Developing a scalable tobacco cessation program for cancer survivors
Faculty Information
Name:
Jenifer LeLaurin PhD
Email
jlelaurin@ufl.edu
Phone
(314) 805-7207
Faculty Department/Division
Health Outcomes and Biomedical Informatics
This project is primarily:
Clinical
Research Project Description:
Background and Significance
17% of cancer survivors (who are currently going through treatment or who have completed treatment) report using tobacco or nicotine products. Tobacco use after a cancer diagnosis increases the risk of cancer-related and all-cause mortality, cancer recurrence, poor cancer treatment response, and increased treatment-related toxicity. Quitting tobacco at any stage of cancer survivorship improves health and quality of life.
Specific Aims:
Primary Aim: To assess the reach of a tailored tobacco cessation intervention for cancer survivors, defined as the proportion of eligible patients enrolled during the study period.
Secondary Aims:
To assess:
1) The reach of the intervention for caregivers/family members of cancer survivors,
2) The effectiveness of the intervention on tobacco cessation outcomes
3) Clinic and provider-level adoption of the intervention
4) Feasibility and acceptability of the intervention and implementation strategies from the perspectives of participants and healthcare providers
We hypothesize that provider training combined with a tailored intervention for cancer survivors will increase the reach of tobacco cessation treatment for cancer survivors when compared to standard care.
Methods:
We will recruit cancer patients, survivors, and their caregivers receiving treatment at UF Health Oncology, Radiation Oncology and Urology clinics who use tobacco or nicotine products. Each survivor can invite one caregiver/family member who also uses tobacco/nicotine products to participate in the intervention with them.
Eligible cancer patients and caregivers will be referred to a tailored nicotine cessation program in collaboration with the Area Health Education Center (AHEC), following screening and brief counseling by their provider. The intervention consists of four 60-minute sessions, delivered via videoconference or phone by a trained Tobacco Treatment Specialist, incorporating evidence-based cessation techniques and nicotine replacement therapy (NRT) if appropriate. Providers will receive training on the AAC model and C-LEAR approach to enhance cessation support. Participants will be followed for three months post-intervention, completing surveys and qualitative interviews to assess program effectiveness and feasibility. Data will be managed through REDCap, with select participants and providers participating in semi-structured interviews.
Role of Medical Student:
Students will assist with participant recruitment, data collection, preliminary data analysis, and qualitative interviewing and analysis. Students will have opportunities for authorship on presentations and manuscripts resulting from this work. This study requires students to be in Gainesville to perform recruitment.
Does this project have an international component or travel?
No
Multi-Omics and AI-Driven Insights for Cancer Therapeutics and Precision Medicine
Faculty Information
Name:
Dr. Qianqian Song
Email
qsong1@ufl.edu
Phone
(336) 926-4972
Faculty Department/Division
Health Outcomes and Biomedical Informatics
This project is primarily:
Translational
Research Project Description:
Project Title: Multi-Omics and AI-Driven Insights for Cancer Therapeutics and Precision Medicine
Background and Significance
Cancer is a complex and heterogeneous disease, requiring a systems biology approach to fully understand tumor evolution, treatment response, and patient outcomes. Advances in multi-omics (genomics, transcriptomics, proteomics, and spatial omics) enable the dissection of cellular heterogeneity, while artificial intelligence (AI) provides powerful tools to integrate these diverse data modalities for actionable insights.
Recent breakthroughs in spatial transcriptomics, single-cell sequencing, and deep learning have revealed novel biomarkers and mechanisms of therapy resistance. However, challenges remain in effectively leveraging multi-omics data to optimize cancer treatment strategies. Our research seeks to bridge this gap by integrating AI-driven computational models with high-dimensional cancer data to enhance patient stratification, biomarker discovery, and therapy response prediction.
Hypothesis and Rationale
We hypothesize that integrating AI-driven models with multi-omics data will allow for a more accurate and mechanistic understanding of tumor biology, leading to improved patient-specific therapeutic strategies. By utilizing deep learning frameworks and graph-based AI models, we can predict drug responses, identify resistance pathways, and uncover novel therapeutic targets in cancer patients.
Specific Aims
- Develop AI-powered multi-omics integration models to identify predictive biomarkers and therapeutic targets for precision oncology.
- Characterize spatial and single-cell tumor heterogeneity using advanced computational techniques to uncover mechanisms of treatment resistance.
- Optimize machine learning approaches for deciphering underlying molecular intricacies across different omics layers.
Methods
- Data Acquisition and Processing: Multi-omics data will be obtained from public repositories (TCGA, GEO, Cancer Cell Line Encyclopedia) and institutional databases.
- AI Model Development: We will employ graph neural networks, deep learning, and transformer-based architectures to analyze tumor heterogeneity and therapy response.
