Bidirectional modulation of TCA cycle metabolites and anaplerosis by metformin and its combination with SGLT2i.

BACKGROUND: Metformin and sodium-glucose-cotransporter-2 inhibitors (SGLT2i) are cornerstone therapies for managing hyperglycemia in diabetes. However, their detailed impacts on metabolic processes, particularly within the citric acid (TCA) cycle and its anaplerotic pathways, remain unclear. This study investigates the tissue-specific metabolic effects of metformin, both as a monotherapy and in combination with SGLT2i, on the TCA cycle and associated anaplerotic reactions in both mice and humans. METHODS: Metformin-specific metabolic changes were initially identified by comparing metformin-treated diabetic mice (MET) with vehicle-treated db/db mice (VG). These findings were then assessed in two human cohorts (KORA and QBB) and a longitudinal KORA study of metformin-naïve patients with Type 2 Diabetes (T2D). We also compared MET with db/db mice on combination therapy (SGLT2i + MET). Metabolic profiling analyzed 716 metabolites from plasma, liver, and kidney tissues post-treatment, using linear regression and Bonferroni correction for statistical analysis, complemented by pathway analyses to explore the pathophysiological implications. RESULTS: Metformin monotherapy significantly upregulated TCA cycle intermediates such as malate, fumarate, and α-ketoglutarate (α-KG) in plasma, and anaplerotic substrates including hepatic glutamate and renal 2-hydroxyglutarate (2-HG) in diabetic mice. Downregulated hepatic taurine was also observed. The addition of SGLT2i, however, reversed these effects, such as downregulating circulating malate and α-KG, and hepatic glutamate and renal 2-HG, but upregulated hepatic taurine. In human T2D patients on metformin therapy, significant systemic alterations in metabolites were observed, including increased malate but decreased citrulline. The bidirectional modulation of TCA cycle intermediates in mice influenced key anaplerotic pathways linked to glutaminolysis, tumorigenesis, immune regulation, and antioxidative responses. CONCLUSION: This study elucidates the specific metabolic consequences of metformin and SGLT2i on the TCA cycle, reflecting potential impacts on the immune system. Metformin shows promise for its anti-inflammatory properties, while the addition of SGLT2i may provide liver protection in conditions like metabolic dysfunction-associated steatotic liver disease (MASLD). These observations underscore the importance of personalized treatment strategies.

Predicting clinically significant prostate cancer following suspicious mpMRI: analyses from a high-volume center.

PURPOSE: mpMRI is routinely used to stratify the risk of clinically significant prostate cancer (csPCa) in men with elevated PSA values before biopsy. This study aimed to calculate a multivariable risk model incorporating standard risk factors and mpMRI findings for predicting csPCa on subsequent prostate biopsy. METHODS: Data from 677 patients undergoing mpMRI ultrasound fusion biopsy of the prostate at the TUM University Hospital tertiary urological center between 2019 and 2023 were analyzed. Patient age at biopsy (67 (median); 33-88 (range) (years)), PSA (7.2; 0.3-439 (ng/ml)), prostate volume (45; 10-300 (ml)), PSA density (0.15; 0.01-8.4), PI-RADS (V.2.0 protocol) score of index lesion (92.2% ≥3), prior negative biopsy (12.9%), suspicious digital rectal examination (31.2%), biopsy cores taken (12; 2-22), and pathological biopsy outcome were analyzed with multivariable logistic regression for independent associations with the detection of csPCa defined as ISUP ≥ 3 (n = 212 (35.2%)) and ISUP ≥ 2 (n = 459 (67.8%) performed on 603 patients with complete information. RESULTS: Older age (OR: 1.64 for a 10-year increase; p < 0.001), higher PSA density (OR: 1.60 for a doubling; p < 0.001), higher PI-RADS score of the index lesion (OR: 2.35 for an increase of 1; p < 0.001), and a prior negative biopsy (OR: 0.43; p = 0.01) were associated with csPCa. CONCLUSION: mpMRI findings are the dominant predictor for csPCa on follow-up prostate biopsy. However, PSA density, age, and prior negative biopsy history are independent predictors. They must be considered when discussing the individual risk for csPCa following suspicious mpMRI and may help facilitate the further diagnostical approach.

