Shannon Lynch, PhD, MPH

Shannon Lynch, PhD, MPH
​​

This Fox Chase professor participates in the Undergraduate Summer Research Fellowship
Learn more about Research Volunteering.

Assistant Professor, Cancer Prevention and Control

Treatment Philosophy

I’m your “neighborhood researcher” in many ways.  I grew up in the Fox Chase area, and my research focuses on understanding how biomarkers and social factors, including neighborhood circumstances, act together to affect cancer.  Born and raised in Philadelphia, I feel privileged that I get the opportunity to help those in my community fight cancer as part of my every day.  I am grateful to all of the patients and families who participate in research programs at Fox Chase.  You are making a difference.  Thank you for your commitment.  It’s so nice to be home. 

Educational Background

  • PhD, Epidemiology, University of Pennsylvania, Philadelphia, PA
  • MPH, Environmental and Occupational Health, The George Washington University, Washington, DC
  • Marymount University, Arlington, VA

Memberships

  • American Association of Cancer Research (AACR)

Honors & Awards

  • Top 20 most downloaded article in Obesity, Science and Practice, 2017
  • Highly rated abstract, invited oral presentation, AACR Annual Meeting, 2016
  • AACR Annual Meeting Women Scholar-in-Training award, 2011
  • AACR Annual Meeting Bristol Scholarship, 2009
  • U.S. Public Health Service Training Award, George Washington University, 2003
  • Tri-Beta Honor Society, 2003
  • Clare Booth Luce full tuition, room, and board Scholarship, Marymount University, 1999-2003

People

Research Interests

  • Multilevel Data Integration/Analysis
  • Empiric Methods Development/Machine Learning
  • Cancer Risk Prediction Modeling
  • Geospatial Cluster Analyses
  • Telomere Length, Aging, Cancer
  • Cancer Education and Screening Interventions
  • Cancer Health Disparities
  • Precision Public Health/Precision Medicine
  • Analysis of secondary data sources: electronic medical records, State cancer registries, prospective cohorts

Lab Overview

Our laboratory specializes in the integration and analysis of complex, multilevel data resources in order to study how the neighborhood, or environment where a person lives, together with a person’s behavior and biology, can impact cancer risk and cancer health disparities.  Specifically, our laboratory develops machine learning and geospatial methods to discover patterns in complex, multilevel risk factor data that can be used to improve clinical risk prediction models for patients and to ensure targeted cancer prevention efforts are deployed to communities disproportionately impacted by poor cancer outcomes.  In line with Precision Public Health, our emphasis is not only on identifying patients or communities with higher than expected rates of cancer or identifying multilevel risk factors (e.g. genetic, biologic, behavioral, and neighborhood or environmental factors) from existing disease surveillance resources, but translating this information into action through cancer interventions.  Our work is currently supported by funding from the Department of Defense, the American Cancer Society, and the Pennsylvania Department of Health.

Lab Description

Our laboratory specializes in the integration and analysis of complex, multilevel data resources in order to study how the neighborhood, or environment where a person lives, together with a person’s behavior and biology can impact cancer risk and cancer health disparities.  Specifically, our laboratory applies machine learning and geospatial methods to discover patterns in complex, multilevel risk factor data that can be used to improve clinical risk prediction models for patients and to ensure targeted cancer prevention efforts are deployed to communities disproportionately impacted by poor cancer outcomes.  In line with Precision Public Health, our emphasis is not only on identifying patients or communities with higher than expected rates of cancer or identifying multilevel risk factors (e.g. genetic, biologic, behavioral, and neighborhood or environmental factors) associated with cancer, but translating this information into action through cancer interventions.

In our lab, we utilize and integrate multiple data resources to identify where the cancer burden is and what risk factors may contribute to this burden.  This approach allows us to identify high-risk patients and communities who might benefit from cancer intervention, particularly those impacted by cancer health disparities.  We currently work with four main data sources:  cancer registry data, electronic medical records (EMR), our neighborhood data platform (NDP), and the national Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) Trial cohort data.   We use Pennsylvania and New Jersey cancer registry data to conduct geospatial analyses to identify where there are higher than expected rates of cancer in PA and NJ neighborhoods (identified at the census tract level).  We have developed a Neighborhood Database Platform for Pennsylvania and New Jersey (NDP-PANJ) that includes over 14K neighborhood socioeconomic, built environment, and health care access variables from multiple sources (the U.S. Census, land use/remote sensing, Environmental Protection Agency, etc.) at the census-tract level.  We link the NDS-PANJ data with PA cancer registry data to conduct association and geospatial studies that can identify neighborhood circumstances related to cancer risk and survival across PA and NJ neighborhoods.  Using liver cancer as an example, we conducted a geospatial study to identify neighborhoods with both a high liver cancer burden and low socioeconomic conditions (SES) to target for liver cancer educational interventions.   This is one example of Precision Public Health approaches by our lab.  Precision Public Health identifies high risk populations through geospatial analysis in order to reduce population targets and maximize often limited resources for interventions in the community.

Our lab also specializes in empiric investigations and machine learning approaches designed to identify neighborhood circumstances that could differentially impact not only neighborhood cancer rates, but state-wide cancer risk overall and by race/ethnicity.  We have developed a novel neighborhood-wide association study (NWAS) that identified from over 14K variables from the NDS, 17 neighborhood socioeconomic factors in White men and 5 in Black men, that were significantly related to diagnosis with advanced prostate cancer in PA.  This data was then translated and tested in clinical prediction models of time to prostate cancer diagnosis in a clinical dataset of Black and White men at high risk for prostate cancer.  We preliminarily found that the addition of neighborhood SES factors might improve clinical decision-making related to prostate cancer treatment, but recommendations might vary by race.  Building on these findings, current funded projects include linking our NDS-PANJ data with electronic medical records from Fox Chase Cancer Center to determine whether the addition of neighborhood factors associated with prostate or pancreatic cancer could improve the identification of patients who are likely to be diagnosed with advanced disease or have worse survival. 

