Clinical benefits of a multivariate prediction model for bladder cancer: a decision analytic approach

Effects and mechanisms of N6-methyladenosine RNA methylation in environmental pollutant-induced carcinogenesis

Environmental pollution, including air pollution, plastic contamination, and heavy metal exposure, is a pressing global issue. This crisis contributes significantly to pollution-related diseases and is a critical risk factor for chronic health conditions, including cancer. Mounting evidence underscores the pivotal role of N6-methyladenosine (m6A) as a crucial regulatory mechanism in pathological processes and cancer progression. Governed by m6A writers, erasers, and readers, m6A orchestrates alterations in target gene expression, consequently playing a vital role in a spectrum of RNA processes, covering mRNA processing, translation, degradation, splicing, nuclear export, and folding. Thus, there is a growing need to pinpoint specific m6A-regulated targets in environmental pollutant-induced carcinogenesis, an emerging area of research in cancer prevention. This review consolidates the understanding of m6A modification in environmental pollutant-induced tumorigenesis, explicitly examining its implications in lung, skin, and bladder cancer. We also investigate the biological mechanisms that underlie carcinogenesis originating from pollution. Specific m6A methylation pathways, such as the HIF1A/METTL3/IGF2BP3/BIRC5 network, METTL3/YTHDF1-mediated m6A modification of IL 24, METTL3/YTHDF2 dynamically catalyzed m6A modification of AKT1, METTL3-mediated m6A-modified oxidative stress, METTL16-mediated m6A modification, site-specific ATG13 methylation-mediated autophagy, and the role of m6A in up-regulating ribosome biogenesis, all come into play in this intricate process. Furthermore, we discuss the direction regarding the interplay between pollutants and RNA metabolism, particularly in immune response, providing new information on RNA modifications for future exploration.

Assessing the clinical utility of genetic profiling in fracture risk prediction: a decision curve analysis

Using decision curve analysis on 2188 women and 1324 men, we found that an osteogenomic profile constructed from 62 genetic variants improved the clinical net benefit of fracture risk prediction over and above that of clinical risk factors and BMD.

INTRODUCTION

Genetic profiling is a promising tool for assessing fracture risk. This study sought to use the decision curve analysis (DCA), a novel approach to determine the impact of genetic profiling on fracture risk prediction.

METHODS

The study involved 2188 women and 1324 men, aged 60 years and above, who were followed for up to 23 years. Bone mineral density (BMD) and clinical risk factors were obtained at baseline. The incidence of fracture and mortality were recorded. A weighted individual genetic risk score (GRS) was constructed from 62 BMD-associated genetic variants. Four models were considered: CRF (clinical risk factors); CRF + GRS; Garvan model (GFRC) including CRF and femoral neck BMD; and GFRC + GRS. The DCA was used to evaluate the clinical net benefit of predictive models at a range of clinically reasonable risk thresholds.

RESULTS

In both women and men, the full model GFRC + GRS achieved the highest net benefits. For 10-year risk threshold > 18% for women and > 15% for men, the GRS provided net benefit above those of the CRF models. At 20% risk threshold, adding the GRS could help to avoid 1 additional treatment per 81 women or 1 per 24 men compared with the Garvan model. At lower risk thresholds, there was no significant difference between the four models.

CONCLUSIONS

The addition of genetic profiling into the clinical risk factors can improve the net clinical benefit at higher risk thresholds of fracture. Although the contribution of genetic profiling was modest in the presence of BMD + CRF, it appeared to be able to replace BMD for fracture prediction.

Evaluating the impact of policies recommending PrEP to subpopulations of men and transgender women who have sex with men based on demographic and behavioral risk factors

INTRODUCTION

Developing guidelines to inform the use of antiretroviral pre-exposure prophylaxis (PrEP) for HIV prevention in resource-limited settings must necessarily be informed by considering the resources and infrastructure needed for PrEP delivery. We describe an approach that identifies subpopulations of cisgender men who have sex with men (MSM) and transgender women (TGW) to prioritize for the rollout of PrEP in resource-limited settings.

METHODS

We use data from the iPrEx study, a multi-national phase III study of PrEP for HIV prevention in MSM/TGW, to build statistical models that identify subpopulations at high risk of HIV acquisition without PrEP, and with high expected PrEP benefit. We then evaluate empirically the population impact of policies recommending PrEP to these subpopulations, and contrast these with existing policies.

