Murphy, Seán

Abstract Background: Germline Pathogenic Variants (PVs) in ATM, BRCA1, BRCA2, CHEK2, and PALB2 are known to increase breast cancer (BCa) risk, but the effect of these PVs on tumor biology is not well-understood. Ki-67 is an established prognostic marker in hormone receptor (HR) positive breast cancer but the distribution of Ki-67 in breast cancer associated with germline PVs is not known. Methods: The study included patients with loco-regional (Stage I-III) HR-positive (ER+ or PR+), HER2-negative invasive ductal or lobular breast cancer evaluated at Mayo Clinic for curative intent treatment between 2012 and 2021. PV carrier status was ascertained in more than two-thirds of cases from germline whole exome sequencing (Regeneron) of patients who participated in a prospective registry at Mayo Clinic. Additional PV carriers were identified through a review of the results of clinical germline genetic testing in electronic medical records aided by natural language processing. Clinically performed Ki-67 values were abstracted from electronic medical records and analyzed as continuous and categorical variables (low: < 20% vs. high: ≥20%). Throughout the timeline of the study, a consistent method for assessment of Ki-67 was utilized at Mayo Clinic, Rochester: Immunohistochemical staining of the Ki-67 antigen in formalin-fixed paraffin-embedded tissue was performed using MIB-1 clone, the scanned slides were reviewed by a technologist and traced areas further analyzed for Ki-67 using Aperio Software. Patients who received neoadjuvant therapy prior to the assessment of Ki-67, with metastatic disease at diagnosis, with PVs in two or more genes of interest, or unknown germline PV or Ki-67 status were excluded from the analysis. Multiple primary tumors in the same patient diagnosed within one year of each other were analyzed independently. The control (non-carrier) group included randomly selected patients from the prospective registry who tested negative for PVs in the five genes over the same time period. Ki-67 values were compared between germline PV carriers and non-carriers in a multivariable logistic regression analysis adjusting for sex, race, age at diagnosis, stage at presentation, and histology of the tumor. Results: A total of 113 HR+/HER2- tumors from 111 PV carriers (ATM: 27, BRCA1: 24, BRCA2: 28, CHEK2: 22, and PALB2:12) along with 431 HR+/HER2- tumors from 422 randomly selected non-carriers were evaluated. The majority (>95%) of patients were non-Hispanic White. Germline PV carriers in each gene were diagnosed at a significantly younger age (P< 0.05) compared to non-carriers. The median Ki-67 value was 12 (Range: 1 to 93) for non-carriers, 16 (Range: 0 to 79) for ATM, 31 (Range: 4 to 86) for BRCA1, 30 (Range: 6 to 80) for BRCA2, 13 (Range: 1 to 38) for CHEK2, and 20 (Range: 3 to 46) for PALB2 PV carriers. In multivariable analysis, compared to non-carriers, we observed significantly higher odds of a high Ki67 value (≥20%) for tumors arising from germline PV carriers in BRCA1 (Odds Ratio (OR): 7.3, 95%CI: 2.3 – 23.3, p< 0.001), BRCA2 (OR: 3.0, 95%CI: 1.2 – 7.4, p=0.02) and PALB2 (OR: 3.9, 95%CI: 1.1 – 14.5, p=0.04). No such relationship was observed with either ATM (OR: 1.1, 95%CI: 0.4 – 2.9, p=0.81) or CHEK2 (OR: 0.8, 95%CI: 0.3 – 2.2, p=0.63). Conclusion: This study reveals that HR+/HER2- tumors in BRCA1, BRCA2, and PALB2 PV carriers have a significantly higher Ki-67 expression compared to non-carriers. These findings have implications for understanding prognosis and endocrine response PV with HR+ breast cancer. Further exploration of the relationships between Ki67 and germline PV status is necessary to personalize systemic therapy options in germline PV carriers. Acknowledgments: Thank you to Regeneron Genetics Center. The study was funded in part by the NIH Specialized Program of Research Excellence in Breast Cancer [P50CA116201] to Mayo Clinic, Mayo Clinic Department of Oncology Small Grants Program, and the Paul Calabresi Program at Mayo Clinic Citation Format: Robert Scheel, Grace Choong, Tara Rao, Nicholas Boddicker, Chunling Hu, Jie Na, Vinod Kaggal, Sean Murphy, Karthik Giridhar, Saba Yasir, Matthew Goetz, Fergus Couch, Siddhartha Yadav. Ki-67 Expression in Breast Cancer associated with ATM, BRCA1, BRCA2, CHEK2 and PALB2 Pathogenic Variants [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-08-07.

Introduction: Predictive algorithms using multiple clinical variables can identify patients at higher risk for abdominal aortic aneurysms (AAA), however they are cumbersome and cannot used directly by patients. A simple, cheap, noninvasive, and readily available (virtual) screening tool for identification of AAA could aid in screening algorithms. Aims: We sought to determine if a machine learning algorithm could predict the presence of an AAA by using a image of the face Methods: Diagnostic imaging studies were extracted from the electronic health record (EHR) from 5/7/2018 through 1/1/2023 and analyzed by a natural language processing algorithm previously validated to identify abdominal aortic aneurysms. Patient EHR profile pictures were extracted and matched to imaging studies. Various deep neural network (DNN) architectures were explored, all trained as classifiers on the face images along with clinical variables to predict clinical outcomes. All models used were CNNs with an EfficientNet architecture. Model performance was evaluated by standard metrics such as the area under the curve (AUC), specificities (Sp), and sensitivities (Sn). All performance metrics reported results from the test subset of the primary dataset and did not contain any observations considered during training. Results: A total of 5522 patients with facial images and AAA diagnostic studies were analyzed, among whom 1314 (23.8%) had a AAA. The optimal model had an AUC of 0.72 (Sn 0.44, Sp 0.83) for predicting AAA and remained similar in women (AUC 0.7), men (AUC 0.65), age >65 (AUC 0.63), and age <=65 (AUC 0.72) (Table). Conclusions: Using only an image of a patients face, the presence of an AAA could be identified with moderate performance in men, women, and in those less than 65. The model’s performance in demographics not typically screened for AAA may allow for targeted screening programs in these groups.

