. METHODS: Hazard ratios (HR) and 95% confidence intervals (CI) for glioma according to circulating biomarkers concentrations were calculated using Cox proportional hazards regression, adjusted for age, sex, race, and education. Analyses were conducted separately for glioma overall and by glioma subtype. RESULTS: We identified 417 incident glioma cases among 428,537 participants with 3,255,815 person-years of follow up. Weak, non-significant associations were observed with increasing levels of these biomarkers for risk of glioma overall or by glioma subtype. Among women only, IGF-1 in the highest quartile was positively associated with glioma risk compared to the lowest quartile (HR=1.64, 95%CI: 1.03-2.60, p-trend=0.08), as was NLR (HR=1.54, 95%CI: 1.00-2.39, p-trend=0.05). CONCLUSION: In this prospective cohort, we found no significant associations between the inflammatory biomarkers CRP and WBC and the development of glioma. NLR and IGF-1 were associated with risk in women, but not men. When considered with previous studies, further investigation of NLR and IGF-1 as markers of glioma risk appears warranted, particularly in women.

A polymorphism in the promoter of FRAS1 is a candidate SNP associated with metastatic prostate cancer.

BACKGROUND: Inflammation and one of its mediators, NF-kappa B (NFκB), have been implicated in prostate cancer carcinogenesis. We assessed whether germline polymorphisms associated with NFκB are associated with the risk of developing lethal disease (metastases or death from prostate cancer). METHODS: Using a Bayesian approach leveraging NFκB biology with integration of publicly available datasets we used a previously defined genome-wide functional association network specific to NFκB and lethal prostate cancer. A dense-module-searching method identified modules enriched with significant genes from a genome-wide association study (GWAS) study in a discovery data set, Physicians' Health Study and Health Professionals Follow-up Study (PHS/HPFS). The top 48 candidate single nucleotide polymorphisms (SNPs) from the dense-module-searching method were then assessed in an independent prostate cancer cohort and the one SNP reproducibly associated with lethality was tested in a third cohort. Logistic regression models evaluated the association between each SNP and lethal prostate cancer. The candidate SNP was assessed for association with lethal prostate cancer in 6 of 28 studies in the prostate cancer association group to investigate cancer associated alterations in the genome (PRACTICAL) Consortium where there was some medical record review for death ascertainment which also had SNP data from the ONCOARRAY platform. All men self-identified as Caucasian. RESULTS: The rs1910301 SNP which was reproducibly associated with lethal disease was nominally associated with lethal disease (odds ratio [OR] = 1.40; p = .02) in the discovery cohort and the minor allele was also associated with lethal disease in two independent cohorts (OR = 1.35; p = .04 and OR = 1.35; p = .07). Fixed effects meta-analysis of all three cohorts found an association: OR = 1.37 (95% confidence interval [CI]: 1.15-1.62, p = .0003). This SNP is in the promoter region of FRAS1, a gene involved in epidermal-basement membrane adhesion and is present at a higher frequency in men with African ancestry. No association was found in the subset of studies from the PRACTICAL consortium studies which had a total of 106 deaths out total of 3263 patients and a median follow-up of 4.4 years. CONCLUSIONS: Through its connection with the NFκB pathway, a candidate SNP with a higher frequency in men of African ancestry without cancer was found to be associated with lethal prostate cancer across three well-annotated independent cohorts of Caucasian men.

Enabling Precision Medicine in Cancer Care Through a Molecular Data Warehouse: The Moffitt Experience.

PURPOSE: The use of genomics within cancer research and clinical oncology practice has become commonplace. Efforts such as The Cancer Genome Atlas have characterized the cancer genome and suggested a wealth of targets for implementing precision medicine strategies for patients with cancer. The data produced from research studies and clinical care have many potential secondary uses beyond their originally intended purpose. Effective storage, query, retrieval, and visualization of these data are essential to create an infrastructure to enable new discoveries in cancer research. METHODS: Moffitt Cancer Center implemented a molecular data warehouse to complement the extensive enterprise clinical data warehouse (Health and Research Informatics). Seven different sequencing experiment types were included in the warehouse, with data from institutional research studies and clinical sequencing. RESULTS: The implementation of the molecular warehouse involved the close collaboration of many teams with different expertise and a use case-focused approach. Cornerstones of project success included project planning, open communication, institutional buy-in, piloting the implementation, implementing custom solutions to address specific problems, data quality improvement, and data governance, unique aspects of which are featured here. We describe our experience in selecting, configuring, and loading molecular data into the molecular data warehouse. Specifically, we developed solutions for heterogeneous genomic sequencing cohorts (many different platforms) and integration with our existing clinical data warehouse. CONCLUSION: The implementation was ultimately successful despite challenges encountered, many of which can be generalized to other research cancer centers.

Distribution of Estimated Lifetime Breast Cancer Risk Among Women Undergoing Screening Mammography.