RESULTS

A policy recommending PrEP to a high risk subpopulation of MSM/TGW reporting condomless receptive anal intercourse over the last 3 months (estimated 3.3% 1-year HIV incidence) yields an estimated 1.95% absolute reduction in 1-year HIV incidence at the population level, and 3.83% reduction over 2 years. Importantly, such a policy requires rolling PrEP out to just 59.7% of MSM/TGW in the iPrEx population. We find that this policy is identical to that which prioritizes MSM/TGW with high expected PrEP benefit. It is estimated to achieve nearly the same reduction in HIV incidence as the PrEP guideline put forth by the US Centers for Disease Control, which relies on the measurement of more behavioral risk factors and which would recommend PrEP to a larger subset of the MSM/TGW population (86% vs. 60%).

CONCLUSIONS

These findings may be used to focus future mathematical modelling studies of PrEP in resource-limited settings on prioritizing PrEP for high-risk subpopulations of MSM/TGW. The statistical approach we took could be employed to develop PrEP policies for other at-risk populations and resource-limited settings.

Machine learning models for predicting post-cystectomy recurrence and survival in bladder cancer patients

Currently in patients with bladder cancer, various clinical evaluations (imaging, operative findings at transurethral resection and radical cystectomy, pathology) are collectively used to determine disease status and prognosis, and recommend neoadjuvant, definitive and adjuvant treatments. We analyze the predictive power of these measurements in forecasting two key long-term outcomes following radical cystectomy, i.e., cancer recurrence and survival. Information theory and machine learning algorithms are employed to create predictive models using a large prospective, continuously collected, temporally resolved, primary bladder cancer dataset comprised of 3503 patients (1971-2016). Patient recurrence and survival one, three, and five years after cystectomy can be predicted with greater than 70% sensitivity and specificity. Such predictions may inform patient monitoring schedules and post-cystectomy treatments. The machine learning models provide a benchmark for predicting oncologic outcomes in patients undergoing radical cystectomy and highlight opportunities for improving care using optimal preoperative and operative data collection.

Do we have biomarkers to predict response to neoadjuvant and adjuvant chemotherapy and immunotherapy in bladder cancer?

Radical cystectomy (RC) is the standard of care treatment of localized muscle-invasive bladder cancer (BC). However, about 50% of patients develop metastases within 2 years after cystectomy. Neoadjuvant cisplatin-based chemotherapy before cystectomy improves the overall survival (OS) in patients with muscle-invasive BC. Pathological response to neoadjuvant treatment is a strong predictor of better disease-specific survival. Nevertheless, some patients do not benefit from chemotherapy. The identification of reliable biomarkers enabling clinicians to identify patients who might benefit from chemotherapy is a very important clinical task. An identification tool could lead to individualized therapy, optimizing response rates. In addition, unnecessary treatment with chemotherapy which potentially leads to a loss of quality of life and which might also might cause a delay of cystectomy in a neoadjuvant setting could be avoided. The present review aims to summarize and discuss the current literature on biomarkers for the prediction of response to systemic therapy in muscle-invasive BC. Tremendous efforts in genetic and molecular characterization have led to the identification of predictive candidate biomarkers in urothelial carcinoma (UC), although prospective validation is pending. Ongoing clinical trials examining the benefit of individual therapies in UC of the bladder (UCB) by molecular patient selection hold promise to shed light on this question.

Prognostic and Prediction Tools in Bladder Cancer: A Comprehensive Review of the Literature

CONTEXT

This review focuses on risk assessment and prediction tools for bladder cancer (BCa).

OBJECTIVE

To review the current knowledge on risk assessment and prediction tools to enhance clinical decision making and counseling of patients with BCa.

EVIDENCE ACQUISITION

A literature search in English was performed using PubMed in July 2013. Relevant risk assessment and prediction tools for BCa were selected. More than 1600 publications were retrieved. Special attention was given to studies that investigated the clinical benefit of a prediction tool.

EVIDENCE SYNTHESIS

Most prediction tools for BCa focus on the prediction of disease recurrence and progression in non-muscle-invasive bladder cancer or disease recurrence and survival after radical cystectomy. Although these tools are helpful, recent prediction tools aim to address a specific clinical problem, such as the prediction of organ-confined disease and lymph node metastasis to help identify patients who might benefit from neoadjuvant chemotherapy. Although a large number of prediction tools have been reported in recent years, many of them lack external validation. Few studies have investigated the clinical utility of any given model as measured by its ability to improve clinical decision making. There is a need for novel biomarkers to improve the accuracy and utility of prediction tools for BCa.