Diabetes, Obesity, and Pathological Upstaging in Renal Cell Carcinoma: Results From a Large Multi-institutional Consortium.

PURPOSE: We sought to determine whether clinical risk factors and morphometric features on preoperative imaging can be utilized to identify those patients with cT1 tumors who are at higher risk of upstaging (pT3a). MATERIALS AND METHODS: We performed a retrospective international case-control study of consecutive patients treated surgically with radical or partial nephrectomy for nonmetastatic renal cell carcinoma (cT1 N0) conducted between January 2010 and December 2018. Multivariable logistic regression models were used to study associations of preoperative risk factors on pT3a pathological upstaging among all patients, as well as subsets with those with preoperative tumors ≤4 cm, renal nephrometry scores, tumors ≤4 cm with nephrometry scores, and clear cell histology. We also examined association with pT3a subsets (renal vein, sinus fat, perinephric fat). RESULTS: Among the 4,092 partial nephrectomy and 2,056 radical nephrectomy patients, pathological upstaging occurred in 4.9% and 23.3%, respectively. Among each group independent factors associated with pT3a upstaging were increasing preoperative tumor size, increasing age, and the presence of diabetes. Specifically, among partial nephrectomy subjects diabetes (OR=1.65; 95% CI 1.17, 2.29), male sex (OR=1.62; 95% CI 1.14, 2.33), and increasing BMI (OR=1.03; 95% CI 1.00, 1.05 per 1 unit BMI) were statistically associated with upstaging. Subset analyses identified hilar tumors as more likely to be upstaged (partial nephrectomy OR=1.91; 95% CI 1.12, 3.16; radical nephrectomy OR=2.16; 95% CI 1.44, 3.25). CONCLUSIONS: Diabetes and higher BMI were associated with pathological upstaging, as were preoperative tumor size, increased age, and male sex. Similarly, hilar tumors were frequently upstaged.

Treatment decision regret in long-term survivors after radical prostatectomy: a longitudinal study.

OBJECTIVES: To investigate prevalence, course, and predictors of longitudinal decision regret in long-term prostate cancer (PCa) survivors treated by radical prostatectomy (RP). PATIENTS AND METHODS: A total of 1003 PCa survivors from the multicentre German Familial PCa Database completed questionnaires on average 7 years after RP in 2007 and at follow-up 13 years later in 2020. Patients completed standardised patient-reported outcome measures on decision regret, decision-making, health-related quality of life, and psychosocial factors. Hierarchical multivariable logistic regression was used to assess predictors of longitudinal decision regret. RESULTS: Decision regret increased significantly over time (9.0% after 6.9 years in 2007 and 12% after 19 years in 2020; P = 0.009). Favourable localised PCa (odds ratio [OR] 1.97, 95% confidence interval [CI] 1.05-3.68), decision regret in 2007 (OR 6.38, 95% CI 3.55-11.47), and a higher depression score (OR 1.37, 95% CI 1.03-1.83) were associated with decision regret in 2020. Shared decision-making (OR 0.55, 95% CI 0.33-0.93) was associated with less decision regret. CONCLUSION: The findings of the present study underline the perseverance of decision regret in long-term PCa survivors and the definitive need for involving patients in the decision-making process to mitigate regret over the long term.

Prevalence and determinants of decision regret in long-term prostate cancer survivors following radical prostatectomy.

