Marc Ryser

Overview:


Marc D. Ryser conducts research in cancer early detection, with a particular focus on breast cancer overdiagnosis and overtreatment. Using a multi-scale approach, his group generates and analyzes biologic, clinical and population data using a variety of mathematical, statistical and computational tools. Examples of ongoing projects include the evolutionary dynamics of early-stage breast cancer; decision support tools for early-stage breast cancer patients; and estimation of breast cancer overdiagnosis. Dr. Ryser has a keen interest in training the next generation of interdisciplinary researchers and teaches an immersive research seminar for undergraduate students called “Math & Medicine.”

Website: https://ryser.netlify.com

Positions:

Assistant Professor in Population Health Sciences

Population Health Sciences
School of Medicine

Member of the Duke Cancer Institute

Duke Cancer Institute
School of Medicine

Education:

Ph.D. 2011

McGill University (Canada)

Grants:

Molecular and Radiologic Predictors of Invasion in a DCIS Active Surveillance Cohort

Awarded By
Breast Cancer Research Foundation
Role
Co Investigator
Start Date
End Date

The Mathematics of Breast Cancer Overtreatment: Improving Treatment Choice through Effective Communication of Personalized Cancer Risk

Awarded By
National Institutes of Health
Role
Principal Investigator
Start Date
End Date

Mathematical Analysis of Spatial Cancer Models

Administered By
Mathematics
Awarded By
National Science Foundation
Role
Co-Principal Investigator
Start Date
End Date

Molecular and Radiologic Predictors of Invasion in a DCIS Active Surveillance Cohort

Awarded By
Breast Cancer Research Foundation
Role
Co Investigator
Start Date
End Date

Modeling to Minimize Detection Bias in Cancer Risk Prediction Studies

Administered By
Population Health Sciences
Awarded By
Fred Hutchinson Cancer Research Center
Role
Principal Investigator
Start Date
End Date

Publications:

Estimation of Breast Cancer Overdiagnosis in a U.S. Breast Screening Cohort.

BACKGROUND: Mammography screening can lead to overdiagnosis-that is, screen-detected breast cancer that would not have caused symptoms or signs in the remaining lifetime. There is no consensus about the frequency of breast cancer overdiagnosis. OBJECTIVE: To estimate the rate of breast cancer overdiagnosis in contemporary mammography practice accounting for the detection of nonprogressive cancer. DESIGN: Bayesian inference of the natural history of breast cancer using individual screening and diagnosis records, allowing for nonprogressive preclinical cancer. Combination of fitted natural history model with life-table data to predict the rate of overdiagnosis among screen-detected cancer under biennial screening. SETTING: Breast Cancer Surveillance Consortium (BCSC) facilities. PARTICIPANTS: Women aged 50 to 74 years at first mammography screen between 2000 and 2018. MEASUREMENTS: Screening mammograms and screen-detected or interval breast cancer. RESULTS: The cohort included 35 986 women, 82 677 mammograms, and 718 breast cancer diagnoses. Among all preclinical cancer cases, 4.5% (95% uncertainty interval [UI], 0.1% to 14.8%) were estimated to be nonprogressive. In a program of biennial screening from age 50 to 74 years, 15.4% (UI, 9.4% to 26.5%) of screen-detected cancer cases were estimated to be overdiagnosed, with 6.1% (UI, 0.2% to 20.1%) due to detecting indolent preclinical cancer and 9.3% (UI, 5.5% to 13.5%) due to detecting progressive preclinical cancer in women who would have died of an unrelated cause before clinical diagnosis. LIMITATIONS: Exclusion of women with first mammography screen outside BCSC. CONCLUSION: On the basis of an authoritative U.S. population data set, the analysis projected that among biennially screened women aged 50 to 74 years, about 1 in 7 cases of screen-detected cancer is overdiagnosed. This information clarifies the risk for breast cancer overdiagnosis in contemporary screening practice and should facilitate shared and informed decision making about mammography screening. PRIMARY FUNDING SOURCE: National Cancer Institute.
Authors
Ryser, MD; Lange, J; Inoue, LYT; O'Meara, ES; Gard, C; Miglioretti, DL; Bulliard, J-L; Brouwer, AF; Hwang, ES; Etzioni, RB
MLA Citation
Ryser, Marc D., et al. “Estimation of Breast Cancer Overdiagnosis in a U.S. Breast Screening Cohort.Ann Intern Med, vol. 175, no. 4, Apr. 2022, pp. 471–78. Pubmed, doi:10.7326/M21-3577.
URI
https://scholars.duke.edu/individual/pub1512139
PMID
35226520
Source
pubmed
Published In
Ann Intern Med
Volume
175
Published Date
Start Page
471
End Page
478
DOI
10.7326/M21-3577

A web-based personalized decision support tool for patients diagnosed with ductal carcinoma in situ: development, content evaluation, and usability testing.

