NCI Cancer Screening Research Network (CSRN) Communication and Coordinating Center (CCC)

The recent development of multicancer detection (MCD) technologies has fueled already strong interest in this topic with information in the lay press preceding compelling evidence of their value for reducing cancer burden. The complexities of implementing MCD tests require careful consideration in relation to the potential impact of MCD based screening on cancer mortality. The Cancer Screening Research Network (CSRN) will be a cooperative group structure with the expertise, infrastructure and access to appropriate patient populations needed to provide high quality evidence on the value of MCD tests in real-world settings. Our objectives are to:  

1. Provide exceptional scientific and clinical expertise in the design and implementation of multicenter cancer screening trials, and particularly randomized trials evaluating MCD assays. We will: 
   • Assist in evaluating and selecting candidate MCD assays for their readiness to test 
   • Model key trial design parameters to enhance trial efficiency, interpretation, and generalizability 
   • Develop guidelines for routine screening and for follow-up diagnostic procedures for positive MCD test 
   • Tailor data collection efforts to Vanguard stage goals while anticipating future studies 
2. Develop the organizational structure and administrative relationships to assure CSRN success. We will:
   •  Define, populate, co-lead, and support an efficient CSRN governance structure 
   • Develop study policies and procedures that ensure scientific integrity and promote strong working relationships between all CSRN organizations, investigators, and staff 
   • Develop and maintain all official study documentation and regulatory processes 
3. Develop and maintain effective communication channels and materials to promote CSRN success by
   • Engaging all CSRN partners in protocol development, implementation, and monitoring 
   • Developing and maintaining a study website and other communicational channels as appropriate to each audience 
   • Creating and supporting an external community advisory group to incorporate the perspectives of patients, providers, payers and industry in the design and implementation of MCD tests 
4. Support CSRN ACCESS sites in their recruitment and retention efforts. This will include:
   • Creating well-tailored and accessible participant materials in multiple formats and languages appropriate to ACCESS Hub populations 
   • Providing ACCESS sites with strategies and resources to engage under-served populations in their catchment areas 
   • Defining and monitoring recruitment and retention goals for minority and under-served populations

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Modeling and Analytics for Cancer Diagnostics

This Research Program details a sequence of projects for two technologies that are generating intense current interest with wide-ranging practice implications and serious evidence gaps:

• Multi-cancer early detection testing, and PSMA-PET/CT for newly diagnosed and recurrent prostate cancer. The MCED work will deepen our understanding of performance characteristics, provide guidance regarding a defensible test confirmation strategy, project benefits and harms of different MCED strategies and offer new ideas for shortcutting the typically lengthy process of cancer screening trials.
• The PSMA-PET/CT work will develop an approach for updating treatment benefit estimated derived from trials that included a mixture of patients with unknown PSMA status and will project lives saved of treatment reallocation on the basis of PSMA-PET.CT result.

The tools and processes developed for modeling these technologies will be applicable to other new diagnostics that emerge during the lifetime of the Research Program. The modeling work will be accompanied by a sequence of real-world analytics projects to assess dissemination of and disparities in uptake of novel diagnostics and their consequences for healthcare utilization and costs.

Modeling and Outcomes Research in Prostate Cancer

Our team’s work focuses on the modeling of prostate cancer progression, detection, and outcomes. We use a combination of mathematical, statistical, and computer simulation modeling. Our models were originally designed to interrogate patterns of prostate cancer incidence and mortality in the US population. Prostate screening was adopted in the population long before results of randomized screening trials became available. Thus, the population has served as an “uncontrolled experiment” offering information on prostate cancer natural history as well as the potential benefits and harms of screening. Modeling provides a sophisticated and coherent approach for unlocking this information and making reasonable inferences from population data.

We have used our models to make inferences about overdiagnosis due to prostate cancer screening and study the role of screening versus treatment in explaining declines in prostate cancer mortality. More recently we have used our models to study the comparative benefits and harms of many competing PSA screening policies. The goal of this work is to provide quantitative information on the tradeoffs of competing policies to screening policy panels as they deliberate their recommendations regarding PSA screening. We have carefully studied the evidence used by the Task Force in developing their recent D recommendation against PSA screening and have suggested, using results from modeling studies, that the Task Force decision was based on evidence that underestimated benefit and overestimated harms of screening.

In our recent articles, our national presentations, and our ongoing work with national policy panels we are actively involved in the great policy debate about prostate cancer screening. New initiatives include using modeling to study the comparative effectiveness of active surveillance and to identify optimal surveillance policies, and to evaluate the harm-benefit tradeoffs of early treatment of recurrent disease.