BACKGROUND: Patients with localized prostate cancer (PC) are faced with a wide spectrum of therapeutic options at initial diagnosis. Following radical prostatectomy (RP), PC patients may experience regret regarding their initial choice of treatment, especially when oncological and functional outcomes are poor. Impacts of psychosocial factors on decision regret, especially after long-term follow-up, are not well understood. This study aimed to investigate the prevalence and determinants of decision regret in long-term PC survivors following RP. METHODS: 3408 PC survivors (mean age 78.8 years, SD = 6.5) from the multicenter German Familial PC Database returned questionnaires after an average of 16.5 (SD = 3.8) years following RP. The outcome of decision regret concerning the initial choice of RP was assessed with one item from the Decision Regret Scale. Health-related quality of life (HRQoL), PC-anxiety, PSA-anxiety, as well as anxiety and depressive symptoms were considered for independent association with decision regret via multivariable logistic regression. RESULTS: 10.9% (373/3408) of PC survivors reported decision regret. Organ-confined disease at RP (OR 1.39, 95%CI 1.02-1.91), biochemical recurrence (OR 1.34, 1.00-1.80), low HRQoL (OR 1.69,1.28-2.24), depressive symptoms (OR 2.32, 1.52-3.53), and prevalent PSA anxiety (OR 1.88,1.17-3.01) were significantly associated with increased risk of decision regret. Shared decision-making reduced the odds of decision regret by 40% (OR 0.59, 0.41-0.86). CONCLUSIONS: PC survivors may experience decision regret even after 16 years following RP. Promoting shared decision-making in light of both established and novel, potentially less invasive treatments at initial diagnosis may help mitigate long-term regret. Awareness regarding patients showing depressive symptoms or PSA anxiety should be encouraged to identify patients at risk of decision regret in need of additional psychological support.

Accommodating population differences when validating risk prediction models.

Validation of risk prediction models in independent data provides a more rigorous assessment of model performance than internal assessment, for example, done by cross-validation in the data used for model development. However, several differences between the populations that gave rise to the training and the validation data can lead to seemingly poor performance of a risk model. In this paper we formalize the notions of "similarity" or "relatedness" of the training and validation data, and define reproducibility and transportability. We address the impact of different distributions of model predictors and differences in verifying the disease status or outcome on measures of calibration, accuracy and discrimination of a model. When individual level information from both the training and validation data sets is available, we propose and study weighted versions of the validation metrics that adjust for differences in the risk factor distributions and in outcome verification between the training and validation data to provide a more comprehensive assessment of model performance. We provide conditions on the risk model and the populations that gave rise to the training and validation data that ensure a model's reproducibility or transportability, and show how to check these conditions using weighted and unweighted performance measures. We illustrate the method by developing and validating a model that predicts the risk of developing prostate cancer using data from two large prostate cancer screening trials.

Accommodating heterogeneous missing data patterns for prostate cancer risk prediction.

BACKGROUND: We compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user. METHODS: Ten North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group ≥ 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach. RESULTS: Among 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history. CONCLUSION: Developers of clinical risk prediction tools should optimize use of available data and sources even in the presence of high amounts of missing data and offer options for users with missing risk factors.

Fear of cancer recurrence and disease progression in long-term prostate cancer survivors after radical prostatectomy: A longitudinal study.

BACKGROUND: Although fear of cancer recurrence (FCR) or disease progression is among the most endorsed unmet needs and concerns of cancer survivors, research on the course of FCR in long-term survivors is scarce. The objective of this study was to assess longitudinally the prevalence and predictors of FCR in long-term prostate cancer (PCa) survivors. METHODS: In all, 2417 survivors from the multicenter German Familial Prostate Cancer Database completed the Fear of Progression Questionnaire-Short Form on average 7 years (T1 in 2010) after radical prostatectomy and at follow-up 9 years later (T2 in 2019). Hierarchical multivariable logistic regression was used to assess predictors of FCR at follow-up. RESULTS: The mean age at the initial assessment was 69.5 years (standard deviation, 5.9 years); 6.5% and 8.4% of patients reported clinical FCR at the initial assessment (T1) and at the follow-up (T2), respectively. In a multivariable analysis controlling for concurrent associations, longitudinal predictors of FCR 9 years later included a lower level of education (odds ratio [OR], 4.35; 95% confidence interval [CI], 2.33-8.33), years since radical prostatectomy (OR, 1.10; 95% CI, 1.03-1.18), biochemical recurrence (OR, 1.67; 95% CI, 1.02-2.72), no current adjuvant therapy (OR, 2.38; 95% CI, 1.19-4.76), FCR (OR, 10.75; 95% CI, 6.18-18.72), and anxiety (OR, 1.35; 95% CI, 1.06-1.72). CONCLUSIONS: FCR remains a burden to certain PCa survivors even many years after their diagnosis and treatment. Health care professionals should monitor for FCR and identify patients at risk to provide appropriate psychosocial care because FCR is leading to limitations in quality of life and psychological well-being.

