Marc Ryser

Overview:



For an up-to-date description of my research program please visit

https://ryser.netlify.com

Areas of Expertise: Multi-scale modeling, early carcinogenesis, cancer evolution

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:

Mathematical Analysis of Spatial Cancer Models

Administered By
Mathematics
Awarded By
National Science Foundation
Role
Co-Principal 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

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

Administered By
Basic Science Departments
Awarded By
National Institutes of Health
Role
Principal 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:

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

<h4>Motivation</h4>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.<h4>Results</h4>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.<h4>Conclusions</h4>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.<h4>Availability</h4>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.<h4>Supplementary information</h4>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 (Oxford, England), Sept. 2021. Epmc, doi:10.1093/bioinformatics/btab637.
URI
https://scholars.duke.edu/individual/pub1496093
PMID
34487148
Source
epmc
Published In
Bioinformatics (Oxford, England)
Published Date
DOI
10.1093/bioinformatics/btab637

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, Aug. 2021. Pubmed, doi:10.1038/s41416-021-01496-6.
URI
https://scholars.duke.edu/individual/pub1494638
PMID
34408284
Source
pubmed
Published In
Br J Cancer
Published Date
DOI
10.1038/s41416-021-01496-6

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

A WEB-BASED PERSONALIZED DECISION TOOL FOR PATIENTS DIAGNOSED WITH DUCTAL CARCINOMA IN SITU: DEVELOPMENT, CONTENT EVALUATION, AND USABILITY TESTING

Authors
Fridman, I; Chan, L; Grant, J; Fish, L; Falkovic, M; Brioux, J; Pollak, KI; Weinfurt, K; Hwang, S; Ryser, MD
MLA Citation
URI
https://scholars.duke.edu/individual/pub1484898
Source
wos-lite
Published In
Medical Decision Making : an International Journal of the Society for Medical Decision Making
Volume
41
Published Date
Start Page
E78
End Page
E80

Relationship Between HCAHPS Scores and Survey Response Rate Is Linked to Hospital Size.

Patient experience is an important dimension of health care quality and is assessed using the standard Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey for inpatients. The HCAHPS scores may vary based on survey response rate and hospital size. The objective of this study was to describe the association between survey response rate and HCAHPS scores and examine whether the relationship varies based on hospital size. Medicare's Hospital Compare publicly reported HCAHPS data were used. Pearson correlation, controlling for number of staffed beds, and linear regression models were used for the analysis. Hospitals were grouped into quartiles based on number of staffed beds to delineate the effect of increasing hospital size on the relationship between survey response rate and HCAHPS scores. A significant association between HCAHPS survey response rate and all examined HCAHPS domain scores was observed. The effect size across HCAHPS domains varied based on hospital size. The relationship between HCAHPS score and survey response rate differed significantly between hospitals in the smallest and largest size quartiles for discharge information, nurse communication, and hospital quietness. While a causal relationship cannot be inferred from this study, the response rate could be a direct and/or indirect driver of HCAHPS scores. Future research should be aimed to further explore the basis of this relationship and to determine how it may inform the interpretation of HCAHPS results.
Authors
Rodriguez-Homs, LG; Hammill, BG; Ryser, MD; Phillips, HR; Mosca, PJ
MLA Citation
Rodriguez-Homs, Larissa G., et al. “Relationship Between HCAHPS Scores and Survey Response Rate Is Linked to Hospital Size.J Patient Exp, vol. 7, no. 6, Dec. 2020, pp. 1543–48. Pubmed, doi:10.1177/2374373520932458.
URI
https://scholars.duke.edu/individual/pub1472628
PMID
33457612
Source
pubmed
Published In
J Patient Exp
Volume
7
Published Date
Start Page
1543
End Page
1548
DOI
10.1177/2374373520932458