PURPOSE: Patients diagnosed with ductal carcinoma in situ (DCIS) face trade-offs when deciding among different treatments, including surgery, radiation, and endocrine therapy. A less chosen option is active monitoring. While evidence from clinical trials is not yet available, observational studies show comparable results for active monitoring and immediate treatment on cancer outcomes in select subgroups of patients. We developed and tested a web-based decision support tool (DST) to help patients explore current knowledge about DCIS and make an informed choice. METHODS: The DST, an interactive web application, was informed by literature reviews and formative work with patients, breast surgeons, and health communication experts. We conducted iterative interviews to evaluate the DST content among women with and without a history of breast cancer, as well as breast cancer experts. For usability testing, we conducted an online survey among women with and without a history of breast cancer. RESULTS: For content evaluation, 5 women with and 10 women without a history of DCIS were interviewed. The sample included 11 White and 4 non-White women, with a mean age of 64 years. The expert sample consisted of 5 attendings and a physician assistant. The feedback was used to add, clarify, or reorganize information in the DST. For usability testing, 22 participants with a mean age of 61 years were recruited including 15 White and 7 Black women and 6 women with a history of DCIS. The mean usability score was 3.7 out of 5. Most participants (86%) found that the DST provided unbiased information about treatments. To improve usability, we reduced the per-page content and added navigation cues. CONCLUSION: Content and usability evaluation showed that the DST helps patients explore trade-offs of active monitoring and immediate treatment. By adopting a personalized approach, the tool will enable informed decisions aligned with patients' values and expectations.
Authors
Fridman, I; Chan, L; Thomas, J; Fish, LJ; Falkovic, M; Brioux, J; Hunter, N; Ryser, DH; Hwang, ES; Pollak, KI; Weinfurt, KP; Ryser, MD
MLA Citation
Fridman, Ilona, et al. “A web-based personalized decision support tool for patients diagnosed with ductal carcinoma in situ: development, content evaluation, and usability testing.Breast Cancer Res Treat, vol. 192, no. 3, 2022, pp. 517–27. Pubmed, doi:10.1007/s10549-022-06512-8.
URI
https://scholars.duke.edu/individual/pub1484898
PMID
35107714
Source
pubmed
Published In
Breast Cancer Res Treat
Volume
192
Published Date
Start Page
517
End Page
527
DOI
10.1007/s10549-022-06512-8

Long-term risk of subsequent ipsilateral lesions after surgery with or without radiotherapy for ductal carcinoma in situ of the breast.

BACKGROUND: Radiotherapy (RT) following breast-conserving surgery (BCS) for ductal carcinoma in situ (DCIS) reduces ipsilateral breast event rates in clinical trials. This study assessed the impact of DCIS treatment on a 20-year risk of ipsilateral DCIS (iDCIS) and ipsilateral invasive breast cancer (iIBC) in a population-based cohort. METHODS: The cohort comprised all women diagnosed with DCIS in the Netherlands during 1989-2004 with follow-up until 2017. Cumulative incidence of iDCIS and iIBC following BCS and BCS + RT were assessed. Associations of DCIS treatment with iDCIS and iIBC risk were estimated in multivariable Cox models. RESULTS: The 20-year cumulative incidence of any ipsilateral breast event was 30.6% (95% confidence interval (CI): 28.9-32.6) after BCS compared to 18.2% (95% CI 16.3-20.3) following BCS  +  RT. Women treated with BCS compared to BCS + RT had higher risk of developing iDCIS and iIBC within 5 years after DCIS diagnosis (for iDCIS: hazard ratio (HR)age < 50 3.2 (95% CI 1.6-6.6); HRage ≥ 50 3.6 (95% CI 2.6-4.8) and for iIBC: HRage<50 2.1 (95% CI 1.4-3.2); HRage ≥ 50 4.3 (95% CI 3.0-6.0)). After 10 years, the risk of iDCIS and iIBC no longer differed for BCS versus BCS + RT (for iDCIS: HRage < 50 0.7 (95% CI 0.3-1.5); HRage ≥ 50 0.7 (95% CI 0.4-1.3) and for iIBC: HRage < 50 0.6 (95% CI 0.4-0.9); HRage ≥ 50 1.2 (95% CI 0.9-1.6)). CONCLUSION: RT is associated with lower iDCIS and iIBC risk up to 10 years after BCS, but this effect wanes thereafter.
Authors
van Seijen, M; Lips, EH; Fu, L; Giardiello, D; van Duijnhoven, F; de Munck, L; Elshof, LE; Thompson, A; Sawyer, E; Ryser, MD; Hwang, ES; Schmidt, MK; Elkhuizen, PHM; Grand Challenge PRECISION Consortium,; Wesseling, J; Schaapveld, M
MLA Citation
van Seijen, Maartje, et al. “Long-term risk of subsequent ipsilateral lesions after surgery with or without radiotherapy for ductal carcinoma in situ of the breast.Br J Cancer, vol. 125, no. 10, Nov. 2021, pp. 1443–49. Pubmed, doi:10.1038/s41416-021-01496-6.
URI
https://scholars.duke.edu/individual/pub1494638
PMID
34408284
Source
pubmed
Published In
Br J Cancer
Volume
125
Published Date
Start Page
1443
End Page
1449
DOI
10.1038/s41416-021-01496-6