Active Projects

Modeling and Analytics for Novel Cancer Diagnostics: Traversing the Data-Evidence Divide

The field of cancer diagnostics is in a rapidly expanding growth phase that goes hand in glove with the precision medicine revolution. However, the rapid pace at which new technologies are entering the marketplace makes rigorous evaluation via the standard clinical trials-based pipeline infeasible for all but a relative few. This means that while we typically have some data about diagnostic test performance, we frequently lack evidence regarding the outcomes that drive clinical and policy decisions. Novel diagnostics for cancer risk prediction, early detection, and prognostic classification are reshaping cancer care and fueling the precision oncology revolution. The major goals of this projects are to develop analytic methods and models to support the optimal use of these technologies in practice, by evaluating their expected benefits, harms, and costs. The research proposal for this award outlined a sequence of projects for two technologies that are generating intense current interest with wide-ranging practice implications and serious evidence gaps:

• Multi-cancer early detection (MCED). New blood tests for cancer offer to detect multiple cancers with a single blood draw. While companies developing the tests are engaged in intensive marketing efforts focusing on their potential benefits, evidence about the magnitude of benefit and about the potential harms of offering the tests remains lacking. Our MCED work will clarify best practices for estimating test performance in the prospective screening setting, project the expected late-stage and mortality reductions expected from tests for specific target cancers, and offer ideas for short-cutting the typically length process of screening trial implementation. In sum, our work will inform realistic expectations regarding the outcomes of MCED testing as well as the design of studies to evaluate their benefits and harms.

• PSMA-PET/CT for prostate cancer. This imaging approach identifies prostate cancer metastases with finer resolution than any technology to date. The consequence is that many men who were previously thought to harbor only localized or regional tumors are now being found to have systemic spread of their disease. The implications of intensifying treatment for these men are not well understood, and the efficacies of treatments within subgroups defined by PSMA-PET status have not been explicitly studies. Our PSMA work will develop frameworks for studying the comparative- and cost-effectiveness of using this technology at diagnosis and at PSA recurrence The tools and processes developed for modeling these technologies will be applicable to other new diagnostics that emerge during the lifetime of the Research Program. In the coming year we will expand our scope to also investigate AI/machine learning, including on images, as an area of investigation.

This work is funded by the National Cancer Institute (R35 grant to Dr. Etzioni).

Key Publications:

Lange JM, Gogebakan KC, Gulati R, Etzioni R. Projecting the Impact of Multi-Cancer Early Detection on Late-Stage Incidence Using Multi-State Disease Modeling. Cancer Epidemiol Biomarkers Prev. 2024;33(6):830-837.

CISNET Prostate

The Cancer Intervention and Surveillance Modeling Network (CISNET) was initiated in 2000 to explain the drivers of trends in cancer incidence and mortality in the United States. At this time, prostate cancer mortality had been declining for a number of years after peaking in the early 1990s. The defining goal of CISNET Prostate was to quantify the roles of screening and changes in primary treatment in driving these mortality declines. The Prostate CISNET group consists of three modeling groups, at Fred Hutch Cancer Center, Erasmus Medical Center, and the University of Michigan. Our overarching goal remains to determine the population impact of changing strategies for prostate cancer control, by linking trends in disease incidence and mortality with trends in screening and treatment. Our methods combine simulation models and maximum likelihood analysis to shed light on two of the most active controversies in prostate cancer research: the value of PSA screening versus advances in prostate cancer treatment, and the link between disparities in care and differences in prostate cancer outcomes between Black and White men in the population. In addition to being a prostate modeling site (with modeling led by Roman Gulati), the  Fred Hutch also serves as the co-ordinating center for CISNET Prostate.

Aim 1: Precision early detection, including risk-stratified screening and biopsy using genetic tests, novel biomarkers, and imaging technology. Develop models to evaluate the impact of screening policies that (a) increase diagnostic intensity in high-risk population strata; (b) adapt screening intervals to individual PSA and biopsy histories; (c) personalize biopsy decisions based on novel biomarkers and/or imaging. Out-comes will be projected under both idealized adherence and under real-world screening and biopsy behaviors. Area 1: Precision screening and new screening technologies.

Aim 2. Precision active surveillance, including adaptive biopsy intervals and imaging technology. Ex-tend existing models to (a) explore personalized biopsy intervals based on PSA histories and novel biomarkers; (b) model MRI as a substitute for biopsy. Area 3: Overdiagnosis and active surveillance.

