The Warner Lab

Increasing Receipt of Guideline Concordant Survivorship Care Among Black Breast Cancer Survivors

Black women experience significant disparities in breast cancer across the care continuum including survivorship. Black women of diverse ethnic backgrounds, particularly those of lower socioeconomic status are less likely to receive guideline concordant survivorship care including: 1) Surveillance for breast cancer recurrence and/or second primary breast cancer; 2) Assessment and management of long-term physical and psychosocial effects of breast cancer and treatment; 3) Healthy lifestyle promotion and maintenance; 4) Care coordination between oncology and primary care. Multiple studies have demonstrated that the transition from active treatment to breast cancer survivorship is fraught with confusion and anxiety for many patients.

The clinical transition from oncology specialist care back to a primary care provider (PCP) is complicated by difficulties in communication and lack of PCP training on cancer survivorship care. Second, patients often are unsure what to expect and lack information. While survivorship care plans have been promoted, they do not fully address patient and provider needs. This project will develop a survivorship education program for patients and PCPs. It builds upon an existing cancer survivorship education program for primary care providers at local health centers and will be conducted in partnership with multiple local organizations including Boston Medical Center, the Boston Breast Cancer Equity Coalition, Boston Public Health Commission, and three local health centers.

Impact of a Comprehensive Patient Navigation Intervention on Endocrine Therapy Adherence and Persistence among Vulnerable Women in Boston

Data suggest that up to 50% of women do not complete five years of endocrine therapy, and this may be even worse among lower income and minority women and may contribute to persistent racial and socioeconomic disparities in breast cancer survival. Previous interventions to address gaps in endocrine therapy (ET) initiation, adherence and persistence have focused on education and cognitive behavioral training among patients, with limited success, however, a small, but growing body of evidence demonstrates system level interventions may be effective. Translating Research into Practice (TRIP) is a three-pronged city-wide patient navigation intervention targeted at breast cancer patients who are at risk for delays in care due to their race/ethnicity, language, or insurance status. We propose to: 1) Determine the feasibility and validity of an online medication database compared to EMR data abstraction to measure ET adherence and persistence; and 2) Evaluate the impact of TRIP on ET early discontinuation and adherence among 342 intervention patients and 263 historic controls with HR+ breast cancer.

Increasing Diverse Women's Awareness of and Participation in Lung Cancer Screening

Limited patient awareness and patient provider communication about lung cancer screening could contribute to low screening uptake among women. The purpose of this mixed methods study is to develop a model for lung cancer screening awareness participation among diverse women defined by race and socioeconomic status. Capitalizing upon an existing community-based project, the Cancer Care Equity Program, and a cohort based at a local federally qualified health center, we propose to: 1) Assess demographic, psychosocial, and health system characteristics associated with lung cancer risk perception, screening awareness, intentions and utilization 2) Determine preferences for lung cancer and lung cancer screening educational materials and methods with attention to specific subgroups.   

Lung Cancer Risk and Early Detection in Boston Firefighters

In partnership with the Boston Fire Department, and leveraging a recent award they received to fund cancer screening among firefighters, we propose to: 1) Compare radiologic findings on low-dose CT (LDCT) between firefighters and non-firefighters matched for age and sex; 2) Examine the association between self-reported occupational exposures, use of safety equipment, and smoking with LDCT radiologic findings; 3) Investigate the ability of a proteomic assay to identify individuals at high risk of lung cancer among firefighters with suspicious LDCT findings. Our goal is to increase lung cancer early detection among firefighters and identify a high-risk subset. More information is available at https://www.firehealthstudy.org/

Improving Lung Cancer Risk Stratification and Expanding Access to Lung Cancer Screening

Lung cancer is the leading cause of cancer-related deaths worldwide. While smoking is the leading cause of lung cancer, lung cancer rates among people that never smoked are on the rise. However, current guidelines for lung cancer screening via low-dose CT only consider age and smoking history for eligibility. We believe that every person has the right to know their risk of developing cancer — Sybil is a deep learning model that accurately predicts a patient’s risk of lung cancer up to six years in advance from analyzing a low-dose CT scan, ensuring that lung cancer is detected in its earliest stages. As part of a team of scientists and clinicians across Mass General Brigham, Massachusetts Institute of Technology, and the world, we are learning how to use Sybil to identify people at increased risk for lung cancer, including people that do not currently qualify for lung cancer screening. Learn more about ongoing research projects here. Watch a video to learn more about Sybil here.

Cancer-related cognitive impairment (CRCI) represents an important clinical issue affecting up to 75% of people before, during, and after diagnosis and treatment. Understanding cognitive trajectories associated with cancer and cancer treatment is particularly important among older adults (age ≥ 65) who may be more susceptible to CRCI. However, older adults have been underrepresented and previous studies have been limited by a predominance of short-term follow-up, focus on breast cancer, and a lack of mechanistic investigation. The overall objective of this application is to determine risk, susceptibility, and biological mechanisms linking cancer, cancer treatment and cognitive functioning.