Diversity of cancer-related identities in long-term prostate cancer survivors after radical prostatectomy.

BACKGROUND: Individuals affected by cancer need to integrate this experience into their personal biography as their life continues after primary therapy, leading to substantial changes in self-perception. This study identified factors uniquely associated with 5 different cancer-related identities in order to improve the understanding of how self-perception in men affected by prostate cancer is associated with certain clinical and psychosocial characteristics. METHODS: In this cross-sectional study, long-term prostate cancer survivors after radical prostatectomy were asked to choose one of 5 cancer-related identities that described them best. Associations with sociodemographic, clinical, and psychological variables were investigated using multivariable logistic regression. RESULTS: Three thousand three hundred forty-seven men (mean age 78.1 years) surveyed on average 15.6 years after prostatectomy were included. Most men favored the terms "someone who has had cancer" (43.9%) which was associated with a mild disease course, and "patient" (26.3%) which was associated with ongoing therapy and biochemical disease recurrence. The self-descriptions "cancer survivor" (16.8%), "cancer conqueror" (10.9%) and "victim" (2.1%) were less common. "Cancer survivor" was associated with high perceived disease severity (OR: 1.86 [1.44-2.40]). "Cancer survivor" and "cancer conqueror" were related to high benefit finding (OR: 1.89 [1.48-2.40], OR: 1.46 [1.12-1.89] respectively), and only "cancer conqueror" was associated with high well-being (OR: 1.84 [1.35-2.50]). Identification as "victim" was associated with a positive depression screening and low well-being (OR: 2.22 [1.15-4.31], OR: 0.38 [0.20-0.72] respectively) (all p < 0.05). CONCLUSIONS: Although long-term survival is common among men affected by PCa, they display a large diversity in cancer-related identities, which are associated with unique clinical and psychological characteristics. These cancer-related identities and their distinctive properties are associated with psychological well-being even after a long follow-up.

Long-term and pathological outcomes of low- and intermediate-risk prostate cancer after radical prostatectomy: implications for active surveillance.

PURPOSE: The safety of active surveillance (AS) in favorable intermediate-risk (FIR) prostate cancer (PCa) remains uncertain. To provide guidance on clinical decision-making, we examined long-term and pathological outcomes of low-risk and intermediate-risk PCa patients after radical prostatectomy (RP). METHODS: The study involved 5693 patients diagnosed between 1994 and 2019 with low-risk, FIR, and unfavorable intermediate-risk (UIR) PCa (stratification according to the AUA guidelines) who underwent RP. Pathological outcomes were compared, and Kaplan-Meier analysis determined biochemical recurrence-free survival (BRFS) and cancer-specific survival (CSS) at 5, 10, 15, and 20 years. Multiple Cox regression was used to simultaneously control for relevant confounders. RESULTS: Those at FIR had higher rates of upgrading and upstaging (12.8% vs. 7.2%, p < 0.001; 19.8% vs. 12.0%, p < 0.001) as well as pathological tumor and node stage (≥ pT3a: 18.8% vs. 11.6%, p < 0.001; pN1: 2.7% vs. 0.8%, p > 0.001) compared to patients at low risk. The 20-year BRFS was 69%, 65%, and 44% and the 20-year CSS was 98%, 95%, and 89% in low-risk, FIR, and UIR patients. On multiple Cox regression, FIR was not associated with a worse BRFS (HR 1.07, CI 0.87-1.32), UIR was associated with a worse BRFS (HR 1.49, CI 1.20-1.85). CONCLUSION: Patients at FIR had only slightly worse pathological and long-term outcomes compared to patients at low risk, whereas the difference compared to patients at UIR was large. This emphasizes AS in these patients as a possible treatment strategy in well-counseled patients.

