Matthew P. Goetz, Vera J. Suman, Joel M. Reid, Don W. Northfelt, Michael A. Mahr, Andrew T. Ralya, Mary Kuffel, Sarah A. Buhrow, Stephanie L. Safgren, Renee M. McGovern, John Black, Travis Dockter, Tufia Haddad, Charles Erlichman, Alex A. Adjei, Dan Vissch
Citations 8
Community-informed development of an in-person forum to promote breast cancer screening and research preparedness in rural and low-socioeconomic status women
Abstract Rural and low-socioeconomic status women face social determinants of health barriers leading to disproportionately low rates of breast cancer screening and markedly reduced participation in clinical trials. To address this gap, we developed the WeCARE (Women’s Engagement for Cancer Awareness, Resources, and Education) intervention using a community-engaged approach. This paper describes how Community Advisory Board feedback informed the development of Mayo Clinic Health System’s WeCARE intervention components. Their input was systematically analyzed using the Consolidated Framework for Implementation Research (CFIR 2.0) to identify determinants of acceptability, appropriateness, and feasibility and to guide actionable refinements to intervention content and delivery.
Collaborative large language models (LLMs) are all you need for screening in systematic reviews
Abstract Background The ability of large language models (LLMs) to work collaboratively and screen studies in a systematic review (SR) is under-explored. Hence, we aimed to evaluate the effectiveness of LLMs in automating the process of screening in systematic reviews. Methods This is an observational study which included labeled data (title and abstracts) for five SRs. Originally, two reviewers screened the citations independently for eligibility. A third reviewer cross-checked each citation for quality assurance. GPT-4, Claude-3-Sonnet, and Gemini-Pro-1.0 were used using zero-shot chain-of-thought prompting. Collaborative approaches included (i): conflict resolution using benefit of the doubt, (ii) majority voting using an independent third LLM and (iii) conflict resolution using an informed third LLM. Performance was assessed using accuracy, precision for exclusion, and recall for inclusion. Work saved over samples (WSS) was computed to estimate the reduction in manual human effort. Results A total of 11300 articles were included in this study. The individual models, GPT-4, Claude-3-Sonnet, and Gemini-Pro-1.0 exhibited a high precision for exclusion, achieving 99.7%, 99.7%, and 99.2% and high recall for inclusion achieving 95.5%, 96.6% and 85.7%, respectively. However, the collaborative approach utilizing the two best-performing models (GPT-4 and Claude-3S) achieved an average precision of 99.9% and a recall of 98.5% (across all collaborative approaches). Furthermore, the proposed collaborative approach resulted in an average WSS of 63.5%, compared to the average WSS of 45.2% for individual models. Conversational LLM interactions showed a consistent pattern of results. Limitations This study was limited due to reliance on proprietary models, and evaluation on oncology datasets. Conclusion Evidence shows that collaborative LLMs enable efficient, high-performing screening in systematic reviews, supporting continuous evidence updates. Primary funding source NIH (U24CA265879-01-1) and Carolyn-Ann-Kennedy-Bacon Fund.
Anastrozole has an Association between Degree of Estrogen Suppression and Outcomes in Early Breast Cancer and is a Ligand for Estrogen Receptor α
Abstract Purpose: To determine if the degree of estrogen suppression with aromatase inhibitors (AI: anastrozole, exemestane, letrozole) is associated with efficacy in early-stage breast cancer, and to examine for differences in the mechanism of action between the three AIs. Experimental Design: Matched case–control studies [247 matched sets from MA.27 (anastrozole vs. exemestane) and PreFace (letrozole) trials] were undertaken to assess whether estrone (E1) or estradiol (E2) concentrations after 6 months of adjuvant therapy were associated with risk of an early breast cancer event (EBCE). Preclinical laboratory studies included luciferase activity, cell proliferation, radio-labeled ligand estrogen receptor binding, surface plasmon resonance ligand receptor binding, and nuclear magnetic resonance assays. Results: Women with E1 ≥1.3 pg/mL and E2 ≥0.5 pg/mL after 6 months of AI treatment had a 2.2-fold increase in risk (P = 0.0005) of an EBCE, and in the anastrozole subgroup, the increase in risk of an EBCE was 3.0-fold (P = 0.001). Preclinical laboratory studies examined mechanisms of action in addition to aromatase inhibition and showed that only anastrozole could directly bind to estrogen receptor α (ERα), activate estrogen response element-dependent transcription, and stimulate growth of an aromatase-deficient CYP19A1−/− T47D breast cancer cell line. Conclusions: This matched case–control clinical study revealed that levels of estrone and estradiol above identified thresholds after 6 months of adjuvant anastrozole treatment were associated with increased risk of an EBCE. Preclinical laboratory studies revealed that anastrozole, but not exemestane or letrozole, is a ligand for ERα. These findings represent potential steps towards individualized anastrozole therapy.
Effect of Pembrolizumab Plus Neoadjuvant Chemotherapy on Pathologic Complete Response in Women With Early-Stage Breast Cancer
A Phase II Trial of Neoadjuvant MK-2206, an AKT Inhibitor, with Anastrozole in Clinical Stage II or III
PIK3CA
-Mutant ER-Positive and HER2-Negative Breast Cancer
Purpose Endoxifen is a tamoxifen metabolite with potent antiestrogenic activity. Patients and Methods We performed a phase I study of oral Z-endoxifen to determine its toxicities, maximum tolerated dose (MTD), pharmacokinetics, and clinical activity. Eligibility included endocrine-refractory, estrogen receptor–positive metastatic breast cancer. An accelerated titration schedule was applied until moderate or dose-limiting toxicity occurred, followed by a 3+3 design and expansion at 40, 80, and 100 mg per day. Tumor DNA from serum (circulating cell free [cf); all patients] and biopsies [160 mg/day and expansion]) was sequenced. Results Of 41 enrolled patients, 38 were evaluable for MTD determination. Prior endocrine regimens during which progression occurred included aromatase inhibitor (n = 36), fulvestrant (n = 21), and tamoxifen (n = 15). Patients received endoxifen once daily at seven dose levels (20 to 160 mg). Dose escalation ceased at 160 mg per day given lack of MTD and endoxifen concentrations > 1,900 ng/mL. Endoxifen clearance was unaffected by CYP2D6 genotype. One patient (60 mg) had cycle 1 dose-limiting toxicity (pulmonary embolus). Overall clinical benefit rate (stable > 6 months [n = 7] or partial response by RECIST criteria [n = 3]) was 26.3% (95% CI, 13.4% to 43.1%) including prior tamoxifen progression (n = 3). cfDNA mutations were observed in 13 patients ( PIK3CA [n = 8], ESR1 [n = 5], TP53 [n = 4], and AKT [n = 1]) with shorter progression-free survival ( v those without cfDNA mutations; median, 61 v 132 days; log-rank P = .046). Clinical benefit was observed in those with ESR1 amplification (tumor; 80 mg/day) and ESR1 mutation (cfDNA; 160 mg/day). Comparing tumor biopsies and cfDNA, some mutations ( PIK3CA, TP53, and AKT) were undetected by cfDNA, whereas cfDNA mutations ( ESR1, TP53, and AKT) were undetected by biopsy. Conclusion In endocrine-refractory metastatic breast cancer, Z-endoxifen provides substantial drug exposure unaffected by CYP2D6 metabolism, acceptable toxicity, and promising antitumor activity.