Aim 3: Precision treatment, including type and timing of initial and salvage therapies. Develop natural history models of events after diagnosis (recurrence, metastasis, death) including (a) estimating risks of metastasis and prostate cancer death and how they are altered by salvage treatments; (b) quantifying the impact of precision salvage therapies compared to non-precision approaches. We expect that this aim will produce novel methodological approaches in addition to a comprehensive assessment of factors associated with post-primary treatment surveillance and treatment intensity in real-world settings. Area 2: Precision treatment.

Aim 4: Targeting screening, biopsy, and treatment policies to reduce racial disparities. This aim will build on our prior models of disease natural history in black men to address what needs to be done differently for black men to close the mortality gap. We will project the mortality reduction and harm-benefit tradeoffs in black men of (a) screening earlier and more frequently; (b) lowering the threshold for prostate biopsy; (c) in-creasing the utilization of primary curative therapies. Area 7: Suggesting routes to reduce health disparities.

Aim 5: Prioritizing screening and treatment interventions in international settings. This aim will project the reduction in prostate cancer mortality under country-specific screening and treatment strategies while modifying the underlying risk of disease and stage-specific survival to match observed disease incidence and mortality in international populations. Application areas will include the Bahamas, British Columbia, the United Kingdom, and Saudi Arabia. Area 6: State, local and international cancer control planning.

Pacific Northwest Specialized Program of Research Excellence (SPORE)

The Pacific NW Prostate Cancer SPORE is a multi-project research program of clinical, translational and population sciences research aimed at ascertaining drivers of metastatic disease risk and developing interventions to prevent and treat high-risk disease. Dr Etzioni is the Principal Investigator and Mr. Gulati is the chief statistical consultant on the Biostatistics Core for the Pacific NW Prostate Cancer SPORE. The Biostatistics Core develops strategies for study design, data collection, measurement, and analysis to validly and rigorously address the critical hypotheses and questions of SPORE projects, reduce systematic bias, and ensure a high likelihood of detection of biologically meaningful effects. The Biostatistics Core also identifies and implements quantitative methods to address scientific questions of interest and develop the evidence needed to support study hypotheses.

Early Detection Research Network Data Management and Coordinating Center

Over the past 20 plus years the Early Detection Research Network (EDRN) Data Management and Coordinating Center (DMCC), located at Fred Hutch Cancer Center, has served a vital role in co-ordinating network research activities, providing statistical support for study design and analysis, and developing innovative statistical methods for cancer biomarker development. The recently renewed DMCC will add a modeling capability for translation of novel biomarker performance to predicted clinical utility and outcomes. Dr Etzioni is one of the multi-PI’s for the renewal. 

Network Coordination and Outreach (Aim 1)
Under the direction of the SC, the DMCC will provide operational support logistical and administrative support for meetings and workshops; provide operational support for EDRN communications, subcommittee meetings, and telephone conference calls; produce and maintain all documents, including the Manual of Operations and procedures manuals; maintain and enhance the EDRN secure web site; maintain and enhance a mailing list system within EDRN; maintain the online review of applications/proposals submitted to the EDRN; disseminate statistical software; and provide contents for EDRN public portal for community outreach.

Data Science, Data Management and Study Protocol Development (Aim 2)
Under the direction of the SC, the DMCC will provide coordination and support for EDRN collaborative validation studies, including: work with study investigators on study design and protocol development; produce data forms and Manuals of Operation; develop and maintain a data management system and a communication system for multi-center studies; monitor protocol adherence, data collection and submission; perform data quality control and quality assurance (QC/QA); analyze data; provide tables, graphics, and other materials for study reports and manuscript preparations; develop uniform protocols for data and specimen collection and sharing; support the formation and distribution of EDRN biospecimen reference sets and analyze data generated from the reference sets; provide a mechanism for rapid and routine information sharing among EDRN investigators and NCI staff. The DMCC will provide EDRN validation studies statistical services and innovations in validation study development, study design, and data analyses, and will apply or develop new data science methodology and tools if necessary, to adapt to the EDRN needs and for broad use by scientific community.

Validation Study Infrastructure Services (Aim 3)
The DMCC will work with the NCI and JPL EDRN Informatics Center (EDRN IC) to specify and design software for the development of the Network-wide informatics enterprise; lead the development of the EDRN secure web site and feed data to EDRN IC for dissemination; maintain and enhance systems to support collection and analysis of data from network collaborative studies; warehouse data to be shared with the scientific community; and maintain and enhance systems in collaboration with EDRN IC to publish EDRN information. The DMCC and EDRN IC will work closely with NCI and the NCI Center for Biomedical Informatics and Information Technology (CBIIT).