No detrimental effect of a positive family history on postoperative upgrading and upstaging in men with low risk and favourable intermediate-risk prostate cancer: implications for active surveillance.

PURPOSE: To assess whether a first-degree family history or a fatal family history of prostate cancer (PCa) are associated with postoperative upgrading and upstaging among men with low risk and favourable intermediate-risk (FIR) PCa and to provide guidance on clinical decision making for active surveillance (AS) in this patient population. METHODS: Participants in the German Familial Prostate Cancer database diagnosed from 1994 to 2019 with (1) low risk (clinical T1c-T2a, biopsy Gleason Grade Group (GGG) 1, PSA < 10 ng/ml), (2) Gleason 6 FIR (clinical T1c-T2a, GGG 1, PSA 10-20 ng/ml), and (3) Gleason 3 + 4 FIR (clinical T1c-T2a, GGG 2, PSA < 10 ng/ml) PCa who were subsequently treated with radical prostatectomy (RP) were analysed for upgrading, defined as postoperative GGG 3 tumour or upstaging, defined as pT3-pT4 or pN1 disease at RP. Logistic regression analysis was used to assess whether PCa family history was associated with postoperative upgrading or upstaging. RESULTS: Among 4091 men who underwent RP, mean age at surgery was 64.4 (SD 6.7) years, 24.7% reported a family history, and 3.4% a fatal family history. Neither family history nor fatal family history were associated with upgrading or upstaging at low risk, Gleason 6 FIR, and Gleason 3 + 4 FIR PCa patients. CONCLUSION: Results from the current study indicated no detrimental effect of family history on postoperative upgrading or upstaging. Therefore, a positive family history or fatal family history of PCa in FIR PCa patients should not be a reason to refrain from AS in men otherwise suitable.

External Validation and Comparison of Prostate Cancer Risk Calculators Incorporating Multiparametric Magnetic Resonance Imaging for Prediction of Clinically Significant Prostate Cancer.

PURPOSE: We sought to externally validate recently published prostate cancer risk calculators incorporating multiparametric magnetic resonance imaging to predict clinically significant prostate cancer. We also compared the performance of these calculators to that of multiparametric magnetic resonance imaging naïve prostate cancer risk calculators. MATERIALS AND METHODS: We identified men without a previous prostate cancer diagnosis who underwent transperineal template saturation prostate biopsy with fusion guided targeted biopsy between November 2014 and March 2018 at our academic tertiary referral center. Any Gleason pattern 4 or greater was defined as clinically significant prostate cancer. Predictors, which were patient age, prostate specific antigen, digital rectal examination, prostate volume, family history, previous prostate biopsy and the highest region of interest according to the PI-RADS™ (Prostate Imaging Reporting and Data System), were retrospectively collected. Four multiparametric magnetic resonance imaging prostate cancer risk calculators and 2 multiparametric magnetic resonance imaging naïve prostate cancer risk calculators were evaluated for discrimination, calibration and the clinical net benefit using ROC analysis, calibration plots and decision curve analysis. RESULTS: Of the 468 men 193 (41%) were diagnosed with clinically significant prostate cancer. Three multiparametric magnetic resonance imaging prostate cancer risk calculators showed similar discrimination with a ROC AUC significantly higher than that of the other prostate cancer risk calculators (AUC 0.83-0.85 vs 0.69-0.74). Calibration in the large showed 2% deviation from the true amount of clinically significant prostate cancer for 2 multiparametric magnetic resonance imaging risk calculators while the other calculators showed worse calibration at 11% to 27%. A clinical net benefit was observed only for 3 multiparametric magnetic resonance imaging risk calculators at biopsy thresholds of 15% or greater. None of the 6 investigated prostate cancer risk calculators demonstrated clinical usefulness against a biopsy all strategy at thresholds less than 15%. CONCLUSIONS: The performance of multiparametric magnetic resonance imaging prostate cancer risk calculators varies but they generally outperform multiparametric magnetic resonance imaging naïve prostate cancer risk calculators in regard to discrimination, calibration and clinical usefulness. External validation in other biopsy settings is highly encouraged.

