UPMC Magee-Women's Hospital known simply as Magee-Womens Hospital, is a nationally ranked, 335-bed non-profit, full service specialty hospital located in South Oakland, Pittsburgh, Pennsylvania. Magee-Womens is a part of the University of Pittsburgh Medical Center (UPMC). The hospital is near UPMC's flagship campus which houses Presbyterian and Montefiore. While the hospital is UPMC's primary facility for women's health, the hospital is a full service hospital that also serves men. As the hospital is a teaching hospital, it is affiliated with University of Pittsburgh School of Medicine.
LBA4 Background: ESR1 mutations ( ESR1 m) constitutively activate the estrogen receptor (ER) and are the most common mechanism of acquired resistance to aromatase inhibitor (AI) + CDK4/6i. Molecular monitoring by ctDNA analysis can detect the emergence of ESR1 m during 1L AI + CDK4/6i. Camizestrant, the next-generation selective ER degrader (SERD) and complete ER antagonist, has shown anti-tumor activity in pts with and without detectable ESR1 m. SERENA-6 is the first global registrational Phase 3 trial assessing a ctDNA-guided approach to detect the emergence of ESR1 m during 1L AI + CDK4/6i to inform a switch in therapy ahead of disease progression. Methods: Pts with HR+/HER2– ABC who had received ≥6 months of 1L AI (anastrozole/letrozole) + CDK4/6i (abemaciclib/palbociclib/ribociclib) were enrolled and had ctDNA tested for ESR1 m every 2–3 months, coinciding with routine imaging. At ESR1 m detection, pts without evidence of disease progression were randomized 1:1 to switch to camizestrant (75 mg) with continued CDK4/6i (type and dose maintained) + placebo for AI vs continuing AI + CDK4/6i + placebo for camizestrant. The primary endpoint was investigator-assessed PFS (per RECIST v1.1). Prespecified interim analysis data cutoff was Nov 28, 2024. Results: 3,256 eligible pts were surveilled for ESR1 m using ctDNA until 315 eligible pts were randomized to switch to camizestrant (n=157) or continue with AI (n=158). All pts remained on the same CDK4/6i. ~50% of randomized pts had ESR1 m detected at the first ctDNA test. Baseline characteristics were well balanced between treatments. After 171 PFS events, hazard ratio for PFS was 0.44 (95% CI 0.31–0.60, p<0.00001; median PFS 16.0 vs 9.2 months). PFS benefit was consistent across subgroups. PFS rate at 12 months was 60.7% (95% CI 51.1–69.0) vs 33.4% (95% CI 24.9–42.2) and at 24 months was 29.7% (95% CI 19.0–41.2) vs 5.4% (95% CI 0.7–18.2). PFS2 hazard ratio was 0.52 (95% CI 0.33–0.81; 27% maturity). OS is immature (12%). Camizestrant + CDK4/6i was well tolerated with safety consistent with the known profiles of camizestrant, and of each CDK4/6i. Rates of treatment discontinuation due to adverse events were 1.3% for camizestrant and 1.9% for AI. Conclusions: Camizestrant + CDK4/6i guided by emergence of ESR1 m during 1L AI + CDK4/6i in pts with HR+/HER2– ABC resulted in a statistically significant and clinically meaningful improvement in PFS. SERENA-6 is the first global Phase 3 trial to demonstrate clinical utility of using ctDNA to detect and treat emerging resistance, ahead of disease progression. These findings represent a potential new treatment strategy to optimize and improve 1L patient outcomes. Clinical trial information: NCT04964934 .
INTRODUCTION:The number of contralateral risk reduction mastectomies (RRM) continues to rise. Despite the existence of many guidelines, there is no general consensus on which patients should be considered for RRM. This survey was distributed among various breast specialists with the goal of evaluating current practices, perspectives, and attitudes towards RRM. METHODS:An English-language survey containing ten questions was designed and distributed electronically to members of the Senologic International Society (SIS). The findings of this survey align with existing research and also provide areas for further investigation. RESULTS:This survey demonstrates the different practices and viewpoints on RRM across various specialties and areas around the world. These variations highlight the need for uniform recommendations and further research to enhance patient outcomes and address the challenges of managing RRM patients.
The objective of the study was to demonstrate the modified Bragg-Williams type analysis of digital microscopic images is an analytically valid methodology for classifying squamous cervical cytology. We analyzed 5318 single cell cervical cytology images from the SIPaKMeD and Herlev databases to calculate a Bragg-Williams order parameter (S2) using a pixel intensity histogram-based approach. A classification model for S2 was created to differentiate normal, LSIL and HSIL. A set of test images from different databases were blindly classified using defined ranges to determine the sensitivity, specificity, and areas under the curve. Results showed distinct ranges of S2 for Normal: 0.7181-0.8633 SD 0.1452, LSIL: 0.580-0.6626 SD 0.0826, and HSIL: 0.3289-0.550 SD 0.2211, with dysplastic cells exhibiting lower degrees of order. The S2 classification model was found to have 100% sensitivity and specificity over the sample set, demonstrating the suitability of the methodology for cervical cytology classification.
Purpose: This retrospective study examined the clinical and genetic characteristics of pediatric patients undergoing clinical exome sequencing (ES) and evaluated the performance of a commercially available artificial intelligence (AI) platform that was integrated into our analysis pipeline. Methods: ES was performed in 822 consecutive patients at a single clinical laboratory. AI-based tools were used to jointly assess genetic information and the proband’s Human Phenotype Ontology terms to support variant prioritization during the initial case review. Results: A definitive molecular diagnosis was established in 22% (181 of 822) of index cases, while 40% (325 of 822) had variants of uncertain significance. Among those with a definitive diagnosis, 93% (168 of 181) had a single finding and 7% (13 of 181) had multiple findings. Of the 152 reported pathogenic/likely pathogenic variants in the fully resolved cases, 98.7% were successfully flagged by AI, and 75.0% ranked among the top 10 “most likely” variants. Conclusion: Clinical ES provides a substantial diagnostic yield in complex pediatric disorders. Integration of AI-powered platforms can accelerate phenotype-driven variant prioritization and facilitate rare disease diagnostics, but underscores the need for careful validation and optimization in clinical workflows.