Management of Core Funds (Aim 4)
The DMCC will work closely with the EDRN SC, the NCI Project Coordinator, and the Fred Hutch Office of Sponsored Research (OSR) to ensure the timely activation of core funds after EDRN SC approval so that EDRN network collaborative studies will not be delayed, and to ensure compliance with all regulatory requirements for subaward management. The Fred Hutch OSR has extensive experience in managing NIH grants and contracts (e.g., Women’s Health Initiatives and the last cycle of EDRN Core Funds).

Key Publications:

Pepe MS, Etzioni R, Feng Z, et al. Phases of biomarker development for early detection of cancer. J Natl Cancer Inst. 2001;93(14):1054-1061.

Past Projects

A Tool to Translate Intermediate Endpoints to Mortality in Cost-Effectiveness Studies (CANTRANce)

Many cost effectiveness studies comparing methods to prevent, treat, or cure cancer do not have the time or the information to evaluate how the approaches being studied affect cancer deaths. Our goal is to develop a software system to translate the results of these studies into projections of the effects of the methods being compared on deaths due to the disease. Learn more about CANTRANce.

Outcomes Based Guideline Development for Prostate Cancer Screening and Treatment (Guidelines)

Clinical practice guidelines for prostate cancer screening impact millions of men at risk of a prostate cancer diagnosis. The research aims to improve how these guidelines are produced by providing policy makers with a computerized decision support tool that will quantify the benefit-harm tradeoffs associated with candidate guidelines.

Modeling to Reduce Detection Bias in Cancer Risk Prediction Studies

Detection bias occurs when the predicted risk of disease due to a specific risk factor is distorted by the association between cancer detection and that risk factor. Detection bias can be due to differential screening or biopsy across subgroups defined by a risk factor (e.g. family history) or to differential performance of an existing screening modality (e.g mammography performance for dense versus non-dense breasts). This project aims to use disease natural history modeling fit to data from a screened cohort to estimate the association between a risk factor and the risk of disease onset rather than between the risk factor and disease diagnosis. We examine natural history as a risk factor for prostate cancer using data from the SELECT prostate cancer prevention trial and race/ethnicity and breast density as risk factors for breast cancer using data from the Breast Cancer Surveillance Consortium. 

Key Publications:

Gard CC, Lange J, Miglioretti DL, O'Meara ES, Lee CI, Etzioni R. Risk of cancer versus risk of cancer diagnosis? Accounting for diagnostic bias in predictions of breast cancer risk by race and ethnicity. J Med Screen. 2023;30(4):209-216. 

Prostate Modeling to Identify Surveillance Strategies (PROMISS)

Widespread PSA screening in the US has created an epidemic of low-risk prostate cancer, the vast majority of which is not lethal. Active surveillance (AS) is an increasingly popular option for managing low-risk prostate cancer, but no evidence-based standard exists for how to implement it. Several AS studies are ongoing but they constitute a loose collection of different approaches and their results cannot be readily compared or integrated. This Prostate Modeling to Identify Surveillance Strategies (PROMISS) research will determine an optimal approach to AS given patient characteristics and preferences.

Estimation and Communication of Overdiagnosis in Cancer Screening (Overdiagnosis)

Overdiagnosis, or the detection by screening of cases that would never become clinically diagnosed, is now recognized as the greatest potential harm of screening. Knowledge about overdiagnosis is critical for well-formed screening policies and for well-informed patient decision making. However, overdiagnosis depends on screening practices and personal factors and many published studies are biased or do not apply to populations that differ from those used for estimation. The Estimation and Communication of Overdiagnosis in Cancer Screening (Overdiagnosis) research will advance knowledge about how to validly estimate overdiagnosis and to provide concrete information about overdiagnosis associated with specific cancer screening settings to inform screening policy development and clinical decision making.

Modeling Outcomes of Reclassifying Low Grade Prostate Cancer

Overdiagnosis and overtreatment of low-grade prostate cancer has long been a concern. The Etzioni group was the first to quantify the frequency of overdiagnosis in prostate cancer screening and has contributed materially to the methodology for estimating overdiagnosis from screening trials and observational cohorts. In recent years, there have been calls to relabel low-grade (Gleason 6) lesions in the prostate as non-cancer, largely due to their ubiquity among men over 50 and their relative clinical indolence in men undergoing active surveillance. However, the population health effects of such a relabeling remain unclear. The Etzioni Lab is collaborating with colleagues from the University of Chicago and Erasmus Medical Center to model the harm-benefit tradeoffs of implementing such a policy, relative to current management practices. 

This work is funded by a contract from the Centers for Disease Control.