The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection required to form the basis for facilitating LHS. A prerequisite for using EHR data within the LHS is an infrastructure that enables access to EHR data longitudinally for health-care analytics and real time for knowledge delivery. Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS. Herein, we share our institutional implementation of a big data-empowered clinical NLP infrastructure, which not only enables health-care analytics but also has real-time NLP processing capability. The infrastructure has been utilized for multiple institutional projects including the MayoExpertAdvisor, an individualized care recommendation solution for clinical care. We compared the advantages of big data over two other environments. Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future.

Abstract Background: Transfusion-related acute lung injury (TRALI) is the leading cause of transfusion-related death in the United States; however, it remains poorly characterized in surgical populations. To better inform perioperative transfusion practice, and to help mitigate perioperative TRALI, the authors aimed to better define its epidemiology before and after TRALI mitigation strategies were introduced. Methods: This retrospective cohort study examined outcomes of adult patients undergoing noncardiac surgery with general anesthesia who received intraoperative transfusions during 2004 (n = 1,817) and 2011 (n = 1,562). The demographics and clinical characteristics of transfusion recipients, blood transfusion descriptors, and combined TRALI/possible TRALI incidence rates were evaluated. Univariate analyses were used to compare associations between patient characteristics, transfusion details, and TRALI mitigation strategies with TRALI/possible TRALI incidence rates in a before-and-after study design. Results: The incidence of TRALI/possible TRALI was 1.3% (23 of 1,613) in 2004 versus 1.4% (22 of 1,562) in 2011 (P = 0.72), with comparable overall rates in males versus females (1.4% [23 of 1,613] vs. 1.2% [22 of 1,766]) (P = 0.65). Overall, thoracic (3.0% [4 of 133]), vascular (2.7% [10 of 375]), and transplant surgeries (2.2% [4 of 178]) carried the highest rates of TRALI/possible TRALI. Obstetric and gynecologic surgical patients had no TRALI episodes. TRALI/possible TRALI incidence increased with larger volumes of blood product transfused (P < 0.001). Conclusions: Perioperative TRALI/possible TRALI is more common than previously reported and its risk increases with greater volumes of blood component therapies. No significant reduction in the combined incidence of TRALI/possible TRALI occurred between 2004 and 2011, despite the introduction of TRALI mitigation strategies. Future efforts to identify specific risk factors for TRALI/possible TRALI in surgical populations may reduce the burden of this life-threatening complication.

Abstract Background: Transfusion-associated circulatory overload (TACO) is a leading cause of transfusion-related fatalities, but its incidence and associated patient and transfusion characteristics are poorly understood. To inform surgical transfusion practice and to begin mitigating perioperative TACO, the authors aimed to define its epidemiology. Methods: In this retrospective cohort study, the medical records of adult patients undergoing noncardiac surgery with general anesthesia during 2004 or 2011 and receiving intraoperative transfusions were screened using an electronic algorithm for identification of TACO. Those patients who were screened as high probability for TACO underwent rigorous manual review. Univariate and multivariate analyses evaluated associations between patient and transfusion characteristics with TACO rates in a before-and-after study design. Results: A total of 2,162 and 1,908 patients met study criteria for 2004 and 2011, respectively. The incidence of TACO was 5.5% (119 of 2,162) in 2004 versus 3.0% (57 of 1,908) in 2011 ( P < 0.001), with comparable rates for men (4.8% [98 of 2,023]) and women (3.8% [78 of 2,047]) ( P = 0.09). Overall, vascular (12.1% [60 of 497]), transplant (8.8% [17 of 193]), and thoracic surgeries (7.2% [10 of 138]) carried the highest TACO rates. Obstetric and gynecologic patients had the lowest rate (1.4% [4 of 295]). The incidence of TACO increased with volume transfused, advancing age, and total intraoperative fluid balance (all P < 0.001). Conclusions: The incidence of perioperative TACO is similar to previous estimates in nonsurgical populations. There was a reduction in TACO rate between 2004 and 2011, with incidence patterns remaining comparable in subgroup analyses. Future efforts exploring risk factors for TACO may guide preventive or therapeutic interventions, helping to further mitigate this transfusion complication.

A large amount of medication information resides in the unstructured text found in electronic medical records, which requires advanced techniques to be properly mined. In clinical notes, medication information follows certain semantic patterns (eg, medication, dosage, frequency, and mode). Some medication descriptions contain additional word(s) between medication attributes. Therefore, it is essential to understand the semantic patterns as well as the patterns of the context interspersed among them (ie, context patterns) to effectively extract comprehensive medication information. In this paper we examined both semantic and context patterns, and compared those found in Mayo Clinic and i2b2 challenge data. We found that some variations exist between the institutions but the dominant patterns are common.