CONCLUSIONS

Decision tools hold the promise of facilitating the shared decision process, potentially improving clinical outcomes for BCa patients. Prediction models need external validation and assessment of clinical utility before they can be incorporated into routine clinical care.

PATIENT SUMMARY

We looked at models that aim to predict outcomes for patients with bladder cancer (BCa). We found a large number of prediction models that hold the promise of facilitating treatment decisions for patients with BCa. However, many models are missing confirmation in a different patient cohort, and only a few studies have tested the clinical utility of any given model as measured by its ability to improve clinical decision making.

Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration

The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.

Accurate determination of the pathological stage with gross dissection protocol for radical cystectomy

The current protocol for reporting urinary bladder cancer in radical cystectomies may exhibit limitations in the diagnostic accuracy, such as a risk of understaging, especially in cases with prostatic involvement. Difficulty can arise in the verification of stage pT0, and the assessment of surgical margins is suboptimal. We have developed a daily gross dissection protocol practice where radical cystectomies are totally embedded and evaluated histologically in whole-mount sections. We report here on the first 138 consecutive specimens from 2008 to the first quarter of 2012 inclusive. The incidence of the cancer stages was compared with data on 15,586 radical cystectomies from the literature. The differences were analyzed with the one-sample z-test (p < 0.05). The following emerged from and our series and the literature data: pT0 8.7 % and 6.1 %; pTa 0.7 % and 2.9 %; pTis 2.9 % and 6 %; pT1 15.2 % and 15.5 %; pT2 21 % and 23.3 %; pT3 34.8 % and 34.3 %; and pT4 16.7 % and 11 %, respectively. Our findings closely reflected the means of the published statistical data based on a large number of cases. The differences were due to the more detailed processing: the case numbers in groups from pTis to pT2 were comparatively low, while those in groups pT3 and pT4 were higher. The difference in group pT4 was significant (p = 0.0494). With this method, only those samples were regarded as pT0 in which the granulomatous area and the hemosiderin deposition indicative of the earlier intervention were observable and the entire preparation was tumor-free.

Risk stratification of pT1-3N0 patients after radical cystectomy for adjuvant chemotherapy counselling

BACKGROUND

In pT1-T3N0 urothelial carcinoma of the bladder (UCB) patients, multi-modal therapy is inconsistently recommended. The aim of the study was to develop a prognostic tool to help decision-making regarding adjuvant therapy.

METHODS

We included 2145 patients with pT1-3N0 UCB after radical cystectomy (RC), naive of neoadjuvant or adjuvant therapy. The cohort was randomly split into development cohort based on the US patients (n=1067) and validation cohort based on the Europe patients (n=1078). Predictive accuracy was quantified using the concordance index.

RESULTS

With a median follow-up of 45 months, 5-year recurrence-free and cancer-specific survival estimates were 68% and 73%, respectively. pT-stage, ge, lymphovascular invasion, and positive margin were significantly associated with both disease recurrence and cancer-specific mortality (P-values ≤ 0.005). The accuracies of the multivariable models at 2, 5, and 7 years for predicting disease recurrence were 67.4%, 65%, and 64.4%, respectively. Accuracies at 2, 5, and 7 years for predicting cancer-specific mortality were 69.3%, 66.4%, and 65.5%, respectively. We developed competing-risk, conditional probability nomograms. External validation revealed minor overestimation.

CONCLUSION

Despite RC, a significant number of patients with pT1-3N0 UCB experience disease recurrence and ultimately die of UCB. We developed and externally validated competing-risk, conditional probability post-RC nomograms for prediction of disease recurrence and cancer-specific mortality.

Who should be included in a clinical trial of screening for bladder cancer?: a decision analysis of data from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial

BACKGROUND

Because of its relatively low incidence, bladder cancer screening might have a better ratio of benefits to harms if it is restricted to a high-risk population. Data from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial were used and simple decision analytic techniques were applied to compare different eligibility criteria for a screening trial.

METHODS

For a variety of possible eligibility criteria, the percentage of the population aged 55 years to 74 years and classified as being at high risk for developing invasive or high-grade carcinoma, and therefore likely to benefit from screening, was calculated. Regression models were used to calculate a risk score based on age, sex, smoking history, and family history of bladder cancer. The reduction in cases was calculated given hypothetical risk reductions associated with screening. The trade-off between patients screened and tumors avoided was calculated as a net benefit.