OBJECTIVE. Supplemental screening breast MRI is recommended for women with an estimated lifetime risk of breast cancer of greater than 20-25%. The performance of risk prediction models varies for each individual and across groups of women. The present study investigates the concordance of three breast cancer risk prediction models among women presenting for screening mammography. SUBJECTS AND METHODS. In this prospective study, we calculated the estimated lifetime risk of breast cancer using the modified Gail, Tyrer-Cuzick version 7, and BRCAPRO models for each woman who presented for screening mammography. Per American Cancer Society guidelines, for each woman the risk was categorized as less than 20% or 20% or greater as well as less than 25% or 25% or greater with use of each model. Venn diagrams were constructed to evaluate concordance across models. The McNemar test was used to test differences in risk group allocations between models, with p ≤ .05 considered to denote statistical significance. RESULTS. Of 3503 screening mammography patients who underwent risk stratification, 3219 (91.9%) were eligible for risk estimation using all three models. Using at least one model, 440 (13.7%) women had a lifetime risk of 20% or greater, including 390 women (12.1%) according to the Tyrer-Cuzick version 7 model, 18 (0.6%) according to the BRCAPRO model, and 141 (4.4%) according to the modified Gail model. Six women (0.2%) had a risk of 20% or greater according to all three models. Women were significantly more likely to be classified as having a high lifetime breast cancer risk by the Tyrer-Cuzick version 7 model compared with the modified Gail model, with thresholds of 20% or greater (odds ratio, 6.4; 95% CI, 4.7-8.7) or 25% or greater (odds ratio, 7.4; 95% CI, 4.7-11.9) used for both models. CONCLUSION. To identify women with a high lifetime breast cancer risk, practices should use estimates of lifetime breast cancer risk derived from multiple risk prediction models.

Tackling Diversity in Prostate Cancer Clinical Trials: A Report From the Diversity Working Group of the IRONMAN Registry.

Prostate cancer disproportionately affects racial and ethnic minority populations. Reasons for disparate outcomes among minority patients are multifaceted and complex, involving factors at the patient, provider, and system levels. Although advancements in our understanding of disease biology have led to novel therapeutics for men with advanced prostate cancer, including the introduction of biomarker-driven therapeutics, pivotal translational studies and clinical trials are underrepresented by minority populations. Despite attempts to bridge the disparities gap, there remains an unmet need to expand minority engagement and participation in clinical trials to better define the impact of therapy on efficacy outcomes, quality of life, and role of biomarkers in diverse patient populations. The IRONMAN registry (ClinicalTrials.gov identifier: NCT03151629), a global, prospective, population-based study, was borne from this unmet medical need to address persistent gaps in our knowledge of advanced prostate cancer. Through integrated collection of clinical outcomes, patient-reported outcomes, epidemiologic data, and biospecimens, IRONMAN has the goal of expanding our understanding of how and why prostate cancer outcomes differ by race and ethnicity. To this end, the Diversity Working Group of the IRONMAN registry has developed informed strategies for site selection, recruitment, engagement and retention, and trial design and eligibility criteria to ensure broad inclusion and needs awareness of minority participants. In concert with systematic strategies to tackle the complex levels of disparate care, our ultimate goal is to expand minority engagement in clinical research and bridge the disparities gap in prostate cancer care.

KLK3 SNP-SNP interactions for prediction of prostate cancer aggressiveness.

Risk classification for prostate cancer (PCa) aggressiveness and underlying mechanisms remain inadequate. Interactions between single nucleotide polymorphisms (SNPs) may provide a solution to fill these gaps. To identify SNP-SNP interactions in the four pathways (the angiogenesis-, mitochondria-, miRNA-, and androgen metabolism-related pathways) associated with PCa aggressiveness, we tested 8587 SNPs for 20,729 cases from the PCa consortium. We identified 3 KLK3 SNPs, and 1083 (P < 3.5 × 10-9) and 3145 (P < 1 × 10-5) SNP-SNP interaction pairs significantly associated with PCa aggressiveness. These SNP pairs associated with PCa aggressiveness were more significant than each of their constituent SNP individual effects. The majority (98.6%) of the 3145 pairs involved KLK3. The 3 most common gene-gene interactions were KLK3-COL4A1:COL4A2, KLK3-CDH13, and KLK3-TGFBR3. Predictions from the SNP interaction-based polygenic risk score based on 24 SNP pairs are promising. The prevalence of PCa aggressiveness was 49.8%, 21.9%, and 7.0% for the PCa cases from our cohort with the top 1%, middle 50%, and bottom 1% risk profiles. Potential biological functions of the identified KLK3 SNP-SNP interactions were supported by gene expression and protein-protein interaction results. Our findings suggest KLK3 SNP interactions may play an important role in PCa aggressiveness.

Circulating lipids and glioma risk: results from the UK Biobank, Nurses' Health Study, and Health Professionals Follow-Up Study.

PURPOSE: Evidence is mixed on whether cholesterol plays a role in the pathogenesis of glioma. We explored the associations between circulating lipids and glioma risk in three prospective cohorts. METHODS: Using prospective data from the UK Biobank, we examined the associations of total cholesterol (TC), high- and low-density lipoprotein cholesterol (HDL-C, LDL-C), and triglycerides (TG) with glioma risk in multivariable (MV)-adjusted Cox proportional hazards models. Within the Nurses' Health Study (NHS) and the Health Professionals Follow-Up Study (HPFS), we carried out a matched, nested case-control study to examine these same associations. RESULTS: In the UK Biobank, 490 gliomas accrued over 2,358,964 person-years. TC was not significantly associated with glioma risk (MV HR = 1.20, 95% CI 0.89-1.61 for highest quartile vs. lowest, p-trend = 0.24). In 4-year lagged analyses (n = 229), higher TC was associated with significantly higher risk of glioma in men (MV HR = 2.26, 95% CI 1.32-3.89, p-trend = 0.002) but not women (MV HR = 1.28, 95% CI 0.61-2.68, p-trend = 0.72); similar findings emerged for HDL-C and, to a lesser extent, LDL-C. In the NHS/HPFS, no significant associations were found between cholesterol and glioma risk. No significant associations were identified for TG. CONCLUSION: In the UK Biobank, higher prediagnostic TC and HDL-C levels were associated with higher risk of glioma in 4-year lagged analyses, but not in non-lagged analyses, in men only. These findings merit further investigation, given that there are few risk factors and no reliable biomarkers of risk identified for glioma.