A strategy for high-dimensional multivariable analysis classifies childhood asthma phenotypes from genetic, immunological, and environmental factors.

BACKGROUND: Associations between childhood asthma phenotypes and genetic, immunological, and environmental factors have been previously established. Yet, strategies to integrate high-dimensional risk factors from multiple distinct data sets, and thereby increase the statistical power of analyses, have been hampered by a preponderance of missing data and lack of methods to accommodate them. METHODS: We assembled questionnaire, diagnostic, genotype, microarray, RT-qPCR, flow cytometry, and cytokine data (referred to as data modalities) to use as input factors for a classifier that could distinguish healthy children, mild-to-moderate allergic asthmatics, and nonallergic asthmatics. Based on data from 260 German children aged 4-14 from our university outpatient clinic, we built a novel multilevel prediction approach for asthma outcome which could deal with a present complex missing data structure. RESULTS: The optimal learning method was boosting based on all data sets, achieving an area underneath the receiver operating characteristic curve (AUC) for three classes of phenotypes of 0.81 (95%-confidence interval (CI): 0.65-0.94) using leave-one-out cross-validation. Besides improving the AUC, our integrative multilevel learning approach led to tighter CIs than using smaller complete predictor data sets (AUC = 0.82 [0.66-0.94] for boosting). The most important variables for classifying childhood asthma phenotypes comprised novel identified genes, namely PKN2 (protein kinase N2), PTK2 (protein tyrosine kinase 2), and ALPP (alkaline phosphatase, placental). CONCLUSION: Our combination of several data modalities using a novel strategy improved classification of childhood asthma phenotypes but requires validation in external populations. The generic approach is applicable to other multilevel data-based risk prediction settings, which typically suffer from incomplete data.

Multi-cohort modeling strategies for scalable globally accessible prostate cancer risk tools.

BACKGROUND: Online clinical risk prediction tools built on data from multiple cohorts are increasingly being utilized for contemporary doctor-patient decision-making and validation. This report outlines a comprehensive data science strategy for building such tools with application to the Prostate Biopsy Collaborative Group prostate cancer risk prediction tool. METHODS: We created models for high-grade prostate cancer risk using six established risk factors. The data comprised 8492 prostate biopsies collected from ten institutions, 2 in Europe and 8 across North America. We calculated area under the receiver operating characteristic curve (AUC) for discrimination, the Hosmer-Lemeshow test statistic (HLS) for calibration and the clinical net benefit at risk threshold 15%. We implemented several internal cross-validation schemes to assess the influence of modeling method and individual cohort on validation performance. RESULTS: High-grade disease prevalence ranged from 18% in Zurich (1863 biopsies) to 39% in UT Health San Antonio (899 biopsies). Visualization revealed outliers in terms of risk factors, including San Juan VA (51% abnormal digital rectal exam), Durham VA (63% African American), and Zurich (2.8% family history). Exclusion of any cohort did not significantly affect the AUC or HLS, nor did the choice of prediction model (pooled, random-effects, meta-analysis). Excluding the lowest-prevalence Zurich cohort from training sets did not statistically significantly change the validation metrics for any of the individual cohorts, except for Sunnybrook, where the effect on the AUC was minimal. Therefore the final multivariable logistic model was built by pooling the data from all cohorts using logistic regression. Higher prostate-specific antigen and age, abnormal digital rectal exam, African ancestry and a family history of prostate cancer increased risk of high-grade prostate cancer, while a history of a prior negative prostate biopsy decreased risk (all p-values < 0.004). CONCLUSIONS: We have outlined a multi-cohort model-building internal validation strategy for developing globally accessible and scalable risk prediction tools.