RESULTS

The 5-year probability of being diagnosed with invasive bladder cancer was 0.24%. Using a risk score > 6 or > 8 as the eligibility criterion for a trial was generally superior to including all older adults. In a typical scenario, a risk score > 6 would result in approximately 25% of the population being screened to prevent 57 invasive or high-grade bladder cancers per 100,000 population; screening the entire population would prevent only an additional 38 cases.

CONCLUSIONS

Screening for bladder cancer can be optimized by restricting it to a subgroup of patients considered to be at elevated risk. Different eligibility criteria for a screening trial can be compared rationally using decision-analytic techniques.

Prediction models in cancer care

Prediction is ubiquitous across the spectrum of cancer care from screening to hospice. Indeed, oncology is often primarily a prediction problem; many of the early stage cancers cause no symptoms, and treatment is recommended because of a prediction that tumor progression would ultimately threaten a patient's quality of life or survival. Recent years have seen attempts to formalize risk prediction in cancer care. In place of qualitative and implicit prediction algorithms, such as cancer stage, researchers have developed statistical prediction tools that provide a quantitative estimate of the probability of a specific event for an individual patient. Prediction models generally have greater accuracy than reliance on stage or risk groupings, can incorporate novel predictors such as genomic data, and can be used more rationally to make treatment decisions. Several prediction models are now widely used in clinical practice, including the Gail model for breast cancer incidence or the Adjuvant! Online prediction model for breast cancer recurrence. Given the burgeoning complexity of diagnostic and prognostic information, there is simply no realistic alternative to incorporating multiple variables into a single prediction model. As such, the question should not be whether but how prediction models should be used to aid decision-making. Key issues will be integration of models into the electronic health record and more careful evaluation of models, particularly with respect to their effects on clinical outcomes.

The Karakiewicz nomogram is the most useful clinical predictor for survival outcomes in patients with localized renal cell carcinoma

BACKGROUND

Outcomes after surgical removal of localized renal cell carcinoma (RCC) are variable. There have been multiple prognostic nomograms and risk groups developed for estimation of survival outcomes, with different models in use for evaluating patient eligibility in ongoing trials of adjuvant therapy. The authors aimed to establish the most useful prognostic model for patients with localized RCC to guide trial design, biomarker research, and clinical counseling.

METHODS

A total of 390 consecutive patients who underwent nephrectomy for sporadic localized RCC in a tertiary institution (1990-2006) with 65 months median follow-up were retrospectively evaluated. The Karakiewicz nomogram, the Kattan nomogram, the Sorbellini nomogram, and the Leibovich model were compared in predicting survival outcomes (overall survival, cancer-specific survival, and freedom from recurrence) using likelihood analysis, adequacy indices, decision curve analysis, calibration, and concordance indices.

RESULTS

Overall, the Karakiewicz nomogram outperformed the Kattan nomogram, the Sorbellini nomogram, and the Leibovich model. Highly improved accuracy was seen using the Karakiewicz nomogram in survival prediction, using likelihood ratio analysis in bivariate models including the competing prognostic models. The Karakiewicz nomogram showed higher adequacy and concordance indices and improved clinical benefit relative to all other nomograms. All 4 models were reasonably calibrated. Exploratory comparisons of prespecified discretized Karakiewicz nomograms and the SORCE trial recruitment criteria (a discretized Leibovich model) of high-risk patients favored the discretized Karakiewicz nomograms.

CONCLUSIONS

The Karakiewicz nomogram was shown to be superior to the other tested nomograms and risk groups in predicting survival outcomes in localized RCC. Routine integration of this model into trial design and biomarker research should be considered.

Statistical consideration for clinical biomarker research in bladder cancer

OBJECTIVE

To critically review and illustrate current methodological and statistical considerations for bladder cancer biomarker discovery and evaluation.

METHODS

Original, review, and methodological articles, and editorials were reviewed and summarized.

