Olson, Joshua

Abstract Twenty percent of patients with follicular lymphoma relapse early with poor outcomes; however, the molecular mechanisms underlying this aggressive behavior are unknown. Using a multiomic approach, we show that patients with follicular lymphoma with elevated IRF4 expression (IRF4hi) have increased transformation risk, dysregulated immune signaling, and a suppressive tumor microenvironment. Loss- and gain-of-function experiments in IRF4hi lymphoma cells, along with chromatin profiling, demonstrate that IRF4 impairs their interaction with T cells by repressing antigen presentation and co-receptor gene modules while promoting the expression of cytokines that antagonize T-follicular helper cell and regulatory T-cell functions. Additionally, IRF4 rewires tumor metabolism and restricts glucose availability to immune cells. Silencing of IRF4 inhibits tumor cell growth and restores immune surveillance mechanisms, thus representing a promising target for therapy. Our data suggest that IRF4hi lymphoma cells co-opt a developmental mechanism used to exit the germinal center response in promoting a more aggressive cancer via engagement of multiple immune-evasive mechanisms. Significance: We show that IRF4 controls immune and metabolic signaling with functional effects on the cross-talk between B and T cells, resulting in a suppressive immune microenvironment when IRF4 is overexpressed in lymphoma. This underscores the importance of IRF4 as a pivotal therapeutic target to restore antitumor immunosurveillance for IRF4-associated lymphomas.

Abstract Disclosure: M.D. Hurtado: None. E. Tama: None. D. Hodge: None. A. Weston: None. S. Fahad: None. K. Ruddy: None. J. Olson: None. F. Coach: None. R. Carter: None. S. D'Andre: None. A. Acosta: Consulting Fee; Self; Amgen Inc. Grant Recipient; Self; Vivus USA, Novo Nordisk. C. Shufelt: None. Background: Breast cancer (BC) is the most common cancer in women. Weight gain after treatment for early-stage BC is associated with a higher risk of recurrence. Excess adiposity is also a major risk factor for cardiovascular disease, the leading cause of non-BC related death in BC survivors. This study aimed to describe weight trends in a large cohort of BC survivors and to identify predictors of weight gain after BC diagnosis. Methods: This was a retrospective study of BC survivors from the prospectively consented Mayo Clinic Breast Cancer Registry, which has enrolled >10,000 patients seen at least once at Mayo Clinic Rochester for a BC diagnosed within the prior year. We extracted data on weights at baseline, and years 1, 4, and 6 after BC diagnosis to generate longitudinal weight trajectory curves on the change from baseline. Weights were either reported by patients in the registry questionnaires or extracted from the electronic health records. We identified BC survivors with weight gain >10% from baseline weight at years 1, 4, and 6. We then conducted univariable logistic regression analyses to identify predictors of >10% weight gain. Variables considered included: demographics, anthropometrics, BC clinical and pathologic characteristics, BRCA 1 and 2 genetic mutation status, and treatment (surgery, neoadjuvant or adjuvant chemotherapy, and/or radiotherapy). Results: We included 4744 BC survivors, mostly white (95%) and non-Hispanic (91%), with a mean age and weight at BC diagnosis of 58 ± 13 years and 76 ± 18 kg. Most patients had clinical stage I (43%) and ER+ and PR+ BC (83% and 76%, respectively). Most patients underwent breast preserving surgery (62%), half received radiation (54%), one third (36%) received systemic chemotherapy, and two thirds (61%) received endocrine therapy. At one year after BC diagnosis the average weight change was negligible compared to baseline (mean difference -0.2 ± 8 kg), but there were small but significant average increases by years 4 and 6 (0.5 ± 9 kg, p=0.002; and 0.8 ± 10 kg, p<0.001; respectively). At years 1, 4, and 6, the percentage of BC survivors gaining >10% of their baseline weight was 10%, 15%, and 18%, respectively. Baseline characteristics associated with >10% weight gain at 6 years included lower baseline weight (p<0.001), younger age at BC diagnosis (p<0.001), more advanced clinical and pathologic BC stage (p=0.008), ER+ and PR+ tumor biology (p=0.02 for both), BRCA2 mutation (p<0.001), mastectomy (compared to breast preserving surgeries, p=0.008), the use of systemic chemotherapy treatment (p<0.001), and the use of endocrine therapy (p=0.03). Conclusion: While most BC survivors do not experience weight gain after survivorship, 1 out 5 may experience excessive weight gain of more than 10%. Baseline anthropometric and BC-related variables are associated with excessive weight gain and could be used as predictors of weight gain in this population. Presentation: 6/3/2024