Men with family history of prostate cancer have a higher risk of disease recurrence after radical prostatectomy.

PURPOSE: We aimed to determine if family history (FH) of prostate cancer (PC) influenced cancer control after radical prostatectomy (RP). METHODS: Patients were evaluated in a prospectively-collected PC family database: The focus was on hereditary prostate cancer (HPC) defined by Johns Hopkins criteria and sporadic prostate cancer (SPC), rigorously defined by absence of prostate cancer in ≥ 2 brothers aged ≥ 60 years. Additionally, patients with first-degree (FPC) and non-first-degree PC (non-FPC) were assessed. Endpoints were biochemical recurrence-free survival (BRFS) and prostate cancer-specific survival (CSS). Finally, clinico-pathological characteristics were compared and multiple proportional hazards regression was used to identify prognostic factors. RESULTS: In total 11,654 patients were included (807 HPC, 2251 FPC, 8072 non-FPC and 524 SPC). Familial imposition (HPC/FPC) was associated with a younger age at diagnosis. Thus, HPC patients were diagnosed 2.9 years earlier than SPC patients with more locally advanced tumors (≥ pT3). With a median follow up of 6.2 years (range 0-31.5) BRFS was significantly different when stratified by FH. In pairwise analyses BRFS differed significantly for HPC compared to SPC (HR = 1.27). Consecutively FH was identified as prognostic factor for BRFS (p = 0.021) together with age, PSA, pathologic characteristics and adjuvant androgen deprivation. Analyses of CSS did not show a difference. CONCLUSION: Patients with FH of PC are likely to be diagnosed earlier and present a higher proportion of locally advanced disease. In addition, men with FH are at higher risk of biochemical recurrence after surgery but reveal similar outcomes regarding prostate cancer-specific survival.

Correlating mammographic and pathologic findings in clinical decision support using natural language processing and data mining methods.

BACKGROUND: A key challenge to mining electronic health records for mammography research is the preponderance of unstructured narrative text, which strikingly limits usable output. The imaging characteristics of breast cancer subtypes have been described previously, but without standardization of parameters for data mining. METHODS: The authors searched the enterprise-wide data warehouse at the Houston Methodist Hospital, the Methodist Environment for Translational Enhancement and Outcomes Research (METEOR), for patients with Breast Imaging Reporting and Data System (BI-RADS) category 5 mammogram readings performed between January 2006 and May 2015 and an available pathology report. The authors developed natural language processing (NLP) software algorithms to automatically extract mammographic and pathologic findings from free text mammogram and pathology reports. The correlation between mammographic imaging features and breast cancer subtype was analyzed using one-way analysis of variance and the Fisher exact test. RESULTS: The NLP algorithm was able to obtain key characteristics for 543 patients who met the inclusion criteria. Patients with estrogen receptor-positive tumors were more likely to have spiculated margins (P = .0008), and those with tumors that overexpressed human epidermal growth factor receptor 2 (HER2) were more likely to have heterogeneous and pleomorphic calcifications (P = .0078 and P = .0002, respectively). CONCLUSIONS: Mammographic imaging characteristics, obtained from an automated text search and the extraction of mammogram reports using NLP techniques, correlated with pathologic breast cancer subtype. The results of the current study validate previously reported trends assessed by manual data collection. Furthermore, NLP provides an automated means with which to scale up data extraction and analysis for clinical decision support. Cancer 2017;114-121. © 2016 American Cancer Society.

Comparison of approaches for incorporating new information into existing risk prediction models.