RESULTS

Biomarkers may be useful at multiple stages of bladder cancer management: early detection, diagnosis, staging, prognosis, and treatment; however, few novel biomarkers are currently used in clinical practice. The reasons for this disjunction are many and reflect the long and difficult pathway from candidate biomarker discovery to clinical assay, and the lack of coherent and comprehensive processes (pipelines) for biomarker development. Conceptually, the development of new biomarkers should be a process that is similar to therapeutic drug evaluation-a highly regulated process with carefully regulated phases from discovery to human applications. In a further effort to address the pervasive problem of inadequacies in the design, analysis, and reporting of biomarker prognostic studies, a set of reporting recommendations are discussed. For example, biomarkers should provide unique information that adds to known clinical and pathologic information. Conventional multivariable analyses are not sufficient to demonstrate improved prediction of outcomes. Predictive models, including or excluding any new putative biomarker, need to show clinically significant improvement of performance in order to claim any real benefit. Towards this end, proper model building, avoidance of overfitting, and external validation are crucial. In addition, it is important to choose appropriate performance measures dependent on outcome and prediction type and to avoid the use of cutpoints. Biomarkers providing a continuous score provide potentially more useful information than cutpoints since risk fits a continuum model. Combination of complementary and independent biomarkers is likely to better capture the biological potential of a tumor than any single biomarker. Finally, methods that incorporate clinical consequences such as decision curve analysis are crucial to the evaluation of biomarkers.

CONCLUSIONS

Attention to sound design and statistical practice should be delivered as early as possible and will help maximize the promise of biomarkers for patient care. Studies should include a measure of predictive accuracy and clinical decision-analysis. External validation using data from an independent cohort provides the strongest evidence that a model is valid. There is a need for adequately assessed clinical biomarkers in bladder cancer.

Predictive and prognostic models in radical prostatectomy candidates: a critical analysis of the literature

CONTEXT

Numerous predictive and prognostic tools have recently been developed for risk stratification of prostate cancer (PCa) patients who are candidates for or have been treated with radical prostatectomy (RP).

OBJECTIVE

To critically review the currently available predictive and prognostic tools for RP patients and to describe the criteria that should be applied in selecting the most accurate and appropriate tool for a given clinical scenario.

EVIDENCE ACQUISITION

A review of the literature was performed using the Medline, Scopus, and Web of Science databases. Relevant reports published between 1996 and January 2010 identified using the keywords prostate cancer, radical prostatectomy, predictive tools, predictive models, and nomograms were critically reviewed and summarised.

EVIDENCE SYNTHESIS

We identified 16 predictive and 22 prognostic validated tools that address a variety of end points related to RP. The majority of tools are prediction models, while a few consist of risk-stratification schemes. Regardless of their format, the tools can be distinguished as preoperative or postoperative. Preoperative tools focus on either predicting pathologic tumour characteristics or assessing the probability of biochemical recurrence (BCR) after RP. Postoperative tools focus on cancer control outcomes (BCR, metastatic progression, PCa-specific mortality [PCSM], overall mortality). Finally, a novel category of tools focuses on functional outcomes. Prediction tools have shown better performance in outcome prediction than the opinions of expert clinicians. The use of these tools in clinical decision-making provides more accurate and highly reproducible estimates of the outcome of interest. Efforts are still needed to improve the available tools' accuracy and to provide more evidence to further justify their routine use in clinical practice. In addition, prediction tools should be externally validated in independent cohorts before they are applied to different patient populations.

CONCLUSIONS

Predictive and prognostic tools represent valuable aids that are meant to consistently and accurately provide most evidence-based estimates of the end points of interest. More accurate, flexible, and easily accessible tools are needed to simplify the practical task of prediction.

Targeting chemotherapy to advanced bladder cancer patients most likely to benefit

Evaluation of: Vickers AJ, Cronin AM, Kattan MW et al.; The International Bladder Cancer Nomogram Consortium: Clinical benefits of a multivariate prediction model for bladder cancer: a decision analytic approach. Cancer 115(23), 5460-5469 (2009). The prognosis from muscle-invasive bladder cancer is poor. Improvements in survival can be made with the use of chemotherapy. The best results are obtained using multiagent regimens, which increase cure rates by approximately 5%. Thus, few patients benefit when compared with treatment morbidity. This low complete response rate makes powering of clinical trials difficult and may prevent them determining which patients benefit most from chemotherapy. Here, we discuss work by Vickers et al. reporting a decision-based analysis using a nomogram to determine the benefit for individual patients from chemotherapy. This decision aid can reduce the number of patients treated by 0.006, without compromising recurrence. The authors conclude that a nomogram-derived 25% risk threshold produced better targeting of chemotherapy than the current standard criteria (mostly using pathological stage).