A Bayesian Hierarchical Model to Estimate DNA Methylation Conservation in Colorectal Tumors.

MOTIVATION: Conservation is broadly used to identify biologically important (epi)genomic regions. In the case of tumor growth, preferential conservation of DNA methylation can be used to identify areas of particular functional importance to the tumor. However, reliable assessment of methylation conservation based on multiple tissue samples per patient requires the decomposition of methylation variation at multiple levels. RESULTS: We developed a Bayesian hierarchical model that allows for variance decomposition of methylation on three levels: between-patient normal tissue variation, between-patient tumor-effect variation, and within-patient tumor variation. We then defined a model-based conservation score to identify loci of reduced within-tumor methylation variation relative to between-patient variation. We fit the model to multi-sample methylation array data from 21 colorectal cancer (CRC) patients using a Monte Carlo Markov Chain algorithm (Stan). Sets of genes implicated in CRC tumorigenesis exhibited preferential conservation, demonstrating the model's ability to identify functionally relevant genes based on methylation conservation. A pathway analysis of preferentially conserved genes implicated several CRC relevant pathways and pathways related to neoantigen presentation and immune evasion. CONCLUSIONS: Our findings suggest that preferential methylation conservation may be used to identify novel gene targets that are not consistently mutated in CRC. The flexible structure makes the model amenable to the analysis of more complex multi-sample data structures. AVAILABILITY: The data underlying this article are available in the NCBI GEO Database, under accession code GSE166212. The R analysis code is available at https://github.com/kevin-murgas/DNAmethylation-hierarchicalmodel. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors
Murgas, KA; Ma, Y; Shahidi, LK; Mukherjee, S; Allen, AS; Shibata, D; Ryser, MD
MLA Citation
Murgas, Kevin A., et al. “A Bayesian Hierarchical Model to Estimate DNA Methylation Conservation in Colorectal Tumors.Bioinformatics, Sept. 2021. Pubmed, doi:10.1093/bioinformatics/btab637.
URI
https://scholars.duke.edu/individual/pub1496093
PMID
34487148
Source
pubmed
Published In
Bioinformatics
Published Date
DOI
10.1093/bioinformatics/btab637

USING PAIRWISE SIMULATED OUTCOMES TO IMPROVE THE UNDERSTANDING OF THE STATISTICAL DIFFERENCES BETWEEN TWO RISK DISTRIBUTIONS

Authors
Chan, L; Fridman, I; Grant, J; Hwang, ES; Weinfurt, K; Ryser, MD
MLA Citation
Chan, Lok, et al. “USING PAIRWISE SIMULATED OUTCOMES TO IMPROVE THE UNDERSTANDING OF THE STATISTICAL DIFFERENCES BETWEEN TWO RISK DISTRIBUTIONS.” Medical Decision Making, vol. 41, no. 4, 2021, pp. E284–86.
URI
https://scholars.duke.edu/individual/pub1484769
Source
wos-lite
Published In
Medical Decision Making : an International Journal of the Society for Medical Decision Making
Volume
41
Published Date
Start Page
E284
End Page
E286