- Spatial and Single-Cell Omics Analysis: Computational frameworks will be designed to integrate spatial transcriptomics and single-cell RNA sequencing data, providing a detailed view of tumor microenvironments.
Plan for Data Analysis
- Feature Selection and Dimensionality Reduction: Implement PCA, t-SNE, and UMAP to extract key biological signals.
- Model Training and Validation: Train AI models on multi-omics datasets and validate using cross-validation and independent test cohorts.
- Pathway and Network Analysis: Utilize knowledge graphs, pathway enrichment analyses, and causal inference models to uncover mechanistic insights.
Role of Medical Student
The medical student will assist with:
- Data preprocessing and curation of multi-omics datasets
- Implementing machine learning pipelines and running bioinformatics workflows
- Conducting literature reviews on AI applications in cancer research
- Assisting in the preparation of manuscripts
Primary Research Location: University of Florida, Department of Health Outcomes and Biomedical Informatics, Gainesville, FL
References:
- Liu X, Wang Q, Zhou M, Zhou X, Song Q*, “DrugFormer: graph-enhanced language model to predict drug sensitivity.” Advanced Science (2024)
- Tang Z, Zhang T, Yang B, Su J, Song Q*, “SiGra: Single-cell spatial elucidation through image-augmented graph transformer.” Nature Communications (2023)
- Bouch RJ, Zhang J, Miller BC, Robbins CJ, Mosher TH, Li W, Krupenko SA, Nagpal R, Zhao J, Lu Y, Nikiforov MA, Song Q*, He Z. “Distinct inflammatory Th17 subsets emerge in autoimmunity and infection.” Journal of Experimental Medicine (2023)
- Tang Z, Zhang T, Yang B, Su J, Song Q*, “SpaRx: Elucidate single-cell spatial heterogeneity of drug responses for personalized treatment.” Briefings in Bioinformatics (2023)
- Tang Z, Zhang T, Yang B, Su J, Song Q*, “spaCI: deciphering spatial cellular communications through adaptive graph model.” Briefings in Bioinformatics (2023)
- Song Q, O’Neill S, Pasche B, Miller L, Ruiz J, Chan M, Soike M, “Single-cell sequencing reveals the landscape of the human brain metastatic microenvironment.” Communications Biology (2023)
Does this project have an international component or travel?
No
Improving Patient Safety and Quality of Care
Name:
Dr. Megan Gregory
Email
megan.gregory@ufl.edu
Phone
(352) 294-8126
Faculty Department/Division
Health Outcomes and Biomedical Informatics
This project is primarily:
Translational
Research Project Description:
Our team works at the intersection of clinical informatics and patient safety/quality of care. We work in several different clinical areas and use both quantitive and qualitative methods and analyses. We have several projects underway in different stages. We will work with you to determine which project and tasks may be the best fit for you. Examples include:
-Recruitment and data collection for an mHealth study on vascular surgery patients
-Recruitment and data collection for a survey study of Spanish-speaking primary care patients
-Analyzing EHR data (quantitively or qualitative) to assess team collaboration in the inpatient setting
-Developing educational tools for Research IT/use of Epic in research
-Various work on projects intended to improve cancer screening and screening for immunotherapy related adverse events in cancer patients
-And more
Does this project have an international component or travel?
No
Implementation of an outreach strategy to improve the uptake of shared decision-making for lung cancer screening
Faculty Information
Name:
Ms. Miranda Reid
Email
miranda.reid@ufl.edu
Phone
(314) 874-2023
Faculty Department/Division
Health Outcomes and Biomedical Informatics
This project is primarily:
Translational
Research Project Description:
Lung cancer is the leading cause of cancer-related deaths in the United States. Despite this, rates of screening have remained persistently low nationwide hovering around 6%. Rates in Florida are even lower with only 3% of eligible individuals screened in 2022, and rural areas consistently face lower rates of screening and higher rates of lung cancer mortality. This project focuses on the implementation of pre-visit decision aid and outreach contact to improve uptake of shared-decision making for lung cancer screening at both an urban and a rural primary care clinic.
Medical students will have an opportunity to participate in both the implementation and evaluation of the project. This includes helping support adoption of the decision aid by providers at both clinics and recruitment of patients to use a pre-visit decision aid. This will allow students to develop important skills in pragmatic clinical research and improve their own patient communication skills in shared-decision making. Additionally, based on student interest there are opportunities for medical students to participate in the mixed methods evaluation of the feasibility and impact of the project, along with economic evaluation of the intervention. Any students that participate will have the opportunity to contribute to resulting publications, including opportunities for authorship on posters and papers. Prior experience is implementation science is not necessary, but students interested in this field are encouraged to reach out.
Does this project have an international component or travel?