We will leverage data from ASPREE (Aspirin in Reducing Events in the Elderly) and its observational follow-up study (ASPREE-XT). ASPREE was a trial of 19,114 individuals aged ≥70 years (≥ 65 for US minorities) enrolled in Australia (n=16,703) or the United States (n=2,411) in which participants were randomly allocated to daily low-dose (100 mg) aspirin or placebo. At enrollment, participants had no history of diagnosed dementia, cardiovascular disease, significant physical disability, or major cognitive impairment. Repeated measures of multiple domains of cognitive function were performed, including memory, executive function, and psychomotor speed, assessed at baseline and every 2 years (2010-2017), and then annually during follow-up (2018-2024).

We will use a prospective longitudinal study design to examine the following specific aims: Aim 1. Determine the impact of cancer and cancer treatment on longitudinal trajectories of cognitive functioning. We will expand the cancer treatment data set to include treatment data for all cancer events through to 2024, and further investigate whether host factors such as age at diagnosis, assignment to aspirin or placebo, educational attainment, APOE risk genotype, dementia polygenic risk score, pre-diagnostic clonal hematopoiesis of indeterminant significance (CHIP), or biomarkers associated with Alzheimer’s Disease and related dementias (ADRD) neuropathology (Aβ, p-tau, NfL, and GFAP) impact risk. Aim 2. Determine the impact of cancer and cancer treatment on CHIP and ADRD biomarkers (Aβ, p-tau, NfL, and GFAP) and investigate potential mediating effects with respect to cognitive functioning. This study is funded by the National Cancer Institute (1R01CA279316-01A1 ).

Early detection of cancers is of substantial benefit, as many malignancies are diagnosed too late; risk prediction in the general population can thus inform surveillance programs that enable earlier diagnosis. The presence of shared genetic, environmental, and lifestyle risk factors across different cancer types motivates a systematic, multi‐cancer risk‐prediction framework that offers greater predictive power than single‐cancer approaches. In our most recent work, we combined medication and diagnosis data from a large-scale VA database for pancreatic cancer risk prediction, achieving performance substantially better than risk estimates based solely on age and sex. Building on this success, we now propose an end‐to‐end, multi‐task deep‐learning architecture that leverages the longitudinal nature of electronic health record data and incorporates prior knowledge of tumor similarities to enhance predictive performance. Our model jointly learns both cancer‐specific and shared risk factors, enabling simultaneous risk prediction across multiple cancer types. We will benchmark this multi-cancer model against single-cancer versions of our earlier architecture and conduct a retrospective, out-of-distribution analysis comparing our AI-driven predictions with current clinical practice. Together with clinical experts, we will then devise a detailed plan for implementing a realistic cancer surveillance program. This project is funded by Innovation in Cancer Informatics. More info here.

Genetic and Lifestyle Predictors of Density

Women with dense tissue in 75% or more of the breast are four to six times more likely to develop breast cancer as those with little or no dense tissue. While breast density (BD) is a strong risk factor for breast cancer, racial/ethnic differences in BD, and its different measures, are unclear. 

Using data from the Boston Mammography Cohort Study (BMCS), a large, multiethnic cohort of women receiving mammograms at Brigham and Women’s Hospital, we will examine the racial/ethnic patterns of mammographic density and change in density, explore early-life and adult predictors of MD and change in density, and investigate whether SNPs associated with percent density in white women are similarly associated among black and Hispanic women.  Our study population is the Boston Mammography Cohort Study (BMCS), a study of mammographic density among over 2,600 women obtaining mammograms at Brigham and Women’s Hospital (BWH) in Boston, MA.

Breast Density Notification

As of January 2020, 38 US states and the District of Columbia require that women receive written notification when dense tissue is found on a mammogram. In March 2019, the Food And Drug Administration proposed a new federal rule requiring all mammography facilities in the United States to notify women about dense breast tissue. Notification is meant to inform women that dense breast tissue is common (40-50% prevalence), associated with breast cancer risk, reduces mammography sensitivity by obscuring tumor tissue, and other screening modalities such as ultrasound or MRI may be warranted. However, notification letters are written above an 8th grade reading level, leaving many women anxious and confused about next steps.

We have several projects ongoing related to breast density notification including the development of a Conversational Agent, an interactive tool that delivers tailored information and support, to help patients and their providers achieve optimal engagement in evidence-based breast cancer screening including understanding of breast density.

Physical and Sedentary Activity in Relation to TDLU Involution and Mammographic Density

Using data from the Komen Tissue Bank (KTB), a repository of breast tissue, DNA, serum and epidemiological data from healthy volunteer donors this project will: 1) Assess the association between current physical and sedentary activity and TDLU counts, median TDLU span, median acini counts/TDLU among 1938 women enrolled in the KTB (1369 premenopausal and 569 postmenopausal); 2) Investigate the association between current physical and sedentary activity with mammographic density (percent, non-dense and dense area) and cross-classified breast density and TDLU involution measures among 485 KTB participants with available digital mammograms and TDLU involution measures