Abstract Background: Mammography (digital 2D or digital 3D/tomosynthesis) is the cornerstone of current screening strategies for breast cancer, but new approaches are needed to further reduce the proportion of cancers diagnosed at advanced stages and more effectively identify those women in need of additional testing and biopsies. Circulating cell-free nucleic acids (cfNAs) shed from tumors, isolated from peripheral blood, and analyzed with ultra-deep and broad sequencing of cancer-associated genes, have great potential for early cancer detection. The ultimate goal is to develop blood cfNA cancer screening tests for use in conjunction with established risk factors and/or radiographic features for improved cancer detection. Development of these tests requires large, well-annotated cohorts of asymptomatic participants with adequate volumes of prediagnostic blood. The STRIVE Study cohort was recently established to train and validate cfNA-based tests for early detection of breast cancer. Eligibility criteria and trial design: The STRIVE Study is a new prospective, multi-ethnic mammography cohort that will recruit 120,000 subjects from 15+ US breast cancer screening centers (including Mayo Clinic and Sutter Health sites). Eligibility criteria require only that a participant has a scheduled routine screening mammogram at a participating center and has not received a biopsy prior to the research blood draw. Participants are recruited within 28 days of screening mammography (digital or tomosynthesis), consent electronically, provide blood samples, and complete an on-line risk factor questionnaire. Participants will be followed for all cancer diagnoses, cancer recurrences, and death for at least 5 years. Pertinent medical record information, imaging findings (including breast density), and follow-up data will be transferred electronically to a central database throughout the study period. Additional blood samples will be collected from participants with abnormal mammogram results, or who are diagnosed with cancer, to document and better understand the evolution of cfNA signals. Recruitment began in February 2017. Primary Aims: To train and validate a cfNA blood-based test to identify breast cancer overall in a cohort of women undergoing screening mammography. Statistical Methods: The study will be divided into a training phase (1/3 of participants) and an independent clinical validation phase (remaining 2/3 of participants). In the training phase, statistical machine learning techniques will be used to develop algorithms incorporating cfNA signals, clinical characteristics, or radiological features. In the validation phase, the prespecified locked algorithm developed from the training phase will be clinically validated in an independent group of women. Contact information for people with a specific interest in the trial: Additional details regarding the STRIVE Study are available on the ClinicalTrials.gov website (NCT03085888). For site-specific questions, please call 844-366-9738 for the Mayo Clinic and 1-855-578-7483 for Sutter Health. Citation Format: Liu MC, Cummings S, Vachon CM, Kerlikowske K, Couch FJ, Morris EA, Olson JE, Polley EC, Conners AL, Ellis RL, Patel B, Maimone IV S, Zhang N, Hamilton S, Clarke CA, Allen BA, Maddala T, Hartman A-R. Development of cell-free nucleic acid-based tests for early detection of breast cancer: The STRIVE study [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr OT3-02-01.

Abstract Background: Mammographic density, the variation in fat, epithelial and stromal tissues seen on screening mammography, is a strong risk factor for breast cancer and can be modified by hormonal agents. Changes in density from tamoxifen or postmenopausal hormone (PMH) use are associated with risk, suggesting that density may be a surrogate marker of therapeutic efficacy. Aromatase inhibitors (AIs) are given as adjuvant therapy in hormone receptor positive postmenopausal breast cancer and are known to decrease levels of estrone and estradiol in both serum and breast tissue. Our goal here was to examine the influence of AIs on mammographic density in women with early breast cancer. Methods: We conducted a case-control study of postmenopausal breast cancer patients initiating adjuvant AI therapy (anastrozole or exemestane) on protocols NCIC CTG MA27, NCCTG N063I and MC (Mayo Clinic) 0532. Eligibility included; an intact contralateral breast with no prior surgery; a screening mammogram within twelve months before AI initiation and at 9-15 months on therapy; no prior endocrine therapy and informed consent. Controls were sampled from the Mayo Mammography Health Study, a cohort of 19,924 receiving screening mammography at the Mayo Clinic, and matched to cases on age, prior PMH use, baseline body mass index (BMI) and interval between mammograms. Pre-treatment and on-study mammograms for cases (corresponding mammograms for controls) were digitized. Change in percent density was estimated on the craniocaudal view of the non-cancerous breast using two methods: a subjective assessment of change by an expert radiologist (within 5%; 5-10% increase, 10-25% increase, 25%+ increase, 5-10% decrease, 10-25% decrease and 25%+ decrease) and a quantitative assessment of absolute change using a computer-assisted thresholding program (Cumulus). Analyses compared magnitude of change in density by both the subjective and quantitative methods between cases and matched controls. Results: 574 pairs were eligible for analyses (MA27-505 cases; N063I-12 cases; MC0532-57 cases). Characteristics of the two groups are shown in the table below. Using either density estimation method, there was a greater decrease in density among women on AI therapy vs. matched controls. In 33% (95% CI: 29-37%) of pairs, there was at least a one greater category decrease for the case relative to her control by subjective estimation. In 14% (95% CI: 11-18%) of the pairs, there was at least a 5% greater decrease for the case relative to her control by quantitative estimation. Data will be available according to AI class (non-steroidal versus steroidal) in November. Conclusions: In the largest report to date to examine the influence of AI therapy on mammographic density, we provide evidence that AI is associated with decreases in density in a small subgroup of women. We are currently examining factors that influence these AI-associated decreases in density and whether these differences are unique to one class of AI. (Supported in part by NIH grants P50CA116201, U01GM61388, U10CA77202, U10CA25224) Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P2-09-03.