No
Sustainability of Tobacco Cessation Programs at NCI-Designated Cancer Centers
Faculty Information
Name:
Dr. Ramzi Salloum
Email
rsalloum@ufl.edu
Phone
(352) 294-4997
Faculty Department/Division
Health Outcomes and Biomedical Informatics
This project is primarily:
Translational
Research Project Description:
Though smoking cessation improves outcomes and is advocated as a standard of care in oncology, tobacco treatment is not consistently delivered as a part of cancer care. To address this challenge, the National Cancer Institute (NCI) launched the Cancer Center Cessation Initiative (C3I) in 2017 to provide financial and technical assistance to 52 NCI-designated cancer centers to implement evidence-based tobacco treatment programs and integrate smoking cessation into routine patient care in oncology settings. Although all funded centers submitted sustainability plans and were required to sustain their programs for a minimum of three years following the funded period, the trajectories and determinants of sustainability for tobacco treatment programs in these cancer centers are unknown.
The objective of this NCI-funded R01 study is to investigate the trajectories and determinants of sustainability across evidence-based tobacco treatment programs in C3I and to identify appropriate strategies for promoting sustainability using an implementation mapping approach (i.e., “sustainability mapping”). This research offers an unprecedented opportunity for identifying how investment in building evidence-based programs is converted into sustainable healthcare systems change, with the long-term goal of developing a generalizable model for sustaining evidence-based tobacco treatment programs in cancer care. We have already conducted qualitative interviews and surveys with program representatives from the tobacco treatment programs to characterize the sustainment of the programs and specify the relationships between the multilevel determinants, strategies, and outcomes of sustainability for tobacco treatment programs within cancer centers. Medical students will have the opportunity to collaborate on the ongoing analysis of quantitative and/or qualitative data and contribute to publications and conference presentations. We anticipate publishing at least three to four papers this upcoming year. Finally, students may assist in developing and testing a toolkit informed by our findings. We will employ sustainability mapping and user-centered design to develop and evaluate an interactive toolkit for sustaining tobacco treatment programs in oncology. The toolkit will be promoted to national audiences through presentations and webinars, and students will have the opportunity to participate in these promotional efforts.
Does this project have an international component or travel?
No
Post-COVID health of older adults
Name:
Dr. Todd Manini
Email
tmanini@ufl.edu
Phone
(352) 273-5914
Faculty Department/Division
Health Outcomes and Biomedical Informatics
This project is primarily:
Clinical
Research Project Description:
This project involves secondary data analysis of a longitudinal survey distributed to older adults at the beginning of the COVID-19 pandemic in May, 2020 and resurveyed in October 2022. The student will receive hands on data management skills and learn basic statistical approaches for analyzing the data. The goal is to generate a scientific question within the bounds of the surveys asked and test a hypothesis — e.g. In older adults, what was the change in depressive symptoms between 2020 and 2022.
The initial purpose of the online survey was to understand changes in behaviors, social activities, health care and medication use, food security, anxiety, depression, technology/telehealth utilization, and mobility patterns during the COVID-19 pandemic. Questions were populated in their exact form from validated questionnaires that were modified to appropriately fit the COVID-19 pandemic (e.g. before vs. after the COVID-19 pandemic). A follow-up of survey with similar questions along with new ones asked about returning to a “new-normal” following vaccine approval and deployment.
The survey was self-administered and taken on a voluntary basis. It was created via the University of Florida Research Electronic Data Capture (REDCAP) secure system and distributed through social media, email lists, websites and in health articles released by UFHealth. Additionally, direct mail post-cards, that advertised the online survey, were sent to 70,000 residents in the north-central region of Florida. The initial survey garnered 1082 responses in May and June 2020. The follow-up survey was completed by 428 respondents in the fall of 2022.
Does this project have an international component or travel?
No
Evaluating the Impact of a Centralized Value-Based Care Team on Healthcare Delivery in Adult Primary Care at UF Health
Faculty Information
Name:
Dr. Maria Kelly
Email
kellymn@peds.ufl.edu
Phone
(352) 265-7231
Faculty Department/Division
Health Outcomes and Biomedical Informatics
This project is primarily:
CQI
Research Project Description:
Working with UF Health physician leadership, this research proposal aims to investigate the impact of a centralized Value-Based Care (VBC) team within the UF Health system. Specifically, the Value-Based Care (VBC) team, partners with >9 UF Health adult primary care clinics and physicians to bridge gaps in primary care delivery. Specifically, this study will analyze how the VBC team, which provides care through a telemedicine and outreach platform, has influenced key areas of healthcare delivery, including transitional care management, annual wellness visits, HCC capture, among others identified, and how it aligns with system-wide health goals.
Additionally, the project should include a cost-savings analysis of the centralized VBC team to demonstrate its value, as more institutions transition to accountable care and shared savings models that value a centralized model. This retrospective study will utilize existing data, with the goal of creating a manuscript for submission by the end of the summer. Findings will also be prepared for presentation at a Vizient or AAMC conference.
Does this project have an international component or travel?
No