We compare the calibration and variability of risk prediction models that were estimated using various approaches for combining information on new predictors, termed 'markers', with parameter information available for other variables from an earlier model, which was estimated from a large data source. We assess the performance of risk prediction models updated based on likelihood ratio (LR) approaches that incorporate dependence between new and old risk factors as well as approaches that assume independence ('naive Bayes' methods). We study the impact of estimating the LR by (i) fitting a single model to cases and non-cases when the distribution of the new markers is in the exponential family or (ii) fitting separate models to cases and non-cases. We also evaluate a new constrained maximum likelihood method. We study updating the risk prediction model when the new data arise from a cohort and extend available methods to accommodate updating when the new data source is a case-control study. To create realistic correlations between predictors, we also based simulations on real data on response to antiviral therapy for hepatitis C. From these studies, we recommend the LR method fit using a single model or constrained maximum likelihood. Copyright © 2016 John Wiley & Sons, Ltd.

Adding genetic risk score to family history identifies twice as many high-risk men for prostate cancer: Results from the prostate cancer prevention trial.

BACKGROUND: While family history (FH) has been widely used to provide risk information, it captures only a small proportion of subjects with higher genetic susceptibility. Our objective is to assess whether a genetic risk score (GRS) calculated from prostate cancer (PCa) risk-associated single nucleotide polymorphisms (SNPs) can supplement FH for more effective risk stratification for PCa screening decision-making. METHODS: A GRS was calculated based on 29 PCa risk-associated SNPs for 4,528 men of European descent in the placebo arm of the Prostate Cancer Prevention Trial (PCPT). At study entry, participants were free of PCa diagnosis. Performance of FH and GRS were measured by observed detection rate of PCa and high-grade PCa (Gleason score ≥7) during the 7-year study. RESULTS: GRS was a significant predictor of PCa in men with or without a positive FH (P = 1.18 × 10(-4) and P = 4.50 × 10(-16) , respectively). Using FH alone, as expected, the 17% of men who were FH+ had a PCa detection rate that was significantly higher (29.02%) than FH- men (23.43%, P = 0.001). When both FH+ or GRS >1.4 are considered, more than twice as many men (36%) can be classified as higher risk, as evidenced by a significantly higher PCa detection rate (30.98%) than in the remaining men (20.61%, P = 5.30 × 10(-15) ). If targeting only FH+ men, four out of five PCa cases would go undetected, as would a similarly large fraction (∼80%) of high-grade PCa cases. In comparison, if targeting FH+ or GRS >1.4 men, almost half of all PCa cases would be detected, including 45% of high-grade PCa cases. CONCLUSIONS: A prostate cancer GRS can supplement family history to better identify higher risk men for targeted intervention. Prostate 76:1120-1129, 2016. © 2016 Wiley Periodicals, Inc.

No Detrimental Effect of a Positive Family History on Long-Term Outcomes Following Radical Prostatectomy.

PURPOSE: Overall 1 in 5 patients with prostate cancer has a positive family history. In this report we evaluated the association between family history and long-term outcomes following radical prostatectomy. MATERIALS AND METHODS: Patients treated with radical prostatectomy were identified from a German registry, and separated into positive first-degree family history vs negative family history (strictly negative, requiring at least 1 male first-degree relative older than 60 years and no prostate cancer in the family). Kaplan-Meier curves and Cox proportional hazards models were used for association analyses with biochemical recurrence-free and prostate cancer specific survival. RESULTS: Median followup for 7,690 men included in the study was 8.4 years. Of the 754 younger patients less than 55 years old 50.9% (384) had a family history compared to 40.4% of the older patients (2,803; p <0.001). The 10-year biochemical recurrence-free (62.5%) and prostate cancer specific survival (96.1%) rates did not differ between patients with vs without a family history, nor between the younger vs older patient groups (all p >0.05). Prostate specific antigen, pathological stage, node stage and Gleason score were the only significant predictors for biochemical recurrence-free survival, while pathological stage, node stage (all p <0.005) and Gleason score (Gleason 7 vs 6 or less-HR 1.711, 95% CI 1.056-2.774, p = 0.03; Gleason 8 or greater vs 6 or less-HR 4.516, 95% CI 2.776-7.347, p <0.0001) were the only predictors for prostate cancer specific survival. CONCLUSIONS: A family history of prostate cancer has no bearing on long-term outcomes after radical prostatectomy.