In addition to neighborhood (or where a person lives), genetics and biology also play a key role in cancer risk and development.  Utilizing the PLCO cohort, we are developing a comprehensive, multilevel risk prediction model for pancreatic cancer that includes genetic and blood biomarkers, in addition to dietary, behavioral, biomedical, and demographic factors using our newly developed, reproducible machine learning pipeline.  This project serves as foundational work to determine if a common set of co-occurring risk factors could be identified to improve the identification of individuals at high risk for pancreatic cancer. 

One particular biomarker of interest in pancreatic cancer, and cancer health disparities research more broadly, is blood leukocyte telomere length.  Our prior studies have found associations between longer telomere length and increased risk for pancreatic cancer, as well as associations between shorter telomere length and perceived stress and neighborhood SES circumstances.  Given these findings, as well as our findings that telomere length can vary by race/ethnicity, we plan to continue to investigate the telomere maintenance pathway, along with other epigenetic markers, as potential biologic linkages between cancer and exposures to unfavorable neighborhood environments under a chronic stress hypothesis.  In this way, our lab continues to take findings from discovery to inform not only risk prediction, but also cancer etiology. 

Selected Publications

Sorice K.A., Fang C.Y., Wiese D., Ortiz A., Chen Y., Henry K.A., Lynch S.M., Systematic review of neighborhood socioeconomic indices studied across the cancer control continuum. Cancer Med. 11(10): 2125-2144, 2022.PMC9119356. https://www.ncbi.nlm.nih.gov/pubmed/35166051.

Mayhand K.N., Handorf E.A., Ortiz A.G., Gonzalez E.T., Devlin A., Sorice K.A., Esnaola N., Fisher S., Lynch S.M., Effect of neighborhood and individual-level socioeconomic factors on colorectal cancer screening adherence. Int J Environ Res Public Health. 18(9)2021. PMC8122519. https://www.ncbi.nlm.nih.gov/pubmed/33919106.

Handorf, E., Yin, Y., Slifker, M. Lynch SM. Variable selection in social-environmental data: sparse regression and tree ensemble machine learning approaches. BMC Med Res Methodol 20, 302 (2020). https://doi.org/10.1186/s12874-020-01183-9

Lynch SM, Wiese D, Ortiz A, Sorice KA, Nguyen M, González ET, Henry KA. Towards precision public health: Geospatial analytics and sensitivity/specificity assessments to inform liver cancer prevention. SSM Popul Health. 2020 Aug 7;12:100640. doi: 10.1016/j.ssmph.2020.100640. PMID: 32885020; PMCID: PMC7451830.

Ortiz AG, Wiese D, Sorice KA, Nguyen M, González ET, Henry KA, Lynch SM. Liver Cancer Incidence and Area-Level Geographic Disparities in Pennsylvania-A Geo-Additive Approach. Int J Environ Res Public Health. 2020 Oct 16;17(20):7526. doi: 10.3390/ijerph17207526. PMID: 33081168; PMCID: PMC7588924.

Wiese D, Stroup AM, Maiti A, Harris G, Lynch SM, Vucetic S, Henry KA. Socioeconomic Disparities in Colon Cancer Survival: Revisiting Neighborhood Poverty Using Residential Histories. Epidemiology. 2020 Sep;31(5):728-735. doi: 10.1097/EDE.0000000000001216. PMID: 32459665.

Lynch SM, Handorf E, Sorice KA, Blackman E, Bealin L, Giri VN, Obeid E, Ragin C, Daly M. The effect of neighborhood social environment on prostate cancer development in black and white men at high risk for prostate cancer. PLoS One. 2020 Aug 13;15(8):e0237332. doi: 10.1371/journal.pone.0237332. PMID: 32790761; PMCID: PMC7425919.

Wiese D, Stroup AM, Maiti A, Harris G, Lynch SM, Vucetic S, Henry KA. Residential Mobility and Geospatial Disparities in Colon Cancer Survival. Cancer Epidemiol Biomarkers Prev. 2020 Nov;29(11):2119-2125. doi: 10.1158/1055-9965.EPI-20-0772. Epub 2020 Aug 5. PMID: 32759382.

Madnick D, Handorf E, Ortiz A, Sorice K, Nagappan L, Moccia M, Cheema K, Vijayvergia N, Dotan E, Lynch SM. Investigating disparities: the effect of social environment on pancreatic cancer survival in metastatic patients. J Gastrointest Oncol. 2020

Lynch SM, Sorice K, Tagai EK, Handorf EA. Use of empiric methods to inform prostate cancer health disparities: Comparison of neighborhood-wide association study "hits" in black and white men. Cancer. 2020 Jan 1;126(9):1949-1957. doi: 10.1002/cncr.32734. Epub 2020 Feb 3. PMID: 32012234; PMCID: PMC7341673... Expand

The following ratings and reviews are based on verified feedback collected from independently administered patient experience surveys. The ratings and comments submitted by patients reflect their own views and opinions. Patient identities are withheld to ensure confidentiality and privacy. Learn more about our Patient Experience Ratings.

Ratings Breakdown

Loading ...

Patient comments

Loading ...
​​

This Fox Chase professor participates in the Undergraduate Summer Research Fellowship
Learn more about Research Volunteering.